From bb70cab3b3b689fefbf55f4ef66c8c40a7842543 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 16 Dec 2025 20:54:12 +0100 Subject: [PATCH 01/44] initial --- examples/discrete_diffusion/README.md | 56 +++ examples/discrete_diffusion/sample_mdlm.py | 101 +++++ examples/discrete_diffusion/train_mdlm.py | 260 ++++++++++++ examples/discrete_diffusion/train_udlm.py | 296 ++++++++++++++ src/diffusers/__init__.py | 5 + src/diffusers/pipelines/__init__.py | 2 + .../pipelines/token_diffusion/__init__.py | 18 + .../pipeline_token_diffusion.py | 216 ++++++++++ src/diffusers/schedulers/__init__.py | 1 + .../schedulers/scheduling_token_diffusion.py | 378 ++++++++++++++++++ .../test_pipeline_token_diffusion.py | 129 ++++++ .../test_scheduler_token_diffusion.py | 100 +++++ 12 files changed, 1562 insertions(+) create mode 100644 examples/discrete_diffusion/README.md create mode 100644 examples/discrete_diffusion/sample_mdlm.py create mode 100644 examples/discrete_diffusion/train_mdlm.py create mode 100644 examples/discrete_diffusion/train_udlm.py create mode 100644 src/diffusers/pipelines/token_diffusion/__init__.py create mode 100644 src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py create mode 100644 src/diffusers/schedulers/scheduling_token_diffusion.py create mode 100644 tests/pipelines/test_pipeline_token_diffusion.py create mode 100644 tests/schedulers/test_scheduler_token_diffusion.py diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md new file mode 100644 index 000000000000..76cd46eb7097 --- /dev/null +++ b/examples/discrete_diffusion/README.md @@ -0,0 +1,56 @@ +# Discrete Token Diffusion (Experimental) + +This folder contains **training examples** for *discrete diffusion over token IDs* (language-model style), built to follow the `diffusers` + `accelerate` training conventions. + +## MDLM-style absorbing diffusion + +`train_mdlm.py` trains a masked/absorbing discrete diffusion model: +- Forward process: with probability `1 - alpha(t)`, replace tokens with `mask_token_id` +- Noise schedule: log-linear `alpha(t) = 1 - (1 - eps) * t` +- Loss: weighted token reconstruction NLL over masked positions + +### Run + +```bash +accelerate launch examples/discrete_diffusion/train_mdlm.py \ + --model_name_or_path bert-base-uncased \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --output_dir mdlm-output \ + --max_train_steps 1000 +``` + +The script saves: +- `transformers` model + tokenizer +- `diffusers.TokenDiffusionScheduler` + +into `--output_dir` checkpoints and `--output_dir/final`. + +### Sample + +```bash +python examples/discrete_diffusion/sample_mdlm.py \ + --checkpoint_path mdlm-output/final \ + --num_samples 4 \ + --seq_len 64 \ + --num_inference_steps 128 +``` + +## UDLM-style uniform diffusion + +`train_udlm.py` trains a uniform token diffusion model: +- Forward process: with probability `1 - alpha(t)`, replace tokens with a uniform random token +- Noise schedule: log-linear `alpha(t) = 1 - (1 - eps) * t` +- Loss: diffusion loss for uniform token diffusion + +### Run + +```bash +accelerate launch examples/discrete_diffusion/train_udlm.py \ + --model_name_or_path bert-base-uncased \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --output_dir udlm-output \ + --max_train_steps 1000 \ + --exclude_mask_from_uniform +``` diff --git a/examples/discrete_diffusion/sample_mdlm.py b/examples/discrete_diffusion/sample_mdlm.py new file mode 100644 index 000000000000..b6add10812f6 --- /dev/null +++ b/examples/discrete_diffusion/sample_mdlm.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from typing import Optional + +import torch +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from diffusers import TokenDiffusionScheduler + + +def parse_args(): + parser = argparse.ArgumentParser(description="Sample from an absorbing token diffusion LM (MDLM-style).") + parser.add_argument( + "--checkpoint_path", type=str, required=True, help="Path saved by train_mdlm.py (or compatible)." + ) + parser.add_argument("--num_samples", type=int, default=4) + parser.add_argument("--seq_len", type=int, default=64) + parser.add_argument("--num_inference_steps", type=int, default=128) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--inject_bos", action="store_true") + return parser.parse_args() + + +@torch.no_grad() +def sample( + model, + tokenizer, + scheduler: TokenDiffusionScheduler, + *, + num_samples: int, + seq_len: int, + num_inference_steps: int, + generator: Optional[torch.Generator], + inject_bos: bool, + device: torch.device, +): + scheduler.set_timesteps(num_inference_steps, device=device) + + x = torch.full((num_samples, seq_len), scheduler.mask_token_id, dtype=torch.long, device=device) + attention_mask = torch.ones_like(x, dtype=torch.long) + + if inject_bos and tokenizer.bos_token_id is not None: + x[:, 0] = int(tokenizer.bos_token_id) + + for t in scheduler.timesteps: + logits = model(input_ids=x, attention_mask=attention_mask).logits # [B, L, V] + x = scheduler.step(logits, t, x, generator=generator, return_dict=True).prev_sample + + if inject_bos and tokenizer.bos_token_id is not None: + x[:, 0] = int(tokenizer.bos_token_id) + + return x + + +def main(): + args = parse_args() + device = torch.device(args.device) + + tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True) + model = AutoModelForMaskedLM.from_pretrained(args.checkpoint_path).to(device) + scheduler = TokenDiffusionScheduler.from_pretrained(args.checkpoint_path) + + model.eval() + + gen = torch.Generator(device=device) + gen.manual_seed(args.seed) + + samples = sample( + model, + tokenizer, + scheduler, + num_samples=args.num_samples, + seq_len=args.seq_len, + num_inference_steps=args.num_inference_steps, + generator=gen, + inject_bos=args.inject_bos, + device=device, + ) + + texts = tokenizer.batch_decode(samples, skip_special_tokens=True) + for i, t in enumerate(texts): + print(f"[{i}] {t}") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_mdlm.py b/examples/discrete_diffusion/train_mdlm.py new file mode 100644 index 000000000000..3e2630b3f4f4 --- /dev/null +++ b/examples/discrete_diffusion/train_mdlm.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import math +import os +from dataclasses import asdict, dataclass +from typing import Dict, Optional + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch.utils.data import DataLoader +from transformers import ( + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + get_scheduler, +) + +from diffusers import TokenDiffusionScheduler + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + model_name_or_path: str + dataset_name: str + dataset_config_name: Optional[str] + text_column: str + + output_dir: str + seed: int + max_train_steps: int + checkpointing_steps: int + logging_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + lr_scheduler: str + lr_warmup_steps: int + + max_length: int + num_train_timesteps: int + eps: float + min_timestep: int + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser(description="Train an absorbing token diffusion LM (MDLM-style) with Accelerate.") + + parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased") + parser.add_argument("--dataset_name", type=str, default="wikitext") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") + parser.add_argument("--text_column", type=str, default="text") + + parser.add_argument("--output_dir", type=str, default="mdlm-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--checkpointing_steps", type=int, default=500) + parser.add_argument("--logging_steps", type=int, default=50) + + parser.add_argument("--per_device_train_batch_size", type=int, default=8) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=5e-5) + parser.add_argument("--weight_decay", type=float, default=0.01) + parser.add_argument( + "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] + ) + parser.add_argument("--lr_warmup_steps", type=int, default=100) + + parser.add_argument("--max_length", type=int, default=256) + + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--eps", type=float, default=1e-3) + parser.add_argument("--min_timestep", type=int, default=1, help="Avoid t=0 to prevent 1/t weighting blow-ups.") + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): + texts = examples[text_column] + # drop empty lines + texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] + return tokenizer( + texts, + truncation=True, + padding=False, + max_length=max_length, + return_special_tokens_mask=True, + ) + + +def main(): + cfg = parse_args() + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True) + if tokenizer.mask_token_id is None: + # MDLM-style absorbing diffusion assumes a mask token exists. + tokenizer.add_special_tokens({"mask_token": "[MASK]"}) + + config = AutoConfig.from_pretrained(cfg.model_name_or_path) + model = AutoModelForMaskedLM.from_pretrained(cfg.model_name_or_path, config=config) + model.resize_token_embeddings(len(tokenizer)) + + scheduler = TokenDiffusionScheduler( + vocab_size=len(tokenizer), + mask_token_id=int(tokenizer.mask_token_id), + num_train_timesteps=cfg.num_train_timesteps, + eps=cfg.eps, + ) + + raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name) + if "train" not in raw_datasets: + raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") + + with accelerator.main_process_first(): + tokenized = raw_datasets["train"].map( + lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), + batched=True, + remove_columns=raw_datasets["train"].column_names, + desc="Tokenizing", + ) + + # We reuse the standard MLM collator to pad and build attention masks; we won't use its masking. + collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") + train_dataloader = DataLoader( + tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True + ) + + optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + lr_scheduler = get_scheduler( + name=cfg.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.lr_warmup_steps, + num_training_steps=cfg.max_train_steps, + ) + + model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + model, optimizer, train_dataloader, lr_scheduler + ) + + global_step = 0 + model.train() + + for epoch in range(num_train_epochs): + for batch in train_dataloader: + with accelerator.accumulate(model): + input_ids = batch["input_ids"] + attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) + + # Sample discrete time indices (avoid timestep 0 for stability with 1/t weighting). + min_t = max(1, int(cfg.min_timestep)) + max_t = scheduler.num_train_timesteps - 1 + timesteps = torch.randint(min_t, max_t + 1, (input_ids.shape[0],), device=input_ids.device) + + # Forward process q(x_t | x_0): replace tokens with [MASK] according to alpha(t). + x_t = scheduler.add_noise(input_ids, noise=None, timesteps=timesteps) + + # Model predicts token logits for x0 reconstruction. + logits = model(input_ids=x_t, attention_mask=attention_mask).logits # [B, L, V] + + # MDLM-style constraints: + # - Do not predict the mask token as x0. + logits = logits.clone() + logits[..., scheduler.mask_token_id] = torch.finfo(logits.dtype).min + + # Only compute loss on tokens that were masked by the forward process. + mask_positions = x_t.eq(scheduler.mask_token_id) & attention_mask.to(dtype=torch.bool) + per_token_ce = F.cross_entropy(logits.view(-1, logits.shape[-1]), input_ids.view(-1), reduction="none") + per_token_ce = per_token_ce.view_as(input_ids) + + # Log-linear schedule gives weight proportional to 1 / t_cont, where t_cont in (0, 1]. + t_cont = timesteps.to(dtype=torch.float32) / float(scheduler.num_train_timesteps - 1) + t_cont = t_cont.clamp_min(1e-6) + weights = (1.0 / t_cont).view(-1, 1) + + loss = (per_token_ce * mask_positions.to(per_token_ce.dtype) * weights).sum() + denom = mask_positions.sum().clamp_min(1) + loss = loss / denom + + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + os.makedirs(save_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(save_dir, save_function=accelerator.save) + tokenizer.save_pretrained(save_dir) + scheduler.save_pretrained(save_dir) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + os.makedirs(final_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(final_dir, save_function=accelerator.save) + tokenizer.save_pretrained(final_dir) + scheduler.save_pretrained(final_dir) + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_udlm.py b/examples/discrete_diffusion/train_udlm.py new file mode 100644 index 000000000000..2c79eef32299 --- /dev/null +++ b/examples/discrete_diffusion/train_udlm.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import math +import os +from dataclasses import asdict, dataclass +from typing import Dict, Optional + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch.utils.data import DataLoader +from transformers import ( + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + get_scheduler, +) + +from diffusers import TokenDiffusionScheduler + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + model_name_or_path: str + dataset_name: str + dataset_config_name: Optional[str] + text_column: str + + output_dir: str + seed: int + max_train_steps: int + checkpointing_steps: int + logging_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + lr_scheduler: str + lr_warmup_steps: int + + max_length: int + num_train_timesteps: int + eps: float + min_timestep: int + exclude_mask_from_uniform: bool + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser(description="Train a uniform token diffusion LM (UDLM-style) with Accelerate.") + + parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased") + parser.add_argument("--dataset_name", type=str, default="wikitext") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") + parser.add_argument("--text_column", type=str, default="text") + + parser.add_argument("--output_dir", type=str, default="udlm-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--checkpointing_steps", type=int, default=500) + parser.add_argument("--logging_steps", type=int, default=50) + + parser.add_argument("--per_device_train_batch_size", type=int, default=8) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=5e-5) + parser.add_argument("--weight_decay", type=float, default=0.01) + parser.add_argument( + "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] + ) + parser.add_argument("--lr_warmup_steps", type=int, default=100) + + parser.add_argument("--max_length", type=int, default=256) + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--eps", type=float, default=1e-3) + parser.add_argument("--min_timestep", type=int, default=1) + parser.add_argument( + "--exclude_mask_from_uniform", action="store_true", help="Exclude mask token from uniform draws." + ) + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): + texts = examples[text_column] + texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] + return tokenizer( + texts, + truncation=True, + padding=False, + max_length=max_length, + return_special_tokens_mask=True, + ) + + +def udlm_diffusion_loss( + logits: torch.Tensor, x0: torch.LongTensor, x_t: torch.LongTensor, *, eps: float, t_cont: torch.Tensor +): + """ + UDLM diffusion loss (continuous-time form). + + Args: + logits: [B, L, V] + x0: [B, L] + x_t: [B, L] + eps: log-linear eps + t_cont: [B] in (0, 1] + Returns: + loss_per_token: [B, L] + """ + log_x_theta = torch.log_softmax(logits, dim=-1) + B, L, V = log_x_theta.shape + + # Treat alpha(t) as (1 - t) for the loss derivation, recovering t from the scheduler alpha. + alpha_sched = 1.0 - (1.0 - eps) * t_cont # scheduler alpha(t) + t = (1.0 - alpha_sched) / (1.0 - eps) + alpha = 1.0 - t # theoretical alpha used in loss + + alpha = alpha.to(dtype=log_x_theta.dtype).view(B, 1, 1) + + x0_one_hot = F.one_hot(x0, V).to(dtype=log_x_theta.dtype) + xt_one_hot = F.one_hot(x_t, V).to(dtype=log_x_theta.dtype) + + x_bar = V * alpha * x0_one_hot + 1.0 - alpha + x_bar_theta = V * alpha * log_x_theta.exp() + 1.0 - alpha + + alpha_prime = -1.0 + coeff = alpha_prime / (V * alpha) + + x_bar_zt = (x_bar * xt_one_hot).sum(dim=-1, keepdim=True) # (B, L, 1) + x_bar_theta_zt = (x_bar_theta * xt_one_hot).sum(dim=-1, keepdim=True) # (B, L, 1) + + term1 = (V / x_bar_zt) - (V / x_bar_theta_zt) + + term2 = ((x_bar / x_bar_zt) * (x_bar_theta_zt.log() - x_bar_theta.log() + x_bar.log() - x_bar_zt.log())).sum( + dim=-1, keepdim=True + ) + + diffusion_loss = (coeff * (term1 - term2)).squeeze(-1) # (B, L) + return diffusion_loss + + +def main(): + cfg = parse_args() + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True) + if tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": "[MASK]"}) + + config = AutoConfig.from_pretrained(cfg.model_name_or_path) + model = AutoModelForMaskedLM.from_pretrained(cfg.model_name_or_path, config=config) + model.resize_token_embeddings(len(tokenizer)) + + scheduler = TokenDiffusionScheduler( + vocab_size=len(tokenizer), + mask_token_id=int(tokenizer.mask_token_id), + num_train_timesteps=cfg.num_train_timesteps, + eps=cfg.eps, + forward_process="uniform", + exclude_mask_from_uniform=cfg.exclude_mask_from_uniform, + ) + + raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name) + if "train" not in raw_datasets: + raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") + + with accelerator.main_process_first(): + tokenized = raw_datasets["train"].map( + lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), + batched=True, + remove_columns=raw_datasets["train"].column_names, + desc="Tokenizing", + ) + + collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") + train_dataloader = DataLoader( + tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True + ) + + optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + lr_scheduler = get_scheduler( + name=cfg.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.lr_warmup_steps, + num_training_steps=cfg.max_train_steps, + ) + + model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + model, optimizer, train_dataloader, lr_scheduler + ) + + global_step = 0 + model.train() + + for epoch in range(num_train_epochs): + for batch in train_dataloader: + with accelerator.accumulate(model): + input_ids = batch["input_ids"] + attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) + + min_t = max(1, int(cfg.min_timestep)) + max_t = scheduler.num_train_timesteps - 1 + timesteps = torch.randint(min_t, max_t + 1, (input_ids.shape[0],), device=input_ids.device) + + x_t = scheduler.add_noise(input_ids, noise=None, timesteps=timesteps) + logits = model(input_ids=x_t, attention_mask=attention_mask).logits + + if scheduler.exclude_mask_from_uniform: + logits = logits.clone() + logits[..., scheduler.mask_token_id] = torch.finfo(logits.dtype).min + + t_cont = timesteps.to(dtype=torch.float32) / float(scheduler.num_train_timesteps - 1) + t_cont = t_cont.clamp_min(1e-6) + + loss_per_token = udlm_diffusion_loss(logits, input_ids, x_t, eps=scheduler.eps, t_cont=t_cont) + loss = (loss_per_token * attention_mask.to(loss_per_token.dtype)).mean() + + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + os.makedirs(save_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(save_dir, save_function=accelerator.save) + tokenizer.save_pretrained(save_dir) + scheduler.save_pretrained(save_dir) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + os.makedirs(final_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(final_dir, save_function=accelerator.save) + tokenizer.save_pretrained(final_dir) + scheduler.save_pretrained(final_dir) + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 6aac3feffd0e..d501eb07d5fb 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -360,12 +360,14 @@ "SCMScheduler", "ScoreSdeVeScheduler", "TCDScheduler", + "TokenDiffusionScheduler", "UnCLIPScheduler", "UniPCMultistepScheduler", "VQDiffusionScheduler", ] ) _import_structure["training_utils"] = ["EMAModel"] + _import_structure["pipelines"].extend(["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"]) try: if not (is_torch_available() and is_scipy_available()): @@ -1057,6 +1059,8 @@ RePaintPipeline, ScoreSdeVePipeline, StableDiffusionMixin, + TokenDiffusionPipeline, + TokenDiffusionPipelineOutput, ) from .quantizers import DiffusersQuantizer from .schedulers import ( @@ -1094,6 +1098,7 @@ SCMScheduler, ScoreSdeVeScheduler, TCDScheduler, + TokenDiffusionScheduler, UnCLIPScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index e8faf868e741..c77a5021ee12 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -52,6 +52,7 @@ _import_structure["ddim"] = ["DDIMPipeline"] _import_structure["ddpm"] = ["DDPMPipeline"] _import_structure["dit"] = ["DiTPipeline"] + _import_structure["token_diffusion"] = ["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"] _import_structure["latent_diffusion"].extend(["LDMSuperResolutionPipeline"]) _import_structure["pipeline_utils"] = [ "AudioPipelineOutput", @@ -557,6 +558,7 @@ ImagePipelineOutput, StableDiffusionMixin, ) + from .token_diffusion import TokenDiffusionPipeline, TokenDiffusionPipelineOutput try: if not (is_torch_available() and is_librosa_available()): diff --git a/src/diffusers/pipelines/token_diffusion/__init__.py b/src/diffusers/pipelines/token_diffusion/__init__.py new file mode 100644 index 000000000000..3fc40f86ee12 --- /dev/null +++ b/src/diffusers/pipelines/token_diffusion/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pipeline_token_diffusion import TokenDiffusionPipeline, TokenDiffusionPipelineOutput + + +__all__ = ["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py new file mode 100644 index 000000000000..6f112ed1308b --- /dev/null +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -0,0 +1,216 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, List, Optional, Tuple, Union + +import torch + +from ...utils import BaseOutput +from ..pipeline_utils import DiffusionPipeline + + +@dataclass +class TokenDiffusionPipelineOutput(BaseOutput): + """ + Output class for token diffusion pipelines. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Sampled token IDs. + texts (`List[str]`, *optional*): + Decoded texts if a tokenizer was provided and `return_text=True`. + """ + + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +class TokenDiffusionPipeline(DiffusionPipeline): + """ + Generic token diffusion sampling pipeline. + + This pipeline is intended as a minimal, diffusers-native wrapper around: + - a token denoiser model (e.g. `transformers.AutoModelForMaskedLM`-like, returning logits over vocab), and + - a discrete token scheduler (e.g. `TokenDiffusionScheduler`) that implements `set_timesteps()` and `step()`. + + The pipeline supports multiple forward processes via the scheduler configuration (e.g. absorbing/mask, uniform). + Conditioning (prefix/infill) is intentionally out of scope for the first version. + """ + + model: Any + tokenizer: Any + scheduler: Any + + def __init__(self, model: Any, scheduler: Any, tokenizer: Optional[Any] = None): + super().__init__() + self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) + + def _resolve_start_token_id(self) -> Optional[int]: + tok = getattr(self, "tokenizer", None) + if tok is None: + return None + for attr in ("bos_token_id", "cls_token_id"): + token_id = getattr(tok, attr, None) + if token_id is not None: + return int(token_id) + return None + + def _init_latents( + self, + batch_size: int, + seq_len: int, + *, + generator: Optional[torch.Generator], + device: torch.device, + ) -> torch.LongTensor: + # Prefer a scheduler-provided prior if available. + if hasattr(self.scheduler, "forward_process") and getattr(self.scheduler, "forward_process") == "uniform": + # Uniform prior over token IDs. Mirror scheduler's exclude-mask behavior. + if getattr(self.scheduler, "exclude_mask_from_uniform", False) and hasattr( + self.scheduler, "_sample_uniform_tokens" + ): + return self.scheduler._sample_uniform_tokens( + torch.Size((batch_size, seq_len)), + device=device, + dtype=torch.long, + generator=generator, + ) + vocab_size = int(getattr(self.scheduler, "vocab_size", 0)) + if vocab_size <= 0: + raise ValueError("Scheduler must define `vocab_size` for uniform prior sampling.") + return torch.randint( + 0, vocab_size, (batch_size, seq_len), device=device, dtype=torch.long, generator=generator + ) + + mask_token_id = getattr(self.scheduler, "mask_token_id", None) + if mask_token_id is None: + raise ValueError("Scheduler must define `mask_token_id` for absorbing prior sampling.") + return torch.full((batch_size, seq_len), int(mask_token_id), device=device, dtype=torch.long) + + def _normalize_prefix_ids( + self, prefix_ids: torch.LongTensor, batch_size: int, device: torch.device + ) -> torch.LongTensor: + if prefix_ids.ndim == 1: + prefix_ids = prefix_ids.unsqueeze(0) + if prefix_ids.ndim != 2: + raise ValueError( + f"`prefix_ids` must have shape [prefix_len] or [batch, prefix_len], got {prefix_ids.shape}." + ) + if prefix_ids.shape[0] not in (1, batch_size): + raise ValueError( + f"`prefix_ids` batch dim must be 1 or batch_size={batch_size}, got {prefix_ids.shape[0]}." + ) + if prefix_ids.dtype != torch.long: + raise ValueError(f"`prefix_ids` must be int64 token IDs, got dtype={prefix_ids.dtype}.") + prefix_ids = prefix_ids.to(device=device) + if prefix_ids.shape[0] == 1 and batch_size > 1: + prefix_ids = prefix_ids.expand(batch_size, -1) + return prefix_ids + + @torch.no_grad() + def __call__( + self, + *, + batch_size: int = 1, + seq_len: int = 64, + num_inference_steps: int = 128, + generator: Optional[torch.Generator] = None, + prefix_ids: Optional[torch.LongTensor] = None, + infill_mask: Optional[torch.BoolTensor] = None, + inject_start_token: bool = False, + return_text: bool = True, + return_dict: bool = True, + **model_kwargs, + ) -> Union[TokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + """ + Args: + batch_size: Number of sequences to generate. + seq_len: Sequence length in tokens. + num_inference_steps: Number of reverse diffusion steps. + generator: Optional torch generator for determinism. + prefix_ids: Optional prefix token IDs to keep fixed at the start of each sequence. Shape `[P]` or + `[batch_size, P]`. + infill_mask: + Optional boolean mask of shape `[batch_size, seq_len]` indicating which positions are editable (`True`) + vs fixed (`False`). Fixed positions are clamped to the initial values on every step. + inject_start_token: If True, inject `bos_token_id` (or `cls_token_id`) into position 0 (if available). + return_text: If True and tokenizer exists, also return decoded strings. + return_dict: If True, returns a `TokenDiffusionPipelineOutput`. + model_kwargs: Forward kwargs passed to `model(...)` (e.g. attention mask overrides). + """ + device = self._execution_device + + self.scheduler.set_timesteps(num_inference_steps, device=device) + + input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + + fixed_mask = None + fixed_values = None + if infill_mask is not None: + if infill_mask.shape != (batch_size, seq_len): + raise ValueError( + f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}." + ) + fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) + fixed_values = input_ids.clone() + + if prefix_ids is not None: + prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) + prefix_len = prefix_ids.shape[1] + if prefix_len > seq_len: + raise ValueError(f"`prefix_ids` length {prefix_len} must be <= seq_len={seq_len}.") + + input_ids[:, :prefix_len] = prefix_ids + if fixed_mask is None: + fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) + fixed_values = input_ids.clone() + fixed_mask[:, :prefix_len] = True + fixed_values[:, :prefix_len] = prefix_ids + + start_token_id = self._resolve_start_token_id() + if inject_start_token and start_token_id is not None: + input_ids[:, 0] = start_token_id + if fixed_mask is not None: + fixed_mask[:, 0] = True + fixed_values[:, 0] = start_token_id + + for t in self.scheduler.timesteps: + out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) + logits = getattr(out, "logits", None) + if logits is None: + # Fall back to tuple-style returns. + logits = out[0] + + input_ids = self.scheduler.step(logits, t, input_ids, generator=generator, return_dict=True).prev_sample + + if fixed_mask is not None: + input_ids = torch.where(fixed_mask, fixed_values, input_ids) + + if inject_start_token and start_token_id is not None: + input_ids[:, 0] = start_token_id + + texts = None + if return_text and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) + + if not return_dict: + return (input_ids, texts) + return TokenDiffusionPipelineOutput(sequences=input_ids, texts=texts) + + +__all__ = ["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"] diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 29052c1ba0cb..699bbf69c2c8 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -72,6 +72,7 @@ _import_structure["scheduling_scm"] = ["SCMScheduler"] _import_structure["scheduling_sde_ve"] = ["ScoreSdeVeScheduler"] _import_structure["scheduling_tcd"] = ["TCDScheduler"] + _import_structure["scheduling_token_diffusion"] = ["TokenDiffusionScheduler"] _import_structure["scheduling_unclip"] = ["UnCLIPScheduler"] _import_structure["scheduling_unipc_multistep"] = ["UniPCMultistepScheduler"] _import_structure["scheduling_utils"] = ["AysSchedules", "KarrasDiffusionSchedulers", "SchedulerMixin"] diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py new file mode 100644 index 000000000000..062a8c12ceaa --- /dev/null +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -0,0 +1,378 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput +from .scheduling_utils import SchedulerMixin + + +@dataclass +class TokenDiffusionSchedulerOutput(BaseOutput): + """ + Output class for discrete token schedulers. + + Args: + prev_sample (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Sample at the previous timestep. This should be fed into the model at the next denoising iteration. + """ + + prev_sample: torch.LongTensor + + +def _gumbel_argmax(logits: torch.Tensor, generator: Optional[torch.Generator] = None) -> torch.LongTensor: + """ + Sample from a categorical distribution defined by (unnormalized) logits via Gumbel-max. + + Args: + logits: Tensor of shape `(..., vocab_size)`. + generator: Optional torch generator for determinism. + + Returns: + `torch.LongTensor` of shape `logits.shape[:-1]` with sampled indices. + """ + # Gumbel(0,1) noise: -log(-log(U)) + uniform = torch.rand(logits.shape, device=logits.device, dtype=logits.dtype, generator=generator).clamp_(1e-30, 1) + gumbel = -torch.log(-torch.log(uniform)) + return (logits + gumbel).argmax(dim=-1) + + +class TokenDiffusionScheduler(SchedulerMixin, ConfigMixin): + """ + Discrete diffusion scheduler over token IDs (categorical states). + + This scheduler is designed for *token-space* diffusion (e.g. masked/absorbing diffusion language models) and + follows the diffusers scheduler API where possible: `set_timesteps()` for inference and `step()` for reverse + updates. + + Currently implemented: + - Forward process: + - `absorbing`: with probability `1 - alpha(t)` replace token with `mask_token_id`. + - `uniform`: with probability `1 - alpha(t)` replace token with a uniform random token. + - Noise schedule: selectable `alpha(t)` families with `t in [0, 1]`. + + Notes: + - `step()` expects the model to return logits over vocabulary for `x0` reconstruction. + - The mask token is treated as an *absorbing state* and is never sampled as an `x0` prediction. + """ + + order = 1 + + @register_to_config + def __init__( + self, + vocab_size: int, + mask_token_id: int, + num_train_timesteps: int = 1000, + alpha_schedule: str = "log_linear", + eps: float = 1e-3, + sigma_min: float = 1e-4, + sigma_max: float = 20.0, + forward_process: str = "absorbing", + exclude_mask_from_uniform: bool = True, + ): + if vocab_size <= 0: + raise ValueError(f"`vocab_size` must be > 0, got {vocab_size}.") + if num_train_timesteps <= 1: + raise ValueError(f"`num_train_timesteps` must be > 1, got {num_train_timesteps}.") + if not (0.0 < eps < 1.0): + raise ValueError(f"`eps` must be in (0, 1), got {eps}.") + if not (0 <= mask_token_id < vocab_size): + raise ValueError(f"`mask_token_id` must be in [0, vocab_size), got {mask_token_id}.") + alpha_schedule = str(alpha_schedule).lower() + if alpha_schedule not in {"log_linear", "linear", "cosine", "geometric"}: + raise ValueError( + "`alpha_schedule` must be one of {'log_linear','linear','cosine','geometric'}, got" + f" {alpha_schedule!r}." + ) + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError(f"`sigma_min` and `sigma_max` must be > 0, got sigma_min={sigma_min}, sigma_max={sigma_max}.") + if sigma_max <= sigma_min: + raise ValueError(f"`sigma_max` must be > `sigma_min`, got sigma_min={sigma_min}, sigma_max={sigma_max}.") + if forward_process not in {"absorbing", "uniform"}: + raise ValueError(f"`forward_process` must be one of {{'absorbing','uniform'}}, got {forward_process!r}.") + + self.vocab_size = int(vocab_size) + self.mask_token_id = int(mask_token_id) + self.num_train_timesteps = int(num_train_timesteps) + self.alpha_schedule = alpha_schedule + self.eps = float(eps) + self.sigma_min = float(sigma_min) + self.sigma_max = float(sigma_max) + self.forward_process = str(forward_process) + self.exclude_mask_from_uniform = bool(exclude_mask_from_uniform) + + self.num_inference_steps = None + self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) + + def _effective_vocab_size(self) -> int: + if self.forward_process == "uniform" and self.exclude_mask_from_uniform: + return self.vocab_size - 1 + return self.vocab_size + + def _sample_uniform_tokens( + self, shape: torch.Size, device: torch.device, dtype: torch.dtype, generator: Optional[torch.Generator] = None + ) -> torch.LongTensor: + """ + Sample uniform token IDs, optionally excluding `mask_token_id` (by shifting indices around it). + """ + if self.forward_process != "uniform": + raise ValueError("Uniform token sampling is only valid for `forward_process='uniform'`.") + + if not self.exclude_mask_from_uniform: + return torch.randint(0, self.vocab_size, shape, device=device, dtype=dtype, generator=generator) + + # Sample in [0, vocab_size-1) and shift around mask_token_id. + v_eff = self.vocab_size - 1 + draw = torch.randint(0, v_eff, shape, device=device, dtype=dtype, generator=generator) + return torch.where(draw >= self.mask_token_id, draw + 1, draw) + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Timesteps are stored in descending order, so `timesteps[0]` is the noisiest step. + """ + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + self.num_inference_steps = int(num_inference_steps) + + # Standard diffusers behavior: map inference steps onto training step indices. + timesteps = torch.linspace( + self.num_train_timesteps - 1, 0, self.num_inference_steps, dtype=torch.float32 + ).round() + self.timesteps = timesteps.to(dtype=torch.long, device=device) + + def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: + return sample + + def _t_from_timestep(self, timestep: Union[int, torch.Tensor], device: torch.device) -> torch.Tensor: + """ + Convert an integer training timestep index into continuous time `t in [0, 1]`. + """ + if isinstance(timestep, torch.Tensor): + t_idx = timestep.to(device=device, dtype=torch.float32) + else: + t_idx = torch.tensor(float(timestep), device=device, dtype=torch.float32) + denom = float(self.num_train_timesteps - 1) + return (t_idx / denom).clamp_(0.0, 1.0) + + def _alpha_t(self, t: torch.Tensor) -> torch.Tensor: + """ + Compute alpha(t) for the configured schedule. + + The returned tensor is expected to be in (0, 1] and monotone decreasing in `t`. + """ + if self.alpha_schedule == "log_linear": + # alpha(t) = 1 - (1 - eps) * t + return 1.0 - (1.0 - self.eps) * t + + if self.alpha_schedule == "linear": + # alpha(t) = (1 - 2*eps) * (1 - t) + eps + return (1.0 - 2.0 * self.eps) * (1.0 - t) + self.eps + + if self.alpha_schedule == "cosine": + # alpha_base(t) = 1 - cos(pi/2 * (1 - t)) + # alpha(t) = (1 - 2*eps) * alpha_base(t) + eps + base = 1.0 - torch.cos(torch.pi / 2.0 * (1.0 - t)) + return (1.0 - 2.0 * self.eps) * base + self.eps + + if self.alpha_schedule == "geometric": + # total_noise(t) = sigma_min^(1-t) * sigma_max^t + # alpha(t) = exp(-total_noise(t)) + sigma_min = torch.as_tensor(self.sigma_min, device=t.device, dtype=t.dtype) + sigma_max = torch.as_tensor(self.sigma_max, device=t.device, dtype=t.dtype) + total_noise = (sigma_min ** (1.0 - t)) * (sigma_max**t) + return (-total_noise).exp() + + raise ValueError(f"Unsupported alpha schedule: {self.alpha_schedule!r}") + + def add_noise( + self, + original_samples: torch.LongTensor, + noise: Optional[torch.Tensor], + timesteps: torch.LongTensor, + ) -> torch.LongTensor: + """ + Apply the (absorbing) forward process q(x_t | x_0). + + The `noise` argument is accepted for API compatibility but is not used for the absorbing kernel. + """ + del noise + + if original_samples.dtype != torch.long: + raise ValueError(f"`original_samples` must be int64 token IDs, got dtype={original_samples.dtype}.") + if timesteps.dtype not in (torch.int32, torch.int64): + raise ValueError(f"`timesteps` must be an integer tensor, got dtype={timesteps.dtype}.") + + batch_size, seq_len = original_samples.shape + device = original_samples.device + + # Convert per-example timesteps into alpha(t) in [eps, 1]. + t = self._t_from_timestep(timesteps.to(device), device=device).view(batch_size, 1) + alpha = self._alpha_t(t).to(dtype=torch.float32) + + p_replace = (1.0 - alpha).expand(batch_size, seq_len) + rand = torch.rand((batch_size, seq_len), device=device, dtype=torch.float32) + replace_positions = rand < p_replace + + if self.forward_process == "absorbing": + replacement = torch.full_like(original_samples, self.mask_token_id) + elif self.forward_process == "uniform": + replacement = self._sample_uniform_tokens( + original_samples.shape, device=device, dtype=original_samples.dtype, generator=None + ) + else: + raise ValueError(f"Unsupported forward process: {self.forward_process!r}") + + return torch.where(replace_positions, replacement, original_samples) + + def step( + self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.LongTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[TokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: + """ + Reverse diffusion step for the configured forward process. + + For `forward_process="absorbing"`, the update mirrors the common absorbing posterior: + - Keep all unmasked positions fixed. + - For masked positions, with probability p_denoise replace mask by a sample from p_theta(x0 | x_t, t). + + For `forward_process="uniform"`, this implements the discrete posterior used by UDLM-style uniform token + diffusion. + """ + if sample.dtype != torch.long: + raise ValueError(f"`sample` must be int64 token IDs, got dtype={sample.dtype}.") + if model_output.ndim != 3 or model_output.shape[-1] != self.vocab_size: + raise ValueError( + f"`model_output` must have shape [batch, seq_len, vocab_size={self.vocab_size}], got {tuple(model_output.shape)}." + ) + if model_output.shape[0] != sample.shape[0] or model_output.shape[1] != sample.shape[1]: + raise ValueError( + f"`model_output` batch/seq dims {tuple(model_output.shape[:2])} must match `sample` {tuple(sample.shape)}." + ) + + device = sample.device + batch_size, seq_len = sample.shape + + # Figure out the previous timestep in the configured inference schedule. + if self.num_inference_steps is None: + raise ValueError("Call `set_timesteps(num_inference_steps, ...)` before calling `step()`.") + + if isinstance(timestep, torch.Tensor): + timestep_int = int(timestep.item()) + else: + timestep_int = int(timestep) + + # Find current index in timesteps and use the next value as "previous" time (less noisy). + # If we are at the end, perform a "noise removal" step (alpha_prev = 1). + current_indices = (self.timesteps == timestep_int).nonzero(as_tuple=False) + if current_indices.numel() == 0: + raise ValueError(f"`timestep` ({timestep_int}) must be one of `self.timesteps`.") + step_index = int(current_indices[0].item()) + is_noise_removal_step = step_index + 1 >= len(self.timesteps) + prev_timestep_int = int(self.timesteps[step_index + 1].item()) if not is_noise_removal_step else 0 + + t = self._t_from_timestep(timestep_int, device=device) + alpha_t = self._alpha_t(t).to(dtype=torch.float32) + if is_noise_removal_step: + alpha_prev = torch.tensor(1.0, device=device, dtype=torch.float32) + else: + t_prev = self._t_from_timestep(prev_timestep_int, device=device) + alpha_prev = self._alpha_t(t_prev).to(dtype=torch.float32) + + if self.forward_process == "uniform": + # Convert logits to probabilities for x0; optionally forbid mask token. + logits = model_output.to(dtype=torch.float32) + if self.exclude_mask_from_uniform: + logits = logits.clone() + logits[..., self.mask_token_id] = torch.finfo(logits.dtype).min + p_x0 = logits.softmax(dim=-1) + + V = self.vocab_size + x = sample + xt_one_hot = F.one_hot(x, V).to(dtype=p_x0.dtype) + + alpha_ts = (alpha_t / alpha_prev).clamp_min(torch.finfo(torch.float32).eps) + + if self.exclude_mask_from_uniform: + limiting = torch.full((V,), 1.0 / float(V - 1), device=device, dtype=p_x0.dtype) + limiting[self.mask_token_id] = 0.0 + else: + limiting = torch.full((V,), 1.0 / float(V), device=device, dtype=p_x0.dtype) + limiting = limiting.view(1, 1, -1) + + alpha_t3 = alpha_t.view(1, 1, 1) + alpha_s3 = alpha_prev.view(1, 1, 1) + alpha_ts3 = alpha_ts.view(1, 1, 1) + + numerator = ( + (alpha_t3 * V * p_x0 * xt_one_hot) + + ((alpha_ts3 - alpha_t3) * xt_one_hot) + + ((alpha_s3 - alpha_t3) * p_x0) + + ((1.0 - alpha_ts3) * (1.0 - alpha_s3) * limiting) + ) + denom = (alpha_t3 * V * p_x0.gather(-1, x.unsqueeze(-1)) + (1.0 - alpha_t3)).clamp_min( + torch.finfo(torch.float32).eps + ) + + q_xs = numerator / denom + q_xs = q_xs.clamp_min(torch.finfo(torch.float32).tiny) + q_xs = q_xs / q_xs.sum(dim=-1, keepdim=True).clamp_min(torch.finfo(torch.float32).eps) + + x_prev = _gumbel_argmax(torch.log(q_xs), generator=generator).to(dtype=torch.long) + + if not return_dict: + return (x_prev,) + return TokenDiffusionSchedulerOutput(prev_sample=x_prev) + + if self.forward_process != "absorbing": + raise ValueError(f"Unsupported forward process for `step()`: {self.forward_process!r}") + + # p_denoise = (alpha_prev - alpha_t) / (1 - alpha_t) + denom = (1.0 - alpha_t).clamp_min(torch.finfo(torch.float32).eps) + p_denoise = ((alpha_prev - alpha_t) / denom).clamp(0.0, 1.0) + + # Sample x0 predictions (never sample the mask token). + logits = model_output.to(dtype=torch.float32) + logits[..., self.mask_token_id] = torch.finfo(logits.dtype).min + sampled_x0 = _gumbel_argmax(logits, generator=generator).to(dtype=torch.long) + + # Only masked positions can change. + is_masked = sample == self.mask_token_id + + # Bernoulli draw for whether to denoise at this step (only matters on masked positions). + rand = torch.rand((batch_size, seq_len), device=device, dtype=torch.float32, generator=generator) + should_denoise = rand < float(p_denoise.item()) + + x_prev = torch.where(is_masked & should_denoise, sampled_x0, sample) + + if not return_dict: + return (x_prev,) + return TokenDiffusionSchedulerOutput(prev_sample=x_prev) + + +__all__ = ["TokenDiffusionScheduler", "TokenDiffusionSchedulerOutput"] diff --git a/tests/pipelines/test_pipeline_token_diffusion.py b/tests/pipelines/test_pipeline_token_diffusion.py new file mode 100644 index 000000000000..57c26d13a6a2 --- /dev/null +++ b/tests/pipelines/test_pipeline_token_diffusion.py @@ -0,0 +1,129 @@ +import unittest + +import torch + +from diffusers import TokenDiffusionPipeline, TokenDiffusionScheduler + + +class _DummyTokenizer: + bos_token_id = None + cls_token_id = 1 + + def batch_decode(self, sequences, skip_special_tokens=True): + # Deterministic, cheap “decode”: join token ids as strings. + out = [] + for row in sequences.tolist(): + out.append(" ".join(str(i) for i in row)) + return out + + +class _DummyModelOutput: + def __init__(self, logits): + self.logits = logits + + +class _DummyMLM(torch.nn.Module): + def __init__(self, vocab_size: int): + super().__init__() + self.vocab_size = vocab_size + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def forward(self, input_ids=None, attention_mask=None, **kwargs): + batch_size, seq_len = input_ids.shape + logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) + return _DummyModelOutput(logits=logits) + + +class TokenDiffusionPipelineTest(unittest.TestCase): + def test_absorbing_pipeline_runs(self): + vocab_size = 32 + scheduler = TokenDiffusionScheduler( + vocab_size=vocab_size, mask_token_id=vocab_size - 1, forward_process="absorbing" + ) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + + pipe = TokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) + pipe = pipe.to("cpu") + + out = pipe(batch_size=2, seq_len=8, num_inference_steps=2, inject_start_token=True) + self.assertEqual(out.sequences.shape, (2, 8)) + self.assertEqual(len(out.texts), 2) + + def test_uniform_pipeline_runs(self): + vocab_size = 32 + scheduler = TokenDiffusionScheduler( + vocab_size=vocab_size, + mask_token_id=vocab_size - 1, + forward_process="uniform", + exclude_mask_from_uniform=True, + ) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + + pipe = TokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) + pipe = pipe.to("cpu") + + gen = torch.Generator().manual_seed(0) + out = pipe(batch_size=2, seq_len=8, num_inference_steps=2, generator=gen, inject_start_token=True) + self.assertEqual(out.sequences.shape, (2, 8)) + self.assertFalse((out.sequences == scheduler.mask_token_id).any().item()) + + def test_prefix_ids_are_fixed(self): + vocab_size = 32 + scheduler = TokenDiffusionScheduler( + vocab_size=vocab_size, mask_token_id=vocab_size - 1, forward_process="absorbing" + ) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + + pipe = TokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to("cpu") + prefix = torch.tensor([5, 6, 7], dtype=torch.long) + out = pipe(batch_size=2, seq_len=8, num_inference_steps=2, prefix_ids=prefix, return_text=False) + + self.assertTrue((out.sequences[:, :3] == prefix.view(1, -1)).all().item()) + + def test_infill_mask_freezes_positions(self): + vocab_size = 32 + scheduler = TokenDiffusionScheduler( + vocab_size=vocab_size, + mask_token_id=vocab_size - 1, + forward_process="uniform", + exclude_mask_from_uniform=True, + ) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + + pipe = TokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to("cpu") + + # Only positions 2..7 are editable, first two positions are fixed to the initial values. + infill_mask = torch.ones((2, 8), dtype=torch.bool) + infill_mask[:, :2] = False + gen = torch.Generator().manual_seed(0) + out = pipe( + batch_size=2, seq_len=8, num_inference_steps=2, generator=gen, infill_mask=infill_mask, return_text=False + ) + + # Fixed positions should be unchanged from the initial latents (for uniform, these are random but clamped). + # Since the model predicts uniform logits and the scheduler would otherwise resample, this checks clamping works. + out2 = pipe( + batch_size=2, + seq_len=8, + num_inference_steps=2, + generator=torch.Generator().manual_seed(0), + infill_mask=infill_mask, + return_text=False, + ) + self.assertTrue((out.sequences[:, :2] == out2.sequences[:, :2]).all().item()) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_token_diffusion.py b/tests/schedulers/test_scheduler_token_diffusion.py new file mode 100644 index 000000000000..384a2581b2c3 --- /dev/null +++ b/tests/schedulers/test_scheduler_token_diffusion.py @@ -0,0 +1,100 @@ +import unittest + +import torch + +from diffusers import TokenDiffusionScheduler + + +class TokenDiffusionSchedulerTest(unittest.TestCase): + def get_scheduler(self, **kwargs): + config = { + "vocab_size": 128, + "mask_token_id": 127, + "num_train_timesteps": 100, + "eps": 1e-3, + } + config.update(kwargs) + return TokenDiffusionScheduler(**config) + + def test_set_timesteps(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(10) + self.assertEqual(len(scheduler.timesteps), 10) + self.assertTrue((scheduler.timesteps[:-1] >= scheduler.timesteps[1:]).all().item()) + + def test_add_noise_absorbing_keeps_shape_dtype(self): + scheduler = self.get_scheduler() + batch_size, seq_len = 4, 16 + x0 = torch.randint(0, scheduler.vocab_size, (batch_size, seq_len), dtype=torch.long) + timesteps = torch.randint(0, scheduler.num_train_timesteps, (batch_size,), dtype=torch.long) + + xt = scheduler.add_noise(x0, noise=None, timesteps=timesteps) + self.assertEqual(xt.shape, x0.shape) + self.assertEqual(xt.dtype, torch.long) + + # xt values must be valid token ids + self.assertTrue(((xt >= 0) & (xt < scheduler.vocab_size)).all().item()) + + def test_step_preserves_unmasked_tokens(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(5) + + batch_size, seq_len = 2, 12 + x_t = torch.randint(0, scheduler.vocab_size, (batch_size, seq_len), dtype=torch.long) + x_t[:, :3] = scheduler.mask_token_id # ensure some masked positions + + # Model predicts uniform logits; step should never change already unmasked positions + logits = torch.zeros((batch_size, seq_len, scheduler.vocab_size), dtype=torch.float32) + out = scheduler.step(logits, scheduler.timesteps[0], x_t, return_dict=True) + x_prev = out.prev_sample + + self.assertTrue((x_prev[:, 3:] == x_t[:, 3:]).all().item()) + + def test_step_never_samples_mask_token(self): + scheduler = self.get_scheduler() + # Use a single inference step so the scheduler denoises to t=0 in one go (p_denoise = 1). + scheduler.set_timesteps(1) + + batch_size, seq_len = 2, 12 + x_t = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) + logits = torch.zeros((batch_size, seq_len, scheduler.vocab_size), dtype=torch.float32) + + gen = torch.Generator().manual_seed(0) + x_prev = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen, return_dict=True).prev_sample + + # Mask token is forbidden as an x0 prediction, and the scheduler performs a final noise-removal step. + self.assertTrue((x_prev != scheduler.mask_token_id).all().item()) + + def test_uniform_add_noise_excludes_mask_if_configured(self): + scheduler = self.get_scheduler(forward_process="uniform", exclude_mask_from_uniform=True) + batch_size, seq_len = 8, 64 + x0 = torch.randint(0, scheduler.vocab_size, (batch_size, seq_len), dtype=torch.long) + # Make sure some originals are mask token too (uniform should still sample non-mask replacements). + x0[:, :5] = scheduler.mask_token_id + + # Use the noisiest time (highest replace probability). + timesteps = torch.full((batch_size,), scheduler.num_train_timesteps - 1, dtype=torch.long) + xt = scheduler.add_noise(x0, noise=None, timesteps=timesteps) + + # Mask token should be rare-to-absent under uniform corruption when excluded. + self.assertFalse((xt == scheduler.mask_token_id).any().item()) + + def test_uniform_step_runs_and_returns_valid_ids(self): + scheduler = self.get_scheduler(forward_process="uniform", exclude_mask_from_uniform=True) + scheduler.set_timesteps(2) + + batch_size, seq_len = 2, 16 + x_t = torch.randint(0, scheduler.vocab_size, (batch_size, seq_len), dtype=torch.long) + logits = torch.zeros((batch_size, seq_len, scheduler.vocab_size), dtype=torch.float32) + + gen = torch.Generator().manual_seed(0) + x_prev = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen, return_dict=True).prev_sample + + self.assertEqual(x_prev.shape, x_t.shape) + self.assertTrue(((x_prev >= 0) & (x_prev < scheduler.vocab_size)).all().item()) + # With exclusion, mask token should not appear. + self.assertFalse((x_prev == scheduler.mask_token_id).any().item()) + + +if __name__ == "__main__": + unittest.main() From f4990a91cf6a8139de9bee1d16026e0f9e45781f Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 16 Dec 2025 21:02:20 +0100 Subject: [PATCH 02/44] use the scheduler --- examples/discrete_diffusion/train_mdlm.py | 19 +++++-- examples/discrete_diffusion/train_udlm.py | 20 +++++++ .../schedulers/scheduling_token_diffusion.py | 53 ++++++++++++++++++- .../test_scheduler_token_diffusion.py | 20 +++++++ 4 files changed, 107 insertions(+), 5 deletions(-) diff --git a/examples/discrete_diffusion/train_mdlm.py b/examples/discrete_diffusion/train_mdlm.py index 3e2630b3f4f4..9a511155f6d3 100644 --- a/examples/discrete_diffusion/train_mdlm.py +++ b/examples/discrete_diffusion/train_mdlm.py @@ -62,7 +62,10 @@ class TrainConfig: max_length: int num_train_timesteps: int + alpha_schedule: str eps: float + sigma_min: float + sigma_max: float min_timestep: int @@ -92,7 +95,15 @@ def parse_args() -> TrainConfig: parser.add_argument("--max_length", type=int, default=256) parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument( + "--alpha_schedule", + type=str, + default="log_linear", + choices=["log_linear", "linear", "cosine", "geometric"], + ) parser.add_argument("--eps", type=float, default=1e-3) + parser.add_argument("--sigma_min", type=float, default=1e-4) + parser.add_argument("--sigma_max", type=float, default=20.0) parser.add_argument("--min_timestep", type=int, default=1, help="Avoid t=0 to prevent 1/t weighting blow-ups.") args = parser.parse_args() @@ -141,7 +152,10 @@ def main(): vocab_size=len(tokenizer), mask_token_id=int(tokenizer.mask_token_id), num_train_timesteps=cfg.num_train_timesteps, + alpha_schedule=cfg.alpha_schedule, eps=cfg.eps, + sigma_min=cfg.sigma_min, + sigma_max=cfg.sigma_max, ) raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name) @@ -208,10 +222,7 @@ def main(): per_token_ce = F.cross_entropy(logits.view(-1, logits.shape[-1]), input_ids.view(-1), reduction="none") per_token_ce = per_token_ce.view_as(input_ids) - # Log-linear schedule gives weight proportional to 1 / t_cont, where t_cont in (0, 1]. - t_cont = timesteps.to(dtype=torch.float32) / float(scheduler.num_train_timesteps - 1) - t_cont = t_cont.clamp_min(1e-6) - weights = (1.0 / t_cont).view(-1, 1) + weights = scheduler.get_mdlm_loss_weights(timesteps) loss = (per_token_ce * mask_positions.to(per_token_ce.dtype) * weights).sum() denom = mask_positions.sum().clamp_min(1) diff --git a/examples/discrete_diffusion/train_udlm.py b/examples/discrete_diffusion/train_udlm.py index 2c79eef32299..806bf8f48d0e 100644 --- a/examples/discrete_diffusion/train_udlm.py +++ b/examples/discrete_diffusion/train_udlm.py @@ -62,7 +62,10 @@ class TrainConfig: max_length: int num_train_timesteps: int + alpha_schedule: str eps: float + sigma_min: float + sigma_max: float min_timestep: int exclude_mask_from_uniform: bool @@ -92,7 +95,16 @@ def parse_args() -> TrainConfig: parser.add_argument("--max_length", type=int, default=256) parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument( + "--alpha_schedule", + type=str, + default="log_linear", + choices=["log_linear", "linear", "cosine", "geometric"], + help="UDLM loss in this example is only implemented for log_linear; other choices will error.", + ) parser.add_argument("--eps", type=float, default=1e-3) + parser.add_argument("--sigma_min", type=float, default=1e-4) + parser.add_argument("--sigma_max", type=float, default=20.0) parser.add_argument("--min_timestep", type=int, default=1) parser.add_argument( "--exclude_mask_from_uniform", action="store_true", help="Exclude mask token from uniform draws." @@ -163,6 +175,11 @@ def udlm_diffusion_loss( def main(): cfg = parse_args() + if cfg.alpha_schedule != "log_linear": + raise ValueError( + "This training script implements the UDLM diffusion loss only for `--alpha_schedule=log_linear`. " + "Use that setting or extend the loss derivation for other schedules." + ) project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) accelerator = Accelerator( @@ -189,7 +206,10 @@ def main(): vocab_size=len(tokenizer), mask_token_id=int(tokenizer.mask_token_id), num_train_timesteps=cfg.num_train_timesteps, + alpha_schedule=cfg.alpha_schedule, eps=cfg.eps, + sigma_min=cfg.sigma_min, + sigma_max=cfg.sigma_max, forward_process="uniform", exclude_mask_from_uniform=cfg.exclude_mask_from_uniform, ) diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index 062a8c12ceaa..87dbd5b089de 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -105,7 +105,9 @@ def __init__( f" {alpha_schedule!r}." ) if sigma_min <= 0 or sigma_max <= 0: - raise ValueError(f"`sigma_min` and `sigma_max` must be > 0, got sigma_min={sigma_min}, sigma_max={sigma_max}.") + raise ValueError( + f"`sigma_min` and `sigma_max` must be > 0, got sigma_min={sigma_min}, sigma_max={sigma_max}." + ) if sigma_max <= sigma_min: raise ValueError(f"`sigma_max` must be > `sigma_min`, got sigma_min={sigma_min}, sigma_max={sigma_max}.") if forward_process not in {"absorbing", "uniform"}: @@ -206,6 +208,55 @@ def _alpha_t(self, t: torch.Tensor) -> torch.Tensor: raise ValueError(f"Unsupported alpha schedule: {self.alpha_schedule!r}") + def _alpha_prime_t(self, t: torch.Tensor) -> torch.Tensor: + """ + Compute d/dt alpha(t) for the configured schedule. + """ + if self.alpha_schedule == "log_linear": + return -(1.0 - self.eps) * torch.ones_like(t) + + if self.alpha_schedule == "linear": + return -(1.0 - 2.0 * self.eps) * torch.ones_like(t) + + if self.alpha_schedule == "cosine": + base_prime = -(torch.pi / 2.0) * torch.sin(torch.pi / 2.0 * (1.0 - t)) + return (1.0 - 2.0 * self.eps) * base_prime + + if self.alpha_schedule == "geometric": + sigma_min = torch.as_tensor(self.sigma_min, device=t.device, dtype=t.dtype) + sigma_max = torch.as_tensor(self.sigma_max, device=t.device, dtype=t.dtype) + total_noise = (sigma_min ** (1.0 - t)) * (sigma_max**t) + alpha = (-total_noise).exp() + rate = total_noise * (sigma_max.log() - sigma_min.log()) + return -alpha * rate + + raise ValueError(f"Unsupported alpha schedule: {self.alpha_schedule!r}") + + def get_mdlm_loss_weights(self, timesteps: torch.LongTensor) -> torch.Tensor: + """ + Return per-example positive loss weights for masked-token reconstruction objectives. + + The weight corresponds to `-alpha'(t) / (1 - alpha(t))`, which is positive for monotone decreasing alpha(t). + + Args: + timesteps (`torch.LongTensor` of shape `(batch_size,)`): + Training timestep indices in `[0, num_train_timesteps-1]`. + + Returns: + `torch.FloatTensor` of shape `(batch_size, 1)`: + Positive weights to multiply token-level cross-entropy by. + """ + if timesteps.dtype not in (torch.int32, torch.int64): + raise ValueError(f"`timesteps` must be an integer tensor, got dtype={timesteps.dtype}.") + device = timesteps.device + t = self._t_from_timestep(timesteps.to(device), device=device) + t = t.to(dtype=torch.float32) + alpha = self._alpha_t(t).to(dtype=torch.float32) + dalpha = self._alpha_prime_t(t).to(dtype=torch.float32) + denom = (1.0 - alpha).clamp_min(torch.finfo(torch.float32).eps) + w = (-dalpha / denom).clamp_min(torch.finfo(torch.float32).tiny) + return w.view(-1, 1) + def add_noise( self, original_samples: torch.LongTensor, diff --git a/tests/schedulers/test_scheduler_token_diffusion.py b/tests/schedulers/test_scheduler_token_diffusion.py index 384a2581b2c3..94697c4145aa 100644 --- a/tests/schedulers/test_scheduler_token_diffusion.py +++ b/tests/schedulers/test_scheduler_token_diffusion.py @@ -22,6 +22,26 @@ def test_set_timesteps(self): self.assertEqual(len(scheduler.timesteps), 10) self.assertTrue((scheduler.timesteps[:-1] >= scheduler.timesteps[1:]).all().item()) + def test_alpha_schedule_monotone_and_bounded(self): + # alpha(t) should be in (0, 1] and non-increasing in t for supported schedules. + schedules = ["log_linear", "linear", "cosine", "geometric"] + t = torch.linspace(0, 1, 33, dtype=torch.float32) + + for name in schedules: + scheduler = self.get_scheduler(alpha_schedule=name) + alpha = scheduler._alpha_t(t) + self.assertTrue(((alpha > 0) & (alpha <= 1)).all().item()) + # monotone non-increasing: alpha[i] >= alpha[i+1] + self.assertTrue((alpha[:-1] >= alpha[1:]).all().item()) + + def test_mdlm_weights_match_log_linear_1_over_t(self): + scheduler = self.get_scheduler(alpha_schedule="log_linear", eps=1e-3, num_train_timesteps=1000) + timesteps = torch.tensor([1, 10, 100, 999], dtype=torch.long) + w = scheduler.get_mdlm_loss_weights(timesteps).squeeze(-1) + t_cont = timesteps.to(dtype=torch.float32) / float(scheduler.num_train_timesteps - 1) + expected = 1.0 / t_cont + self.assertTrue(torch.allclose(w, expected, rtol=5e-5, atol=1e-5)) + def test_add_noise_absorbing_keeps_shape_dtype(self): scheduler = self.get_scheduler() batch_size, seq_len = 4, 16 From c74d66daf93f9e255c29cf6880e926e98e79b725 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 16 Dec 2025 21:21:30 +0100 Subject: [PATCH 03/44] added Block-wise sampling --- examples/discrete_diffusion/README.md | 14 ++ .../sample_block_token_diffusion.py | 66 ++++++ src/diffusers/__init__.py | 13 +- src/diffusers/pipelines/__init__.py | 2 + .../block_token_diffusion/__init__.py | 18 ++ .../pipeline_block_token_diffusion.py | 215 ++++++++++++++++++ src/diffusers/schedulers/__init__.py | 1 + .../scheduling_block_token_diffusion.py | 95 ++++++++ .../test_pipeline_block_token_diffusion.py | 59 +++++ .../test_scheduler_block_token_diffusion.py | 30 +++ 10 files changed, 512 insertions(+), 1 deletion(-) create mode 100644 examples/discrete_diffusion/sample_block_token_diffusion.py create mode 100644 src/diffusers/pipelines/block_token_diffusion/__init__.py create mode 100644 src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py create mode 100644 src/diffusers/schedulers/scheduling_block_token_diffusion.py create mode 100644 tests/pipelines/test_pipeline_block_token_diffusion.py create mode 100644 tests/schedulers/test_scheduler_block_token_diffusion.py diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index 76cd46eb7097..be7e1fe70ec3 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -36,6 +36,20 @@ python examples/discrete_diffusion/sample_mdlm.py \ --num_inference_steps 128 ``` +## Block-wise sampling + +Block-wise sampling updates the sequence in chunks, refining only the active block at a time. + +```bash +python examples/discrete_diffusion/sample_block_token_diffusion.py \ + --checkpoint_path mdlm-output/final \ + --num_samples 4 \ + --seq_len 256 \ + --block_size 32 \ + --num_inference_steps 64 \ + --top_p 0.9 +``` + ## UDLM-style uniform diffusion `train_udlm.py` trains a uniform token diffusion model: diff --git a/examples/discrete_diffusion/sample_block_token_diffusion.py b/examples/discrete_diffusion/sample_block_token_diffusion.py new file mode 100644 index 000000000000..5fb7c5a3d121 --- /dev/null +++ b/examples/discrete_diffusion/sample_block_token_diffusion.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python + +import argparse +from typing import Optional + +import torch +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from diffusers import BlockTokenDiffusionPipeline, BlockTokenDiffusionScheduler + + +def parse_args(): + parser = argparse.ArgumentParser(description="Sample with block-wise token diffusion.") + parser.add_argument( + "--checkpoint_path", type=str, required=True, help="Path saved by train scripts (or compatible)." + ) + parser.add_argument("--prompt", type=str, default=None, help="Optional prompt; will be used as a fixed prefix.") + parser.add_argument("--num_samples", type=int, default=4) + parser.add_argument("--seq_len", type=int, default=64) + parser.add_argument("--block_size", type=int, default=16) + parser.add_argument("--num_inference_steps", type=int, default=32) + parser.add_argument("--top_p", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--inject_start_token", action="store_true") + return parser.parse_args() + + +def main(): + args = parse_args() + device = torch.device(args.device) + + tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True) + model = AutoModelForMaskedLM.from_pretrained(args.checkpoint_path).to(device) + scheduler = BlockTokenDiffusionScheduler.from_pretrained(args.checkpoint_path) + + pipe = BlockTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to(device) + model.eval() + + generator: Optional[torch.Generator] = torch.Generator(device=device).manual_seed(args.seed) + + prefix_ids = None + if args.prompt is not None: + encoded = tokenizer(args.prompt, return_tensors="pt", add_special_tokens=True) + prefix_ids = encoded["input_ids"].to(device=device, dtype=torch.long) + if prefix_ids.shape[1] > args.seq_len: + raise ValueError(f"--seq_len ({args.seq_len}) must be >= prompt length ({prefix_ids.shape[1]}).") + + out = pipe( + batch_size=args.num_samples, + seq_len=args.seq_len, + block_size=args.block_size, + num_inference_steps=args.num_inference_steps, + generator=generator, + prefix_ids=prefix_ids, + inject_start_token=args.inject_start_token, + top_p=args.top_p, + return_text=True, + ) + + for i, t in enumerate(out.texts or []): + print(f"[{i}] {t}") + + +if __name__ == "__main__": + main() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index d501eb07d5fb..e095c63ea3d3 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -364,10 +364,18 @@ "UnCLIPScheduler", "UniPCMultistepScheduler", "VQDiffusionScheduler", + "BlockTokenDiffusionScheduler", ] ) _import_structure["training_utils"] = ["EMAModel"] - _import_structure["pipelines"].extend(["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"]) + _import_structure["pipelines"].extend( + [ + "BlockTokenDiffusionPipeline", + "BlockTokenDiffusionPipelineOutput", + "TokenDiffusionPipeline", + "TokenDiffusionPipelineOutput", + ] + ) try: if not (is_torch_available() and is_scipy_available()): @@ -1044,6 +1052,8 @@ AutoPipelineForText2Image, BlipDiffusionControlNetPipeline, BlipDiffusionPipeline, + BlockTokenDiffusionPipeline, + BlockTokenDiffusionPipelineOutput, CLIPImageProjection, ConsistencyModelPipeline, DanceDiffusionPipeline, @@ -1065,6 +1075,7 @@ from .quantizers import DiffusersQuantizer from .schedulers import ( AmusedScheduler, + BlockTokenDiffusionScheduler, CMStochasticIterativeScheduler, CogVideoXDDIMScheduler, CogVideoXDPMScheduler, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index c77a5021ee12..f8c136f1c9cc 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -47,6 +47,7 @@ "AutoPipelineForInpainting", "AutoPipelineForText2Image", ] + _import_structure["block_token_diffusion"] = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] _import_structure["consistency_models"] = ["ConsistencyModelPipeline"] _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"] _import_structure["ddim"] = ["DDIMPipeline"] @@ -545,6 +546,7 @@ AutoPipelineForInpainting, AutoPipelineForText2Image, ) + from .block_token_diffusion import BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput from .consistency_models import ConsistencyModelPipeline from .dance_diffusion import DanceDiffusionPipeline from .ddim import DDIMPipeline diff --git a/src/diffusers/pipelines/block_token_diffusion/__init__.py b/src/diffusers/pipelines/block_token_diffusion/__init__.py new file mode 100644 index 000000000000..ab331c11f765 --- /dev/null +++ b/src/diffusers/pipelines/block_token_diffusion/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pipeline_block_token_diffusion import BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput + + +__all__ = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py new file mode 100644 index 000000000000..24aebdc9d945 --- /dev/null +++ b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py @@ -0,0 +1,215 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, List, Optional, Tuple, Union + +import torch + +from ...utils import BaseOutput +from ..pipeline_utils import DiffusionPipeline + + +@dataclass +class BlockTokenDiffusionPipelineOutput(BaseOutput): + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +def _top_p_filtering(logits: torch.Tensor, top_p: float) -> torch.Tensor: + if top_p >= 1.0: + return logits + if not (0.0 < top_p <= 1.0): + raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") + + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = torch.softmax(sorted_logits, dim=-1) + cumulative_probs = sorted_probs.cumsum(dim=-1) + + sorted_indices_to_remove = cumulative_probs > top_p + sorted_indices_to_remove[..., 0] = 0 + + sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) + filtered = logits.scatter(-1, sorted_indices, sorted_logits) + return filtered + + +class BlockTokenDiffusionPipeline(DiffusionPipeline): + """ + Block-wise token diffusion sampling pipeline. + + Compared to `TokenDiffusionPipeline`, this pipeline updates the sequence in blocks. Only the current block's + positions are allowed to change during the inner denoising loop. + """ + + model: Any + tokenizer: Any + scheduler: Any + + def __init__(self, model: Any, scheduler: Any, tokenizer: Optional[Any] = None): + super().__init__() + self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) + + def _resolve_start_token_id(self) -> Optional[int]: + tok = getattr(self, "tokenizer", None) + if tok is None: + return None + for attr in ("bos_token_id", "cls_token_id"): + token_id = getattr(tok, attr, None) + if token_id is not None: + return int(token_id) + return None + + def _normalize_prefix_ids( + self, prefix_ids: torch.LongTensor, batch_size: int, device: torch.device + ) -> torch.LongTensor: + if prefix_ids.ndim == 1: + prefix_ids = prefix_ids.unsqueeze(0) + if prefix_ids.ndim != 2: + raise ValueError( + f"`prefix_ids` must have shape [prefix_len] or [batch, prefix_len], got {prefix_ids.shape}." + ) + if prefix_ids.shape[0] not in (1, batch_size): + raise ValueError( + f"`prefix_ids` batch dim must be 1 or batch_size={batch_size}, got {prefix_ids.shape[0]}." + ) + if prefix_ids.dtype != torch.long: + raise ValueError(f"`prefix_ids` must be int64 token IDs, got dtype={prefix_ids.dtype}.") + prefix_ids = prefix_ids.to(device=device) + if prefix_ids.shape[0] == 1 and batch_size > 1: + prefix_ids = prefix_ids.expand(batch_size, -1) + return prefix_ids + + def _init_latents( + self, + batch_size: int, + seq_len: int, + *, + generator: Optional[torch.Generator], + device: torch.device, + ) -> torch.LongTensor: + mask_token_id = getattr(self.scheduler, "mask_token_id", None) + if mask_token_id is None: + raise ValueError("Scheduler must define `mask_token_id` for block diffusion sampling.") + return torch.full((batch_size, seq_len), int(mask_token_id), device=device, dtype=torch.long) + + @torch.no_grad() + def __call__( + self, + *, + batch_size: int = 1, + seq_len: int = 64, + block_size: int = 32, + num_inference_steps: int = 64, + generator: Optional[torch.Generator] = None, + prefix_ids: Optional[torch.LongTensor] = None, + infill_mask: Optional[torch.BoolTensor] = None, + inject_start_token: bool = False, + top_p: float = 1.0, + return_text: bool = True, + return_dict: bool = True, + **model_kwargs, + ) -> Union[BlockTokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + device = self._execution_device + self.scheduler.set_timesteps(num_inference_steps, device=device) + + input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + + fixed_mask = None + fixed_values = None + if infill_mask is not None: + if infill_mask.shape != (batch_size, seq_len): + raise ValueError( + f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}." + ) + fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) + fixed_values = input_ids.clone() + + if prefix_ids is not None: + prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) + prefix_len = prefix_ids.shape[1] + if prefix_len > seq_len: + raise ValueError(f"`prefix_ids` length {prefix_len} must be <= seq_len={seq_len}.") + + input_ids[:, :prefix_len] = prefix_ids + if fixed_mask is None: + fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) + fixed_values = input_ids.clone() + fixed_mask[:, :prefix_len] = True + fixed_values[:, :prefix_len] = prefix_ids + + start_token_id = self._resolve_start_token_id() + if inject_start_token and start_token_id is not None: + input_ids[:, 0] = start_token_id + if fixed_mask is None: + fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) + fixed_values = input_ids.clone() + fixed_mask[:, 0] = True + fixed_values[:, 0] = start_token_id + + if block_size <= 0 or block_size > seq_len: + raise ValueError(f"`block_size` must be in [1, seq_len], got block_size={block_size}, seq_len={seq_len}.") + + num_blocks = (seq_len + block_size - 1) // block_size + for block_idx in range(num_blocks): + start = block_idx * block_size + end = min((block_idx + 1) * block_size, seq_len) + + block_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) + block_mask[:, start:end] = True + if fixed_mask is not None: + block_mask = block_mask & (~fixed_mask) + + if not torch.any(block_mask): + continue + + input_ids = torch.where(block_mask, int(self.scheduler.mask_token_id), input_ids) + + for t in self.scheduler.timesteps: + out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) + logits = getattr(out, "logits", None) + if logits is None: + logits = out[0] + + if top_p < 1.0: + logits_block = logits[block_mask].view(-1, logits.shape[-1]) + logits_block = _top_p_filtering(logits_block, top_p=top_p) + logits = logits.clone() + logits[block_mask] = logits_block.view(-1, logits.shape[-1]) + + input_ids = self.scheduler.step( + logits, + t, + input_ids, + generator=generator, + return_dict=True, + block_mask=block_mask, + ).prev_sample + + if fixed_mask is not None: + input_ids = torch.where(fixed_mask, fixed_values, input_ids) + + texts = None + if return_text and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) + + if not return_dict: + return (input_ids, texts) + return BlockTokenDiffusionPipelineOutput(sequences=input_ids, texts=texts) + + +__all__ = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 699bbf69c2c8..ab47da99ceab 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -48,6 +48,7 @@ _import_structure["scheduling_ddim_parallel"] = ["DDIMParallelScheduler"] _import_structure["scheduling_ddpm"] = ["DDPMScheduler"] _import_structure["scheduling_ddpm_parallel"] = ["DDPMParallelScheduler"] + _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_ddpm_wuerstchen"] = ["DDPMWuerstchenScheduler"] _import_structure["scheduling_deis_multistep"] = ["DEISMultistepScheduler"] _import_structure["scheduling_dpm_cogvideox"] = ["CogVideoXDPMScheduler"] diff --git a/src/diffusers/schedulers/scheduling_block_token_diffusion.py b/src/diffusers/schedulers/scheduling_block_token_diffusion.py new file mode 100644 index 000000000000..4d743cf93dbc --- /dev/null +++ b/src/diffusers/schedulers/scheduling_block_token_diffusion.py @@ -0,0 +1,95 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from typing import Optional, Tuple, Union + +import torch + +from .scheduling_token_diffusion import TokenDiffusionScheduler, TokenDiffusionSchedulerOutput + + +class BlockTokenDiffusionScheduler(TokenDiffusionScheduler): + """ + A token diffusion scheduler that supports updating only a subset of positions (e.g. a block). + + This scheduler reuses the same alpha schedules and forward processes as `TokenDiffusionScheduler`, but allows + callers to restrict noising/denoising to a boolean `block_mask` of shape `[batch, seq_len]`. + """ + + @classmethod + def from_config(cls, config, **kwargs): + # TokenDiffusionScheduler doesn't have compatibles; keep standard ConfigMixin behavior. + return super().from_config(config, **kwargs) + + def add_noise( + self, + original_samples: torch.LongTensor, + noise: Optional[torch.Tensor], + timesteps: torch.LongTensor, + block_mask: Optional[torch.BoolTensor] = None, + ) -> torch.LongTensor: + if block_mask is None: + return super().add_noise(original_samples=original_samples, noise=noise, timesteps=timesteps) + + if block_mask.dtype != torch.bool: + raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") + if block_mask.shape != original_samples.shape: + raise ValueError( + f"`block_mask` must have shape {tuple(original_samples.shape)}, got {tuple(block_mask.shape)}." + ) + + noised = super().add_noise(original_samples=original_samples, noise=noise, timesteps=timesteps) + return torch.where(block_mask.to(device=original_samples.device), noised, original_samples) + + def step( + self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.LongTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + block_mask: Optional[torch.BoolTensor] = None, + ) -> Union[TokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: + if block_mask is None: + return super().step( + model_output=model_output, + timestep=timestep, + sample=sample, + generator=generator, + return_dict=return_dict, + ) + + if block_mask.dtype != torch.bool: + raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") + if block_mask.shape != sample.shape: + raise ValueError(f"`block_mask` must have shape {tuple(sample.shape)}, got {tuple(block_mask.shape)}.") + + out = super().step( + model_output=model_output, + timestep=timestep, + sample=sample, + generator=generator, + return_dict=True, + ) + prev = out.prev_sample + prev = torch.where(block_mask.to(device=prev.device), prev, sample) + + if not return_dict: + return (prev,) + return TokenDiffusionSchedulerOutput(prev_sample=prev) + + +__all__ = ["BlockTokenDiffusionScheduler"] diff --git a/tests/pipelines/test_pipeline_block_token_diffusion.py b/tests/pipelines/test_pipeline_block_token_diffusion.py new file mode 100644 index 000000000000..e005310be974 --- /dev/null +++ b/tests/pipelines/test_pipeline_block_token_diffusion.py @@ -0,0 +1,59 @@ +import unittest + +import torch + +from diffusers import BlockTokenDiffusionPipeline, BlockTokenDiffusionScheduler + + +class _DummyTokenizer: + cls_token_id = 1 + bos_token_id = None + + def batch_decode(self, sequences, skip_special_tokens=True): + return [" ".join(map(str, row)) for row in sequences.tolist()] + + +class _DummyModelOutput: + def __init__(self, logits): + self.logits = logits + + +class _DummyMLM(torch.nn.Module): + def __init__(self, vocab_size: int): + super().__init__() + self.vocab_size = vocab_size + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def forward(self, input_ids=None, attention_mask=None, **kwargs): + batch_size, seq_len = input_ids.shape + logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) + return _DummyModelOutput(logits=logits) + + +class BlockTokenDiffusionPipelineTest(unittest.TestCase): + def test_pipeline_runs_and_respects_prefix(self): + vocab_size = 32 + scheduler = BlockTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + pipe = BlockTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to("cpu") + + prefix = torch.tensor([5, 6, 7], dtype=torch.long) + gen = torch.Generator().manual_seed(0) + out = pipe(batch_size=2, seq_len=10, block_size=4, num_inference_steps=2, generator=gen, prefix_ids=prefix) + + self.assertEqual(out.sequences.shape, (2, 10)) + self.assertTrue((out.sequences[:, :3] == prefix.view(1, -1)).all().item()) + self.assertEqual(len(out.texts), 2) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_block_token_diffusion.py b/tests/schedulers/test_scheduler_block_token_diffusion.py new file mode 100644 index 000000000000..7b5e833b21d0 --- /dev/null +++ b/tests/schedulers/test_scheduler_block_token_diffusion.py @@ -0,0 +1,30 @@ +import unittest + +import torch + +from diffusers import BlockTokenDiffusionScheduler + + +class BlockTokenDiffusionSchedulerTest(unittest.TestCase): + def test_step_respects_block_mask(self): + vocab_size = 32 + scheduler = BlockTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) + scheduler.set_timesteps(1) + + batch_size, seq_len = 2, 8 + x = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) + block_mask = torch.zeros_like(x, dtype=torch.bool) + block_mask[:, :4] = True + + logits = torch.zeros((batch_size, seq_len, vocab_size), dtype=torch.float32) + gen = torch.Generator().manual_seed(0) + out = scheduler.step(logits, scheduler.timesteps[0], x, generator=gen, return_dict=True, block_mask=block_mask) + + # Block positions should be denoised (non-mask) after the final noise-removal step. + self.assertTrue((out.prev_sample[:, :4] != scheduler.mask_token_id).all().item()) + # Outside the block, tokens should remain unchanged (still mask). + self.assertTrue((out.prev_sample[:, 4:] == scheduler.mask_token_id).all().item()) + + +if __name__ == "__main__": + unittest.main() From 9a9470cb45a02376e5fd276cef2aa4df7934e71b Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 16 Dec 2025 21:30:05 +0100 Subject: [PATCH 04/44] add hybrid --- src/diffusers/__init__.py | 6 + src/diffusers/pipelines/__init__.py | 2 + .../hybrid_token_diffusion/__init__.py | 18 ++ .../pipeline_hybrid_token_diffusion.py | 24 ++ src/diffusers/schedulers/__init__.py | 4 + .../scheduling_hybrid_token_diffusion.py | 243 ++++++++++++++++++ .../test_pipeline_hybrid_token_diffusion.py | 56 ++++ .../test_scheduler_hybrid_token_diffusion.py | 29 +++ 8 files changed, 382 insertions(+) create mode 100644 src/diffusers/pipelines/hybrid_token_diffusion/__init__.py create mode 100644 src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py create mode 100644 src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py create mode 100644 tests/pipelines/test_pipeline_hybrid_token_diffusion.py create mode 100644 tests/schedulers/test_scheduler_hybrid_token_diffusion.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index e095c63ea3d3..8f8cb519fea9 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -365,6 +365,8 @@ "UniPCMultistepScheduler", "VQDiffusionScheduler", "BlockTokenDiffusionScheduler", + "HybridTokenDiffusionScheduler", + "HybridTokenDiffusionSchedulerOutput", ] ) _import_structure["training_utils"] = ["EMAModel"] @@ -372,6 +374,7 @@ [ "BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput", + "HybridTokenDiffusionPipeline", "TokenDiffusionPipeline", "TokenDiffusionPipelineOutput", ] @@ -1061,6 +1064,7 @@ DDPMPipeline, DiffusionPipeline, DiTPipeline, + HybridTokenDiffusionPipeline, ImagePipelineOutput, KarrasVePipeline, LDMPipeline, @@ -1097,6 +1101,8 @@ FlowMatchHeunDiscreteScheduler, FlowMatchLCMScheduler, HeunDiscreteScheduler, + HybridTokenDiffusionScheduler, + HybridTokenDiffusionSchedulerOutput, IPNDMScheduler, KarrasVeScheduler, KDPM2AncestralDiscreteScheduler, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index f8c136f1c9cc..4feb16b3a629 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -53,6 +53,7 @@ _import_structure["ddim"] = ["DDIMPipeline"] _import_structure["ddpm"] = ["DDPMPipeline"] _import_structure["dit"] = ["DiTPipeline"] + _import_structure["hybrid_token_diffusion"] = ["HybridTokenDiffusionPipeline"] _import_structure["token_diffusion"] = ["TokenDiffusionPipeline", "TokenDiffusionPipelineOutput"] _import_structure["latent_diffusion"].extend(["LDMSuperResolutionPipeline"]) _import_structure["pipeline_utils"] = [ @@ -553,6 +554,7 @@ from .ddpm import DDPMPipeline from .deprecated import KarrasVePipeline, LDMPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline from .dit import DiTPipeline + from .hybrid_token_diffusion import HybridTokenDiffusionPipeline from .latent_diffusion import LDMSuperResolutionPipeline from .pipeline_utils import ( AudioPipelineOutput, diff --git a/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py b/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py new file mode 100644 index 000000000000..9fb3efbe4709 --- /dev/null +++ b/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pipeline_hybrid_token_diffusion import HybridTokenDiffusionPipeline + + +__all__ = ["HybridTokenDiffusionPipeline"] diff --git a/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py b/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py new file mode 100644 index 000000000000..bdc372960dee --- /dev/null +++ b/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py @@ -0,0 +1,24 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from ..token_diffusion.pipeline_token_diffusion import TokenDiffusionPipeline + + +class HybridTokenDiffusionPipeline(TokenDiffusionPipeline): + """Alias of `TokenDiffusionPipeline` for hybrid-transition schedulers.""" + + +__all__ = ["HybridTokenDiffusionPipeline"] diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index ab47da99ceab..e0f32d79a44a 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -51,6 +51,10 @@ _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_ddpm_wuerstchen"] = ["DDPMWuerstchenScheduler"] _import_structure["scheduling_deis_multistep"] = ["DEISMultistepScheduler"] + _import_structure["scheduling_hybrid_token_diffusion"] = [ + "HybridTokenDiffusionScheduler", + "HybridTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_dpm_cogvideox"] = ["CogVideoXDPMScheduler"] _import_structure["scheduling_dpmsolver_multistep"] = ["DPMSolverMultistepScheduler"] _import_structure["scheduling_dpmsolver_multistep_inverse"] = ["DPMSolverMultistepInverseScheduler"] diff --git a/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py new file mode 100644 index 000000000000..70982cc70d24 --- /dev/null +++ b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py @@ -0,0 +1,243 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput +from .scheduling_token_diffusion import _gumbel_argmax +from .scheduling_utils import SchedulerMixin + + +@dataclass +class HybridTokenDiffusionSchedulerOutput(BaseOutput): + prev_sample: torch.LongTensor + + +class HybridTokenDiffusionScheduler(SchedulerMixin, ConfigMixin): + """ + Hybrid-transition discrete token diffusion scheduler. + + This scheduler defines a forward transition kernel that mixes: + - keeping the current token (scaled by alpha(t)) + - moving toward a mixture distribution over tokens (beta_pi(t)) + + The scheduler exposes: + - `add_noise(...)` for forward corruption + - `step(...)` for reverse updates using the model's predicted token distribution + """ + + order = 1 + + @register_to_config + def __init__( + self, + vocab_size: int, + mask_token_id: int, + num_train_timesteps: int = 1000, + t_eps: float = 1e-4, + p_uniform: float = 0.0, + clip_noise: float = 20.0, + gamma: float = 1.0, + ): + if vocab_size <= 0: + raise ValueError(f"`vocab_size` must be > 0, got {vocab_size}.") + if not (0 <= mask_token_id < vocab_size): + raise ValueError(f"`mask_token_id` must be in [0, vocab_size), got {mask_token_id}.") + if num_train_timesteps <= 1: + raise ValueError(f"`num_train_timesteps` must be > 1, got {num_train_timesteps}.") + if not (0.0 < t_eps < 0.5): + raise ValueError(f"`t_eps` must be in (0, 0.5), got {t_eps}.") + if gamma <= 0: + raise ValueError(f"`gamma` must be > 0, got {gamma}.") + + self.vocab_size = int(vocab_size) + self.mask_token_id = int(mask_token_id) + self.num_train_timesteps = int(num_train_timesteps) + self.t_eps = float(t_eps) + + p_uniform = max(math.exp(-float(clip_noise)), float(p_uniform)) + log_B = float(gamma) * math.log(2.0) + math.log(p_uniform) - math.log(1.0 - p_uniform) + log_B = float(np.clip(log_B, -float(clip_noise), float(clip_noise))) + self.log_B = float(log_B) + self.log_gamma = float(math.log(float(gamma))) + + self.num_inference_steps = None + self.timesteps = None + self._timesteps_with_end = None + + mask = torch.zeros(self.vocab_size, dtype=torch.float32) + mask[self.mask_token_id] = 1.0 + self.mask = mask + + unif = (1.0 - mask) / max(self.vocab_size - 1, 1) + self.unif = unif + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + self.num_inference_steps = int(num_inference_steps) + + t0 = 1.0 - float(self.t_eps) + t1 = float(self.t_eps) + timesteps = torch.linspace(t0, t1, self.num_inference_steps + 1, dtype=torch.float32, device=device) + self._timesteps_with_end = timesteps + self.timesteps = timesteps[:-1] + + def scale_model_input( + self, sample: torch.Tensor, timestep: Optional[Union[int, torch.Tensor]] = None + ) -> torch.Tensor: + return sample + + def _to_continuous_t(self, timesteps: torch.Tensor, device: torch.device) -> torch.Tensor: + if timesteps.dtype in (torch.float16, torch.float32, torch.float64, torch.bfloat16): + t = timesteps.to(device=device, dtype=torch.float32) + return t.clamp(float(self.t_eps), 1.0 - float(self.t_eps)) + + if timesteps.dtype not in (torch.int32, torch.int64): + raise ValueError(f"`timesteps` must be float or int, got dtype={timesteps.dtype}.") + + t = timesteps.to(device=device, dtype=torch.float32) / float(self.num_train_timesteps - 1) + t = (1.0 - 2.0 * float(self.t_eps)) * t + float(self.t_eps) + return t.clamp(float(self.t_eps), 1.0 - float(self.t_eps)) + + def _get_alpha_betapi(self, t: torch.Tensor, eps: float = 1e-6) -> Tuple[torch.Tensor, torch.Tensor]: + t = t.view(-1, 1) + t1m = 1.0 - t + + gamma = float(math.exp(self.log_gamma)) + B = float(math.exp(self.log_B)) + c_t = (t.pow(gamma / 2.0) * t1m.pow(gamma / 2.0) * B).to(dtype=torch.float32) + C_t = (1.0 + c_t).clamp_min(eps) + + alpha_t = t1m / C_t + beta_pi = ( + t * self.mask.to(device=t.device, dtype=torch.float32) + + c_t * self.unif.to(device=t.device, dtype=torch.float32) + ) / C_t + return alpha_t, beta_pi + + def _probs_at_t(self, probs: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + alpha_t, beta_pi = self._get_alpha_betapi(t) + alpha_t = alpha_t.to(dtype=probs.dtype) + beta_pi = beta_pi.to(dtype=probs.dtype) + + out = probs.mul(alpha_t.unsqueeze(1)) + out[..., : beta_pi.shape[-1]].add_(beta_pi.unsqueeze(1)) + return out + + def _sample_categorical(self, probs: torch.Tensor, generator: Optional[torch.Generator]) -> torch.LongTensor: + bsz, seqlen, vocab = probs.shape + flat = probs.view(-1, vocab).clamp_min(torch.finfo(probs.dtype).tiny) + flat = flat / flat.sum(dim=-1, keepdim=True).clamp_min(torch.finfo(probs.dtype).eps) + sample = torch.multinomial(flat, num_samples=1, generator=generator).view(bsz, seqlen) + return sample.to(dtype=torch.long) + + def add_noise( + self, + original_samples: torch.LongTensor, + noise: Optional[torch.Tensor], + timesteps: torch.Tensor, + ) -> torch.LongTensor: + del noise + if original_samples.dtype != torch.long: + raise ValueError(f"`original_samples` must be int64 token IDs, got dtype={original_samples.dtype}.") + + device = original_samples.device + t = self._to_continuous_t(timesteps.to(device=device), device=device) + onehot = F.one_hot(original_samples, num_classes=self.vocab_size).to(dtype=torch.float32) + probs = self._probs_at_t(onehot, t) + return self._sample_categorical(probs, generator=None) + + def _index_for_timestep(self, timestep: Union[float, torch.Tensor]) -> int: + if self.timesteps is None: + raise ValueError("Call `set_timesteps(...)` before calling `step()`.") + + if isinstance(timestep, torch.Tensor): + t = float(timestep.detach().cpu().item()) + else: + t = float(timestep) + + idx = int(torch.argmin(torch.abs(self.timesteps.detach().cpu() - torch.tensor(t))).item()) + return idx + + def step( + self, + model_output: torch.Tensor, + timestep: Union[float, torch.Tensor], + sample: torch.LongTensor, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[HybridTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: + if sample.dtype != torch.long: + raise ValueError(f"`sample` must be int64 token IDs, got dtype={sample.dtype}.") + if model_output.ndim != 3 or model_output.shape[-1] != self.vocab_size: + raise ValueError( + f"`model_output` must have shape [batch, seq_len, vocab_size={self.vocab_size}], got {tuple(model_output.shape)}." + ) + if model_output.shape[0] != sample.shape[0] or model_output.shape[1] != sample.shape[1]: + raise ValueError( + f"`model_output` batch/seq dims {tuple(model_output.shape[:2])} must match `sample` {tuple(sample.shape)}." + ) + + if self._timesteps_with_end is None: + raise ValueError("Call `set_timesteps(...)` before calling `step()`.") + + device = sample.device + batch_size, seq_len = sample.shape + + step_index = self._index_for_timestep(timestep) + t_val = self._timesteps_with_end[step_index].to(device=device) + s_val = self._timesteps_with_end[step_index + 1].to(device=device) + + t = t_val * torch.ones(batch_size, device=device, dtype=torch.float32) + s = s_val * torch.ones(batch_size, device=device, dtype=torch.float32) + + logits = model_output.to(dtype=torch.float32) + logits = logits.clone() + logits[..., self.mask_token_id] = torch.finfo(logits.dtype).min + probs = logits.softmax(dim=-1) + + q_s = self._probs_at_t(probs, s) + q_t = self._probs_at_t(probs, t) + q_zt = q_t.gather(-1, sample.unsqueeze(-1)).clamp_min(torch.finfo(torch.float32).eps) + + alpha_t, beta_pi_t = self._get_alpha_betapi(t) + alpha_s, beta_pi_s = self._get_alpha_betapi(s) + + alpha_ts = (alpha_t / alpha_s).clamp_min(torch.finfo(torch.float32).eps) + beta_pi_ts = beta_pi_t - (alpha_t / alpha_s) * beta_pi_s + + vz_t = F.one_hot(sample, num_classes=self.vocab_size).to(dtype=torch.float32) + beta_pi_ts_at_zt = beta_pi_ts.unsqueeze(1).expand_as(vz_t).gather(-1, sample.unsqueeze(-1)) + q_ts = alpha_ts.view(batch_size, 1, 1) * vz_t + beta_pi_ts_at_zt + + q_st = q_ts * q_s / q_zt + q_st = q_st.clamp_min(torch.finfo(torch.float32).tiny) + q_st = q_st / q_st.sum(dim=-1, keepdim=True).clamp_min(torch.finfo(torch.float32).eps) + + x_prev = _gumbel_argmax(torch.log(q_st), generator=generator).to(dtype=torch.long) + + if not return_dict: + return (x_prev,) + return HybridTokenDiffusionSchedulerOutput(prev_sample=x_prev) + + +__all__ = ["HybridTokenDiffusionScheduler", "HybridTokenDiffusionSchedulerOutput"] diff --git a/tests/pipelines/test_pipeline_hybrid_token_diffusion.py b/tests/pipelines/test_pipeline_hybrid_token_diffusion.py new file mode 100644 index 000000000000..25c2f7a9ae82 --- /dev/null +++ b/tests/pipelines/test_pipeline_hybrid_token_diffusion.py @@ -0,0 +1,56 @@ +import unittest + +import torch + +from diffusers import HybridTokenDiffusionPipeline, HybridTokenDiffusionScheduler + + +class _DummyTokenizer: + cls_token_id = 1 + bos_token_id = None + + def batch_decode(self, sequences, skip_special_tokens=True): + return [" ".join(map(str, row)) for row in sequences.tolist()] + + +class _DummyModelOutput: + def __init__(self, logits): + self.logits = logits + + +class _DummyMLM(torch.nn.Module): + def __init__(self, vocab_size: int): + super().__init__() + self.vocab_size = vocab_size + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def forward(self, input_ids=None, attention_mask=None, **kwargs): + batch_size, seq_len = input_ids.shape + logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) + return _DummyModelOutput(logits=logits) + + +class HybridTokenDiffusionPipelineTest(unittest.TestCase): + def test_pipeline_runs(self): + vocab_size = 32 + scheduler = HybridTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) + model = _DummyMLM(vocab_size=vocab_size) + tokenizer = _DummyTokenizer() + pipe = HybridTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to("cpu") + + gen = torch.Generator().manual_seed(0) + out = pipe(batch_size=2, seq_len=8, num_inference_steps=2, generator=gen, inject_start_token=True) + self.assertEqual(out.sequences.shape, (2, 8)) + self.assertEqual(len(out.texts), 2) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_hybrid_token_diffusion.py b/tests/schedulers/test_scheduler_hybrid_token_diffusion.py new file mode 100644 index 000000000000..a3146698f22b --- /dev/null +++ b/tests/schedulers/test_scheduler_hybrid_token_diffusion.py @@ -0,0 +1,29 @@ +import unittest + +import torch + +from diffusers import HybridTokenDiffusionScheduler + + +class HybridTokenDiffusionSchedulerTest(unittest.TestCase): + def test_add_noise_and_step_shapes(self): + vocab_size = 32 + scheduler = HybridTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) + scheduler.set_timesteps(4, device="cpu") + + batch_size, seq_len = 2, 8 + x0 = torch.randint(0, vocab_size, (batch_size, seq_len), dtype=torch.long) + timesteps = torch.randint(0, scheduler.num_train_timesteps, (batch_size,), dtype=torch.long) + x_t = scheduler.add_noise(x0, noise=None, timesteps=timesteps) + self.assertEqual(x_t.shape, x0.shape) + self.assertEqual(x_t.dtype, torch.long) + + logits = torch.zeros((batch_size, seq_len, vocab_size), dtype=torch.float32) + gen = torch.Generator().manual_seed(0) + out = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen, return_dict=True) + self.assertEqual(out.prev_sample.shape, x0.shape) + self.assertTrue(((out.prev_sample >= 0) & (out.prev_sample < vocab_size)).all().item()) + + +if __name__ == "__main__": + unittest.main() From 886a76d2fb93b92663cb2c3c138045b689cb73bf Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 16 Dec 2025 21:41:16 +0100 Subject: [PATCH 05/44] hybrid sample training --- examples/discrete_diffusion/README.md | 24 ++ .../sample_hybrid_token_diffusion.py | 62 +++++ .../train_hybrid_token_diffusion.py | 232 ++++++++++++++++++ 3 files changed, 318 insertions(+) create mode 100644 examples/discrete_diffusion/sample_hybrid_token_diffusion.py create mode 100644 examples/discrete_diffusion/train_hybrid_token_diffusion.py diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index be7e1fe70ec3..72351324dd53 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -50,6 +50,30 @@ python examples/discrete_diffusion/sample_block_token_diffusion.py \ --top_p 0.9 ``` +## Hybrid sampling + +Hybrid sampling uses a different transition kernel than absorbing/uniform diffusion and requires a compatible scheduler +configuration saved in the checkpoint directory. + +```bash +python examples/discrete_diffusion/sample_hybrid_token_diffusion.py \ + --checkpoint_path hybrid-output/final \ + --num_samples 4 \ + --seq_len 256 \ + --num_inference_steps 64 +``` + +### Train + +```bash +accelerate launch examples/discrete_diffusion/train_hybrid_token_diffusion.py \ + --model_name_or_path bert-base-uncased \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --output_dir hybrid-output \ + --max_train_steps 1000 +``` + ## UDLM-style uniform diffusion `train_udlm.py` trains a uniform token diffusion model: diff --git a/examples/discrete_diffusion/sample_hybrid_token_diffusion.py b/examples/discrete_diffusion/sample_hybrid_token_diffusion.py new file mode 100644 index 000000000000..81f35ae5b9c6 --- /dev/null +++ b/examples/discrete_diffusion/sample_hybrid_token_diffusion.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python + +import argparse +from typing import Optional + +import torch +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from diffusers import HybridTokenDiffusionPipeline, HybridTokenDiffusionScheduler + + +def parse_args(): + parser = argparse.ArgumentParser(description="Sample with a hybrid-transition token diffusion scheduler.") + parser.add_argument( + "--checkpoint_path", type=str, required=True, help="Path containing a model + scheduler config." + ) + parser.add_argument("--prompt", type=str, default=None, help="Optional prompt; will be used as a fixed prefix.") + parser.add_argument("--num_samples", type=int, default=4) + parser.add_argument("--seq_len", type=int, default=64) + parser.add_argument("--num_inference_steps", type=int, default=64) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--inject_start_token", action="store_true") + return parser.parse_args() + + +def main(): + args = parse_args() + device = torch.device(args.device) + + tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True) + model = AutoModelForMaskedLM.from_pretrained(args.checkpoint_path).to(device) + scheduler = HybridTokenDiffusionScheduler.from_pretrained(args.checkpoint_path) + + pipe = HybridTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to(device) + model.eval() + + generator: Optional[torch.Generator] = torch.Generator(device=device).manual_seed(args.seed) + + prefix_ids = None + if args.prompt is not None: + encoded = tokenizer(args.prompt, return_tensors="pt", add_special_tokens=True) + prefix_ids = encoded["input_ids"].to(device=device, dtype=torch.long) + if prefix_ids.shape[1] > args.seq_len: + raise ValueError(f"--seq_len ({args.seq_len}) must be >= prompt length ({prefix_ids.shape[1]}).") + + out = pipe( + batch_size=args.num_samples, + seq_len=args.seq_len, + num_inference_steps=args.num_inference_steps, + generator=generator, + prefix_ids=prefix_ids, + inject_start_token=args.inject_start_token, + return_text=True, + ) + + for i, t in enumerate(out.texts or []): + print(f"[{i}] {t}") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_hybrid_token_diffusion.py b/examples/discrete_diffusion/train_hybrid_token_diffusion.py new file mode 100644 index 000000000000..7b7295b28e30 --- /dev/null +++ b/examples/discrete_diffusion/train_hybrid_token_diffusion.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python + +import argparse +import math +import os +from dataclasses import asdict, dataclass +from typing import Dict, Optional + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch.utils.data import DataLoader +from transformers import ( + AutoConfig, + AutoModelForMaskedLM, + AutoTokenizer, + DataCollatorForLanguageModeling, + get_scheduler, +) + +from diffusers import HybridTokenDiffusionScheduler + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + model_name_or_path: str + dataset_name: str + dataset_config_name: Optional[str] + text_column: str + + output_dir: str + seed: int + max_train_steps: int + checkpointing_steps: int + logging_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + lr_scheduler: str + lr_warmup_steps: int + + max_length: int + num_train_timesteps: int + t_eps: float + p_uniform: float + gamma: float + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser(description="Train a hybrid-transition token diffusion model with Accelerate.") + + parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased") + parser.add_argument("--dataset_name", type=str, default="wikitext") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") + parser.add_argument("--text_column", type=str, default="text") + + parser.add_argument("--output_dir", type=str, default="hybrid-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--checkpointing_steps", type=int, default=500) + parser.add_argument("--logging_steps", type=int, default=50) + + parser.add_argument("--per_device_train_batch_size", type=int, default=8) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=5e-5) + parser.add_argument("--weight_decay", type=float, default=0.01) + parser.add_argument( + "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] + ) + parser.add_argument("--lr_warmup_steps", type=int, default=100) + + parser.add_argument("--max_length", type=int, default=256) + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--t_eps", type=float, default=1e-4) + parser.add_argument("--p_uniform", type=float, default=0.0) + parser.add_argument("--gamma", type=float, default=1.0) + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): + texts = examples[text_column] + texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] + return tokenizer( + texts, + truncation=True, + padding=False, + max_length=max_length, + return_special_tokens_mask=True, + ) + + +def main(): + cfg = parse_args() + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True) + if tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": "[MASK]"}) + + config = AutoConfig.from_pretrained(cfg.model_name_or_path) + model = AutoModelForMaskedLM.from_pretrained(cfg.model_name_or_path, config=config) + model.resize_token_embeddings(len(tokenizer)) + + scheduler = HybridTokenDiffusionScheduler( + vocab_size=len(tokenizer), + mask_token_id=int(tokenizer.mask_token_id), + num_train_timesteps=cfg.num_train_timesteps, + t_eps=cfg.t_eps, + p_uniform=cfg.p_uniform, + gamma=cfg.gamma, + ) + + raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name) + if "train" not in raw_datasets: + raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") + + with accelerator.main_process_first(): + tokenized = raw_datasets["train"].map( + lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), + batched=True, + remove_columns=raw_datasets["train"].column_names, + desc="Tokenizing", + ) + + collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") + train_dataloader = DataLoader( + tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True + ) + + optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + lr_scheduler = get_scheduler( + name=cfg.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.lr_warmup_steps, + num_training_steps=cfg.max_train_steps, + ) + + model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + model, optimizer, train_dataloader, lr_scheduler + ) + + global_step = 0 + model.train() + + for epoch in range(num_train_epochs): + for batch in train_dataloader: + with accelerator.accumulate(model): + input_ids = batch["input_ids"] + attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) + + timesteps = torch.randint( + 0, scheduler.num_train_timesteps, (input_ids.shape[0],), device=input_ids.device, dtype=torch.long + ) + + x_t = scheduler.add_noise(input_ids, noise=None, timesteps=timesteps) + logits = model(input_ids=x_t, attention_mask=attention_mask).logits + + # For this hybrid kernel, we use a simple denoising objective: predict x0 from z_t. + logits = logits.clone() + logits[..., scheduler.mask_token_id] = torch.finfo(logits.dtype).min + + loss = F.cross_entropy(logits.view(-1, logits.shape[-1]), input_ids.view(-1), reduction="none") + loss = loss.view_as(input_ids) + loss = (loss * attention_mask.to(loss.dtype)).sum() / attention_mask.sum().clamp_min(1) + + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + os.makedirs(save_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(save_dir, save_function=accelerator.save) + tokenizer.save_pretrained(save_dir) + scheduler.save_pretrained(save_dir) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + os.makedirs(final_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(model) + unwrapped.save_pretrained(final_dir, save_function=accelerator.save) + tokenizer.save_pretrained(final_dir) + scheduler.save_pretrained(final_dir) + + logger.info("Done.") + + +if __name__ == "__main__": + main() From 6f94f4fe5cdb582057b79621ee0fac4f546500ad Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sat, 20 Dec 2025 16:25:26 +0100 Subject: [PATCH 06/44] initial trainer --- examples/discrete_diffusion/README.md | 69 +++- .../sample_block_refinement.py | 66 +++ .../train_block_refinement_cap.py | 387 ++++++++++++++++++ .../train_block_refinement_qwen_cap.py | 359 ++++++++++++++++ .../train_hybrid_token_diffusion.py | 38 +- examples/discrete_diffusion/train_mdlm.py | 41 +- examples/discrete_diffusion/train_udlm.py | 39 +- src/diffusers/__init__.py | 4 + src/diffusers/pipelines/__init__.py | 2 + .../pipelines/block_refinement/__init__.py | 18 + .../pipeline_block_refinement.py | 363 ++++++++++++++++ .../schedulers/scheduling_token_diffusion.py | 38 ++ src/diffusers/training_utils.py | 87 ++++ tests/others/test_training.py | 46 ++- .../test_pipeline_block_refinement.py | 93 +++++ .../test_scheduler_token_diffusion.py | 10 + 16 files changed, 1622 insertions(+), 38 deletions(-) create mode 100644 examples/discrete_diffusion/sample_block_refinement.py create mode 100644 examples/discrete_diffusion/train_block_refinement_cap.py create mode 100644 examples/discrete_diffusion/train_block_refinement_qwen_cap.py create mode 100644 src/diffusers/pipelines/block_refinement/__init__.py create mode 100644 src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py create mode 100644 tests/pipelines/test_pipeline_block_refinement.py diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index 72351324dd53..fee10a9c7f00 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -2,6 +2,24 @@ This folder contains **training examples** for *discrete diffusion over token IDs* (language-model style), built to follow the `diffusers` + `accelerate` training conventions. +## Quickstart: block refinement with Qwen (causal LM) + +If you want a causal-LM example, start here. This trains block refinement with a CAP-style confidence loss. + +```bash +accelerate launch examples/discrete_diffusion/train_block_refinement_qwen_cap.py \ + --model_name_or_path Qwen/Qwen2.5-0.5B \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --text_column text \ + --output_dir qwen-block-refinement-output \ + --max_train_steps 1000 \ + --prompt_length 32 \ + --block_length 32 \ + --lambda_conf 2.0 \ + --conf_temperature 0.5 +``` + ## MDLM-style absorbing diffusion `train_mdlm.py` trains a masked/absorbing discrete diffusion model: @@ -17,7 +35,9 @@ accelerate launch examples/discrete_diffusion/train_mdlm.py \ --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 \ --output_dir mdlm-output \ - --max_train_steps 1000 + --max_train_steps 1000 \ + --lambda_conf 0.0 \ + --conf_temperature 1.0 ``` The script saves: @@ -50,6 +70,47 @@ python examples/discrete_diffusion/sample_block_token_diffusion.py \ --top_p 0.9 ``` +## Block refinement (commit-by-confidence) with Qwen + +For causal LMs that only support a 2D `attention_mask`, run `BlockRefinementPipeline` with `--attention_mask_mode 2d`. + +### Train + +```bash +accelerate launch examples/discrete_diffusion/train_block_refinement_qwen_cap.py \ + --model_name_or_path Qwen/Qwen2.5-0.5B \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --text_column text \ + --output_dir qwen-block-refinement-output \ + --max_train_steps 1000 \ + --prompt_length 32 \ + --block_length 32 \ + --lambda_conf 2.0 \ + --conf_temperature 0.5 +``` + +If you don't want to download a dataset, you can use random-token data: + +```bash +accelerate launch examples/discrete_diffusion/train_block_refinement_qwen_cap.py \ + --model_name_or_path Qwen/Qwen2.5-0.5B \ + --output_dir qwen-block-refinement-output \ + --use_dummy_data \ + --num_dummy_samples 2048 +``` + +### Sample + +```bash +python examples/discrete_diffusion/sample_block_refinement.py \ + --checkpoint_path qwen-block-refinement-output/final \ + --device cuda \ + --attention_mask_mode 2d \ + --prompt "Write a short paragraph about diffusion models." \ + --gen_length 128 +``` + ## Hybrid sampling Hybrid sampling uses a different transition kernel than absorbing/uniform diffusion and requires a compatible scheduler @@ -71,14 +132,16 @@ accelerate launch examples/discrete_diffusion/train_hybrid_token_diffusion.py \ --dataset_name wikitext \ --dataset_config_name wikitext-2-raw-v1 \ --output_dir hybrid-output \ - --max_train_steps 1000 + --max_train_steps 1000 \ + --lambda_conf 0.0 \ + --conf_temperature 1.0 ``` ## UDLM-style uniform diffusion `train_udlm.py` trains a uniform token diffusion model: - Forward process: with probability `1 - alpha(t)`, replace tokens with a uniform random token -- Noise schedule: log-linear `alpha(t) = 1 - (1 - eps) * t` +- Noise schedule: configurable via `--alpha_schedule` (`log_linear`, `linear`, `cosine`, `geometric`) - Loss: diffusion loss for uniform token diffusion ### Run diff --git a/examples/discrete_diffusion/sample_block_refinement.py b/examples/discrete_diffusion/sample_block_refinement.py new file mode 100644 index 000000000000..72e728d876f8 --- /dev/null +++ b/examples/discrete_diffusion/sample_block_refinement.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python + +import argparse + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from diffusers import BlockRefinementPipeline + + +def main(): + parser = argparse.ArgumentParser(description="Sample with BlockRefinementPipeline using a transformers causal LM.") + parser.add_argument("--checkpoint_path", type=str, required=True) + parser.add_argument("--cache_dir", type=str, default=None) + parser.add_argument("--prompt", type=str, default="Write a short paragraph about diffusion models.") + parser.add_argument("--gen_length", type=int, default=128) + parser.add_argument("--block_length", type=int, default=32) + parser.add_argument("--steps", type=int, default=32) + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--top_p", type=float, default=1.0) + parser.add_argument("--top_k", type=int, default=0) + parser.add_argument("--threshold", type=float, default=0.95) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") + parser.add_argument("--attention_mask_mode", type=str, default="2d", choices=["auto", "4d", "2d", "none"]) + + args = parser.parse_args() + + tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True, cache_dir=args.cache_dir) + model = AutoModelForCausalLM.from_pretrained( + args.checkpoint_path, + torch_dtype=torch.bfloat16 if args.device.startswith("cuda") else torch.float32, + cache_dir=args.cache_dir, + ) + model.to(args.device) + model.eval() + + if tokenizer.mask_token_id is None: + raise ValueError("Tokenizer must have `mask_token_id` for block refinement sampling.") + + pipe = BlockRefinementPipeline(model=model, tokenizer=tokenizer).to(args.device) + gen = torch.Generator(device=args.device).manual_seed(args.seed) + + prompt_ids = tokenizer(args.prompt, return_tensors="pt")["input_ids"].to(args.device) + out = pipe( + prompt_ids=prompt_ids, + gen_length=int(args.gen_length), + block_length=int(args.block_length), + steps=int(args.steps), + temperature=float(args.temperature), + top_p=None if args.top_p >= 1.0 else float(args.top_p), + top_k=None if args.top_k <= 0 else int(args.top_k), + threshold=float(args.threshold), + eos_early_stop=True, + eos_token_id=int(tokenizer.eos_token_id) if tokenizer.eos_token_id is not None else None, + mask_token_id=int(tokenizer.mask_token_id), + attention_mask_mode=args.attention_mask_mode, + generator=gen, + return_text=True, + ) + + print(out.texts[0] if out.texts is not None else tokenizer.decode(out.sequences[0], skip_special_tokens=True)) + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_block_refinement_cap.py b/examples/discrete_diffusion/train_block_refinement_cap.py new file mode 100644 index 000000000000..21fabc8d7da1 --- /dev/null +++ b/examples/discrete_diffusion/train_block_refinement_cap.py @@ -0,0 +1,387 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import json +import math +import os +from dataclasses import asdict, dataclass +from typing import Optional, Tuple + +import torch +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from torch.utils.data import DataLoader, Dataset + +from diffusers import BlockRefinementPipeline +from diffusers.training_utils import compute_confidence_aware_loss + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + output_dir: str + seed: int + max_train_steps: int + logging_steps: int + checkpointing_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + + vocab_size: int + mask_token_id: int + eos_token_id: int + max_length: int + prompt_length: int + + block_length: int + steps: int + lambda_conf: float + conf_temperature: float + temperature: float + threshold: float + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser( + description="Train a block-wise refinement model with a confidence-aware objective (CAP-style)." + ) + + parser.add_argument("--output_dir", type=str, default="block-refinement-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--logging_steps", type=int, default=50) + parser.add_argument("--checkpointing_steps", type=int, default=500) + + parser.add_argument("--per_device_train_batch_size", type=int, default=64) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=2e-4) + parser.add_argument("--weight_decay", type=float, default=0.0) + + parser.add_argument("--vocab_size", type=int, default=256) + parser.add_argument("--mask_token_id", type=int, default=255) + parser.add_argument("--eos_token_id", type=int, default=254) + parser.add_argument("--max_length", type=int, default=64) + parser.add_argument("--prompt_length", type=int, default=8) + + parser.add_argument("--block_length", type=int, default=16) + parser.add_argument("--steps", type=int, default=16) + parser.add_argument("--lambda_conf", type=float, default=2.0) + parser.add_argument("--conf_temperature", type=float, default=0.5) + + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--threshold", type=float, default=0.95) + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def build_block_attention_mask( + *, + num_blocks: int, + block_length: int, + total_length: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=torch.bool)) + attn = ( + block_mask.repeat_interleave(block_length, dim=0) + .repeat_interleave(block_length, dim=1) + .unsqueeze(0) + .unsqueeze(0) + ) + attn = attn[:, :, :total_length, :total_length] + return torch.where( + attn, torch.zeros((), device=device, dtype=dtype), torch.full((), float("-inf"), device=device, dtype=dtype) + ) + + +def forward_process_semi_ar( + input_ids: torch.LongTensor, + *, + prompt_length: int, + block_length: int, + mask_token_id: int, + generator: Optional[torch.Generator], +) -> Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]: + batch_size, seq_len = input_ids.shape + device = input_ids.device + + noisy = input_ids.clone() + noisy_rev = input_ids.clone() + masked = torch.zeros_like(input_ids, dtype=torch.bool) + masked_rev = torch.zeros_like(input_ids, dtype=torch.bool) + + start = int(prompt_length) + for block_start in range(start, seq_len, int(block_length)): + block_end = min(seq_len, block_start + int(block_length)) + seg_len = block_end - block_start + if seg_len <= 0: + continue + + p_mask = torch.rand((batch_size, 1), device=device, generator=generator) + seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask + seg_rev = ~seg + + masked[:, block_start:block_end] = seg + masked_rev[:, block_start:block_end] = seg_rev + + noisy = torch.where(masked, torch.full_like(noisy, int(mask_token_id)), noisy) + noisy_rev = torch.where(masked_rev, torch.full_like(noisy_rev, int(mask_token_id)), noisy_rev) + return noisy, noisy_rev, masked, masked_rev + + +class RandomTokenDataset(Dataset): + def __init__(self, *, num_samples: int, seq_len: int, vocab_size: int, eos_token_id: int): + self.num_samples = int(num_samples) + self.seq_len = int(seq_len) + self.vocab_size = int(vocab_size) + self.eos_token_id = int(eos_token_id) + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + del idx + # Keep EOS out of the training distribution to avoid trivial early-stops during sampling. + ids = torch.randint(0, self.vocab_size - 2, (self.seq_len,), dtype=torch.long) + return {"input_ids": ids} + + +class TinyBlockRefinementLM(torch.nn.Module): + def __init__(self, *, vocab_size: int, hidden_size: int = 128, num_heads: int = 4, num_layers: int = 4): + super().__init__() + self.vocab_size = int(vocab_size) + self.hidden_size = int(hidden_size) + + self.token_emb = torch.nn.Embedding(self.vocab_size, self.hidden_size) + self.pos_emb = torch.nn.Embedding(2048, self.hidden_size) + enc_layer = torch.nn.TransformerEncoderLayer( + d_model=self.hidden_size, + nhead=int(num_heads), + dim_feedforward=self.hidden_size * 4, + dropout=0.0, + activation="gelu", + batch_first=True, + norm_first=True, + ) + self.encoder = torch.nn.TransformerEncoder(enc_layer, num_layers=int(num_layers)) + self.lm_head = torch.nn.Linear(self.hidden_size, self.vocab_size, bias=False) + + @property + def dtype(self): + return next(self.parameters()).dtype + + def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): + if position_ids is None: + position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0).expand_as(input_ids) + + x = self.token_emb(input_ids) + self.pos_emb(position_ids) + + attn_mask = None + if attention_mask is not None: + if attention_mask.ndim == 4: + attn_mask = attention_mask[0, 0] + elif attention_mask.ndim == 2: + attn_mask = attention_mask + else: + raise ValueError(f"Unsupported `attention_mask` shape: {attention_mask.shape}") + attn_mask = attn_mask.to(dtype=torch.float32) + + hidden = self.encoder(x, mask=attn_mask) + logits = self.lm_head(hidden) + return type("Output", (), {"logits": logits}) + + +def save_checkpoint(output_dir: str, *, model: torch.nn.Module, cfg: TrainConfig): + os.makedirs(output_dir, exist_ok=True) + torch.save(model.state_dict(), os.path.join(output_dir, "pytorch_model.bin")) + with open(os.path.join(output_dir, "training_config.json"), "w", encoding="utf-8") as f: + json.dump(asdict(cfg), f, indent=2, sort_keys=True) + + +def main(): + cfg = parse_args() + if cfg.mask_token_id >= cfg.vocab_size: + raise ValueError("`mask_token_id` must be < `vocab_size`.") + if cfg.eos_token_id >= cfg.vocab_size: + raise ValueError("`eos_token_id` must be < `vocab_size`.") + if cfg.prompt_length >= cfg.max_length: + raise ValueError("`prompt_length` must be < `max_length`.") + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + dataset = RandomTokenDataset( + num_samples=max(cfg.max_train_steps * cfg.per_device_train_batch_size, 4096), + seq_len=cfg.max_length, + vocab_size=cfg.vocab_size, + eos_token_id=cfg.eos_token_id, + ) + dataloader = DataLoader(dataset, batch_size=cfg.per_device_train_batch_size, shuffle=True, drop_last=True) + + model = TinyBlockRefinementLM(vocab_size=cfg.vocab_size) + pipe = BlockRefinementPipeline(model=model, tokenizer=None) + + optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) + pipe = pipe.to(accelerator.device) + + global_step = 0 + model.train() + + for _epoch in range(num_train_epochs): + for batch in dataloader: + with accelerator.accumulate(model): + input_ids = batch["input_ids"] + + # Build the same attention mask that the sampler uses. + prompt_len = int(cfg.prompt_length) + num_blocks = (prompt_len + int(cfg.max_length - prompt_len) + int(cfg.block_length) - 1) // int( + cfg.block_length + ) + total_length = int(num_blocks) * int(cfg.block_length) + total_length = max(total_length, int(cfg.max_length)) + attn_mask = build_block_attention_mask( + num_blocks=(total_length + int(cfg.block_length) - 1) // int(cfg.block_length), + block_length=int(cfg.block_length), + total_length=int(cfg.max_length), + device=input_ids.device, + dtype=torch.bfloat16 if input_ids.device.type == "cuda" else torch.float32, + ) + position_ids = ( + torch.arange(int(cfg.max_length), device=input_ids.device, dtype=torch.long) + .unsqueeze(0) + .expand_as(input_ids) + ) + + gen = None + if accelerator.is_local_main_process: + gen = torch.Generator(device=input_ids.device).manual_seed(cfg.seed + global_step) + + noisy, noisy_rev, masked, masked_rev = forward_process_semi_ar( + input_ids, + prompt_length=prompt_len, + block_length=int(cfg.block_length), + mask_token_id=int(cfg.mask_token_id), + generator=gen, + ) + + logits = model(noisy, attention_mask=attn_mask, position_ids=position_ids).logits + logits_rev = model(noisy_rev, attention_mask=attn_mask, position_ids=position_ids).logits + + # Do not allow predicting mask_id. + logits = logits.clone() + logits[..., int(cfg.mask_token_id)] = torch.finfo(logits.dtype).min + logits_rev = logits_rev.clone() + logits_rev[..., int(cfg.mask_token_id)] = torch.finfo(logits_rev.dtype).min + + labels = input_ids.clone() + labels[~masked] = -100 + labels_rev = input_ids.clone() + labels_rev[~masked_rev] = -100 + + weights = masked.to(dtype=logits.dtype) + weights_rev = masked_rev.to(dtype=logits.dtype) + + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, + labels, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=weights, + ) + loss_rev, loss_sft_rev, loss_conf_rev = compute_confidence_aware_loss( + logits_rev, + labels_rev, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=weights_rev, + ) + + total_loss = loss + loss_rev + accelerator.backward(total_loss) + optimizer.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info( + "step=%d loss=%.4f sft=%.4f conf=%.4f", + global_step, + total_loss.item(), + (loss_sft + loss_sft_rev).item(), + (loss_conf + loss_conf_rev).item(), + ) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + save_checkpoint(save_dir, model=accelerator.unwrap_model(model), cfg=cfg) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + save_checkpoint(final_dir, model=accelerator.unwrap_model(model), cfg=cfg) + + # Quick sampler smoke to ensure the pipeline runs with the trained weights. + out = pipe( + prompt_ids=torch.randint(0, cfg.vocab_size - 2, (1, cfg.prompt_length), device=accelerator.device), + gen_length=int(cfg.max_length - cfg.prompt_length), + block_length=int(cfg.block_length), + steps=int(cfg.steps), + temperature=float(cfg.temperature), + threshold=float(cfg.threshold), + eos_early_stop=False, + eos_token_id=int(cfg.eos_token_id), + mask_token_id=int(cfg.mask_token_id), + return_text=False, + ) + logger.info("sample shape=%s", tuple(out.sequences.shape)) + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_block_refinement_qwen_cap.py b/examples/discrete_diffusion/train_block_refinement_qwen_cap.py new file mode 100644 index 000000000000..5149f3ba61d0 --- /dev/null +++ b/examples/discrete_diffusion/train_block_refinement_qwen_cap.py @@ -0,0 +1,359 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import math +import os +from dataclasses import asdict, dataclass +from typing import Dict, Optional, Tuple + +import torch +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch.utils.data import DataLoader +from transformers import AutoModelForCausalLM, AutoTokenizer, DataCollatorForLanguageModeling, get_scheduler + +from diffusers.training_utils import compute_confidence_aware_loss + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + model_name_or_path: str + dataset_name: str + dataset_config_name: Optional[str] + text_column: str + cache_dir: Optional[str] + use_dummy_data: bool + num_dummy_samples: int + + output_dir: str + seed: int + max_train_steps: int + checkpointing_steps: int + logging_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + lr_scheduler: str + lr_warmup_steps: int + + max_length: int + prompt_length: int + block_length: int + + lambda_conf: float + conf_temperature: float + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser(description="Train block-refinement with a confidence-aware loss on a causal LM.") + + parser.add_argument("--model_name_or_path", type=str, default="Qwen/Qwen2.5-0.5B") + parser.add_argument("--dataset_name", type=str, default="wikitext") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") + parser.add_argument("--text_column", type=str, default="text") + parser.add_argument("--cache_dir", type=str, default=None) + parser.add_argument("--use_dummy_data", action="store_true", help="Use random-token data instead of downloading.") + parser.add_argument("--num_dummy_samples", type=int, default=2048) + + parser.add_argument("--output_dir", type=str, default="qwen-block-refinement-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--checkpointing_steps", type=int, default=500) + parser.add_argument("--logging_steps", type=int, default=50) + + parser.add_argument("--per_device_train_batch_size", type=int, default=1) + parser.add_argument("--gradient_accumulation_steps", type=int, default=8) + parser.add_argument("--learning_rate", type=float, default=2e-5) + parser.add_argument("--weight_decay", type=float, default=0.0) + parser.add_argument( + "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] + ) + parser.add_argument("--lr_warmup_steps", type=int, default=100) + + parser.add_argument("--max_length", type=int, default=256) + parser.add_argument("--prompt_length", type=int, default=32) + parser.add_argument("--block_length", type=int, default=32) + + parser.add_argument("--lambda_conf", type=float, default=2.0) + parser.add_argument("--conf_temperature", type=float, default=0.5) + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): + texts = examples[text_column] + texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] + return tokenizer(texts, truncation=True, padding=False, max_length=max_length) + + +class RandomTokenDataset(torch.utils.data.Dataset): + def __init__(self, *, num_samples: int, seq_len: int, vocab_size: int, pad_token_id: int): + self.num_samples = int(num_samples) + self.seq_len = int(seq_len) + self.vocab_size = int(vocab_size) + self.pad_token_id = int(pad_token_id) + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + del idx + input_ids = torch.randint(0, self.vocab_size, (self.seq_len,), dtype=torch.long) + attention_mask = torch.ones_like(input_ids) + return {"input_ids": input_ids, "attention_mask": attention_mask} + + +def forward_process_semi_ar( + input_ids: torch.LongTensor, + attention_mask: torch.LongTensor, + *, + prompt_length: int, + block_length: int, + mask_token_id: int, + generator: Optional[torch.Generator], +) -> Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]: + batch_size, seq_len = input_ids.shape + device = input_ids.device + + noisy = input_ids.clone() + noisy_rev = input_ids.clone() + masked = torch.zeros_like(input_ids, dtype=torch.bool) + masked_rev = torch.zeros_like(input_ids, dtype=torch.bool) + + # Only mask non-padding positions after the prompt. + valid = attention_mask.to(dtype=torch.bool) + start = int(prompt_length) + for block_start in range(start, seq_len, int(block_length)): + block_end = min(seq_len, block_start + int(block_length)) + seg_len = block_end - block_start + if seg_len <= 0: + continue + + p_mask = torch.rand((batch_size, 1), device=device, generator=generator) + seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask + seg = seg & valid[:, block_start:block_end] + seg_rev = (~seg) & valid[:, block_start:block_end] + + masked[:, block_start:block_end] = seg + masked_rev[:, block_start:block_end] = seg_rev + + noisy = torch.where(masked, torch.full_like(noisy, int(mask_token_id)), noisy) + noisy_rev = torch.where(masked_rev, torch.full_like(noisy_rev, int(mask_token_id)), noisy_rev) + return noisy, noisy_rev, masked, masked_rev + + +def main(): + cfg = parse_args() + if cfg.prompt_length >= cfg.max_length: + raise ValueError("`prompt_length` must be < `max_length`.") + if cfg.block_length <= 0: + raise ValueError("`block_length` must be > 0.") + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True, cache_dir=cfg.cache_dir) + if tokenizer.pad_token_id is None: + tokenizer.pad_token = tokenizer.eos_token + + if tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": "[MASK]"}) + + load_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32 + model = AutoModelForCausalLM.from_pretrained( + cfg.model_name_or_path, cache_dir=cfg.cache_dir, torch_dtype=load_dtype + ) + model.resize_token_embeddings(len(tokenizer)) + if load_dtype == torch.float32: + model.to(dtype=torch.float32) + + mask_token_id = int(tokenizer.mask_token_id) + + if cfg.use_dummy_data: + dataset = RandomTokenDataset( + num_samples=cfg.num_dummy_samples, + seq_len=cfg.max_length, + vocab_size=len(tokenizer), + pad_token_id=int(tokenizer.pad_token_id), + ) + train_dataloader = DataLoader( + dataset, + shuffle=True, + batch_size=cfg.per_device_train_batch_size, + drop_last=True, + ) + else: + raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name, cache_dir=cfg.cache_dir) + if "train" not in raw_datasets: + raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") + + with accelerator.main_process_first(): + tokenized = raw_datasets["train"].map( + lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), + batched=True, + remove_columns=raw_datasets["train"].column_names, + desc="Tokenizing", + ) + + collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") + train_dataloader = DataLoader( + tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True + ) + + optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + lr_scheduler = get_scheduler( + name=cfg.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.lr_warmup_steps, + num_training_steps=cfg.max_train_steps, + ) + + model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + model, optimizer, train_dataloader, lr_scheduler + ) + + global_step = 0 + model.train() + + for _epoch in range(num_train_epochs): + for batch in train_dataloader: + with accelerator.accumulate(model): + input_ids = batch["input_ids"] + attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) + + gen = torch.Generator(device=input_ids.device).manual_seed(cfg.seed + global_step) + noisy, noisy_rev, masked, masked_rev = forward_process_semi_ar( + input_ids, + attention_mask, + prompt_length=int(cfg.prompt_length), + block_length=int(cfg.block_length), + mask_token_id=mask_token_id, + generator=gen, + ) + + position_ids = ( + torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0).expand_as(input_ids) + ) + + logits = model(input_ids=noisy, attention_mask=attention_mask, position_ids=position_ids).logits + logits_rev = model( + input_ids=noisy_rev, attention_mask=attention_mask, position_ids=position_ids + ).logits + + logits = logits.clone() + logits[..., mask_token_id] = torch.finfo(logits.dtype).min + logits_rev = logits_rev.clone() + logits_rev[..., mask_token_id] = torch.finfo(logits_rev.dtype).min + + valid = attention_mask.to(dtype=torch.bool) + masked = masked & valid + masked_rev = masked_rev & valid + + labels = input_ids.clone() + labels[~masked] = -100 + labels_rev = input_ids.clone() + labels_rev[~masked_rev] = -100 + + weights = masked.to(dtype=logits.dtype) + weights_rev = masked_rev.to(dtype=logits.dtype) + + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, + labels, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=weights, + ) + loss_rev, loss_sft_rev, loss_conf_rev = compute_confidence_aware_loss( + logits_rev, + labels_rev, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=weights_rev, + ) + + total_loss = loss + loss_rev + accelerator.backward(total_loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info( + "step=%d loss=%.4f sft=%.4f conf=%.4f lr=%.6g", + global_step, + total_loss.item(), + (loss_sft + loss_sft_rev).item(), + (loss_conf + loss_conf_rev).item(), + lr_scheduler.get_last_lr()[0], + ) + print( + f"step={global_step} loss={total_loss.item():.4f} " + f"sft={(loss_sft + loss_sft_rev).item():.4f} " + f"conf={(loss_conf + loss_conf_rev).item():.4f} " + f"lr={lr_scheduler.get_last_lr()[0]:.6g}" + ) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + os.makedirs(save_dir, exist_ok=True) + accelerator.unwrap_model(model).save_pretrained(save_dir, save_function=accelerator.save) + tokenizer.save_pretrained(save_dir) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + os.makedirs(final_dir, exist_ok=True) + accelerator.unwrap_model(model).save_pretrained(final_dir, save_function=accelerator.save) + tokenizer.save_pretrained(final_dir) + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_hybrid_token_diffusion.py b/examples/discrete_diffusion/train_hybrid_token_diffusion.py index 7b7295b28e30..ee23b430f555 100644 --- a/examples/discrete_diffusion/train_hybrid_token_diffusion.py +++ b/examples/discrete_diffusion/train_hybrid_token_diffusion.py @@ -7,7 +7,6 @@ from typing import Dict, Optional import torch -import torch.nn.functional as F from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed @@ -22,6 +21,7 @@ ) from diffusers import HybridTokenDiffusionScheduler +from diffusers.training_utils import compute_confidence_aware_loss logger = get_logger(__name__) @@ -52,6 +52,8 @@ class TrainConfig: t_eps: float p_uniform: float gamma: float + lambda_conf: float + conf_temperature: float def parse_args() -> TrainConfig: @@ -82,6 +84,18 @@ def parse_args() -> TrainConfig: parser.add_argument("--t_eps", type=float, default=1e-4) parser.add_argument("--p_uniform", type=float, default=0.0) parser.add_argument("--gamma", type=float, default=1.0) + parser.add_argument( + "--lambda_conf", + type=float, + default=0.0, + help="Optional confidence-aware penalty weight (entropy on correctly predicted tokens).", + ) + parser.add_argument( + "--conf_temperature", + type=float, + default=1.0, + help="Temperature for the confidence term only; lower values sharpen the entropy penalty.", + ) args = parser.parse_args() return TrainConfig(**vars(args)) @@ -185,9 +199,16 @@ def main(): logits = logits.clone() logits[..., scheduler.mask_token_id] = torch.finfo(logits.dtype).min - loss = F.cross_entropy(logits.view(-1, logits.shape[-1]), input_ids.view(-1), reduction="none") - loss = loss.view_as(input_ids) - loss = (loss * attention_mask.to(loss.dtype)).sum() / attention_mask.sum().clamp_min(1) + labels = input_ids.clone() + labels[attention_mask.eq(0)] = -100 + per_token_weights = attention_mask.to(dtype=logits.dtype) + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, + labels, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=per_token_weights, + ) accelerator.backward(loss) optimizer.step() @@ -198,7 +219,14 @@ def main(): global_step += 1 if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: - logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + logger.info( + "step=%d loss=%.4f loss_sft=%.4f loss_conf=%.4f lr=%.6g", + global_step, + loss.item(), + loss_sft.item(), + loss_conf.item(), + lr_scheduler.get_last_lr()[0], + ) if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: accelerator.wait_for_everyone() diff --git a/examples/discrete_diffusion/train_mdlm.py b/examples/discrete_diffusion/train_mdlm.py index 9a511155f6d3..59b323d35275 100644 --- a/examples/discrete_diffusion/train_mdlm.py +++ b/examples/discrete_diffusion/train_mdlm.py @@ -20,7 +20,6 @@ from typing import Dict, Optional import torch -import torch.nn.functional as F from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed @@ -35,6 +34,7 @@ ) from diffusers import TokenDiffusionScheduler +from diffusers.training_utils import compute_confidence_aware_loss logger = get_logger(__name__) @@ -67,6 +67,8 @@ class TrainConfig: sigma_min: float sigma_max: float min_timestep: int + lambda_conf: float + conf_temperature: float def parse_args() -> TrainConfig: @@ -105,6 +107,18 @@ def parse_args() -> TrainConfig: parser.add_argument("--sigma_min", type=float, default=1e-4) parser.add_argument("--sigma_max", type=float, default=20.0) parser.add_argument("--min_timestep", type=int, default=1, help="Avoid t=0 to prevent 1/t weighting blow-ups.") + parser.add_argument( + "--lambda_conf", + type=float, + default=0.0, + help="Optional confidence-aware penalty weight (entropy on correctly predicted tokens).", + ) + parser.add_argument( + "--conf_temperature", + type=float, + default=1.0, + help="Temperature for the confidence term only; lower values sharpen the entropy penalty.", + ) args = parser.parse_args() return TrainConfig(**vars(args)) @@ -219,14 +233,20 @@ def main(): # Only compute loss on tokens that were masked by the forward process. mask_positions = x_t.eq(scheduler.mask_token_id) & attention_mask.to(dtype=torch.bool) - per_token_ce = F.cross_entropy(logits.view(-1, logits.shape[-1]), input_ids.view(-1), reduction="none") - per_token_ce = per_token_ce.view_as(input_ids) weights = scheduler.get_mdlm_loss_weights(timesteps) - loss = (per_token_ce * mask_positions.to(per_token_ce.dtype) * weights).sum() - denom = mask_positions.sum().clamp_min(1) - loss = loss / denom + labels = input_ids.clone() + labels[~mask_positions] = -100 + + per_token_weights = weights.to(dtype=logits.dtype).expand_as(labels) + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, + labels, + lambda_conf=cfg.lambda_conf, + temperature=cfg.conf_temperature, + per_token_weights=per_token_weights, + ) accelerator.backward(loss) optimizer.step() @@ -237,7 +257,14 @@ def main(): global_step += 1 if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: - logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + logger.info( + "step=%d loss=%.4f loss_sft=%.4f loss_conf=%.4f lr=%.6g", + global_step, + loss.item(), + loss_sft.item(), + loss_conf.item(), + lr_scheduler.get_last_lr()[0], + ) if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: accelerator.wait_for_everyone() diff --git a/examples/discrete_diffusion/train_udlm.py b/examples/discrete_diffusion/train_udlm.py index 806bf8f48d0e..8c61790defc2 100644 --- a/examples/discrete_diffusion/train_udlm.py +++ b/examples/discrete_diffusion/train_udlm.py @@ -100,7 +100,7 @@ def parse_args() -> TrainConfig: type=str, default="log_linear", choices=["log_linear", "linear", "cosine", "geometric"], - help="UDLM loss in this example is only implemented for log_linear; other choices will error.", + help="Alpha schedule used for the uniform forward process and the continuous-time UDLM objective.", ) parser.add_argument("--eps", type=float, default=1e-3) parser.add_argument("--sigma_min", type=float, default=1e-4) @@ -127,7 +127,12 @@ def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): def udlm_diffusion_loss( - logits: torch.Tensor, x0: torch.LongTensor, x_t: torch.LongTensor, *, eps: float, t_cont: torch.Tensor + logits: torch.Tensor, + x0: torch.LongTensor, + x_t: torch.LongTensor, + *, + alpha_t: torch.Tensor, + dalpha_t: torch.Tensor, ): """ UDLM diffusion loss (continuous-time form). @@ -136,20 +141,16 @@ def udlm_diffusion_loss( logits: [B, L, V] x0: [B, L] x_t: [B, L] - eps: log-linear eps - t_cont: [B] in (0, 1] + alpha_t: [B, 1] alpha(t) for the uniform forward process. + dalpha_t: [B, 1] time derivative alpha'(t) with respect to continuous time t in [0, 1]. Returns: loss_per_token: [B, L] """ log_x_theta = torch.log_softmax(logits, dim=-1) B, L, V = log_x_theta.shape - # Treat alpha(t) as (1 - t) for the loss derivation, recovering t from the scheduler alpha. - alpha_sched = 1.0 - (1.0 - eps) * t_cont # scheduler alpha(t) - t = (1.0 - alpha_sched) / (1.0 - eps) - alpha = 1.0 - t # theoretical alpha used in loss - - alpha = alpha.to(dtype=log_x_theta.dtype).view(B, 1, 1) + alpha = alpha_t.to(dtype=log_x_theta.dtype).view(B, 1, 1) + alpha_prime = dalpha_t.to(dtype=log_x_theta.dtype).view(B, 1, 1) x0_one_hot = F.one_hot(x0, V).to(dtype=log_x_theta.dtype) xt_one_hot = F.one_hot(x_t, V).to(dtype=log_x_theta.dtype) @@ -157,8 +158,7 @@ def udlm_diffusion_loss( x_bar = V * alpha * x0_one_hot + 1.0 - alpha x_bar_theta = V * alpha * log_x_theta.exp() + 1.0 - alpha - alpha_prime = -1.0 - coeff = alpha_prime / (V * alpha) + coeff = alpha_prime / (V * alpha.clamp_min(torch.finfo(alpha.dtype).eps)) x_bar_zt = (x_bar * xt_one_hot).sum(dim=-1, keepdim=True) # (B, L, 1) x_bar_theta_zt = (x_bar_theta * xt_one_hot).sum(dim=-1, keepdim=True) # (B, L, 1) @@ -175,11 +175,6 @@ def udlm_diffusion_loss( def main(): cfg = parse_args() - if cfg.alpha_schedule != "log_linear": - raise ValueError( - "This training script implements the UDLM diffusion loss only for `--alpha_schedule=log_linear`. " - "Use that setting or extend the loss derivation for other schedules." - ) project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) accelerator = Accelerator( @@ -267,11 +262,11 @@ def main(): logits = logits.clone() logits[..., scheduler.mask_token_id] = torch.finfo(logits.dtype).min - t_cont = timesteps.to(dtype=torch.float32) / float(scheduler.num_train_timesteps - 1) - t_cont = t_cont.clamp_min(1e-6) - - loss_per_token = udlm_diffusion_loss(logits, input_ids, x_t, eps=scheduler.eps, t_cont=t_cont) - loss = (loss_per_token * attention_mask.to(loss_per_token.dtype)).mean() + alpha_t = scheduler.get_alpha(timesteps) + dalpha_t = scheduler.get_alpha_prime(timesteps) + loss_per_token = udlm_diffusion_loss(logits, input_ids, x_t, alpha_t=alpha_t, dalpha_t=dalpha_t) + loss = (loss_per_token * attention_mask.to(loss_per_token.dtype)).sum() + loss = loss / attention_mask.sum().clamp_min(1) accelerator.backward(loss) optimizer.step() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 8f8cb519fea9..2220ffcc0f0d 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -372,6 +372,8 @@ _import_structure["training_utils"] = ["EMAModel"] _import_structure["pipelines"].extend( [ + "BlockRefinementPipeline", + "BlockRefinementPipelineOutput", "BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput", "HybridTokenDiffusionPipeline", @@ -1055,6 +1057,8 @@ AutoPipelineForText2Image, BlipDiffusionControlNetPipeline, BlipDiffusionPipeline, + BlockRefinementPipeline, + BlockRefinementPipelineOutput, BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput, CLIPImageProjection, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 4feb16b3a629..4ae5beeaac0b 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -47,6 +47,7 @@ "AutoPipelineForInpainting", "AutoPipelineForText2Image", ] + _import_structure["block_refinement"] = ["BlockRefinementPipeline", "BlockRefinementPipelineOutput"] _import_structure["block_token_diffusion"] = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] _import_structure["consistency_models"] = ["ConsistencyModelPipeline"] _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"] @@ -547,6 +548,7 @@ AutoPipelineForInpainting, AutoPipelineForText2Image, ) + from .block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput from .block_token_diffusion import BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput from .consistency_models import ConsistencyModelPipeline from .dance_diffusion import DanceDiffusionPipeline diff --git a/src/diffusers/pipelines/block_refinement/__init__.py b/src/diffusers/pipelines/block_refinement/__init__.py new file mode 100644 index 000000000000..1eec2ee97e81 --- /dev/null +++ b/src/diffusers/pipelines/block_refinement/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .pipeline_block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput + + +__all__ = ["BlockRefinementPipeline", "BlockRefinementPipelineOutput"] diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py new file mode 100644 index 000000000000..a45fb397cf21 --- /dev/null +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -0,0 +1,363 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, List, Optional, Union + +import torch + +from ...utils import BaseOutput +from ..pipeline_utils import DiffusionPipeline + + +@dataclass +class BlockRefinementPipelineOutput(BaseOutput): + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +def _top_k_filtering(logits: torch.Tensor, top_k: Optional[int]) -> torch.Tensor: + if top_k is None or top_k <= 0: + return logits + if top_k >= logits.shape[-1]: + return logits + values, _ = torch.topk(logits, k=int(top_k), dim=-1) + min_keep = values[..., -1, None] + return logits.masked_fill(logits < min_keep, torch.finfo(logits.dtype).min) + + +def _top_p_filtering(logits: torch.Tensor, top_p: Optional[float]) -> torch.Tensor: + if top_p is None or top_p >= 1.0: + return logits + if not (0.0 < top_p <= 1.0): + raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") + + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = torch.softmax(sorted_logits, dim=-1) + cumulative_probs = sorted_probs.cumsum(dim=-1) + + sorted_indices_to_remove = cumulative_probs > float(top_p) + sorted_indices_to_remove[..., 0] = 0 + + sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) + filtered = logits.scatter(-1, sorted_indices, sorted_logits) + return filtered + + +def _sample_with_temperature_topk_topp( + logits: torch.Tensor, + *, + temperature: float, + top_k: Optional[int], + top_p: Optional[float], + generator: Optional[torch.Generator], +) -> tuple[torch.LongTensor, torch.Tensor]: + vocab_size = logits.shape[-1] + flat_logits = logits.reshape(-1, vocab_size) + + filtered = _top_k_filtering(flat_logits, top_k=top_k) + filtered = _top_p_filtering(filtered, top_p=top_p) + + if temperature == 0.0: + token = filtered.argmax(dim=-1, keepdim=True) + probs = torch.softmax(filtered.float(), dim=-1) + token_prob = torch.gather(probs, -1, token) + else: + if temperature < 0: + raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + scaled = filtered / float(temperature) + probs = torch.softmax(scaled.float(), dim=-1) + token = torch.multinomial(probs, num_samples=1, generator=generator) + token_prob = torch.gather(probs, -1, token) + + return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) + + +def _get_num_transfer_tokens(block_length: int, steps: int) -> torch.LongTensor: + if steps <= 0: + return torch.zeros((0,), dtype=torch.long) + base = int(block_length) // int(steps) + remainder = int(block_length) % int(steps) + out = torch.full((int(steps),), base, dtype=torch.long) + out[:remainder] += 1 + return out + + +class BlockRefinementPipeline(DiffusionPipeline): + """ + Block-wise iterative refinement pipeline for token generation. + + This pipeline maintains a template sequence filled with a `mask_token_id` and refines it in blocks. In each + refinement step, it samples candidate tokens for the active block and commits a subset based on confidence. + + The model is expected to accept an additive attention mask of shape `[batch, 1, seq, seq]` (0 for allowed, `-inf` + for disallowed) and `position_ids`, and to return logits of shape `[batch, seq, vocab_size]`. + """ + + model: Any + tokenizer: Any + + def __init__(self, model: Any, tokenizer: Optional[Any] = None): + super().__init__() + self.register_modules(model=model, tokenizer=tokenizer) + + def _model_forward_logits( + self, + input_ids: torch.LongTensor, + *, + attention_mask_4d: Optional[torch.Tensor], + attention_mask_2d: Optional[torch.Tensor], + position_ids: torch.LongTensor, + attention_mask_mode: str, + ) -> tuple[torch.Tensor, str]: + if attention_mask_mode not in {"auto", "4d", "2d", "none"}: + raise ValueError( + f"`attention_mask_mode` must be one of {{'auto','4d','2d','none'}}, got {attention_mask_mode!r}." + ) + + def _call(mask): + return self.model(input_ids, attention_mask=mask, position_ids=position_ids).logits + + if attention_mask_mode == "none": + return _call(None), "none" + if attention_mask_mode == "2d": + return _call(attention_mask_2d), "2d" + if attention_mask_mode == "4d": + return _call(attention_mask_4d), "4d" + + # auto: try 4d additive mask first, then fall back to 2d padding mask, then no mask. + try: + return _call(attention_mask_4d), "4d" + except (TypeError, ValueError, RuntimeError): + pass + try: + return _call(attention_mask_2d), "2d" + except (TypeError, ValueError, RuntimeError): + return _call(None), "none" + + def _build_block_attention_mask( + self, + *, + num_blocks: int, + block_length: int, + total_length: int, + device: torch.device, + dtype: torch.dtype, + ) -> torch.Tensor: + block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=torch.bool)) + attn = ( + block_mask.repeat_interleave(block_length, dim=0) + .repeat_interleave(block_length, dim=1) + .unsqueeze(0) + .unsqueeze(0) + ) + attn = attn[:, :, :total_length, :total_length] + return torch.where( + attn, + torch.zeros((), device=device, dtype=dtype), + torch.full((), float("-inf"), device=device, dtype=dtype), + ) + + def _encode_prompt( + self, + prompt: Optional[Union[str, List[str]]], + prompt_ids: Optional[torch.LongTensor], + *, + device: torch.device, + ) -> torch.LongTensor: + if prompt_ids is not None: + if prompt_ids.ndim == 1: + prompt_ids = prompt_ids.unsqueeze(0) + if prompt_ids.ndim != 2: + raise ValueError( + f"`prompt_ids` must have shape [prompt_len] or [batch, prompt_len], got {prompt_ids.shape}." + ) + if prompt_ids.dtype != torch.long: + raise ValueError(f"`prompt_ids` must be int64 token IDs, got dtype={prompt_ids.dtype}.") + return prompt_ids.to(device=device) + + if prompt is None: + return torch.zeros((1, 0), device=device, dtype=torch.long) + if getattr(self, "tokenizer", None) is None: + raise ValueError("`prompt` requires a tokenizer, but no tokenizer was provided to the pipeline.") + + encoded = self.tokenizer(prompt, return_tensors="pt", padding=True) + return encoded["input_ids"].to(device=device) + + @torch.no_grad() + def __call__( + self, + *, + prompt: Optional[Union[str, List[str]]] = None, + prompt_ids: Optional[torch.LongTensor] = None, + gen_length: int = 128, + block_length: int = 32, + steps: int = 32, + temperature: float = 0.0, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + threshold: float = 0.95, + minimal_topk: int = 1, + eos_early_stop: bool = False, + eos_token_id: Optional[int] = None, + mask_token_id: Optional[int] = None, + attention_mask_mode: str = "auto", + generator: Optional[torch.Generator] = None, + return_text: bool = True, + ) -> BlockRefinementPipelineOutput: + if gen_length <= 0: + raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") + if block_length <= 0: + raise ValueError(f"`block_length` must be > 0, got {block_length}.") + if steps <= 0: + raise ValueError(f"`steps` must be > 0, got {steps}.") + if minimal_topk <= 0: + raise ValueError(f"`minimal_topk` must be > 0, got {minimal_topk}.") + if not (0.0 <= threshold <= 1.0) and not (threshold > 1.0): + raise ValueError(f"`threshold` must be in [0, 1] (or > 1 to force top-k commits), got {threshold}.") + + model_params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] + model_device = model_params[0].device if len(model_params) > 0 else torch.device("cpu") + + prompt_ids = self._encode_prompt(prompt, prompt_ids, device=model_device) + batch_size, prompt_length = prompt_ids.shape + + if eos_token_id is None: + eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) + if mask_token_id is None: + mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + + steps = min(int(steps), int(gen_length) // int(minimal_topk)) + + num_blocks = (prompt_length + int(gen_length) + int(block_length) - 1) // int(block_length) + total_length = int(num_blocks) * int(block_length) + + dtype = getattr(self.model, "dtype", torch.float32) + attn_dtype = torch.bfloat16 if dtype in (torch.bfloat16, torch.float16) else torch.float32 + attn_mask_4d = self._build_block_attention_mask( + num_blocks=num_blocks, + block_length=block_length, + total_length=total_length, + device=model_device, + dtype=attn_dtype, + ) + attn_mask_2d_full = torch.ones((batch_size, total_length), device=model_device, dtype=torch.long) + position_ids = ( + torch.arange(total_length, device=model_device, dtype=torch.long).unsqueeze(0).expand(batch_size, -1) + ) + + x = torch.full((batch_size, total_length), int(mask_token_id), device=model_device, dtype=torch.long) + if prompt_length > 0: + x[:, :prompt_length] = prompt_ids.to(device=model_device) + + prefill_blocks = prompt_length // int(block_length) + transfer_schedule = _get_num_transfer_tokens(int(block_length), int(steps)).to(device=model_device) + + finished = torch.zeros((batch_size,), device=model_device, dtype=torch.bool) + resolved_attention_mode: str = str(attention_mask_mode) + + for num_block in range(int(prefill_blocks), int(num_blocks)): + current_window_end = (num_block + 1) * int(block_length) + cur_x = x[:, :current_window_end] + cur_attn_mask_4d = attn_mask_4d[:, :, :current_window_end, :current_window_end] + cur_attn_mask_2d = attn_mask_2d_full[:, :current_window_end] + cur_position_ids = position_ids[:, :current_window_end] + + for step_idx in range(int(steps)): + if finished.all(): + break + + active_block = cur_x[:, -int(block_length) :] == int(mask_token_id) + if active_block.sum() == 0: + break + + logits, resolved_attention_mode = self._model_forward_logits( + cur_x, + attention_mask_4d=cur_attn_mask_4d, + attention_mask_2d=cur_attn_mask_2d, + position_ids=cur_position_ids, + attention_mask_mode=resolved_attention_mode, + ) + block_logits = logits[:, -int(block_length) :, :] + + x0, x0_p = _sample_with_temperature_topk_topp( + block_logits, + temperature=float(temperature), + top_k=top_k, + top_p=top_p, + generator=generator, + ) + + num_to_transfer = int(transfer_schedule[step_idx].item()) + transfer_index = torch.zeros_like(x0, dtype=torch.bool) + + confidence = torch.where( + active_block, x0_p.to(dtype=torch.float32), torch.full_like(x0_p, -torch.inf, dtype=torch.float32) + ) + + for b in range(batch_size): + if finished[b]: + continue + + high_conf = confidence[b] > float(threshold) + if high_conf.sum().item() >= num_to_transfer: + transfer_index[b] = high_conf + else: + k = min(num_to_transfer, int(active_block[b].sum().item())) + if k > 0: + _, idx = torch.topk(confidence[b], k=k) + transfer_index[b, idx] = True + + if transfer_index.any(): + updated = cur_x[:, -int(block_length) :].clone() + updated[transfer_index] = x0[transfer_index] + cur_x[:, -int(block_length) :] = updated + + if eos_early_stop and eos_token_id is not None: + for b in range(batch_size): + if finished[b]: + continue + eos_in_commits = (x0[b][transfer_index[b]] == int(eos_token_id)).any().item() + if not eos_in_commits: + continue + eos_pos = (cur_x[b] == int(eos_token_id)).nonzero(as_tuple=True) + if len(eos_pos[0]) == 0: + continue + eos_pos = int(eos_pos[0][0].item()) + if prompt_length >= eos_pos: + continue + if (cur_x[b, prompt_length:eos_pos] != int(mask_token_id)).all().item(): + finished[b] = True + + x[:, :current_window_end] = cur_x + if eos_token_id is not None and (x[:, prompt_length:current_window_end] == int(eos_token_id)).any().item(): + if eos_early_stop: + break + + generated = x[:, : prompt_length + int(gen_length)] + sequences = generated[:, prompt_length:] + if eos_token_id is not None and batch_size == 1: + eos_positions = (sequences[0] == int(eos_token_id)).nonzero(as_tuple=True)[0] + if len(eos_positions) > 0: + sequences = sequences[:, : int(eos_positions[0].item()) + 1] + + texts = None + if return_text and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) + + return BlockRefinementPipelineOutput(sequences=sequences.to(device=model_device), texts=texts) diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index 87dbd5b089de..ee08a6e221a8 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -257,6 +257,44 @@ def get_mdlm_loss_weights(self, timesteps: torch.LongTensor) -> torch.Tensor: w = (-dalpha / denom).clamp_min(torch.finfo(torch.float32).tiny) return w.view(-1, 1) + def get_alpha(self, timesteps: torch.LongTensor) -> torch.Tensor: + """ + Return per-example alpha(t) values for the configured schedule. + + Args: + timesteps (`torch.LongTensor` of shape `(batch_size,)`): + Training timestep indices in `[0, num_train_timesteps-1]`. + + Returns: + `torch.FloatTensor` of shape `(batch_size, 1)`: + Alpha values in `(0, 1]` for each example. + """ + if timesteps.dtype not in (torch.int32, torch.int64): + raise ValueError(f"`timesteps` must be an integer tensor, got dtype={timesteps.dtype}.") + device = timesteps.device + t = self._t_from_timestep(timesteps.to(device), device=device).to(dtype=torch.float32) + alpha = self._alpha_t(t).to(dtype=torch.float32) + return alpha.view(-1, 1) + + def get_alpha_prime(self, timesteps: torch.LongTensor) -> torch.Tensor: + """ + Return per-example time derivative alpha'(t) for the configured schedule. + + Args: + timesteps (`torch.LongTensor` of shape `(batch_size,)`): + Training timestep indices in `[0, num_train_timesteps-1]`. + + Returns: + `torch.FloatTensor` of shape `(batch_size, 1)`: + Alpha derivatives with respect to continuous time `t in [0, 1]`. + """ + if timesteps.dtype not in (torch.int32, torch.int64): + raise ValueError(f"`timesteps` must be an integer tensor, got dtype={timesteps.dtype}.") + device = timesteps.device + t = self._t_from_timestep(timesteps.to(device), device=device).to(dtype=torch.float32) + dalpha = self._alpha_prime_t(t).to(dtype=torch.float32) + return dalpha.view(-1, 1) + def add_noise( self, original_samples: torch.LongTensor, diff --git a/src/diffusers/training_utils.py b/src/diffusers/training_utils.py index 7a98fa3da14a..205d13fd4d6a 100644 --- a/src/diffusers/training_utils.py +++ b/src/diffusers/training_utils.py @@ -10,6 +10,7 @@ import numpy as np import torch +import torch.nn.functional as F from .models import UNet2DConditionModel from .pipelines import DiffusionPipeline @@ -98,6 +99,92 @@ def compute_snr(noise_scheduler, timesteps): return snr +def compute_confidence_aware_loss( + logits: torch.Tensor, + labels: torch.Tensor, + *, + lambda_conf: float = 0.0, + temperature: float = 1.0, + per_token_weights: Optional[torch.Tensor] = None, + ignore_index: int = -100, +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Computes a confidence-aware training loss for token classification-style heads. + + This loss combines: + - `loss_sft`: standard supervised cross-entropy on all non-ignored labels. + - `loss_conf`: an entropy penalty applied only on tokens that are already predicted correctly. + + Args: + logits (`torch.Tensor`): Logits of shape `(..., vocab_size)`. + labels (`torch.Tensor`): Labels of shape `(...)`, matching `logits.shape[:-1]`. Values set to `ignore_index` + are excluded from both losses. + lambda_conf (`float`, *optional*, defaults to `0.0`): Weight for the confidence term. + temperature (`float`, *optional*, defaults to `1.0`): Temperature used for the entropy term only. Lower values + sharpen the distribution and change the strength of the confidence gradients. + per_token_weights (`torch.Tensor`, *optional*): Optional weights of shape `(...)` to reweight both losses per + token (e.g. schedule-aware weights). Tokens with weight `0` contribute nothing. + ignore_index (`int`, *optional*, defaults to `-100`): Ignore index for labels. + + Returns: + `Tuple[torch.Tensor, torch.Tensor, torch.Tensor]`: `(loss, loss_sft, loss_conf)`. + """ + if logits.ndim < 2: + raise ValueError(f"`logits` must have at least 2 dims, got shape {tuple(logits.shape)}.") + if labels.shape != logits.shape[:-1]: + raise ValueError( + f"`labels` shape must match `logits.shape[:-1]`, got labels={tuple(labels.shape)} logits={tuple(logits.shape)}." + ) + if temperature <= 0: + raise ValueError(f"`temperature` must be > 0, got {temperature}.") + + valid = labels.ne(ignore_index) + if per_token_weights is None: + weights = torch.ones_like(labels, dtype=logits.dtype) + else: + if per_token_weights.shape != labels.shape: + raise ValueError( + f"`per_token_weights` shape must match `labels` shape, got {tuple(per_token_weights.shape)} != {tuple(labels.shape)}." + ) + weights = per_token_weights.to(dtype=logits.dtype) + + # Supervised CE (optionally weighted). + vocab_size = logits.shape[-1] + per_token_nll = F.cross_entropy( + logits.reshape(-1, vocab_size), + labels.reshape(-1), + reduction="none", + ignore_index=ignore_index, + ).reshape_as(labels) + + denom_sft = (weights * valid.to(weights.dtype)).sum().clamp_min(1) + loss_sft = (per_token_nll * weights * valid.to(per_token_nll.dtype)).sum() / denom_sft + + # Confidence loss: penalize entropy only where prediction is already correct. + if lambda_conf == 0.0: + loss_conf = torch.zeros((), device=logits.device, dtype=loss_sft.dtype) + return loss_sft, loss_sft, loss_conf + + with torch.no_grad(): + pred = logits.argmax(dim=-1) + correct = valid & pred.eq(labels) + + scaled_logits = logits.float() + if temperature != 1.0: + scaled_logits = scaled_logits / float(temperature) + + probs = torch.softmax(scaled_logits, dim=-1) + eps = torch.finfo(probs.dtype).tiny + log_probs = torch.log(probs.clamp_min(eps)) + entropy = -(probs * log_probs).sum(dim=-1).to(dtype=logits.dtype) + + denom_conf = (weights * correct.to(weights.dtype)).sum().clamp_min(1) + loss_conf = (entropy * weights * correct.to(entropy.dtype)).sum() / denom_conf + + loss = loss_sft + float(lambda_conf) * loss_conf + return loss, loss_sft, loss_conf + + def resolve_interpolation_mode(interpolation_type: str): """ Maps a string describing an interpolation function to the corresponding torchvision `InterpolationMode` enum. The diff --git a/tests/others/test_training.py b/tests/others/test_training.py index 2038a98a813e..d8e86984ef1e 100644 --- a/tests/others/test_training.py +++ b/tests/others/test_training.py @@ -18,7 +18,7 @@ import torch from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel -from diffusers.training_utils import set_seed +from diffusers.training_utils import compute_confidence_aware_loss, set_seed from ..testing_utils import slow @@ -85,3 +85,47 @@ def test_training_step_equality(self): self.assertTrue(torch.allclose(ddpm_noisy_images, ddim_noisy_images, atol=1e-5)) self.assertTrue(torch.allclose(ddpm_noise_pred, ddim_noise_pred, atol=1e-5)) + + def test_confidence_aware_loss(self): + logits = torch.tensor([[[5.0, 0.0], [0.0, 5.0]]]) + labels = torch.tensor([[0, 0]]) + weights = torch.tensor([[1.0, 2.0]]) + + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, labels, lambda_conf=0.0, per_token_weights=weights + ) + self.assertTrue(torch.allclose(loss, loss_sft)) + self.assertTrue(torch.allclose(loss_conf, torch.zeros_like(loss_conf))) + + lambda_conf = 0.25 + loss, loss_sft, loss_conf = compute_confidence_aware_loss( + logits, labels, lambda_conf=lambda_conf, per_token_weights=weights + ) + + # Manual expected values for the small 2-class case. + per_token_nll = torch.nn.functional.cross_entropy(logits.view(-1, 2), labels.view(-1), reduction="none").view( + 1, 2 + ) + expected_sft = (per_token_nll * weights).sum() / weights.sum() + + pred = logits.argmax(dim=-1) + correct = pred.eq(labels) + log_probs = torch.log_softmax(logits.float(), dim=-1) + probs = log_probs.exp() + entropy = -(probs * log_probs).sum(dim=-1).to(dtype=logits.dtype) + expected_conf = (entropy * weights * correct.to(entropy.dtype)).sum() / ( + weights * correct.to(weights.dtype) + ).sum().clamp_min(1) + + expected = expected_sft + lambda_conf * expected_conf + self.assertTrue(torch.allclose(loss_sft, expected_sft)) + self.assertTrue(torch.allclose(loss_conf, expected_conf)) + self.assertTrue(torch.allclose(loss, expected)) + + # Temperature affects only the confidence term. + loss_t, loss_sft_t, loss_conf_t = compute_confidence_aware_loss( + logits, labels, lambda_conf=lambda_conf, temperature=0.5, per_token_weights=weights + ) + self.assertTrue(torch.allclose(loss_sft_t, expected_sft)) + self.assertFalse(torch.allclose(loss_conf_t, expected_conf)) + self.assertTrue(torch.allclose(loss_t, loss_sft_t + lambda_conf * loss_conf_t)) diff --git a/tests/pipelines/test_pipeline_block_refinement.py b/tests/pipelines/test_pipeline_block_refinement.py new file mode 100644 index 000000000000..d4b841268b53 --- /dev/null +++ b/tests/pipelines/test_pipeline_block_refinement.py @@ -0,0 +1,93 @@ +import unittest + +import torch + +from diffusers import BlockRefinementPipeline + + +class _DummyModelOutput: + def __init__(self, logits): + self.logits = logits + + +class _DummyCausalLM(torch.nn.Module): + def __init__(self, vocab_size: int): + super().__init__() + self.vocab_size = int(vocab_size) + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): + batch_size, seq_len = input_ids.shape + logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) + + # Make confidence vary with token position so top-k commits are deterministic. + positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.float32).view(1, seq_len, 1) + token_ids = (torch.arange(seq_len, device=input_ids.device) % (self.vocab_size - 2)).view(1, seq_len, 1) + logits.scatter_(2, token_ids.expand(batch_size, -1, -1), 1.0 + positions.expand(batch_size, -1, -1) * 0.1) + return _DummyModelOutput(logits=logits) + + +class _DummyCausalLM2DOnly(_DummyCausalLM): + def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): + if attention_mask is not None and attention_mask.ndim != 2: + raise ValueError("2D attention_mask required") + return super().forward(input_ids, attention_mask=attention_mask, position_ids=position_ids, **kwargs) + + +class BlockRefinementPipelineTest(unittest.TestCase): + def test_pipeline_runs(self): + vocab_size = 32 + model = _DummyCausalLM(vocab_size=vocab_size) + pipe = BlockRefinementPipeline(model=model, tokenizer=None).to("cpu") + + prompt_ids = torch.tensor([[5, 6, 7, 8], [1, 2, 3, 4]], dtype=torch.long) + out = pipe( + prompt_ids=prompt_ids, + gen_length=24, + block_length=8, + steps=8, + temperature=0.0, + threshold=2.0, # force top-k commits + minimal_topk=1, + eos_early_stop=False, + mask_token_id=vocab_size - 1, + eos_token_id=None, + return_text=False, + ) + + self.assertEqual(out.sequences.shape, (2, 24)) + self.assertFalse((out.sequences == vocab_size - 1).any().item()) + + def test_pipeline_falls_back_to_2d_attention_mask(self): + vocab_size = 32 + model = _DummyCausalLM2DOnly(vocab_size=vocab_size) + pipe = BlockRefinementPipeline(model=model, tokenizer=None).to("cpu") + + out = pipe( + prompt_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=16, + block_length=8, + steps=4, + temperature=0.0, + threshold=2.0, + minimal_topk=1, + eos_early_stop=False, + mask_token_id=vocab_size - 1, + eos_token_id=None, + attention_mask_mode="auto", + return_text=False, + ) + + self.assertEqual(out.sequences.shape, (1, 16)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_token_diffusion.py b/tests/schedulers/test_scheduler_token_diffusion.py index 94697c4145aa..1812f3f28630 100644 --- a/tests/schedulers/test_scheduler_token_diffusion.py +++ b/tests/schedulers/test_scheduler_token_diffusion.py @@ -115,6 +115,16 @@ def test_uniform_step_runs_and_returns_valid_ids(self): # With exclusion, mask token should not appear. self.assertFalse((x_prev == scheduler.mask_token_id).any().item()) + def test_alpha_helpers_shapes(self): + scheduler = self.get_scheduler(num_train_timesteps=10) + timesteps = torch.tensor([0, 1, 9], dtype=torch.long) + + alpha = scheduler.get_alpha(timesteps) + dalpha = scheduler.get_alpha_prime(timesteps) + + self.assertEqual(alpha.shape, (3, 1)) + self.assertEqual(dalpha.shape, (3, 1)) + if __name__ == "__main__": unittest.main() From c1bf227a8c54eef514479f2c87766fab77eb219b Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Thu, 1 Jan 2026 23:08:02 +0100 Subject: [PATCH 07/44] added llada2 --- src/diffusers/__init__.py | 4 + src/diffusers/pipelines/__init__.py | 2 + src/diffusers/pipelines/llada2/__init__.py | 47 ++++++ .../pipelines/llada2/pipeline_llada2.py | 157 ++++++++++++++++++ .../dummy_torch_and_transformers_objects.py | 14 ++ 5 files changed, 224 insertions(+) create mode 100644 src/diffusers/pipelines/llada2/__init__.py create mode 100644 src/diffusers/pipelines/llada2/pipeline_llada2.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 2220ffcc0f0d..99edb7f981e6 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -521,6 +521,8 @@ "IFPipeline", "IFSuperResolutionPipeline", "ImageTextPipelineOutput", + "LLaDA2Pipeline", + "LLaDA2PipelineOutput", "Kandinsky3Img2ImgPipeline", "Kandinsky3Pipeline", "Kandinsky5I2IPipeline", @@ -1245,6 +1247,8 @@ IFPipeline, IFSuperResolutionPipeline, ImageTextPipelineOutput, + LLaDA2Pipeline, + LLaDA2PipelineOutput, Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline, Kandinsky5I2IPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 4ae5beeaac0b..a58d5ca4148b 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -410,6 +410,7 @@ "Kandinsky5T2IPipeline", "Kandinsky5I2IPipeline", ] + _import_structure["llada2"] = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] _import_structure["z_image"] = [ "ZImageImg2ImgPipeline", "ZImagePipeline", @@ -723,6 +724,7 @@ Kandinsky5T2IPipeline, Kandinsky5T2VPipeline, ) + from .llada2 import LLaDA2Pipeline, LLaDA2PipelineOutput from .latent_consistency_models import ( LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline, diff --git a/src/diffusers/pipelines/llada2/__init__.py b/src/diffusers/pipelines/llada2/__init__.py new file mode 100644 index 000000000000..45a02e6851e2 --- /dev/null +++ b/src/diffusers/pipelines/llada2/__init__.py @@ -0,0 +1,47 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_llada2"] = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 + else: + from .pipeline_llada2 import LLaDA2Pipeline, LLaDA2PipelineOutput +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py new file mode 100644 index 000000000000..72940fdb69c3 --- /dev/null +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -0,0 +1,157 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple, Union + +import torch + +from ...utils import BaseOutput, logging, replace_example_docstring +from ..block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput + + +logger = logging.get_logger(__name__) + + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> from transformers import AutoModelForCausalLM, AutoTokenizer + >>> from diffusers import LLaDA2Pipeline + + >>> model_id = "inclusionAI/LLaDA2.0-mini" + >>> model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16) + >>> tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) + >>> model = model.to("cuda") + + >>> pipe = LLaDA2Pipeline(model=model, tokenizer=tokenizer) + >>> output = pipe(prompt="What is the meaning of life?", gen_length=256) + >>> print(output.texts[0]) + ``` +""" + + +@dataclass +class LLaDA2PipelineOutput(BaseOutput): + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +class LLaDA2Pipeline(BlockRefinementPipeline): + r""" + Adapter pipeline for LLaDA2-style discrete diffusion generation. + + This pipeline subclasses [`BlockRefinementPipeline`] and reuses its sampling loop. It only adapts + prompt preparation (including chat templates) and output formatting. + """ + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Optional[Union[str, List[str]]] = None, + *, + messages: Optional[List[Dict[str, str]]] = None, + input_ids: Optional[torch.LongTensor] = None, + use_chat_template: bool = True, + add_generation_prompt: bool = True, + gen_length: int = 512, + block_length: int = 32, + steps: int = 32, + temperature: float = 0.0, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + threshold: float = 0.95, + minimal_topk: int = 1, + eos_early_stop: bool = True, + eos_token_id: Optional[int] = None, + mask_token_id: Optional[int] = None, + attention_mask_mode: str = "auto", + generator: Optional[torch.Generator] = None, + return_text: bool = True, + return_dict: bool = True, + ) -> Union[LLaDA2PipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + prompt_ids = self._prepare_prompt_ids( + prompt=prompt, + messages=messages, + input_ids=input_ids, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + ) + + output: BlockRefinementPipelineOutput = super().__call__( + prompt_ids=prompt_ids, + gen_length=gen_length, + block_length=block_length, + steps=steps, + temperature=temperature, + top_p=top_p, + top_k=top_k, + threshold=threshold, + minimal_topk=minimal_topk, + eos_early_stop=eos_early_stop, + eos_token_id=eos_token_id, + mask_token_id=mask_token_id, + attention_mask_mode=attention_mask_mode, + generator=generator, + return_text=return_text, + ) + + if not return_dict: + return output.sequences, output.texts + return LLaDA2PipelineOutput(sequences=output.sequences, texts=output.texts) + + def _prepare_prompt_ids( + self, + *, + prompt: Optional[Union[str, List[str]]], + messages: Optional[List[Dict[str, str]]], + input_ids: Optional[torch.LongTensor], + use_chat_template: bool, + add_generation_prompt: bool, + ) -> Optional[torch.LongTensor]: + if input_ids is not None: + return input_ids + + if self.tokenizer is None: + if prompt is None and messages is None: + return None + raise ValueError("Tokenizer is required to encode `prompt` or `messages`.") + + if messages is not None: + return self.tokenizer.apply_chat_template( + messages, add_generation_prompt=add_generation_prompt, tokenize=True, return_tensors="pt" + ) + + if prompt is None: + return None + + if use_chat_template and getattr(self.tokenizer, "chat_template", None): + if isinstance(prompt, list): + raise ValueError("`prompt` must be a string when `use_chat_template=True`.") + return self.tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + ) + + encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) + return encoded["input_ids"] + + +__all__ = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index 4e1eae211c6f..2e23efa8c2e2 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -1403,6 +1403,20 @@ class ImageTextPipelineOutput(metaclass=DummyObject): def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) + +class LLaDA2PipelineOutput(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + +class LLaDA2Pipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) From f2b9223585bf1fac4617ee836de1fd8165472c7e Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Thu, 1 Jan 2026 23:28:21 +0100 Subject: [PATCH 08/44] add sample_llada2.py --- examples/discrete_diffusion/sample_llada2.py | 229 +++++++++++++++++++ 1 file changed, 229 insertions(+) create mode 100644 examples/discrete_diffusion/sample_llada2.py diff --git a/examples/discrete_diffusion/sample_llada2.py b/examples/discrete_diffusion/sample_llada2.py new file mode 100644 index 000000000000..5e338fe5d22f --- /dev/null +++ b/examples/discrete_diffusion/sample_llada2.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Sample script for LLaDA2-style discrete diffusion text generation. + +This script demonstrates how to use the LLaDA2Pipeline for text generation +using block-wise iterative refinement. + +Example usage: + python sample_llada2.py --model_id inclusionAI/LLaDA2.0-mini --prompt "What is the capital of France?" + python sample_llada2.py --model_id inclusionAI/LLaDA2.0-flash-CAP --prompt "Explain quantum computing." --temperature 0.7 +""" + +import argparse + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from diffusers import LLaDA2Pipeline +from diffusers.hooks import apply_group_offloading + + +def main(): + parser = argparse.ArgumentParser( + description="Generate text using LLaDA2Pipeline with block-wise discrete diffusion." + ) + parser.add_argument( + "--model_id", + type=str, + default="inclusionAI/LLaDA2.0-mini", + help="HuggingFace model ID or path to local model.", + ) + parser.add_argument( + "--prompt", + type=str, + default="Why does Camus think that Sisyphus is happy?", + help="Text prompt to generate from.", + ) + parser.add_argument( + "--gen_length", + type=int, + default=512, + help="Number of tokens to generate.", + ) + parser.add_argument( + "--block_length", + type=int, + default=32, + help="Size of each generation block.", + ) + parser.add_argument( + "--steps", + type=int, + default=32, + help="Number of refinement steps per block.", + ) + parser.add_argument( + "--temperature", + type=float, + default=0.0, + help="Sampling temperature (0.0 for greedy).", + ) + parser.add_argument( + "--top_p", + type=float, + default=None, + help="Nucleus sampling probability threshold.", + ) + parser.add_argument( + "--top_k", + type=int, + default=None, + help="Top-k sampling parameter.", + ) + parser.add_argument( + "--threshold", + type=float, + default=0.95, + help="Confidence threshold for committing tokens.", + ) + parser.add_argument( + "--eos_early_stop", + action="store_true", + help="Stop generation early when EOS token is generated.", + ) + parser.add_argument( + "--use_chat_template", + action="store_true", + help="Use the tokenizer chat template for the prompt.", + ) + parser.add_argument( + "--add_generation_prompt", + action="store_true", + help="Add the generation prompt when using the chat template.", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + help="Device to run inference on.", + ) + parser.add_argument( + "--dtype", + type=str, + default="bfloat16", + choices=["float32", "float16", "bfloat16"], + help="Model dtype.", + ) + parser.add_argument( + "--seed", + type=int, + default=None, + help="Random seed for reproducibility.", + ) + parser.add_argument( + "--offload", + type=str, + default=None, + choices=["group", "sequential"], + help="Memory offloading strategy: 'group' for group offloading (faster), 'sequential' for sequential CPU offload (slower but lower memory).", + ) + + args = parser.parse_args() + + # Parse dtype + dtype_map = { + "float32": torch.float32, + "float16": torch.float16, + "bfloat16": torch.bfloat16, + } + torch_dtype = dtype_map[args.dtype] + + print(f"Loading model: {args.model_id}") + tokenizer = AutoTokenizer.from_pretrained(args.model_id, trust_remote_code=True) + + # Load model with appropriate memory settings based on offload strategy + if args.offload == "group": + # For group offloading, load to CPU first then apply hooks + print("Using group offloading for memory efficiency...") + model = AutoModelForCausalLM.from_pretrained( + args.model_id, + trust_remote_code=True, + torch_dtype=torch_dtype, + low_cpu_mem_usage=True, + ) + # Apply group offloading with CUDA streams for better performance + onload_device = torch.device(args.device) + offload_device = torch.device("cpu") + apply_group_offloading( + model, + onload_device=onload_device, + offload_device=offload_device, + offload_type="leaf_level", + use_stream=True, + ) + elif args.offload == "sequential": + # For sequential offloading, load to CPU first + print("Using sequential CPU offloading (slower but lower memory)...") + model = AutoModelForCausalLM.from_pretrained( + args.model_id, + trust_remote_code=True, + torch_dtype=torch_dtype, + low_cpu_mem_usage=True, + ) + # Sequential offloading will be applied via pipeline + else: + # Default: use device_map="auto" for automatic memory management + model = AutoModelForCausalLM.from_pretrained( + args.model_id, + trust_remote_code=True, + torch_dtype=torch_dtype, + device_map="auto", + low_cpu_mem_usage=True, + ) + model.eval() + + # Create pipeline + pipe = LLaDA2Pipeline(model=model, tokenizer=tokenizer) + + # Apply sequential CPU offload if requested + if args.offload == "sequential": + pipe.enable_sequential_cpu_offload() + + # Set up generator for reproducibility + generator = None + if args.seed is not None: + generator = torch.Generator(device=args.device).manual_seed(args.seed) + + print(f"\nPrompt: {args.prompt}") + print(f"Generating {args.gen_length} tokens with block_length={args.block_length}, steps={args.steps}") + print("-" * 50) + + # Generate + output = pipe( + prompt=args.prompt, + use_chat_template=args.use_chat_template, + add_generation_prompt=args.add_generation_prompt, + gen_length=args.gen_length, + block_length=args.block_length, + steps=args.steps, + temperature=args.temperature, + top_p=args.top_p, + top_k=args.top_k, + threshold=args.threshold, + eos_early_stop=args.eos_early_stop, + generator=generator, + ) + + print("\nGenerated text:") + print(output.texts[0]) + + print(f"\nGenerated {output.sequences.shape[1]} tokens") + + +if __name__ == "__main__": + main() From cf18acdbf2a5e2bb5ae003ca269f00762b5b9899 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 4 Jan 2026 22:52:47 +0000 Subject: [PATCH 09/44] added api --- .../en/api/pipelines/block_refinement.md | 24 +++++++++++++++++++ .../en/api/pipelines/block_token_diffusion.md | 23 ++++++++++++++++++ .../api/pipelines/hybrid_token_diffusion.md | 23 ++++++++++++++++++ docs/source/en/api/pipelines/llada2.md | 23 ++++++++++++++++++ docs/source/en/api/pipelines/overview.md | 5 ++++ .../en/api/pipelines/token_diffusion.md | 24 +++++++++++++++++++ .../api/schedulers/block_token_diffusion.md | 21 ++++++++++++++++ .../api/schedulers/hybrid_token_diffusion.md | 22 +++++++++++++++++ docs/source/en/api/schedulers/overview.md | 5 ++++ .../en/api/schedulers/token_diffusion.md | 22 +++++++++++++++++ 10 files changed, 192 insertions(+) create mode 100644 docs/source/en/api/pipelines/block_refinement.md create mode 100644 docs/source/en/api/pipelines/block_token_diffusion.md create mode 100644 docs/source/en/api/pipelines/hybrid_token_diffusion.md create mode 100644 docs/source/en/api/pipelines/llada2.md create mode 100644 docs/source/en/api/pipelines/token_diffusion.md create mode 100644 docs/source/en/api/schedulers/block_token_diffusion.md create mode 100644 docs/source/en/api/schedulers/hybrid_token_diffusion.md create mode 100644 docs/source/en/api/schedulers/token_diffusion.md diff --git a/docs/source/en/api/pipelines/block_refinement.md b/docs/source/en/api/pipelines/block_refinement.md new file mode 100644 index 000000000000..2cf4bba28f22 --- /dev/null +++ b/docs/source/en/api/pipelines/block_refinement.md @@ -0,0 +1,24 @@ + + +# Block Refinement + +`BlockRefinementPipeline` performs block-wise iterative refinement over a masked token template, sampling and +committing tokens based on confidence. + +## BlockRefinementPipeline +[[autodoc]] BlockRefinementPipeline + - all + - __call__ + +## BlockRefinementPipelineOutput +[[autodoc]] pipelines.BlockRefinementPipelineOutput diff --git a/docs/source/en/api/pipelines/block_token_diffusion.md b/docs/source/en/api/pipelines/block_token_diffusion.md new file mode 100644 index 000000000000..df25001ffa03 --- /dev/null +++ b/docs/source/en/api/pipelines/block_token_diffusion.md @@ -0,0 +1,23 @@ + + +# Block Token Diffusion + +`BlockTokenDiffusionPipeline` performs token diffusion by iterating over fixed-size blocks of the sequence. + +## BlockTokenDiffusionPipeline +[[autodoc]] BlockTokenDiffusionPipeline + - all + - __call__ + +## BlockTokenDiffusionPipelineOutput +[[autodoc]] pipelines.BlockTokenDiffusionPipelineOutput diff --git a/docs/source/en/api/pipelines/hybrid_token_diffusion.md b/docs/source/en/api/pipelines/hybrid_token_diffusion.md new file mode 100644 index 000000000000..56ccd61bfbc8 --- /dev/null +++ b/docs/source/en/api/pipelines/hybrid_token_diffusion.md @@ -0,0 +1,23 @@ + + +# Hybrid Token Diffusion + +`HybridTokenDiffusionPipeline` is an alias of `TokenDiffusionPipeline` for hybrid-transition schedulers. + +## HybridTokenDiffusionPipeline +[[autodoc]] HybridTokenDiffusionPipeline + - all + - __call__ + +## TokenDiffusionPipelineOutput +[[autodoc]] pipelines.TokenDiffusionPipelineOutput diff --git a/docs/source/en/api/pipelines/llada2.md b/docs/source/en/api/pipelines/llada2.md new file mode 100644 index 000000000000..ba9330e4f5b3 --- /dev/null +++ b/docs/source/en/api/pipelines/llada2.md @@ -0,0 +1,23 @@ + + +# LLaDA2 + +`LLaDA2Pipeline` adapts block refinement sampling for LLaDA2-style token diffusion models. + +## LLaDA2Pipeline +[[autodoc]] LLaDA2Pipeline + - all + - __call__ + +## LLaDA2PipelineOutput +[[autodoc]] pipelines.LLaDA2PipelineOutput diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index 22fcf560eaca..29e68ba6d395 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -34,6 +34,8 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [AudioLDM2](audioldm2) | text2audio | | [AuraFlow](aura_flow) | text2image | | [BLIP Diffusion](blip_diffusion) | text2image | +| [Block Refinement](block_refinement) | text2text | +| [Block Token Diffusion](block_token_diffusion) | text2text | | [Bria 3.2](bria_3_2) | text2image | | [CogVideoX](cogvideox) | text2video | | [Consistency Models](consistency_models) | unconditional image generation | @@ -52,6 +54,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [DiT](dit) | text2image | | [Flux](flux) | text2image | | [Hunyuan-DiT](hunyuandit) | text2image | +| [Hybrid Token Diffusion](hybrid_token_diffusion) | text2text | | [I2VGen-XL](i2vgenxl) | image2video | | [InstructPix2Pix](pix2pix) | image editing | | [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation | @@ -62,6 +65,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [Latent Diffusion](latent_diffusion) | text2image, super-resolution | | [Latte](latte) | text2image | | [LEDITS++](ledits_pp) | image editing | +| [LLaDA2](llada2) | text2text | | [Lumina-T2X](lumina) | text2image | | [Marigold](marigold) | depth-estimation, normals-estimation, intrinsic-decomposition | | [MultiDiffusion](panorama) | text2image | @@ -83,6 +87,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [T2I-Adapter](stable_diffusion/adapter) | text2image | | [Text2Video](text_to_video) | text2video, video2video | | [Text2Video-Zero](text_to_video_zero) | text2video | +| [Token Diffusion](token_diffusion) | text2text | | [unCLIP](unclip) | text2image, image variation | | [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation | | [Value-guided planning](value_guided_sampling) | value guided sampling | diff --git a/docs/source/en/api/pipelines/token_diffusion.md b/docs/source/en/api/pipelines/token_diffusion.md new file mode 100644 index 000000000000..c105f53abece --- /dev/null +++ b/docs/source/en/api/pipelines/token_diffusion.md @@ -0,0 +1,24 @@ + + +# Token Diffusion + +`TokenDiffusionPipeline` provides a generic token-space diffusion sampler for discrete denoising over token IDs. It +pairs a token denoiser model with a token diffusion scheduler. + +## TokenDiffusionPipeline +[[autodoc]] TokenDiffusionPipeline + - all + - __call__ + +## TokenDiffusionPipelineOutput +[[autodoc]] pipelines.TokenDiffusionPipelineOutput diff --git a/docs/source/en/api/schedulers/block_token_diffusion.md b/docs/source/en/api/schedulers/block_token_diffusion.md new file mode 100644 index 000000000000..2ad92117fb0f --- /dev/null +++ b/docs/source/en/api/schedulers/block_token_diffusion.md @@ -0,0 +1,21 @@ + + +# BlockTokenDiffusionScheduler + +`BlockTokenDiffusionScheduler` extends `TokenDiffusionScheduler` with block-wise updates over token positions. + +## BlockTokenDiffusionScheduler +[[autodoc]] BlockTokenDiffusionScheduler + +## TokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_token_diffusion.TokenDiffusionSchedulerOutput diff --git a/docs/source/en/api/schedulers/hybrid_token_diffusion.md b/docs/source/en/api/schedulers/hybrid_token_diffusion.md new file mode 100644 index 000000000000..4dcdda0ea49c --- /dev/null +++ b/docs/source/en/api/schedulers/hybrid_token_diffusion.md @@ -0,0 +1,22 @@ + + +# HybridTokenDiffusionScheduler + +`HybridTokenDiffusionScheduler` defines hybrid discrete token diffusion updates with separate transitions for +masked and unmasked tokens. + +## HybridTokenDiffusionScheduler +[[autodoc]] HybridTokenDiffusionScheduler + +## HybridTokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_hybrid_token_diffusion.HybridTokenDiffusionSchedulerOutput diff --git a/docs/source/en/api/schedulers/overview.md b/docs/source/en/api/schedulers/overview.md index a57e99a3e46e..3d9c48e8171e 100644 --- a/docs/source/en/api/schedulers/overview.md +++ b/docs/source/en/api/schedulers/overview.md @@ -54,6 +54,11 @@ Many schedulers are implemented from the [k-diffusion](https://github.com/crowso | exponential | init with `timestep_spacing="linspace"`, `use_exponential_sigmas=True` | | beta | init with `timestep_spacing="linspace"`, `use_beta_sigmas=True` | +## Token diffusion schedulers +- [TokenDiffusionScheduler](token_diffusion) +- [BlockTokenDiffusionScheduler](block_token_diffusion) +- [HybridTokenDiffusionScheduler](hybrid_token_diffusion) + All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. ## SchedulerMixin diff --git a/docs/source/en/api/schedulers/token_diffusion.md b/docs/source/en/api/schedulers/token_diffusion.md new file mode 100644 index 000000000000..fe5305c00ae5 --- /dev/null +++ b/docs/source/en/api/schedulers/token_diffusion.md @@ -0,0 +1,22 @@ + + +# TokenDiffusionScheduler + +`TokenDiffusionScheduler` defines discrete token diffusion updates over categorical token IDs and supports multiple +forward processes and alpha schedules. + +## TokenDiffusionScheduler +[[autodoc]] TokenDiffusionScheduler + +## TokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_token_diffusion.TokenDiffusionSchedulerOutput From 920992450886063074780a7d9c8c3004564592c0 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 4 Jan 2026 22:54:00 +0000 Subject: [PATCH 10/44] fix llada2 sampling --- examples/discrete_diffusion/sample_llada2.py | 10 ++++++- .../pipeline_block_refinement.py | 29 +++++++++++++------ .../pipelines/llada2/pipeline_llada2.py | 28 ++++++++++++++---- 3 files changed, 51 insertions(+), 16 deletions(-) diff --git a/examples/discrete_diffusion/sample_llada2.py b/examples/discrete_diffusion/sample_llada2.py index 5e338fe5d22f..3e0fd0434008 100644 --- a/examples/discrete_diffusion/sample_llada2.py +++ b/examples/discrete_diffusion/sample_llada2.py @@ -52,7 +52,7 @@ def main(): parser.add_argument( "--gen_length", type=int, - default=512, + default=2048, help="Number of tokens to generate.", ) parser.add_argument( @@ -91,6 +91,13 @@ def main(): default=0.95, help="Confidence threshold for committing tokens.", ) + parser.add_argument( + "--sampling_method", + type=str, + default="multinomial", + choices=["auto", "greedy", "multinomial"], + help="Sampling method for block refinement.", + ) parser.add_argument( "--eos_early_stop", action="store_true", @@ -215,6 +222,7 @@ def main(): top_p=args.top_p, top_k=args.top_k, threshold=args.threshold, + sampling_method=args.sampling_method, eos_early_stop=args.eos_early_stop, generator=generator, ) diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index a45fb397cf21..a09bff71b178 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -64,6 +64,7 @@ def _sample_with_temperature_topk_topp( top_k: Optional[int], top_p: Optional[float], generator: Optional[torch.Generator], + use_multinomial: bool, ) -> tuple[torch.LongTensor, torch.Tensor]: vocab_size = logits.shape[-1] flat_logits = logits.reshape(-1, vocab_size) @@ -71,17 +72,19 @@ def _sample_with_temperature_topk_topp( filtered = _top_k_filtering(flat_logits, top_k=top_k) filtered = _top_p_filtering(filtered, top_p=top_p) - if temperature == 0.0: - token = filtered.argmax(dim=-1, keepdim=True) - probs = torch.softmax(filtered.float(), dim=-1) - token_prob = torch.gather(probs, -1, token) - else: - if temperature < 0: - raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + if temperature < 0: + raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + + scaled = filtered + if temperature > 0.0 and temperature != 1.0: scaled = filtered / float(temperature) - probs = torch.softmax(scaled.float(), dim=-1) + + probs = torch.softmax(scaled.float(), dim=-1) + if use_multinomial: token = torch.multinomial(probs, num_samples=1, generator=generator) - token_prob = torch.gather(probs, -1, token) + else: + token = scaled.argmax(dim=-1, keepdim=True) + token_prob = torch.gather(probs, -1, token) return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) @@ -209,6 +212,7 @@ def __call__( temperature: float = 0.0, top_p: Optional[float] = None, top_k: Optional[int] = None, + sampling_method: str = "auto", threshold: float = 0.95, minimal_topk: int = 1, eos_early_stop: bool = False, @@ -228,6 +232,10 @@ def __call__( raise ValueError(f"`minimal_topk` must be > 0, got {minimal_topk}.") if not (0.0 <= threshold <= 1.0) and not (threshold > 1.0): raise ValueError(f"`threshold` must be in [0, 1] (or > 1 to force top-k commits), got {threshold}.") + if sampling_method not in {"auto", "greedy", "multinomial"}: + raise ValueError( + f"`sampling_method` must be one of {{'auto','greedy','multinomial'}}, got {sampling_method!r}." + ) model_params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] model_device = model_params[0].device if len(model_params) > 0 else torch.device("cpu") @@ -271,6 +279,8 @@ def __call__( finished = torch.zeros((batch_size,), device=model_device, dtype=torch.bool) resolved_attention_mode: str = str(attention_mask_mode) + use_multinomial = sampling_method == "multinomial" or (sampling_method == "auto" and float(temperature) != 0.0) + for num_block in range(int(prefill_blocks), int(num_blocks)): current_window_end = (num_block + 1) * int(block_length) cur_x = x[:, :current_window_end] @@ -301,6 +311,7 @@ def __call__( top_k=top_k, top_p=top_p, generator=generator, + use_multinomial=use_multinomial, ) num_to_transfer = int(transfer_schedule[step_idx].item()) diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index 72940fdb69c3..bccfe5590b87 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -69,22 +69,28 @@ def __call__( input_ids: Optional[torch.LongTensor] = None, use_chat_template: bool = True, add_generation_prompt: bool = True, - gen_length: int = 512, + gen_length: int = 2048, block_length: int = 32, steps: int = 32, temperature: float = 0.0, top_p: Optional[float] = None, top_k: Optional[int] = None, + sampling_method: str = "multinomial", threshold: float = 0.95, minimal_topk: int = 1, - eos_early_stop: bool = True, + eos_early_stop: bool = False, eos_token_id: Optional[int] = None, mask_token_id: Optional[int] = None, - attention_mask_mode: str = "auto", + attention_mask_mode: str = "4d", generator: Optional[torch.Generator] = None, return_text: bool = True, return_dict: bool = True, ) -> Union[LLaDA2PipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + """ + Generate text with block-wise refinement. + + Examples: + """ prompt_ids = self._prepare_prompt_ids( prompt=prompt, messages=messages, @@ -101,6 +107,7 @@ def __call__( temperature=temperature, top_p=top_p, top_k=top_k, + sampling_method=sampling_method, threshold=threshold, minimal_topk=minimal_topk, eos_early_stop=eos_early_stop, @@ -132,10 +139,18 @@ def _prepare_prompt_ids( return None raise ValueError("Tokenizer is required to encode `prompt` or `messages`.") + def _extract_input_ids(encoded): + if isinstance(encoded, dict) and "input_ids" in encoded: + return encoded["input_ids"] + if hasattr(encoded, "input_ids"): + return encoded.input_ids + return encoded + if messages is not None: - return self.tokenizer.apply_chat_template( + encoded = self.tokenizer.apply_chat_template( messages, add_generation_prompt=add_generation_prompt, tokenize=True, return_tensors="pt" ) + return _extract_input_ids(encoded) if prompt is None: return None @@ -143,15 +158,16 @@ def _prepare_prompt_ids( if use_chat_template and getattr(self.tokenizer, "chat_template", None): if isinstance(prompt, list): raise ValueError("`prompt` must be a string when `use_chat_template=True`.") - return self.tokenizer.apply_chat_template( + encoded = self.tokenizer.apply_chat_template( [{"role": "user", "content": prompt}], add_generation_prompt=add_generation_prompt, tokenize=True, return_tensors="pt", ) + return _extract_input_ids(encoded) encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return encoded["input_ids"] + return _extract_input_ids(encoded) __all__ = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] From 5835d2c5f4541ba71f5465c3fc43a16dee93643e Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 4 Jan 2026 23:26:53 +0000 Subject: [PATCH 11/44] formatting --- .../en/api/pipelines/block_refinement.md | 39 +++++ src/diffusers/__init__.py | 36 ++-- src/diffusers/pipelines/__init__.py | 2 +- .../pipeline_block_refinement.py | 155 ++++++++++++++++-- .../pipeline_block_token_diffusion.py | 82 ++++++++- .../pipelines/llada2/pipeline_llada2.py | 10 +- .../pipeline_token_diffusion.py | 76 ++++++++- src/diffusers/schedulers/__init__.py | 10 +- 8 files changed, 356 insertions(+), 54 deletions(-) diff --git a/docs/source/en/api/pipelines/block_refinement.md b/docs/source/en/api/pipelines/block_refinement.md index 2cf4bba28f22..96e2f64e8660 100644 --- a/docs/source/en/api/pipelines/block_refinement.md +++ b/docs/source/en/api/pipelines/block_refinement.md @@ -15,6 +15,45 @@ specific language governing permissions and limitations under the License. `BlockRefinementPipeline` performs block-wise iterative refinement over a masked token template, sampling and committing tokens based on confidence. +## Config defaults + +You can set default sampling parameters when creating the pipeline. Passing `None` for a parameter in `__call__` +falls back to `pipe.config`. + +```py +from diffusers import BlockRefinementPipeline + +pipe = BlockRefinementPipeline( + model=model, + tokenizer=tokenizer, + gen_length=256, + block_length=32, + steps=16, + temperature=0.8, + sampling_method="multinomial", +) + +out = pipe(prompt="Explain gradient descent.") +print(out.texts[0]) +``` + +## Callbacks + +Callbacks run after each refinement step and can inspect or override the current tokens. + +```py +def on_step_end(pipe, step, timestep, callback_kwargs): + cur_x = callback_kwargs["cur_x"] + # Inspect or modify `cur_x` here. + return {"cur_x": cur_x} + +out = pipe( + prompt="Write a short poem.", + callback_on_step_end=on_step_end, + callback_on_step_end_tensor_inputs=["cur_x"], +) +``` + ## BlockRefinementPipeline [[autodoc]] BlockRefinementPipeline - all diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 99edb7f981e6..c053cdd9436d 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -323,10 +323,22 @@ "StableDiffusionMixin", ] ) + _import_structure["pipelines"].extend( + [ + "BlockRefinementPipeline", + "BlockRefinementPipelineOutput", + "BlockTokenDiffusionPipeline", + "BlockTokenDiffusionPipelineOutput", + "HybridTokenDiffusionPipeline", + "TokenDiffusionPipeline", + "TokenDiffusionPipelineOutput", + ] + ) _import_structure["quantizers"] = ["DiffusersQuantizer"] _import_structure["schedulers"].extend( [ "AmusedScheduler", + "BlockTokenDiffusionScheduler", "CMStochasticIterativeScheduler", "CogVideoXDDIMScheduler", "CogVideoXDPMScheduler", @@ -348,6 +360,8 @@ "FlowMatchHeunDiscreteScheduler", "FlowMatchLCMScheduler", "HeunDiscreteScheduler", + "HybridTokenDiffusionScheduler", + "HybridTokenDiffusionSchedulerOutput", "IPNDMScheduler", "KarrasVeScheduler", "KDPM2AncestralDiscreteScheduler", @@ -364,23 +378,9 @@ "UnCLIPScheduler", "UniPCMultistepScheduler", "VQDiffusionScheduler", - "BlockTokenDiffusionScheduler", - "HybridTokenDiffusionScheduler", - "HybridTokenDiffusionSchedulerOutput", ] ) _import_structure["training_utils"] = ["EMAModel"] - _import_structure["pipelines"].extend( - [ - "BlockRefinementPipeline", - "BlockRefinementPipelineOutput", - "BlockTokenDiffusionPipeline", - "BlockTokenDiffusionPipelineOutput", - "HybridTokenDiffusionPipeline", - "TokenDiffusionPipeline", - "TokenDiffusionPipelineOutput", - ] - ) try: if not (is_torch_available() and is_scipy_available()): @@ -521,8 +521,6 @@ "IFPipeline", "IFSuperResolutionPipeline", "ImageTextPipelineOutput", - "LLaDA2Pipeline", - "LLaDA2PipelineOutput", "Kandinsky3Img2ImgPipeline", "Kandinsky3Pipeline", "Kandinsky5I2IPipeline", @@ -552,6 +550,8 @@ "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", + "LLaDA2Pipeline", + "LLaDA2PipelineOutput", "LongCatImageEditPipeline", "LongCatImagePipeline", "LTXConditionPipeline", @@ -1247,8 +1247,6 @@ IFPipeline, IFSuperResolutionPipeline, ImageTextPipelineOutput, - LLaDA2Pipeline, - LLaDA2PipelineOutput, Kandinsky3Img2ImgPipeline, Kandinsky3Pipeline, Kandinsky5I2IPipeline, @@ -1278,6 +1276,8 @@ LDMTextToImagePipeline, LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, + LLaDA2Pipeline, + LLaDA2PipelineOutput, LongCatImageEditPipeline, LongCatImagePipeline, LTXConditionPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index a58d5ca4148b..c611a01985fc 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -724,7 +724,6 @@ Kandinsky5T2IPipeline, Kandinsky5T2VPipeline, ) - from .llada2 import LLaDA2Pipeline, LLaDA2PipelineOutput from .latent_consistency_models import ( LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline, @@ -737,6 +736,7 @@ LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, ) + from .llada2 import LLaDA2Pipeline, LLaDA2PipelineOutput from .longcat_image import LongCatImageEditPipeline, LongCatImagePipeline from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXLatentUpsamplePipeline, LTXPipeline from .lucy import LucyEditPipeline diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index a09bff71b178..9b5ea4695f4e 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -15,10 +15,11 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Union import torch +from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput from ..pipeline_utils import DiffusionPipeline @@ -113,9 +114,44 @@ class BlockRefinementPipeline(DiffusionPipeline): model: Any tokenizer: Any - def __init__(self, model: Any, tokenizer: Optional[Any] = None): + _callback_tensor_inputs = ["cur_x", "x0", "x0_p", "transfer_index", "confidence", "active_block"] + + def __init__( + self, + model: Any, + tokenizer: Optional[Any] = None, + *, + gen_length: int = 128, + block_length: int = 32, + steps: int = 32, + temperature: float = 0.0, + top_p: Optional[float] = None, + top_k: Optional[int] = None, + sampling_method: str = "auto", + threshold: float = 0.95, + minimal_topk: int = 1, + eos_early_stop: bool = False, + attention_mask_mode: str = "auto", + ): super().__init__() self.register_modules(model=model, tokenizer=tokenizer) + self.register_to_config( + gen_length=gen_length, + block_length=block_length, + steps=steps, + temperature=temperature, + top_p=top_p, + top_k=top_k, + sampling_method=sampling_method, + threshold=threshold, + minimal_topk=minimal_topk, + eos_early_stop=eos_early_stop, + attention_mask_mode=attention_mask_mode, + ) + + @property + def num_timesteps(self): + return self._num_timesteps def _model_forward_logits( self, @@ -206,22 +242,94 @@ def __call__( *, prompt: Optional[Union[str, List[str]]] = None, prompt_ids: Optional[torch.LongTensor] = None, - gen_length: int = 128, - block_length: int = 32, - steps: int = 32, - temperature: float = 0.0, + gen_length: Optional[int] = None, + block_length: Optional[int] = None, + steps: Optional[int] = None, + temperature: Optional[float] = None, top_p: Optional[float] = None, top_k: Optional[int] = None, - sampling_method: str = "auto", - threshold: float = 0.95, - minimal_topk: int = 1, - eos_early_stop: bool = False, + sampling_method: Optional[str] = None, + threshold: Optional[float] = None, + minimal_topk: Optional[int] = None, + eos_early_stop: Optional[bool] = None, eos_token_id: Optional[int] = None, mask_token_id: Optional[int] = None, - attention_mask_mode: str = "auto", + attention_mask_mode: Optional[str] = None, generator: Optional[torch.Generator] = None, return_text: bool = True, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, ) -> BlockRefinementPipelineOutput: + """ + Generate tokens with block-wise refinement. + + Args: + prompt (`str` or `List[str]`, *optional*): + Prompt text to encode with the tokenizer. + prompt_ids (`torch.LongTensor`, *optional*): + Pre-tokenized prompt IDs with shape `[prompt_len]` or `[batch, prompt_len]`. + gen_length (`int`, *optional*): + Number of tokens to generate. If `None`, uses `pipe.config.gen_length`. + block_length (`int`, *optional*): + Block size for refinement. If `None`, uses `pipe.config.block_length`. + steps (`int`, *optional*): + Refinement steps per block. If `None`, uses `pipe.config.steps`. + temperature (`float`, *optional*): + Sampling temperature. If `None`, uses `pipe.config.temperature`. + top_p (`float`, *optional*): + Nucleus sampling cutoff. If `None`, uses `pipe.config.top_p`. + top_k (`int`, *optional*): + Top-k sampling cutoff. If `None`, uses `pipe.config.top_k`. + sampling_method (`str`, *optional*): + Sampling method (`auto`, `greedy`, `multinomial`). If `None`, uses `pipe.config.sampling_method`. + threshold (`float`, *optional*): + Confidence threshold for committing tokens. If `None`, uses `pipe.config.threshold`. + minimal_topk (`int`, *optional*): + Minimum number of tokens to commit per step. If `None`, uses `pipe.config.minimal_topk`. + eos_early_stop (`bool`, *optional*): + Whether to stop after committing EOS in a block. If `None`, uses `pipe.config.eos_early_stop`. + eos_token_id (`int`, *optional*): + EOS token ID to use for early stopping. + mask_token_id (`int`, *optional*): + Mask token ID to use for the template. + attention_mask_mode (`str`, *optional*): + Attention mask mode (`auto`, `4d`, `2d`, `none`). If `None`, uses `pipe.config.attention_mask_mode`. + generator (`torch.Generator`, *optional*): + RNG for sampling. + return_text (`bool`, *optional*, defaults to `True`): + Whether to decode sequences into text when a tokenizer is available. + callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): + Callback executed after each refinement step with signature `callback_on_step_end(self, step: int, + timestep: int, callback_kwargs: Dict)`. + callback_on_step_end_tensor_inputs (`List[str]`, *optional*): + Tensor keys to pass to the callback. Allowed keys: `cur_x`, `x0`, `x0_p`, `transfer_index`, + `confidence`, `active_block`. + """ + if gen_length is None: + gen_length = int(self.config.gen_length) + if block_length is None: + block_length = int(self.config.block_length) + if steps is None: + steps = int(self.config.steps) + if temperature is None: + temperature = float(self.config.temperature) + if top_p is None: + top_p = self.config.top_p + if top_k is None: + top_k = self.config.top_k + if sampling_method is None: + sampling_method = str(self.config.sampling_method) + if threshold is None: + threshold = float(self.config.threshold) + if minimal_topk is None: + minimal_topk = int(self.config.minimal_topk) + if eos_early_stop is None: + eos_early_stop = bool(self.config.eos_early_stop) + if attention_mask_mode is None: + attention_mask_mode = str(self.config.attention_mask_mode) + if gen_length <= 0: raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") if block_length <= 0: @@ -237,6 +345,20 @@ def __call__( f"`sampling_method` must be one of {{'auto','greedy','multinomial'}}, got {sampling_method!r}." ) + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["cur_x"] + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + model_params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] model_device = model_params[0].device if len(model_params) > 0 else torch.device("cpu") @@ -274,12 +396,14 @@ def __call__( x[:, :prompt_length] = prompt_ids.to(device=model_device) prefill_blocks = prompt_length // int(block_length) + self._num_timesteps = int(steps) * max(int(num_blocks) - int(prefill_blocks), 0) transfer_schedule = _get_num_transfer_tokens(int(block_length), int(steps)).to(device=model_device) finished = torch.zeros((batch_size,), device=model_device, dtype=torch.bool) resolved_attention_mode: str = str(attention_mask_mode) use_multinomial = sampling_method == "multinomial" or (sampling_method == "auto" and float(temperature) != 0.0) + global_step = 0 for num_block in range(int(prefill_blocks), int(num_blocks)): current_window_end = (num_block + 1) * int(block_length) @@ -355,6 +479,15 @@ def __call__( if (cur_x[b, prompt_length:eos_pos] != int(mask_token_id)).all().item(): finished[b] = True + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, global_step, step_idx, callback_kwargs) + cur_x = callback_outputs.pop("cur_x", cur_x) + + global_step += 1 + x[:, :current_window_end] = cur_x if eos_token_id is not None and (x[:, prompt_length:current_window_end] == int(eos_token_id)).any().item(): if eos_early_stop: diff --git a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py index 24aebdc9d945..43f6a71621a8 100644 --- a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py +++ b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py @@ -15,10 +15,11 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, List, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch +from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput from ..pipeline_utils import DiffusionPipeline @@ -59,9 +60,33 @@ class BlockTokenDiffusionPipeline(DiffusionPipeline): tokenizer: Any scheduler: Any - def __init__(self, model: Any, scheduler: Any, tokenizer: Optional[Any] = None): + _callback_tensor_inputs = ["input_ids", "logits", "block_mask"] + + def __init__( + self, + model: Any, + scheduler: Any, + tokenizer: Optional[Any] = None, + *, + seq_len: int = 64, + block_size: int = 32, + num_inference_steps: int = 64, + inject_start_token: bool = False, + top_p: float = 1.0, + ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) + self.register_to_config( + seq_len=seq_len, + block_size=block_size, + num_inference_steps=num_inference_steps, + inject_start_token=inject_start_token, + top_p=top_p, + ) + + @property + def num_timesteps(self): + return self._num_timesteps def _resolve_start_token_id(self) -> Optional[int]: tok = getattr(self, "tokenizer", None) @@ -111,20 +136,50 @@ def __call__( self, *, batch_size: int = 1, - seq_len: int = 64, - block_size: int = 32, - num_inference_steps: int = 64, + seq_len: Optional[int] = None, + block_size: Optional[int] = None, + num_inference_steps: Optional[int] = None, generator: Optional[torch.Generator] = None, prefix_ids: Optional[torch.LongTensor] = None, infill_mask: Optional[torch.BoolTensor] = None, - inject_start_token: bool = False, - top_p: float = 1.0, + inject_start_token: Optional[bool] = None, + top_p: Optional[float] = None, return_text: bool = True, return_dict: bool = True, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, **model_kwargs, ) -> Union[BlockTokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + if seq_len is None: + seq_len = int(self.config.seq_len) + if block_size is None: + block_size = int(self.config.block_size) + if num_inference_steps is None: + num_inference_steps = int(self.config.num_inference_steps) + if inject_start_token is None: + inject_start_token = bool(self.config.inject_start_token) + if top_p is None: + top_p = float(self.config.top_p) + + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["input_ids"] + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + device = self._execution_device self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) attention_mask = torch.ones_like(input_ids, dtype=torch.long) @@ -165,6 +220,8 @@ def __call__( raise ValueError(f"`block_size` must be in [1, seq_len], got block_size={block_size}, seq_len={seq_len}.") num_blocks = (seq_len + block_size - 1) // block_size + self._num_timesteps = len(timesteps) * int(num_blocks) + global_step = 0 for block_idx in range(num_blocks): start = block_idx * block_size end = min((block_idx + 1) * block_size, seq_len) @@ -179,7 +236,7 @@ def __call__( input_ids = torch.where(block_mask, int(self.scheduler.mask_token_id), input_ids) - for t in self.scheduler.timesteps: + for step_idx, t in enumerate(timesteps): out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) logits = getattr(out, "logits", None) if logits is None: @@ -203,6 +260,15 @@ def __call__( if fixed_mask is not None: input_ids = torch.where(fixed_mask, fixed_values, input_ids) + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, global_step, t, callback_kwargs) + input_ids = callback_outputs.pop("input_ids", input_ids) + + global_step += 1 + texts = None if return_text and getattr(self, "tokenizer", None) is not None: texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index bccfe5590b87..e1d9d22537da 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -15,7 +15,7 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import torch @@ -55,8 +55,8 @@ class LLaDA2Pipeline(BlockRefinementPipeline): r""" Adapter pipeline for LLaDA2-style discrete diffusion generation. - This pipeline subclasses [`BlockRefinementPipeline`] and reuses its sampling loop. It only adapts - prompt preparation (including chat templates) and output formatting. + This pipeline subclasses [`BlockRefinementPipeline`] and reuses its sampling loop. It only adapts prompt + preparation (including chat templates) and output formatting. """ @torch.no_grad() @@ -85,6 +85,8 @@ def __call__( generator: Optional[torch.Generator] = None, return_text: bool = True, return_dict: bool = True, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, ) -> Union[LLaDA2PipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: """ Generate text with block-wise refinement. @@ -116,6 +118,8 @@ def __call__( attention_mask_mode=attention_mask_mode, generator=generator, return_text=return_text, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, ) if not return_dict: diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py index 6f112ed1308b..217a533a1119 100644 --- a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -15,10 +15,11 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, List, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch +from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput from ..pipeline_utils import DiffusionPipeline @@ -55,9 +56,29 @@ class TokenDiffusionPipeline(DiffusionPipeline): tokenizer: Any scheduler: Any - def __init__(self, model: Any, scheduler: Any, tokenizer: Optional[Any] = None): + _callback_tensor_inputs = ["input_ids", "logits"] + + def __init__( + self, + model: Any, + scheduler: Any, + tokenizer: Optional[Any] = None, + *, + seq_len: int = 64, + num_inference_steps: int = 128, + inject_start_token: bool = False, + ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) + self.register_to_config( + seq_len=seq_len, + num_inference_steps=num_inference_steps, + inject_start_token=inject_start_token, + ) + + @property + def num_timesteps(self): + return self._num_timesteps def _resolve_start_token_id(self) -> Optional[int]: tok = getattr(self, "tokenizer", None) @@ -126,21 +147,26 @@ def __call__( self, *, batch_size: int = 1, - seq_len: int = 64, - num_inference_steps: int = 128, + seq_len: Optional[int] = None, + num_inference_steps: Optional[int] = None, generator: Optional[torch.Generator] = None, prefix_ids: Optional[torch.LongTensor] = None, infill_mask: Optional[torch.BoolTensor] = None, - inject_start_token: bool = False, + inject_start_token: Optional[bool] = None, return_text: bool = True, return_dict: bool = True, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, **model_kwargs, ) -> Union[TokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: """ Args: batch_size: Number of sequences to generate. - seq_len: Sequence length in tokens. - num_inference_steps: Number of reverse diffusion steps. + seq_len: Sequence length in tokens. Uses `pipe.config.seq_len` when `None`. + num_inference_steps: + Number of reverse diffusion steps. Uses `pipe.config.num_inference_steps` when `None`. generator: Optional torch generator for determinism. prefix_ids: Optional prefix token IDs to keep fixed at the start of each sequence. Shape `[P]` or `[batch_size, P]`. @@ -148,13 +174,40 @@ def __call__( Optional boolean mask of shape `[batch_size, seq_len]` indicating which positions are editable (`True`) vs fixed (`False`). Fixed positions are clamped to the initial values on every step. inject_start_token: If True, inject `bos_token_id` (or `cls_token_id`) into position 0 (if available). + Uses `pipe.config.inject_start_token` when `None`. return_text: If True and tokenizer exists, also return decoded strings. return_dict: If True, returns a `TokenDiffusionPipelineOutput`. + callback_on_step_end: A function called after each denoising step with signature + `callback_on_step_end(self, step: int, timestep: int, callback_kwargs: Dict)`. + callback_on_step_end_tensor_inputs: List of tensor keys to include in `callback_kwargs`. model_kwargs: Forward kwargs passed to `model(...)` (e.g. attention mask overrides). """ + if seq_len is None: + seq_len = int(self.config.seq_len) + if num_inference_steps is None: + num_inference_steps = int(self.config.num_inference_steps) + if inject_start_token is None: + inject_start_token = bool(self.config.inject_start_token) + + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["input_ids"] + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + device = self._execution_device self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + self._num_timesteps = len(timesteps) input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) attention_mask = torch.ones_like(input_ids, dtype=torch.long) @@ -189,7 +242,7 @@ def __call__( fixed_mask[:, 0] = True fixed_values[:, 0] = start_token_id - for t in self.scheduler.timesteps: + for step_idx, t in enumerate(timesteps): out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) logits = getattr(out, "logits", None) if logits is None: @@ -204,6 +257,13 @@ def __call__( if inject_start_token and start_token_id is not None: input_ids[:, 0] = start_token_id + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, step_idx, t, callback_kwargs) + input_ids = callback_outputs.pop("input_ids", input_ids) + texts = None if return_text and getattr(self, "tokenizer", None) is not None: texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index e0f32d79a44a..bec678d07ec1 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -40,6 +40,7 @@ else: _import_structure["deprecated"] = ["KarrasVeScheduler", "ScoreSdeVpScheduler"] _import_structure["scheduling_amused"] = ["AmusedScheduler"] + _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"] _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"] _import_structure["scheduling_ddim"] = ["DDIMScheduler"] @@ -48,13 +49,8 @@ _import_structure["scheduling_ddim_parallel"] = ["DDIMParallelScheduler"] _import_structure["scheduling_ddpm"] = ["DDPMScheduler"] _import_structure["scheduling_ddpm_parallel"] = ["DDPMParallelScheduler"] - _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_ddpm_wuerstchen"] = ["DDPMWuerstchenScheduler"] _import_structure["scheduling_deis_multistep"] = ["DEISMultistepScheduler"] - _import_structure["scheduling_hybrid_token_diffusion"] = [ - "HybridTokenDiffusionScheduler", - "HybridTokenDiffusionSchedulerOutput", - ] _import_structure["scheduling_dpm_cogvideox"] = ["CogVideoXDPMScheduler"] _import_structure["scheduling_dpmsolver_multistep"] = ["DPMSolverMultistepScheduler"] _import_structure["scheduling_dpmsolver_multistep_inverse"] = ["DPMSolverMultistepInverseScheduler"] @@ -67,6 +63,10 @@ _import_structure["scheduling_flow_match_heun_discrete"] = ["FlowMatchHeunDiscreteScheduler"] _import_structure["scheduling_flow_match_lcm"] = ["FlowMatchLCMScheduler"] _import_structure["scheduling_heun_discrete"] = ["HeunDiscreteScheduler"] + _import_structure["scheduling_hybrid_token_diffusion"] = [ + "HybridTokenDiffusionScheduler", + "HybridTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_ipndm"] = ["IPNDMScheduler"] _import_structure["scheduling_k_dpm_2_ancestral_discrete"] = ["KDPM2AncestralDiscreteScheduler"] _import_structure["scheduling_k_dpm_2_discrete"] = ["KDPM2DiscreteScheduler"] From 1b3ffd71dd4c5781f6ebceda959b9f90f02ca5b7 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 11 Jan 2026 16:08:57 +0000 Subject: [PATCH 12/44] make fix-copies --- src/diffusers/utils/dummy_pt_objects.py | 165 ++++++++++++++++++ .../dummy_torch_and_transformers_objects.py | 44 +++-- 2 files changed, 195 insertions(+), 14 deletions(-) diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index bb94c94da360..b8aa154f2661 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2034,6 +2034,66 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class BlockRefinementPipeline(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class BlockRefinementPipelineOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class BlockTokenDiffusionPipeline(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class BlockTokenDiffusionPipelineOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class CLIPImageProjection(metaclass=DummyObject): _backends = ["torch"] @@ -2139,6 +2199,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class HybridTokenDiffusionPipeline(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class ImagePipelineOutput(metaclass=DummyObject): _backends = ["torch"] @@ -2259,6 +2334,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class TokenDiffusionPipeline(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class TokenDiffusionPipelineOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class DiffusersQuantizer(metaclass=DummyObject): _backends = ["torch"] @@ -2289,6 +2394,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class BlockTokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class CMStochasticIterativeScheduler(metaclass=DummyObject): _backends = ["torch"] @@ -2604,6 +2724,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class HybridTokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class HybridTokenDiffusionSchedulerOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class IPNDMScheduler(metaclass=DummyObject): _backends = ["torch"] @@ -2799,6 +2949,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class TokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class UnCLIPScheduler(metaclass=DummyObject): _backends = ["torch"] diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index c9c300f2d0af..c308d3f8fc00 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -1433,20 +1433,6 @@ class ImageTextPipelineOutput(metaclass=DummyObject): def __init__(self, *args, **kwargs): requires_backends(self, ["torch", "transformers"]) - -class LLaDA2PipelineOutput(metaclass=DummyObject): - _backends = ["torch", "transformers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "transformers"]) - - -class LLaDA2Pipeline(metaclass=DummyObject): - _backends = ["torch", "transformers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "transformers"]) - @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) @@ -1891,6 +1877,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class LLaDA2Pipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + +class LLaDA2PipelineOutput(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class LongCatImageEditPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] From c263f166017f73e22b586b6056eabcc02b48349a Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 11 Jan 2026 16:18:42 +0000 Subject: [PATCH 13/44] fix docs --- docs/source/en/api/schedulers/overview.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/docs/source/en/api/schedulers/overview.md b/docs/source/en/api/schedulers/overview.md index 3d9c48e8171e..f1d31a4c9982 100644 --- a/docs/source/en/api/schedulers/overview.md +++ b/docs/source/en/api/schedulers/overview.md @@ -55,9 +55,15 @@ Many schedulers are implemented from the [k-diffusion](https://github.com/crowso | beta | init with `timestep_spacing="linspace"`, `use_beta_sigmas=True` | ## Token diffusion schedulers -- [TokenDiffusionScheduler](token_diffusion) -- [BlockTokenDiffusionScheduler](block_token_diffusion) -- [HybridTokenDiffusionScheduler](hybrid_token_diffusion) + +These schedulers operate over categorical token IDs instead of continuous latents. They are designed for discrete +token diffusion models and expose the same `set_timesteps`/`step` interface as other schedulers. + +[[autodoc]] TokenDiffusionScheduler + +[[autodoc]] BlockTokenDiffusionScheduler + +[[autodoc]] HybridTokenDiffusionScheduler All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. From b97e11cd6b34433bbaf69b150b4b60143594edc5 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 11 Jan 2026 18:38:10 +0000 Subject: [PATCH 14/44] add dflash pipeline --- docs/source/en/api/pipelines/overview.md | 1 + docs/source/en/api/schedulers/overview.md | 2 + examples/discrete_diffusion/README.md | 14 + examples/discrete_diffusion/sample_dflash.py | 144 ++++++ src/diffusers/__init__.py | 8 + src/diffusers/pipelines/__init__.py | 2 + src/diffusers/pipelines/dflash/__init__.py | 47 ++ .../pipelines/dflash/pipeline_dflash.py | 436 ++++++++++++++++++ .../pipelines/llada2/pipeline_llada2.py | 7 +- src/diffusers/schedulers/__init__.py | 8 + .../scheduling_dflash_token_diffusion.py | 104 +++++ src/diffusers/utils/dummy_pt_objects.py | 30 ++ .../dummy_torch_and_transformers_objects.py | 30 ++ 13 files changed, 832 insertions(+), 1 deletion(-) create mode 100644 examples/discrete_diffusion/sample_dflash.py create mode 100644 src/diffusers/pipelines/dflash/__init__.py create mode 100644 src/diffusers/pipelines/dflash/pipeline_dflash.py create mode 100644 src/diffusers/schedulers/scheduling_dflash_token_diffusion.py diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index 29e68ba6d395..093a70b5b180 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -49,6 +49,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [Dance Diffusion](dance_diffusion) | unconditional audio generation | | [DDIM](ddim) | unconditional image generation | | [DDPM](ddpm) | unconditional image generation | +| [DFlash](dflash) | text2text | | [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution | | [DiffEdit](diffedit) | inpainting | | [DiT](dit) | text2image | diff --git a/docs/source/en/api/schedulers/overview.md b/docs/source/en/api/schedulers/overview.md index f1d31a4c9982..4017523bce78 100644 --- a/docs/source/en/api/schedulers/overview.md +++ b/docs/source/en/api/schedulers/overview.md @@ -65,6 +65,8 @@ token diffusion models and expose the same `set_timesteps`/`step` interface as o [[autodoc]] HybridTokenDiffusionScheduler +[[autodoc]] DFlashTokenDiffusionScheduler + All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. ## SchedulerMixin diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index fee10a9c7f00..7bf0e6892a70 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -111,6 +111,20 @@ python examples/discrete_diffusion/sample_block_refinement.py \ --gen_length 128 ``` +## DFlash speculative decoding + +Use a diffusion draft model with a target causal LM for block-wise speculative decoding. + +```bash +python examples/discrete_diffusion/sample_dflash.py \ + --draft_model_id z-lab/Qwen3-8B-DFlash-b16 \ + --target_model_id Qwen/Qwen3-8B \ + --prompt "How many positive whole-number divisors does 196 have?" \ + --max_new_tokens 256 \ + --use_chat_template \ + --add_generation_prompt +``` + ## Hybrid sampling Hybrid sampling uses a different transition kernel than absorbing/uniform diffusion and requires a compatible scheduler diff --git a/examples/discrete_diffusion/sample_dflash.py b/examples/discrete_diffusion/sample_dflash.py new file mode 100644 index 000000000000..2c172dc04464 --- /dev/null +++ b/examples/discrete_diffusion/sample_dflash.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Sample script for DFlash speculative decoding. + +Example: + python sample_dflash.py \ + --draft_model_id z-lab/Qwen3-8B-DFlash-b16 \ + --target_model_id Qwen/Qwen3-8B \ + --prompt "How many positive whole-number divisors does 196 have?" \ + --max_new_tokens 256 +""" + +import argparse + +import torch + +from diffusers import DFlashPipeline + + +def main(): + parser = argparse.ArgumentParser(description="Run DFlash speculative decoding.") + parser.add_argument( + "--draft_model_id", + type=str, + default="z-lab/Qwen3-8B-DFlash-b16", + help="Draft model ID or local path.", + ) + parser.add_argument( + "--target_model_id", + type=str, + default="Qwen/Qwen3-8B", + help="Target model ID or local path.", + ) + parser.add_argument( + "--prompt", + type=str, + default="How many positive whole-number divisors does 196 have?", + help="Prompt text to generate from.", + ) + parser.add_argument( + "--max_new_tokens", + type=int, + default=2048, + help="Maximum number of new tokens to generate.", + ) + parser.add_argument( + "--temperature", + type=float, + default=0.0, + help="Sampling temperature.", + ) + parser.add_argument( + "--use_chat_template", + action="store_true", + help="Use the tokenizer chat template for the prompt.", + ) + parser.add_argument( + "--add_generation_prompt", + action="store_true", + help="Add the generation prompt when using the chat template.", + ) + parser.add_argument( + "--enable_thinking", + action="store_true", + help="Enable chat-template thinking mode if supported by the tokenizer.", + ) + parser.add_argument( + "--mask_token", + type=str, + default="<|MASK|>", + help="Mask token to add if the tokenizer does not define one.", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + help="Device to run inference on.", + ) + parser.add_argument( + "--dtype", + type=str, + default="auto", + choices=["auto", "float32", "float16", "bfloat16"], + help="Model dtype.", + ) + + args = parser.parse_args() + + dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16} + torch_dtype = dtype_map.get(args.dtype) + + print(f"Loading draft model: {args.draft_model_id}") + print(f"Loading target model: {args.target_model_id}") + dtype_arg = torch_dtype if torch_dtype is not None else "auto" + pipe = DFlashPipeline.from_pretrained( + draft_model_id=args.draft_model_id, + target_model_id=args.target_model_id, + mask_token=args.mask_token, + draft_model_kwargs={ + "trust_remote_code": True, + "dtype": dtype_arg, + "device_map": args.device, + }, + target_model_kwargs={ + "dtype": dtype_arg, + "device_map": args.device, + }, + max_new_tokens=args.max_new_tokens, + temperature=args.temperature, + ) + + chat_kwargs = {"enable_thinking": args.enable_thinking} + + print(f"\nPrompt: {args.prompt}") + output = pipe( + prompt=args.prompt, + max_new_tokens=args.max_new_tokens, + temperature=args.temperature, + use_chat_template=args.use_chat_template, + add_generation_prompt=args.add_generation_prompt, + chat_template_kwargs=chat_kwargs, + ) + + print("\nGenerated text:") + print(output.texts[0]) + print(f"\nGenerated {output.sequences.shape[1]} tokens") + + +if __name__ == "__main__": + main() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 3d7f7c83773d..d313156d82b9 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -342,6 +342,8 @@ [ "AmusedScheduler", "BlockTokenDiffusionScheduler", + "DFlashTokenDiffusionScheduler", + "DFlashTokenDiffusionSchedulerOutput", "CMStochasticIterativeScheduler", "CogVideoXDDIMScheduler", "CogVideoXDPMScheduler", @@ -556,6 +558,8 @@ "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", + "DFlashPipeline", + "DFlashPipelineOutput", "LLaDA2Pipeline", "LLaDA2PipelineOutput", "LongCatImageEditPipeline", @@ -1110,6 +1114,8 @@ DDPMScheduler, DDPMWuerstchenScheduler, DEISMultistepScheduler, + DFlashTokenDiffusionScheduler, + DFlashTokenDiffusionSchedulerOutput, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, @@ -1227,6 +1233,8 @@ CosmosTextToWorldPipeline, CosmosVideoToWorldPipeline, CycleDiffusionPipeline, + DFlashPipeline, + DFlashPipelineOutput, EasyAnimateControlPipeline, EasyAnimateInpaintPipeline, EasyAnimatePipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 59cfa352e011..b9b2f5e0e5f9 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -412,6 +412,7 @@ "Kandinsky5T2IPipeline", "Kandinsky5I2IPipeline", ] + _import_structure["dflash"] = ["DFlashPipeline", "DFlashPipelineOutput"] _import_structure["llada2"] = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] _import_structure["z_image"] = [ "ZImageImg2ImgPipeline", @@ -663,6 +664,7 @@ VersatileDiffusionTextToImagePipeline, VQDiffusionPipeline, ) + from .dflash import DFlashPipeline, DFlashPipelineOutput from .easyanimate import ( EasyAnimateControlPipeline, EasyAnimateInpaintPipeline, diff --git a/src/diffusers/pipelines/dflash/__init__.py b/src/diffusers/pipelines/dflash/__init__.py new file mode 100644 index 000000000000..c5d0f5fae4cd --- /dev/null +++ b/src/diffusers/pipelines/dflash/__init__.py @@ -0,0 +1,47 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_dflash"] = ["DFlashPipeline", "DFlashPipelineOutput"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 + else: + from .pipeline_dflash import DFlashPipeline, DFlashPipelineOutput +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py new file mode 100644 index 000000000000..b7e61b6df475 --- /dev/null +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -0,0 +1,436 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple, Union + +import torch +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, DynamicCache + +from ...schedulers import DFlashTokenDiffusionScheduler +from ...utils import BaseOutput, logging, replace_example_docstring +from ..pipeline_utils import DiffusionPipeline + + +logger = logging.get_logger(__name__) + + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> from diffusers import DFlashPipeline + + >>> draft_id = "z-lab/Qwen3-8B-DFlash-b16" + >>> target_id = "Qwen/Qwen3-8B" + >>> pipe = DFlashPipeline.from_pretrained( + ... draft_model_id=draft_id, + ... target_model_id=target_id, + ... draft_model_kwargs={"trust_remote_code": True, "dtype": torch.bfloat16}, + ... target_model_kwargs={"dtype": torch.bfloat16}, + ... ) + >>> out = pipe(prompt="How many positive whole-number divisors does 196 have?") + >>> print(out.texts[0]) + ``` +""" + + +@dataclass +class DFlashPipelineOutput(BaseOutput): + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +def _build_target_layer_ids(num_target_layers: int, num_draft_layers: int) -> List[int]: + if num_draft_layers == 1: + return [int(num_target_layers // 2)] + start = 1 + end = int(num_target_layers) - 3 + span = end - start + return [int(round(start + (i * span) / (num_draft_layers - 1))) for i in range(int(num_draft_layers))] + + +def _extract_context_feature(hidden_states: List[torch.Tensor], layer_ids: List[int]) -> torch.Tensor: + offset = 1 + selected_states = [hidden_states[layer_id + offset] for layer_id in layer_ids] + return torch.cat(selected_states, dim=-1) + + +class DFlashPipeline(DiffusionPipeline): + r""" + Block diffusion pipeline for speculative decoding with a DFlash draft model and a target causal LM. + """ + + draft_model: torch.nn.Module + target_model: torch.nn.Module + tokenizer: Optional[object] + scheduler: DFlashTokenDiffusionScheduler + + def __init__( + self, + draft_model: torch.nn.Module, + target_model: torch.nn.Module, + tokenizer: Optional[object] = None, + scheduler: Optional[DFlashTokenDiffusionScheduler] = None, + *, + max_new_tokens: int = 2048, + temperature: float = 0.0, + use_chat_template: bool = True, + add_generation_prompt: bool = True, + ): + super().__init__() + if scheduler is None: + scheduler = DFlashTokenDiffusionScheduler() + self.register_modules(draft_model=draft_model, target_model=target_model, tokenizer=tokenizer, scheduler=scheduler) + self.register_to_config( + max_new_tokens=max_new_tokens, + temperature=temperature, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + ) + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path: Optional[str] = None, + *, + draft_model_id: Optional[str] = None, + target_model_id: Optional[str] = None, + tokenizer_id: Optional[str] = None, + mask_token: Optional[str] = "<|MASK|>", + scheduler: Optional[DFlashTokenDiffusionScheduler] = None, + draft_model_kwargs: Optional[Dict[str, object]] = None, + target_model_kwargs: Optional[Dict[str, object]] = None, + tokenizer_kwargs: Optional[Dict[str, object]] = None, + **pipeline_kwargs, + ) -> "DFlashPipeline": + if draft_model_id is None and target_model_id is None and pretrained_model_name_or_path is not None: + return super().from_pretrained(pretrained_model_name_or_path, **pipeline_kwargs) + + if draft_model_id is None: + if pretrained_model_name_or_path is None: + raise ValueError("Provide `draft_model_id` or `pretrained_model_name_or_path`.") + draft_model_id = str(pretrained_model_name_or_path) + if target_model_id is None: + raise ValueError("`target_model_id` must be provided when loading draft/target models separately.") + + draft_model_kwargs = dict(draft_model_kwargs or {}) + draft_model_kwargs.setdefault("trust_remote_code", True) + target_model_kwargs = dict(target_model_kwargs or {}) + tokenizer_kwargs = dict(tokenizer_kwargs or {}) + + draft = AutoModel.from_pretrained(draft_model_id, **draft_model_kwargs) + target = AutoModelForCausalLM.from_pretrained(target_model_id, **target_model_kwargs) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id or target_model_id, **tokenizer_kwargs) + + if mask_token is not None and tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": mask_token}) + + return cls( + draft_model=draft, + target_model=target, + tokenizer=tokenizer, + scheduler=scheduler, + **pipeline_kwargs, + ) + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Optional[Union[str, List[str]]] = None, + *, + messages: Optional[List[Dict[str, str]]] = None, + input_ids: Optional[torch.LongTensor] = None, + max_new_tokens: Optional[int] = None, + temperature: Optional[float] = None, + stop_token_ids: Optional[List[int]] = None, + mask_token_id: Optional[int] = None, + use_chat_template: Optional[bool] = None, + add_generation_prompt: Optional[bool] = None, + chat_template_kwargs: Optional[Dict[str, object]] = None, + return_text: bool = True, + return_dict: bool = True, + ) -> Union[DFlashPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + """ + Generate text using block-diffusion speculative decoding. + + Examples: + """ + if max_new_tokens is None: + max_new_tokens = int(self.config.max_new_tokens) + if temperature is None: + temperature = float(self.config.temperature) + if use_chat_template is None: + use_chat_template = bool(self.config.use_chat_template) + if add_generation_prompt is None: + add_generation_prompt = bool(self.config.add_generation_prompt) + + input_ids = self._prepare_input_ids( + prompt=prompt, + messages=messages, + input_ids=input_ids, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + chat_template_kwargs=chat_template_kwargs, + ) + + if input_ids.shape[0] != 1: + raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.") + + target_params = list(self.target_model.parameters()) if hasattr(self.target_model, "parameters") else [] + device = target_params[0].device if len(target_params) > 0 else torch.device("cpu") + input_ids = input_ids.to(device=device) + draft_params = list(self.draft_model.parameters()) if hasattr(self.draft_model, "parameters") else [] + draft_device = draft_params[0].device if len(draft_params) > 0 else device + if draft_device != device: + logger.warning( + "Draft model is on %s while target model is on %s. For best performance, place both on the same device.", + draft_device, + device, + ) + + if mask_token_id is None: + mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + + if stop_token_ids is None: + eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) + stop_token_ids = [int(eos_token_id)] if eos_token_id is not None else None + if stop_token_ids is not None: + stop_token_ids = [int(token_id) for token_id in stop_token_ids] + + self.draft_model.eval() + self.target_model.eval() + self.scheduler.set_timesteps(1, device=device) + + block_size = self._get_block_size() + target_layer_ids = self._get_target_layer_ids() + input_embeddings = self._get_target_input_embeddings() + output_embeddings = self._get_target_output_embeddings() + + num_input_tokens = input_ids.shape[1] + max_length = num_input_tokens + int(max_new_tokens) + output_ids = torch.full( + (1, max_length + int(block_size)), + int(mask_token_id), + dtype=torch.long, + device=device, + ) + position_ids = torch.arange(output_ids.shape[1], device=device).unsqueeze(0) + + past_key_values_target = DynamicCache() + past_key_values_draft = DynamicCache() + + output = self._target_forward( + input_ids=input_ids, + position_ids=position_ids[:, :num_input_tokens], + past_key_values=past_key_values_target, + output_hidden_states=True, + logits_to_keep=1, + ) + output_ids[:, :num_input_tokens] = input_ids + output_ids[:, num_input_tokens : num_input_tokens + 1] = self.scheduler.sample( + output.logits[:, -1:], temperature=temperature + ) + target_hidden = _extract_context_feature(output.hidden_states, target_layer_ids) + + start = num_input_tokens + stop_tensor = None + if stop_token_ids is not None: + stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) + + while start < max_length: + block_output_ids = output_ids[:, start : start + int(block_size)].clone() + block_position_ids = position_ids[:, start : start + int(block_size)] + noise_embedding = input_embeddings(block_output_ids) + draft_hidden = self.draft_model( + target_hidden=target_hidden, + noise_embedding=noise_embedding, + position_ids=position_ids[:, past_key_values_draft.get_seq_length() : start + int(block_size)], + past_key_values=past_key_values_draft, + use_cache=True, + is_causal=False, + ) + if not torch.is_tensor(draft_hidden): + draft_hidden = getattr(draft_hidden, "last_hidden_state", draft_hidden[0]) + draft_logits = output_embeddings(draft_hidden[:, -int(block_size) + 1 :, :]) + past_key_values_draft.crop(start) + block_output_ids[:, 1:] = self.scheduler.sample(draft_logits, temperature=temperature) + + output = self._target_forward( + input_ids=block_output_ids, + position_ids=block_position_ids, + past_key_values=past_key_values_target, + output_hidden_states=True, + logits_to_keep=None, + ) + step_output = self.scheduler.step( + block_output_ids, output.logits, temperature=temperature, return_dict=True + ) + acceptance_length = int(step_output.accepted_length[0].item()) + output_ids[:, start : start + acceptance_length + 1] = block_output_ids[:, : acceptance_length + 1] + output_ids[:, start + acceptance_length + 1] = step_output.next_token + start += acceptance_length + 1 + past_key_values_target.crop(start) + target_hidden = _extract_context_feature(output.hidden_states, target_layer_ids)[:, : acceptance_length + 1, :] + + if stop_tensor is not None and torch.isin(output_ids[:, num_input_tokens:], stop_tensor).any(): + break + + output_ids = output_ids[:, :max_length] + output_ids = output_ids[:, output_ids[0] != int(mask_token_id)] + if stop_tensor is not None: + stop_positions = torch.isin(output_ids[0, num_input_tokens:], stop_tensor).nonzero(as_tuple=True)[0] + if stop_positions.numel() > 0: + output_ids = output_ids[:, : num_input_tokens + int(stop_positions[0].item()) + 1] + + prompt_len = input_ids.shape[1] + sequences = output_ids[:, prompt_len:] + + texts = None + if return_text and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) + + if not return_dict: + return sequences, texts + return DFlashPipelineOutput(sequences=sequences, texts=texts) + + def _get_block_size(self) -> int: + block_size = getattr(self.draft_model, "block_size", None) + if block_size is None: + block_size = getattr(getattr(self.draft_model, "config", None), "block_size", None) + if block_size is None: + raise ValueError("`draft_model` must define `block_size` on the module or its config.") + return int(block_size) + + def _get_target_layer_ids(self) -> List[int]: + layer_ids = getattr(self.draft_model, "target_layer_ids", None) + if layer_ids is not None: + return list(layer_ids) + cfg = getattr(self.draft_model, "config", None) + num_target_layers = getattr(cfg, "num_target_layers", None) + num_hidden_layers = getattr(cfg, "num_hidden_layers", None) + if num_target_layers is None or num_hidden_layers is None: + raise ValueError("`draft_model` must define `target_layer_ids` or expose `num_target_layers` in config.") + return _build_target_layer_ids(int(num_target_layers), int(num_hidden_layers)) + + def _get_target_input_embeddings(self) -> torch.nn.Module: + embeddings = self.target_model.get_input_embeddings() + if embeddings is None: + base_model = getattr(self.target_model, "model", None) + embeddings = getattr(base_model, "embed_tokens", None) + if embeddings is None: + raise ValueError("`target_model` must provide input embeddings for DFlash decoding.") + return embeddings + + def _get_target_output_embeddings(self) -> torch.nn.Module: + embeddings = self.target_model.get_output_embeddings() + if embeddings is None: + embeddings = getattr(self.target_model, "lm_head", None) + if embeddings is None: + raise ValueError("`target_model` must provide output embeddings for DFlash decoding.") + return embeddings + + def _target_forward( + self, + *, + input_ids: torch.LongTensor, + position_ids: torch.LongTensor, + past_key_values: DynamicCache, + output_hidden_states: bool, + logits_to_keep: Optional[int], + ): + kwargs = { + "input_ids": input_ids, + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": True, + "output_hidden_states": output_hidden_states, + } + if logits_to_keep is not None: + try: + return self.target_model(**kwargs, logits_to_keep=logits_to_keep) + except TypeError: + pass + return self.target_model(**kwargs) + + def _prepare_input_ids( + self, + *, + prompt: Optional[Union[str, List[str]]], + messages: Optional[List[Dict[str, str]]], + input_ids: Optional[torch.LongTensor], + use_chat_template: bool, + add_generation_prompt: bool, + chat_template_kwargs: Optional[Dict[str, object]], + ) -> torch.LongTensor: + if input_ids is not None: + if input_ids.ndim == 1: + input_ids = input_ids.unsqueeze(0) + if input_ids.ndim != 2: + raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + return input_ids + + if self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + if messages is not None and prompt is not None: + raise ValueError("Provide either `prompt` or `messages`, not both.") + if messages is None and prompt is None: + raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") + + chat_template_kwargs = chat_template_kwargs or {} + + def _extract_input_ids(encoded): + if isinstance(encoded, dict) and "input_ids" in encoded: + return encoded["input_ids"] + if hasattr(encoded, "input_ids"): + return encoded.input_ids + return encoded + + if messages is not None: + encoded = self.tokenizer.apply_chat_template( + messages, + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + if use_chat_template and getattr(self.tokenizer, "chat_template", None): + if isinstance(prompt, list): + raise ValueError("`prompt` must be a string when `use_chat_template=True`.") + encoded = self.tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) + return _extract_input_ids(encoded) + + +__all__ = ["DFlashPipeline", "DFlashPipelineOutput"] diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index e1d9d22537da..acdb686b3aaf 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -152,7 +152,11 @@ def _extract_input_ids(encoded): if messages is not None: encoded = self.tokenizer.apply_chat_template( - messages, add_generation_prompt=add_generation_prompt, tokenize=True, return_tensors="pt" + messages, + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, ) return _extract_input_ids(encoded) @@ -167,6 +171,7 @@ def _extract_input_ids(encoded): add_generation_prompt=add_generation_prompt, tokenize=True, return_tensors="pt", + return_dict=True, ) return _extract_input_ids(encoded) diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index c31e6632ee91..1876a5cac89c 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -43,6 +43,10 @@ _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"] _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"] + _import_structure["scheduling_dflash_token_diffusion"] = [ + "DFlashTokenDiffusionScheduler", + "DFlashTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_ddim"] = ["DDIMScheduler"] _import_structure["scheduling_ddim_cogvideox"] = ["CogVideoXDDIMScheduler"] _import_structure["scheduling_ddim_inverse"] = ["DDIMInverseScheduler"] @@ -159,6 +163,10 @@ from .scheduling_ddpm_parallel import DDPMParallelScheduler from .scheduling_ddpm_wuerstchen import DDPMWuerstchenScheduler from .scheduling_deis_multistep import DEISMultistepScheduler + from .scheduling_dflash_token_diffusion import ( + DFlashTokenDiffusionScheduler, + DFlashTokenDiffusionSchedulerOutput, + ) from .scheduling_dpm_cogvideox import CogVideoXDPMScheduler from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler diff --git a/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py new file mode 100644 index 000000000000..f34a37ca0db4 --- /dev/null +++ b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py @@ -0,0 +1,104 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput +from .scheduling_utils import SchedulerMixin + + +@dataclass +class DFlashTokenDiffusionSchedulerOutput(BaseOutput): + """ + Output class for DFlash-style speculative token scheduling. + + Args: + prev_sample (`torch.LongTensor` of shape `(batch_size, block_size)`): + The proposed block tokens from the draft model. + accepted_length (`torch.LongTensor` of shape `(batch_size,)`): + Number of consecutive accepted tokens from the block. + next_token (`torch.LongTensor` of shape `(batch_size,)`): + Next token sampled from the target posterior at the first rejection. + posterior (`torch.LongTensor` of shape `(batch_size, block_size)`): + Sampled tokens from the target posterior used for acceptance checks. + """ + + prev_sample: torch.LongTensor + accepted_length: torch.LongTensor + next_token: torch.LongTensor + posterior: torch.LongTensor + + +class DFlashTokenDiffusionScheduler(SchedulerMixin, ConfigMixin): + """ + Scheduler for DFlash-style block diffusion speculative decoding. + + This scheduler samples target posteriors and computes acceptance lengths for draft blocks. + """ + + order = 1 + + @register_to_config + def __init__(self): + self.num_inference_steps = 1 + self.timesteps = torch.tensor([0], dtype=torch.long) + + def set_timesteps(self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None) -> None: + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + self.num_inference_steps = int(num_inference_steps) + self.timesteps = torch.arange(self.num_inference_steps - 1, -1, -1, device=device, dtype=torch.long) + + def sample(self, logits: torch.Tensor, temperature: float = 0.0) -> torch.LongTensor: + if temperature < 1e-5: + return torch.argmax(logits, dim=-1) + bsz, seq_len, vocab_size = logits.shape + flat = logits.view(-1, vocab_size) / float(temperature) + probs = torch.softmax(flat, dim=-1) + return torch.multinomial(probs, num_samples=1).view(bsz, seq_len) + + def step( + self, + draft_tokens: torch.LongTensor, + target_logits: torch.Tensor, + *, + temperature: float = 0.0, + return_dict: bool = True, + ) -> Union[DFlashTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor]]: + posterior = self.sample(target_logits, temperature=temperature) + if draft_tokens.shape[1] > 1: + matches = draft_tokens[:, 1:] == posterior[:, :-1] + accepted_length = matches.int().cumprod(dim=1).sum(dim=1) + else: + accepted_length = torch.zeros((draft_tokens.shape[0],), device=draft_tokens.device, dtype=torch.long) + + next_token = posterior.gather(1, accepted_length.unsqueeze(1)).squeeze(1) + + if not return_dict: + return draft_tokens, accepted_length, next_token, posterior + return DFlashTokenDiffusionSchedulerOutput( + prev_sample=draft_tokens, + accepted_length=accepted_length, + next_token=next_token, + posterior=posterior, + ) + + +__all__ = ["DFlashTokenDiffusionScheduler", "DFlashTokenDiffusionSchedulerOutput"] diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index b8aa154f2661..0ab6311fc2bf 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2409,6 +2409,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class DFlashTokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class DFlashTokenDiffusionSchedulerOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class CMStochasticIterativeScheduler(metaclass=DummyObject): _backends = ["torch"] diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index c308d3f8fc00..a8bb6c62d3dd 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -1877,6 +1877,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class DFlashPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + +class DFlashPipelineOutput(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class LLaDA2Pipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] From 471bfd39ae55649653ddc8759440c2ed0d8005ac Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 11 Jan 2026 20:10:21 +0000 Subject: [PATCH 15/44] added SDAR JET pipeline and scheduler --- docs/source/en/api/pipelines/overview.md | 1 + docs/source/en/api/schedulers/overview.md | 9 + examples/discrete_diffusion/README.md | 30 + src/diffusers/__init__.py | 16 +- src/diffusers/pipelines/__init__.py | 2 + .../pipelines/dflash/pipeline_dflash.py | 41 +- src/diffusers/pipelines/sdar/__init__.py | 47 ++ src/diffusers/pipelines/sdar/pipeline_sdar.py | 516 ++++++++++++++++++ src/diffusers/schedulers/__init__.py | 13 +- .../scheduling_dflash_token_diffusion.py | 5 +- .../scheduling_sdar_token_diffusion.py | 238 ++++++++ src/diffusers/utils/dummy_pt_objects.py | 54 +- .../dummy_torch_and_transformers_objects.py | 90 ++- 13 files changed, 1008 insertions(+), 54 deletions(-) create mode 100644 src/diffusers/pipelines/sdar/__init__.py create mode 100644 src/diffusers/pipelines/sdar/pipeline_sdar.py create mode 100644 src/diffusers/schedulers/scheduling_sdar_token_diffusion.py diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index 093a70b5b180..4124761f86a9 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -50,6 +50,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [DDIM](ddim) | unconditional image generation | | [DDPM](ddpm) | unconditional image generation | | [DFlash](dflash) | text2text | +| [SDAR](sdar) | text2text | | [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution | | [DiffEdit](diffedit) | inpainting | | [DiT](dit) | text2image | diff --git a/docs/source/en/api/schedulers/overview.md b/docs/source/en/api/schedulers/overview.md index 4017523bce78..2dc8feae6964 100644 --- a/docs/source/en/api/schedulers/overview.md +++ b/docs/source/en/api/schedulers/overview.md @@ -59,6 +59,13 @@ Many schedulers are implemented from the [k-diffusion](https://github.com/crowso These schedulers operate over categorical token IDs instead of continuous latents. They are designed for discrete token diffusion models and expose the same `set_timesteps`/`step` interface as other schedulers. +Differences between the discrete token schedulers: +- `TokenDiffusionScheduler`: token-level diffusion with per-token corruption (e.g. mask/uniform) and a single-step `step` to denoise logits. +- `BlockTokenDiffusionScheduler`: block-wise token diffusion that updates fixed-size blocks in parallel. +- `HybridTokenDiffusionScheduler`: hybrid transitions that combine token- and block-wise updates in the same schedule. +- `DFlashTokenDiffusionScheduler`: block diffusion scheduler specialized for speculative decoding with a draft model and target acceptance. +- `SDARTokenDiffusionScheduler`: block diffusion scheduler with remasking strategies (sequential/low-confidence/entropy-bounded) per step. + [[autodoc]] TokenDiffusionScheduler [[autodoc]] BlockTokenDiffusionScheduler @@ -67,6 +74,8 @@ token diffusion models and expose the same `set_timesteps`/`step` interface as o [[autodoc]] DFlashTokenDiffusionScheduler +[[autodoc]] SDARTokenDiffusionScheduler + All schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers. ## SchedulerMixin diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index 7bf0e6892a70..9da3d084e846 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -125,6 +125,36 @@ python examples/discrete_diffusion/sample_dflash.py \ --add_generation_prompt ``` +## SDAR block diffusion decoding + +Run SDAR-style block diffusion sampling with remasking strategies. + +```bash +python examples/discrete_diffusion/sample_sdar.py \ + --model_id JetLM/SDAR-1.7B-Chat \ + --prompt "Explain what reinforcement learning is in simple terms." \ + --max_new_tokens 256 \ + --block_length 4 \ + --denoising_steps 4 \ + --remasking_strategy low_confidence_dynamic \ + --confidence_threshold 0.9 \ + --use_chat_template \ + --add_generation_prompt +``` + +### Fine-tune (draft model) + +```bash +accelerate launch examples/discrete_diffusion/train_dflash.py \ + --draft_model_id z-lab/Qwen3-4B-DFlash-b16 \ + --target_model_id Qwen/Qwen3-4B \ + --dataset_name wikitext \ + --dataset_config_name wikitext-2-raw-v1 \ + --output_dir dflash-output \ + --max_train_steps 100 \ + --logging_steps 10 +``` + ## Hybrid sampling Hybrid sampling uses a different transition kernel than absorbing/uniform diffusion and requires a compatible scheduler diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index d313156d82b9..e03c94d28d26 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -342,8 +342,6 @@ [ "AmusedScheduler", "BlockTokenDiffusionScheduler", - "DFlashTokenDiffusionScheduler", - "DFlashTokenDiffusionSchedulerOutput", "CMStochasticIterativeScheduler", "CogVideoXDDIMScheduler", "CogVideoXDPMScheduler", @@ -354,6 +352,8 @@ "DDPMScheduler", "DDPMWuerstchenScheduler", "DEISMultistepScheduler", + "DFlashTokenDiffusionScheduler", + "DFlashTokenDiffusionSchedulerOutput", "DPMSolverMultistepInverseScheduler", "DPMSolverMultistepScheduler", "DPMSolverSinglestepScheduler", @@ -379,6 +379,8 @@ "SchedulerMixin", "SCMScheduler", "ScoreSdeVeScheduler", + "SDARTokenDiffusionScheduler", + "SDARTokenDiffusionSchedulerOutput", "TCDScheduler", "TokenDiffusionScheduler", "UnCLIPScheduler", @@ -492,6 +494,8 @@ "CosmosTextToWorldPipeline", "CosmosVideoToWorldPipeline", "CycleDiffusionPipeline", + "DFlashPipeline", + "DFlashPipelineOutput", "EasyAnimateControlPipeline", "EasyAnimateInpaintPipeline", "EasyAnimatePipeline", @@ -558,8 +562,6 @@ "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", - "DFlashPipeline", - "DFlashPipelineOutput", "LLaDA2Pipeline", "LLaDA2PipelineOutput", "LongCatImageEditPipeline", @@ -608,6 +610,8 @@ "SanaSprintPipeline", "SanaVideoPipeline", "SanaVideoPipeline", + "SDARPipeline", + "SDARPipelineOutput", "SemanticStableDiffusionPipeline", "ShapEImg2ImgPipeline", "ShapEPipeline", @@ -1141,6 +1145,8 @@ SchedulerMixin, SCMScheduler, ScoreSdeVeScheduler, + SDARTokenDiffusionScheduler, + SDARTokenDiffusionSchedulerOutput, TCDScheduler, TokenDiffusionScheduler, UnCLIPScheduler, @@ -1348,6 +1354,8 @@ SanaSprintImg2ImgPipeline, SanaSprintPipeline, SanaVideoPipeline, + SDARPipeline, + SDARPipelineOutput, SemanticStableDiffusionPipeline, ShapEImg2ImgPipeline, ShapEPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index b9b2f5e0e5f9..db14c6f64f54 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -413,6 +413,7 @@ "Kandinsky5I2IPipeline", ] _import_structure["dflash"] = ["DFlashPipeline", "DFlashPipelineOutput"] + _import_structure["sdar"] = ["SDARPipeline", "SDARPipelineOutput"] _import_structure["llada2"] = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] _import_structure["z_image"] = [ "ZImageImg2ImgPipeline", @@ -804,6 +805,7 @@ SanaSprintPipeline, ) from .sana_video import SanaImageToVideoPipeline, SanaVideoPipeline + from .sdar import SDARPipeline, SDARPipelineOutput from .semantic_stable_diffusion import SemanticStableDiffusionPipeline from .shap_e import ShapEImg2ImgPipeline, ShapEPipeline from .stable_audio import StableAudioPipeline, StableAudioProjectionModel diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py index b7e61b6df475..ae7f11f3b628 100644 --- a/src/diffusers/pipelines/dflash/pipeline_dflash.py +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -15,11 +15,12 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import torch from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, DynamicCache +from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...schedulers import DFlashTokenDiffusionScheduler from ...utils import BaseOutput, logging, replace_example_docstring from ..pipeline_utils import DiffusionPipeline @@ -78,6 +79,7 @@ class DFlashPipeline(DiffusionPipeline): target_model: torch.nn.Module tokenizer: Optional[object] scheduler: DFlashTokenDiffusionScheduler + _callback_tensor_inputs = ["block_output_ids", "draft_logits", "accepted_length", "next_token", "output_ids"] def __init__( self, @@ -94,7 +96,9 @@ def __init__( super().__init__() if scheduler is None: scheduler = DFlashTokenDiffusionScheduler() - self.register_modules(draft_model=draft_model, target_model=target_model, tokenizer=tokenizer, scheduler=scheduler) + self.register_modules( + draft_model=draft_model, target_model=target_model, tokenizer=tokenizer, scheduler=scheduler + ) self.register_to_config( max_new_tokens=max_new_tokens, temperature=temperature, @@ -164,6 +168,10 @@ def __call__( chat_template_kwargs: Optional[Dict[str, object]] = None, return_text: bool = True, return_dict: bool = True, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, ) -> Union[DFlashPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: """ Generate text using block-diffusion speculative decoding. @@ -179,6 +187,20 @@ def __call__( if add_generation_prompt is None: add_generation_prompt = bool(self.config.add_generation_prompt) + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["block_output_ids"] + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + input_ids = self._prepare_input_ids( prompt=prompt, messages=messages, @@ -250,6 +272,7 @@ def __call__( target_hidden = _extract_context_feature(output.hidden_states, target_layer_ids) start = num_input_tokens + global_step = 0 stop_tensor = None if stop_token_ids is not None: stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) @@ -282,12 +305,24 @@ def __call__( step_output = self.scheduler.step( block_output_ids, output.logits, temperature=temperature, return_dict=True ) + accepted_length = step_output.accepted_length + next_token = step_output.next_token acceptance_length = int(step_output.accepted_length[0].item()) output_ids[:, start : start + acceptance_length + 1] = block_output_ids[:, : acceptance_length + 1] output_ids[:, start + acceptance_length + 1] = step_output.next_token start += acceptance_length + 1 past_key_values_target.crop(start) - target_hidden = _extract_context_feature(output.hidden_states, target_layer_ids)[:, : acceptance_length + 1, :] + target_hidden = _extract_context_feature(output.hidden_states, target_layer_ids)[ + :, : acceptance_length + 1, : + ] + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, global_step, 0, callback_kwargs) + output_ids = callback_outputs.pop("output_ids", output_ids) + global_step += 1 if stop_tensor is not None and torch.isin(output_ids[:, num_input_tokens:], stop_tensor).any(): break diff --git a/src/diffusers/pipelines/sdar/__init__.py b/src/diffusers/pipelines/sdar/__init__.py new file mode 100644 index 000000000000..13f8e30c9962 --- /dev/null +++ b/src/diffusers/pipelines/sdar/__init__.py @@ -0,0 +1,47 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_sdar"] = ["SDARPipeline", "SDARPipelineOutput"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 + else: + from .pipeline_sdar import SDARPipeline, SDARPipelineOutput +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/sdar/pipeline_sdar.py b/src/diffusers/pipelines/sdar/pipeline_sdar.py new file mode 100644 index 000000000000..fbd35e95adea --- /dev/null +++ b/src/diffusers/pipelines/sdar/pipeline_sdar.py @@ -0,0 +1,516 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Callable, Dict, List, Optional, Tuple, Union + +import torch +from transformers import DynamicCache + +from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...schedulers import SDARTokenDiffusionScheduler +from ...utils import BaseOutput, logging, replace_example_docstring +from ..pipeline_utils import DiffusionPipeline + + +logger = logging.get_logger(__name__) + + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> from transformers import AutoModelForCausalLM, AutoTokenizer + >>> from diffusers import SDARPipeline + + >>> model_id = "JetLM/SDAR-1.7B-Chat" + >>> model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, dtype=torch.bfloat16) + >>> tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) + >>> tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) + + >>> pipe = SDARPipeline(model=model, tokenizer=tokenizer) + >>> out = pipe(prompt="Explain what reinforcement learning is in simple terms.") + >>> print(out.texts[0]) + ``` +""" + + +@dataclass +class SDARPipelineOutput(BaseOutput): + sequences: torch.LongTensor + texts: Optional[List[str]] = None + + +class SDARPipeline(DiffusionPipeline): + r""" + Block diffusion pipeline for SDAR-style token generation. + """ + + model: torch.nn.Module + tokenizer: Optional[object] + scheduler: SDARTokenDiffusionScheduler + _callback_tensor_inputs = ["cur_x", "logits", "sampled_tokens", "sampled_probs", "transfer_index"] + + def __init__( + self, + model: torch.nn.Module, + tokenizer: Optional[object] = None, + scheduler: Optional[SDARTokenDiffusionScheduler] = None, + *, + max_new_tokens: int = 256, + block_length: int = 4, + denoising_steps: int = 4, + temperature: float = 1.0, + top_k: int = 0, + top_p: float = 1.0, + remasking_strategy: str = "low_confidence_dynamic", + confidence_threshold: float = 0.9, + entropy_threshold: float = 0.35, + use_chat_template: bool = True, + add_generation_prompt: bool = True, + attention_mask_mode: str = "3d", + ): + super().__init__() + if scheduler is None: + scheduler = SDARTokenDiffusionScheduler( + block_length=block_length, + denoising_steps=denoising_steps, + remasking_strategy=remasking_strategy, + confidence_threshold=confidence_threshold, + entropy_threshold=entropy_threshold, + temperature=temperature, + top_k=top_k, + top_p=top_p, + ) + self.register_modules(model=model, tokenizer=tokenizer, scheduler=scheduler) + self.register_to_config( + max_new_tokens=max_new_tokens, + block_length=block_length, + denoising_steps=denoising_steps, + temperature=temperature, + top_k=top_k, + top_p=top_p, + remasking_strategy=remasking_strategy, + confidence_threshold=confidence_threshold, + entropy_threshold=entropy_threshold, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + attention_mask_mode=attention_mask_mode, + ) + self._store_kv_supported: Optional[bool] = None + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Optional[Union[str, List[str]]] = None, + *, + messages: Optional[List[Dict[str, str]]] = None, + input_ids: Optional[torch.LongTensor] = None, + max_new_tokens: Optional[int] = None, + block_length: Optional[int] = None, + denoising_steps: Optional[int] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + remasking_strategy: Optional[str] = None, + confidence_threshold: Optional[float] = None, + entropy_threshold: Optional[float] = None, + stop_token_ids: Optional[List[int]] = None, + mask_token_id: Optional[int] = None, + attention_mask_mode: Optional[str] = None, + use_chat_template: Optional[bool] = None, + add_generation_prompt: Optional[bool] = None, + chat_template_kwargs: Optional[Dict[str, object]] = None, + return_text: bool = True, + return_dict: bool = True, + callback_on_step_end: Optional[ + Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] + ] = None, + callback_on_step_end_tensor_inputs: Optional[List[str]] = None, + ) -> Union[SDARPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + """ + Generate text using SDAR-style block diffusion decoding. + + Examples: + """ + if max_new_tokens is None: + max_new_tokens = int(self.config.max_new_tokens) + if block_length is None: + model_block_length = getattr(self.model, "block_length", None) + if model_block_length is None: + model_block_length = getattr(getattr(self.model, "config", None), "block_length", None) + block_length = int(model_block_length) if model_block_length is not None else int(self.config.block_length) + if denoising_steps is None: + denoising_steps = int(self.config.denoising_steps) + if temperature is None: + temperature = float(self.config.temperature) + if top_k is None: + top_k = int(self.config.top_k) + if top_p is None: + top_p = float(self.config.top_p) + if remasking_strategy is None: + remasking_strategy = str(self.config.remasking_strategy) + if confidence_threshold is None: + confidence_threshold = float(self.config.confidence_threshold) + if entropy_threshold is None: + entropy_threshold = float(self.config.entropy_threshold) + if attention_mask_mode is None: + attention_mask_mode = str(self.config.attention_mask_mode) + if use_chat_template is None: + use_chat_template = bool(self.config.use_chat_template) + if add_generation_prompt is None: + add_generation_prompt = bool(self.config.add_generation_prompt) + + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["cur_x"] + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + input_ids = self._prepare_input_ids( + prompt=prompt, + messages=messages, + input_ids=input_ids, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + chat_template_kwargs=chat_template_kwargs, + ) + + if input_ids.shape[0] != 1: + raise ValueError("SDARPipeline currently supports batch_size=1 input_ids.") + + params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] + device = params[0].device if len(params) > 0 else torch.device("cpu") + input_ids = input_ids.to(device=device) + + if mask_token_id is None: + mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + + if stop_token_ids is None: + eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) + stop_token_ids = [int(eos_token_id)] if eos_token_id is not None else None + if stop_token_ids is not None: + stop_token_ids = [int(token_id) for token_id in stop_token_ids] + + self.model.eval() + if block_length <= 0: + raise ValueError(f"`block_length` must be > 0, got {block_length}.") + if denoising_steps <= 0: + raise ValueError(f"`denoising_steps` must be > 0, got {denoising_steps}.") + + self.scheduler.set_timesteps(int(denoising_steps), device=device) + + prompt_length = input_ids.shape[1] + num_blocks = (prompt_length + int(max_new_tokens) + int(block_length) - 1) // int(block_length) + total_length = int(num_blocks) * int(block_length) + + block_mask_3d = self._build_block_attention_mask_3d( + num_blocks=num_blocks, + block_length=block_length, + total_length=total_length, + device=device, + dtype=torch.float32, + ) + block_mask_4d = self._build_block_attention_mask_4d(block_mask_3d, dtype=torch.float32) + block_mask_2d = block_mask_3d[0] + + x = torch.full( + (1, total_length), + int(mask_token_id), + dtype=torch.long, + device=device, + ) + x[:, :prompt_length] = input_ids + + position_ids = torch.arange(total_length, device=device).unsqueeze(0) + past_key_values = DynamicCache() + + prefill_blocks = prompt_length // int(block_length) + prefill_length = int(prefill_blocks) * int(block_length) + resolved_attention_mode = str(attention_mask_mode) + + if prefill_length > 0: + cur_x = x[:, :prefill_length] + cur_position_ids = position_ids[:, :prefill_length] + cur_attn_mask = block_mask_3d[:, :prefill_length, :prefill_length] + cur_attn_mask_4d = block_mask_4d[:, :, :prefill_length, :prefill_length] + cur_attn_mask_2d = block_mask_2d[:prefill_length, :prefill_length] + _, resolved_attention_mode = self._model_forward_logits( + input_ids=cur_x, + attention_mask_3d=cur_attn_mask, + attention_mask_4d=cur_attn_mask_4d, + attention_mask_2d=cur_attn_mask_2d, + position_ids=cur_position_ids, + attention_mask_mode=resolved_attention_mode, + past_key_values=past_key_values, + store_kv=True, + ) + + num_transfer_tokens = self.scheduler.get_num_transfer_tokens(int(block_length), int(denoising_steps)).to( + device=device + ) + + stop_tensor = None + if stop_token_ids is not None: + stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) + + global_step = 0 + for block_idx in range(prefill_blocks, int(num_blocks)): + start = int(block_idx) * int(block_length) + end = start + int(block_length) + cur_x = x[:, start:end].clone() + cur_position_ids = position_ids[:, start:end] + cur_attn_mask = block_mask_3d[:, start:end, :end] + cur_attn_mask_4d = block_mask_4d[:, :, start:end, :end] + cur_attn_mask_2d = block_mask_2d[start:end, :end] + + for step in range(int(denoising_steps) + 1): + mask_index = cur_x == int(mask_token_id) + if mask_index.sum() == 0: + _, resolved_attention_mode = self._model_forward_logits( + input_ids=cur_x, + attention_mask_3d=cur_attn_mask, + attention_mask_4d=cur_attn_mask_4d, + attention_mask_2d=cur_attn_mask_2d, + position_ids=cur_position_ids, + attention_mask_mode=resolved_attention_mode, + past_key_values=past_key_values, + store_kv=True, + ) + break + + logits, resolved_attention_mode = self._model_forward_logits( + input_ids=cur_x, + attention_mask_3d=cur_attn_mask, + attention_mask_4d=cur_attn_mask_4d, + attention_mask_2d=cur_attn_mask_2d, + position_ids=cur_position_ids, + attention_mask_mode=resolved_attention_mode, + past_key_values=past_key_values, + store_kv=False, + ) + + step_output = self.scheduler.step( + logits, + step, + cur_x, + mask_token_id=int(mask_token_id), + num_transfer_tokens=num_transfer_tokens, + remasking_strategy=remasking_strategy, + confidence_threshold=confidence_threshold, + entropy_threshold=entropy_threshold, + temperature=temperature, + top_k=top_k, + top_p=top_p, + return_dict=True, + ) + cur_x = step_output.prev_sample + transfer_index = step_output.transfer_index + sampled_tokens = step_output.sampled_tokens + sampled_probs = step_output.sampled_probs + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, global_step, step, callback_kwargs) + cur_x = callback_outputs.pop("cur_x", cur_x) + + global_step += 1 + + x[:, start:end] = cur_x + if stop_tensor is not None and torch.isin(x[:, prompt_length:], stop_tensor).any(): + break + + output_ids = x[:, : prompt_length + int(max_new_tokens)] + if stop_tensor is not None: + stop_positions = torch.isin(output_ids[0, prompt_length:], stop_tensor).nonzero(as_tuple=True)[0] + if stop_positions.numel() > 0: + output_ids = output_ids[:, : prompt_length + int(stop_positions[0].item()) + 1] + + if output_ids.shape[0] == 1: + output_ids = output_ids[:, output_ids[0] != int(mask_token_id)] + + sequences = output_ids[:, prompt_length:] + texts = None + if return_text and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) + + if not return_dict: + return sequences, texts + return SDARPipelineOutput(sequences=sequences, texts=texts) + + def _model_forward_logits( + self, + *, + input_ids: torch.LongTensor, + attention_mask_3d: Optional[torch.Tensor], + attention_mask_4d: Optional[torch.Tensor], + attention_mask_2d: Optional[torch.Tensor], + position_ids: torch.LongTensor, + attention_mask_mode: str, + past_key_values: DynamicCache, + store_kv: bool, + ) -> Tuple[torch.Tensor, str]: + if attention_mask_mode not in {"auto", "3d", "4d", "2d", "none"}: + raise ValueError( + f"`attention_mask_mode` must be one of {{'auto','3d','4d','2d','none'}}, got {attention_mask_mode!r}." + ) + + def _call(mask): + kwargs = { + "input_ids": input_ids, + "attention_mask": mask, + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": True, + } + if self._store_kv_supported is False: + output = self.model(**kwargs) + return output.logits if hasattr(output, "logits") else output[0] + if self._store_kv_supported is True: + kwargs["store_kv"] = store_kv + output = self.model(**kwargs) + return output.logits if hasattr(output, "logits") else output[0] + try: + kwargs["store_kv"] = store_kv + output = self.model(**kwargs) + self._store_kv_supported = True + return output.logits if hasattr(output, "logits") else output[0] + except TypeError: + output = self.model(**kwargs) + self._store_kv_supported = False + return output.logits if hasattr(output, "logits") else output[0] + + if attention_mask_mode == "none": + return _call(None), "none" + if attention_mask_mode == "2d": + return _call(attention_mask_2d), "2d" + if attention_mask_mode == "3d": + return _call(attention_mask_3d), "3d" + if attention_mask_mode == "4d": + return _call(attention_mask_4d), "4d" + + try: + return _call(attention_mask_3d), "3d" + except (TypeError, ValueError, RuntimeError): + pass + try: + return _call(attention_mask_4d), "4d" + except (TypeError, ValueError, RuntimeError): + pass + try: + return _call(attention_mask_2d), "2d" + except (TypeError, ValueError, RuntimeError): + return _call(None), "none" + + def _build_block_attention_mask_3d( + self, + *, + num_blocks: int, + block_length: int, + total_length: int, + device: torch.device, + dtype: torch.dtype, + ) -> torch.Tensor: + block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=dtype)) + attn = block_mask.repeat_interleave(block_length, dim=0).repeat_interleave(block_length, dim=1).unsqueeze(0) + return attn[:, :total_length, :total_length] + + def _build_block_attention_mask_4d(self, mask_3d: torch.Tensor, *, dtype: torch.dtype) -> torch.Tensor: + attn = mask_3d.unsqueeze(1).to(dtype=dtype) + return torch.where( + attn > 0, + torch.zeros((), device=attn.device, dtype=dtype), + torch.full((), float("-inf"), device=attn.device, dtype=dtype), + ) + + def _prepare_input_ids( + self, + *, + prompt: Optional[Union[str, List[str]]], + messages: Optional[List[Dict[str, str]]], + input_ids: Optional[torch.LongTensor], + use_chat_template: bool, + add_generation_prompt: bool, + chat_template_kwargs: Optional[Dict[str, object]], + ) -> torch.LongTensor: + if input_ids is not None: + if input_ids.ndim == 1: + input_ids = input_ids.unsqueeze(0) + if input_ids.ndim != 2: + raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + return input_ids + + if self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + if messages is not None and prompt is not None: + raise ValueError("Provide either `prompt` or `messages`, not both.") + if messages is None and prompt is None: + raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") + + chat_template_kwargs = chat_template_kwargs or {} + + def _extract_input_ids(encoded): + if isinstance(encoded, dict) and "input_ids" in encoded: + return encoded["input_ids"] + if hasattr(encoded, "input_ids"): + return encoded.input_ids + return encoded + + if messages is not None: + encoded = self.tokenizer.apply_chat_template( + messages, + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + if use_chat_template and getattr(self.tokenizer, "chat_template", None): + if isinstance(prompt, list): + raise ValueError("`prompt` must be a string when `use_chat_template=True`.") + encoded = self.tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) + return _extract_input_ids(encoded) + + +__all__ = ["SDARPipeline", "SDARPipelineOutput"] diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 1876a5cac89c..c6025293fb4c 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -43,10 +43,6 @@ _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"] _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"] - _import_structure["scheduling_dflash_token_diffusion"] = [ - "DFlashTokenDiffusionScheduler", - "DFlashTokenDiffusionSchedulerOutput", - ] _import_structure["scheduling_ddim"] = ["DDIMScheduler"] _import_structure["scheduling_ddim_cogvideox"] = ["CogVideoXDDIMScheduler"] _import_structure["scheduling_ddim_inverse"] = ["DDIMInverseScheduler"] @@ -55,6 +51,10 @@ _import_structure["scheduling_ddpm_parallel"] = ["DDPMParallelScheduler"] _import_structure["scheduling_ddpm_wuerstchen"] = ["DDPMWuerstchenScheduler"] _import_structure["scheduling_deis_multistep"] = ["DEISMultistepScheduler"] + _import_structure["scheduling_dflash_token_diffusion"] = [ + "DFlashTokenDiffusionScheduler", + "DFlashTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_dpm_cogvideox"] = ["CogVideoXDPMScheduler"] _import_structure["scheduling_dpmsolver_multistep"] = ["DPMSolverMultistepScheduler"] _import_structure["scheduling_dpmsolver_multistep_inverse"] = ["DPMSolverMultistepInverseScheduler"] @@ -80,6 +80,10 @@ _import_structure["scheduling_repaint"] = ["RePaintScheduler"] _import_structure["scheduling_sasolver"] = ["SASolverScheduler"] _import_structure["scheduling_scm"] = ["SCMScheduler"] + _import_structure["scheduling_sdar_token_diffusion"] = [ + "SDARTokenDiffusionScheduler", + "SDARTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_sde_ve"] = ["ScoreSdeVeScheduler"] _import_structure["scheduling_tcd"] = ["TCDScheduler"] _import_structure["scheduling_token_diffusion"] = ["TokenDiffusionScheduler"] @@ -188,6 +192,7 @@ from .scheduling_repaint import RePaintScheduler from .scheduling_sasolver import SASolverScheduler from .scheduling_scm import SCMScheduler + from .scheduling_sdar_token_diffusion import SDARTokenDiffusionScheduler, SDARTokenDiffusionSchedulerOutput from .scheduling_sde_ve import ScoreSdeVeScheduler from .scheduling_tcd import TCDScheduler from .scheduling_unclip import UnCLIPScheduler diff --git a/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py index f34a37ca0db4..24e8238321c9 100644 --- a/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py @@ -81,7 +81,10 @@ def step( *, temperature: float = 0.0, return_dict: bool = True, - ) -> Union[DFlashTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor]]: + ) -> Union[ + DFlashTokenDiffusionSchedulerOutput, + Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor], + ]: posterior = self.sample(target_logits, temperature=temperature) if draft_tokens.shape[1] > 1: matches = draft_tokens[:, 1:] == posterior[:, :-1] diff --git a/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py new file mode 100644 index 000000000000..a5d86441491c --- /dev/null +++ b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py @@ -0,0 +1,238 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +import torch.nn.functional as F + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput +from .scheduling_utils import SchedulerMixin + + +@dataclass +class SDARTokenDiffusionSchedulerOutput(BaseOutput): + """ + Output class for SDAR-style block diffusion scheduling. + + Args: + prev_sample (`torch.LongTensor` of shape `(batch_size, block_length)`): + Updated block tokens after the current denoising step. + transfer_index (`torch.BoolTensor` of shape `(batch_size, block_length)`): + Boolean mask indicating which tokens were updated. + sampled_tokens (`torch.LongTensor` of shape `(batch_size, block_length)`): + Sampled token IDs from the model logits. + sampled_probs (`torch.Tensor` of shape `(batch_size, block_length)`): + Probabilities of the sampled tokens. + """ + + prev_sample: torch.LongTensor + transfer_index: torch.BoolTensor + sampled_tokens: torch.LongTensor + sampled_probs: torch.Tensor + + +class SDARTokenDiffusionScheduler(SchedulerMixin, ConfigMixin): + """ + Scheduler for SDAR-style block diffusion decoding. + """ + + order = 1 + + @register_to_config + def __init__( + self, + block_length: int = 4, + denoising_steps: int = 4, + remasking_strategy: str = "low_confidence_dynamic", + confidence_threshold: float = 0.9, + entropy_threshold: float = 0.35, + temperature: float = 1.0, + top_k: int = 0, + top_p: float = 1.0, + ): + self.num_inference_steps = int(denoising_steps) + self.timesteps = torch.arange(self.num_inference_steps - 1, -1, -1, dtype=torch.long) + + def set_timesteps(self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None) -> None: + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + self.num_inference_steps = int(num_inference_steps) + self.timesteps = torch.arange(self.num_inference_steps - 1, -1, -1, device=device, dtype=torch.long) + + def get_num_transfer_tokens(self, block_length: int, num_inference_steps: int) -> torch.LongTensor: + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + base = int(block_length) // int(num_inference_steps) + remainder = int(block_length) % int(num_inference_steps) + num_transfer_tokens = torch.zeros(int(num_inference_steps), dtype=torch.long) + num_transfer_tokens += base + if remainder > 0: + num_transfer_tokens[:remainder] += 1 + return num_transfer_tokens + + def _top_k_logits(self, logits: torch.Tensor, k: int) -> torch.Tensor: + if k <= 0: + return logits + values, _ = torch.topk(logits, k) + min_values = values[..., -1, None] + return torch.where(logits < min_values, torch.full_like(logits, float("-inf")), logits) + + def _top_p_logits(self, logits: torch.Tensor, p: float) -> torch.Tensor: + if p >= 1.0: + return logits + sorted_logits, sorted_indices = torch.sort(logits, descending=True) + cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) + sorted_mask = cumulative_probs > p + sorted_mask[..., 1:] = sorted_mask[..., :-1].clone() + sorted_mask[..., 0] = False + mask_indices = torch.scatter(torch.zeros_like(logits, dtype=torch.bool), -1, sorted_indices, sorted_mask) + return logits.masked_fill(mask_indices, float("-inf")) + + def sample( + self, + logits: torch.Tensor, + *, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + generator: Optional[torch.Generator] = None, + ) -> Tuple[torch.LongTensor, torch.Tensor]: + if temperature is None: + temperature = float(self.config.temperature) + if top_k is None: + top_k = int(self.config.top_k) + if top_p is None: + top_p = float(self.config.top_p) + + orig_shape = logits.shape[:-1] + vocab_size = logits.shape[-1] + flat = logits.view(-1, vocab_size) + + if temperature < 1e-5: + probs = F.softmax(flat, dim=-1) + tokens = torch.argmax(flat, dim=-1, keepdim=True) + token_probs = torch.gather(probs, -1, tokens) + return tokens.view(*orig_shape), token_probs.view(*orig_shape) + + flat = flat / float(temperature) + flat = self._top_k_logits(flat, int(top_k)) + flat = self._top_p_logits(flat, float(top_p)) + probs = F.softmax(flat, dim=-1) + tokens = torch.multinomial(probs, num_samples=1, generator=generator) + token_probs = torch.gather(probs, -1, tokens) + return tokens.view(*orig_shape), token_probs.view(*orig_shape) + + def step( + self, + model_output: torch.Tensor, + timestep: Union[int, torch.Tensor], + sample: torch.LongTensor, + *, + mask_token_id: int, + num_transfer_tokens: torch.LongTensor, + remasking_strategy: Optional[str] = None, + confidence_threshold: Optional[float] = None, + entropy_threshold: Optional[float] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[ + SDARTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor, torch.BoolTensor, torch.LongTensor, torch.Tensor] + ]: + if remasking_strategy is None: + remasking_strategy = str(self.config.remasking_strategy) + if confidence_threshold is None: + confidence_threshold = float(self.config.confidence_threshold) + if entropy_threshold is None: + entropy_threshold = float(self.config.entropy_threshold) + + sampled_tokens, sampled_probs = self.sample( + model_output, temperature=temperature, top_k=top_k, top_p=top_p, generator=generator + ) + mask_index = sample == int(mask_token_id) + transfer_index = torch.zeros_like(mask_index) + + if isinstance(timestep, torch.Tensor): + step_index = int(timestep.item()) + else: + step_index = int(timestep) + + if step_index >= int(num_transfer_tokens.numel()): + step_index = int(num_transfer_tokens.numel()) - 1 + step_transfer = int(num_transfer_tokens[step_index].item()) + + if remasking_strategy == "sequential": + for j in range(sample.shape[0]): + if not mask_index[j].any(): + continue + num_masked = int(mask_index[j].sum().item()) + k = min(step_transfer, num_masked) + first_mask_index = mask_index[j].nonzero(as_tuple=True)[0].min().item() + transfer_index[j, first_mask_index : first_mask_index + k] = True + + elif remasking_strategy in {"low_confidence_static", "low_confidence_dynamic"}: + confidence = torch.where(mask_index, sampled_probs, torch.full_like(sampled_probs, float("-inf"))) + for j in range(confidence.shape[0]): + if not mask_index[j].any(): + continue + num_masked = int(mask_index[j].sum().item()) + k = min(step_transfer, num_masked) + if remasking_strategy == "low_confidence_dynamic": + high_conf_mask = confidence[j] > confidence_threshold + if int(high_conf_mask.sum().item()) >= k: + transfer_index[j] = high_conf_mask + continue + _, idx = torch.topk(confidence[j], k) + transfer_index[j, idx] = True + + elif remasking_strategy == "entropy_bounded": + eps = 1e-12 + entropies = -(sampled_probs.clamp_min(eps) * sampled_probs.clamp_min(eps).log()).sum(dim=-1) + entropies = torch.where(mask_index, entropies, torch.full_like(sampled_probs, float("inf"))) + ent_sorted, order = torch.sort(entropies, dim=1, descending=False) + cumsum = torch.cumsum(ent_sorted, dim=1) + for j in range(sampled_probs.shape[0]): + if not mask_index[j].any(): + continue + threshold_tensor = torch.tensor(entropy_threshold, device=sampled_probs.device) + k = int(torch.searchsorted(cumsum[j], threshold_tensor, right=False).item()) + num_masked = int(mask_index[j].sum().item()) + k = max(1, min(k, num_masked)) + selected_token_indices = order[j, :k] + transfer_index[j, selected_token_indices] = True + + else: + raise ValueError(f"Unknown remasking strategy: {remasking_strategy}") + + prev_sample = sample.clone() + prev_sample[transfer_index] = sampled_tokens[transfer_index] + + if not return_dict: + return prev_sample, transfer_index, sampled_tokens, sampled_probs + return SDARTokenDiffusionSchedulerOutput( + prev_sample=prev_sample, + transfer_index=transfer_index, + sampled_tokens=sampled_tokens, + sampled_probs=sampled_probs, + ) + + +__all__ = ["SDARTokenDiffusionScheduler", "SDARTokenDiffusionSchedulerOutput"] diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 0ab6311fc2bf..622dd94fee35 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2409,7 +2409,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DFlashTokenDiffusionScheduler(metaclass=DummyObject): +class CMStochasticIterativeScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2424,7 +2424,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DFlashTokenDiffusionSchedulerOutput(metaclass=DummyObject): +class CogVideoXDDIMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2439,7 +2439,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class CMStochasticIterativeScheduler(metaclass=DummyObject): +class CogVideoXDPMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2454,7 +2454,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class CogVideoXDDIMScheduler(metaclass=DummyObject): +class DDIMInverseScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2469,7 +2469,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class CogVideoXDPMScheduler(metaclass=DummyObject): +class DDIMParallelScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2484,7 +2484,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDIMInverseScheduler(metaclass=DummyObject): +class DDIMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2499,7 +2499,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDIMParallelScheduler(metaclass=DummyObject): +class DDPMParallelScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2514,7 +2514,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDIMScheduler(metaclass=DummyObject): +class DDPMScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2529,7 +2529,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDPMParallelScheduler(metaclass=DummyObject): +class DDPMWuerstchenScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2544,7 +2544,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDPMScheduler(metaclass=DummyObject): +class DEISMultistepScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2559,7 +2559,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDPMWuerstchenScheduler(metaclass=DummyObject): +class DFlashTokenDiffusionScheduler(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2574,7 +2574,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DEISMultistepScheduler(metaclass=DummyObject): +class DFlashTokenDiffusionSchedulerOutput(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2964,6 +2964,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class SDARTokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class SDARTokenDiffusionSchedulerOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class TCDScheduler(metaclass=DummyObject): _backends = ["torch"] diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index a8bb6c62d3dd..15223187178f 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -887,6 +887,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class DFlashPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + +class DFlashPipelineOutput(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class EasyAnimateControlPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] @@ -1877,36 +1907,6 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) -class DFlashPipeline(metaclass=DummyObject): - _backends = ["torch", "transformers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "transformers"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch", "transformers"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch", "transformers"]) - - -class DFlashPipelineOutput(metaclass=DummyObject): - _backends = ["torch", "transformers"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "transformers"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch", "transformers"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch", "transformers"]) - - class LLaDA2Pipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] @@ -2612,6 +2612,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class SDARPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + +class SDARPipelineOutput(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class SemanticStableDiffusionPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] From 88fe29dd7bed6d424872c94a3e4c9516594f5a88 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Mon, 9 Feb 2026 21:21:38 +0000 Subject: [PATCH 16/44] initial review changes --- .../pipeline_block_refinement.py | 188 ++++++++---------- .../pipeline_block_token_diffusion.py | 108 +++++----- .../pipelines/dflash/pipeline_dflash.py | 97 ++++----- .../pipelines/llada2/pipeline_llada2.py | 1 - src/diffusers/pipelines/sdar/pipeline_sdar.py | 177 +++++++---------- .../pipeline_token_diffusion.py | 114 +++++------ .../scheduling_hybrid_token_diffusion.py | 25 +++ .../schedulers/scheduling_token_diffusion.py | 27 +++ 8 files changed, 367 insertions(+), 370 deletions(-) diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index 9b5ea4695f4e..9dc17c713a8b 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -120,34 +120,9 @@ def __init__( self, model: Any, tokenizer: Optional[Any] = None, - *, - gen_length: int = 128, - block_length: int = 32, - steps: int = 32, - temperature: float = 0.0, - top_p: Optional[float] = None, - top_k: Optional[int] = None, - sampling_method: str = "auto", - threshold: float = 0.95, - minimal_topk: int = 1, - eos_early_stop: bool = False, - attention_mask_mode: str = "auto", ): super().__init__() self.register_modules(model=model, tokenizer=tokenizer) - self.register_to_config( - gen_length=gen_length, - block_length=block_length, - steps=steps, - temperature=temperature, - top_p=top_p, - top_k=top_k, - sampling_method=sampling_method, - threshold=threshold, - minimal_topk=minimal_topk, - eos_early_stop=eos_early_stop, - attention_mask_mode=attention_mask_mode, - ) @property def num_timesteps(self): @@ -236,25 +211,70 @@ def _encode_prompt( encoded = self.tokenizer(prompt, return_tensors="pt", padding=True) return encoded["input_ids"].to(device=device) + def prepare_latents( + self, + batch_size: int, + total_length: int, + mask_token_id: int, + device: torch.device, + ) -> torch.LongTensor: + return torch.full((batch_size, total_length), int(mask_token_id), device=device, dtype=torch.long) + + def check_inputs( + self, + gen_length: int, + block_length: int, + steps: int, + minimal_topk: int, + threshold: float, + sampling_method: str, + callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], + callback_on_step_end_tensor_inputs: Optional[List[str]], + ): + if gen_length <= 0: + raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") + if block_length <= 0: + raise ValueError(f"`block_length` must be > 0, got {block_length}.") + if steps <= 0: + raise ValueError(f"`steps` must be > 0, got {steps}.") + if minimal_topk <= 0: + raise ValueError(f"`minimal_topk` must be > 0, got {minimal_topk}.") + if not (0.0 <= threshold <= 1.0) and not (threshold > 1.0): + raise ValueError(f"`threshold` must be in [0, 1] (or > 1 to force top-k commits), got {threshold}.") + if sampling_method not in {"auto", "greedy", "multinomial"}: + raise ValueError( + f"`sampling_method` must be one of {{'auto','greedy','multinomial'}}, got {sampling_method!r}." + ) + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + @torch.no_grad() def __call__( self, - *, prompt: Optional[Union[str, List[str]]] = None, prompt_ids: Optional[torch.LongTensor] = None, - gen_length: Optional[int] = None, - block_length: Optional[int] = None, - steps: Optional[int] = None, - temperature: Optional[float] = None, + gen_length: int = 128, + block_length: int = 32, + steps: int = 32, + temperature: float = 0.0, top_p: Optional[float] = None, top_k: Optional[int] = None, - sampling_method: Optional[str] = None, - threshold: Optional[float] = None, - minimal_topk: Optional[int] = None, - eos_early_stop: Optional[bool] = None, + sampling_method: str = "auto", + threshold: float = 0.95, + minimal_topk: int = 1, + eos_early_stop: bool = False, eos_token_id: Optional[int] = None, mask_token_id: Optional[int] = None, - attention_mask_mode: Optional[str] = None, + attention_mask_mode: str = "auto", generator: Optional[torch.Generator] = None, return_text: bool = True, callback_on_step_end: Optional[ @@ -270,32 +290,32 @@ def __call__( Prompt text to encode with the tokenizer. prompt_ids (`torch.LongTensor`, *optional*): Pre-tokenized prompt IDs with shape `[prompt_len]` or `[batch, prompt_len]`. - gen_length (`int`, *optional*): - Number of tokens to generate. If `None`, uses `pipe.config.gen_length`. - block_length (`int`, *optional*): - Block size for refinement. If `None`, uses `pipe.config.block_length`. - steps (`int`, *optional*): - Refinement steps per block. If `None`, uses `pipe.config.steps`. - temperature (`float`, *optional*): - Sampling temperature. If `None`, uses `pipe.config.temperature`. + gen_length (`int`): + Number of tokens to generate. + block_length (`int`): + Block size for refinement. + steps (`int`): + Refinement steps per block. + temperature (`float`): + Sampling temperature. top_p (`float`, *optional*): - Nucleus sampling cutoff. If `None`, uses `pipe.config.top_p`. + Nucleus sampling cutoff. top_k (`int`, *optional*): - Top-k sampling cutoff. If `None`, uses `pipe.config.top_k`. - sampling_method (`str`, *optional*): - Sampling method (`auto`, `greedy`, `multinomial`). If `None`, uses `pipe.config.sampling_method`. - threshold (`float`, *optional*): - Confidence threshold for committing tokens. If `None`, uses `pipe.config.threshold`. - minimal_topk (`int`, *optional*): - Minimum number of tokens to commit per step. If `None`, uses `pipe.config.minimal_topk`. - eos_early_stop (`bool`, *optional*): - Whether to stop after committing EOS in a block. If `None`, uses `pipe.config.eos_early_stop`. + Top-k sampling cutoff. + sampling_method (`str`): + Sampling method (`auto`, `greedy`, `multinomial`). + threshold (`float`): + Confidence threshold for committing tokens. + minimal_topk (`int`): + Minimum number of tokens to commit per step. + eos_early_stop (`bool`): + Whether to stop after committing EOS in a block. eos_token_id (`int`, *optional*): EOS token ID to use for early stopping. mask_token_id (`int`, *optional*): Mask token ID to use for the template. - attention_mask_mode (`str`, *optional*): - Attention mask mode (`auto`, `4d`, `2d`, `none`). If `None`, uses `pipe.config.attention_mask_mode`. + attention_mask_mode (`str`): + Attention mask mode (`auto`, `4d`, `2d`, `none`). generator (`torch.Generator`, *optional*): RNG for sampling. return_text (`bool`, *optional*, defaults to `True`): @@ -307,57 +327,23 @@ def __call__( Tensor keys to pass to the callback. Allowed keys: `cur_x`, `x0`, `x0_p`, `transfer_index`, `confidence`, `active_block`. """ - if gen_length is None: - gen_length = int(self.config.gen_length) - if block_length is None: - block_length = int(self.config.block_length) - if steps is None: - steps = int(self.config.steps) - if temperature is None: - temperature = float(self.config.temperature) - if top_p is None: - top_p = self.config.top_p - if top_k is None: - top_k = self.config.top_k - if sampling_method is None: - sampling_method = str(self.config.sampling_method) - if threshold is None: - threshold = float(self.config.threshold) - if minimal_topk is None: - minimal_topk = int(self.config.minimal_topk) - if eos_early_stop is None: - eos_early_stop = bool(self.config.eos_early_stop) - if attention_mask_mode is None: - attention_mask_mode = str(self.config.attention_mask_mode) - - if gen_length <= 0: - raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") - if block_length <= 0: - raise ValueError(f"`block_length` must be > 0, got {block_length}.") - if steps <= 0: - raise ValueError(f"`steps` must be > 0, got {steps}.") - if minimal_topk <= 0: - raise ValueError(f"`minimal_topk` must be > 0, got {minimal_topk}.") - if not (0.0 <= threshold <= 1.0) and not (threshold > 1.0): - raise ValueError(f"`threshold` must be in [0, 1] (or > 1 to force top-k commits), got {threshold}.") - if sampling_method not in {"auto", "greedy", "multinomial"}: - raise ValueError( - f"`sampling_method` must be one of {{'auto','greedy','multinomial'}}, got {sampling_method!r}." - ) - if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["cur_x"] - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) + + self.check_inputs( + gen_length=gen_length, + block_length=block_length, + steps=steps, + minimal_topk=minimal_topk, + threshold=threshold, + sampling_method=sampling_method, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + ) model_params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] model_device = model_params[0].device if len(model_params) > 0 else torch.device("cpu") @@ -391,7 +377,7 @@ def __call__( torch.arange(total_length, device=model_device, dtype=torch.long).unsqueeze(0).expand(batch_size, -1) ) - x = torch.full((batch_size, total_length), int(mask_token_id), device=model_device, dtype=torch.long) + x = self.prepare_latents(batch_size, total_length, int(mask_token_id), model_device) if prompt_length > 0: x[:, :prompt_length] = prompt_ids.to(device=model_device) diff --git a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py index 43f6a71621a8..82a5c81544ba 100644 --- a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py +++ b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py @@ -67,22 +67,9 @@ def __init__( model: Any, scheduler: Any, tokenizer: Optional[Any] = None, - *, - seq_len: int = 64, - block_size: int = 32, - num_inference_steps: int = 64, - inject_start_token: bool = False, - top_p: float = 1.0, ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) - self.register_to_config( - seq_len=seq_len, - block_size=block_size, - num_inference_steps=num_inference_steps, - inject_start_token=inject_start_token, - top_p=top_p, - ) @property def num_timesteps(self): @@ -118,32 +105,60 @@ def _normalize_prefix_ids( prefix_ids = prefix_ids.expand(batch_size, -1) return prefix_ids - def _init_latents( + def prepare_latents( self, batch_size: int, seq_len: int, - *, - generator: Optional[torch.Generator], - device: torch.device, + generator: Optional[torch.Generator] = None, + device: Optional[torch.device] = None, ) -> torch.LongTensor: - mask_token_id = getattr(self.scheduler, "mask_token_id", None) - if mask_token_id is None: - raise ValueError("Scheduler must define `mask_token_id` for block diffusion sampling.") - return torch.full((batch_size, seq_len), int(mask_token_id), device=device, dtype=torch.long) + shape = torch.Size((batch_size, seq_len)) + return self.scheduler.sample_prior(shape, device=device, generator=generator) + + def check_inputs( + self, + batch_size: int, + seq_len: int, + block_size: int, + callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], + callback_on_step_end_tensor_inputs: Optional[List[str]], + infill_mask: Optional[torch.BoolTensor], + prefix_ids: Optional[torch.LongTensor], + ): + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + if infill_mask is not None and infill_mask.shape != (batch_size, seq_len): + raise ValueError(f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}.") + if prefix_ids is not None: + p = prefix_ids + if p.ndim == 1: + p = p.unsqueeze(0) + if p.ndim == 2 and p.shape[1] > seq_len: + raise ValueError(f"`prefix_ids` length {p.shape[1]} must be <= seq_len={seq_len}.") + if block_size <= 0 or block_size > seq_len: + raise ValueError(f"`block_size` must be in [1, seq_len], got block_size={block_size}, seq_len={seq_len}.") @torch.no_grad() def __call__( self, - *, batch_size: int = 1, - seq_len: Optional[int] = None, - block_size: Optional[int] = None, - num_inference_steps: Optional[int] = None, + seq_len: int = 64, + block_size: int = 32, + num_inference_steps: int = 64, generator: Optional[torch.Generator] = None, prefix_ids: Optional[torch.LongTensor] = None, infill_mask: Optional[torch.BoolTensor] = None, - inject_start_token: Optional[bool] = None, - top_p: Optional[float] = None, + inject_start_token: bool = False, + top_p: float = 1.0, return_text: bool = True, return_dict: bool = True, callback_on_step_end: Optional[ @@ -152,53 +167,39 @@ def __call__( callback_on_step_end_tensor_inputs: Optional[List[str]] = None, **model_kwargs, ) -> Union[BlockTokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: - if seq_len is None: - seq_len = int(self.config.seq_len) - if block_size is None: - block_size = int(self.config.block_size) - if num_inference_steps is None: - num_inference_steps = int(self.config.num_inference_steps) - if inject_start_token is None: - inject_start_token = bool(self.config.inject_start_token) - if top_p is None: - top_p = float(self.config.top_p) - if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["input_ids"] - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) + + self.check_inputs( + batch_size=batch_size, + seq_len=seq_len, + block_size=block_size, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + infill_mask=infill_mask, + prefix_ids=prefix_ids, + ) device = self._execution_device self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps - input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) + input_ids = self.prepare_latents(batch_size, seq_len, generator=generator, device=device) attention_mask = torch.ones_like(input_ids, dtype=torch.long) fixed_mask = None fixed_values = None if infill_mask is not None: - if infill_mask.shape != (batch_size, seq_len): - raise ValueError( - f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}." - ) fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) fixed_values = input_ids.clone() if prefix_ids is not None: prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) prefix_len = prefix_ids.shape[1] - if prefix_len > seq_len: - raise ValueError(f"`prefix_ids` length {prefix_len} must be <= seq_len={seq_len}.") input_ids[:, :prefix_len] = prefix_ids if fixed_mask is None: @@ -216,9 +217,6 @@ def __call__( fixed_mask[:, 0] = True fixed_values[:, 0] = start_token_id - if block_size <= 0 or block_size > seq_len: - raise ValueError(f"`block_size` must be in [1, seq_len], got block_size={block_size}, seq_len={seq_len}.") - num_blocks = (seq_len + block_size - 1) // block_size self._num_timesteps = len(timesteps) * int(num_blocks) global_step = 0 diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py index ae7f11f3b628..0fec57e08645 100644 --- a/src/diffusers/pipelines/dflash/pipeline_dflash.py +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -87,11 +87,6 @@ def __init__( target_model: torch.nn.Module, tokenizer: Optional[object] = None, scheduler: Optional[DFlashTokenDiffusionScheduler] = None, - *, - max_new_tokens: int = 2048, - temperature: float = 0.0, - use_chat_template: bool = True, - add_generation_prompt: bool = True, ): super().__init__() if scheduler is None: @@ -99,12 +94,6 @@ def __init__( self.register_modules( draft_model=draft_model, target_model=target_model, tokenizer=tokenizer, scheduler=scheduler ) - self.register_to_config( - max_new_tokens=max_new_tokens, - temperature=temperature, - use_chat_template=use_chat_template, - add_generation_prompt=add_generation_prompt, - ) @classmethod def from_pretrained( @@ -151,20 +140,56 @@ def from_pretrained( **pipeline_kwargs, ) + def check_inputs( + self, + input_ids: torch.LongTensor, + mask_token_id: Optional[int], + callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], + callback_on_step_end_tensor_inputs: Optional[List[str]], + ): + if input_ids.shape[0] != 1: + raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.") + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + def prepare_latents( + self, + max_length: int, + block_size: int, + mask_token_id: int, + device: torch.device, + ) -> torch.LongTensor: + return torch.full( + (1, max_length + int(block_size)), + int(mask_token_id), + dtype=torch.long, + device=device, + ) + @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Optional[Union[str, List[str]]] = None, - *, messages: Optional[List[Dict[str, str]]] = None, input_ids: Optional[torch.LongTensor] = None, - max_new_tokens: Optional[int] = None, - temperature: Optional[float] = None, + max_new_tokens: int = 2048, + temperature: float = 0.0, stop_token_ids: Optional[List[int]] = None, mask_token_id: Optional[int] = None, - use_chat_template: Optional[bool] = None, - add_generation_prompt: Optional[bool] = None, + use_chat_template: bool = True, + add_generation_prompt: bool = True, chat_template_kwargs: Optional[Dict[str, object]] = None, return_text: bool = True, return_dict: bool = True, @@ -178,28 +203,12 @@ def __call__( Examples: """ - if max_new_tokens is None: - max_new_tokens = int(self.config.max_new_tokens) - if temperature is None: - temperature = float(self.config.temperature) - if use_chat_template is None: - use_chat_template = bool(self.config.use_chat_template) - if add_generation_prompt is None: - add_generation_prompt = bool(self.config.add_generation_prompt) - if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["block_output_ids"] - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) input_ids = self._prepare_input_ids( prompt=prompt, @@ -210,8 +219,15 @@ def __call__( chat_template_kwargs=chat_template_kwargs, ) - if input_ids.shape[0] != 1: - raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.") + if mask_token_id is None: + mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) + + self.check_inputs( + input_ids=input_ids, + mask_token_id=mask_token_id, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + ) target_params = list(self.target_model.parameters()) if hasattr(self.target_model, "parameters") else [] device = target_params[0].device if len(target_params) > 0 else torch.device("cpu") @@ -225,11 +241,6 @@ def __call__( device, ) - if mask_token_id is None: - mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) - if mask_token_id is None: - raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") - if stop_token_ids is None: eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) stop_token_ids = [int(eos_token_id)] if eos_token_id is not None else None @@ -247,12 +258,8 @@ def __call__( num_input_tokens = input_ids.shape[1] max_length = num_input_tokens + int(max_new_tokens) - output_ids = torch.full( - (1, max_length + int(block_size)), - int(mask_token_id), - dtype=torch.long, - device=device, - ) + + output_ids = self.prepare_latents(max_length, block_size, int(mask_token_id), device) position_ids = torch.arange(output_ids.shape[1], device=device).unsqueeze(0) past_key_values_target = DynamicCache() diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index acdb686b3aaf..c7d73fd124df 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -64,7 +64,6 @@ class LLaDA2Pipeline(BlockRefinementPipeline): def __call__( self, prompt: Optional[Union[str, List[str]]] = None, - *, messages: Optional[List[Dict[str, str]]] = None, input_ids: Optional[torch.LongTensor] = None, use_chat_template: bool = True, diff --git a/src/diffusers/pipelines/sdar/pipeline_sdar.py b/src/diffusers/pipelines/sdar/pipeline_sdar.py index fbd35e95adea..8c74ef0d527e 100644 --- a/src/diffusers/pipelines/sdar/pipeline_sdar.py +++ b/src/diffusers/pipelines/sdar/pipeline_sdar.py @@ -69,71 +69,76 @@ def __init__( model: torch.nn.Module, tokenizer: Optional[object] = None, scheduler: Optional[SDARTokenDiffusionScheduler] = None, - *, - max_new_tokens: int = 256, - block_length: int = 4, - denoising_steps: int = 4, - temperature: float = 1.0, - top_k: int = 0, - top_p: float = 1.0, - remasking_strategy: str = "low_confidence_dynamic", - confidence_threshold: float = 0.9, - entropy_threshold: float = 0.35, - use_chat_template: bool = True, - add_generation_prompt: bool = True, - attention_mask_mode: str = "3d", ): super().__init__() if scheduler is None: - scheduler = SDARTokenDiffusionScheduler( - block_length=block_length, - denoising_steps=denoising_steps, - remasking_strategy=remasking_strategy, - confidence_threshold=confidence_threshold, - entropy_threshold=entropy_threshold, - temperature=temperature, - top_k=top_k, - top_p=top_p, - ) + scheduler = SDARTokenDiffusionScheduler() self.register_modules(model=model, tokenizer=tokenizer, scheduler=scheduler) - self.register_to_config( - max_new_tokens=max_new_tokens, - block_length=block_length, - denoising_steps=denoising_steps, - temperature=temperature, - top_k=top_k, - top_p=top_p, - remasking_strategy=remasking_strategy, - confidence_threshold=confidence_threshold, - entropy_threshold=entropy_threshold, - use_chat_template=use_chat_template, - add_generation_prompt=add_generation_prompt, - attention_mask_mode=attention_mask_mode, - ) self._store_kv_supported: Optional[bool] = None + def check_inputs( + self, + input_ids: torch.LongTensor, + block_length: int, + denoising_steps: int, + mask_token_id: Optional[int], + callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], + callback_on_step_end_tensor_inputs: Optional[List[str]], + ): + if input_ids.shape[0] != 1: + raise ValueError("SDARPipeline currently supports batch_size=1 input_ids.") + if block_length <= 0: + raise ValueError(f"`block_length` must be > 0, got {block_length}.") + if denoising_steps <= 0: + raise ValueError(f"`denoising_steps` must be > 0, got {denoising_steps}.") + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + def prepare_latents( + self, + total_length: int, + mask_token_id: int, + device: torch.device, + ) -> torch.LongTensor: + return torch.full( + (1, total_length), + int(mask_token_id), + dtype=torch.long, + device=device, + ) + @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Optional[Union[str, List[str]]] = None, - *, messages: Optional[List[Dict[str, str]]] = None, input_ids: Optional[torch.LongTensor] = None, - max_new_tokens: Optional[int] = None, - block_length: Optional[int] = None, - denoising_steps: Optional[int] = None, - temperature: Optional[float] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - remasking_strategy: Optional[str] = None, - confidence_threshold: Optional[float] = None, - entropy_threshold: Optional[float] = None, + max_new_tokens: int = 256, + block_length: int = 4, + denoising_steps: int = 4, + temperature: float = 1.0, + top_k: int = 0, + top_p: float = 1.0, + remasking_strategy: str = "low_confidence_dynamic", + confidence_threshold: float = 0.9, + entropy_threshold: float = 0.35, stop_token_ids: Optional[List[int]] = None, mask_token_id: Optional[int] = None, - attention_mask_mode: Optional[str] = None, - use_chat_template: Optional[bool] = None, - add_generation_prompt: Optional[bool] = None, + attention_mask_mode: str = "3d", + use_chat_template: bool = True, + add_generation_prompt: bool = True, chat_template_kwargs: Optional[Dict[str, object]] = None, return_text: bool = True, return_dict: bool = True, @@ -147,47 +152,19 @@ def __call__( Examples: """ - if max_new_tokens is None: - max_new_tokens = int(self.config.max_new_tokens) - if block_length is None: - model_block_length = getattr(self.model, "block_length", None) - if model_block_length is None: - model_block_length = getattr(getattr(self.model, "config", None), "block_length", None) - block_length = int(model_block_length) if model_block_length is not None else int(self.config.block_length) - if denoising_steps is None: - denoising_steps = int(self.config.denoising_steps) - if temperature is None: - temperature = float(self.config.temperature) - if top_k is None: - top_k = int(self.config.top_k) - if top_p is None: - top_p = float(self.config.top_p) - if remasking_strategy is None: - remasking_strategy = str(self.config.remasking_strategy) - if confidence_threshold is None: - confidence_threshold = float(self.config.confidence_threshold) - if entropy_threshold is None: - entropy_threshold = float(self.config.entropy_threshold) - if attention_mask_mode is None: - attention_mask_mode = str(self.config.attention_mask_mode) - if use_chat_template is None: - use_chat_template = bool(self.config.use_chat_template) - if add_generation_prompt is None: - add_generation_prompt = bool(self.config.add_generation_prompt) - if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["cur_x"] - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) + + # Resolve block_length from model if not explicitly overridden by the user + model_block_length = getattr(self.model, "block_length", None) + if model_block_length is None: + model_block_length = getattr(getattr(self.model, "config", None), "block_length", None) + if model_block_length is not None: + block_length = int(model_block_length) input_ids = self._prepare_input_ids( prompt=prompt, @@ -198,18 +175,22 @@ def __call__( chat_template_kwargs=chat_template_kwargs, ) - if input_ids.shape[0] != 1: - raise ValueError("SDARPipeline currently supports batch_size=1 input_ids.") + if mask_token_id is None: + mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) + + self.check_inputs( + input_ids=input_ids, + block_length=block_length, + denoising_steps=denoising_steps, + mask_token_id=mask_token_id, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + ) params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] device = params[0].device if len(params) > 0 else torch.device("cpu") input_ids = input_ids.to(device=device) - if mask_token_id is None: - mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) - if mask_token_id is None: - raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") - if stop_token_ids is None: eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) stop_token_ids = [int(eos_token_id)] if eos_token_id is not None else None @@ -217,11 +198,6 @@ def __call__( stop_token_ids = [int(token_id) for token_id in stop_token_ids] self.model.eval() - if block_length <= 0: - raise ValueError(f"`block_length` must be > 0, got {block_length}.") - if denoising_steps <= 0: - raise ValueError(f"`denoising_steps` must be > 0, got {denoising_steps}.") - self.scheduler.set_timesteps(int(denoising_steps), device=device) prompt_length = input_ids.shape[1] @@ -238,12 +214,7 @@ def __call__( block_mask_4d = self._build_block_attention_mask_4d(block_mask_3d, dtype=torch.float32) block_mask_2d = block_mask_3d[0] - x = torch.full( - (1, total_length), - int(mask_token_id), - dtype=torch.long, - device=device, - ) + x = self.prepare_latents(total_length, int(mask_token_id), device) x[:, :prompt_length] = input_ids position_ids = torch.arange(total_length, device=device).unsqueeze(0) diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py index 217a533a1119..ebd8e8fb59a4 100644 --- a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -63,18 +63,9 @@ def __init__( model: Any, scheduler: Any, tokenizer: Optional[Any] = None, - *, - seq_len: int = 64, - num_inference_steps: int = 128, - inject_start_token: bool = False, ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) - self.register_to_config( - seq_len=seq_len, - num_inference_steps=num_inference_steps, - inject_start_token=inject_start_token, - ) @property def num_timesteps(self): @@ -90,37 +81,15 @@ def _resolve_start_token_id(self) -> Optional[int]: return int(token_id) return None - def _init_latents( + def prepare_latents( self, batch_size: int, seq_len: int, - *, - generator: Optional[torch.Generator], - device: torch.device, + generator: Optional[torch.Generator] = None, + device: Optional[torch.device] = None, ) -> torch.LongTensor: - # Prefer a scheduler-provided prior if available. - if hasattr(self.scheduler, "forward_process") and getattr(self.scheduler, "forward_process") == "uniform": - # Uniform prior over token IDs. Mirror scheduler's exclude-mask behavior. - if getattr(self.scheduler, "exclude_mask_from_uniform", False) and hasattr( - self.scheduler, "_sample_uniform_tokens" - ): - return self.scheduler._sample_uniform_tokens( - torch.Size((batch_size, seq_len)), - device=device, - dtype=torch.long, - generator=generator, - ) - vocab_size = int(getattr(self.scheduler, "vocab_size", 0)) - if vocab_size <= 0: - raise ValueError("Scheduler must define `vocab_size` for uniform prior sampling.") - return torch.randint( - 0, vocab_size, (batch_size, seq_len), device=device, dtype=torch.long, generator=generator - ) - - mask_token_id = getattr(self.scheduler, "mask_token_id", None) - if mask_token_id is None: - raise ValueError("Scheduler must define `mask_token_id` for absorbing prior sampling.") - return torch.full((batch_size, seq_len), int(mask_token_id), device=device, dtype=torch.long) + shape = torch.Size((batch_size, seq_len)) + return self.scheduler.sample_prior(shape, device=device, generator=generator) def _normalize_prefix_ids( self, prefix_ids: torch.LongTensor, batch_size: int, device: torch.device @@ -142,17 +111,45 @@ def _normalize_prefix_ids( prefix_ids = prefix_ids.expand(batch_size, -1) return prefix_ids + def check_inputs( + self, + batch_size: int, + seq_len: int, + callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], + callback_on_step_end_tensor_inputs: Optional[List[str]], + infill_mask: Optional[torch.BoolTensor], + prefix_ids: Optional[torch.LongTensor], + ): + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + if infill_mask is not None and infill_mask.shape != (batch_size, seq_len): + raise ValueError(f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}.") + if prefix_ids is not None: + p = prefix_ids + if p.ndim == 1: + p = p.unsqueeze(0) + if p.ndim == 2 and p.shape[1] > seq_len: + raise ValueError(f"`prefix_ids` length {p.shape[1]} must be <= seq_len={seq_len}.") + @torch.no_grad() def __call__( self, - *, batch_size: int = 1, - seq_len: Optional[int] = None, - num_inference_steps: Optional[int] = None, + seq_len: int = 64, + num_inference_steps: int = 128, generator: Optional[torch.Generator] = None, prefix_ids: Optional[torch.LongTensor] = None, infill_mask: Optional[torch.BoolTensor] = None, - inject_start_token: Optional[bool] = None, + inject_start_token: bool = False, return_text: bool = True, return_dict: bool = True, callback_on_step_end: Optional[ @@ -164,9 +161,8 @@ def __call__( """ Args: batch_size: Number of sequences to generate. - seq_len: Sequence length in tokens. Uses `pipe.config.seq_len` when `None`. - num_inference_steps: - Number of reverse diffusion steps. Uses `pipe.config.num_inference_steps` when `None`. + seq_len: Sequence length in tokens. + num_inference_steps: Number of reverse diffusion steps. generator: Optional torch generator for determinism. prefix_ids: Optional prefix token IDs to keep fixed at the start of each sequence. Shape `[P]` or `[batch_size, P]`. @@ -174,7 +170,6 @@ def __call__( Optional boolean mask of shape `[batch_size, seq_len]` indicating which positions are editable (`True`) vs fixed (`False`). Fixed positions are clamped to the initial values on every step. inject_start_token: If True, inject `bos_token_id` (or `cls_token_id`) into position 0 (if available). - Uses `pipe.config.inject_start_token` when `None`. return_text: If True and tokenizer exists, also return decoded strings. return_dict: If True, returns a `TokenDiffusionPipelineOutput`. callback_on_step_end: A function called after each denoising step with signature @@ -182,26 +177,21 @@ def __call__( callback_on_step_end_tensor_inputs: List of tensor keys to include in `callback_kwargs`. model_kwargs: Forward kwargs passed to `model(...)` (e.g. attention mask overrides). """ - if seq_len is None: - seq_len = int(self.config.seq_len) - if num_inference_steps is None: - num_inference_steps = int(self.config.num_inference_steps) - if inject_start_token is None: - inject_start_token = bool(self.config.inject_start_token) - if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["input_ids"] - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) + + self.check_inputs( + batch_size=batch_size, + seq_len=seq_len, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + infill_mask=infill_mask, + prefix_ids=prefix_ids, + ) device = self._execution_device @@ -209,24 +199,18 @@ def __call__( timesteps = self.scheduler.timesteps self._num_timesteps = len(timesteps) - input_ids = self._init_latents(batch_size, seq_len, generator=generator, device=device) + input_ids = self.prepare_latents(batch_size, seq_len, generator=generator, device=device) attention_mask = torch.ones_like(input_ids, dtype=torch.long) fixed_mask = None fixed_values = None if infill_mask is not None: - if infill_mask.shape != (batch_size, seq_len): - raise ValueError( - f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}." - ) fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) fixed_values = input_ids.clone() if prefix_ids is not None: prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) prefix_len = prefix_ids.shape[1] - if prefix_len > seq_len: - raise ValueError(f"`prefix_ids` length {prefix_len} must be <= seq_len={seq_len}.") input_ids[:, :prefix_len] = prefix_ids if fixed_mask is None: diff --git a/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py index 70982cc70d24..6b816481a9c1 100644 --- a/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py @@ -90,6 +90,31 @@ def __init__( unif = (1.0 - mask) / max(self.vocab_size - 1, 1) self.unif = unif + def sample_prior( + self, + shape: torch.Size, + device: torch.device, + generator: Optional[torch.Generator] = None, + ) -> torch.LongTensor: + """ + Sample from the prior distribution at t=1. + + At t=1, the stationary distribution concentrates on the mask token, so this returns a tensor filled with + `mask_token_id`. + + Args: + shape (`torch.Size`): + Desired output shape, e.g. `(batch_size, seq_len)`. + device (`torch.device`): + Device for the output tensor. + generator (`torch.Generator`, *optional*): + Optional generator for determinism (unused for the absorbing prior). + + Returns: + `torch.LongTensor` of shape `shape` with `mask_token_id`. + """ + return torch.full(shape, self.mask_token_id, device=device, dtype=torch.long) + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index ee08a6e221a8..3607c97d9bad 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -148,6 +148,33 @@ def _sample_uniform_tokens( draw = torch.randint(0, v_eff, shape, device=device, dtype=dtype, generator=generator) return torch.where(draw >= self.mask_token_id, draw + 1, draw) + def sample_prior( + self, + shape: torch.Size, + device: torch.device, + generator: Optional[torch.Generator] = None, + ) -> torch.LongTensor: + """ + Sample from the prior distribution of the forward process at t=1. + + For `forward_process="absorbing"`, returns a tensor filled with `mask_token_id`. + For `forward_process="uniform"`, returns uniform random token IDs (optionally excluding `mask_token_id`). + + Args: + shape (`torch.Size`): + Desired output shape, e.g. `(batch_size, seq_len)`. + device (`torch.device`): + Device for the output tensor. + generator (`torch.Generator`, *optional*): + Optional generator for determinism (only used for the uniform process). + + Returns: + `torch.LongTensor` of shape `shape` with sampled prior token IDs. + """ + if self.forward_process == "uniform": + return self._sample_uniform_tokens(shape, device=device, dtype=torch.long, generator=generator) + return torch.full(shape, self.mask_token_id, device=device, dtype=torch.long) + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). From 61fdfda90caceb14881e02cb4591e4288c8f8b3a Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Mon, 9 Feb 2026 21:46:16 +0000 Subject: [PATCH 17/44] add discrete diffusion mixin --- .../pipeline_block_refinement.py | 66 +------ .../pipeline_block_token_diffusion.py | 54 +----- .../pipelines/dflash/pipeline_dflash.py | 67 +------ src/diffusers/pipelines/pipeline_utils.py | 173 ++++++++++++++++++ src/diffusers/pipelines/sdar/pipeline_sdar.py | 67 +------ .../pipeline_token_diffusion.py | 34 +--- .../schedulers/scheduling_token_diffusion.py | 4 +- 7 files changed, 187 insertions(+), 278 deletions(-) diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index 9dc17c713a8b..bb529a9df0a3 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -21,7 +21,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin @dataclass @@ -30,66 +30,6 @@ class BlockRefinementPipelineOutput(BaseOutput): texts: Optional[List[str]] = None -def _top_k_filtering(logits: torch.Tensor, top_k: Optional[int]) -> torch.Tensor: - if top_k is None or top_k <= 0: - return logits - if top_k >= logits.shape[-1]: - return logits - values, _ = torch.topk(logits, k=int(top_k), dim=-1) - min_keep = values[..., -1, None] - return logits.masked_fill(logits < min_keep, torch.finfo(logits.dtype).min) - - -def _top_p_filtering(logits: torch.Tensor, top_p: Optional[float]) -> torch.Tensor: - if top_p is None or top_p >= 1.0: - return logits - if not (0.0 < top_p <= 1.0): - raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") - - sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) - sorted_probs = torch.softmax(sorted_logits, dim=-1) - cumulative_probs = sorted_probs.cumsum(dim=-1) - - sorted_indices_to_remove = cumulative_probs > float(top_p) - sorted_indices_to_remove[..., 0] = 0 - - sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) - filtered = logits.scatter(-1, sorted_indices, sorted_logits) - return filtered - - -def _sample_with_temperature_topk_topp( - logits: torch.Tensor, - *, - temperature: float, - top_k: Optional[int], - top_p: Optional[float], - generator: Optional[torch.Generator], - use_multinomial: bool, -) -> tuple[torch.LongTensor, torch.Tensor]: - vocab_size = logits.shape[-1] - flat_logits = logits.reshape(-1, vocab_size) - - filtered = _top_k_filtering(flat_logits, top_k=top_k) - filtered = _top_p_filtering(filtered, top_p=top_p) - - if temperature < 0: - raise ValueError(f"`temperature` must be >= 0, got {temperature}.") - - scaled = filtered - if temperature > 0.0 and temperature != 1.0: - scaled = filtered / float(temperature) - - probs = torch.softmax(scaled.float(), dim=-1) - if use_multinomial: - token = torch.multinomial(probs, num_samples=1, generator=generator) - else: - token = scaled.argmax(dim=-1, keepdim=True) - token_prob = torch.gather(probs, -1, token) - - return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) - - def _get_num_transfer_tokens(block_length: int, steps: int) -> torch.LongTensor: if steps <= 0: return torch.zeros((0,), dtype=torch.long) @@ -100,7 +40,7 @@ def _get_num_transfer_tokens(block_length: int, steps: int) -> torch.LongTensor: return out -class BlockRefinementPipeline(DiffusionPipeline): +class BlockRefinementPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): """ Block-wise iterative refinement pipeline for token generation. @@ -415,7 +355,7 @@ def __call__( ) block_logits = logits[:, -int(block_length) :, :] - x0, x0_p = _sample_with_temperature_topk_topp( + x0, x0_p = self._sample_with_temperature_topk_topp( block_logits, temperature=float(temperature), top_k=top_k, diff --git a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py index 82a5c81544ba..126648847b38 100644 --- a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py +++ b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py @@ -21,7 +21,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin @dataclass @@ -30,25 +30,7 @@ class BlockTokenDiffusionPipelineOutput(BaseOutput): texts: Optional[List[str]] = None -def _top_p_filtering(logits: torch.Tensor, top_p: float) -> torch.Tensor: - if top_p >= 1.0: - return logits - if not (0.0 < top_p <= 1.0): - raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") - - sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) - sorted_probs = torch.softmax(sorted_logits, dim=-1) - cumulative_probs = sorted_probs.cumsum(dim=-1) - - sorted_indices_to_remove = cumulative_probs > top_p - sorted_indices_to_remove[..., 0] = 0 - - sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) - filtered = logits.scatter(-1, sorted_indices, sorted_logits) - return filtered - - -class BlockTokenDiffusionPipeline(DiffusionPipeline): +class BlockTokenDiffusionPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): """ Block-wise token diffusion sampling pipeline. @@ -75,36 +57,6 @@ def __init__( def num_timesteps(self): return self._num_timesteps - def _resolve_start_token_id(self) -> Optional[int]: - tok = getattr(self, "tokenizer", None) - if tok is None: - return None - for attr in ("bos_token_id", "cls_token_id"): - token_id = getattr(tok, attr, None) - if token_id is not None: - return int(token_id) - return None - - def _normalize_prefix_ids( - self, prefix_ids: torch.LongTensor, batch_size: int, device: torch.device - ) -> torch.LongTensor: - if prefix_ids.ndim == 1: - prefix_ids = prefix_ids.unsqueeze(0) - if prefix_ids.ndim != 2: - raise ValueError( - f"`prefix_ids` must have shape [prefix_len] or [batch, prefix_len], got {prefix_ids.shape}." - ) - if prefix_ids.shape[0] not in (1, batch_size): - raise ValueError( - f"`prefix_ids` batch dim must be 1 or batch_size={batch_size}, got {prefix_ids.shape[0]}." - ) - if prefix_ids.dtype != torch.long: - raise ValueError(f"`prefix_ids` must be int64 token IDs, got dtype={prefix_ids.dtype}.") - prefix_ids = prefix_ids.to(device=device) - if prefix_ids.shape[0] == 1 and batch_size > 1: - prefix_ids = prefix_ids.expand(batch_size, -1) - return prefix_ids - def prepare_latents( self, batch_size: int, @@ -242,7 +194,7 @@ def __call__( if top_p < 1.0: logits_block = logits[block_mask].view(-1, logits.shape[-1]) - logits_block = _top_p_filtering(logits_block, top_p=top_p) + logits_block = self._top_p_filtering(logits_block, top_p=top_p) logits = logits.clone() logits[block_mask] = logits_block.view(-1, logits.shape[-1]) diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py index 0fec57e08645..2569c18180a5 100644 --- a/src/diffusers/pipelines/dflash/pipeline_dflash.py +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -23,7 +23,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...schedulers import DFlashTokenDiffusionScheduler from ...utils import BaseOutput, logging, replace_example_docstring -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin logger = logging.get_logger(__name__) @@ -70,7 +70,7 @@ def _extract_context_feature(hidden_states: List[torch.Tensor], layer_ids: List[ return torch.cat(selected_states, dim=-1) -class DFlashPipeline(DiffusionPipeline): +class DFlashPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): r""" Block diffusion pipeline for speculative decoding with a DFlash draft model and a target causal LM. """ @@ -411,68 +411,5 @@ def _target_forward( pass return self.target_model(**kwargs) - def _prepare_input_ids( - self, - *, - prompt: Optional[Union[str, List[str]]], - messages: Optional[List[Dict[str, str]]], - input_ids: Optional[torch.LongTensor], - use_chat_template: bool, - add_generation_prompt: bool, - chat_template_kwargs: Optional[Dict[str, object]], - ) -> torch.LongTensor: - if input_ids is not None: - if input_ids.ndim == 1: - input_ids = input_ids.unsqueeze(0) - if input_ids.ndim != 2: - raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") - if input_ids.dtype != torch.long: - raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") - return input_ids - - if self.tokenizer is None: - raise ValueError("Tokenizer is required when `input_ids` is not provided.") - - if messages is not None and prompt is not None: - raise ValueError("Provide either `prompt` or `messages`, not both.") - if messages is None and prompt is None: - raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") - - chat_template_kwargs = chat_template_kwargs or {} - - def _extract_input_ids(encoded): - if isinstance(encoded, dict) and "input_ids" in encoded: - return encoded["input_ids"] - if hasattr(encoded, "input_ids"): - return encoded.input_ids - return encoded - - if messages is not None: - encoded = self.tokenizer.apply_chat_template( - messages, - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return _extract_input_ids(encoded) - - if use_chat_template and getattr(self.tokenizer, "chat_template", None): - if isinstance(prompt, list): - raise ValueError("`prompt` must be a string when `use_chat_template=True`.") - encoded = self.tokenizer.apply_chat_template( - [{"role": "user", "content": prompt}], - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return _extract_input_ids(encoded) - - encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return _extract_input_ids(encoded) - __all__ = ["DFlashPipeline", "DFlashPipelineOutput"] diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index b96305c74131..652e8bf59398 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -2335,3 +2335,176 @@ def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True): else: self.vae.unfuse_qkv_projections() self.fusing_vae = False + + +class DiscreteDiffusionPipelineMixin: + """Shared utilities for discrete (token) diffusion pipelines. + + Provides SAR sampling techniques (top-p, top-k) and common helper methods for pipelines that operate on discrete + token sequences. + """ + + # --- SAR sampling utilities (static methods) --- + + @staticmethod + def _top_p_filtering(logits: "torch.Tensor", top_p: Optional[float]) -> "torch.Tensor": + """Nucleus (top-p) logit filtering.""" + if top_p is None or top_p >= 1.0: + return logits + if not (0.0 < top_p <= 1.0): + raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") + + sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) + sorted_probs = torch.softmax(sorted_logits, dim=-1) + cumulative_probs = sorted_probs.cumsum(dim=-1) + + sorted_indices_to_remove = cumulative_probs > float(top_p) + sorted_indices_to_remove[..., 0] = 0 + + sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) + filtered = logits.scatter(-1, sorted_indices, sorted_logits) + return filtered + + @staticmethod + def _top_k_filtering(logits: "torch.Tensor", top_k: Optional[int]) -> "torch.Tensor": + """Top-k logit filtering.""" + if top_k is None or top_k <= 0: + return logits + if top_k >= logits.shape[-1]: + return logits + values, _ = torch.topk(logits, k=int(top_k), dim=-1) + min_keep = values[..., -1, None] + return logits.masked_fill(logits < min_keep, torch.finfo(logits.dtype).min) + + @staticmethod + def _sample_with_temperature_topk_topp( + logits: "torch.Tensor", + *, + temperature: float, + top_k: Optional[int], + top_p: Optional[float], + generator: Optional["torch.Generator"], + use_multinomial: bool, + ) -> "tuple[torch.LongTensor, torch.Tensor]": + """Sample tokens from logits with temperature scaling, top-k, and top-p.""" + vocab_size = logits.shape[-1] + flat_logits = logits.reshape(-1, vocab_size) + + filtered = DiscreteDiffusionPipelineMixin._top_k_filtering(flat_logits, top_k=top_k) + filtered = DiscreteDiffusionPipelineMixin._top_p_filtering(filtered, top_p=top_p) + + if temperature < 0: + raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + + scaled = filtered + if temperature > 0.0 and temperature != 1.0: + scaled = filtered / float(temperature) + + probs = torch.softmax(scaled.float(), dim=-1) + if use_multinomial: + token = torch.multinomial(probs, num_samples=1, generator=generator) + else: + token = scaled.argmax(dim=-1, keepdim=True) + token_prob = torch.gather(probs, -1, token) + + return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) + + # --- Token/prefix utilities (instance methods) --- + + def _resolve_start_token_id(self) -> Optional[int]: + """Resolve BOS or CLS token ID from self.tokenizer.""" + tok = getattr(self, "tokenizer", None) + if tok is None: + return None + for attr in ("bos_token_id", "cls_token_id"): + token_id = getattr(tok, attr, None) + if token_id is not None: + return int(token_id) + return None + + def _normalize_prefix_ids( + self, prefix_ids: "torch.LongTensor", batch_size: int, device: "torch.device" + ) -> "torch.LongTensor": + """Validate shape/dtype and broadcast prefix token IDs.""" + if prefix_ids.ndim == 1: + prefix_ids = prefix_ids.unsqueeze(0) + if prefix_ids.ndim != 2: + raise ValueError( + f"`prefix_ids` must have shape [prefix_len] or [batch, prefix_len], got {prefix_ids.shape}." + ) + if prefix_ids.shape[0] not in (1, batch_size): + raise ValueError( + f"`prefix_ids` batch dim must be 1 or batch_size={batch_size}, got {prefix_ids.shape[0]}." + ) + if prefix_ids.dtype != torch.long: + raise ValueError(f"`prefix_ids` must be int64 token IDs, got dtype={prefix_ids.dtype}.") + prefix_ids = prefix_ids.to(device=device) + if prefix_ids.shape[0] == 1 and batch_size > 1: + prefix_ids = prefix_ids.expand(batch_size, -1) + return prefix_ids + + # --- Prompt encoding (instance method) --- + + def _prepare_input_ids( + self, + *, + prompt: Optional[Union[str, List[str]]], + messages: Optional[List[Dict[str, str]]], + input_ids: Optional["torch.LongTensor"], + use_chat_template: bool, + add_generation_prompt: bool, + chat_template_kwargs: Optional[Dict[str, object]], + ) -> "torch.LongTensor": + """Convert prompt/messages/input_ids to a [batch, seq] LongTensor.""" + if input_ids is not None: + if input_ids.ndim == 1: + input_ids = input_ids.unsqueeze(0) + if input_ids.ndim != 2: + raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + return input_ids + + if self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + if messages is not None and prompt is not None: + raise ValueError("Provide either `prompt` or `messages`, not both.") + if messages is None and prompt is None: + raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") + + chat_template_kwargs = chat_template_kwargs or {} + + def _extract_input_ids(encoded): + if isinstance(encoded, dict) and "input_ids" in encoded: + return encoded["input_ids"] + if hasattr(encoded, "input_ids"): + return encoded.input_ids + return encoded + + if messages is not None: + encoded = self.tokenizer.apply_chat_template( + messages, + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + if use_chat_template and getattr(self.tokenizer, "chat_template", None): + if isinstance(prompt, list): + raise ValueError("`prompt` must be a string when `use_chat_template=True`.") + encoded = self.tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + add_generation_prompt=add_generation_prompt, + tokenize=True, + return_tensors="pt", + return_dict=True, + **chat_template_kwargs, + ) + return _extract_input_ids(encoded) + + encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) + return _extract_input_ids(encoded) diff --git a/src/diffusers/pipelines/sdar/pipeline_sdar.py b/src/diffusers/pipelines/sdar/pipeline_sdar.py index 8c74ef0d527e..fdc97ff0fe58 100644 --- a/src/diffusers/pipelines/sdar/pipeline_sdar.py +++ b/src/diffusers/pipelines/sdar/pipeline_sdar.py @@ -23,7 +23,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...schedulers import SDARTokenDiffusionScheduler from ...utils import BaseOutput, logging, replace_example_docstring -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin logger = logging.get_logger(__name__) @@ -54,7 +54,7 @@ class SDARPipelineOutput(BaseOutput): texts: Optional[List[str]] = None -class SDARPipeline(DiffusionPipeline): +class SDARPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): r""" Block diffusion pipeline for SDAR-style token generation. """ @@ -420,68 +420,5 @@ def _build_block_attention_mask_4d(self, mask_3d: torch.Tensor, *, dtype: torch. torch.full((), float("-inf"), device=attn.device, dtype=dtype), ) - def _prepare_input_ids( - self, - *, - prompt: Optional[Union[str, List[str]]], - messages: Optional[List[Dict[str, str]]], - input_ids: Optional[torch.LongTensor], - use_chat_template: bool, - add_generation_prompt: bool, - chat_template_kwargs: Optional[Dict[str, object]], - ) -> torch.LongTensor: - if input_ids is not None: - if input_ids.ndim == 1: - input_ids = input_ids.unsqueeze(0) - if input_ids.ndim != 2: - raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") - if input_ids.dtype != torch.long: - raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") - return input_ids - - if self.tokenizer is None: - raise ValueError("Tokenizer is required when `input_ids` is not provided.") - - if messages is not None and prompt is not None: - raise ValueError("Provide either `prompt` or `messages`, not both.") - if messages is None and prompt is None: - raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") - - chat_template_kwargs = chat_template_kwargs or {} - - def _extract_input_ids(encoded): - if isinstance(encoded, dict) and "input_ids" in encoded: - return encoded["input_ids"] - if hasattr(encoded, "input_ids"): - return encoded.input_ids - return encoded - - if messages is not None: - encoded = self.tokenizer.apply_chat_template( - messages, - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return _extract_input_ids(encoded) - - if use_chat_template and getattr(self.tokenizer, "chat_template", None): - if isinstance(prompt, list): - raise ValueError("`prompt` must be a string when `use_chat_template=True`.") - encoded = self.tokenizer.apply_chat_template( - [{"role": "user", "content": prompt}], - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return _extract_input_ids(encoded) - - encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return _extract_input_ids(encoded) - __all__ = ["SDARPipeline", "SDARPipelineOutput"] diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py index ebd8e8fb59a4..1cd395e1ffb0 100644 --- a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -21,7 +21,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...utils import BaseOutput -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin @dataclass @@ -40,7 +40,7 @@ class TokenDiffusionPipelineOutput(BaseOutput): texts: Optional[List[str]] = None -class TokenDiffusionPipeline(DiffusionPipeline): +class TokenDiffusionPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): """ Generic token diffusion sampling pipeline. @@ -71,16 +71,6 @@ def __init__( def num_timesteps(self): return self._num_timesteps - def _resolve_start_token_id(self) -> Optional[int]: - tok = getattr(self, "tokenizer", None) - if tok is None: - return None - for attr in ("bos_token_id", "cls_token_id"): - token_id = getattr(tok, attr, None) - if token_id is not None: - return int(token_id) - return None - def prepare_latents( self, batch_size: int, @@ -91,26 +81,6 @@ def prepare_latents( shape = torch.Size((batch_size, seq_len)) return self.scheduler.sample_prior(shape, device=device, generator=generator) - def _normalize_prefix_ids( - self, prefix_ids: torch.LongTensor, batch_size: int, device: torch.device - ) -> torch.LongTensor: - if prefix_ids.ndim == 1: - prefix_ids = prefix_ids.unsqueeze(0) - if prefix_ids.ndim != 2: - raise ValueError( - f"`prefix_ids` must have shape [prefix_len] or [batch, prefix_len], got {prefix_ids.shape}." - ) - if prefix_ids.shape[0] not in (1, batch_size): - raise ValueError( - f"`prefix_ids` batch dim must be 1 or batch_size={batch_size}, got {prefix_ids.shape[0]}." - ) - if prefix_ids.dtype != torch.long: - raise ValueError(f"`prefix_ids` must be int64 token IDs, got dtype={prefix_ids.dtype}.") - prefix_ids = prefix_ids.to(device=device) - if prefix_ids.shape[0] == 1 and batch_size > 1: - prefix_ids = prefix_ids.expand(batch_size, -1) - return prefix_ids - def check_inputs( self, batch_size: int, diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index 3607c97d9bad..01022e2abc9a 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -157,8 +157,8 @@ def sample_prior( """ Sample from the prior distribution of the forward process at t=1. - For `forward_process="absorbing"`, returns a tensor filled with `mask_token_id`. - For `forward_process="uniform"`, returns uniform random token IDs (optionally excluding `mask_token_id`). + For `forward_process="absorbing"`, returns a tensor filled with `mask_token_id`. For + `forward_process="uniform"`, returns uniform random token IDs (optionally excluding `mask_token_id`). Args: shape (`torch.Size`): From 939c7ef9b35374e9784270b70b47d3dbf26b4a8b Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Mon, 9 Feb 2026 21:57:16 +0000 Subject: [PATCH 18/44] Pre-compute alpha schedule and Restructure step() with if/elif/else for readability --- .../schedulers/scheduling_token_diffusion.py | 84 ++++++++++++------- .../test_scheduler_token_diffusion.py | 60 +++++++++++++ 2 files changed, 112 insertions(+), 32 deletions(-) diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index 01022e2abc9a..e9b80d2a5aae 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -125,6 +125,8 @@ def __init__( self.num_inference_steps = None self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy()) + self.alphas = None + self._step_index_map = None def _effective_vocab_size(self) -> int: if self.forward_process == "uniform" and self.exclude_mask_from_uniform: @@ -180,6 +182,8 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic Sets the discrete timesteps used for the diffusion chain (to be run before inference). Timesteps are stored in descending order, so `timesteps[0]` is the noisiest step. + Alpha values are pre-computed for each timestep so that `step()` can look them up + by index instead of recomputing them on every call. """ if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") @@ -191,6 +195,14 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic ).round() self.timesteps = timesteps.to(dtype=torch.long, device=device) + # Pre-compute alpha(t) for every inference timestep. + t_continuous = timesteps / float(self.num_train_timesteps - 1) + t_continuous = t_continuous.clamp_(0.0, 1.0) + self.alphas = self._alpha_t(t_continuous).to(dtype=torch.float32, device=device) + + # Build a map from timestep value → index for O(1) lookup in step(). + self._step_index_map = {int(self.timesteps[i].item()): i for i in range(len(self.timesteps))} + def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: return sample @@ -403,22 +415,33 @@ def step( else: timestep_int = int(timestep) - # Find current index in timesteps and use the next value as "previous" time (less noisy). - # If we are at the end, perform a "noise removal" step (alpha_prev = 1). - current_indices = (self.timesteps == timestep_int).nonzero(as_tuple=False) - if current_indices.numel() == 0: - raise ValueError(f"`timestep` ({timestep_int}) must be one of `self.timesteps`.") - step_index = int(current_indices[0].item()) + # Look up the step index and pre-computed alpha values. + if self._step_index_map is not None and timestep_int in self._step_index_map: + step_index = self._step_index_map[timestep_int] + else: + current_indices = (self.timesteps == timestep_int).nonzero(as_tuple=False) + if current_indices.numel() == 0: + raise ValueError(f"`timestep` ({timestep_int}) must be one of `self.timesteps`.") + step_index = int(current_indices[0].item()) + is_noise_removal_step = step_index + 1 >= len(self.timesteps) - prev_timestep_int = int(self.timesteps[step_index + 1].item()) if not is_noise_removal_step else 0 - t = self._t_from_timestep(timestep_int, device=device) - alpha_t = self._alpha_t(t).to(dtype=torch.float32) - if is_noise_removal_step: - alpha_prev = torch.tensor(1.0, device=device, dtype=torch.float32) + # Use pre-computed alphas when available, otherwise fall back to computing on the fly. + if self.alphas is not None: + alpha_t = self.alphas[step_index].to(device=device) + if is_noise_removal_step: + alpha_prev = torch.tensor(1.0, device=device, dtype=torch.float32) + else: + alpha_prev = self.alphas[step_index + 1].to(device=device) else: - t_prev = self._t_from_timestep(prev_timestep_int, device=device) - alpha_prev = self._alpha_t(t_prev).to(dtype=torch.float32) + t = self._t_from_timestep(timestep_int, device=device) + alpha_t = self._alpha_t(t).to(dtype=torch.float32) + if is_noise_removal_step: + alpha_prev = torch.tensor(1.0, device=device, dtype=torch.float32) + else: + prev_timestep_int = int(self.timesteps[step_index + 1].item()) + t_prev = self._t_from_timestep(prev_timestep_int, device=device) + alpha_prev = self._alpha_t(t_prev).to(dtype=torch.float32) if self.forward_process == "uniform": # Convert logits to probabilities for x0; optionally forbid mask token. @@ -461,30 +484,27 @@ def step( x_prev = _gumbel_argmax(torch.log(q_xs), generator=generator).to(dtype=torch.long) - if not return_dict: - return (x_prev,) - return TokenDiffusionSchedulerOutput(prev_sample=x_prev) + elif self.forward_process == "absorbing": + # p_denoise = (alpha_prev - alpha_t) / (1 - alpha_t) + denom = (1.0 - alpha_t).clamp_min(torch.finfo(torch.float32).eps) + p_denoise = ((alpha_prev - alpha_t) / denom).clamp(0.0, 1.0) - if self.forward_process != "absorbing": - raise ValueError(f"Unsupported forward process for `step()`: {self.forward_process!r}") - - # p_denoise = (alpha_prev - alpha_t) / (1 - alpha_t) - denom = (1.0 - alpha_t).clamp_min(torch.finfo(torch.float32).eps) - p_denoise = ((alpha_prev - alpha_t) / denom).clamp(0.0, 1.0) + # Sample x0 predictions (never sample the mask token). + logits = model_output.to(dtype=torch.float32) + logits[..., self.mask_token_id] = torch.finfo(logits.dtype).min + sampled_x0 = _gumbel_argmax(logits, generator=generator).to(dtype=torch.long) - # Sample x0 predictions (never sample the mask token). - logits = model_output.to(dtype=torch.float32) - logits[..., self.mask_token_id] = torch.finfo(logits.dtype).min - sampled_x0 = _gumbel_argmax(logits, generator=generator).to(dtype=torch.long) + # Only masked positions can change. + is_masked = sample == self.mask_token_id - # Only masked positions can change. - is_masked = sample == self.mask_token_id + # Bernoulli draw for whether to denoise at this step (only matters on masked positions). + rand = torch.rand((batch_size, seq_len), device=device, dtype=torch.float32, generator=generator) + should_denoise = rand < float(p_denoise.item()) - # Bernoulli draw for whether to denoise at this step (only matters on masked positions). - rand = torch.rand((batch_size, seq_len), device=device, dtype=torch.float32, generator=generator) - should_denoise = rand < float(p_denoise.item()) + x_prev = torch.where(is_masked & should_denoise, sampled_x0, sample) - x_prev = torch.where(is_masked & should_denoise, sampled_x0, sample) + else: + raise ValueError(f"Unsupported forward process for `step()`: {self.forward_process!r}") if not return_dict: return (x_prev,) diff --git a/tests/schedulers/test_scheduler_token_diffusion.py b/tests/schedulers/test_scheduler_token_diffusion.py index 1812f3f28630..f7ace043133c 100644 --- a/tests/schedulers/test_scheduler_token_diffusion.py +++ b/tests/schedulers/test_scheduler_token_diffusion.py @@ -125,6 +125,66 @@ def test_alpha_helpers_shapes(self): self.assertEqual(alpha.shape, (3, 1)) self.assertEqual(dalpha.shape, (3, 1)) + def test_set_timesteps_precomputes_alphas(self): + for schedule in ["log_linear", "linear", "cosine", "geometric"]: + scheduler = self.get_scheduler(alpha_schedule=schedule) + scheduler.set_timesteps(10) + + self.assertIsNotNone(scheduler.alphas) + self.assertEqual(len(scheduler.alphas), 10) + + # Verify pre-computed alphas match on-the-fly computation. + for i, ts in enumerate(scheduler.timesteps): + t = scheduler._t_from_timestep(int(ts.item()), device=torch.device("cpu")) + expected = scheduler._alpha_t(t).to(dtype=torch.float32) + self.assertTrue( + torch.allclose(scheduler.alphas[i].cpu(), expected, atol=1e-6), + f"Alpha mismatch at step {i} for schedule {schedule}", + ) + + def test_set_timesteps_builds_step_index_map(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(5) + + self.assertIsNotNone(scheduler._step_index_map) + self.assertEqual(len(scheduler._step_index_map), 5) + + for i, ts in enumerate(scheduler.timesteps): + self.assertEqual(scheduler._step_index_map[int(ts.item())], i) + + def test_step_uses_precomputed_alphas_consistent_with_recompute(self): + """Verify step() produces identical results whether using pre-computed or recomputed alphas.""" + for process in ["absorbing", "uniform"]: + scheduler = self.get_scheduler(forward_process=process) + scheduler.set_timesteps(3) + + batch_size, seq_len = 2, 8 + if process == "absorbing": + x_t = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) + else: + x_t = torch.randint(0, scheduler.vocab_size, (batch_size, seq_len), dtype=torch.long) + logits = torch.randn((batch_size, seq_len, scheduler.vocab_size), dtype=torch.float32) + + gen1 = torch.Generator().manual_seed(42) + out1 = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen1, return_dict=True) + + # Temporarily clear pre-computed alphas to force recomputation. + saved_alphas = scheduler.alphas + saved_map = scheduler._step_index_map + scheduler.alphas = None + scheduler._step_index_map = None + + gen2 = torch.Generator().manual_seed(42) + out2 = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen2, return_dict=True) + + scheduler.alphas = saved_alphas + scheduler._step_index_map = saved_map + + self.assertTrue( + torch.equal(out1.prev_sample, out2.prev_sample), + f"Mismatch for forward_process={process}", + ) + if __name__ == "__main__": unittest.main() From 597060d48eb7838d0683e678c31b733f4c34b82b Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Mon, 9 Feb 2026 22:07:20 +0000 Subject: [PATCH 19/44] merge BlockTokenDiffusionScheduler into TokenDiffusionScheduler --- .../scheduling_block_token_diffusion.py | 80 ++----------------- .../schedulers/scheduling_token_diffusion.py | 40 ++++++++-- .../test_scheduler_token_diffusion.py | 49 ++++++++++++ 3 files changed, 89 insertions(+), 80 deletions(-) diff --git a/src/diffusers/schedulers/scheduling_block_token_diffusion.py b/src/diffusers/schedulers/scheduling_block_token_diffusion.py index 4d743cf93dbc..b8116daac249 100644 --- a/src/diffusers/schedulers/scheduling_block_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_block_token_diffusion.py @@ -14,82 +14,12 @@ from __future__ import annotations -from typing import Optional, Tuple, Union +from .scheduling_token_diffusion import TokenDiffusionScheduler -import torch - -from .scheduling_token_diffusion import TokenDiffusionScheduler, TokenDiffusionSchedulerOutput - - -class BlockTokenDiffusionScheduler(TokenDiffusionScheduler): - """ - A token diffusion scheduler that supports updating only a subset of positions (e.g. a block). - - This scheduler reuses the same alpha schedules and forward processes as `TokenDiffusionScheduler`, but allows - callers to restrict noising/denoising to a boolean `block_mask` of shape `[batch, seq_len]`. - """ - - @classmethod - def from_config(cls, config, **kwargs): - # TokenDiffusionScheduler doesn't have compatibles; keep standard ConfigMixin behavior. - return super().from_config(config, **kwargs) - - def add_noise( - self, - original_samples: torch.LongTensor, - noise: Optional[torch.Tensor], - timesteps: torch.LongTensor, - block_mask: Optional[torch.BoolTensor] = None, - ) -> torch.LongTensor: - if block_mask is None: - return super().add_noise(original_samples=original_samples, noise=noise, timesteps=timesteps) - - if block_mask.dtype != torch.bool: - raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") - if block_mask.shape != original_samples.shape: - raise ValueError( - f"`block_mask` must have shape {tuple(original_samples.shape)}, got {tuple(block_mask.shape)}." - ) - - noised = super().add_noise(original_samples=original_samples, noise=noise, timesteps=timesteps) - return torch.where(block_mask.to(device=original_samples.device), noised, original_samples) - - def step( - self, - model_output: torch.Tensor, - timestep: Union[int, torch.Tensor], - sample: torch.LongTensor, - generator: Optional[torch.Generator] = None, - return_dict: bool = True, - block_mask: Optional[torch.BoolTensor] = None, - ) -> Union[TokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: - if block_mask is None: - return super().step( - model_output=model_output, - timestep=timestep, - sample=sample, - generator=generator, - return_dict=return_dict, - ) - - if block_mask.dtype != torch.bool: - raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") - if block_mask.shape != sample.shape: - raise ValueError(f"`block_mask` must have shape {tuple(sample.shape)}, got {tuple(block_mask.shape)}.") - - out = super().step( - model_output=model_output, - timestep=timestep, - sample=sample, - generator=generator, - return_dict=True, - ) - prev = out.prev_sample - prev = torch.where(block_mask.to(device=prev.device), prev, sample) - - if not return_dict: - return (prev,) - return TokenDiffusionSchedulerOutput(prev_sample=prev) +# Backwards-compatible alias. `TokenDiffusionScheduler` now natively supports +# the `block_mask` parameter in `add_noise()` and `step()`, so a separate +# subclass is no longer needed. +BlockTokenDiffusionScheduler = TokenDiffusionScheduler __all__ = ["BlockTokenDiffusionScheduler"] diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index e9b80d2a5aae..04021550a1c8 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -181,9 +181,8 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). - Timesteps are stored in descending order, so `timesteps[0]` is the noisiest step. - Alpha values are pre-computed for each timestep so that `step()` can look them up - by index instead of recomputing them on every call. + Timesteps are stored in descending order, so `timesteps[0]` is the noisiest step. Alpha values are pre-computed + for each timestep so that `step()` can look them up by index instead of recomputing them on every call. """ if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") @@ -339,11 +338,23 @@ def add_noise( original_samples: torch.LongTensor, noise: Optional[torch.Tensor], timesteps: torch.LongTensor, + block_mask: Optional[torch.BoolTensor] = None, ) -> torch.LongTensor: """ - Apply the (absorbing) forward process q(x_t | x_0). + Apply the forward process q(x_t | x_0). The `noise` argument is accepted for API compatibility but is not used for the absorbing kernel. + + Args: + original_samples (`torch.LongTensor`): + Original token IDs of shape `(batch_size, seq_len)`. + noise (`torch.Tensor`, *optional*): + Accepted for API compatibility; unused. + timesteps (`torch.LongTensor`): + Per-example timestep indices of shape `(batch_size,)`. + block_mask (`torch.BoolTensor`, *optional*): + Boolean mask of shape `(batch_size, seq_len)`. When provided, only positions where `block_mask` is + `True` are noised; other positions are kept unchanged. """ del noise @@ -351,6 +362,13 @@ def add_noise( raise ValueError(f"`original_samples` must be int64 token IDs, got dtype={original_samples.dtype}.") if timesteps.dtype not in (torch.int32, torch.int64): raise ValueError(f"`timesteps` must be an integer tensor, got dtype={timesteps.dtype}.") + if block_mask is not None: + if block_mask.dtype != torch.bool: + raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") + if block_mask.shape != original_samples.shape: + raise ValueError( + f"`block_mask` must have shape {tuple(original_samples.shape)}, got {tuple(block_mask.shape)}." + ) batch_size, seq_len = original_samples.shape device = original_samples.device @@ -372,7 +390,10 @@ def add_noise( else: raise ValueError(f"Unsupported forward process: {self.forward_process!r}") - return torch.where(replace_positions, replacement, original_samples) + noised = torch.where(replace_positions, replacement, original_samples) + if block_mask is not None: + noised = torch.where(block_mask.to(device=device), noised, original_samples) + return noised def step( self, @@ -381,6 +402,7 @@ def step( sample: torch.LongTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, + block_mask: Optional[torch.BoolTensor] = None, ) -> Union[TokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: """ Reverse diffusion step for the configured forward process. @@ -402,6 +424,11 @@ def step( raise ValueError( f"`model_output` batch/seq dims {tuple(model_output.shape[:2])} must match `sample` {tuple(sample.shape)}." ) + if block_mask is not None: + if block_mask.dtype != torch.bool: + raise ValueError(f"`block_mask` must be boolean, got dtype={block_mask.dtype}.") + if block_mask.shape != sample.shape: + raise ValueError(f"`block_mask` must have shape {tuple(sample.shape)}, got {tuple(block_mask.shape)}.") device = sample.device batch_size, seq_len = sample.shape @@ -506,6 +533,9 @@ def step( else: raise ValueError(f"Unsupported forward process for `step()`: {self.forward_process!r}") + if block_mask is not None: + x_prev = torch.where(block_mask.to(device=device), x_prev, sample) + if not return_dict: return (x_prev,) return TokenDiffusionSchedulerOutput(prev_sample=x_prev) diff --git a/tests/schedulers/test_scheduler_token_diffusion.py b/tests/schedulers/test_scheduler_token_diffusion.py index f7ace043133c..66738dceedc4 100644 --- a/tests/schedulers/test_scheduler_token_diffusion.py +++ b/tests/schedulers/test_scheduler_token_diffusion.py @@ -152,6 +152,55 @@ def test_set_timesteps_builds_step_index_map(self): for i, ts in enumerate(scheduler.timesteps): self.assertEqual(scheduler._step_index_map[int(ts.item())], i) + def test_step_respects_block_mask(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(1) + + batch_size, seq_len = 2, 8 + x = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) + block_mask = torch.zeros_like(x, dtype=torch.bool) + block_mask[:, :4] = True + + logits = torch.zeros((batch_size, seq_len, scheduler.config.vocab_size), dtype=torch.float32) + gen = torch.Generator().manual_seed(0) + out = scheduler.step(logits, scheduler.timesteps[0], x, generator=gen, return_dict=True, block_mask=block_mask) + + # Block positions should be denoised (non-mask) after the final noise-removal step. + self.assertTrue((out.prev_sample[:, :4] != scheduler.mask_token_id).all().item()) + # Outside the block, tokens should remain unchanged (still mask). + self.assertTrue((out.prev_sample[:, 4:] == scheduler.mask_token_id).all().item()) + + def test_add_noise_respects_block_mask(self): + scheduler = self.get_scheduler() + batch_size, seq_len = 2, 16 + x0 = torch.randint(0, scheduler.config.vocab_size - 1, (batch_size, seq_len), dtype=torch.long) + block_mask = torch.zeros_like(x0, dtype=torch.bool) + block_mask[:, :4] = True + + # Use high noise timestep so almost all block positions get noised. + timesteps = torch.full((batch_size,), scheduler.num_train_timesteps - 1, dtype=torch.long) + xt = scheduler.add_noise(x0, noise=None, timesteps=timesteps, block_mask=block_mask) + + # Outside the block, tokens must be unchanged. + self.assertTrue(torch.equal(xt[:, 4:], x0[:, 4:])) + + def test_step_without_block_mask_unchanged(self): + """Passing block_mask=None should produce the same result as before.""" + scheduler = self.get_scheduler() + scheduler.set_timesteps(3) + + batch_size, seq_len = 2, 8 + x_t = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) + logits = torch.randn((batch_size, seq_len, scheduler.config.vocab_size), dtype=torch.float32) + + gen1 = torch.Generator().manual_seed(42) + out1 = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen1, return_dict=True) + + gen2 = torch.Generator().manual_seed(42) + out2 = scheduler.step(logits, scheduler.timesteps[0], x_t, generator=gen2, return_dict=True, block_mask=None) + + self.assertTrue(torch.equal(out1.prev_sample, out2.prev_sample)) + def test_step_uses_precomputed_alphas_consistent_with_recompute(self): """Verify step() produces identical results whether using pre-computed or recomputed alphas.""" for process in ["absorbing", "uniform"]: From 20b113444aaef24d769792a16ead959d37b251e3 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 10 Feb 2026 13:51:23 +0000 Subject: [PATCH 20/44] support for llada 2.1 --- examples/discrete_diffusion/sample_dflash.py | 2 - examples/discrete_diffusion/sample_llada2.py | 14 +++ .../pipeline_block_refinement.py | 93 ++++++++++++++----- .../pipelines/llada2/pipeline_llada2.py | 4 + 4 files changed, 89 insertions(+), 24 deletions(-) diff --git a/examples/discrete_diffusion/sample_dflash.py b/examples/discrete_diffusion/sample_dflash.py index 2c172dc04464..6ff0f293410d 100644 --- a/examples/discrete_diffusion/sample_dflash.py +++ b/examples/discrete_diffusion/sample_dflash.py @@ -119,8 +119,6 @@ def main(): "dtype": dtype_arg, "device_map": args.device, }, - max_new_tokens=args.max_new_tokens, - temperature=args.temperature, ) chat_kwargs = {"enable_thinking": args.enable_thinking} diff --git a/examples/discrete_diffusion/sample_llada2.py b/examples/discrete_diffusion/sample_llada2.py index 3e0fd0434008..bcb0c4c47dc2 100644 --- a/examples/discrete_diffusion/sample_llada2.py +++ b/examples/discrete_diffusion/sample_llada2.py @@ -91,6 +91,18 @@ def main(): default=0.95, help="Confidence threshold for committing tokens.", ) + parser.add_argument( + "--editing_threshold", + type=float, + default=None, + help="Confidence threshold for editing already-committed tokens. Set to enable post-mask editing (e.g. 0.5).", + ) + parser.add_argument( + "--max_post_steps", + type=int, + default=0, + help="Maximum post-mask editing iterations per block (e.g. 16). Only used when --editing_threshold is set.", + ) parser.add_argument( "--sampling_method", type=str, @@ -222,6 +234,8 @@ def main(): top_p=args.top_p, top_k=args.top_k, threshold=args.threshold, + editing_threshold=args.editing_threshold, + max_post_steps=args.max_post_steps, sampling_method=args.sampling_method, eos_early_stop=args.eos_early_stop, generator=generator, diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index bb529a9df0a3..389c2feb1583 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -210,6 +210,8 @@ def __call__( top_k: Optional[int] = None, sampling_method: str = "auto", threshold: float = 0.95, + editing_threshold: Optional[float] = None, + max_post_steps: int = 0, minimal_topk: int = 1, eos_early_stop: bool = False, eos_token_id: Optional[int] = None, @@ -246,6 +248,14 @@ def __call__( Sampling method (`auto`, `greedy`, `multinomial`). threshold (`float`): Confidence threshold for committing tokens. + editing_threshold (`float`, *optional*): + Confidence threshold for editing already-committed (non-mask) tokens. When set, after all mask tokens + in a block are resolved, the pipeline continues refining: if the model predicts a different token with + confidence above this threshold, the existing token is replaced. Set to `None` or a negative value to + disable editing. Defaults to `None` (disabled). + max_post_steps (`int`): + Maximum number of additional refinement iterations after all mask tokens in a block are resolved. Only + used when `editing_threshold` is enabled. Defaults to `0` (no post-mask editing steps). minimal_topk (`int`): Minimum number of tokens to commit per step. eos_early_stop (`bool`): @@ -329,6 +339,7 @@ def __call__( resolved_attention_mode: str = str(attention_mask_mode) use_multinomial = sampling_method == "multinomial" or (sampling_method == "auto" and float(temperature) != 0.0) + editing_enabled = editing_threshold is not None and editing_threshold >= 0.0 global_step = 0 for num_block in range(int(prefill_blocks), int(num_blocks)): @@ -338,13 +349,28 @@ def __call__( cur_attn_mask_2d = attn_mask_2d_full[:, :current_window_end] cur_position_ids = position_ids[:, :current_window_end] - for step_idx in range(int(steps)): + # Identify which positions in the block are prompt (non-editable). + block_start_pos = num_block * int(block_length) + prompt_mask_in_block = torch.zeros(int(block_length), device=model_device, dtype=torch.bool) + if block_start_pos < prompt_length: + prompt_end_in_block = min(prompt_length - block_start_pos, int(block_length)) + prompt_mask_in_block[:prompt_end_in_block] = True + + post_steps = 0 + step_idx = 0 + while step_idx < int(steps) or (editing_enabled and post_steps <= int(max_post_steps)): if finished.all(): break active_block = cur_x[:, -int(block_length) :] == int(mask_token_id) - if active_block.sum() == 0: + masks_remaining = active_block.sum() > 0 + + if not masks_remaining and not editing_enabled: break + if not masks_remaining: + post_steps += 1 + if post_steps > int(max_post_steps): + break logits, resolved_attention_mode = self._model_forward_logits( cur_x, @@ -364,36 +390,57 @@ def __call__( use_multinomial=use_multinomial, ) - num_to_transfer = int(transfer_schedule[step_idx].item()) + # --- Mask-filling transfer --- transfer_index = torch.zeros_like(x0, dtype=torch.bool) + if masks_remaining and step_idx < int(steps): + clamped_step = min(step_idx, len(transfer_schedule) - 1) + num_to_transfer = int(transfer_schedule[clamped_step].item()) - confidence = torch.where( - active_block, x0_p.to(dtype=torch.float32), torch.full_like(x0_p, -torch.inf, dtype=torch.float32) - ) - - for b in range(batch_size): - if finished[b]: - continue - - high_conf = confidence[b] > float(threshold) - if high_conf.sum().item() >= num_to_transfer: - transfer_index[b] = high_conf - else: - k = min(num_to_transfer, int(active_block[b].sum().item())) - if k > 0: - _, idx = torch.topk(confidence[b], k=k) - transfer_index[b, idx] = True + confidence = torch.where( + active_block, + x0_p.to(dtype=torch.float32), + torch.full_like(x0_p, -torch.inf, dtype=torch.float32), + ) - if transfer_index.any(): + for b in range(batch_size): + if finished[b]: + continue + high_conf = confidence[b] > float(threshold) + if high_conf.sum().item() >= num_to_transfer: + transfer_index[b] = high_conf + else: + k = min(num_to_transfer, int(active_block[b].sum().item())) + if k > 0: + _, idx = torch.topk(confidence[b], k=k) + transfer_index[b, idx] = True + + # --- Editing transfer (non-mask, non-prompt positions) --- + editing_transfer_index = torch.zeros_like(x0, dtype=torch.bool) + if editing_enabled: + old_block_tokens = cur_x[:, -int(block_length) :] + editable = (~active_block) & (~prompt_mask_in_block.unsqueeze(0)) + editing_conf = torch.where( + editable, x0_p.to(dtype=torch.float32), torch.full_like(x0_p, -torch.inf, dtype=torch.float32) + ) + high_conf_edit = editing_conf > float(editing_threshold) + token_changed = x0 != old_block_tokens + editing_transfer_index = high_conf_edit & token_changed & editable + + final_transfer = transfer_index | editing_transfer_index + if final_transfer.any(): updated = cur_x[:, -int(block_length) :].clone() - updated[transfer_index] = x0[transfer_index] + updated[final_transfer] = x0[final_transfer] cur_x[:, -int(block_length) :] = updated + # Break if no masks remain and no edits were made. + if not masks_remaining and not editing_transfer_index.any(): + break + if eos_early_stop and eos_token_id is not None: for b in range(batch_size): if finished[b]: continue - eos_in_commits = (x0[b][transfer_index[b]] == int(eos_token_id)).any().item() + eos_in_commits = (x0[b][final_transfer[b]] == int(eos_token_id)).any().item() if not eos_in_commits: continue eos_pos = (cur_x[b] == int(eos_token_id)).nonzero(as_tuple=True) @@ -413,6 +460,8 @@ def __call__( cur_x = callback_outputs.pop("cur_x", cur_x) global_step += 1 + if masks_remaining: + step_idx += 1 x[:, :current_window_end] = cur_x if eos_token_id is not None and (x[:, prompt_length:current_window_end] == int(eos_token_id)).any().item(): diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index c7d73fd124df..b520271244c9 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -76,6 +76,8 @@ def __call__( top_k: Optional[int] = None, sampling_method: str = "multinomial", threshold: float = 0.95, + editing_threshold: Optional[float] = None, + max_post_steps: int = 0, minimal_topk: int = 1, eos_early_stop: bool = False, eos_token_id: Optional[int] = None, @@ -110,6 +112,8 @@ def __call__( top_k=top_k, sampling_method=sampling_method, threshold=threshold, + editing_threshold=editing_threshold, + max_post_steps=max_post_steps, minimal_topk=minimal_topk, eos_early_stop=eos_early_stop, eos_token_id=eos_token_id, From 70fc4c1f962b4dfc6cfe488bef9a82593033eb5f Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 10 Feb 2026 14:49:13 +0000 Subject: [PATCH 21/44] fix sampling example to use pipeline --- examples/discrete_diffusion/sample_mdlm.py | 93 ++++++++----------- .../pipeline_token_diffusion.py | 3 +- 2 files changed, 40 insertions(+), 56 deletions(-) diff --git a/examples/discrete_diffusion/sample_mdlm.py b/examples/discrete_diffusion/sample_mdlm.py index b6add10812f6..a598ea17f45c 100644 --- a/examples/discrete_diffusion/sample_mdlm.py +++ b/examples/discrete_diffusion/sample_mdlm.py @@ -13,19 +13,31 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Sample script for MDLM-style absorbing token diffusion text generation. + +This script demonstrates how to use the TokenDiffusionPipeline for unconditional +text generation using absorbing-state discrete diffusion. + +Example usage: + python sample_mdlm.py --model_id kuleshov-group/mdlm-owt --num_samples 4 --seq_len 64 +""" + import argparse -from typing import Optional import torch from transformers import AutoModelForMaskedLM, AutoTokenizer -from diffusers import TokenDiffusionScheduler +from diffusers import TokenDiffusionPipeline, TokenDiffusionScheduler -def parse_args(): +def main(): parser = argparse.ArgumentParser(description="Sample from an absorbing token diffusion LM (MDLM-style).") parser.add_argument( - "--checkpoint_path", type=str, required=True, help="Path saved by train_mdlm.py (or compatible)." + "--model_id", + type=str, + default="kuleshov-group/mdlm-owt", + help="HuggingFace model ID or path to local checkpoint.", ) parser.add_argument("--num_samples", type=int, default=4) parser.add_argument("--seq_len", type=int, default=64) @@ -33,68 +45,39 @@ def parse_args(): parser.add_argument("--seed", type=int, default=0) parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") parser.add_argument("--inject_bos", action="store_true") - return parser.parse_args() - - -@torch.no_grad() -def sample( - model, - tokenizer, - scheduler: TokenDiffusionScheduler, - *, - num_samples: int, - seq_len: int, - num_inference_steps: int, - generator: Optional[torch.Generator], - inject_bos: bool, - device: torch.device, -): - scheduler.set_timesteps(num_inference_steps, device=device) - - x = torch.full((num_samples, seq_len), scheduler.mask_token_id, dtype=torch.long, device=device) - attention_mask = torch.ones_like(x, dtype=torch.long) - - if inject_bos and tokenizer.bos_token_id is not None: - x[:, 0] = int(tokenizer.bos_token_id) + parser.add_argument("--trust_remote_code", action="store_true", help="Trust remote code for model/tokenizer.") - for t in scheduler.timesteps: - logits = model(input_ids=x, attention_mask=attention_mask).logits # [B, L, V] - x = scheduler.step(logits, t, x, generator=generator, return_dict=True).prev_sample + args = parser.parse_args() + device = torch.device(args.device) - if inject_bos and tokenizer.bos_token_id is not None: - x[:, 0] = int(tokenizer.bos_token_id) + print(f"Loading model: {args.model_id}") + tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True) + model = AutoModelForMaskedLM.from_pretrained( + args.model_id, trust_remote_code=args.trust_remote_code + ).to(device) + model.eval() - return x + mask_token_id = len(tokenizer) # MDLM appends mask token after vocab + vocab_size = mask_token_id + 1 + scheduler = TokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=mask_token_id) + pipe = TokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) -def main(): - args = parse_args() - device = torch.device(args.device) - - tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True) - model = AutoModelForMaskedLM.from_pretrained(args.checkpoint_path).to(device) - scheduler = TokenDiffusionScheduler.from_pretrained(args.checkpoint_path) - - model.eval() + generator = torch.Generator(device=device).manual_seed(args.seed) - gen = torch.Generator(device=device) - gen.manual_seed(args.seed) + print(f"Generating {args.num_samples} samples of {args.seq_len} tokens with {args.num_inference_steps} steps") + print("-" * 50) - samples = sample( - model, - tokenizer, - scheduler, - num_samples=args.num_samples, + output = pipe( + batch_size=args.num_samples, seq_len=args.seq_len, num_inference_steps=args.num_inference_steps, - generator=gen, - inject_bos=args.inject_bos, - device=device, + generator=generator, + inject_start_token=args.inject_bos, ) - texts = tokenizer.batch_decode(samples, skip_special_tokens=True) - for i, t in enumerate(texts): - print(f"[{i}] {t}") + for i, text in enumerate(output.texts): + print(f"[{i}] {text}") if __name__ == "__main__": diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py index 1cd395e1ffb0..f53736505e49 100644 --- a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -197,7 +197,8 @@ def __call__( fixed_values[:, 0] = start_token_id for step_idx, t in enumerate(timesteps): - out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) + timestep = t.expand(batch_size) + out = self.model(input_ids=input_ids, timesteps=timestep, return_dict=True, **model_kwargs) logits = getattr(out, "logits", None) if logits is None: # Fall back to tuple-style returns. From 0432b80962b7529601d1fc25f37da2797fdc0c7d Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 10 Feb 2026 16:57:14 +0000 Subject: [PATCH 22/44] formatting --- examples/discrete_diffusion/sample_mdlm.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/discrete_diffusion/sample_mdlm.py b/examples/discrete_diffusion/sample_mdlm.py index a598ea17f45c..bc435e647967 100644 --- a/examples/discrete_diffusion/sample_mdlm.py +++ b/examples/discrete_diffusion/sample_mdlm.py @@ -52,9 +52,7 @@ def main(): print(f"Loading model: {args.model_id}") tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True) - model = AutoModelForMaskedLM.from_pretrained( - args.model_id, trust_remote_code=args.trust_remote_code - ).to(device) + model = AutoModelForMaskedLM.from_pretrained(args.model_id, trust_remote_code=args.trust_remote_code).to(device) model.eval() mask_token_id = len(tokenizer) # MDLM appends mask token after vocab From 4e702279ca7c0b65193519d305ac4d809a4a6a0e Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 8 Mar 2026 17:22:30 +0000 Subject: [PATCH 23/44] fix imports --- src/diffusers/pipelines/pipeline_utils.py | 2 +- src/diffusers/training_utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index 27f045dfb950..3b074832ed59 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -22,7 +22,7 @@ import types from dataclasses import dataclass from pathlib import Path -from typing import Any, Callable, Dict, List, Union, get_args, get_origin, get_type_hints +from typing import Any, Callable, Dict, List, Optional, Union, get_args, get_origin, get_type_hints import httpx import numpy as np diff --git a/src/diffusers/training_utils.py b/src/diffusers/training_utils.py index b6070fa4a6a9..fbccd82180a5 100644 --- a/src/diffusers/training_utils.py +++ b/src/diffusers/training_utils.py @@ -7,7 +7,7 @@ import warnings from contextlib import contextmanager from functools import partial -from typing import Any, Iterable +from typing import Any, Iterable, Optional, Tuple import numpy as np import torch From ae25ba1192b44cf822bec9f45f0a9dd0eb2e1de4 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Sun, 15 Mar 2026 11:21:21 +0000 Subject: [PATCH 24/44] fix: port fixes from llada2-support branch - Attention mask: use 1/0 float instead of 0/-inf (matches official LLaDA2 code) - Sampling: add shift-right in top-p to preserve at least one token - Sampling: apply temperature before top-k/top-p filtering - Sampling: separate greedy branch for temperature=0 - LLaDA2Pipeline: update defaults to LLaDA2.1 quality mode (threshold=0.7, editing_threshold=0.5, max_post_steps=16, eos_early_stop=True) - LLaDA2Pipeline: remove duplicate _prepare_prompt_ids, reuse mixin's _prepare_input_ids - LLaDA2Pipeline: fix EXAMPLE_DOC_STRING (model ID, dtype, scheduler) - Simplify _extract_input_ids since return_dict=True - Replace deprecated torch_dtype with dtype in example scripts - Add all discrete diffusion pipeline/scheduler pages to docs toctree --- docs/source/en/_toctree.yml | 26 +++++++ .../sample_block_refinement.py | 2 +- examples/discrete_diffusion/sample_llada2.py | 6 +- .../pipeline_block_refinement.py | 9 +-- .../pipelines/llada2/pipeline_llada2.py | 75 ++++--------------- src/diffusers/pipelines/pipeline_utils.py | 50 +++++++------ 6 files changed, 72 insertions(+), 96 deletions(-) diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index c69bcd340b27..11af43d0c774 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -500,6 +500,10 @@ title: AuraFlow - local: api/pipelines/blip_diffusion title: BLIP-Diffusion + - local: api/pipelines/block_refinement + title: Block Refinement + - local: api/pipelines/block_token_diffusion + title: Block Token Diffusion - local: api/pipelines/bria_3_2 title: Bria 3.2 - local: api/pipelines/bria_fibo @@ -538,6 +542,8 @@ title: DDPM - local: api/pipelines/deepfloyd_if title: DeepFloyd IF + - local: api/pipelines/dflash + title: DFlash - local: api/pipelines/diffedit title: DiffEdit - local: api/pipelines/dit @@ -558,6 +564,8 @@ title: Hunyuan-DiT - local: api/pipelines/hunyuanimage21 title: HunyuanImage2.1 + - local: api/pipelines/hybrid_token_diffusion + title: Hybrid Token Diffusion - local: api/pipelines/pix2pix title: InstructPix2Pix - local: api/pipelines/kandinsky @@ -608,6 +616,8 @@ title: Sana Sprint - local: api/pipelines/sana_video title: Sana Video + - local: api/pipelines/sdar + title: SDAR - local: api/pipelines/self_attention_guidance title: Self-Attention Guidance - local: api/pipelines/semantic_stable_diffusion @@ -653,6 +663,8 @@ title: Stable Diffusion - local: api/pipelines/stable_unclip title: Stable unCLIP + - local: api/pipelines/token_diffusion + title: Token Diffusion - local: api/pipelines/unclip title: unCLIP - local: api/pipelines/unidiffuser @@ -691,6 +703,8 @@ title: Kandinsky 5.0 Video - local: api/pipelines/latte title: Latte + - local: api/pipelines/llada2 + title: LLaDA2 - local: api/pipelines/ltx2 title: LTX-2 - local: api/pipelines/ltx_video @@ -714,6 +728,10 @@ - sections: - local: api/schedulers/overview title: Overview + - local: api/schedulers/bd3lm_token_diffusion + title: BD3LMTokenDiffusionScheduler + - local: api/schedulers/block_token_diffusion + title: BlockTokenDiffusionScheduler - local: api/schedulers/cm_stochastic_iterative title: CMStochasticIterativeScheduler - local: api/schedulers/ddim_cogvideox @@ -732,6 +750,8 @@ title: DDPMScheduler - local: api/schedulers/deis title: DEISMultistepScheduler + - local: api/schedulers/dflash_token_diffusion + title: DFlashTokenDiffusionScheduler - local: api/schedulers/multistep_dpm_solver_inverse title: DPMSolverMultistepInverse - local: api/schedulers/multistep_dpm_solver @@ -758,6 +778,8 @@ title: HeliosScheduler - local: api/schedulers/heun title: HeunDiscreteScheduler + - local: api/schedulers/hybrid_token_diffusion + title: HybridTokenDiffusionScheduler - local: api/schedulers/ipndm title: IPNDMScheduler - local: api/schedulers/stochastic_karras_ve @@ -778,8 +800,12 @@ title: ScoreSdeVeScheduler - local: api/schedulers/score_sde_vp title: ScoreSdeVpScheduler + - local: api/schedulers/sdar_token_diffusion + title: SDARTokenDiffusionScheduler - local: api/schedulers/tcd title: TCDScheduler + - local: api/schedulers/token_diffusion + title: TokenDiffusionScheduler - local: api/schedulers/unipc title: UniPCMultistepScheduler - local: api/schedulers/vq_diffusion diff --git a/examples/discrete_diffusion/sample_block_refinement.py b/examples/discrete_diffusion/sample_block_refinement.py index 72e728d876f8..2b9b5c3ba764 100644 --- a/examples/discrete_diffusion/sample_block_refinement.py +++ b/examples/discrete_diffusion/sample_block_refinement.py @@ -29,7 +29,7 @@ def main(): tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True, cache_dir=args.cache_dir) model = AutoModelForCausalLM.from_pretrained( args.checkpoint_path, - torch_dtype=torch.bfloat16 if args.device.startswith("cuda") else torch.float32, + dtype=torch.bfloat16 if args.device.startswith("cuda") else torch.float32, cache_dir=args.cache_dir, ) model.to(args.device) diff --git a/examples/discrete_diffusion/sample_llada2.py b/examples/discrete_diffusion/sample_llada2.py index bcb0c4c47dc2..2769f24856e2 100644 --- a/examples/discrete_diffusion/sample_llada2.py +++ b/examples/discrete_diffusion/sample_llada2.py @@ -172,7 +172,7 @@ def main(): model = AutoModelForCausalLM.from_pretrained( args.model_id, trust_remote_code=True, - torch_dtype=torch_dtype, + dtype=torch_dtype, low_cpu_mem_usage=True, ) # Apply group offloading with CUDA streams for better performance @@ -191,7 +191,7 @@ def main(): model = AutoModelForCausalLM.from_pretrained( args.model_id, trust_remote_code=True, - torch_dtype=torch_dtype, + dtype=torch_dtype, low_cpu_mem_usage=True, ) # Sequential offloading will be applied via pipeline @@ -200,7 +200,7 @@ def main(): model = AutoModelForCausalLM.from_pretrained( args.model_id, trust_remote_code=True, - torch_dtype=torch_dtype, + dtype=torch_dtype, device_map="auto", low_cpu_mem_usage=True, ) diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py index 389c2feb1583..3ba9f7e379be 100644 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py @@ -111,19 +111,14 @@ def _build_block_attention_mask( device: torch.device, dtype: torch.dtype, ) -> torch.Tensor: - block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=torch.bool)) + block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=dtype)) attn = ( block_mask.repeat_interleave(block_length, dim=0) .repeat_interleave(block_length, dim=1) .unsqueeze(0) .unsqueeze(0) ) - attn = attn[:, :, :total_length, :total_length] - return torch.where( - attn, - torch.zeros((), device=device, dtype=dtype), - torch.full((), float("-inf"), device=device, dtype=dtype), - ) + return attn[:, :, :total_length, :total_length] def _encode_prompt( self, diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index b520271244c9..bd9c814ec18e 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -31,14 +31,16 @@ ```python >>> import torch >>> from transformers import AutoModelForCausalLM, AutoTokenizer - >>> from diffusers import LLaDA2Pipeline + >>> from diffusers import BlockRefinementScheduler, LLaDA2Pipeline - >>> model_id = "inclusionAI/LLaDA2.0-mini" - >>> model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.bfloat16) + >>> model_id = "inclusionAI/LLaDA2.1-mini" + >>> model = AutoModelForCausalLM.from_pretrained( + ... model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="auto" + ... ) >>> tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) - >>> model = model.to("cuda") + >>> scheduler = BlockRefinementScheduler() - >>> pipe = LLaDA2Pipeline(model=model, tokenizer=tokenizer) + >>> pipe = LLaDA2Pipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) >>> output = pipe(prompt="What is the meaning of life?", gen_length=256) >>> print(output.texts[0]) ``` @@ -75,11 +77,11 @@ def __call__( top_p: Optional[float] = None, top_k: Optional[int] = None, sampling_method: str = "multinomial", - threshold: float = 0.95, - editing_threshold: Optional[float] = None, - max_post_steps: int = 0, + threshold: float = 0.7, + editing_threshold: Optional[float] = 0.5, + max_post_steps: int = 16, minimal_topk: int = 1, - eos_early_stop: bool = False, + eos_early_stop: bool = True, eos_token_id: Optional[int] = None, mask_token_id: Optional[int] = None, attention_mask_mode: str = "4d", @@ -94,12 +96,13 @@ def __call__( Examples: """ - prompt_ids = self._prepare_prompt_ids( + prompt_ids = self._prepare_input_ids( prompt=prompt, messages=messages, input_ids=input_ids, use_chat_template=use_chat_template, add_generation_prompt=add_generation_prompt, + chat_template_kwargs=None, ) output: BlockRefinementPipelineOutput = super().__call__( @@ -129,57 +132,5 @@ def __call__( return output.sequences, output.texts return LLaDA2PipelineOutput(sequences=output.sequences, texts=output.texts) - def _prepare_prompt_ids( - self, - *, - prompt: Optional[Union[str, List[str]]], - messages: Optional[List[Dict[str, str]]], - input_ids: Optional[torch.LongTensor], - use_chat_template: bool, - add_generation_prompt: bool, - ) -> Optional[torch.LongTensor]: - if input_ids is not None: - return input_ids - - if self.tokenizer is None: - if prompt is None and messages is None: - return None - raise ValueError("Tokenizer is required to encode `prompt` or `messages`.") - - def _extract_input_ids(encoded): - if isinstance(encoded, dict) and "input_ids" in encoded: - return encoded["input_ids"] - if hasattr(encoded, "input_ids"): - return encoded.input_ids - return encoded - - if messages is not None: - encoded = self.tokenizer.apply_chat_template( - messages, - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - ) - return _extract_input_ids(encoded) - - if prompt is None: - return None - - if use_chat_template and getattr(self.tokenizer, "chat_template", None): - if isinstance(prompt, list): - raise ValueError("`prompt` must be a string when `use_chat_template=True`.") - encoded = self.tokenizer.apply_chat_template( - [{"role": "user", "content": prompt}], - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - ) - return _extract_input_ids(encoded) - - encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return _extract_input_ids(encoded) - __all__ = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index 3b074832ed59..9975a28b4928 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -2407,6 +2407,8 @@ def _top_p_filtering(logits: "torch.Tensor", top_p: Optional[float]) -> "torch.T cumulative_probs = sorted_probs.cumsum(dim=-1) sorted_indices_to_remove = cumulative_probs > float(top_p) + # Shift right to keep at least the first token above the threshold + sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) @@ -2434,25 +2436,34 @@ def _sample_with_temperature_topk_topp( generator: Optional["torch.Generator"], use_multinomial: bool, ) -> "tuple[torch.LongTensor, torch.Tensor]": - """Sample tokens from logits with temperature scaling, top-k, and top-p.""" + """Sample tokens from logits with temperature scaling, top-k, and top-p. + + Follows the official LLaDA2 ordering: temperature scaling is applied before top-k/top-p filtering so that + filtering operates on the scaled logit distribution. + """ + if temperature < 0: + raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + vocab_size = logits.shape[-1] flat_logits = logits.reshape(-1, vocab_size) - filtered = DiscreteDiffusionPipelineMixin._top_k_filtering(flat_logits, top_k=top_k) - filtered = DiscreteDiffusionPipelineMixin._top_p_filtering(filtered, top_p=top_p) + # Greedy: return argmax directly (matches official LLaDA2 temperature=0 branch) + if temperature == 0.0 or not use_multinomial: + probs = torch.softmax(flat_logits.float(), dim=-1) + token = flat_logits.argmax(dim=-1, keepdim=True) + token_prob = torch.gather(probs, -1, token) + return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) - if temperature < 0: - raise ValueError(f"`temperature` must be >= 0, got {temperature}.") + # Temperature scaling before filtering (matches official LLaDA2 code) + scaled = flat_logits + if temperature != 1.0: + scaled = flat_logits / float(temperature) - scaled = filtered - if temperature > 0.0 and temperature != 1.0: - scaled = filtered / float(temperature) + filtered = DiscreteDiffusionPipelineMixin._top_k_filtering(scaled, top_k=top_k) + filtered = DiscreteDiffusionPipelineMixin._top_p_filtering(filtered, top_p=top_p) - probs = torch.softmax(scaled.float(), dim=-1) - if use_multinomial: - token = torch.multinomial(probs, num_samples=1, generator=generator) - else: - token = scaled.argmax(dim=-1, keepdim=True) + probs = torch.softmax(filtered.float(), dim=-1) + token = torch.multinomial(probs, num_samples=1, generator=generator) token_prob = torch.gather(probs, -1, token) return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) @@ -2523,13 +2534,6 @@ def _prepare_input_ids( chat_template_kwargs = chat_template_kwargs or {} - def _extract_input_ids(encoded): - if isinstance(encoded, dict) and "input_ids" in encoded: - return encoded["input_ids"] - if hasattr(encoded, "input_ids"): - return encoded.input_ids - return encoded - if messages is not None: encoded = self.tokenizer.apply_chat_template( messages, @@ -2539,7 +2543,7 @@ def _extract_input_ids(encoded): return_dict=True, **chat_template_kwargs, ) - return _extract_input_ids(encoded) + return encoded["input_ids"] if use_chat_template and getattr(self.tokenizer, "chat_template", None): if isinstance(prompt, list): @@ -2552,7 +2556,7 @@ def _extract_input_ids(encoded): return_dict=True, **chat_template_kwargs, ) - return _extract_input_ids(encoded) + return encoded["input_ids"] encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return _extract_input_ids(encoded) + return encoded["input_ids"] From cfc36c0a16a59d4c9267fdd554f169d1c50adbdd Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 10:54:21 +0000 Subject: [PATCH 25/44] Remove llada2/block_refinement files that now exist in main These files were part of the merged llada2-support PR and will be replaced by main's versions during merge. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../en/api/pipelines/{llada2.md => bd3lm.md} | 12 +- .../en/api/pipelines/block_refinement.md | 63 --- docs/source/en/api/pipelines/dflash.md | 24 + docs/source/en/api/pipelines/sdar.md | 23 + .../api/schedulers/bd3lm_token_diffusion.md | 21 + .../api/schedulers/dflash_token_diffusion.md | 22 + .../en/api/schedulers/sdar_token_diffusion.md | 21 + examples/discrete_diffusion/sample_bd3lm.py | 193 +++++++ .../sample_block_refinement.py | 66 --- examples/discrete_diffusion/sample_llada2.py | 251 --------- examples/discrete_diffusion/sample_sdar.py | 148 ++++++ .../train_block_refinement_cap.py | 387 -------------- .../train_block_refinement_qwen_cap.py | 359 ------------- examples/discrete_diffusion/train_dflash.py | 319 ++++++++++++ .../pipelines/block_refinement/__init__.py | 18 - .../pipeline_block_refinement.py | 477 ------------------ src/diffusers/pipelines/llada2/__init__.py | 47 -- .../pipelines/llada2/pipeline_llada2.py | 136 ----- .../test_pipeline_block_refinement.py | 93 ---- 19 files changed, 777 insertions(+), 1903 deletions(-) rename docs/source/en/api/pipelines/{llada2.md => bd3lm.md} (74%) delete mode 100644 docs/source/en/api/pipelines/block_refinement.md create mode 100644 docs/source/en/api/pipelines/dflash.md create mode 100644 docs/source/en/api/pipelines/sdar.md create mode 100644 docs/source/en/api/schedulers/bd3lm_token_diffusion.md create mode 100644 docs/source/en/api/schedulers/dflash_token_diffusion.md create mode 100644 docs/source/en/api/schedulers/sdar_token_diffusion.md create mode 100644 examples/discrete_diffusion/sample_bd3lm.py delete mode 100644 examples/discrete_diffusion/sample_block_refinement.py delete mode 100644 examples/discrete_diffusion/sample_llada2.py create mode 100644 examples/discrete_diffusion/sample_sdar.py delete mode 100644 examples/discrete_diffusion/train_block_refinement_cap.py delete mode 100644 examples/discrete_diffusion/train_block_refinement_qwen_cap.py create mode 100644 examples/discrete_diffusion/train_dflash.py delete mode 100644 src/diffusers/pipelines/block_refinement/__init__.py delete mode 100644 src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py delete mode 100644 src/diffusers/pipelines/llada2/__init__.py delete mode 100644 src/diffusers/pipelines/llada2/pipeline_llada2.py delete mode 100644 tests/pipelines/test_pipeline_block_refinement.py diff --git a/docs/source/en/api/pipelines/llada2.md b/docs/source/en/api/pipelines/bd3lm.md similarity index 74% rename from docs/source/en/api/pipelines/llada2.md rename to docs/source/en/api/pipelines/bd3lm.md index ba9330e4f5b3..545f6f31db42 100644 --- a/docs/source/en/api/pipelines/llada2.md +++ b/docs/source/en/api/pipelines/bd3lm.md @@ -10,14 +10,14 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o specific language governing permissions and limitations under the License. --> -# LLaDA2 +# BD3LM -`LLaDA2Pipeline` adapts block refinement sampling for LLaDA2-style token diffusion models. +`BD3LMPipeline` performs block diffusion decoding in the style of BD3LM models. -## LLaDA2Pipeline -[[autodoc]] LLaDA2Pipeline +## BD3LMPipeline +[[autodoc]] BD3LMPipeline - all - __call__ -## LLaDA2PipelineOutput -[[autodoc]] pipelines.LLaDA2PipelineOutput +## BD3LMPipelineOutput +[[autodoc]] pipelines.BD3LMPipelineOutput diff --git a/docs/source/en/api/pipelines/block_refinement.md b/docs/source/en/api/pipelines/block_refinement.md deleted file mode 100644 index 96e2f64e8660..000000000000 --- a/docs/source/en/api/pipelines/block_refinement.md +++ /dev/null @@ -1,63 +0,0 @@ - - -# Block Refinement - -`BlockRefinementPipeline` performs block-wise iterative refinement over a masked token template, sampling and -committing tokens based on confidence. - -## Config defaults - -You can set default sampling parameters when creating the pipeline. Passing `None` for a parameter in `__call__` -falls back to `pipe.config`. - -```py -from diffusers import BlockRefinementPipeline - -pipe = BlockRefinementPipeline( - model=model, - tokenizer=tokenizer, - gen_length=256, - block_length=32, - steps=16, - temperature=0.8, - sampling_method="multinomial", -) - -out = pipe(prompt="Explain gradient descent.") -print(out.texts[0]) -``` - -## Callbacks - -Callbacks run after each refinement step and can inspect or override the current tokens. - -```py -def on_step_end(pipe, step, timestep, callback_kwargs): - cur_x = callback_kwargs["cur_x"] - # Inspect or modify `cur_x` here. - return {"cur_x": cur_x} - -out = pipe( - prompt="Write a short poem.", - callback_on_step_end=on_step_end, - callback_on_step_end_tensor_inputs=["cur_x"], -) -``` - -## BlockRefinementPipeline -[[autodoc]] BlockRefinementPipeline - - all - - __call__ - -## BlockRefinementPipelineOutput -[[autodoc]] pipelines.BlockRefinementPipelineOutput diff --git a/docs/source/en/api/pipelines/dflash.md b/docs/source/en/api/pipelines/dflash.md new file mode 100644 index 000000000000..95847e1fdd82 --- /dev/null +++ b/docs/source/en/api/pipelines/dflash.md @@ -0,0 +1,24 @@ + + +# DFlash + +`DFlashPipeline` performs block-diffusion speculative decoding using a diffusion draft model and a target causal LM. +The draft model is conditioned on target hidden features extracted during prefill and verification steps. + +## DFlashPipeline +[[autodoc]] DFlashPipeline + - all + - __call__ + +## DFlashPipelineOutput +[[autodoc]] pipelines.DFlashPipelineOutput diff --git a/docs/source/en/api/pipelines/sdar.md b/docs/source/en/api/pipelines/sdar.md new file mode 100644 index 000000000000..bfa6aa3fb104 --- /dev/null +++ b/docs/source/en/api/pipelines/sdar.md @@ -0,0 +1,23 @@ + + +# SDAR + +`SDARPipeline` performs block diffusion decoding with iterative remasking strategies. + +## SDARPipeline +[[autodoc]] SDARPipeline + - all + - __call__ + +## SDARPipelineOutput +[[autodoc]] pipelines.SDARPipelineOutput diff --git a/docs/source/en/api/schedulers/bd3lm_token_diffusion.md b/docs/source/en/api/schedulers/bd3lm_token_diffusion.md new file mode 100644 index 000000000000..650b89031ca3 --- /dev/null +++ b/docs/source/en/api/schedulers/bd3lm_token_diffusion.md @@ -0,0 +1,21 @@ + + +# BD3LMTokenDiffusionScheduler + +`BD3LMTokenDiffusionScheduler` implements block diffusion remasking and sampling for BD3LM-style decoding. + +## BD3LMTokenDiffusionScheduler +[[autodoc]] BD3LMTokenDiffusionScheduler + +## BD3LMTokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_bd3lm_token_diffusion.BD3LMTokenDiffusionSchedulerOutput diff --git a/docs/source/en/api/schedulers/dflash_token_diffusion.md b/docs/source/en/api/schedulers/dflash_token_diffusion.md new file mode 100644 index 000000000000..c98b11bc9963 --- /dev/null +++ b/docs/source/en/api/schedulers/dflash_token_diffusion.md @@ -0,0 +1,22 @@ + + +# DFlashTokenDiffusionScheduler + +`DFlashTokenDiffusionScheduler` implements the acceptance and posterior sampling logic used in DFlash-style block +diffusion speculative decoding. + +## DFlashTokenDiffusionScheduler +[[autodoc]] DFlashTokenDiffusionScheduler + +## DFlashTokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_dflash_token_diffusion.DFlashTokenDiffusionSchedulerOutput diff --git a/docs/source/en/api/schedulers/sdar_token_diffusion.md b/docs/source/en/api/schedulers/sdar_token_diffusion.md new file mode 100644 index 000000000000..7af2185f190f --- /dev/null +++ b/docs/source/en/api/schedulers/sdar_token_diffusion.md @@ -0,0 +1,21 @@ + + +# SDARTokenDiffusionScheduler + +`SDARTokenDiffusionScheduler` implements block diffusion remasking and sampling for SDAR-style decoding. + +## SDARTokenDiffusionScheduler +[[autodoc]] SDARTokenDiffusionScheduler + +## SDARTokenDiffusionSchedulerOutput +[[autodoc]] schedulers.scheduling_sdar_token_diffusion.SDARTokenDiffusionSchedulerOutput diff --git a/examples/discrete_diffusion/sample_bd3lm.py b/examples/discrete_diffusion/sample_bd3lm.py new file mode 100644 index 000000000000..dbf9b7999ac6 --- /dev/null +++ b/examples/discrete_diffusion/sample_bd3lm.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Sample script for BD3LM-style block diffusion decoding. + +Example: + python sample_bd3lm.py \ + --model_id kuleshov-group/bd3-lm \ + --prompt "Explain what reinforcement learning is in simple terms." \ + --max_new_tokens 256 +""" + +import argparse + +import torch +from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer +from transformers.dynamic_module_utils import get_class_from_dynamic_module + +from diffusers import BD3LMModel, BD3LMPipeline + + +def main(): + parser = argparse.ArgumentParser(description="Run BD3LM block diffusion decoding.") + parser.add_argument( + "--model_id", + type=str, + default="kuleshov-group/bd3-lm", + help="Model ID or local path.", + ) + parser.add_argument( + "--use_diffusers_model", + action="store_true", + help="Load a diffusers-native BD3LM checkpoint instead of transformers.", + ) + parser.add_argument( + "--tokenizer_id", + type=str, + default=None, + help="Tokenizer ID or local path (defaults to `model_id`, falls back to `gpt2`).", + ) + parser.add_argument( + "--prompt", + type=str, + default="Explain what reinforcement learning is in simple terms.", + help="Prompt text to generate from.", + ) + parser.add_argument("--max_new_tokens", type=int, default=256) + parser.add_argument("--block_length", type=int, default=32) + parser.add_argument("--denoising_steps", type=int, default=64) + parser.add_argument("--p_nucleus", type=float, default=1.0) + parser.add_argument("--first_hitting", action="store_true") + parser.add_argument("--no_first_hitting", action="store_true") + parser.add_argument("--use_kv_cache", action="store_true") + parser.add_argument("--max_window", type=int, default=1024) + parser.add_argument("--entropy_threshold", type=float, default=4.0) + parser.add_argument("--var_length", action="store_true") + parser.add_argument("--mask_token_id", type=int, default=None) + parser.add_argument( + "--use_chat_template", + action="store_true", + help="Use the tokenizer chat template for the prompt.", + ) + parser.add_argument( + "--add_generation_prompt", + action="store_true", + help="Add the generation prompt when using the chat template.", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + help="Device to run inference on.", + ) + parser.add_argument( + "--dtype", + type=str, + default="auto", + choices=["auto", "float32", "float16", "bfloat16"], + help="Model dtype.", + ) + + args = parser.parse_args() + + dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16} + torch_dtype = dtype_map.get(args.dtype) + + print(f"Loading model: {args.model_id}") + if args.use_diffusers_model: + model_kwargs = {} + if torch_dtype is not None: + model_kwargs["torch_dtype"] = torch_dtype + model = BD3LMModel.from_pretrained(args.model_id, **model_kwargs) + config = model.config + else: + config = AutoConfig.from_pretrained(args.model_id, trust_remote_code=True) + if not hasattr(config, "get"): + config.get = lambda key, default=None: getattr(config, key, default) + + try: + model = AutoModelForMaskedLM.from_pretrained( + args.model_id, + trust_remote_code=True, + dtype=torch_dtype if torch_dtype is not None else "auto", + config=config, + ) + except AttributeError as err: + if "all_tied_weights_keys" not in str(err): + raise + class_ref = config.auto_map.get("AutoModelForMaskedLM") + if class_ref is None: + raise ValueError("BD3LM config does not define AutoModelForMaskedLM in auto_map.") from err + model_class = get_class_from_dynamic_module( + class_ref, args.model_id, revision=None, trust_remote_code=True + ) + if not hasattr(model_class, "all_tied_weights_keys"): + model_class.all_tied_weights_keys = {} + model = model_class.from_pretrained( + args.model_id, + trust_remote_code=True, + dtype=torch_dtype if torch_dtype is not None else "auto", + config=config, + ) + model = model.to(args.device) + tokenizer = None + tokenizer_id = args.tokenizer_id or args.model_id + tokenizer_kwargs = {} + if not args.use_diffusers_model: + tokenizer_kwargs["config"] = config + try: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True, **tokenizer_kwargs) + except Exception: + tokenizer_id = "gpt2" if args.tokenizer_id is None else args.tokenizer_id + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True, **tokenizer_kwargs) + + if tokenizer.mask_token_id is None: + if getattr(config, "vocab_size", None) == len(tokenizer) + 1: + tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) + elif getattr(config, "vocab_size", None) == len(tokenizer): + raise ValueError( + "Tokenizer has no mask token but matches the model vocab size. " + "Provide a tokenizer that already includes a mask token." + ) + else: + tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) + + if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None: + tokenizer.pad_token = tokenizer.eos_token + + first_hitting = args.first_hitting or not args.no_first_hitting + + pipe = BD3LMPipeline(model=model, tokenizer=tokenizer) + + print(f"\nPrompt: {args.prompt}") + output = pipe( + prompt=args.prompt, + max_new_tokens=args.max_new_tokens, + block_length=args.block_length, + denoising_steps=args.denoising_steps, + p_nucleus=args.p_nucleus, + first_hitting=first_hitting, + use_kv_cache=args.use_kv_cache, + max_window=args.max_window, + entropy_threshold=args.entropy_threshold, + var_length=args.var_length, + mask_token_id=args.mask_token_id, + use_chat_template=args.use_chat_template, + add_generation_prompt=args.add_generation_prompt, + ) + + print("\nGenerated text:") + print( + output.texts[0] + if output.texts is not None + else tokenizer.decode(output.sequences[0], skip_special_tokens=True) + ) + print(f"\nGenerated {output.sequences.shape[1]} tokens") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/sample_block_refinement.py b/examples/discrete_diffusion/sample_block_refinement.py deleted file mode 100644 index 2b9b5c3ba764..000000000000 --- a/examples/discrete_diffusion/sample_block_refinement.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python - -import argparse - -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - -from diffusers import BlockRefinementPipeline - - -def main(): - parser = argparse.ArgumentParser(description="Sample with BlockRefinementPipeline using a transformers causal LM.") - parser.add_argument("--checkpoint_path", type=str, required=True) - parser.add_argument("--cache_dir", type=str, default=None) - parser.add_argument("--prompt", type=str, default="Write a short paragraph about diffusion models.") - parser.add_argument("--gen_length", type=int, default=128) - parser.add_argument("--block_length", type=int, default=32) - parser.add_argument("--steps", type=int, default=32) - parser.add_argument("--temperature", type=float, default=0.0) - parser.add_argument("--top_p", type=float, default=1.0) - parser.add_argument("--top_k", type=int, default=0) - parser.add_argument("--threshold", type=float, default=0.95) - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") - parser.add_argument("--attention_mask_mode", type=str, default="2d", choices=["auto", "4d", "2d", "none"]) - - args = parser.parse_args() - - tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True, cache_dir=args.cache_dir) - model = AutoModelForCausalLM.from_pretrained( - args.checkpoint_path, - dtype=torch.bfloat16 if args.device.startswith("cuda") else torch.float32, - cache_dir=args.cache_dir, - ) - model.to(args.device) - model.eval() - - if tokenizer.mask_token_id is None: - raise ValueError("Tokenizer must have `mask_token_id` for block refinement sampling.") - - pipe = BlockRefinementPipeline(model=model, tokenizer=tokenizer).to(args.device) - gen = torch.Generator(device=args.device).manual_seed(args.seed) - - prompt_ids = tokenizer(args.prompt, return_tensors="pt")["input_ids"].to(args.device) - out = pipe( - prompt_ids=prompt_ids, - gen_length=int(args.gen_length), - block_length=int(args.block_length), - steps=int(args.steps), - temperature=float(args.temperature), - top_p=None if args.top_p >= 1.0 else float(args.top_p), - top_k=None if args.top_k <= 0 else int(args.top_k), - threshold=float(args.threshold), - eos_early_stop=True, - eos_token_id=int(tokenizer.eos_token_id) if tokenizer.eos_token_id is not None else None, - mask_token_id=int(tokenizer.mask_token_id), - attention_mask_mode=args.attention_mask_mode, - generator=gen, - return_text=True, - ) - - print(out.texts[0] if out.texts is not None else tokenizer.decode(out.sequences[0], skip_special_tokens=True)) - - -if __name__ == "__main__": - main() diff --git a/examples/discrete_diffusion/sample_llada2.py b/examples/discrete_diffusion/sample_llada2.py deleted file mode 100644 index 2769f24856e2..000000000000 --- a/examples/discrete_diffusion/sample_llada2.py +++ /dev/null @@ -1,251 +0,0 @@ -#!/usr/bin/env python -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Sample script for LLaDA2-style discrete diffusion text generation. - -This script demonstrates how to use the LLaDA2Pipeline for text generation -using block-wise iterative refinement. - -Example usage: - python sample_llada2.py --model_id inclusionAI/LLaDA2.0-mini --prompt "What is the capital of France?" - python sample_llada2.py --model_id inclusionAI/LLaDA2.0-flash-CAP --prompt "Explain quantum computing." --temperature 0.7 -""" - -import argparse - -import torch -from transformers import AutoModelForCausalLM, AutoTokenizer - -from diffusers import LLaDA2Pipeline -from diffusers.hooks import apply_group_offloading - - -def main(): - parser = argparse.ArgumentParser( - description="Generate text using LLaDA2Pipeline with block-wise discrete diffusion." - ) - parser.add_argument( - "--model_id", - type=str, - default="inclusionAI/LLaDA2.0-mini", - help="HuggingFace model ID or path to local model.", - ) - parser.add_argument( - "--prompt", - type=str, - default="Why does Camus think that Sisyphus is happy?", - help="Text prompt to generate from.", - ) - parser.add_argument( - "--gen_length", - type=int, - default=2048, - help="Number of tokens to generate.", - ) - parser.add_argument( - "--block_length", - type=int, - default=32, - help="Size of each generation block.", - ) - parser.add_argument( - "--steps", - type=int, - default=32, - help="Number of refinement steps per block.", - ) - parser.add_argument( - "--temperature", - type=float, - default=0.0, - help="Sampling temperature (0.0 for greedy).", - ) - parser.add_argument( - "--top_p", - type=float, - default=None, - help="Nucleus sampling probability threshold.", - ) - parser.add_argument( - "--top_k", - type=int, - default=None, - help="Top-k sampling parameter.", - ) - parser.add_argument( - "--threshold", - type=float, - default=0.95, - help="Confidence threshold for committing tokens.", - ) - parser.add_argument( - "--editing_threshold", - type=float, - default=None, - help="Confidence threshold for editing already-committed tokens. Set to enable post-mask editing (e.g. 0.5).", - ) - parser.add_argument( - "--max_post_steps", - type=int, - default=0, - help="Maximum post-mask editing iterations per block (e.g. 16). Only used when --editing_threshold is set.", - ) - parser.add_argument( - "--sampling_method", - type=str, - default="multinomial", - choices=["auto", "greedy", "multinomial"], - help="Sampling method for block refinement.", - ) - parser.add_argument( - "--eos_early_stop", - action="store_true", - help="Stop generation early when EOS token is generated.", - ) - parser.add_argument( - "--use_chat_template", - action="store_true", - help="Use the tokenizer chat template for the prompt.", - ) - parser.add_argument( - "--add_generation_prompt", - action="store_true", - help="Add the generation prompt when using the chat template.", - ) - parser.add_argument( - "--device", - type=str, - default="cuda" if torch.cuda.is_available() else "cpu", - help="Device to run inference on.", - ) - parser.add_argument( - "--dtype", - type=str, - default="bfloat16", - choices=["float32", "float16", "bfloat16"], - help="Model dtype.", - ) - parser.add_argument( - "--seed", - type=int, - default=None, - help="Random seed for reproducibility.", - ) - parser.add_argument( - "--offload", - type=str, - default=None, - choices=["group", "sequential"], - help="Memory offloading strategy: 'group' for group offloading (faster), 'sequential' for sequential CPU offload (slower but lower memory).", - ) - - args = parser.parse_args() - - # Parse dtype - dtype_map = { - "float32": torch.float32, - "float16": torch.float16, - "bfloat16": torch.bfloat16, - } - torch_dtype = dtype_map[args.dtype] - - print(f"Loading model: {args.model_id}") - tokenizer = AutoTokenizer.from_pretrained(args.model_id, trust_remote_code=True) - - # Load model with appropriate memory settings based on offload strategy - if args.offload == "group": - # For group offloading, load to CPU first then apply hooks - print("Using group offloading for memory efficiency...") - model = AutoModelForCausalLM.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype, - low_cpu_mem_usage=True, - ) - # Apply group offloading with CUDA streams for better performance - onload_device = torch.device(args.device) - offload_device = torch.device("cpu") - apply_group_offloading( - model, - onload_device=onload_device, - offload_device=offload_device, - offload_type="leaf_level", - use_stream=True, - ) - elif args.offload == "sequential": - # For sequential offloading, load to CPU first - print("Using sequential CPU offloading (slower but lower memory)...") - model = AutoModelForCausalLM.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype, - low_cpu_mem_usage=True, - ) - # Sequential offloading will be applied via pipeline - else: - # Default: use device_map="auto" for automatic memory management - model = AutoModelForCausalLM.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype, - device_map="auto", - low_cpu_mem_usage=True, - ) - model.eval() - - # Create pipeline - pipe = LLaDA2Pipeline(model=model, tokenizer=tokenizer) - - # Apply sequential CPU offload if requested - if args.offload == "sequential": - pipe.enable_sequential_cpu_offload() - - # Set up generator for reproducibility - generator = None - if args.seed is not None: - generator = torch.Generator(device=args.device).manual_seed(args.seed) - - print(f"\nPrompt: {args.prompt}") - print(f"Generating {args.gen_length} tokens with block_length={args.block_length}, steps={args.steps}") - print("-" * 50) - - # Generate - output = pipe( - prompt=args.prompt, - use_chat_template=args.use_chat_template, - add_generation_prompt=args.add_generation_prompt, - gen_length=args.gen_length, - block_length=args.block_length, - steps=args.steps, - temperature=args.temperature, - top_p=args.top_p, - top_k=args.top_k, - threshold=args.threshold, - editing_threshold=args.editing_threshold, - max_post_steps=args.max_post_steps, - sampling_method=args.sampling_method, - eos_early_stop=args.eos_early_stop, - generator=generator, - ) - - print("\nGenerated text:") - print(output.texts[0]) - - print(f"\nGenerated {output.sequences.shape[1]} tokens") - - -if __name__ == "__main__": - main() diff --git a/examples/discrete_diffusion/sample_sdar.py b/examples/discrete_diffusion/sample_sdar.py new file mode 100644 index 000000000000..24977bc6899e --- /dev/null +++ b/examples/discrete_diffusion/sample_sdar.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Sample script for SDAR-style block diffusion decoding. + +Example: + python sample_sdar.py \ + --model_id JetLM/SDAR-1.7B-Chat \ + --prompt "Explain what reinforcement learning is in simple terms." \ + --max_new_tokens 256 +""" + +import argparse + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from diffusers import SDARPipeline + + +def main(): + parser = argparse.ArgumentParser(description="Run SDAR block diffusion decoding.") + parser.add_argument( + "--model_id", + type=str, + default="JetLM/SDAR-1.7B-Chat", + help="Model ID or local path.", + ) + parser.add_argument( + "--prompt", + type=str, + default="Explain what reinforcement learning is in simple terms.", + help="Prompt text to generate from.", + ) + parser.add_argument("--max_new_tokens", type=int, default=256) + parser.add_argument("--block_length", type=int, default=4) + parser.add_argument("--denoising_steps", type=int, default=4) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--top_k", type=int, default=0) + parser.add_argument("--top_p", type=float, default=1.0) + parser.add_argument( + "--remasking_strategy", + type=str, + default="low_confidence_dynamic", + choices=["low_confidence_dynamic", "low_confidence_static", "sequential", "entropy_bounded"], + ) + parser.add_argument("--confidence_threshold", type=float, default=0.9) + parser.add_argument("--entropy_threshold", type=float, default=0.35) + parser.add_argument("--mask_token_id", type=int, default=None) + parser.add_argument( + "--use_chat_template", + action="store_true", + help="Use the tokenizer chat template for the prompt.", + ) + parser.add_argument( + "--add_generation_prompt", + action="store_true", + help="Add the generation prompt when using the chat template.", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + help="Device to run inference on.", + ) + parser.add_argument( + "--dtype", + type=str, + default="auto", + choices=["auto", "float32", "float16", "bfloat16"], + help="Model dtype.", + ) + parser.add_argument( + "--attention_mask_mode", + type=str, + default="3d", + choices=["auto", "3d", "4d", "2d", "none"], + help="Attention mask mode to use.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + help="Model revision (branch, tag, or commit hash).", + ) + + args = parser.parse_args() + + dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16} + torch_dtype = dtype_map.get(args.dtype) + + print(f"Loading model: {args.model_id}") + model = AutoModelForCausalLM.from_pretrained( + args.model_id, + trust_remote_code=True, + torch_dtype=torch_dtype if torch_dtype is not None else "auto", + device_map=args.device, + revision=args.revision, + ) + tokenizer = AutoTokenizer.from_pretrained(args.model_id, trust_remote_code=True, revision=args.revision) + + if tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) + + pipe = SDARPipeline(model=model, tokenizer=tokenizer) + + print(f"\nPrompt: {args.prompt}") + output = pipe( + prompt=args.prompt, + max_new_tokens=args.max_new_tokens, + block_length=args.block_length, + denoising_steps=args.denoising_steps, + temperature=args.temperature, + top_k=args.top_k, + top_p=args.top_p, + remasking_strategy=args.remasking_strategy, + confidence_threshold=args.confidence_threshold, + entropy_threshold=args.entropy_threshold, + mask_token_id=args.mask_token_id, + use_chat_template=args.use_chat_template, + add_generation_prompt=args.add_generation_prompt, + attention_mask_mode=args.attention_mask_mode, + ) + + print("\nGenerated text:") + print( + output.texts[0] + if output.texts is not None + else tokenizer.decode(output.sequences[0], skip_special_tokens=True) + ) + print(f"\nGenerated {output.sequences.shape[1]} tokens") + + +if __name__ == "__main__": + main() diff --git a/examples/discrete_diffusion/train_block_refinement_cap.py b/examples/discrete_diffusion/train_block_refinement_cap.py deleted file mode 100644 index 21fabc8d7da1..000000000000 --- a/examples/discrete_diffusion/train_block_refinement_cap.py +++ /dev/null @@ -1,387 +0,0 @@ -#!/usr/bin/env python -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import json -import math -import os -from dataclasses import asdict, dataclass -from typing import Optional, Tuple - -import torch -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from torch.utils.data import DataLoader, Dataset - -from diffusers import BlockRefinementPipeline -from diffusers.training_utils import compute_confidence_aware_loss - - -logger = get_logger(__name__) - - -@dataclass -class TrainConfig: - output_dir: str - seed: int - max_train_steps: int - logging_steps: int - checkpointing_steps: int - - per_device_train_batch_size: int - gradient_accumulation_steps: int - learning_rate: float - weight_decay: float - - vocab_size: int - mask_token_id: int - eos_token_id: int - max_length: int - prompt_length: int - - block_length: int - steps: int - lambda_conf: float - conf_temperature: float - temperature: float - threshold: float - - -def parse_args() -> TrainConfig: - parser = argparse.ArgumentParser( - description="Train a block-wise refinement model with a confidence-aware objective (CAP-style)." - ) - - parser.add_argument("--output_dir", type=str, default="block-refinement-output") - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--max_train_steps", type=int, default=1000) - parser.add_argument("--logging_steps", type=int, default=50) - parser.add_argument("--checkpointing_steps", type=int, default=500) - - parser.add_argument("--per_device_train_batch_size", type=int, default=64) - parser.add_argument("--gradient_accumulation_steps", type=int, default=1) - parser.add_argument("--learning_rate", type=float, default=2e-4) - parser.add_argument("--weight_decay", type=float, default=0.0) - - parser.add_argument("--vocab_size", type=int, default=256) - parser.add_argument("--mask_token_id", type=int, default=255) - parser.add_argument("--eos_token_id", type=int, default=254) - parser.add_argument("--max_length", type=int, default=64) - parser.add_argument("--prompt_length", type=int, default=8) - - parser.add_argument("--block_length", type=int, default=16) - parser.add_argument("--steps", type=int, default=16) - parser.add_argument("--lambda_conf", type=float, default=2.0) - parser.add_argument("--conf_temperature", type=float, default=0.5) - - parser.add_argument("--temperature", type=float, default=0.0) - parser.add_argument("--threshold", type=float, default=0.95) - - args = parser.parse_args() - return TrainConfig(**vars(args)) - - -def build_block_attention_mask( - *, - num_blocks: int, - block_length: int, - total_length: int, - device: torch.device, - dtype: torch.dtype, -) -> torch.Tensor: - block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=torch.bool)) - attn = ( - block_mask.repeat_interleave(block_length, dim=0) - .repeat_interleave(block_length, dim=1) - .unsqueeze(0) - .unsqueeze(0) - ) - attn = attn[:, :, :total_length, :total_length] - return torch.where( - attn, torch.zeros((), device=device, dtype=dtype), torch.full((), float("-inf"), device=device, dtype=dtype) - ) - - -def forward_process_semi_ar( - input_ids: torch.LongTensor, - *, - prompt_length: int, - block_length: int, - mask_token_id: int, - generator: Optional[torch.Generator], -) -> Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]: - batch_size, seq_len = input_ids.shape - device = input_ids.device - - noisy = input_ids.clone() - noisy_rev = input_ids.clone() - masked = torch.zeros_like(input_ids, dtype=torch.bool) - masked_rev = torch.zeros_like(input_ids, dtype=torch.bool) - - start = int(prompt_length) - for block_start in range(start, seq_len, int(block_length)): - block_end = min(seq_len, block_start + int(block_length)) - seg_len = block_end - block_start - if seg_len <= 0: - continue - - p_mask = torch.rand((batch_size, 1), device=device, generator=generator) - seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask - seg_rev = ~seg - - masked[:, block_start:block_end] = seg - masked_rev[:, block_start:block_end] = seg_rev - - noisy = torch.where(masked, torch.full_like(noisy, int(mask_token_id)), noisy) - noisy_rev = torch.where(masked_rev, torch.full_like(noisy_rev, int(mask_token_id)), noisy_rev) - return noisy, noisy_rev, masked, masked_rev - - -class RandomTokenDataset(Dataset): - def __init__(self, *, num_samples: int, seq_len: int, vocab_size: int, eos_token_id: int): - self.num_samples = int(num_samples) - self.seq_len = int(seq_len) - self.vocab_size = int(vocab_size) - self.eos_token_id = int(eos_token_id) - - def __len__(self): - return self.num_samples - - def __getitem__(self, idx): - del idx - # Keep EOS out of the training distribution to avoid trivial early-stops during sampling. - ids = torch.randint(0, self.vocab_size - 2, (self.seq_len,), dtype=torch.long) - return {"input_ids": ids} - - -class TinyBlockRefinementLM(torch.nn.Module): - def __init__(self, *, vocab_size: int, hidden_size: int = 128, num_heads: int = 4, num_layers: int = 4): - super().__init__() - self.vocab_size = int(vocab_size) - self.hidden_size = int(hidden_size) - - self.token_emb = torch.nn.Embedding(self.vocab_size, self.hidden_size) - self.pos_emb = torch.nn.Embedding(2048, self.hidden_size) - enc_layer = torch.nn.TransformerEncoderLayer( - d_model=self.hidden_size, - nhead=int(num_heads), - dim_feedforward=self.hidden_size * 4, - dropout=0.0, - activation="gelu", - batch_first=True, - norm_first=True, - ) - self.encoder = torch.nn.TransformerEncoder(enc_layer, num_layers=int(num_layers)) - self.lm_head = torch.nn.Linear(self.hidden_size, self.vocab_size, bias=False) - - @property - def dtype(self): - return next(self.parameters()).dtype - - def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): - if position_ids is None: - position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0).expand_as(input_ids) - - x = self.token_emb(input_ids) + self.pos_emb(position_ids) - - attn_mask = None - if attention_mask is not None: - if attention_mask.ndim == 4: - attn_mask = attention_mask[0, 0] - elif attention_mask.ndim == 2: - attn_mask = attention_mask - else: - raise ValueError(f"Unsupported `attention_mask` shape: {attention_mask.shape}") - attn_mask = attn_mask.to(dtype=torch.float32) - - hidden = self.encoder(x, mask=attn_mask) - logits = self.lm_head(hidden) - return type("Output", (), {"logits": logits}) - - -def save_checkpoint(output_dir: str, *, model: torch.nn.Module, cfg: TrainConfig): - os.makedirs(output_dir, exist_ok=True) - torch.save(model.state_dict(), os.path.join(output_dir, "pytorch_model.bin")) - with open(os.path.join(output_dir, "training_config.json"), "w", encoding="utf-8") as f: - json.dump(asdict(cfg), f, indent=2, sort_keys=True) - - -def main(): - cfg = parse_args() - if cfg.mask_token_id >= cfg.vocab_size: - raise ValueError("`mask_token_id` must be < `vocab_size`.") - if cfg.eos_token_id >= cfg.vocab_size: - raise ValueError("`eos_token_id` must be < `vocab_size`.") - if cfg.prompt_length >= cfg.max_length: - raise ValueError("`prompt_length` must be < `max_length`.") - - project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) - accelerator = Accelerator( - gradient_accumulation_steps=cfg.gradient_accumulation_steps, - project_config=project_config, - ) - if accelerator.is_main_process: - os.makedirs(cfg.output_dir, exist_ok=True) - accelerator.wait_for_everyone() - - set_seed(cfg.seed) - logger.info("Training configuration: %s", asdict(cfg)) - - dataset = RandomTokenDataset( - num_samples=max(cfg.max_train_steps * cfg.per_device_train_batch_size, 4096), - seq_len=cfg.max_length, - vocab_size=cfg.vocab_size, - eos_token_id=cfg.eos_token_id, - ) - dataloader = DataLoader(dataset, batch_size=cfg.per_device_train_batch_size, shuffle=True, drop_last=True) - - model = TinyBlockRefinementLM(vocab_size=cfg.vocab_size) - pipe = BlockRefinementPipeline(model=model, tokenizer=None) - - optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) - - num_update_steps_per_epoch = math.ceil(len(dataloader) / cfg.gradient_accumulation_steps) - num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) - - model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader) - pipe = pipe.to(accelerator.device) - - global_step = 0 - model.train() - - for _epoch in range(num_train_epochs): - for batch in dataloader: - with accelerator.accumulate(model): - input_ids = batch["input_ids"] - - # Build the same attention mask that the sampler uses. - prompt_len = int(cfg.prompt_length) - num_blocks = (prompt_len + int(cfg.max_length - prompt_len) + int(cfg.block_length) - 1) // int( - cfg.block_length - ) - total_length = int(num_blocks) * int(cfg.block_length) - total_length = max(total_length, int(cfg.max_length)) - attn_mask = build_block_attention_mask( - num_blocks=(total_length + int(cfg.block_length) - 1) // int(cfg.block_length), - block_length=int(cfg.block_length), - total_length=int(cfg.max_length), - device=input_ids.device, - dtype=torch.bfloat16 if input_ids.device.type == "cuda" else torch.float32, - ) - position_ids = ( - torch.arange(int(cfg.max_length), device=input_ids.device, dtype=torch.long) - .unsqueeze(0) - .expand_as(input_ids) - ) - - gen = None - if accelerator.is_local_main_process: - gen = torch.Generator(device=input_ids.device).manual_seed(cfg.seed + global_step) - - noisy, noisy_rev, masked, masked_rev = forward_process_semi_ar( - input_ids, - prompt_length=prompt_len, - block_length=int(cfg.block_length), - mask_token_id=int(cfg.mask_token_id), - generator=gen, - ) - - logits = model(noisy, attention_mask=attn_mask, position_ids=position_ids).logits - logits_rev = model(noisy_rev, attention_mask=attn_mask, position_ids=position_ids).logits - - # Do not allow predicting mask_id. - logits = logits.clone() - logits[..., int(cfg.mask_token_id)] = torch.finfo(logits.dtype).min - logits_rev = logits_rev.clone() - logits_rev[..., int(cfg.mask_token_id)] = torch.finfo(logits_rev.dtype).min - - labels = input_ids.clone() - labels[~masked] = -100 - labels_rev = input_ids.clone() - labels_rev[~masked_rev] = -100 - - weights = masked.to(dtype=logits.dtype) - weights_rev = masked_rev.to(dtype=logits.dtype) - - loss, loss_sft, loss_conf = compute_confidence_aware_loss( - logits, - labels, - lambda_conf=cfg.lambda_conf, - temperature=cfg.conf_temperature, - per_token_weights=weights, - ) - loss_rev, loss_sft_rev, loss_conf_rev = compute_confidence_aware_loss( - logits_rev, - labels_rev, - lambda_conf=cfg.lambda_conf, - temperature=cfg.conf_temperature, - per_token_weights=weights_rev, - ) - - total_loss = loss + loss_rev - accelerator.backward(total_loss) - optimizer.step() - optimizer.zero_grad(set_to_none=True) - - if accelerator.sync_gradients: - global_step += 1 - - if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: - logger.info( - "step=%d loss=%.4f sft=%.4f conf=%.4f", - global_step, - total_loss.item(), - (loss_sft + loss_sft_rev).item(), - (loss_conf + loss_conf_rev).item(), - ) - - if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: - accelerator.wait_for_everyone() - if accelerator.is_main_process: - save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") - save_checkpoint(save_dir, model=accelerator.unwrap_model(model), cfg=cfg) - - if global_step >= cfg.max_train_steps: - break - - if global_step >= cfg.max_train_steps: - break - - accelerator.wait_for_everyone() - if accelerator.is_main_process: - final_dir = os.path.join(cfg.output_dir, "final") - save_checkpoint(final_dir, model=accelerator.unwrap_model(model), cfg=cfg) - - # Quick sampler smoke to ensure the pipeline runs with the trained weights. - out = pipe( - prompt_ids=torch.randint(0, cfg.vocab_size - 2, (1, cfg.prompt_length), device=accelerator.device), - gen_length=int(cfg.max_length - cfg.prompt_length), - block_length=int(cfg.block_length), - steps=int(cfg.steps), - temperature=float(cfg.temperature), - threshold=float(cfg.threshold), - eos_early_stop=False, - eos_token_id=int(cfg.eos_token_id), - mask_token_id=int(cfg.mask_token_id), - return_text=False, - ) - logger.info("sample shape=%s", tuple(out.sequences.shape)) - - logger.info("Done.") - - -if __name__ == "__main__": - main() diff --git a/examples/discrete_diffusion/train_block_refinement_qwen_cap.py b/examples/discrete_diffusion/train_block_refinement_qwen_cap.py deleted file mode 100644 index 5149f3ba61d0..000000000000 --- a/examples/discrete_diffusion/train_block_refinement_qwen_cap.py +++ /dev/null @@ -1,359 +0,0 @@ -#!/usr/bin/env python -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import math -import os -from dataclasses import asdict, dataclass -from typing import Dict, Optional, Tuple - -import torch -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from datasets import load_dataset -from torch.utils.data import DataLoader -from transformers import AutoModelForCausalLM, AutoTokenizer, DataCollatorForLanguageModeling, get_scheduler - -from diffusers.training_utils import compute_confidence_aware_loss - - -logger = get_logger(__name__) - - -@dataclass -class TrainConfig: - model_name_or_path: str - dataset_name: str - dataset_config_name: Optional[str] - text_column: str - cache_dir: Optional[str] - use_dummy_data: bool - num_dummy_samples: int - - output_dir: str - seed: int - max_train_steps: int - checkpointing_steps: int - logging_steps: int - - per_device_train_batch_size: int - gradient_accumulation_steps: int - learning_rate: float - weight_decay: float - lr_scheduler: str - lr_warmup_steps: int - - max_length: int - prompt_length: int - block_length: int - - lambda_conf: float - conf_temperature: float - - -def parse_args() -> TrainConfig: - parser = argparse.ArgumentParser(description="Train block-refinement with a confidence-aware loss on a causal LM.") - - parser.add_argument("--model_name_or_path", type=str, default="Qwen/Qwen2.5-0.5B") - parser.add_argument("--dataset_name", type=str, default="wikitext") - parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") - parser.add_argument("--text_column", type=str, default="text") - parser.add_argument("--cache_dir", type=str, default=None) - parser.add_argument("--use_dummy_data", action="store_true", help="Use random-token data instead of downloading.") - parser.add_argument("--num_dummy_samples", type=int, default=2048) - - parser.add_argument("--output_dir", type=str, default="qwen-block-refinement-output") - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--max_train_steps", type=int, default=1000) - parser.add_argument("--checkpointing_steps", type=int, default=500) - parser.add_argument("--logging_steps", type=int, default=50) - - parser.add_argument("--per_device_train_batch_size", type=int, default=1) - parser.add_argument("--gradient_accumulation_steps", type=int, default=8) - parser.add_argument("--learning_rate", type=float, default=2e-5) - parser.add_argument("--weight_decay", type=float, default=0.0) - parser.add_argument( - "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] - ) - parser.add_argument("--lr_warmup_steps", type=int, default=100) - - parser.add_argument("--max_length", type=int, default=256) - parser.add_argument("--prompt_length", type=int, default=32) - parser.add_argument("--block_length", type=int, default=32) - - parser.add_argument("--lambda_conf", type=float, default=2.0) - parser.add_argument("--conf_temperature", type=float, default=0.5) - - args = parser.parse_args() - return TrainConfig(**vars(args)) - - -def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): - texts = examples[text_column] - texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] - return tokenizer(texts, truncation=True, padding=False, max_length=max_length) - - -class RandomTokenDataset(torch.utils.data.Dataset): - def __init__(self, *, num_samples: int, seq_len: int, vocab_size: int, pad_token_id: int): - self.num_samples = int(num_samples) - self.seq_len = int(seq_len) - self.vocab_size = int(vocab_size) - self.pad_token_id = int(pad_token_id) - - def __len__(self): - return self.num_samples - - def __getitem__(self, idx): - del idx - input_ids = torch.randint(0, self.vocab_size, (self.seq_len,), dtype=torch.long) - attention_mask = torch.ones_like(input_ids) - return {"input_ids": input_ids, "attention_mask": attention_mask} - - -def forward_process_semi_ar( - input_ids: torch.LongTensor, - attention_mask: torch.LongTensor, - *, - prompt_length: int, - block_length: int, - mask_token_id: int, - generator: Optional[torch.Generator], -) -> Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]: - batch_size, seq_len = input_ids.shape - device = input_ids.device - - noisy = input_ids.clone() - noisy_rev = input_ids.clone() - masked = torch.zeros_like(input_ids, dtype=torch.bool) - masked_rev = torch.zeros_like(input_ids, dtype=torch.bool) - - # Only mask non-padding positions after the prompt. - valid = attention_mask.to(dtype=torch.bool) - start = int(prompt_length) - for block_start in range(start, seq_len, int(block_length)): - block_end = min(seq_len, block_start + int(block_length)) - seg_len = block_end - block_start - if seg_len <= 0: - continue - - p_mask = torch.rand((batch_size, 1), device=device, generator=generator) - seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask - seg = seg & valid[:, block_start:block_end] - seg_rev = (~seg) & valid[:, block_start:block_end] - - masked[:, block_start:block_end] = seg - masked_rev[:, block_start:block_end] = seg_rev - - noisy = torch.where(masked, torch.full_like(noisy, int(mask_token_id)), noisy) - noisy_rev = torch.where(masked_rev, torch.full_like(noisy_rev, int(mask_token_id)), noisy_rev) - return noisy, noisy_rev, masked, masked_rev - - -def main(): - cfg = parse_args() - if cfg.prompt_length >= cfg.max_length: - raise ValueError("`prompt_length` must be < `max_length`.") - if cfg.block_length <= 0: - raise ValueError("`block_length` must be > 0.") - - project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) - accelerator = Accelerator( - gradient_accumulation_steps=cfg.gradient_accumulation_steps, - project_config=project_config, - ) - if accelerator.is_main_process: - os.makedirs(cfg.output_dir, exist_ok=True) - accelerator.wait_for_everyone() - - set_seed(cfg.seed) - logger.info("Training configuration: %s", asdict(cfg)) - - tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path, use_fast=True, cache_dir=cfg.cache_dir) - if tokenizer.pad_token_id is None: - tokenizer.pad_token = tokenizer.eos_token - - if tokenizer.mask_token_id is None: - tokenizer.add_special_tokens({"mask_token": "[MASK]"}) - - load_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32 - model = AutoModelForCausalLM.from_pretrained( - cfg.model_name_or_path, cache_dir=cfg.cache_dir, torch_dtype=load_dtype - ) - model.resize_token_embeddings(len(tokenizer)) - if load_dtype == torch.float32: - model.to(dtype=torch.float32) - - mask_token_id = int(tokenizer.mask_token_id) - - if cfg.use_dummy_data: - dataset = RandomTokenDataset( - num_samples=cfg.num_dummy_samples, - seq_len=cfg.max_length, - vocab_size=len(tokenizer), - pad_token_id=int(tokenizer.pad_token_id), - ) - train_dataloader = DataLoader( - dataset, - shuffle=True, - batch_size=cfg.per_device_train_batch_size, - drop_last=True, - ) - else: - raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name, cache_dir=cfg.cache_dir) - if "train" not in raw_datasets: - raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") - - with accelerator.main_process_first(): - tokenized = raw_datasets["train"].map( - lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), - batched=True, - remove_columns=raw_datasets["train"].column_names, - desc="Tokenizing", - ) - - collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") - train_dataloader = DataLoader( - tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True - ) - - optimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) - - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) - num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) - - lr_scheduler = get_scheduler( - name=cfg.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=cfg.lr_warmup_steps, - num_training_steps=cfg.max_train_steps, - ) - - model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - model, optimizer, train_dataloader, lr_scheduler - ) - - global_step = 0 - model.train() - - for _epoch in range(num_train_epochs): - for batch in train_dataloader: - with accelerator.accumulate(model): - input_ids = batch["input_ids"] - attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) - - gen = torch.Generator(device=input_ids.device).manual_seed(cfg.seed + global_step) - noisy, noisy_rev, masked, masked_rev = forward_process_semi_ar( - input_ids, - attention_mask, - prompt_length=int(cfg.prompt_length), - block_length=int(cfg.block_length), - mask_token_id=mask_token_id, - generator=gen, - ) - - position_ids = ( - torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0).expand_as(input_ids) - ) - - logits = model(input_ids=noisy, attention_mask=attention_mask, position_ids=position_ids).logits - logits_rev = model( - input_ids=noisy_rev, attention_mask=attention_mask, position_ids=position_ids - ).logits - - logits = logits.clone() - logits[..., mask_token_id] = torch.finfo(logits.dtype).min - logits_rev = logits_rev.clone() - logits_rev[..., mask_token_id] = torch.finfo(logits_rev.dtype).min - - valid = attention_mask.to(dtype=torch.bool) - masked = masked & valid - masked_rev = masked_rev & valid - - labels = input_ids.clone() - labels[~masked] = -100 - labels_rev = input_ids.clone() - labels_rev[~masked_rev] = -100 - - weights = masked.to(dtype=logits.dtype) - weights_rev = masked_rev.to(dtype=logits.dtype) - - loss, loss_sft, loss_conf = compute_confidence_aware_loss( - logits, - labels, - lambda_conf=cfg.lambda_conf, - temperature=cfg.conf_temperature, - per_token_weights=weights, - ) - loss_rev, loss_sft_rev, loss_conf_rev = compute_confidence_aware_loss( - logits_rev, - labels_rev, - lambda_conf=cfg.lambda_conf, - temperature=cfg.conf_temperature, - per_token_weights=weights_rev, - ) - - total_loss = loss + loss_rev - accelerator.backward(total_loss) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad(set_to_none=True) - - if accelerator.sync_gradients: - global_step += 1 - - if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: - logger.info( - "step=%d loss=%.4f sft=%.4f conf=%.4f lr=%.6g", - global_step, - total_loss.item(), - (loss_sft + loss_sft_rev).item(), - (loss_conf + loss_conf_rev).item(), - lr_scheduler.get_last_lr()[0], - ) - print( - f"step={global_step} loss={total_loss.item():.4f} " - f"sft={(loss_sft + loss_sft_rev).item():.4f} " - f"conf={(loss_conf + loss_conf_rev).item():.4f} " - f"lr={lr_scheduler.get_last_lr()[0]:.6g}" - ) - - if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: - accelerator.wait_for_everyone() - if accelerator.is_main_process: - save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") - os.makedirs(save_dir, exist_ok=True) - accelerator.unwrap_model(model).save_pretrained(save_dir, save_function=accelerator.save) - tokenizer.save_pretrained(save_dir) - - if global_step >= cfg.max_train_steps: - break - - if global_step >= cfg.max_train_steps: - break - - accelerator.wait_for_everyone() - if accelerator.is_main_process: - final_dir = os.path.join(cfg.output_dir, "final") - os.makedirs(final_dir, exist_ok=True) - accelerator.unwrap_model(model).save_pretrained(final_dir, save_function=accelerator.save) - tokenizer.save_pretrained(final_dir) - - logger.info("Done.") - - -if __name__ == "__main__": - main() diff --git a/examples/discrete_diffusion/train_dflash.py b/examples/discrete_diffusion/train_dflash.py new file mode 100644 index 000000000000..673a2173a058 --- /dev/null +++ b/examples/discrete_diffusion/train_dflash.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import math +import os +from dataclasses import asdict, dataclass +from typing import Dict, Optional + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch.utils.data import DataLoader +from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, DataCollatorForLanguageModeling, get_scheduler + + +logger = get_logger(__name__) + + +@dataclass +class TrainConfig: + draft_model_id: str + target_model_id: str + dataset_name: str + dataset_config_name: Optional[str] + text_column: str + + output_dir: str + seed: int + max_train_steps: int + checkpointing_steps: int + logging_steps: int + + per_device_train_batch_size: int + gradient_accumulation_steps: int + learning_rate: float + weight_decay: float + lr_scheduler: str + lr_warmup_steps: int + + max_length: int + block_size: int + mask_token: str + + +def parse_args() -> TrainConfig: + parser = argparse.ArgumentParser(description="Fine-tune a DFlash draft model with target-conditioned blocks.") + + parser.add_argument("--draft_model_id", type=str, default="z-lab/Qwen3-4B-DFlash-b16") + parser.add_argument("--target_model_id", type=str, default="Qwen/Qwen3-4B") + parser.add_argument("--dataset_name", type=str, default="wikitext") + parser.add_argument("--dataset_config_name", type=str, default="wikitext-2-raw-v1") + parser.add_argument("--text_column", type=str, default="text") + + parser.add_argument("--output_dir", type=str, default="dflash-output") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--max_train_steps", type=int, default=1000) + parser.add_argument("--checkpointing_steps", type=int, default=500) + parser.add_argument("--logging_steps", type=int, default=50) + + parser.add_argument("--per_device_train_batch_size", type=int, default=2) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=2e-5) + parser.add_argument("--weight_decay", type=float, default=0.0) + parser.add_argument( + "--lr_scheduler", type=str, default="cosine", choices=["linear", "cosine", "cosine_with_restarts"] + ) + parser.add_argument("--lr_warmup_steps", type=int, default=100) + + parser.add_argument("--max_length", type=int, default=512) + parser.add_argument( + "--block_size", type=int, default=0, help="Override draft block size (0 uses the model config)." + ) + parser.add_argument("--mask_token", type=str, default="<|MASK|>") + + args = parser.parse_args() + return TrainConfig(**vars(args)) + + +def tokenize_fn(examples: Dict, tokenizer, text_column: str, max_length: int): + texts = examples[text_column] + texts = [t for t in texts if isinstance(t, str) and len(t.strip()) > 0] + return tokenizer(texts, truncation=True, padding=False, max_length=max_length) + + +def build_target_layer_ids(num_target_layers: int, num_draft_layers: int): + if num_draft_layers == 1: + return [int(num_target_layers // 2)] + start = 1 + end = int(num_target_layers) - 3 + span = end - start + return [int(round(start + (i * span) / (num_draft_layers - 1))) for i in range(int(num_draft_layers))] + + +def extract_context_feature(hidden_states, layer_ids): + offset = 1 + selected_states = [hidden_states[layer_id + offset] for layer_id in layer_ids] + return torch.cat(selected_states, dim=-1) + + +def get_target_input_embeddings(model: torch.nn.Module) -> torch.nn.Module: + embeddings = model.get_input_embeddings() + if embeddings is None: + base = getattr(model, "model", None) + embeddings = getattr(base, "embed_tokens", None) + if embeddings is None: + raise ValueError("Target model must expose input embeddings.") + return embeddings + + +def get_target_output_embeddings(model: torch.nn.Module) -> torch.nn.Module: + embeddings = model.get_output_embeddings() + if embeddings is None: + embeddings = getattr(model, "lm_head", None) + if embeddings is None: + raise ValueError("Target model must expose output embeddings.") + return embeddings + + +def main(): + cfg = parse_args() + + project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=os.path.join(cfg.output_dir, "logs")) + accelerator = Accelerator( + gradient_accumulation_steps=cfg.gradient_accumulation_steps, + project_config=project_config, + ) + + if accelerator.is_main_process: + os.makedirs(cfg.output_dir, exist_ok=True) + accelerator.wait_for_everyone() + + set_seed(cfg.seed) + logger.info("Training configuration: %s", asdict(cfg)) + + tokenizer = AutoTokenizer.from_pretrained(cfg.target_model_id, use_fast=True) + if tokenizer.pad_token_id is None: + tokenizer.pad_token = tokenizer.eos_token + if tokenizer.mask_token_id is None: + tokenizer.add_special_tokens({"mask_token": cfg.mask_token}) + + draft_model = AutoModel.from_pretrained(cfg.draft_model_id, trust_remote_code=True) + target_model = AutoModelForCausalLM.from_pretrained(cfg.target_model_id) + target_model.eval() + target_model.requires_grad_(False) + + mask_token_id = tokenizer.mask_token_id + if mask_token_id is None: + raise ValueError("Tokenizer must define a mask token for DFlash training.") + + input_embeddings = get_target_input_embeddings(target_model) + output_embeddings = get_target_output_embeddings(target_model) + + block_size = int(cfg.block_size) + if block_size <= 0: + block_size = getattr(draft_model, "block_size", None) or getattr( + getattr(draft_model, "config", None), "block_size", None + ) + if block_size is None: + raise ValueError("Draft model must define `block_size` or pass --block_size.") + block_size = int(block_size) + if block_size < 2: + raise ValueError("`block_size` must be at least 2 for DFlash training.") + + layer_ids = getattr(draft_model, "target_layer_ids", None) + if layer_ids is None: + cfg_draft = getattr(draft_model, "config", None) + num_target_layers = getattr(cfg_draft, "num_target_layers", None) + num_hidden_layers = getattr(cfg_draft, "num_hidden_layers", None) + if num_target_layers is None or num_hidden_layers is None: + raise ValueError("Draft model must expose `target_layer_ids` or `num_target_layers` in config.") + layer_ids = build_target_layer_ids(int(num_target_layers), int(num_hidden_layers)) + + raw_datasets = load_dataset(cfg.dataset_name, cfg.dataset_config_name) + if "train" not in raw_datasets: + raise ValueError(f"Dataset {cfg.dataset_name} has no 'train' split.") + + with accelerator.main_process_first(): + tokenized = raw_datasets["train"].map( + lambda ex: tokenize_fn(ex, tokenizer, cfg.text_column, cfg.max_length), + batched=True, + remove_columns=raw_datasets["train"].column_names, + desc="Tokenizing", + ) + + collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="pt") + train_dataloader = DataLoader( + tokenized, shuffle=True, collate_fn=collator, batch_size=cfg.per_device_train_batch_size, drop_last=True + ) + + optimizer = torch.optim.AdamW(draft_model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / cfg.gradient_accumulation_steps) + num_train_epochs = math.ceil(cfg.max_train_steps / num_update_steps_per_epoch) + + lr_scheduler = get_scheduler( + name=cfg.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=cfg.lr_warmup_steps, + num_training_steps=cfg.max_train_steps, + ) + + draft_model, optimizer, train_dataloader, lr_scheduler, target_model = accelerator.prepare( + draft_model, optimizer, train_dataloader, lr_scheduler, target_model + ) + input_embeddings = get_target_input_embeddings(target_model) + output_embeddings = get_target_output_embeddings(target_model) + + global_step = 0 + draft_model.train() + + for epoch in range(num_train_epochs): + for batch in train_dataloader: + with accelerator.accumulate(draft_model): + input_ids = batch["input_ids"] + attention_mask = batch.get("attention_mask", torch.ones_like(input_ids)) + + valid_lengths = attention_mask.sum(dim=1) + min_valid = int(valid_lengths.min().item()) + if min_valid <= block_size: + continue + + max_start = min_valid - block_size + start = torch.randint(1, max_start + 1, (1,), device=input_ids.device).item() + + block_output_ids = torch.full( + (input_ids.shape[0], block_size), + int(mask_token_id), + device=input_ids.device, + dtype=torch.long, + ) + block_output_ids[:, 0] = input_ids[:, start] + block_targets = input_ids[:, start + 1 : start + block_size] + block_mask = attention_mask[:, start + 1 : start + block_size] + + position_ids = torch.arange(start, start + block_size, device=input_ids.device).unsqueeze(0) + position_ids = position_ids.expand(input_ids.shape[0], -1) + + with torch.no_grad(): + target_out = target_model( + input_ids=input_ids, + attention_mask=attention_mask, + output_hidden_states=True, + use_cache=False, + ) + target_hidden = extract_context_feature(target_out.hidden_states, layer_ids) + target_hidden = target_hidden[:, :start, :] + + noise_embedding = input_embeddings(block_output_ids) + draft_hidden = draft_model( + target_hidden=target_hidden, + noise_embedding=noise_embedding, + position_ids=position_ids, + use_cache=False, + is_causal=False, + ) + if not torch.is_tensor(draft_hidden): + draft_hidden = getattr(draft_hidden, "last_hidden_state", draft_hidden[0]) + + logits = output_embeddings(draft_hidden[:, -block_size + 1 :, :]) + vocab_size = logits.shape[-1] + loss = F.cross_entropy(logits.view(-1, vocab_size), block_targets.reshape(-1), reduction="none") + loss = loss.view(block_targets.shape[0], -1) + loss = (loss * block_mask.to(loss.dtype)).sum() / block_mask.sum().clamp_min(1) + + accelerator.backward(loss) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + if accelerator.sync_gradients: + global_step += 1 + + if global_step % cfg.logging_steps == 0 and accelerator.is_main_process: + logger.info("step=%d loss=%.4f lr=%.6g", global_step, loss.item(), lr_scheduler.get_last_lr()[0]) + + if cfg.checkpointing_steps > 0 and global_step % cfg.checkpointing_steps == 0: + accelerator.wait_for_everyone() + if accelerator.is_main_process: + save_dir = os.path.join(cfg.output_dir, f"checkpoint-{global_step}") + os.makedirs(save_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(draft_model) + unwrapped.save_pretrained(save_dir, save_function=accelerator.save) + tokenizer.save_pretrained(save_dir) + + if global_step >= cfg.max_train_steps: + break + + if global_step >= cfg.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = os.path.join(cfg.output_dir, "final") + os.makedirs(final_dir, exist_ok=True) + unwrapped = accelerator.unwrap_model(draft_model) + unwrapped.save_pretrained(final_dir, save_function=accelerator.save) + tokenizer.save_pretrained(final_dir) + + logger.info("Done.") + + +if __name__ == "__main__": + main() diff --git a/src/diffusers/pipelines/block_refinement/__init__.py b/src/diffusers/pipelines/block_refinement/__init__.py deleted file mode 100644 index 1eec2ee97e81..000000000000 --- a/src/diffusers/pipelines/block_refinement/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from .pipeline_block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput - - -__all__ = ["BlockRefinementPipeline", "BlockRefinementPipelineOutput"] diff --git a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py b/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py deleted file mode 100644 index 3ba9f7e379be..000000000000 --- a/src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py +++ /dev/null @@ -1,477 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Union - -import torch - -from ...callbacks import MultiPipelineCallbacks, PipelineCallback -from ...utils import BaseOutput -from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin - - -@dataclass -class BlockRefinementPipelineOutput(BaseOutput): - sequences: torch.LongTensor - texts: Optional[List[str]] = None - - -def _get_num_transfer_tokens(block_length: int, steps: int) -> torch.LongTensor: - if steps <= 0: - return torch.zeros((0,), dtype=torch.long) - base = int(block_length) // int(steps) - remainder = int(block_length) % int(steps) - out = torch.full((int(steps),), base, dtype=torch.long) - out[:remainder] += 1 - return out - - -class BlockRefinementPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): - """ - Block-wise iterative refinement pipeline for token generation. - - This pipeline maintains a template sequence filled with a `mask_token_id` and refines it in blocks. In each - refinement step, it samples candidate tokens for the active block and commits a subset based on confidence. - - The model is expected to accept an additive attention mask of shape `[batch, 1, seq, seq]` (0 for allowed, `-inf` - for disallowed) and `position_ids`, and to return logits of shape `[batch, seq, vocab_size]`. - """ - - model: Any - tokenizer: Any - - _callback_tensor_inputs = ["cur_x", "x0", "x0_p", "transfer_index", "confidence", "active_block"] - - def __init__( - self, - model: Any, - tokenizer: Optional[Any] = None, - ): - super().__init__() - self.register_modules(model=model, tokenizer=tokenizer) - - @property - def num_timesteps(self): - return self._num_timesteps - - def _model_forward_logits( - self, - input_ids: torch.LongTensor, - *, - attention_mask_4d: Optional[torch.Tensor], - attention_mask_2d: Optional[torch.Tensor], - position_ids: torch.LongTensor, - attention_mask_mode: str, - ) -> tuple[torch.Tensor, str]: - if attention_mask_mode not in {"auto", "4d", "2d", "none"}: - raise ValueError( - f"`attention_mask_mode` must be one of {{'auto','4d','2d','none'}}, got {attention_mask_mode!r}." - ) - - def _call(mask): - return self.model(input_ids, attention_mask=mask, position_ids=position_ids).logits - - if attention_mask_mode == "none": - return _call(None), "none" - if attention_mask_mode == "2d": - return _call(attention_mask_2d), "2d" - if attention_mask_mode == "4d": - return _call(attention_mask_4d), "4d" - - # auto: try 4d additive mask first, then fall back to 2d padding mask, then no mask. - try: - return _call(attention_mask_4d), "4d" - except (TypeError, ValueError, RuntimeError): - pass - try: - return _call(attention_mask_2d), "2d" - except (TypeError, ValueError, RuntimeError): - return _call(None), "none" - - def _build_block_attention_mask( - self, - *, - num_blocks: int, - block_length: int, - total_length: int, - device: torch.device, - dtype: torch.dtype, - ) -> torch.Tensor: - block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=dtype)) - attn = ( - block_mask.repeat_interleave(block_length, dim=0) - .repeat_interleave(block_length, dim=1) - .unsqueeze(0) - .unsqueeze(0) - ) - return attn[:, :, :total_length, :total_length] - - def _encode_prompt( - self, - prompt: Optional[Union[str, List[str]]], - prompt_ids: Optional[torch.LongTensor], - *, - device: torch.device, - ) -> torch.LongTensor: - if prompt_ids is not None: - if prompt_ids.ndim == 1: - prompt_ids = prompt_ids.unsqueeze(0) - if prompt_ids.ndim != 2: - raise ValueError( - f"`prompt_ids` must have shape [prompt_len] or [batch, prompt_len], got {prompt_ids.shape}." - ) - if prompt_ids.dtype != torch.long: - raise ValueError(f"`prompt_ids` must be int64 token IDs, got dtype={prompt_ids.dtype}.") - return prompt_ids.to(device=device) - - if prompt is None: - return torch.zeros((1, 0), device=device, dtype=torch.long) - if getattr(self, "tokenizer", None) is None: - raise ValueError("`prompt` requires a tokenizer, but no tokenizer was provided to the pipeline.") - - encoded = self.tokenizer(prompt, return_tensors="pt", padding=True) - return encoded["input_ids"].to(device=device) - - def prepare_latents( - self, - batch_size: int, - total_length: int, - mask_token_id: int, - device: torch.device, - ) -> torch.LongTensor: - return torch.full((batch_size, total_length), int(mask_token_id), device=device, dtype=torch.long) - - def check_inputs( - self, - gen_length: int, - block_length: int, - steps: int, - minimal_topk: int, - threshold: float, - sampling_method: str, - callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], - callback_on_step_end_tensor_inputs: Optional[List[str]], - ): - if gen_length <= 0: - raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") - if block_length <= 0: - raise ValueError(f"`block_length` must be > 0, got {block_length}.") - if steps <= 0: - raise ValueError(f"`steps` must be > 0, got {steps}.") - if minimal_topk <= 0: - raise ValueError(f"`minimal_topk` must be > 0, got {minimal_topk}.") - if not (0.0 <= threshold <= 1.0) and not (threshold > 1.0): - raise ValueError(f"`threshold` must be in [0, 1] (or > 1 to force top-k commits), got {threshold}.") - if sampling_method not in {"auto", "greedy", "multinomial"}: - raise ValueError( - f"`sampling_method` must be one of {{'auto','greedy','multinomial'}}, got {sampling_method!r}." - ) - if callback_on_step_end is not None and isinstance( - callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) - ): - callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - - @torch.no_grad() - def __call__( - self, - prompt: Optional[Union[str, List[str]]] = None, - prompt_ids: Optional[torch.LongTensor] = None, - gen_length: int = 128, - block_length: int = 32, - steps: int = 32, - temperature: float = 0.0, - top_p: Optional[float] = None, - top_k: Optional[int] = None, - sampling_method: str = "auto", - threshold: float = 0.95, - editing_threshold: Optional[float] = None, - max_post_steps: int = 0, - minimal_topk: int = 1, - eos_early_stop: bool = False, - eos_token_id: Optional[int] = None, - mask_token_id: Optional[int] = None, - attention_mask_mode: str = "auto", - generator: Optional[torch.Generator] = None, - return_text: bool = True, - callback_on_step_end: Optional[ - Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] - ] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, - ) -> BlockRefinementPipelineOutput: - """ - Generate tokens with block-wise refinement. - - Args: - prompt (`str` or `List[str]`, *optional*): - Prompt text to encode with the tokenizer. - prompt_ids (`torch.LongTensor`, *optional*): - Pre-tokenized prompt IDs with shape `[prompt_len]` or `[batch, prompt_len]`. - gen_length (`int`): - Number of tokens to generate. - block_length (`int`): - Block size for refinement. - steps (`int`): - Refinement steps per block. - temperature (`float`): - Sampling temperature. - top_p (`float`, *optional*): - Nucleus sampling cutoff. - top_k (`int`, *optional*): - Top-k sampling cutoff. - sampling_method (`str`): - Sampling method (`auto`, `greedy`, `multinomial`). - threshold (`float`): - Confidence threshold for committing tokens. - editing_threshold (`float`, *optional*): - Confidence threshold for editing already-committed (non-mask) tokens. When set, after all mask tokens - in a block are resolved, the pipeline continues refining: if the model predicts a different token with - confidence above this threshold, the existing token is replaced. Set to `None` or a negative value to - disable editing. Defaults to `None` (disabled). - max_post_steps (`int`): - Maximum number of additional refinement iterations after all mask tokens in a block are resolved. Only - used when `editing_threshold` is enabled. Defaults to `0` (no post-mask editing steps). - minimal_topk (`int`): - Minimum number of tokens to commit per step. - eos_early_stop (`bool`): - Whether to stop after committing EOS in a block. - eos_token_id (`int`, *optional*): - EOS token ID to use for early stopping. - mask_token_id (`int`, *optional*): - Mask token ID to use for the template. - attention_mask_mode (`str`): - Attention mask mode (`auto`, `4d`, `2d`, `none`). - generator (`torch.Generator`, *optional*): - RNG for sampling. - return_text (`bool`, *optional*, defaults to `True`): - Whether to decode sequences into text when a tokenizer is available. - callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): - Callback executed after each refinement step with signature `callback_on_step_end(self, step: int, - timestep: int, callback_kwargs: Dict)`. - callback_on_step_end_tensor_inputs (`List[str]`, *optional*): - Tensor keys to pass to the callback. Allowed keys: `cur_x`, `x0`, `x0_p`, `transfer_index`, - `confidence`, `active_block`. - """ - if callback_on_step_end is not None and isinstance( - callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) - ): - callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs - if callback_on_step_end_tensor_inputs is None: - callback_on_step_end_tensor_inputs = ["cur_x"] - - self.check_inputs( - gen_length=gen_length, - block_length=block_length, - steps=steps, - minimal_topk=minimal_topk, - threshold=threshold, - sampling_method=sampling_method, - callback_on_step_end=callback_on_step_end, - callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, - ) - - model_params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] - model_device = model_params[0].device if len(model_params) > 0 else torch.device("cpu") - - prompt_ids = self._encode_prompt(prompt, prompt_ids, device=model_device) - batch_size, prompt_length = prompt_ids.shape - - if eos_token_id is None: - eos_token_id = getattr(getattr(self, "tokenizer", None), "eos_token_id", None) - if mask_token_id is None: - mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) - if mask_token_id is None: - raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") - - steps = min(int(steps), int(gen_length) // int(minimal_topk)) - - num_blocks = (prompt_length + int(gen_length) + int(block_length) - 1) // int(block_length) - total_length = int(num_blocks) * int(block_length) - - dtype = getattr(self.model, "dtype", torch.float32) - attn_dtype = torch.bfloat16 if dtype in (torch.bfloat16, torch.float16) else torch.float32 - attn_mask_4d = self._build_block_attention_mask( - num_blocks=num_blocks, - block_length=block_length, - total_length=total_length, - device=model_device, - dtype=attn_dtype, - ) - attn_mask_2d_full = torch.ones((batch_size, total_length), device=model_device, dtype=torch.long) - position_ids = ( - torch.arange(total_length, device=model_device, dtype=torch.long).unsqueeze(0).expand(batch_size, -1) - ) - - x = self.prepare_latents(batch_size, total_length, int(mask_token_id), model_device) - if prompt_length > 0: - x[:, :prompt_length] = prompt_ids.to(device=model_device) - - prefill_blocks = prompt_length // int(block_length) - self._num_timesteps = int(steps) * max(int(num_blocks) - int(prefill_blocks), 0) - transfer_schedule = _get_num_transfer_tokens(int(block_length), int(steps)).to(device=model_device) - - finished = torch.zeros((batch_size,), device=model_device, dtype=torch.bool) - resolved_attention_mode: str = str(attention_mask_mode) - - use_multinomial = sampling_method == "multinomial" or (sampling_method == "auto" and float(temperature) != 0.0) - editing_enabled = editing_threshold is not None and editing_threshold >= 0.0 - global_step = 0 - - for num_block in range(int(prefill_blocks), int(num_blocks)): - current_window_end = (num_block + 1) * int(block_length) - cur_x = x[:, :current_window_end] - cur_attn_mask_4d = attn_mask_4d[:, :, :current_window_end, :current_window_end] - cur_attn_mask_2d = attn_mask_2d_full[:, :current_window_end] - cur_position_ids = position_ids[:, :current_window_end] - - # Identify which positions in the block are prompt (non-editable). - block_start_pos = num_block * int(block_length) - prompt_mask_in_block = torch.zeros(int(block_length), device=model_device, dtype=torch.bool) - if block_start_pos < prompt_length: - prompt_end_in_block = min(prompt_length - block_start_pos, int(block_length)) - prompt_mask_in_block[:prompt_end_in_block] = True - - post_steps = 0 - step_idx = 0 - while step_idx < int(steps) or (editing_enabled and post_steps <= int(max_post_steps)): - if finished.all(): - break - - active_block = cur_x[:, -int(block_length) :] == int(mask_token_id) - masks_remaining = active_block.sum() > 0 - - if not masks_remaining and not editing_enabled: - break - if not masks_remaining: - post_steps += 1 - if post_steps > int(max_post_steps): - break - - logits, resolved_attention_mode = self._model_forward_logits( - cur_x, - attention_mask_4d=cur_attn_mask_4d, - attention_mask_2d=cur_attn_mask_2d, - position_ids=cur_position_ids, - attention_mask_mode=resolved_attention_mode, - ) - block_logits = logits[:, -int(block_length) :, :] - - x0, x0_p = self._sample_with_temperature_topk_topp( - block_logits, - temperature=float(temperature), - top_k=top_k, - top_p=top_p, - generator=generator, - use_multinomial=use_multinomial, - ) - - # --- Mask-filling transfer --- - transfer_index = torch.zeros_like(x0, dtype=torch.bool) - if masks_remaining and step_idx < int(steps): - clamped_step = min(step_idx, len(transfer_schedule) - 1) - num_to_transfer = int(transfer_schedule[clamped_step].item()) - - confidence = torch.where( - active_block, - x0_p.to(dtype=torch.float32), - torch.full_like(x0_p, -torch.inf, dtype=torch.float32), - ) - - for b in range(batch_size): - if finished[b]: - continue - high_conf = confidence[b] > float(threshold) - if high_conf.sum().item() >= num_to_transfer: - transfer_index[b] = high_conf - else: - k = min(num_to_transfer, int(active_block[b].sum().item())) - if k > 0: - _, idx = torch.topk(confidence[b], k=k) - transfer_index[b, idx] = True - - # --- Editing transfer (non-mask, non-prompt positions) --- - editing_transfer_index = torch.zeros_like(x0, dtype=torch.bool) - if editing_enabled: - old_block_tokens = cur_x[:, -int(block_length) :] - editable = (~active_block) & (~prompt_mask_in_block.unsqueeze(0)) - editing_conf = torch.where( - editable, x0_p.to(dtype=torch.float32), torch.full_like(x0_p, -torch.inf, dtype=torch.float32) - ) - high_conf_edit = editing_conf > float(editing_threshold) - token_changed = x0 != old_block_tokens - editing_transfer_index = high_conf_edit & token_changed & editable - - final_transfer = transfer_index | editing_transfer_index - if final_transfer.any(): - updated = cur_x[:, -int(block_length) :].clone() - updated[final_transfer] = x0[final_transfer] - cur_x[:, -int(block_length) :] = updated - - # Break if no masks remain and no edits were made. - if not masks_remaining and not editing_transfer_index.any(): - break - - if eos_early_stop and eos_token_id is not None: - for b in range(batch_size): - if finished[b]: - continue - eos_in_commits = (x0[b][final_transfer[b]] == int(eos_token_id)).any().item() - if not eos_in_commits: - continue - eos_pos = (cur_x[b] == int(eos_token_id)).nonzero(as_tuple=True) - if len(eos_pos[0]) == 0: - continue - eos_pos = int(eos_pos[0][0].item()) - if prompt_length >= eos_pos: - continue - if (cur_x[b, prompt_length:eos_pos] != int(mask_token_id)).all().item(): - finished[b] = True - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, global_step, step_idx, callback_kwargs) - cur_x = callback_outputs.pop("cur_x", cur_x) - - global_step += 1 - if masks_remaining: - step_idx += 1 - - x[:, :current_window_end] = cur_x - if eos_token_id is not None and (x[:, prompt_length:current_window_end] == int(eos_token_id)).any().item(): - if eos_early_stop: - break - - generated = x[:, : prompt_length + int(gen_length)] - sequences = generated[:, prompt_length:] - if eos_token_id is not None and batch_size == 1: - eos_positions = (sequences[0] == int(eos_token_id)).nonzero(as_tuple=True)[0] - if len(eos_positions) > 0: - sequences = sequences[:, : int(eos_positions[0].item()) + 1] - - texts = None - if return_text and getattr(self, "tokenizer", None) is not None: - texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) - - return BlockRefinementPipelineOutput(sequences=sequences.to(device=model_device), texts=texts) diff --git a/src/diffusers/pipelines/llada2/__init__.py b/src/diffusers/pipelines/llada2/__init__.py deleted file mode 100644 index 45a02e6851e2..000000000000 --- a/src/diffusers/pipelines/llada2/__init__.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import TYPE_CHECKING - -from ...utils import ( - DIFFUSERS_SLOW_IMPORT, - OptionalDependencyNotAvailable, - _LazyModule, - get_objects_from_module, - is_torch_available, - is_transformers_available, -) - - -_dummy_objects = {} -_import_structure = {} - - -try: - if not (is_transformers_available() and is_torch_available()): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - from ...utils import dummy_torch_and_transformers_objects # noqa F403 - - _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) -else: - _import_structure["pipeline_llada2"] = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] - -if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: - try: - if not (is_transformers_available() and is_torch_available()): - raise OptionalDependencyNotAvailable() - - except OptionalDependencyNotAvailable: - from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 - else: - from .pipeline_llada2 import LLaDA2Pipeline, LLaDA2PipelineOutput -else: - import sys - - sys.modules[__name__] = _LazyModule( - __name__, - globals()["__file__"], - _import_structure, - module_spec=__spec__, - ) - - for name, value in _dummy_objects.items(): - setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py deleted file mode 100644 index bd9c814ec18e..000000000000 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ /dev/null @@ -1,136 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from dataclasses import dataclass -from typing import Callable, Dict, List, Optional, Tuple, Union - -import torch - -from ...utils import BaseOutput, logging, replace_example_docstring -from ..block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput - - -logger = logging.get_logger(__name__) - - -EXAMPLE_DOC_STRING = """ - Examples: - ```python - >>> import torch - >>> from transformers import AutoModelForCausalLM, AutoTokenizer - >>> from diffusers import BlockRefinementScheduler, LLaDA2Pipeline - - >>> model_id = "inclusionAI/LLaDA2.1-mini" - >>> model = AutoModelForCausalLM.from_pretrained( - ... model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="auto" - ... ) - >>> tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) - >>> scheduler = BlockRefinementScheduler() - - >>> pipe = LLaDA2Pipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) - >>> output = pipe(prompt="What is the meaning of life?", gen_length=256) - >>> print(output.texts[0]) - ``` -""" - - -@dataclass -class LLaDA2PipelineOutput(BaseOutput): - sequences: torch.LongTensor - texts: Optional[List[str]] = None - - -class LLaDA2Pipeline(BlockRefinementPipeline): - r""" - Adapter pipeline for LLaDA2-style discrete diffusion generation. - - This pipeline subclasses [`BlockRefinementPipeline`] and reuses its sampling loop. It only adapts prompt - preparation (including chat templates) and output formatting. - """ - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Optional[Union[str, List[str]]] = None, - messages: Optional[List[Dict[str, str]]] = None, - input_ids: Optional[torch.LongTensor] = None, - use_chat_template: bool = True, - add_generation_prompt: bool = True, - gen_length: int = 2048, - block_length: int = 32, - steps: int = 32, - temperature: float = 0.0, - top_p: Optional[float] = None, - top_k: Optional[int] = None, - sampling_method: str = "multinomial", - threshold: float = 0.7, - editing_threshold: Optional[float] = 0.5, - max_post_steps: int = 16, - minimal_topk: int = 1, - eos_early_stop: bool = True, - eos_token_id: Optional[int] = None, - mask_token_id: Optional[int] = None, - attention_mask_mode: str = "4d", - generator: Optional[torch.Generator] = None, - return_text: bool = True, - return_dict: bool = True, - callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, - ) -> Union[LLaDA2PipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: - """ - Generate text with block-wise refinement. - - Examples: - """ - prompt_ids = self._prepare_input_ids( - prompt=prompt, - messages=messages, - input_ids=input_ids, - use_chat_template=use_chat_template, - add_generation_prompt=add_generation_prompt, - chat_template_kwargs=None, - ) - - output: BlockRefinementPipelineOutput = super().__call__( - prompt_ids=prompt_ids, - gen_length=gen_length, - block_length=block_length, - steps=steps, - temperature=temperature, - top_p=top_p, - top_k=top_k, - sampling_method=sampling_method, - threshold=threshold, - editing_threshold=editing_threshold, - max_post_steps=max_post_steps, - minimal_topk=minimal_topk, - eos_early_stop=eos_early_stop, - eos_token_id=eos_token_id, - mask_token_id=mask_token_id, - attention_mask_mode=attention_mask_mode, - generator=generator, - return_text=return_text, - callback_on_step_end=callback_on_step_end, - callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, - ) - - if not return_dict: - return output.sequences, output.texts - return LLaDA2PipelineOutput(sequences=output.sequences, texts=output.texts) - - -__all__ = ["LLaDA2Pipeline", "LLaDA2PipelineOutput"] diff --git a/tests/pipelines/test_pipeline_block_refinement.py b/tests/pipelines/test_pipeline_block_refinement.py deleted file mode 100644 index d4b841268b53..000000000000 --- a/tests/pipelines/test_pipeline_block_refinement.py +++ /dev/null @@ -1,93 +0,0 @@ -import unittest - -import torch - -from diffusers import BlockRefinementPipeline - - -class _DummyModelOutput: - def __init__(self, logits): - self.logits = logits - - -class _DummyCausalLM(torch.nn.Module): - def __init__(self, vocab_size: int): - super().__init__() - self.vocab_size = int(vocab_size) - self.register_buffer("_device_anchor", torch.empty(0)) - - @property - def dtype(self): - return torch.float32 - - @property - def device(self): - return self._device_anchor.device - - def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): - batch_size, seq_len = input_ids.shape - logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) - - # Make confidence vary with token position so top-k commits are deterministic. - positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.float32).view(1, seq_len, 1) - token_ids = (torch.arange(seq_len, device=input_ids.device) % (self.vocab_size - 2)).view(1, seq_len, 1) - logits.scatter_(2, token_ids.expand(batch_size, -1, -1), 1.0 + positions.expand(batch_size, -1, -1) * 0.1) - return _DummyModelOutput(logits=logits) - - -class _DummyCausalLM2DOnly(_DummyCausalLM): - def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): - if attention_mask is not None and attention_mask.ndim != 2: - raise ValueError("2D attention_mask required") - return super().forward(input_ids, attention_mask=attention_mask, position_ids=position_ids, **kwargs) - - -class BlockRefinementPipelineTest(unittest.TestCase): - def test_pipeline_runs(self): - vocab_size = 32 - model = _DummyCausalLM(vocab_size=vocab_size) - pipe = BlockRefinementPipeline(model=model, tokenizer=None).to("cpu") - - prompt_ids = torch.tensor([[5, 6, 7, 8], [1, 2, 3, 4]], dtype=torch.long) - out = pipe( - prompt_ids=prompt_ids, - gen_length=24, - block_length=8, - steps=8, - temperature=0.0, - threshold=2.0, # force top-k commits - minimal_topk=1, - eos_early_stop=False, - mask_token_id=vocab_size - 1, - eos_token_id=None, - return_text=False, - ) - - self.assertEqual(out.sequences.shape, (2, 24)) - self.assertFalse((out.sequences == vocab_size - 1).any().item()) - - def test_pipeline_falls_back_to_2d_attention_mask(self): - vocab_size = 32 - model = _DummyCausalLM2DOnly(vocab_size=vocab_size) - pipe = BlockRefinementPipeline(model=model, tokenizer=None).to("cpu") - - out = pipe( - prompt_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), - gen_length=16, - block_length=8, - steps=4, - temperature=0.0, - threshold=2.0, - minimal_topk=1, - eos_early_stop=False, - mask_token_id=vocab_size - 1, - eos_token_id=None, - attention_mask_mode="auto", - return_text=False, - ) - - self.assertEqual(out.sequences.shape, (1, 16)) - - -if __name__ == "__main__": - unittest.main() From 50878e54bebab1a353951d0bce6d72720740c885 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 11:20:59 +0000 Subject: [PATCH 26/44] Refactor discrete diffusion pipelines to follow LLaDA2 conventions - Remove BlockTokenDiffusion (folded into LLaDA2Pipeline in main) - TokenDiffusion, HybridTokenDiffusion, DFlash, SDAR: apply output_type, check_inputs first, 3.9+ annotations, progress bars, scheduler-owned stopping logic - SDAR: rename denoising_steps to num_inference_steps, remove attention_mask_mode, add add_noise/check_should_stop to scheduler - DFlash: add add_noise/check_should_stop to scheduler - TokenDiffusion: add enforce_fixed_masks to scheduler --- docs/source/en/_toctree.yml | 4 - .../en/api/pipelines/block_token_diffusion.md | 23 -- docs/source/en/api/pipelines/overview.md | 1 - .../api/schedulers/block_token_diffusion.md | 21 -- docs/source/en/api/schedulers/overview.md | 3 - .../sample_block_token_diffusion.py | 66 ---- src/diffusers/__init__.py | 6 - src/diffusers/pipelines/__init__.py | 2 - .../block_token_diffusion/__init__.py | 18 - .../pipeline_block_token_diffusion.py | 231 ------------ .../pipelines/dflash/pipeline_dflash.py | 183 ++++++--- .../hybrid_token_diffusion/__init__.py | 4 +- .../pipeline_hybrid_token_diffusion.py | 222 ++++++++++- src/diffusers/pipelines/sdar/pipeline_sdar.py | 351 ++++++++++-------- .../pipeline_token_diffusion.py | 84 +++-- src/diffusers/schedulers/__init__.py | 2 - .../scheduling_block_token_diffusion.py | 25 -- .../scheduling_dflash_token_diffusion.py | 92 ++++- .../scheduling_hybrid_token_diffusion.py | 25 +- .../scheduling_sdar_token_diffusion.py | 121 +++++- .../schedulers/scheduling_token_diffusion.py | 49 ++- src/diffusers/utils/dummy_pt_objects.py | 45 --- .../test_pipeline_block_token_diffusion.py | 59 --- .../test_scheduler_block_token_diffusion.py | 30 -- 24 files changed, 850 insertions(+), 817 deletions(-) delete mode 100644 docs/source/en/api/pipelines/block_token_diffusion.md delete mode 100644 docs/source/en/api/schedulers/block_token_diffusion.md delete mode 100644 examples/discrete_diffusion/sample_block_token_diffusion.py delete mode 100644 src/diffusers/pipelines/block_token_diffusion/__init__.py delete mode 100644 src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py delete mode 100644 src/diffusers/schedulers/scheduling_block_token_diffusion.py delete mode 100644 tests/pipelines/test_pipeline_block_token_diffusion.py delete mode 100644 tests/schedulers/test_scheduler_block_token_diffusion.py diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index a2ad6be74b0c..6c456fde4d71 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -506,8 +506,6 @@ title: BLIP-Diffusion - local: api/pipelines/block_refinement title: Block Refinement - - local: api/pipelines/block_token_diffusion - title: Block Token Diffusion - local: api/pipelines/bria_3_2 title: Bria 3.2 - local: api/pipelines/bria_fibo @@ -738,8 +736,6 @@ title: BD3LMTokenDiffusionScheduler - local: api/schedulers/block_refinement title: BlockRefinementScheduler - - local: api/schedulers/block_token_diffusion - title: BlockTokenDiffusionScheduler - local: api/schedulers/cm_stochastic_iterative title: CMStochasticIterativeScheduler - local: api/schedulers/ddim_cogvideox diff --git a/docs/source/en/api/pipelines/block_token_diffusion.md b/docs/source/en/api/pipelines/block_token_diffusion.md deleted file mode 100644 index df25001ffa03..000000000000 --- a/docs/source/en/api/pipelines/block_token_diffusion.md +++ /dev/null @@ -1,23 +0,0 @@ - - -# Block Token Diffusion - -`BlockTokenDiffusionPipeline` performs token diffusion by iterating over fixed-size blocks of the sequence. - -## BlockTokenDiffusionPipeline -[[autodoc]] BlockTokenDiffusionPipeline - - all - - __call__ - -## BlockTokenDiffusionPipelineOutput -[[autodoc]] pipelines.BlockTokenDiffusionPipelineOutput diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index c397570c0190..3a0683257d1d 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -35,7 +35,6 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [AuraFlow](aura_flow) | text2image | | [BLIP Diffusion](blip_diffusion) | text2image | | [Block Refinement](block_refinement) | text2text | -| [Block Token Diffusion](block_token_diffusion) | text2text | | [Bria 3.2](bria_3_2) | text2image | | [CogVideoX](cogvideox) | text2video | | [Consistency Models](consistency_models) | unconditional image generation | diff --git a/docs/source/en/api/schedulers/block_token_diffusion.md b/docs/source/en/api/schedulers/block_token_diffusion.md deleted file mode 100644 index 2ad92117fb0f..000000000000 --- a/docs/source/en/api/schedulers/block_token_diffusion.md +++ /dev/null @@ -1,21 +0,0 @@ - - -# BlockTokenDiffusionScheduler - -`BlockTokenDiffusionScheduler` extends `TokenDiffusionScheduler` with block-wise updates over token positions. - -## BlockTokenDiffusionScheduler -[[autodoc]] BlockTokenDiffusionScheduler - -## TokenDiffusionSchedulerOutput -[[autodoc]] schedulers.scheduling_token_diffusion.TokenDiffusionSchedulerOutput diff --git a/docs/source/en/api/schedulers/overview.md b/docs/source/en/api/schedulers/overview.md index 2dc8feae6964..5f171266b4a8 100644 --- a/docs/source/en/api/schedulers/overview.md +++ b/docs/source/en/api/schedulers/overview.md @@ -61,15 +61,12 @@ token diffusion models and expose the same `set_timesteps`/`step` interface as o Differences between the discrete token schedulers: - `TokenDiffusionScheduler`: token-level diffusion with per-token corruption (e.g. mask/uniform) and a single-step `step` to denoise logits. -- `BlockTokenDiffusionScheduler`: block-wise token diffusion that updates fixed-size blocks in parallel. - `HybridTokenDiffusionScheduler`: hybrid transitions that combine token- and block-wise updates in the same schedule. - `DFlashTokenDiffusionScheduler`: block diffusion scheduler specialized for speculative decoding with a draft model and target acceptance. - `SDARTokenDiffusionScheduler`: block diffusion scheduler with remasking strategies (sequential/low-confidence/entropy-bounded) per step. [[autodoc]] TokenDiffusionScheduler -[[autodoc]] BlockTokenDiffusionScheduler - [[autodoc]] HybridTokenDiffusionScheduler [[autodoc]] DFlashTokenDiffusionScheduler diff --git a/examples/discrete_diffusion/sample_block_token_diffusion.py b/examples/discrete_diffusion/sample_block_token_diffusion.py deleted file mode 100644 index 5fb7c5a3d121..000000000000 --- a/examples/discrete_diffusion/sample_block_token_diffusion.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python - -import argparse -from typing import Optional - -import torch -from transformers import AutoModelForMaskedLM, AutoTokenizer - -from diffusers import BlockTokenDiffusionPipeline, BlockTokenDiffusionScheduler - - -def parse_args(): - parser = argparse.ArgumentParser(description="Sample with block-wise token diffusion.") - parser.add_argument( - "--checkpoint_path", type=str, required=True, help="Path saved by train scripts (or compatible)." - ) - parser.add_argument("--prompt", type=str, default=None, help="Optional prompt; will be used as a fixed prefix.") - parser.add_argument("--num_samples", type=int, default=4) - parser.add_argument("--seq_len", type=int, default=64) - parser.add_argument("--block_size", type=int, default=16) - parser.add_argument("--num_inference_steps", type=int, default=32) - parser.add_argument("--top_p", type=float, default=1.0) - parser.add_argument("--seed", type=int, default=0) - parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") - parser.add_argument("--inject_start_token", action="store_true") - return parser.parse_args() - - -def main(): - args = parse_args() - device = torch.device(args.device) - - tokenizer = AutoTokenizer.from_pretrained(args.checkpoint_path, use_fast=True) - model = AutoModelForMaskedLM.from_pretrained(args.checkpoint_path).to(device) - scheduler = BlockTokenDiffusionScheduler.from_pretrained(args.checkpoint_path) - - pipe = BlockTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to(device) - model.eval() - - generator: Optional[torch.Generator] = torch.Generator(device=device).manual_seed(args.seed) - - prefix_ids = None - if args.prompt is not None: - encoded = tokenizer(args.prompt, return_tensors="pt", add_special_tokens=True) - prefix_ids = encoded["input_ids"].to(device=device, dtype=torch.long) - if prefix_ids.shape[1] > args.seq_len: - raise ValueError(f"--seq_len ({args.seq_len}) must be >= prompt length ({prefix_ids.shape[1]}).") - - out = pipe( - batch_size=args.num_samples, - seq_len=args.seq_len, - block_size=args.block_size, - num_inference_steps=args.num_inference_steps, - generator=generator, - prefix_ids=prefix_ids, - inject_start_token=args.inject_start_token, - top_p=args.top_p, - return_text=True, - ) - - for i, t in enumerate(out.texts or []): - print(f"[{i}] {t}") - - -if __name__ == "__main__": - main() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index de550c4cfa56..e6d1841b80ee 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -344,8 +344,6 @@ [ "BlockRefinementPipeline", "BlockRefinementPipelineOutput", - "BlockTokenDiffusionPipeline", - "BlockTokenDiffusionPipelineOutput", "HybridTokenDiffusionPipeline", "TokenDiffusionPipeline", "TokenDiffusionPipelineOutput", @@ -357,7 +355,6 @@ "AmusedScheduler", "BlockRefinementScheduler", "BlockRefinementSchedulerOutput", - "BlockTokenDiffusionScheduler", "CMStochasticIterativeScheduler", "CogVideoXDDIMScheduler", "CogVideoXDPMScheduler", @@ -1134,8 +1131,6 @@ BlipDiffusionPipeline, BlockRefinementPipeline, BlockRefinementPipelineOutput, - BlockTokenDiffusionPipeline, - BlockTokenDiffusionPipelineOutput, CLIPImageProjection, ConsistencyModelPipeline, DanceDiffusionPipeline, @@ -1160,7 +1155,6 @@ AmusedScheduler, BlockRefinementScheduler, BlockRefinementSchedulerOutput, - BlockTokenDiffusionScheduler, CMStochasticIterativeScheduler, CogVideoXDDIMScheduler, CogVideoXDPMScheduler, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index a4cc460592dc..7db40e8b57c8 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -48,7 +48,6 @@ "AutoPipelineForText2Image", ] _import_structure["block_refinement"] = ["BlockRefinementPipeline", "BlockRefinementPipelineOutput"] - _import_structure["block_token_diffusion"] = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] _import_structure["consistency_models"] = ["ConsistencyModelPipeline"] _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"] _import_structure["ddim"] = ["DDIMPipeline"] @@ -551,7 +550,6 @@ AutoPipelineForText2Image, ) from .block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput - from .block_token_diffusion import BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput from .consistency_models import ConsistencyModelPipeline from .dance_diffusion import DanceDiffusionPipeline from .ddim import DDIMPipeline diff --git a/src/diffusers/pipelines/block_token_diffusion/__init__.py b/src/diffusers/pipelines/block_token_diffusion/__init__.py deleted file mode 100644 index ab331c11f765..000000000000 --- a/src/diffusers/pipelines/block_token_diffusion/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from .pipeline_block_token_diffusion import BlockTokenDiffusionPipeline, BlockTokenDiffusionPipelineOutput - - -__all__ = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py b/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py deleted file mode 100644 index 126648847b38..000000000000 --- a/src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py +++ /dev/null @@ -1,231 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import torch - -from ...callbacks import MultiPipelineCallbacks, PipelineCallback -from ...utils import BaseOutput -from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin - - -@dataclass -class BlockTokenDiffusionPipelineOutput(BaseOutput): - sequences: torch.LongTensor - texts: Optional[List[str]] = None - - -class BlockTokenDiffusionPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): - """ - Block-wise token diffusion sampling pipeline. - - Compared to `TokenDiffusionPipeline`, this pipeline updates the sequence in blocks. Only the current block's - positions are allowed to change during the inner denoising loop. - """ - - model: Any - tokenizer: Any - scheduler: Any - - _callback_tensor_inputs = ["input_ids", "logits", "block_mask"] - - def __init__( - self, - model: Any, - scheduler: Any, - tokenizer: Optional[Any] = None, - ): - super().__init__() - self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) - - @property - def num_timesteps(self): - return self._num_timesteps - - def prepare_latents( - self, - batch_size: int, - seq_len: int, - generator: Optional[torch.Generator] = None, - device: Optional[torch.device] = None, - ) -> torch.LongTensor: - shape = torch.Size((batch_size, seq_len)) - return self.scheduler.sample_prior(shape, device=device, generator=generator) - - def check_inputs( - self, - batch_size: int, - seq_len: int, - block_size: int, - callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], - callback_on_step_end_tensor_inputs: Optional[List[str]], - infill_mask: Optional[torch.BoolTensor], - prefix_ids: Optional[torch.LongTensor], - ): - if callback_on_step_end is not None and isinstance( - callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) - ): - callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs - ): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " - f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - if infill_mask is not None and infill_mask.shape != (batch_size, seq_len): - raise ValueError(f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}.") - if prefix_ids is not None: - p = prefix_ids - if p.ndim == 1: - p = p.unsqueeze(0) - if p.ndim == 2 and p.shape[1] > seq_len: - raise ValueError(f"`prefix_ids` length {p.shape[1]} must be <= seq_len={seq_len}.") - if block_size <= 0 or block_size > seq_len: - raise ValueError(f"`block_size` must be in [1, seq_len], got block_size={block_size}, seq_len={seq_len}.") - - @torch.no_grad() - def __call__( - self, - batch_size: int = 1, - seq_len: int = 64, - block_size: int = 32, - num_inference_steps: int = 64, - generator: Optional[torch.Generator] = None, - prefix_ids: Optional[torch.LongTensor] = None, - infill_mask: Optional[torch.BoolTensor] = None, - inject_start_token: bool = False, - top_p: float = 1.0, - return_text: bool = True, - return_dict: bool = True, - callback_on_step_end: Optional[ - Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] - ] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, - **model_kwargs, - ) -> Union[BlockTokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: - if callback_on_step_end is not None and isinstance( - callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) - ): - callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs - if callback_on_step_end_tensor_inputs is None: - callback_on_step_end_tensor_inputs = ["input_ids"] - - self.check_inputs( - batch_size=batch_size, - seq_len=seq_len, - block_size=block_size, - callback_on_step_end=callback_on_step_end, - callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, - infill_mask=infill_mask, - prefix_ids=prefix_ids, - ) - - device = self._execution_device - self.scheduler.set_timesteps(num_inference_steps, device=device) - timesteps = self.scheduler.timesteps - - input_ids = self.prepare_latents(batch_size, seq_len, generator=generator, device=device) - attention_mask = torch.ones_like(input_ids, dtype=torch.long) - - fixed_mask = None - fixed_values = None - if infill_mask is not None: - fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) - fixed_values = input_ids.clone() - - if prefix_ids is not None: - prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) - prefix_len = prefix_ids.shape[1] - - input_ids[:, :prefix_len] = prefix_ids - if fixed_mask is None: - fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) - fixed_values = input_ids.clone() - fixed_mask[:, :prefix_len] = True - fixed_values[:, :prefix_len] = prefix_ids - - start_token_id = self._resolve_start_token_id() - if inject_start_token and start_token_id is not None: - input_ids[:, 0] = start_token_id - if fixed_mask is None: - fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) - fixed_values = input_ids.clone() - fixed_mask[:, 0] = True - fixed_values[:, 0] = start_token_id - - num_blocks = (seq_len + block_size - 1) // block_size - self._num_timesteps = len(timesteps) * int(num_blocks) - global_step = 0 - for block_idx in range(num_blocks): - start = block_idx * block_size - end = min((block_idx + 1) * block_size, seq_len) - - block_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) - block_mask[:, start:end] = True - if fixed_mask is not None: - block_mask = block_mask & (~fixed_mask) - - if not torch.any(block_mask): - continue - - input_ids = torch.where(block_mask, int(self.scheduler.mask_token_id), input_ids) - - for step_idx, t in enumerate(timesteps): - out = self.model(input_ids=input_ids, attention_mask=attention_mask, **model_kwargs) - logits = getattr(out, "logits", None) - if logits is None: - logits = out[0] - - if top_p < 1.0: - logits_block = logits[block_mask].view(-1, logits.shape[-1]) - logits_block = self._top_p_filtering(logits_block, top_p=top_p) - logits = logits.clone() - logits[block_mask] = logits_block.view(-1, logits.shape[-1]) - - input_ids = self.scheduler.step( - logits, - t, - input_ids, - generator=generator, - return_dict=True, - block_mask=block_mask, - ).prev_sample - - if fixed_mask is not None: - input_ids = torch.where(fixed_mask, fixed_values, input_ids) - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end(self, global_step, t, callback_kwargs) - input_ids = callback_outputs.pop("input_ids", input_ids) - - global_step += 1 - - texts = None - if return_text and getattr(self, "tokenizer", None) is not None: - texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) - - if not return_dict: - return (input_ids, texts) - return BlockTokenDiffusionPipelineOutput(sequences=input_ids, texts=texts) - - -__all__ = ["BlockTokenDiffusionPipeline", "BlockTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py index 2569c18180a5..a61431fbbf5f 100644 --- a/src/diffusers/pipelines/dflash/pipeline_dflash.py +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -15,9 +15,10 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import Any, Callable import torch +from tqdm.auto import tqdm from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, DynamicCache from ...callbacks import MultiPipelineCallbacks, PipelineCallback @@ -52,10 +53,10 @@ @dataclass class DFlashPipelineOutput(BaseOutput): sequences: torch.LongTensor - texts: Optional[List[str]] = None + texts: list[str] | None = None -def _build_target_layer_ids(num_target_layers: int, num_draft_layers: int) -> List[int]: +def _build_target_layer_ids(num_target_layers: int, num_draft_layers: int) -> list[int]: if num_draft_layers == 1: return [int(num_target_layers // 2)] start = 1 @@ -64,7 +65,7 @@ def _build_target_layer_ids(num_target_layers: int, num_draft_layers: int) -> Li return [int(round(start + (i * span) / (num_draft_layers - 1))) for i in range(int(num_draft_layers))] -def _extract_context_feature(hidden_states: List[torch.Tensor], layer_ids: List[int]) -> torch.Tensor: +def _extract_context_feature(hidden_states: list[torch.Tensor], layer_ids: list[int]) -> torch.Tensor: offset = 1 selected_states = [hidden_states[layer_id + offset] for layer_id in layer_ids] return torch.cat(selected_states, dim=-1) @@ -75,9 +76,9 @@ class DFlashPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): Block diffusion pipeline for speculative decoding with a DFlash draft model and a target causal LM. """ - draft_model: torch.nn.Module - target_model: torch.nn.Module - tokenizer: Optional[object] + draft_model: Any + target_model: Any + tokenizer: Any scheduler: DFlashTokenDiffusionScheduler _callback_tensor_inputs = ["block_output_ids", "draft_logits", "accepted_length", "next_token", "output_ids"] @@ -85,8 +86,8 @@ def __init__( self, draft_model: torch.nn.Module, target_model: torch.nn.Module, - tokenizer: Optional[object] = None, - scheduler: Optional[DFlashTokenDiffusionScheduler] = None, + tokenizer: Any | None = None, + scheduler: DFlashTokenDiffusionScheduler | None = None, ): super().__init__() if scheduler is None: @@ -98,16 +99,16 @@ def __init__( @classmethod def from_pretrained( cls, - pretrained_model_name_or_path: Optional[str] = None, + pretrained_model_name_or_path: str | None = None, *, - draft_model_id: Optional[str] = None, - target_model_id: Optional[str] = None, - tokenizer_id: Optional[str] = None, - mask_token: Optional[str] = "<|MASK|>", - scheduler: Optional[DFlashTokenDiffusionScheduler] = None, - draft_model_kwargs: Optional[Dict[str, object]] = None, - target_model_kwargs: Optional[Dict[str, object]] = None, - tokenizer_kwargs: Optional[Dict[str, object]] = None, + draft_model_id: str | None = None, + target_model_id: str | None = None, + tokenizer_id: str | None = None, + mask_token: str | None = "<|MASK|>", + scheduler: DFlashTokenDiffusionScheduler | None = None, + draft_model_kwargs: dict[str, object] | None = None, + target_model_kwargs: dict[str, object] | None = None, + tokenizer_kwargs: dict[str, object] | None = None, **pipeline_kwargs, ) -> "DFlashPipeline": if draft_model_id is None and target_model_id is None and pretrained_model_name_or_path is not None: @@ -142,15 +143,36 @@ def from_pretrained( def check_inputs( self, - input_ids: torch.LongTensor, - mask_token_id: Optional[int], - callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], - callback_on_step_end_tensor_inputs: Optional[List[str]], + prompt: str | list[str] | None, + messages: list[dict[str, str]] | None, + input_ids: torch.LongTensor | None, + max_new_tokens: int, + output_type: str, + callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, + callback_on_step_end_tensor_inputs: list[str] | None, ): - if input_ids.shape[0] != 1: - raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.") - if mask_token_id is None: - raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + # Input source validation + if prompt is None and messages is None and input_ids is None: + raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") + if prompt is not None and messages is not None: + raise ValueError("Provide either `prompt` or `messages`, not both.") + if input_ids is not None: + if input_ids.ndim not in (1, 2): + raise ValueError(f"`input_ids` must be 1D or 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + if prompt is not None and input_ids is None and self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + if messages is not None and input_ids is None and self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + # Generation parameter validation + if max_new_tokens <= 0: + raise ValueError(f"`max_new_tokens` must be > 0, got {max_new_tokens}.") + if output_type not in {"seq", "text"}: + raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") + + # Callback validation if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -181,28 +203,62 @@ def prepare_latents( @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, - prompt: Optional[Union[str, List[str]]] = None, - messages: Optional[List[Dict[str, str]]] = None, - input_ids: Optional[torch.LongTensor] = None, + prompt: str | list[str] | None = None, + messages: list[dict[str, str]] | None = None, + input_ids: torch.LongTensor | None = None, max_new_tokens: int = 2048, temperature: float = 0.0, - stop_token_ids: Optional[List[int]] = None, - mask_token_id: Optional[int] = None, + stop_token_ids: list[int] | None = None, + mask_token_id: int | None = None, use_chat_template: bool = True, add_generation_prompt: bool = True, - chat_template_kwargs: Optional[Dict[str, object]] = None, - return_text: bool = True, + chat_template_kwargs: dict[str, object] | None = None, + output_type: str = "text", return_dict: bool = True, - callback_on_step_end: Optional[ - Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] - ] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, - ) -> Union[DFlashPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + callback_on_step_end: Callable[[int, int, dict], None] + | PipelineCallback + | MultiPipelineCallbacks + | None = None, + callback_on_step_end_tensor_inputs: list[str] | None = None, + ) -> DFlashPipelineOutput | tuple[torch.LongTensor, list[str] | None]: """ Generate text using block-diffusion speculative decoding. + Args: + prompt (`str` or `list[str]`, *optional*): + Prompt text. When `use_chat_template` is `True` (default) and a tokenizer with a chat template is + available, the prompt is wrapped in a chat message before tokenization. + messages (`list[dict[str, str]]`, *optional*): + Chat messages to encode. Takes precedence over `prompt` when provided. + input_ids (`torch.LongTensor`, *optional*): + Pre-tokenized input IDs. Takes precedence over `prompt` and `messages`. + max_new_tokens (`int`): + Maximum number of new tokens to generate. + temperature (`float`): + Sampling temperature. + stop_token_ids (`list[int]`, *optional*): + Token IDs that signal generation should stop. + mask_token_id (`int`, *optional*): + Mask token ID for the draft model. + use_chat_template (`bool`, defaults to `True`): + Whether to wrap the prompt in a chat template. + add_generation_prompt (`bool`, defaults to `True`): + Whether to add the generation prompt when using chat templates. + chat_template_kwargs (`dict[str, object]`, *optional*): + Additional keyword arguments for the chat template. + output_type (`str`, defaults to `"text"`): + Output format. `"text"` decodes sequences into strings (requires a tokenizer). `"seq"` returns raw + token ID sequences only. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`DFlashPipelineOutput`] instead of a tuple. + callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): + Callback executed after each speculative decoding step. + callback_on_step_end_tensor_inputs (`list[str]`, *optional*): + Tensor keys to pass to the callback. + Examples: """ + # 1. Check inputs early if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -210,6 +266,17 @@ def __call__( if callback_on_step_end_tensor_inputs is None: callback_on_step_end_tensor_inputs = ["block_output_ids"] + self.check_inputs( + prompt=prompt, + messages=messages, + input_ids=input_ids, + max_new_tokens=max_new_tokens, + output_type=output_type, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + ) + + # 2. Prepare input IDs from prompt/messages/input_ids input_ids = self._prepare_input_ids( prompt=prompt, messages=messages, @@ -221,13 +288,10 @@ def __call__( if mask_token_id is None: mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) - - self.check_inputs( - input_ids=input_ids, - mask_token_id=mask_token_id, - callback_on_step_end=callback_on_step_end, - callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, - ) + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + if input_ids.shape[0] != 1: + raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.") target_params = list(self.target_model.parameters()) if hasattr(self.target_model, "parameters") else [] device = target_params[0].device if len(target_params) > 0 else torch.device("cpu") @@ -247,6 +311,7 @@ def __call__( if stop_token_ids is not None: stop_token_ids = [int(token_id) for token_id in stop_token_ids] + # 3. Setup models and scheduler self.draft_model.eval() self.target_model.eval() self.scheduler.set_timesteps(1, device=device) @@ -265,6 +330,7 @@ def __call__( past_key_values_target = DynamicCache() past_key_values_draft = DynamicCache() + # 4. Prefill step output = self._target_forward( input_ids=input_ids, position_ids=position_ids[:, :num_input_tokens], @@ -280,11 +346,18 @@ def __call__( start = num_input_tokens global_step = 0 - stop_tensor = None - if stop_token_ids is not None: - stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) + num_blocks = (max_length - num_input_tokens + block_size - 1) // block_size + + # 5. Block-wise speculative decoding loop + block_progress_bar_config = getattr(self, "_progress_bar_config", {}).copy() + block_progress_bar_config["position"] = 0 + block_progress_bar_config["desc"] = "Blocks" + block_iter = tqdm(range(num_blocks), **block_progress_bar_config) + + for _block_idx in block_iter: + if start >= max_length: + break - while start < max_length: block_output_ids = output_ids[:, start : start + int(block_size)].clone() block_position_ids = position_ids[:, start : start + int(block_size)] noise_embedding = input_embeddings(block_output_ids) @@ -331,12 +404,14 @@ def __call__( output_ids = callback_outputs.pop("output_ids", output_ids) global_step += 1 - if stop_tensor is not None and torch.isin(output_ids[:, num_input_tokens:], stop_tensor).any(): + if self.scheduler.check_should_stop(output_ids, stop_token_ids, num_input_tokens): break + # 6. Post-process output output_ids = output_ids[:, :max_length] output_ids = output_ids[:, output_ids[0] != int(mask_token_id)] - if stop_tensor is not None: + if stop_token_ids is not None: + stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) stop_positions = torch.isin(output_ids[0, num_input_tokens:], stop_tensor).nonzero(as_tuple=True)[0] if stop_positions.numel() > 0: output_ids = output_ids[:, : num_input_tokens + int(stop_positions[0].item()) + 1] @@ -345,7 +420,7 @@ def __call__( sequences = output_ids[:, prompt_len:] texts = None - if return_text and getattr(self, "tokenizer", None) is not None: + if output_type == "text" and getattr(self, "tokenizer", None) is not None: texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) if not return_dict: @@ -360,7 +435,7 @@ def _get_block_size(self) -> int: raise ValueError("`draft_model` must define `block_size` on the module or its config.") return int(block_size) - def _get_target_layer_ids(self) -> List[int]: + def _get_target_layer_ids(self) -> list[int]: layer_ids = getattr(self.draft_model, "target_layer_ids", None) if layer_ids is not None: return list(layer_ids) @@ -395,7 +470,7 @@ def _target_forward( position_ids: torch.LongTensor, past_key_values: DynamicCache, output_hidden_states: bool, - logits_to_keep: Optional[int], + logits_to_keep: int | None, ): kwargs = { "input_ids": input_ids, diff --git a/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py b/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py index 9fb3efbe4709..b3a1792f2a64 100644 --- a/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py +++ b/src/diffusers/pipelines/hybrid_token_diffusion/__init__.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .pipeline_hybrid_token_diffusion import HybridTokenDiffusionPipeline +from .pipeline_hybrid_token_diffusion import HybridTokenDiffusionPipeline, HybridTokenDiffusionPipelineOutput -__all__ = ["HybridTokenDiffusionPipeline"] +__all__ = ["HybridTokenDiffusionPipeline", "HybridTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py b/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py index bdc372960dee..e2869eabec2b 100644 --- a/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py +++ b/src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py @@ -14,11 +14,229 @@ from __future__ import annotations +from dataclasses import dataclass +from typing import Callable + +import torch + +from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...utils import BaseOutput, logging from ..token_diffusion.pipeline_token_diffusion import TokenDiffusionPipeline +logger = logging.get_logger(__name__) + + +@dataclass +class HybridTokenDiffusionPipelineOutput(BaseOutput): + """ + Output class for hybrid token diffusion pipelines. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Sampled token IDs. + texts (`list[str]`, *optional*): + Decoded texts if a tokenizer was provided and `output_type="text"`. + """ + + sequences: torch.LongTensor + texts: list[str] | None = None + + class HybridTokenDiffusionPipeline(TokenDiffusionPipeline): - """Alias of `TokenDiffusionPipeline` for hybrid-transition schedulers.""" + """ + Pipeline for hybrid-transition discrete token diffusion sampling. + + This pipeline extends `TokenDiffusionPipeline` with conventions aligned to LLaDA2-style pipelines: `output_type` + parameter, input validation via `check_inputs`, and progress bar support. + """ + + def check_inputs( + self, + batch_size: int, + seq_len: int, + num_inference_steps: int, + output_type: str, + callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, + callback_on_step_end_tensor_inputs: list[str] | None, + infill_mask: torch.BoolTensor | None, + prefix_ids: torch.LongTensor | None, + ): + # Generation parameter validation + if batch_size <= 0: + raise ValueError(f"`batch_size` must be > 0, got {batch_size}.") + if seq_len <= 0: + raise ValueError(f"`seq_len` must be > 0, got {seq_len}.") + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + if output_type not in {"seq", "text"}: + raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") + + # Callback validation + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + # Conditioning validation + if infill_mask is not None and infill_mask.shape != (batch_size, seq_len): + raise ValueError(f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}.") + if prefix_ids is not None: + p = prefix_ids + if p.ndim == 1: + p = p.unsqueeze(0) + if p.ndim == 2 and p.shape[1] > seq_len: + raise ValueError(f"`prefix_ids` length {p.shape[1]} must be <= seq_len={seq_len}.") + + @torch.no_grad() + def __call__( + self, + batch_size: int = 1, + seq_len: int = 64, + num_inference_steps: int = 128, + generator: torch.Generator | None = None, + prefix_ids: torch.LongTensor | None = None, + infill_mask: torch.BoolTensor | None = None, + inject_start_token: bool = False, + output_type: str = "text", + return_dict: bool = True, + callback_on_step_end: Callable[[int, int, dict], None] + | PipelineCallback + | MultiPipelineCallbacks + | None = None, + callback_on_step_end_tensor_inputs: list[str] | None = None, + **model_kwargs, + ) -> HybridTokenDiffusionPipelineOutput | tuple[torch.LongTensor, list[str] | None]: + """ + Generate token sequences via hybrid-transition discrete diffusion. + + Args: + batch_size (`int`, defaults to `1`): + Number of sequences to generate. + seq_len (`int`, defaults to `64`): + Sequence length in tokens. + num_inference_steps (`int`, defaults to `128`): + Number of reverse diffusion steps. + generator (`torch.Generator`, *optional*): + Optional torch generator for determinism. + prefix_ids (`torch.LongTensor`, *optional*): + Optional prefix token IDs to keep fixed at the start of each sequence. Shape `[P]` or `[batch_size, + P]`. + infill_mask (`torch.BoolTensor`, *optional*): + Optional boolean mask of shape `[batch_size, seq_len]` indicating which positions are editable (`True`) + vs fixed (`False`). Fixed positions are clamped to the initial values on every step. + inject_start_token (`bool`, defaults to `False`): + If True, inject `bos_token_id` (or `cls_token_id`) into position 0 (if available). + output_type (`str`, defaults to `"text"`): + Output format. `"text"` decodes sequences into strings (requires a tokenizer). `"seq"` returns raw + token ID sequences only. + return_dict (`bool`, defaults to `True`): + Whether to return a [`HybridTokenDiffusionPipelineOutput`] instead of a tuple. + callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): + A function called after each denoising step with signature `callback_on_step_end(self, step: int, + timestep: int, callback_kwargs: Dict)`. + callback_on_step_end_tensor_inputs (`list[str]`, *optional*): + List of tensor keys to include in `callback_kwargs`. + model_kwargs: + Forward kwargs passed to `model(...)` (e.g. attention mask overrides). + """ + # 1. Check inputs early + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["input_ids"] + + self.check_inputs( + batch_size=batch_size, + seq_len=seq_len, + num_inference_steps=num_inference_steps, + output_type=output_type, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + infill_mask=infill_mask, + prefix_ids=prefix_ids, + ) + + # 2. Set up device and scheduler + device = self._execution_device + + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + self._num_timesteps = len(timesteps) + + # 3. Prepare latents + input_ids = self.prepare_latents(batch_size, seq_len, generator=generator, device=device) + attention_mask = torch.ones_like(input_ids, dtype=torch.long) + + fixed_mask = None + fixed_values = None + if infill_mask is not None: + fixed_mask = (~infill_mask.to(device=device)).to(dtype=torch.bool) + fixed_values = input_ids.clone() + + if prefix_ids is not None: + prefix_ids = self._normalize_prefix_ids(prefix_ids, batch_size=batch_size, device=device) + prefix_len = prefix_ids.shape[1] + + input_ids[:, :prefix_len] = prefix_ids + if fixed_mask is None: + fixed_mask = torch.zeros((batch_size, seq_len), device=device, dtype=torch.bool) + fixed_values = input_ids.clone() + fixed_mask[:, :prefix_len] = True + fixed_values[:, :prefix_len] = prefix_ids + + start_token_id = self._resolve_start_token_id() + if inject_start_token and start_token_id is not None: + input_ids[:, 0] = start_token_id + if fixed_mask is not None: + fixed_mask[:, 0] = True + fixed_values[:, 0] = start_token_id + + # 4. Denoising loop with progress bar + progress_bar = self.progress_bar(total=len(timesteps)) + for step_idx, t in enumerate(timesteps): + timestep = t.expand(batch_size) + out = self.model(input_ids=input_ids, timesteps=timestep, return_dict=True, **model_kwargs) + logits = getattr(out, "logits", None) + if logits is None: + # Fall back to tuple-style returns. + logits = out[0] + + input_ids = self.scheduler.step(logits, t, input_ids, generator=generator, return_dict=True).prev_sample + + if fixed_mask is not None: + input_ids = torch.where(fixed_mask, fixed_values, input_ids) + + if inject_start_token and start_token_id is not None: + input_ids[:, 0] = start_token_id + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, step_idx, t, callback_kwargs) + input_ids = callback_outputs.pop("input_ids", input_ids) + + progress_bar.update(1) + progress_bar.close() + + # 5. Post-process output + texts = None + if output_type == "text" and getattr(self, "tokenizer", None) is not None: + texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) + + if not return_dict: + return (input_ids, texts) + return HybridTokenDiffusionPipelineOutput(sequences=input_ids, texts=texts) -__all__ = ["HybridTokenDiffusionPipeline"] +__all__ = ["HybridTokenDiffusionPipeline", "HybridTokenDiffusionPipelineOutput"] diff --git a/src/diffusers/pipelines/sdar/pipeline_sdar.py b/src/diffusers/pipelines/sdar/pipeline_sdar.py index fdc97ff0fe58..b63b0620420b 100644 --- a/src/diffusers/pipelines/sdar/pipeline_sdar.py +++ b/src/diffusers/pipelines/sdar/pipeline_sdar.py @@ -15,9 +15,10 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import Any, Callable import torch +from tqdm.auto import tqdm from transformers import DynamicCache from ...callbacks import MultiPipelineCallbacks, PipelineCallback @@ -51,48 +52,83 @@ @dataclass class SDARPipelineOutput(BaseOutput): sequences: torch.LongTensor - texts: Optional[List[str]] = None + texts: list[str] | None = None class SDARPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): r""" Block diffusion pipeline for SDAR-style token generation. + + This pipeline generates text by processing blocks of tokens in a semi-autoregressive fashion. Each block is + iteratively denoised using a masked diffusion process, where tokens are progressively revealed based on model + confidence. + + The model is expected to accept an attention mask and `position_ids`, and to return logits of shape `[batch, seq, + vocab_size]`. """ - model: torch.nn.Module - tokenizer: Optional[object] + model: Any scheduler: SDARTokenDiffusionScheduler + tokenizer: Any + _callback_tensor_inputs = ["cur_x", "logits", "sampled_tokens", "sampled_probs", "transfer_index"] def __init__( self, - model: torch.nn.Module, - tokenizer: Optional[object] = None, - scheduler: Optional[SDARTokenDiffusionScheduler] = None, + model: Any, + scheduler: SDARTokenDiffusionScheduler | None = None, + tokenizer: Any | None = None, ): super().__init__() if scheduler is None: scheduler = SDARTokenDiffusionScheduler() self.register_modules(model=model, tokenizer=tokenizer, scheduler=scheduler) - self._store_kv_supported: Optional[bool] = None + self._store_kv_supported: bool | None = None + + @property + def num_timesteps(self): + return self._num_timesteps def check_inputs( self, - input_ids: torch.LongTensor, + prompt: str | list[str] | None, + messages: list[dict[str, str]] | None, + input_ids: torch.LongTensor | None, block_length: int, - denoising_steps: int, - mask_token_id: Optional[int], - callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], - callback_on_step_end_tensor_inputs: Optional[List[str]], + num_inference_steps: int, + mask_token_id: int | None, + output_type: str, + callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, + callback_on_step_end_tensor_inputs: list[str] | None, ): - if input_ids.shape[0] != 1: - raise ValueError("SDARPipeline currently supports batch_size=1 input_ids.") + # Input source validation + if prompt is None and messages is None and input_ids is None: + raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") + if prompt is not None and messages is not None: + raise ValueError("Provide either `prompt` or `messages`, not both.") + if input_ids is not None: + if input_ids.ndim not in (1, 2): + raise ValueError(f"`input_ids` must be 1D or 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + if input_ids.ndim == 2 and input_ids.shape[0] != 1: + raise ValueError("SDARPipeline currently supports batch_size=1 input_ids.") + if prompt is not None and input_ids is None and self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + if messages is not None and input_ids is None and self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + # Generation parameter validation if block_length <= 0: raise ValueError(f"`block_length` must be > 0, got {block_length}.") - if denoising_steps <= 0: - raise ValueError(f"`denoising_steps` must be > 0, got {denoising_steps}.") + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") if mask_token_id is None: raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + if output_type not in {"seq", "text"}: + raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") + + # Callback validation if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -122,36 +158,88 @@ def prepare_latents( @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, - prompt: Optional[Union[str, List[str]]] = None, - messages: Optional[List[Dict[str, str]]] = None, - input_ids: Optional[torch.LongTensor] = None, + prompt: str | list[str] | None = None, + messages: list[dict[str, str]] | None = None, + input_ids: torch.LongTensor | None = None, max_new_tokens: int = 256, block_length: int = 4, - denoising_steps: int = 4, + num_inference_steps: int = 4, temperature: float = 1.0, top_k: int = 0, top_p: float = 1.0, remasking_strategy: str = "low_confidence_dynamic", confidence_threshold: float = 0.9, entropy_threshold: float = 0.35, - stop_token_ids: Optional[List[int]] = None, - mask_token_id: Optional[int] = None, - attention_mask_mode: str = "3d", + stop_token_ids: list[int] | None = None, + mask_token_id: int | None = None, use_chat_template: bool = True, add_generation_prompt: bool = True, - chat_template_kwargs: Optional[Dict[str, object]] = None, - return_text: bool = True, + chat_template_kwargs: dict[str, object] | None = None, + generator: torch.Generator | None = None, + output_type: str = "text", return_dict: bool = True, - callback_on_step_end: Optional[ - Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] - ] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, - ) -> Union[SDARPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + callback_on_step_end: Callable[[int, int, dict], None] + | PipelineCallback + | MultiPipelineCallbacks + | None = None, + callback_on_step_end_tensor_inputs: list[str] | None = None, + ) -> SDARPipelineOutput | tuple[torch.LongTensor, list[str] | None]: """ Generate text using SDAR-style block diffusion decoding. + Args: + prompt (`str` or `List[str]`, *optional*): + Prompt text. When `use_chat_template` is `True` (default) and a tokenizer with a chat template is + available, the prompt is wrapped in a chat message before tokenization. + messages (`List[Dict[str, str]]`, *optional*): + Chat messages to encode (e.g. `[{"role": "user", "content": "Hello"}]`). Takes precedence over `prompt` + when provided. Requires a tokenizer with `apply_chat_template`. + input_ids (`torch.LongTensor`, *optional*): + Pre-tokenized input IDs. Takes precedence over `prompt` and `messages`. + max_new_tokens (`int`): + Number of tokens to generate. + block_length (`int`): + Block size for denoising. + num_inference_steps (`int`): + Number of denoising steps per block. + temperature (`float`): + Sampling temperature. + top_k (`int`): + Top-k sampling cutoff. + top_p (`float`): + Nucleus sampling cutoff. + remasking_strategy (`str`): + Strategy for selecting which tokens to commit (`sequential`, `low_confidence_static`, + `low_confidence_dynamic`, `entropy_bounded`). + confidence_threshold (`float`): + Confidence threshold for dynamic remasking. + entropy_threshold (`float`): + Entropy threshold for entropy-bounded remasking. + stop_token_ids (`list[int]`, *optional*): + Token IDs that signal generation should stop. + mask_token_id (`int`, *optional*): + Mask token ID to use for the template. + use_chat_template (`bool`, defaults to `True`): + Whether to wrap the prompt in a chat template. + add_generation_prompt (`bool`, defaults to `True`): + Whether to add the generation prompt when using chat templates. + chat_template_kwargs (`dict`, *optional*): + Extra kwargs for `apply_chat_template`. + generator (`torch.Generator`, *optional*): + RNG for sampling. + output_type (`str`, defaults to `"text"`): + Output format. `"text"` decodes sequences into strings (requires a tokenizer). `"seq"` returns raw + token ID sequences only. + return_dict (`bool`, *optional*, defaults to `True`): + Whether to return a [`SDARPipelineOutput`] instead of a tuple. + callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): + Callback executed after each denoising step. + callback_on_step_end_tensor_inputs (`List[str]`, *optional*): + Tensor keys to pass to the callback. + Examples: """ + # 1. Check inputs early if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -166,29 +254,32 @@ def __call__( if model_block_length is not None: block_length = int(model_block_length) - input_ids = self._prepare_input_ids( - prompt=prompt, - messages=messages, - input_ids=input_ids, - use_chat_template=use_chat_template, - add_generation_prompt=add_generation_prompt, - chat_template_kwargs=chat_template_kwargs, - ) - if mask_token_id is None: mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) self.check_inputs( + prompt=prompt, + messages=messages, input_ids=input_ids, block_length=block_length, - denoising_steps=denoising_steps, + num_inference_steps=num_inference_steps, mask_token_id=mask_token_id, + output_type=output_type, callback_on_step_end=callback_on_step_end, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, ) - params = list(self.model.parameters()) if hasattr(self.model, "parameters") else [] - device = params[0].device if len(params) > 0 else torch.device("cpu") + # 2. Prepare input IDs from prompt/messages/input_ids + input_ids = self._prepare_input_ids( + prompt=prompt, + messages=messages, + input_ids=input_ids, + use_chat_template=use_chat_template, + add_generation_prompt=add_generation_prompt, + chat_template_kwargs=chat_template_kwargs, + ) + + device = self._execution_device input_ids = input_ids.to(device=device) if stop_token_ids is None: @@ -198,21 +289,19 @@ def __call__( stop_token_ids = [int(token_id) for token_id in stop_token_ids] self.model.eval() - self.scheduler.set_timesteps(int(denoising_steps), device=device) + self.scheduler.set_timesteps(int(num_inference_steps), device=device) prompt_length = input_ids.shape[1] num_blocks = (prompt_length + int(max_new_tokens) + int(block_length) - 1) // int(block_length) total_length = int(num_blocks) * int(block_length) - block_mask_3d = self._build_block_attention_mask_3d( + # 3. Build 2D attention mask — the model handles backend-specific conversion internally. + attn_mask = self._build_block_attention_mask_2d( num_blocks=num_blocks, block_length=block_length, total_length=total_length, device=device, - dtype=torch.float32, ) - block_mask_4d = self._build_block_attention_mask_4d(block_mask_3d, dtype=torch.float32) - block_mask_2d = block_mask_3d[0] x = self.prepare_latents(total_length, int(mask_token_id), device) x[:, :prompt_length] = input_ids @@ -222,65 +311,57 @@ def __call__( prefill_blocks = prompt_length // int(block_length) prefill_length = int(prefill_blocks) * int(block_length) - resolved_attention_mode = str(attention_mask_mode) + + self._num_timesteps = num_inference_steps * max(num_blocks - prefill_blocks, 0) if prefill_length > 0: cur_x = x[:, :prefill_length] cur_position_ids = position_ids[:, :prefill_length] - cur_attn_mask = block_mask_3d[:, :prefill_length, :prefill_length] - cur_attn_mask_4d = block_mask_4d[:, :, :prefill_length, :prefill_length] - cur_attn_mask_2d = block_mask_2d[:prefill_length, :prefill_length] - _, resolved_attention_mode = self._model_forward_logits( + cur_attn_mask = attn_mask[:prefill_length, :prefill_length].unsqueeze(0) + self._model_forward_logits( input_ids=cur_x, - attention_mask_3d=cur_attn_mask, - attention_mask_4d=cur_attn_mask_4d, - attention_mask_2d=cur_attn_mask_2d, + attention_mask=cur_attn_mask, position_ids=cur_position_ids, - attention_mask_mode=resolved_attention_mode, past_key_values=past_key_values, store_kv=True, ) - num_transfer_tokens = self.scheduler.get_num_transfer_tokens(int(block_length), int(denoising_steps)).to( + num_transfer_tokens = self.scheduler.get_num_transfer_tokens(int(block_length), int(num_inference_steps)).to( device=device ) - stop_tensor = None - if stop_token_ids is not None: - stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) - global_step = 0 - for block_idx in range(prefill_blocks, int(num_blocks)): + + # 4. Block-wise generation loop + block_progress_bar_config = getattr(self, "_progress_bar_config", {}).copy() + block_progress_bar_config["position"] = 0 + block_progress_bar_config["desc"] = "Blocks" + for block_idx in tqdm(range(prefill_blocks, int(num_blocks)), **block_progress_bar_config): start = int(block_idx) * int(block_length) end = start + int(block_length) cur_x = x[:, start:end].clone() cur_position_ids = position_ids[:, start:end] - cur_attn_mask = block_mask_3d[:, start:end, :end] - cur_attn_mask_4d = block_mask_4d[:, :, start:end, :end] - cur_attn_mask_2d = block_mask_2d[start:end, :end] + cur_attn_mask = attn_mask[start:end, :end].unsqueeze(0) + + self.set_progress_bar_config(position=1, leave=False, desc=f"Block {block_idx} Inference Steps") + progress_bar = self.progress_bar(total=num_inference_steps) - for step in range(int(denoising_steps) + 1): + for step in range(int(num_inference_steps) + 1): mask_index = cur_x == int(mask_token_id) if mask_index.sum() == 0: - _, resolved_attention_mode = self._model_forward_logits( + self._model_forward_logits( input_ids=cur_x, - attention_mask_3d=cur_attn_mask, - attention_mask_4d=cur_attn_mask_4d, - attention_mask_2d=cur_attn_mask_2d, + attention_mask=cur_attn_mask, position_ids=cur_position_ids, - attention_mask_mode=resolved_attention_mode, past_key_values=past_key_values, store_kv=True, ) break - logits, resolved_attention_mode = self._model_forward_logits( + logits = self._model_forward_logits( input_ids=cur_x, - attention_mask_3d=cur_attn_mask, - attention_mask_4d=cur_attn_mask_4d, - attention_mask_2d=cur_attn_mask_2d, + attention_mask=cur_attn_mask, position_ids=cur_position_ids, - attention_mask_mode=resolved_attention_mode, past_key_values=past_key_values, store_kv=False, ) @@ -297,6 +378,7 @@ def __call__( temperature=temperature, top_k=top_k, top_p=top_p, + generator=generator, return_dict=True, ) cur_x = step_output.prev_sample @@ -312,13 +394,18 @@ def __call__( cur_x = callback_outputs.pop("cur_x", cur_x) global_step += 1 + progress_bar.update(1) + progress_bar.close() x[:, start:end] = cur_x - if stop_tensor is not None and torch.isin(x[:, prompt_length:], stop_tensor).any(): + + if self.scheduler.check_should_stop(x, prompt_length, stop_token_ids): break + # 5. Post-process output output_ids = x[:, : prompt_length + int(max_new_tokens)] - if stop_tensor is not None: + if stop_token_ids is not None: + stop_tensor = torch.tensor(stop_token_ids, device=device, dtype=torch.long) stop_positions = torch.isin(output_ids[0, prompt_length:], stop_tensor).nonzero(as_tuple=True)[0] if stop_positions.numel() > 0: output_ids = output_ids[:, : prompt_length + int(stop_positions[0].item()) + 1] @@ -328,7 +415,7 @@ def __call__( sequences = output_ids[:, prompt_length:] texts = None - if return_text and getattr(self, "tokenizer", None) is not None: + if output_type == "text" and self.tokenizer is not None: texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) if not return_dict: @@ -339,86 +426,54 @@ def _model_forward_logits( self, *, input_ids: torch.LongTensor, - attention_mask_3d: Optional[torch.Tensor], - attention_mask_4d: Optional[torch.Tensor], - attention_mask_2d: Optional[torch.Tensor], + attention_mask: torch.Tensor, position_ids: torch.LongTensor, - attention_mask_mode: str, past_key_values: DynamicCache, store_kv: bool, - ) -> Tuple[torch.Tensor, str]: - if attention_mask_mode not in {"auto", "3d", "4d", "2d", "none"}: - raise ValueError( - f"`attention_mask_mode` must be one of {{'auto','3d','4d','2d','none'}}, got {attention_mask_mode!r}." - ) - - def _call(mask): - kwargs = { - "input_ids": input_ids, - "attention_mask": mask, - "position_ids": position_ids, - "past_key_values": past_key_values, - "use_cache": True, - } - if self._store_kv_supported is False: - output = self.model(**kwargs) - return output.logits if hasattr(output, "logits") else output[0] - if self._store_kv_supported is True: - kwargs["store_kv"] = store_kv - output = self.model(**kwargs) - return output.logits if hasattr(output, "logits") else output[0] - try: - kwargs["store_kv"] = store_kv - output = self.model(**kwargs) - self._store_kv_supported = True - return output.logits if hasattr(output, "logits") else output[0] - except TypeError: - output = self.model(**kwargs) - self._store_kv_supported = False - return output.logits if hasattr(output, "logits") else output[0] - - if attention_mask_mode == "none": - return _call(None), "none" - if attention_mask_mode == "2d": - return _call(attention_mask_2d), "2d" - if attention_mask_mode == "3d": - return _call(attention_mask_3d), "3d" - if attention_mask_mode == "4d": - return _call(attention_mask_4d), "4d" + ) -> torch.Tensor: + """Run the model forward pass and return logits. + Passes a 2D attention mask and lets the model handle internal conversion. + """ + kwargs = { + "input_ids": input_ids, + "attention_mask": attention_mask, + "position_ids": position_ids, + "past_key_values": past_key_values, + "use_cache": True, + } + if self._store_kv_supported is False: + output = self.model(**kwargs) + return output.logits if hasattr(output, "logits") else output[0] + if self._store_kv_supported is True: + kwargs["store_kv"] = store_kv + output = self.model(**kwargs) + return output.logits if hasattr(output, "logits") else output[0] try: - return _call(attention_mask_3d), "3d" - except (TypeError, ValueError, RuntimeError): - pass - try: - return _call(attention_mask_4d), "4d" - except (TypeError, ValueError, RuntimeError): - pass - try: - return _call(attention_mask_2d), "2d" - except (TypeError, ValueError, RuntimeError): - return _call(None), "none" - - def _build_block_attention_mask_3d( + kwargs["store_kv"] = store_kv + output = self.model(**kwargs) + self._store_kv_supported = True + return output.logits if hasattr(output, "logits") else output[0] + except TypeError: + output = self.model(**kwargs) + self._store_kv_supported = False + return output.logits if hasattr(output, "logits") else output[0] + + def _build_block_attention_mask_2d( self, *, num_blocks: int, block_length: int, total_length: int, device: torch.device, - dtype: torch.dtype, ) -> torch.Tensor: - block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=dtype)) - attn = block_mask.repeat_interleave(block_length, dim=0).repeat_interleave(block_length, dim=1).unsqueeze(0) - return attn[:, :total_length, :total_length] - - def _build_block_attention_mask_4d(self, mask_3d: torch.Tensor, *, dtype: torch.dtype) -> torch.Tensor: - attn = mask_3d.unsqueeze(1).to(dtype=dtype) - return torch.where( - attn > 0, - torch.zeros((), device=attn.device, dtype=dtype), - torch.full((), float("-inf"), device=attn.device, dtype=dtype), - ) + """Build a 2D block-causal attention mask of shape `(total_length, total_length)`. + + Each position can attend to all positions in the same or earlier blocks. + """ + block_mask = torch.tril(torch.ones(num_blocks, num_blocks, device=device, dtype=torch.long)) + attn = block_mask.repeat_interleave(block_length, dim=0).repeat_interleave(block_length, dim=1) + return attn[:total_length, :total_length] __all__ = ["SDARPipeline", "SDARPipelineOutput"] diff --git a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py index f53736505e49..e93762b8c4dd 100644 --- a/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py +++ b/src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py @@ -15,15 +15,18 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from typing import Any, Callable import torch from ...callbacks import MultiPipelineCallbacks, PipelineCallback -from ...utils import BaseOutput +from ...utils import BaseOutput, logging from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin +logger = logging.get_logger(__name__) + + @dataclass class TokenDiffusionPipelineOutput(BaseOutput): """ @@ -32,12 +35,12 @@ class TokenDiffusionPipelineOutput(BaseOutput): Args: sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Sampled token IDs. - texts (`List[str]`, *optional*): - Decoded texts if a tokenizer was provided and `return_text=True`. + texts (`list[str]`, *optional*): + Decoded texts if a tokenizer was provided and `output_type="text"`. """ sequences: torch.LongTensor - texts: Optional[List[str]] = None + texts: list[str] | None = None class TokenDiffusionPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): @@ -62,7 +65,7 @@ def __init__( self, model: Any, scheduler: Any, - tokenizer: Optional[Any] = None, + tokenizer: Any | None = None, ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) @@ -75,8 +78,8 @@ def prepare_latents( self, batch_size: int, seq_len: int, - generator: Optional[torch.Generator] = None, - device: Optional[torch.device] = None, + generator: torch.Generator | None = None, + device: torch.device | None = None, ) -> torch.LongTensor: shape = torch.Size((batch_size, seq_len)) return self.scheduler.sample_prior(shape, device=device, generator=generator) @@ -85,11 +88,24 @@ def check_inputs( self, batch_size: int, seq_len: int, - callback_on_step_end: Optional[Union[Callable, PipelineCallback, MultiPipelineCallbacks]], - callback_on_step_end_tensor_inputs: Optional[List[str]], - infill_mask: Optional[torch.BoolTensor], - prefix_ids: Optional[torch.LongTensor], + num_inference_steps: int, + output_type: str, + callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, + callback_on_step_end_tensor_inputs: list[str] | None, + infill_mask: torch.BoolTensor | None, + prefix_ids: torch.LongTensor | None, ): + # Generation parameter validation + if batch_size <= 0: + raise ValueError(f"`batch_size` must be > 0, got {batch_size}.") + if seq_len <= 0: + raise ValueError(f"`seq_len` must be > 0, got {seq_len}.") + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + if output_type not in {"seq", "text"}: + raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") + + # Callback validation if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -101,6 +117,8 @@ def check_inputs( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) + + # Mask / prefix validation if infill_mask is not None and infill_mask.shape != (batch_size, seq_len): raise ValueError(f"`infill_mask` must have shape {(batch_size, seq_len)}, got {tuple(infill_mask.shape)}.") if prefix_ids is not None: @@ -116,18 +134,19 @@ def __call__( batch_size: int = 1, seq_len: int = 64, num_inference_steps: int = 128, - generator: Optional[torch.Generator] = None, - prefix_ids: Optional[torch.LongTensor] = None, - infill_mask: Optional[torch.BoolTensor] = None, + generator: torch.Generator | None = None, + prefix_ids: torch.LongTensor | None = None, + infill_mask: torch.BoolTensor | None = None, inject_start_token: bool = False, - return_text: bool = True, + output_type: str = "text", return_dict: bool = True, - callback_on_step_end: Optional[ - Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] - ] = None, - callback_on_step_end_tensor_inputs: Optional[List[str]] = None, + callback_on_step_end: Callable[[int, int, dict], None] + | PipelineCallback + | MultiPipelineCallbacks + | None = None, + callback_on_step_end_tensor_inputs: list[str] | None = None, **model_kwargs, - ) -> Union[TokenDiffusionPipelineOutput, Tuple[torch.LongTensor, Optional[List[str]]]]: + ) -> TokenDiffusionPipelineOutput | tuple[torch.LongTensor, list[str] | None]: """ Args: batch_size: Number of sequences to generate. @@ -140,13 +159,16 @@ def __call__( Optional boolean mask of shape `[batch_size, seq_len]` indicating which positions are editable (`True`) vs fixed (`False`). Fixed positions are clamped to the initial values on every step. inject_start_token: If True, inject `bos_token_id` (or `cls_token_id`) into position 0 (if available). - return_text: If True and tokenizer exists, also return decoded strings. + output_type (`str`, defaults to `"text"`): + Output format. `"text"` decodes sequences into strings (requires a tokenizer). `"seq"` returns raw + token ID sequences only. return_dict: If True, returns a `TokenDiffusionPipelineOutput`. callback_on_step_end: A function called after each denoising step with signature - `callback_on_step_end(self, step: int, timestep: int, callback_kwargs: Dict)`. + `callback_on_step_end(self, step: int, timestep: int, callback_kwargs: dict)`. callback_on_step_end_tensor_inputs: List of tensor keys to include in `callback_kwargs`. model_kwargs: Forward kwargs passed to `model(...)` (e.g. attention mask overrides). """ + # 1. Check inputs early if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -157,21 +179,26 @@ def __call__( self.check_inputs( batch_size=batch_size, seq_len=seq_len, + num_inference_steps=num_inference_steps, + output_type=output_type, callback_on_step_end=callback_on_step_end, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, infill_mask=infill_mask, prefix_ids=prefix_ids, ) + # 2. Prepare timesteps device = self._execution_device self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps self._num_timesteps = len(timesteps) + # 3. Prepare latents input_ids = self.prepare_latents(batch_size, seq_len, generator=generator, device=device) attention_mask = torch.ones_like(input_ids, dtype=torch.long) + # 4. Build fixed masks for prefix / infill conditioning fixed_mask = None fixed_values = None if infill_mask is not None: @@ -196,6 +223,8 @@ def __call__( fixed_mask[:, 0] = True fixed_values[:, 0] = start_token_id + # 5. Denoising loop + progress_bar = self.progress_bar(total=num_inference_steps) for step_idx, t in enumerate(timesteps): timestep = t.expand(batch_size) out = self.model(input_ids=input_ids, timesteps=timestep, return_dict=True, **model_kwargs) @@ -206,8 +235,9 @@ def __call__( input_ids = self.scheduler.step(logits, t, input_ids, generator=generator, return_dict=True).prev_sample + # Enforce fixed masks (prefix / infill conditioning) if fixed_mask is not None: - input_ids = torch.where(fixed_mask, fixed_values, input_ids) + input_ids = self.scheduler.enforce_fixed_masks(input_ids, fixed_mask, fixed_values) if inject_start_token and start_token_id is not None: input_ids[:, 0] = start_token_id @@ -219,8 +249,12 @@ def __call__( callback_outputs = callback_on_step_end(self, step_idx, t, callback_kwargs) input_ids = callback_outputs.pop("input_ids", input_ids) + progress_bar.update(1) + progress_bar.close() + + # 6. Post-process output texts = None - if return_text and getattr(self, "tokenizer", None) is not None: + if output_type == "text" and getattr(self, "tokenizer", None) is not None: texts = self.tokenizer.batch_decode(input_ids, skip_special_tokens=True) if not return_dict: diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index b44e725e484f..74196f778bba 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -41,7 +41,6 @@ _import_structure["deprecated"] = ["KarrasVeScheduler", "ScoreSdeVpScheduler"] _import_structure["scheduling_amused"] = ["AmusedScheduler"] _import_structure["scheduling_block_refinement"] = ["BlockRefinementScheduler", "BlockRefinementSchedulerOutput"] - _import_structure["scheduling_block_token_diffusion"] = ["BlockTokenDiffusionScheduler"] _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"] _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"] _import_structure["scheduling_ddim"] = ["DDIMScheduler"] @@ -161,7 +160,6 @@ from .deprecated import KarrasVeScheduler, ScoreSdeVpScheduler from .scheduling_amused import AmusedScheduler from .scheduling_block_refinement import BlockRefinementScheduler, BlockRefinementSchedulerOutput - from .scheduling_block_token_diffusion import BlockTokenDiffusionScheduler from .scheduling_consistency_decoder import ConsistencyDecoderScheduler from .scheduling_consistency_models import CMStochasticIterativeScheduler from .scheduling_ddim import DDIMScheduler diff --git a/src/diffusers/schedulers/scheduling_block_token_diffusion.py b/src/diffusers/schedulers/scheduling_block_token_diffusion.py deleted file mode 100644 index b8116daac249..000000000000 --- a/src/diffusers/schedulers/scheduling_block_token_diffusion.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from __future__ import annotations - -from .scheduling_token_diffusion import TokenDiffusionScheduler - - -# Backwards-compatible alias. `TokenDiffusionScheduler` now natively supports -# the `block_mask` parameter in `add_noise()` and `step()`, so a separate -# subclass is no longer needed. -BlockTokenDiffusionScheduler = TokenDiffusionScheduler - -__all__ = ["BlockTokenDiffusionScheduler"] diff --git a/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py index 24e8238321c9..1190cc5fe123 100644 --- a/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_dflash_token_diffusion.py @@ -15,7 +15,6 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Optional, Tuple, Union import torch @@ -60,7 +59,7 @@ def __init__(self): self.num_inference_steps = 1 self.timesteps = torch.tensor([0], dtype=torch.long) - def set_timesteps(self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None) -> None: + def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") self.num_inference_steps = int(num_inference_steps) @@ -81,10 +80,10 @@ def step( *, temperature: float = 0.0, return_dict: bool = True, - ) -> Union[ - DFlashTokenDiffusionSchedulerOutput, - Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor], - ]: + ) -> ( + DFlashTokenDiffusionSchedulerOutput + | tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor] + ): posterior = self.sample(target_logits, temperature=temperature) if draft_tokens.shape[1] > 1: matches = draft_tokens[:, 1:] == posterior[:, :-1] @@ -103,5 +102,86 @@ def step( posterior=posterior, ) + @staticmethod + def check_should_stop( + output_ids: torch.LongTensor, + stop_token_ids: list[int] | None, + num_input_tokens: int, + ) -> bool: + """ + Check whether any stop token has been generated in the output sequence. + + Args: + output_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Current output token IDs including prompt and generated tokens. + stop_token_ids (`list[int]` or `None`): + Token IDs that signal generation should stop. + num_input_tokens (`int`): + Number of prompt tokens at the start of the sequence. + + Returns: + `bool`: `True` if generation should stop, `False` otherwise. + """ + if stop_token_ids is None: + return False + stop_tensor = torch.tensor(stop_token_ids, device=output_ids.device, dtype=torch.long) + return torch.isin(output_ids[:, num_input_tokens:], stop_tensor).any().item() + + def add_noise( + self, + original_samples: torch.LongTensor, + attention_mask: torch.LongTensor, + *, + prompt_length: int, + block_size: int, + mask_token_id: int, + generator: torch.Generator | None = None, + ) -> tuple[torch.LongTensor, torch.BoolTensor]: + """ + Apply the forward (noising) process for DFlash-style block diffusion training. + + For each block after the prompt, a random fraction of valid (non-padding) tokens are replaced with + `mask_token_id`. + + Args: + original_samples (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Clean token IDs. + attention_mask (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Padding mask (1 for valid, 0 for padding). + prompt_length (`int`): + Number of leading prompt tokens to keep unmasked. + block_size (`int`): + Block size for masking. + mask_token_id (`int`): + Token ID to use for masked positions. + generator (`torch.Generator`, *optional*): + RNG for reproducibility. + + Returns: + `tuple[torch.LongTensor, torch.BoolTensor]`: + `(noisy, masked)` -- the noisy sequence and the boolean mask indicating which positions were masked. + """ + batch_size, seq_len = original_samples.shape + device = original_samples.device + + noisy = original_samples.clone() + masked = torch.zeros_like(original_samples, dtype=torch.bool) + + valid = attention_mask.to(dtype=torch.bool) + for block_start in range(prompt_length, seq_len, block_size): + block_end = min(seq_len, block_start + block_size) + seg_len = block_end - block_start + if seg_len <= 0: + continue + + p_mask = torch.rand((batch_size, 1), device=device, generator=generator) + seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask + seg = seg & valid[:, block_start:block_end] + + masked[:, block_start:block_end] = seg + + noisy = torch.where(masked, torch.full_like(noisy, mask_token_id), noisy) + return noisy, masked + __all__ = ["DFlashTokenDiffusionScheduler", "DFlashTokenDiffusionSchedulerOutput"] diff --git a/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py index 6b816481a9c1..b738895e3221 100644 --- a/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py @@ -12,9 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import math from dataclasses import dataclass -from typing import Optional, Tuple, Union import numpy as np import torch @@ -94,7 +95,7 @@ def sample_prior( self, shape: torch.Size, device: torch.device, - generator: Optional[torch.Generator] = None, + generator: torch.Generator | None = None, ) -> torch.LongTensor: """ Sample from the prior distribution at t=1. @@ -115,7 +116,7 @@ def sample_prior( """ return torch.full(shape, self.mask_token_id, device=device, dtype=torch.long) - def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: + def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") self.num_inference_steps = int(num_inference_steps) @@ -126,9 +127,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic self._timesteps_with_end = timesteps self.timesteps = timesteps[:-1] - def scale_model_input( - self, sample: torch.Tensor, timestep: Optional[Union[int, torch.Tensor]] = None - ) -> torch.Tensor: + def scale_model_input(self, sample: torch.Tensor, timestep: int | torch.Tensor | None = None) -> torch.Tensor: return sample def _to_continuous_t(self, timesteps: torch.Tensor, device: torch.device) -> torch.Tensor: @@ -143,7 +142,7 @@ def _to_continuous_t(self, timesteps: torch.Tensor, device: torch.device) -> tor t = (1.0 - 2.0 * float(self.t_eps)) * t + float(self.t_eps) return t.clamp(float(self.t_eps), 1.0 - float(self.t_eps)) - def _get_alpha_betapi(self, t: torch.Tensor, eps: float = 1e-6) -> Tuple[torch.Tensor, torch.Tensor]: + def _get_alpha_betapi(self, t: torch.Tensor, eps: float = 1e-6) -> tuple[torch.Tensor, torch.Tensor]: t = t.view(-1, 1) t1m = 1.0 - t @@ -168,7 +167,7 @@ def _probs_at_t(self, probs: torch.Tensor, t: torch.Tensor) -> torch.Tensor: out[..., : beta_pi.shape[-1]].add_(beta_pi.unsqueeze(1)) return out - def _sample_categorical(self, probs: torch.Tensor, generator: Optional[torch.Generator]) -> torch.LongTensor: + def _sample_categorical(self, probs: torch.Tensor, generator: torch.Generator | None) -> torch.LongTensor: bsz, seqlen, vocab = probs.shape flat = probs.view(-1, vocab).clamp_min(torch.finfo(probs.dtype).tiny) flat = flat / flat.sum(dim=-1, keepdim=True).clamp_min(torch.finfo(probs.dtype).eps) @@ -178,7 +177,7 @@ def _sample_categorical(self, probs: torch.Tensor, generator: Optional[torch.Gen def add_noise( self, original_samples: torch.LongTensor, - noise: Optional[torch.Tensor], + noise: torch.Tensor | None, timesteps: torch.Tensor, ) -> torch.LongTensor: del noise @@ -191,7 +190,7 @@ def add_noise( probs = self._probs_at_t(onehot, t) return self._sample_categorical(probs, generator=None) - def _index_for_timestep(self, timestep: Union[float, torch.Tensor]) -> int: + def _index_for_timestep(self, timestep: float | torch.Tensor) -> int: if self.timesteps is None: raise ValueError("Call `set_timesteps(...)` before calling `step()`.") @@ -206,11 +205,11 @@ def _index_for_timestep(self, timestep: Union[float, torch.Tensor]) -> int: def step( self, model_output: torch.Tensor, - timestep: Union[float, torch.Tensor], + timestep: float | torch.Tensor, sample: torch.LongTensor, - generator: Optional[torch.Generator] = None, + generator: torch.Generator | None = None, return_dict: bool = True, - ) -> Union[HybridTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: + ) -> HybridTokenDiffusionSchedulerOutput | tuple[torch.LongTensor]: if sample.dtype != torch.long: raise ValueError(f"`sample` must be int64 token IDs, got dtype={sample.dtype}.") if model_output.ndim != 3 or model_output.shape[-1] != self.vocab_size: diff --git a/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py index a5d86441491c..886523a0d80a 100644 --- a/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py @@ -15,7 +15,6 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Optional, Tuple, Union import torch import torch.nn.functional as F @@ -69,7 +68,7 @@ def __init__( self.num_inference_steps = int(denoising_steps) self.timesteps = torch.arange(self.num_inference_steps - 1, -1, -1, dtype=torch.long) - def set_timesteps(self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None) -> None: + def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: if num_inference_steps <= 0: raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") self.num_inference_steps = int(num_inference_steps) @@ -108,11 +107,11 @@ def sample( self, logits: torch.Tensor, *, - temperature: Optional[float] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - generator: Optional[torch.Generator] = None, - ) -> Tuple[torch.LongTensor, torch.Tensor]: + temperature: float | None = None, + top_k: int | None = None, + top_p: float | None = None, + generator: torch.Generator | None = None, + ) -> tuple[torch.LongTensor, torch.Tensor]: if temperature is None: temperature = float(self.config.temperature) if top_k is None: @@ -138,25 +137,111 @@ def sample( token_probs = torch.gather(probs, -1, tokens) return tokens.view(*orig_shape), token_probs.view(*orig_shape) + def check_should_stop( + self, + sequences: torch.LongTensor, + prompt_length: int, + stop_token_ids: list[int] | None = None, + ) -> bool: + """ + Check whether generation should stop based on stop token IDs. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Current full sequence including prompt. + prompt_length (`int`): + Number of prompt tokens at the start of the sequence. + stop_token_ids (`list[int]`, *optional*): + Token IDs that signal generation should stop. + + Returns: + `bool`: `True` if any stop token is found in the generated portion. + """ + if stop_token_ids is None or len(stop_token_ids) == 0: + return False + stop_tensor = torch.tensor(stop_token_ids, device=sequences.device, dtype=torch.long) + return torch.isin(sequences[:, prompt_length:], stop_tensor).any().item() + + def add_noise( + self, + original_samples: torch.LongTensor, + attention_mask: torch.LongTensor, + *, + prompt_length: int, + block_length: int, + mask_token_id: int, + generator: torch.Generator | None = None, + ) -> tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]: + """ + Apply the forward (noising) process for semi-autoregressive block masking. + + For each block after the prompt, a random fraction of valid (non-padding) tokens are replaced with + `mask_token_id`. Two complementary views are returned: `noisy` and `noisy_rev`, where the masked positions in + one are the unmasked positions in the other. + + Args: + original_samples (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Clean token IDs. + attention_mask (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Padding mask (1 for valid, 0 for padding). + prompt_length (`int`): + Number of leading prompt tokens to keep unmasked. + block_length (`int`): + Block size for masking. + mask_token_id (`int`): + Token ID to use for masked positions. + generator (`torch.Generator`, *optional*): + RNG for reproducibility. + + Returns: + `tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor, torch.BoolTensor]`: + `(noisy, noisy_rev, masked, masked_rev)` — the two complementary noisy sequences and their + corresponding boolean masks. + """ + batch_size, seq_len = original_samples.shape + device = original_samples.device + + noisy = original_samples.clone() + noisy_rev = original_samples.clone() + masked = torch.zeros_like(original_samples, dtype=torch.bool) + masked_rev = torch.zeros_like(original_samples, dtype=torch.bool) + + valid = attention_mask.to(dtype=torch.bool) + for block_start in range(prompt_length, seq_len, block_length): + block_end = min(seq_len, block_start + block_length) + seg_len = block_end - block_start + if seg_len <= 0: + continue + + p_mask = torch.rand((batch_size, 1), device=device, generator=generator) + seg = torch.rand((batch_size, seg_len), device=device, generator=generator) < p_mask + seg = seg & valid[:, block_start:block_end] + seg_rev = (~seg) & valid[:, block_start:block_end] + + masked[:, block_start:block_end] = seg + masked_rev[:, block_start:block_end] = seg_rev + + noisy = torch.where(masked, torch.full_like(noisy, mask_token_id), noisy) + noisy_rev = torch.where(masked_rev, torch.full_like(noisy_rev, mask_token_id), noisy_rev) + return noisy, noisy_rev, masked, masked_rev + def step( self, model_output: torch.Tensor, - timestep: Union[int, torch.Tensor], + timestep: int | torch.Tensor, sample: torch.LongTensor, *, mask_token_id: int, num_transfer_tokens: torch.LongTensor, - remasking_strategy: Optional[str] = None, - confidence_threshold: Optional[float] = None, - entropy_threshold: Optional[float] = None, - temperature: Optional[float] = None, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - generator: Optional[torch.Generator] = None, + remasking_strategy: str | None = None, + confidence_threshold: float | None = None, + entropy_threshold: float | None = None, + temperature: float | None = None, + top_k: int | None = None, + top_p: float | None = None, + generator: torch.Generator | None = None, return_dict: bool = True, - ) -> Union[ - SDARTokenDiffusionSchedulerOutput, Tuple[torch.LongTensor, torch.BoolTensor, torch.LongTensor, torch.Tensor] - ]: + ) -> SDARTokenDiffusionSchedulerOutput | tuple[torch.LongTensor, torch.BoolTensor, torch.LongTensor, torch.Tensor]: if remasking_strategy is None: remasking_strategy = str(self.config.remasking_strategy) if confidence_threshold is None: diff --git a/src/diffusers/schedulers/scheduling_token_diffusion.py b/src/diffusers/schedulers/scheduling_token_diffusion.py index 04021550a1c8..b00a1a743b97 100644 --- a/src/diffusers/schedulers/scheduling_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_token_diffusion.py @@ -15,7 +15,6 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Optional, Tuple, Union import numpy as np import torch @@ -39,7 +38,7 @@ class TokenDiffusionSchedulerOutput(BaseOutput): prev_sample: torch.LongTensor -def _gumbel_argmax(logits: torch.Tensor, generator: Optional[torch.Generator] = None) -> torch.LongTensor: +def _gumbel_argmax(logits: torch.Tensor, generator: torch.Generator | None = None) -> torch.LongTensor: """ Sample from a categorical distribution defined by (unnormalized) logits via Gumbel-max. @@ -134,7 +133,7 @@ def _effective_vocab_size(self) -> int: return self.vocab_size def _sample_uniform_tokens( - self, shape: torch.Size, device: torch.device, dtype: torch.dtype, generator: Optional[torch.Generator] = None + self, shape: torch.Size, device: torch.device, dtype: torch.dtype, generator: torch.Generator | None = None ) -> torch.LongTensor: """ Sample uniform token IDs, optionally excluding `mask_token_id` (by shifting indices around it). @@ -154,7 +153,7 @@ def sample_prior( self, shape: torch.Size, device: torch.device, - generator: Optional[torch.Generator] = None, + generator: torch.Generator | None = None, ) -> torch.LongTensor: """ Sample from the prior distribution of the forward process at t=1. @@ -177,7 +176,7 @@ def sample_prior( return self._sample_uniform_tokens(shape, device=device, dtype=torch.long, generator=generator) return torch.full(shape, self.mask_token_id, device=device, dtype=torch.long) - def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device, None] = None) -> None: + def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). @@ -202,10 +201,10 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic # Build a map from timestep value → index for O(1) lookup in step(). self._step_index_map = {int(self.timesteps[i].item()): i for i in range(len(self.timesteps))} - def scale_model_input(self, sample: torch.Tensor, timestep: Optional[int] = None) -> torch.Tensor: + def scale_model_input(self, sample: torch.Tensor, timestep: int | None = None) -> torch.Tensor: return sample - def _t_from_timestep(self, timestep: Union[int, torch.Tensor], device: torch.device) -> torch.Tensor: + def _t_from_timestep(self, timestep: int | torch.Tensor, device: torch.device) -> torch.Tensor: """ Convert an integer training timestep index into continuous time `t in [0, 1]`. """ @@ -336,9 +335,9 @@ def get_alpha_prime(self, timesteps: torch.LongTensor) -> torch.Tensor: def add_noise( self, original_samples: torch.LongTensor, - noise: Optional[torch.Tensor], + noise: torch.Tensor | None, timesteps: torch.LongTensor, - block_mask: Optional[torch.BoolTensor] = None, + block_mask: torch.BoolTensor | None = None, ) -> torch.LongTensor: """ Apply the forward process q(x_t | x_0). @@ -395,15 +394,39 @@ def add_noise( noised = torch.where(block_mask.to(device=device), noised, original_samples) return noised + def enforce_fixed_masks( + self, + sample: torch.LongTensor, + fixed_mask: torch.BoolTensor, + fixed_values: torch.LongTensor, + ) -> torch.LongTensor: + """ + Re-apply fixed token values at positions indicated by `fixed_mask`. + + This is used by the pipeline to enforce prefix / infill conditioning after each scheduler step. + + Args: + sample (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Current token IDs after a scheduler step. + fixed_mask (`torch.BoolTensor` of shape `(batch_size, seq_len)`): + Boolean mask where `True` indicates a position whose value must be restored. + fixed_values (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Token IDs to restore at the fixed positions. + + Returns: + `torch.LongTensor`: Token IDs with fixed positions restored. + """ + return torch.where(fixed_mask, fixed_values, sample) + def step( self, model_output: torch.Tensor, - timestep: Union[int, torch.Tensor], + timestep: int | torch.Tensor, sample: torch.LongTensor, - generator: Optional[torch.Generator] = None, + generator: torch.Generator | None = None, return_dict: bool = True, - block_mask: Optional[torch.BoolTensor] = None, - ) -> Union[TokenDiffusionSchedulerOutput, Tuple[torch.LongTensor]]: + block_mask: torch.BoolTensor | None = None, + ) -> TokenDiffusionSchedulerOutput | tuple[torch.LongTensor]: """ Reverse diffusion step for the configured forward process. diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 3fdaf46dfb5d..0354542166ce 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2293,36 +2293,6 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BlockTokenDiffusionPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class BlockTokenDiffusionPipelineOutput(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - class CLIPImageProjection(metaclass=DummyObject): _backends = ["torch"] @@ -2653,21 +2623,6 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BlockTokenDiffusionScheduler(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - class CMStochasticIterativeScheduler(metaclass=DummyObject): _backends = ["torch"] diff --git a/tests/pipelines/test_pipeline_block_token_diffusion.py b/tests/pipelines/test_pipeline_block_token_diffusion.py deleted file mode 100644 index e005310be974..000000000000 --- a/tests/pipelines/test_pipeline_block_token_diffusion.py +++ /dev/null @@ -1,59 +0,0 @@ -import unittest - -import torch - -from diffusers import BlockTokenDiffusionPipeline, BlockTokenDiffusionScheduler - - -class _DummyTokenizer: - cls_token_id = 1 - bos_token_id = None - - def batch_decode(self, sequences, skip_special_tokens=True): - return [" ".join(map(str, row)) for row in sequences.tolist()] - - -class _DummyModelOutput: - def __init__(self, logits): - self.logits = logits - - -class _DummyMLM(torch.nn.Module): - def __init__(self, vocab_size: int): - super().__init__() - self.vocab_size = vocab_size - self.register_buffer("_device_anchor", torch.empty(0)) - - @property - def dtype(self): - return torch.float32 - - @property - def device(self): - return self._device_anchor.device - - def forward(self, input_ids=None, attention_mask=None, **kwargs): - batch_size, seq_len = input_ids.shape - logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) - return _DummyModelOutput(logits=logits) - - -class BlockTokenDiffusionPipelineTest(unittest.TestCase): - def test_pipeline_runs_and_respects_prefix(self): - vocab_size = 32 - scheduler = BlockTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) - model = _DummyMLM(vocab_size=vocab_size) - tokenizer = _DummyTokenizer() - pipe = BlockTokenDiffusionPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer).to("cpu") - - prefix = torch.tensor([5, 6, 7], dtype=torch.long) - gen = torch.Generator().manual_seed(0) - out = pipe(batch_size=2, seq_len=10, block_size=4, num_inference_steps=2, generator=gen, prefix_ids=prefix) - - self.assertEqual(out.sequences.shape, (2, 10)) - self.assertTrue((out.sequences[:, :3] == prefix.view(1, -1)).all().item()) - self.assertEqual(len(out.texts), 2) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/schedulers/test_scheduler_block_token_diffusion.py b/tests/schedulers/test_scheduler_block_token_diffusion.py deleted file mode 100644 index 7b5e833b21d0..000000000000 --- a/tests/schedulers/test_scheduler_block_token_diffusion.py +++ /dev/null @@ -1,30 +0,0 @@ -import unittest - -import torch - -from diffusers import BlockTokenDiffusionScheduler - - -class BlockTokenDiffusionSchedulerTest(unittest.TestCase): - def test_step_respects_block_mask(self): - vocab_size = 32 - scheduler = BlockTokenDiffusionScheduler(vocab_size=vocab_size, mask_token_id=vocab_size - 1) - scheduler.set_timesteps(1) - - batch_size, seq_len = 2, 8 - x = torch.full((batch_size, seq_len), scheduler.mask_token_id, dtype=torch.long) - block_mask = torch.zeros_like(x, dtype=torch.bool) - block_mask[:, :4] = True - - logits = torch.zeros((batch_size, seq_len, vocab_size), dtype=torch.float32) - gen = torch.Generator().manual_seed(0) - out = scheduler.step(logits, scheduler.timesteps[0], x, generator=gen, return_dict=True, block_mask=block_mask) - - # Block positions should be denoised (non-mask) after the final noise-removal step. - self.assertTrue((out.prev_sample[:, :4] != scheduler.mask_token_id).all().item()) - # Outside the block, tokens should remain unchanged (still mask). - self.assertTrue((out.prev_sample[:, 4:] == scheduler.mask_token_id).all().item()) - - -if __name__ == "__main__": - unittest.main() From a112493c51186e24a1d7d53388edf6e7a6122b43 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 11:24:10 +0000 Subject: [PATCH 27/44] fix: rename denoising_steps to num_inference_steps, remove attention_mask_mode, fix broken doc link --- docs/source/en/api/pipelines/overview.md | 1 - examples/discrete_diffusion/README.md | 2 +- examples/discrete_diffusion/sample_sdar.py | 12 ++---------- .../schedulers/scheduling_sdar_token_diffusion.py | 4 ++-- 4 files changed, 5 insertions(+), 14 deletions(-) diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index 3a0683257d1d..6042cc44c1a4 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -34,7 +34,6 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [AudioLDM2](audioldm2) | text2audio | | [AuraFlow](aura_flow) | text2image | | [BLIP Diffusion](blip_diffusion) | text2image | -| [Block Refinement](block_refinement) | text2text | | [Bria 3.2](bria_3_2) | text2image | | [CogVideoX](cogvideox) | text2video | | [Consistency Models](consistency_models) | unconditional image generation | diff --git a/examples/discrete_diffusion/README.md b/examples/discrete_diffusion/README.md index 7e3f30e4656e..9257fbcefb63 100644 --- a/examples/discrete_diffusion/README.md +++ b/examples/discrete_diffusion/README.md @@ -123,7 +123,7 @@ python examples/discrete_diffusion/sample_sdar.py \ --prompt "Explain what reinforcement learning is in simple terms." \ --max_new_tokens 256 \ --block_length 4 \ - --denoising_steps 4 \ + --num_inference_steps 4 \ --remasking_strategy low_confidence_dynamic \ --confidence_threshold 0.9 \ --use_chat_template \ diff --git a/examples/discrete_diffusion/sample_sdar.py b/examples/discrete_diffusion/sample_sdar.py index 24977bc6899e..2d671df542bc 100644 --- a/examples/discrete_diffusion/sample_sdar.py +++ b/examples/discrete_diffusion/sample_sdar.py @@ -47,7 +47,7 @@ def main(): ) parser.add_argument("--max_new_tokens", type=int, default=256) parser.add_argument("--block_length", type=int, default=4) - parser.add_argument("--denoising_steps", type=int, default=4) + parser.add_argument("--num_inference_steps", type=int, default=4) parser.add_argument("--temperature", type=float, default=1.0) parser.add_argument("--top_k", type=int, default=0) parser.add_argument("--top_p", type=float, default=1.0) @@ -83,13 +83,6 @@ def main(): choices=["auto", "float32", "float16", "bfloat16"], help="Model dtype.", ) - parser.add_argument( - "--attention_mask_mode", - type=str, - default="3d", - choices=["auto", "3d", "4d", "2d", "none"], - help="Attention mask mode to use.", - ) parser.add_argument( "--revision", type=str, @@ -122,7 +115,7 @@ def main(): prompt=args.prompt, max_new_tokens=args.max_new_tokens, block_length=args.block_length, - denoising_steps=args.denoising_steps, + num_inference_steps=args.num_inference_steps, temperature=args.temperature, top_k=args.top_k, top_p=args.top_p, @@ -132,7 +125,6 @@ def main(): mask_token_id=args.mask_token_id, use_chat_template=args.use_chat_template, add_generation_prompt=args.add_generation_prompt, - attention_mask_mode=args.attention_mask_mode, ) print("\nGenerated text:") diff --git a/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py index 886523a0d80a..e52eb95e04c3 100644 --- a/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_sdar_token_diffusion.py @@ -57,7 +57,7 @@ class SDARTokenDiffusionScheduler(SchedulerMixin, ConfigMixin): def __init__( self, block_length: int = 4, - denoising_steps: int = 4, + num_inference_steps: int = 4, remasking_strategy: str = "low_confidence_dynamic", confidence_threshold: float = 0.9, entropy_threshold: float = 0.35, @@ -65,7 +65,7 @@ def __init__( top_k: int = 0, top_p: float = 1.0, ): - self.num_inference_steps = int(denoising_steps) + self.num_inference_steps = num_inference_steps self.timesteps = torch.arange(self.num_inference_steps - 1, -1, -1, dtype=torch.long) def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: From 6aa3e52f48467d76e317993227e01f29cfedc631 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 11:38:50 +0000 Subject: [PATCH 28/44] Add tests for DFlash and SDAR pipelines and schedulers --- tests/pipelines/dflash/__init__.py | 1 + tests/pipelines/dflash/test_dflash.py | 375 ++++++++++++++++++ tests/pipelines/sdar/__init__.py | 0 tests/pipelines/sdar/test_sdar.py | 261 ++++++++++++ .../test_scheduler_dflash_token_diffusion.py | 308 ++++++++++++++ .../test_scheduler_sdar_token_diffusion.py | 317 +++++++++++++++ 6 files changed, 1262 insertions(+) create mode 100644 tests/pipelines/dflash/__init__.py create mode 100644 tests/pipelines/dflash/test_dflash.py create mode 100644 tests/pipelines/sdar/__init__.py create mode 100644 tests/pipelines/sdar/test_sdar.py create mode 100644 tests/schedulers/test_scheduler_dflash_token_diffusion.py create mode 100644 tests/schedulers/test_scheduler_sdar_token_diffusion.py diff --git a/tests/pipelines/dflash/__init__.py b/tests/pipelines/dflash/__init__.py new file mode 100644 index 000000000000..8b137891791f --- /dev/null +++ b/tests/pipelines/dflash/__init__.py @@ -0,0 +1 @@ + diff --git a/tests/pipelines/dflash/test_dflash.py b/tests/pipelines/dflash/test_dflash.py new file mode 100644 index 000000000000..288865844721 --- /dev/null +++ b/tests/pipelines/dflash/test_dflash.py @@ -0,0 +1,375 @@ +import unittest + +import torch + +from diffusers import DFlashTokenDiffusionScheduler +from diffusers.pipelines.dflash.pipeline_dflash import DFlashPipeline + + +class _DummyModelOutput: + def __init__(self, logits, hidden_states=None): + self.logits = logits + self.hidden_states = hidden_states + + +class _DummyConfig: + def __init__(self, block_size, num_target_layers, num_hidden_layers): + self.block_size = block_size + self.num_target_layers = num_target_layers + self.num_hidden_layers = num_hidden_layers + + +class _DummyTargetModel(torch.nn.Module): + """Minimal target (causal LM) model that returns logits and hidden_states.""" + + def __init__(self, vocab_size: int, hidden_dim: int, num_layers: int): + super().__init__() + self.vocab_size = vocab_size + self.hidden_dim = hidden_dim + self.num_layers = num_layers + self.embed = torch.nn.Embedding(vocab_size, hidden_dim) + self.lm_head = torch.nn.Linear(hidden_dim, vocab_size, bias=False) + + def get_input_embeddings(self): + return self.embed + + def get_output_embeddings(self): + return self.lm_head + + def forward(self, input_ids, position_ids=None, past_key_values=None, + use_cache=False, output_hidden_states=False, logits_to_keep=None, **kwargs): + bsz, seq_len = input_ids.shape + h = self.embed(input_ids) + # Create hidden_states list: one entry per layer + 1 for the embedding layer + hidden_states = [h] * (self.num_layers + 1) if output_hidden_states else None + logits = self.lm_head(h) + # Make token 0 the most likely so acceptance is deterministic + logits[:, :, 0] = 10.0 + return _DummyModelOutput(logits=logits, hidden_states=hidden_states) + + def parameters(self): + return super().parameters() + + +class _DummyDraftModel(torch.nn.Module): + """Minimal draft model that returns hidden states of the expected shape.""" + + def __init__(self, hidden_dim: int, num_target_layers: int, block_size: int): + super().__init__() + self.block_size = block_size + self.config = _DummyConfig( + block_size=block_size, + num_target_layers=num_target_layers, + num_hidden_layers=1, + ) + # The draft model receives concatenated hidden states from num_target_layers target layers, + # each of dim hidden_dim, and produces a hidden state of dim hidden_dim. + self.proj = torch.nn.Linear(hidden_dim * num_target_layers, hidden_dim, bias=False) + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def device(self): + return self._device_anchor.device + + def forward(self, target_hidden, noise_embedding, position_ids=None, + past_key_values=None, use_cache=False, is_causal=False, **kwargs): + # Return a tensor with shape (batch, seq_len, hidden_dim) + bsz = noise_embedding.shape[0] + seq_len = position_ids.shape[1] if position_ids is not None else noise_embedding.shape[1] + h = torch.zeros(bsz, seq_len, self.proj.out_features, device=noise_embedding.device) + return h + + +def _make_pipeline(tokenizer=None, vocab_size=32, hidden_dim=16, num_target_layers=4, block_size=4): + target = _DummyTargetModel(vocab_size=vocab_size, hidden_dim=hidden_dim, num_layers=num_target_layers) + draft = _DummyDraftModel(hidden_dim=hidden_dim, num_target_layers=1, block_size=block_size) + # Set target_layer_ids directly so we skip the config-based computation. + draft.target_layer_ids = [1] + scheduler = DFlashTokenDiffusionScheduler() + return DFlashPipeline(draft_model=draft, target_model=target, tokenizer=tokenizer, scheduler=scheduler) + + +class DFlashPipelineTest(unittest.TestCase): + # ------------------------------------------------------------------ + # Pipeline runs + # ------------------------------------------------------------------ + def test_pipeline_runs_with_input_ids(self): + pipe = _make_pipeline() + input_ids = torch.tensor([[5, 6, 7, 8]], dtype=torch.long) + + out = pipe( + input_ids=input_ids, + max_new_tokens=8, + temperature=0.0, + mask_token_id=31, + stop_token_ids=None, + output_type="seq", + ) + + self.assertIsNotNone(out.sequences) + self.assertEqual(out.sequences.ndim, 2) + self.assertEqual(out.sequences.shape[0], 1) + # Generated tokens should not be longer than max_new_tokens + self.assertLessEqual(out.sequences.shape[1], 8) + + # ------------------------------------------------------------------ + # output_type="seq" + # ------------------------------------------------------------------ + def test_output_type_seq(self): + pipe = _make_pipeline() + input_ids = torch.tensor([[1, 2, 3]], dtype=torch.long) + + out = pipe( + input_ids=input_ids, + max_new_tokens=8, + temperature=0.0, + mask_token_id=31, + output_type="seq", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + # ------------------------------------------------------------------ + # output_type="text" with mock tokenizer + # ------------------------------------------------------------------ + def test_output_type_text_with_tokenizer(self): + tok = type( + "Tok", + (), + { + "eos_token_id": None, + "mask_token_id": 31, + "batch_decode": lambda self, seqs, **kw: [f"decoded_{len(s)}" for s in seqs], + }, + )() + pipe = _make_pipeline(tokenizer=tok) + + out = pipe( + input_ids=torch.tensor([[1, 2, 3]], dtype=torch.long), + max_new_tokens=8, + temperature=0.0, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNotNone(out.texts) + self.assertEqual(len(out.texts), 1) + self.assertTrue(out.texts[0].startswith("decoded_")) + + def test_output_type_text_without_tokenizer(self): + """output_type='text' without a tokenizer should return texts=None.""" + pipe = _make_pipeline(tokenizer=None) + + out = pipe( + input_ids=torch.tensor([[1, 2, 3]], dtype=torch.long), + max_new_tokens=8, + temperature=0.0, + mask_token_id=31, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + # ------------------------------------------------------------------ + # output_type invalid + # ------------------------------------------------------------------ + def test_output_type_invalid_raises(self): + pipe = _make_pipeline() + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[1, 2, 3]], dtype=torch.long), + max_new_tokens=8, + mask_token_id=31, + output_type="invalid", + ) + + # ------------------------------------------------------------------ + # return_dict=False + # ------------------------------------------------------------------ + def test_pipeline_return_tuple(self): + pipe = _make_pipeline() + input_ids = torch.tensor([[1, 2, 3]], dtype=torch.long) + + result = pipe( + input_ids=input_ids, + max_new_tokens=8, + temperature=0.0, + mask_token_id=31, + output_type="seq", + return_dict=False, + ) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 2) + sequences, texts = result + self.assertIsNotNone(sequences) + self.assertIsNone(texts) + + # ------------------------------------------------------------------ + # check_inputs validation + # ------------------------------------------------------------------ + def test_check_inputs_no_inputs_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=None, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_both_prompt_and_messages_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt="hello", + messages=[{"role": "user", "content": "hi"}], + input_ids=None, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_invalid_input_ids_ndim_raises(self): + pipe = _make_pipeline() + bad_ids = torch.zeros(2, 3, 4, dtype=torch.long) + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=bad_ids, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_invalid_input_ids_dtype_raises(self): + pipe = _make_pipeline() + bad_ids = torch.zeros(1, 4, dtype=torch.float32) + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=bad_ids, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_invalid_max_new_tokens_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + max_new_tokens=0, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_invalid_output_type_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + max_new_tokens=16, + output_type="bad", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_prompt_without_tokenizer_raises(self): + pipe = _make_pipeline(tokenizer=None) + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt="hello", + messages=None, + input_ids=None, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_messages_without_tokenizer_raises(self): + pipe = _make_pipeline(tokenizer=None) + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=[{"role": "user", "content": "hi"}], + input_ids=None, + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_valid_input_ids_passes(self): + pipe = _make_pipeline() + # Should not raise. + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=torch.tensor([[1, 2, 3]], dtype=torch.long), + max_new_tokens=16, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + # ------------------------------------------------------------------ + # _prepare_input_ids + # ------------------------------------------------------------------ + def test_prepare_input_ids_from_tensor(self): + pipe = _make_pipeline() + ids = torch.tensor([[1, 2, 3]], dtype=torch.long) + result = pipe._prepare_input_ids( + prompt=None, + messages=None, + input_ids=ids, + use_chat_template=False, + add_generation_prompt=False, + chat_template_kwargs=None, + ) + self.assertTrue(torch.equal(result, ids)) + + def test_prepare_input_ids_from_1d_tensor(self): + pipe = _make_pipeline() + ids = torch.tensor([1, 2, 3], dtype=torch.long) + result = pipe._prepare_input_ids( + prompt=None, + messages=None, + input_ids=ids, + use_chat_template=False, + add_generation_prompt=False, + chat_template_kwargs=None, + ) + self.assertEqual(result.shape, (1, 3)) + + # ------------------------------------------------------------------ + # prepare_latents + # ------------------------------------------------------------------ + def test_prepare_latents(self): + pipe = _make_pipeline() + mask_token_id = 99 + latents = pipe.prepare_latents(max_length=10, block_size=4, mask_token_id=mask_token_id, device=torch.device("cpu")) + self.assertEqual(latents.shape, (1, 14)) # 10 + 4 + self.assertTrue((latents == mask_token_id).all().item()) + self.assertEqual(latents.dtype, torch.long) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/pipelines/sdar/__init__.py b/tests/pipelines/sdar/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/sdar/test_sdar.py b/tests/pipelines/sdar/test_sdar.py new file mode 100644 index 000000000000..830e1ec0da54 --- /dev/null +++ b/tests/pipelines/sdar/test_sdar.py @@ -0,0 +1,261 @@ +import unittest + +import torch + +from diffusers import SDARPipeline, SDARTokenDiffusionScheduler + + +class _DummyModelOutput: + def __init__(self, logits): + self.logits = logits + + +class _DummyCausalLM(torch.nn.Module): + """Minimal causal LM that returns deterministic logits given input_ids.""" + + def __init__(self, vocab_size: int): + super().__init__() + self.vocab_size = int(vocab_size) + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def forward(self, input_ids, attention_mask=None, position_ids=None, **kwargs): + batch_size, seq_len = input_ids.shape + logits = torch.zeros((batch_size, seq_len, self.vocab_size), device=input_ids.device, dtype=torch.float32) + # Make confidence vary with token position so top-k commits are deterministic. + positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.float32).view(1, seq_len, 1) + token_ids = (torch.arange(seq_len, device=input_ids.device) % (self.vocab_size - 2)).view(1, seq_len, 1) + logits.scatter_(2, token_ids.expand(batch_size, -1, -1), 1.0 + positions.expand(batch_size, -1, -1) * 0.1) + return _DummyModelOutput(logits=logits) + + +def _make_pipeline(tokenizer=None): + model = _DummyCausalLM(vocab_size=32) + scheduler = SDARTokenDiffusionScheduler() + return SDARPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) + + +class SDARPipelineTest(unittest.TestCase): + # ------------------------------------------------------------------ + # Basic pipeline run + # ------------------------------------------------------------------ + def test_pipeline_runs_with_input_ids(self): + pipe = _make_pipeline().to("cpu") + + input_ids = torch.tensor([[5, 6, 7, 8]], dtype=torch.long) + out = pipe( + input_ids=input_ids, + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + temperature=0.0, + mask_token_id=31, + output_type="seq", + ) + + self.assertIsNotNone(out.sequences) + self.assertEqual(out.sequences.ndim, 2) + # Generated tokens only (prompt stripped) + self.assertGreater(out.sequences.shape[1], 0) + + # ------------------------------------------------------------------ + # output_type="seq" → texts is None + # ------------------------------------------------------------------ + def test_output_type_seq(self): + pipe = _make_pipeline().to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + temperature=0.0, + mask_token_id=31, + output_type="seq", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + # ------------------------------------------------------------------ + # output_type="text" with dummy tokenizer + # ------------------------------------------------------------------ + def test_output_type_text_with_tokenizer(self): + tok = type( + "Tok", + (), + { + "eos_token_id": None, + "mask_token_id": 31, + "batch_decode": lambda self, seqs, **kw: [f"decoded_{len(s)}" for s in seqs], + }, + )() + pipe = _make_pipeline(tokenizer=tok).to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + temperature=0.0, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNotNone(out.texts) + self.assertEqual(len(out.texts), 1) + self.assertTrue(out.texts[0].startswith("decoded_")) + + # ------------------------------------------------------------------ + # output_type="text" without tokenizer → texts is None + # ------------------------------------------------------------------ + def test_output_type_text_without_tokenizer(self): + pipe = _make_pipeline(tokenizer=None).to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + temperature=0.0, + mask_token_id=31, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + # ------------------------------------------------------------------ + # Invalid output_type raises ValueError + # ------------------------------------------------------------------ + def test_output_type_invalid_raises(self): + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + mask_token_id=31, + output_type="invalid", + ) + + # ------------------------------------------------------------------ + # check_inputs validation + # ------------------------------------------------------------------ + def test_check_inputs_no_source_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=None, + block_length=4, + num_inference_steps=4, + mask_token_id=31, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_prompt_and_messages_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt="hello", + messages=[{"role": "user", "content": "hi"}], + input_ids=None, + block_length=4, + num_inference_steps=4, + mask_token_id=31, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_bad_input_ids_ndim_raises(self): + pipe = _make_pipeline() + bad_ids = torch.zeros(2, 3, 4, dtype=torch.long) + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=bad_ids, + block_length=4, + num_inference_steps=4, + mask_token_id=31, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_bad_block_length_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + block_length=0, + num_inference_steps=4, + mask_token_id=31, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + def test_check_inputs_no_mask_token_raises(self): + pipe = _make_pipeline() + with self.assertRaises(ValueError): + pipe.check_inputs( + prompt=None, + messages=None, + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + block_length=4, + num_inference_steps=4, + mask_token_id=None, + output_type="seq", + callback_on_step_end=None, + callback_on_step_end_tensor_inputs=None, + ) + + # ------------------------------------------------------------------ + # return_dict=False returns tuple + # ------------------------------------------------------------------ + def test_return_dict_false(self): + pipe = _make_pipeline().to("cpu") + + result = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + use_chat_template=False, + max_new_tokens=16, + block_length=4, + num_inference_steps=4, + temperature=0.0, + mask_token_id=31, + output_type="seq", + return_dict=False, + ) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 2) + sequences, texts = result + self.assertIsNotNone(sequences) + self.assertIsNone(texts) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_dflash_token_diffusion.py b/tests/schedulers/test_scheduler_dflash_token_diffusion.py new file mode 100644 index 000000000000..00f4ccad221f --- /dev/null +++ b/tests/schedulers/test_scheduler_dflash_token_diffusion.py @@ -0,0 +1,308 @@ +import tempfile +import unittest + +import torch + +from diffusers import DFlashTokenDiffusionScheduler + + +class DFlashTokenDiffusionSchedulerTest(unittest.TestCase): + def get_scheduler(self): + return DFlashTokenDiffusionScheduler() + + # ------------------------------------------------------------------ + # set_timesteps + # ------------------------------------------------------------------ + def test_set_timesteps(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(4) + self.assertEqual(scheduler.num_inference_steps, 4) + self.assertEqual(len(scheduler.timesteps), 4) + self.assertEqual(scheduler.timesteps[0].item(), 3) + self.assertEqual(scheduler.timesteps[-1].item(), 0) + + def test_set_timesteps_single(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(1) + self.assertEqual(scheduler.num_inference_steps, 1) + self.assertEqual(len(scheduler.timesteps), 1) + self.assertEqual(scheduler.timesteps[0].item(), 0) + + def test_set_timesteps_invalid(self): + scheduler = self.get_scheduler() + with self.assertRaises(ValueError): + scheduler.set_timesteps(0) + with self.assertRaises(ValueError): + scheduler.set_timesteps(-1) + + # ------------------------------------------------------------------ + # Config round-trip + # ------------------------------------------------------------------ + def test_save_load_config_round_trip(self): + scheduler = self.get_scheduler() + with tempfile.TemporaryDirectory() as tmpdir: + scheduler.save_config(tmpdir) + loaded = DFlashTokenDiffusionScheduler.from_pretrained(tmpdir) + # The scheduler has no user-configurable params, but it should survive the round-trip. + self.assertIsInstance(loaded, DFlashTokenDiffusionScheduler) + self.assertEqual(loaded.order, 1) + + def test_from_config(self): + scheduler = self.get_scheduler() + new_scheduler = DFlashTokenDiffusionScheduler.from_config(scheduler.config) + self.assertIsInstance(new_scheduler, DFlashTokenDiffusionScheduler) + self.assertEqual(new_scheduler.order, 1) + + # ------------------------------------------------------------------ + # sample() – greedy + # ------------------------------------------------------------------ + def test_sample_greedy(self): + scheduler = self.get_scheduler() + logits = torch.tensor([[[1.0, 5.0, 2.0], [3.0, 1.0, 4.0]]]) # (1, 2, 3) + tokens = scheduler.sample(logits, temperature=0.0) + self.assertEqual(tokens.shape, (1, 2)) + self.assertEqual(tokens[0, 0].item(), 1) # argmax of [1,5,2] + self.assertEqual(tokens[0, 1].item(), 2) # argmax of [3,1,4] + + def test_sample_greedy_batched(self): + scheduler = self.get_scheduler() + logits = torch.tensor([ + [[10.0, 0.0], [0.0, 10.0]], + [[0.0, 10.0], [10.0, 0.0]], + ]) # (2, 2, 2) + tokens = scheduler.sample(logits, temperature=0.0) + self.assertEqual(tokens.shape, (2, 2)) + self.assertEqual(tokens[0, 0].item(), 0) + self.assertEqual(tokens[0, 1].item(), 1) + self.assertEqual(tokens[1, 0].item(), 1) + self.assertEqual(tokens[1, 1].item(), 0) + + # ------------------------------------------------------------------ + # sample() – multinomial + # ------------------------------------------------------------------ + def test_sample_multinomial(self): + scheduler = self.get_scheduler() + # One token has overwhelming probability; multinomial should pick it. + logits = torch.tensor([[[0.0, 100.0, -100.0]]]) # (1, 1, 3) + tokens = scheduler.sample(logits, temperature=1.0) + self.assertEqual(tokens.shape, (1, 1)) + self.assertEqual(tokens[0, 0].item(), 1) + + # ------------------------------------------------------------------ + # step() – return dict + # ------------------------------------------------------------------ + def test_step_all_accepted(self): + """All draft tokens match the posterior => accepted_length == block_size - 1.""" + scheduler = self.get_scheduler() + batch_size, block_size, vocab_size = 1, 4, 8 + + # Draft tokens: [0, 3, 3, 3] + draft_tokens = torch.tensor([[0, 3, 3, 3]], dtype=torch.long) + # Target logits: make argmax = [3, 3, 3, X] so posterior[:, :-1] matches draft[:, 1:] + logits = torch.zeros(batch_size, block_size, vocab_size) + logits[:, 0, 3] = 10.0 + logits[:, 1, 3] = 10.0 + logits[:, 2, 3] = 10.0 + logits[:, 3, 5] = 10.0 # last posterior token (next_token candidate) + + out = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=True) + + self.assertEqual(out.prev_sample.shape, (1, 4)) + self.assertEqual(out.accepted_length.shape, (1,)) + self.assertEqual(out.accepted_length[0].item(), 3) # all 3 comparisons match + self.assertEqual(out.next_token.shape, (1,)) + self.assertEqual(out.next_token[0].item(), 5) + self.assertEqual(out.posterior.shape, (1, 4)) + + def test_step_none_accepted(self): + """First draft token already mismatches => accepted_length == 0.""" + scheduler = self.get_scheduler() + batch_size, block_size, vocab_size = 1, 4, 8 + + draft_tokens = torch.tensor([[0, 1, 2, 3]], dtype=torch.long) + logits = torch.zeros(batch_size, block_size, vocab_size) + logits[:, 0, 5] = 10.0 # posterior[0] = 5, but draft[1] = 1 => mismatch + logits[:, 1, 2] = 10.0 + logits[:, 2, 3] = 10.0 + logits[:, 3, 4] = 10.0 + + out = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=True) + + self.assertEqual(out.accepted_length[0].item(), 0) + self.assertEqual(out.next_token[0].item(), 5) # posterior at index 0 + + def test_step_partial_accepted(self): + """First two match, third does not => accepted_length == 2.""" + scheduler = self.get_scheduler() + batch_size, block_size, vocab_size = 1, 5, 8 + + # draft: [0, 3, 4, 7, 2] + draft_tokens = torch.tensor([[0, 3, 4, 7, 2]], dtype=torch.long) + logits = torch.zeros(batch_size, block_size, vocab_size) + logits[:, 0, 3] = 10.0 # match draft[1]=3 + logits[:, 1, 4] = 10.0 # match draft[2]=4 + logits[:, 2, 0] = 10.0 # mismatch draft[3]=7 + logits[:, 3, 2] = 10.0 + logits[:, 4, 6] = 10.0 + + out = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=True) + + self.assertEqual(out.accepted_length[0].item(), 2) + self.assertEqual(out.next_token[0].item(), 0) # posterior at index 2 + + def test_step_single_token_block(self): + """Block with a single token => accepted_length == 0.""" + scheduler = self.get_scheduler() + draft_tokens = torch.tensor([[5]], dtype=torch.long) + logits = torch.zeros(1, 1, 8) + logits[:, 0, 3] = 10.0 + + out = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=True) + self.assertEqual(out.accepted_length[0].item(), 0) + self.assertEqual(out.next_token[0].item(), 3) + + # ------------------------------------------------------------------ + # step() – return tuple + # ------------------------------------------------------------------ + def test_step_return_tuple(self): + scheduler = self.get_scheduler() + draft_tokens = torch.tensor([[0, 1, 2]], dtype=torch.long) + logits = torch.randn(1, 3, 8) + + result = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=False) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 4) + prev_sample, accepted_length, next_token, posterior = result + self.assertEqual(prev_sample.shape, (1, 3)) + self.assertEqual(accepted_length.shape, (1,)) + self.assertEqual(next_token.shape, (1,)) + self.assertEqual(posterior.shape, (1, 3)) + + # ------------------------------------------------------------------ + # step() – batched + # ------------------------------------------------------------------ + def test_step_batched(self): + scheduler = self.get_scheduler() + batch_size, block_size, vocab_size = 3, 4, 16 + draft_tokens = torch.randint(0, vocab_size, (batch_size, block_size)) + logits = torch.randn(batch_size, block_size, vocab_size) + + out = scheduler.step(draft_tokens, logits, temperature=0.0, return_dict=True) + + self.assertEqual(out.prev_sample.shape, (batch_size, block_size)) + self.assertEqual(out.accepted_length.shape, (batch_size,)) + self.assertEqual(out.next_token.shape, (batch_size,)) + self.assertEqual(out.posterior.shape, (batch_size, block_size)) + + # ------------------------------------------------------------------ + # check_should_stop() + # ------------------------------------------------------------------ + def test_check_should_stop_no_stop_tokens(self): + output_ids = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long) + self.assertFalse(DFlashTokenDiffusionScheduler.check_should_stop(output_ids, None, 2)) + + def test_check_should_stop_found(self): + # Stop token 99 is in the generated portion (after num_input_tokens=2). + output_ids = torch.tensor([[1, 2, 3, 99, 5]], dtype=torch.long) + self.assertTrue(DFlashTokenDiffusionScheduler.check_should_stop(output_ids, [99], 2)) + + def test_check_should_stop_only_in_prompt(self): + # Stop token 1 is only in the prompt portion => should NOT stop. + output_ids = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long) + self.assertFalse(DFlashTokenDiffusionScheduler.check_should_stop(output_ids, [1], 2)) + + def test_check_should_stop_multiple_stop_tokens(self): + output_ids = torch.tensor([[10, 20, 30, 40, 50]], dtype=torch.long) + self.assertTrue(DFlashTokenDiffusionScheduler.check_should_stop(output_ids, [40, 99], 2)) + self.assertFalse(DFlashTokenDiffusionScheduler.check_should_stop(output_ids, [99, 100], 2)) + + # ------------------------------------------------------------------ + # add_noise() + # ------------------------------------------------------------------ + def test_add_noise_prompt_preserved(self): + scheduler = self.get_scheduler() + original = torch.tensor([[10, 11, 12, 13, 14, 15, 16, 17]], dtype=torch.long) + attention_mask = torch.ones_like(original) + mask_token_id = 99 + prompt_length = 3 + + gen = torch.Generator().manual_seed(42) + noisy, masked = scheduler.add_noise( + original, + attention_mask, + prompt_length=prompt_length, + block_size=4, + mask_token_id=mask_token_id, + generator=gen, + ) + + # Prompt positions should never be masked. + self.assertFalse(masked[0, :prompt_length].any().item()) + # Prompt tokens should be unchanged. + self.assertTrue(torch.equal(noisy[0, :prompt_length], original[0, :prompt_length])) + + def test_add_noise_masked_positions(self): + scheduler = self.get_scheduler() + original = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=torch.long) + attention_mask = torch.ones_like(original) + mask_token_id = 99 + + gen = torch.Generator().manual_seed(0) + noisy, masked = scheduler.add_noise( + original, + attention_mask, + prompt_length=2, + block_size=3, + mask_token_id=mask_token_id, + generator=gen, + ) + + # Where masked is True, noisy should equal mask_token_id. + self.assertTrue((noisy[masked] == mask_token_id).all().item()) + # Where masked is False, noisy should equal original. + self.assertTrue(torch.equal(noisy[~masked], original[~masked])) + + def test_add_noise_respects_attention_mask(self): + scheduler = self.get_scheduler() + original = torch.tensor([[1, 2, 3, 4, 0, 0]], dtype=torch.long) + attention_mask = torch.tensor([[1, 1, 1, 1, 0, 0]], dtype=torch.long) + mask_token_id = 99 + + gen = torch.Generator().manual_seed(42) + noisy, masked = scheduler.add_noise( + original, + attention_mask, + prompt_length=1, + block_size=3, + mask_token_id=mask_token_id, + generator=gen, + ) + + # Padding positions (attention_mask=0) should never be masked. + self.assertFalse(masked[0, 4].item()) + self.assertFalse(masked[0, 5].item()) + + def test_add_noise_output_shapes(self): + scheduler = self.get_scheduler() + batch_size, seq_len = 2, 10 + original = torch.randint(0, 50, (batch_size, seq_len)) + attention_mask = torch.ones(batch_size, seq_len, dtype=torch.long) + mask_token_id = 99 + + noisy, masked = scheduler.add_noise( + original, + attention_mask, + prompt_length=2, + block_size=4, + mask_token_id=mask_token_id, + ) + + self.assertEqual(noisy.shape, (batch_size, seq_len)) + self.assertEqual(masked.shape, (batch_size, seq_len)) + self.assertEqual(noisy.dtype, torch.long) + self.assertEqual(masked.dtype, torch.bool) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_sdar_token_diffusion.py b/tests/schedulers/test_scheduler_sdar_token_diffusion.py new file mode 100644 index 000000000000..a51fe13514cb --- /dev/null +++ b/tests/schedulers/test_scheduler_sdar_token_diffusion.py @@ -0,0 +1,317 @@ +import tempfile +import unittest + +import torch + +from diffusers import SDARTokenDiffusionScheduler + + +class SDARTokenDiffusionSchedulerTest(unittest.TestCase): + def get_scheduler(self, **kwargs): + config = { + "block_length": 32, + "num_inference_steps": 8, + "remasking_strategy": "low_confidence_dynamic", + "confidence_threshold": 0.9, + "entropy_threshold": 0.35, + "temperature": 1.0, + "top_k": 0, + "top_p": 1.0, + } + config.update(kwargs) + return SDARTokenDiffusionScheduler(**config) + + # ------------------------------------------------------------------ + # set_timesteps + # ------------------------------------------------------------------ + def test_set_timesteps(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + self.assertEqual(scheduler.num_inference_steps, 8) + self.assertEqual(len(scheduler.timesteps), 8) + self.assertEqual(scheduler.timesteps[0].item(), 7) + self.assertEqual(scheduler.timesteps[-1].item(), 0) + + def test_set_timesteps_invalid(self): + scheduler = self.get_scheduler() + with self.assertRaises(ValueError): + scheduler.set_timesteps(0) + + # ------------------------------------------------------------------ + # get_num_transfer_tokens + # ------------------------------------------------------------------ + def test_get_num_transfer_tokens_even(self): + scheduler = self.get_scheduler() + schedule = scheduler.get_num_transfer_tokens(block_length=32, num_inference_steps=8) + self.assertEqual(schedule.sum().item(), 32) + self.assertEqual(len(schedule), 8) + self.assertTrue((schedule == 4).all().item()) + + def test_get_num_transfer_tokens_remainder(self): + scheduler = self.get_scheduler() + schedule = scheduler.get_num_transfer_tokens(block_length=10, num_inference_steps=3) + self.assertEqual(schedule.sum().item(), 10) + self.assertEqual(len(schedule), 3) + # First `remainder` entries get +1 + self.assertEqual(schedule[0].item(), 4) + self.assertEqual(schedule[1].item(), 3) + self.assertEqual(schedule[2].item(), 3) + + # ------------------------------------------------------------------ + # save / load config round trip + # ------------------------------------------------------------------ + def test_save_load_config_round_trip(self): + scheduler = self.get_scheduler( + block_length=64, + remasking_strategy="sequential", + confidence_threshold=0.8, + entropy_threshold=0.5, + ) + with tempfile.TemporaryDirectory() as tmpdir: + scheduler.save_config(tmpdir) + loaded = SDARTokenDiffusionScheduler.from_pretrained(tmpdir) + + self.assertEqual(loaded.config.block_length, 64) + self.assertEqual(loaded.config.remasking_strategy, "sequential") + self.assertEqual(loaded.config.confidence_threshold, 0.8) + self.assertEqual(loaded.config.entropy_threshold, 0.5) + + # ------------------------------------------------------------------ + # from_config + # ------------------------------------------------------------------ + def test_from_config(self): + scheduler = self.get_scheduler(block_length=16, remasking_strategy="entropy_bounded") + new_scheduler = SDARTokenDiffusionScheduler.from_config(scheduler.config) + self.assertEqual(new_scheduler.config.block_length, 16) + self.assertEqual(new_scheduler.config.remasking_strategy, "entropy_bounded") + + # ------------------------------------------------------------------ + # step – remasking strategies + # ------------------------------------------------------------------ + def _make_step_inputs(self, batch_size=1, block_length=8, vocab_size=32, mask_id=31, num_steps=2): + sample = torch.full((batch_size, block_length), mask_id, dtype=torch.long) + logits = torch.zeros(batch_size, block_length, vocab_size) + for i in range(block_length): + logits[:, i, i % (vocab_size - 1)] = 10.0 - i # decreasing confidence + scheduler = self.get_scheduler(block_length=block_length, num_inference_steps=num_steps) + scheduler.set_timesteps(num_steps) + num_transfer_tokens = scheduler.get_num_transfer_tokens(block_length, num_steps) + return scheduler, logits, sample, num_transfer_tokens, mask_id + + def test_step_sequential(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + remasking_strategy="sequential", + temperature=0.0, + return_dict=True, + ) + # With 8 tokens and 2 steps, first step commits 4 tokens sequentially from the first mask + committed = out.transfer_index[0].sum().item() + self.assertEqual(committed, 4) + # Sequential: first 4 positions should be committed + self.assertTrue(out.transfer_index[0, :4].all().item()) + self.assertFalse(out.transfer_index[0, 4:].any().item()) + + def test_step_low_confidence_static(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + remasking_strategy="low_confidence_static", + temperature=0.0, + return_dict=True, + ) + committed = out.transfer_index[0].sum().item() + self.assertEqual(committed, 4) + + def test_step_low_confidence_dynamic(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + remasking_strategy="low_confidence_dynamic", + confidence_threshold=0.9, + temperature=0.0, + return_dict=True, + ) + # Should commit at least step_transfer tokens + committed = out.transfer_index[0].sum().item() + self.assertGreaterEqual(committed, 4) + + def test_step_entropy_bounded(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + remasking_strategy="entropy_bounded", + entropy_threshold=0.35, + temperature=0.0, + return_dict=True, + ) + committed = out.transfer_index[0].sum().item() + self.assertGreater(committed, 0) + + def test_step_unknown_strategy_raises(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + with self.assertRaises(ValueError): + scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + remasking_strategy="nonexistent", + temperature=0.0, + ) + + # ------------------------------------------------------------------ + # step – output shapes + # ------------------------------------------------------------------ + def test_step_output_shapes(self): + batch_size, block_length, vocab_size = 2, 8, 32 + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs( + batch_size=batch_size, block_length=block_length, vocab_size=vocab_size + ) + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + temperature=0.0, + return_dict=True, + ) + self.assertEqual(out.prev_sample.shape, (batch_size, block_length)) + self.assertEqual(out.transfer_index.shape, (batch_size, block_length)) + self.assertEqual(out.sampled_tokens.shape, (batch_size, block_length)) + self.assertEqual(out.sampled_probs.shape, (batch_size, block_length)) + + # ------------------------------------------------------------------ + # step – return_dict=False + # ------------------------------------------------------------------ + def test_step_return_tuple(self): + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs() + result = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + temperature=0.0, + return_dict=False, + ) + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 4) + + # ------------------------------------------------------------------ + # step – batched + # ------------------------------------------------------------------ + def test_step_batched(self): + batch_size = 3 + scheduler, logits, sample, ntt, mask_id = self._make_step_inputs(batch_size=batch_size) + out = scheduler.step( + model_output=logits, + timestep=0, + sample=sample, + mask_token_id=mask_id, + num_transfer_tokens=ntt, + temperature=0.0, + return_dict=True, + ) + self.assertEqual(out.prev_sample.shape, (batch_size, 8)) + self.assertEqual(out.transfer_index.shape, (batch_size, 8)) + + # ------------------------------------------------------------------ + # sample – greedy and multinomial + # ------------------------------------------------------------------ + def test_sample_greedy(self): + scheduler = self.get_scheduler() + logits = torch.tensor([[[1.0, 5.0, 2.0]]]) # (1, 1, 3) + tokens, probs = scheduler.sample(logits, temperature=0.0) + self.assertEqual(tokens.item(), 1) + self.assertEqual(tokens.shape, (1, 1)) + self.assertEqual(probs.shape, (1, 1)) + + def test_sample_multinomial(self): + scheduler = self.get_scheduler() + logits = torch.tensor([[[0.0, 100.0, -100.0]]]) + gen = torch.Generator().manual_seed(42) + tokens, probs = scheduler.sample(logits, temperature=1.0, generator=gen) + self.assertEqual(tokens.item(), 1) + + # ------------------------------------------------------------------ + # check_should_stop + # ------------------------------------------------------------------ + def test_check_should_stop_with_stop_tokens(self): + scheduler = self.get_scheduler() + sequences = torch.tensor([[1, 2, 3, 99, 5]], dtype=torch.long) + self.assertTrue(scheduler.check_should_stop(sequences, prompt_length=2, stop_token_ids=[99])) + + def test_check_should_stop_without_stop_tokens(self): + scheduler = self.get_scheduler() + sequences = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long) + self.assertFalse(scheduler.check_should_stop(sequences, prompt_length=2, stop_token_ids=None)) + + def test_check_should_stop_no_match(self): + scheduler = self.get_scheduler() + sequences = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long) + self.assertFalse(scheduler.check_should_stop(sequences, prompt_length=2, stop_token_ids=[99])) + + def test_check_should_stop_in_prompt_only(self): + scheduler = self.get_scheduler() + # Stop token present only in the prompt region — should NOT trigger stop + sequences = torch.tensor([[99, 2, 3, 4, 5]], dtype=torch.long) + self.assertFalse(scheduler.check_should_stop(sequences, prompt_length=2, stop_token_ids=[99])) + + # ------------------------------------------------------------------ + # add_noise + # ------------------------------------------------------------------ + def test_add_noise(self): + scheduler = self.get_scheduler(block_length=4) + input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=torch.long) + attention_mask = torch.ones_like(input_ids) + mask_token_id = 99 + + gen = torch.Generator().manual_seed(42) + noisy, noisy_rev, masked, masked_rev = scheduler.add_noise( + input_ids, + attention_mask, + prompt_length=2, + block_length=4, + mask_token_id=mask_token_id, + generator=gen, + ) + + # Prompt positions should never be masked + self.assertFalse(masked[0, 0].item()) + self.assertFalse(masked[0, 1].item()) + self.assertFalse(masked_rev[0, 0].item()) + self.assertFalse(masked_rev[0, 1].item()) + + # Noisy should have mask_token_id where masked is True + self.assertTrue((noisy[masked] == mask_token_id).all().item()) + self.assertTrue((noisy_rev[masked_rev] == mask_token_id).all().item()) + + # masked and masked_rev should be complementary within valid non-prompt positions + non_prompt = torch.zeros_like(masked) + non_prompt[0, 2:] = True + combined = masked | masked_rev + self.assertTrue((combined[0, 2:] == non_prompt[0, 2:]).all().item()) + + +if __name__ == "__main__": + unittest.main() From 1d96bbeabeea5ee46ce878ad4bc11592ada267c7 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 11:42:47 +0000 Subject: [PATCH 29/44] Clean up DiscreteDiffusionPipelineMixin: remove sampling methods, LLaDA2 reuses mixin --- .../pipelines/llada2/pipeline_llada2.py | 63 +------------ src/diffusers/pipelines/pipeline_utils.py | 94 ++----------------- 2 files changed, 9 insertions(+), 148 deletions(-) diff --git a/src/diffusers/pipelines/llada2/pipeline_llada2.py b/src/diffusers/pipelines/llada2/pipeline_llada2.py index d4b037ada151..7655cb92ab8e 100644 --- a/src/diffusers/pipelines/llada2/pipeline_llada2.py +++ b/src/diffusers/pipelines/llada2/pipeline_llada2.py @@ -23,7 +23,7 @@ from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...schedulers import BlockRefinementScheduler from ...utils import BaseOutput, logging, replace_example_docstring -from ..pipeline_utils import DiffusionPipeline +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin logger = logging.get_logger(__name__) @@ -56,7 +56,7 @@ class LLaDA2PipelineOutput(BaseOutput): texts: list[str] | None = None -class LLaDA2Pipeline(DiffusionPipeline): +class LLaDA2Pipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): r""" Pipeline for LLaDA2-style discrete diffusion text generation via block-wise iterative refinement. @@ -88,65 +88,6 @@ def __init__( def num_timesteps(self): return self._num_timesteps - # --- Prompt encoding --- - - def _prepare_input_ids( - self, - *, - prompt: str | list[str] | None, - messages: list[dict[str, str]] | None, - input_ids: torch.LongTensor | None, - use_chat_template: bool, - add_generation_prompt: bool, - chat_template_kwargs: dict[str, Any] | None, - ) -> torch.LongTensor: - """Convert prompt/messages/input_ids to a [batch, seq] LongTensor.""" - if input_ids is not None: - if input_ids.ndim == 1: - input_ids = input_ids.unsqueeze(0) - if input_ids.ndim != 2: - raise ValueError(f"`input_ids` must be 2D, got shape {tuple(input_ids.shape)}.") - if input_ids.dtype != torch.long: - raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") - return input_ids - - if self.tokenizer is None: - raise ValueError("Tokenizer is required when `input_ids` is not provided.") - - if messages is not None and prompt is not None: - raise ValueError("Provide either `prompt` or `messages`, not both.") - if messages is None and prompt is None: - raise ValueError("Provide one of `prompt`, `messages`, or `input_ids`.") - - chat_template_kwargs = chat_template_kwargs or {} - - if messages is not None: - encoded = self.tokenizer.apply_chat_template( - messages, - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return encoded["input_ids"] - - if use_chat_template and getattr(self.tokenizer, "chat_template", None): - if isinstance(prompt, list): - raise ValueError("`prompt` must be a string when `use_chat_template=True`.") - encoded = self.tokenizer.apply_chat_template( - [{"role": "user", "content": prompt}], - add_generation_prompt=add_generation_prompt, - tokenize=True, - return_tensors="pt", - return_dict=True, - **chat_template_kwargs, - ) - return encoded["input_ids"] - - encoded = self.tokenizer(prompt, return_tensors="pt", padding=isinstance(prompt, list)) - return encoded["input_ids"] - def check_inputs( self, prompt: str | list[str] | None, diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index 9975a28b4928..b2b6ccf0d123 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -2388,89 +2388,11 @@ def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True): class DiscreteDiffusionPipelineMixin: """Shared utilities for discrete (token) diffusion pipelines. - Provides SAR sampling techniques (top-p, top-k) and common helper methods for pipelines that operate on discrete - token sequences. + Provides common helper methods for pipelines that operate on discrete token sequences, including prompt encoding, + prefix handling, and start token resolution. """ - # --- SAR sampling utilities (static methods) --- - - @staticmethod - def _top_p_filtering(logits: "torch.Tensor", top_p: Optional[float]) -> "torch.Tensor": - """Nucleus (top-p) logit filtering.""" - if top_p is None or top_p >= 1.0: - return logits - if not (0.0 < top_p <= 1.0): - raise ValueError(f"`top_p` must be in (0, 1], got {top_p}.") - - sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) - sorted_probs = torch.softmax(sorted_logits, dim=-1) - cumulative_probs = sorted_probs.cumsum(dim=-1) - - sorted_indices_to_remove = cumulative_probs > float(top_p) - # Shift right to keep at least the first token above the threshold - sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() - sorted_indices_to_remove[..., 0] = 0 - - sorted_logits = sorted_logits.masked_fill(sorted_indices_to_remove, torch.finfo(sorted_logits.dtype).min) - filtered = logits.scatter(-1, sorted_indices, sorted_logits) - return filtered - - @staticmethod - def _top_k_filtering(logits: "torch.Tensor", top_k: Optional[int]) -> "torch.Tensor": - """Top-k logit filtering.""" - if top_k is None or top_k <= 0: - return logits - if top_k >= logits.shape[-1]: - return logits - values, _ = torch.topk(logits, k=int(top_k), dim=-1) - min_keep = values[..., -1, None] - return logits.masked_fill(logits < min_keep, torch.finfo(logits.dtype).min) - - @staticmethod - def _sample_with_temperature_topk_topp( - logits: "torch.Tensor", - *, - temperature: float, - top_k: Optional[int], - top_p: Optional[float], - generator: Optional["torch.Generator"], - use_multinomial: bool, - ) -> "tuple[torch.LongTensor, torch.Tensor]": - """Sample tokens from logits with temperature scaling, top-k, and top-p. - - Follows the official LLaDA2 ordering: temperature scaling is applied before top-k/top-p filtering so that - filtering operates on the scaled logit distribution. - """ - if temperature < 0: - raise ValueError(f"`temperature` must be >= 0, got {temperature}.") - - vocab_size = logits.shape[-1] - flat_logits = logits.reshape(-1, vocab_size) - - # Greedy: return argmax directly (matches official LLaDA2 temperature=0 branch) - if temperature == 0.0 or not use_multinomial: - probs = torch.softmax(flat_logits.float(), dim=-1) - token = flat_logits.argmax(dim=-1, keepdim=True) - token_prob = torch.gather(probs, -1, token) - return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) - - # Temperature scaling before filtering (matches official LLaDA2 code) - scaled = flat_logits - if temperature != 1.0: - scaled = flat_logits / float(temperature) - - filtered = DiscreteDiffusionPipelineMixin._top_k_filtering(scaled, top_k=top_k) - filtered = DiscreteDiffusionPipelineMixin._top_p_filtering(filtered, top_p=top_p) - - probs = torch.softmax(filtered.float(), dim=-1) - token = torch.multinomial(probs, num_samples=1, generator=generator) - token_prob = torch.gather(probs, -1, token) - - return token.view(*logits.shape[:-1]), token_prob.view(*logits.shape[:-1]) - - # --- Token/prefix utilities (instance methods) --- - - def _resolve_start_token_id(self) -> Optional[int]: + def _resolve_start_token_id(self) -> "int | None": """Resolve BOS or CLS token ID from self.tokenizer.""" tok = getattr(self, "tokenizer", None) if tok is None: @@ -2502,17 +2424,15 @@ def _normalize_prefix_ids( prefix_ids = prefix_ids.expand(batch_size, -1) return prefix_ids - # --- Prompt encoding (instance method) --- - def _prepare_input_ids( self, *, - prompt: Optional[Union[str, List[str]]], - messages: Optional[List[Dict[str, str]]], - input_ids: Optional["torch.LongTensor"], + prompt: "str | list[str] | None", + messages: "list[dict[str, str]] | None", + input_ids: "torch.LongTensor | None", use_chat_template: bool, add_generation_prompt: bool, - chat_template_kwargs: Optional[Dict[str, object]], + chat_template_kwargs: "dict[str, object] | None", ) -> "torch.LongTensor": """Convert prompt/messages/input_ids to a [batch, seq] LongTensor.""" if input_ids is not None: From 4cd7dd64546c4663cab12989125ecc4ff72bd9b0 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Wed, 25 Mar 2026 11:57:56 +0000 Subject: [PATCH 30/44] fix: remove stale BlockRefinementPipeline refs, clean up tracked files --- 3rd_party/Dream | 1 - 3rd_party/LLaDOU | 1 - 3rd_party/SDAR | 1 - 3rd_party/Score-Entropy-Discrete-Diffusion | 1 - 3rd_party/UNI-D2 | 1 - 3rd_party/bd3lms | 1 - 3rd_party/dUltra-os | 1 - 3rd_party/dflash | 1 - 3rd_party/dllm-dev | 1 - 3rd_party/mdlm | 1 - ...-dinov2-wReg-base-ViTXL-n08-i512_input.png | Bin 302881 -> 0 bytes ...-dinov2-wReg-base-ViTXL-n08-i512_recon.png | Bin 383064 -> 0 bytes .../RAE-dinov2-wReg-base-ViTXL-n08_input.png | Bin 64968 -> 0 bytes .../RAE-dinov2-wReg-base-ViTXL-n08_recon.png | Bin 99064 -> 0 bytes .../RAE-dinov2-wReg-large-ViTXL-n08_input.png | Bin 64968 -> 0 bytes .../RAE-dinov2-wReg-large-ViTXL-n08_recon.png | Bin 107481 -> 0 bytes .../RAE-dinov2-wReg-small-ViTXL-n08_input.png | Bin 64968 -> 0 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zT^UZN-DHm@LrKu}h3E`$679=Hz1#Xc`COE=Yf#RFrF3tzG3eg5lWcEbr_&2#0*!bt zc5_RJFYCOWtwb8_CK7n>s~ZYv82W4;Pf~R~KIjzJZX!SR|5N|JW&H~R8v%<YF&!_Y zcO00#;cLYmK>9@F?Ov;s3<wKsZujTPs}=9&*>2+c$GU-#2yrcLENp%rA672j)3!_1 z#3->3r9gmDix#>AUNf`HQW4;46Kz49L``X`M%R&fRE=Y36jg(=kRoebBeX<_=n+N% zG8A4bsD0UolkH|zq!d~VDT<`yw6L;+jm!2scCwf`<|oKC1(vRPV^aogktDheItqxA zwk;qn8wNw#w6(LZuu!gAY$2kmp4?y(B~nBI0s2~H5ud|l8gX14D51d8&@9y`i4=xh zzJQA0(7fh^hepK7y7qzw7-p+Cw?Ps{FZXB9Ax37Cjehp-kYI_9vyt}YdA^E1HZFp4 zHHnHHrbgxvuALk@mz>iHv9ogJei)9Ov{&HmwJXVjy~z}ieUUSl?b0wi#D*_)Pl&y{ uPkXh)T#+Kexd3E@#!vnK)c<c;|Nno#?%2Qhua)`$0000<MNUMnLSTZqxFX{K diff --git a/scripts/__init__.py b/scripts/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/scripts/benchmark_qwenimage_branch_compare.py b/scripts/benchmark_qwenimage_branch_compare.py deleted file mode 100644 index d1f1e1930656..000000000000 --- a/scripts/benchmark_qwenimage_branch_compare.py +++ /dev/null @@ -1,101 +0,0 @@ -import argparse -import json -import os -import subprocess -import sys -import time - -import torch - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Benchmark QwenImage pipeline runtime.") - parser.add_argument("--repo-root", default=None, help="Path to diffusers repo to benchmark.") - parser.add_argument("--model-path", required=True, help="Local path or hub id for QwenImage.") - parser.add_argument("--prompt", default="A serene mountain landscape at sunset", help="Prompt text.") - parser.add_argument("--height", type=int, default=512) - parser.add_argument("--width", type=int, default=512) - parser.add_argument("--steps", type=int, default=20) - parser.add_argument("--runs", type=int, default=5) - parser.add_argument("--warmup", type=int, default=1) - parser.add_argument("--device", default="cuda") - parser.add_argument("--attn-backend", default=None, help="Set DIFFUSERS_ATTN_BACKEND if provided.") - parser.add_argument("--output-json", required=True, help="Output JSON path.") - return parser.parse_args() - - -def _git_rev(repo_root: str | None) -> str: - if not repo_root: - return "" - try: - out = subprocess.check_output(["git", "-C", repo_root, "rev-parse", "--short", "HEAD"], text=True) - return out.strip() - except Exception: - return "" - - -def main() -> None: - args = parse_args() - - if args.repo_root: - sys.path.insert(0, args.repo_root) - - if args.attn_backend: - os.environ["DIFFUSERS_ATTN_BACKEND"] = args.attn_backend - - from diffusers import QwenImagePipeline - - device = torch.device(args.device) - - pipe = QwenImagePipeline.from_pretrained(args.model_path, torch_dtype=torch.bfloat16) - pipe.to(device) - pipe.set_progress_bar_config(disable=True) - - inputs = { - "prompt": args.prompt, - "height": args.height, - "width": args.width, - "num_inference_steps": args.steps, - } - - with torch.inference_mode(): - for _ in range(args.warmup): - _ = pipe(**inputs) - torch.cuda.synchronize() - - times = [] - for _ in range(args.runs): - torch.cuda.synchronize() - start = time.perf_counter() - _ = pipe(**inputs) - torch.cuda.synchronize() - times.append(time.perf_counter() - start) - - mean = sum(times) / len(times) - std = (sum((t - mean) ** 2 for t in times) / len(times)) ** 0.5 - - result = { - "repo_root": args.repo_root, - "git_rev": _git_rev(args.repo_root), - "model_path": args.model_path, - "prompt": args.prompt, - "height": args.height, - "width": args.width, - "steps": args.steps, - "runs": args.runs, - "warmup": args.warmup, - "attn_backend": args.attn_backend, - "times": times, - "mean_seconds": mean, - "std_seconds": std, - } - - with open(args.output_json, "w", encoding="utf-8") as f: - json.dump(result, f, indent=2) - - print(f"Saved results to {args.output_json}") - print(f"Mean: {mean:.4f}s, Std: {std:.4f}s") - - -if __name__ == "__main__": - main() diff --git a/scripts/benchmark_qwenimage_editplus_branch_compare.py b/scripts/benchmark_qwenimage_editplus_branch_compare.py deleted file mode 100644 index 3677b83e3a30..000000000000 --- a/scripts/benchmark_qwenimage_editplus_branch_compare.py +++ /dev/null @@ -1,131 +0,0 @@ -import argparse -import json -import subprocess -import sys -import time - -import torch - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Benchmark QwenImageEditPlus pipeline runtime.") - parser.add_argument("--repo-root", default=None, help="Path to diffusers repo to benchmark.") - parser.add_argument("--model-id", default="Qwen/Qwen-Image-Edit-2511", help="Model id or local path.") - parser.add_argument( - "--prompt", - default=( - "The magician bear is on the left, the alchemist bear is on the right, " - "facing each other in the central park square." - ), - help="Prompt text.", - ) - parser.add_argument( - "--image-1", - default="https://github.com/vipshop/cache-dit/raw/main/examples/data/edit2509_1.jpg", - help="First input image path or URL.", - ) - parser.add_argument( - "--image-2", - default="https://github.com/vipshop/cache-dit/raw/main/examples/data/edit2509_2.jpg", - help="Second input image path or URL.", - ) - parser.add_argument("--height", type=int, default=None, help="Optional output height.") - parser.add_argument("--width", type=int, default=None, help="Optional output width.") - parser.add_argument("--steps", type=int, default=40) - parser.add_argument("--runs", type=int, default=3) - parser.add_argument("--warmup", type=int, default=1) - parser.add_argument("--device", default="cuda") - parser.add_argument("--output-json", required=True, help="Output JSON path.") - return parser.parse_args() - - -def _git_rev(repo_root: str | None) -> str: - if not repo_root: - return "" - try: - out = subprocess.check_output(["git", "-C", repo_root, "rev-parse", "--short", "HEAD"], text=True) - return out.strip() - except Exception: - return "" - - -def main() -> None: - args = parse_args() - - if args.repo_root: - sys.path.insert(0, args.repo_root) - - from diffusers import QwenImageEditPlusPipeline - from diffusers.utils import load_image - - device = torch.device(args.device) - - pipe = QwenImageEditPlusPipeline.from_pretrained(args.model_id, torch_dtype=torch.bfloat16) - pipe.to(device) - pipe.set_progress_bar_config(disable=True) - print(f"Running on device={device}, dtype={pipe.dtype}") - - image1 = load_image(args.image_1) - image2 = load_image(args.image_2) - - generator = torch.Generator(device=device).manual_seed(0) - - inputs = { - "image": [image1, image2], - "prompt": args.prompt, - "generator": generator, - "true_cfg_scale": 4.0, - "negative_prompt": " ", - "num_inference_steps": args.steps, - "guidance_scale": 1.0, - "num_images_per_prompt": 1, - } - if args.height is not None: - inputs["height"] = args.height - if args.width is not None: - inputs["width"] = args.width - - with torch.inference_mode(): - for _ in range(args.warmup): - _ = pipe(**inputs) - torch.cuda.synchronize() - - times = [] - for _ in range(args.runs): - torch.cuda.synchronize() - start = time.perf_counter() - _ = pipe(**inputs) - torch.cuda.synchronize() - times.append(time.perf_counter() - start) - - mean = sum(times) / len(times) - std = (sum((t - mean) ** 2 for t in times) / len(times)) ** 0.5 - - result = { - "repo_root": args.repo_root, - "git_rev": _git_rev(args.repo_root), - "model_id": args.model_id, - "prompt": args.prompt, - "image_1": args.image_1, - "image_2": args.image_2, - "height": args.height, - "width": args.width, - "steps": args.steps, - "runs": args.runs, - "warmup": args.warmup, - "device": str(device), - "dtype": str(pipe.dtype), - "times": times, - "mean_seconds": mean, - "std_seconds": std, - } - - with open(args.output_json, "w", encoding="utf-8") as f: - json.dump(result, f, indent=2) - - print(f"Saved results to {args.output_json}") - print(f"Mean: {mean:.4f}s, Std: {std:.4f}s") - - -if __name__ == "__main__": - main() diff --git a/scripts/change_naming_configs_and_checkpoints.py b/scripts/change_naming_configs_and_checkpoints.py deleted file mode 100644 index 4220901c13bf..000000000000 --- a/scripts/change_naming_configs_and_checkpoints.py +++ /dev/null @@ -1,113 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the LDM checkpoints.""" - -import argparse -import json -import os - -import torch -from transformers.file_utils import has_file - -from diffusers import UNet2DConditionModel, UNet2DModel - - -do_only_config = False -do_only_weights = True -do_only_renaming = False - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--repo_path", - default=None, - type=str, - required=True, - help="The config json file corresponding to the architecture.", - ) - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - args = parser.parse_args() - - config_parameters_to_change = { - "image_size": "sample_size", - "num_res_blocks": "layers_per_block", - "block_channels": "block_out_channels", - "down_blocks": "down_block_types", - "up_blocks": "up_block_types", - "downscale_freq_shift": "freq_shift", - "resnet_num_groups": "norm_num_groups", - "resnet_act_fn": "act_fn", - "resnet_eps": "norm_eps", - "num_head_channels": "attention_head_dim", - } - - key_parameters_to_change = { - "time_steps": "time_proj", - "mid": "mid_block", - "downsample_blocks": "down_blocks", - "upsample_blocks": "up_blocks", - } - - subfolder = "" if has_file(args.repo_path, "config.json") else "unet" - - with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader: - text = reader.read() - config = json.loads(text) - - if do_only_config: - for key in config_parameters_to_change.keys(): - config.pop(key, None) - - if has_file(args.repo_path, "config.json"): - model = UNet2DModel(**config) - else: - class_name = UNet2DConditionModel if "ldm-text2im-large-256" in args.repo_path else UNet2DModel - model = class_name(**config) - - if do_only_config: - model.save_config(os.path.join(args.repo_path, subfolder)) - - config = dict(model.config) - - if do_only_renaming: - for key, value in config_parameters_to_change.items(): - if key in config: - config[value] = config[key] - del config[key] - - config["down_block_types"] = [k.replace("UNetRes", "") for k in config["down_block_types"]] - config["up_block_types"] = [k.replace("UNetRes", "") for k in config["up_block_types"]] - - if do_only_weights: - state_dict = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin")) - - new_state_dict = {} - for param_key, param_value in state_dict.items(): - if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"): - continue - has_changed = False - for key, new_key in key_parameters_to_change.items(): - if not has_changed and param_key.split(".")[0] == key: - new_state_dict[".".join([new_key] + param_key.split(".")[1:])] = param_value - has_changed = True - if not has_changed: - new_state_dict[param_key] = param_value - - model.load_state_dict(new_state_dict) - model.save_pretrained(os.path.join(args.repo_path, subfolder)) diff --git a/scripts/compare_output_images.py b/scripts/compare_output_images.py deleted file mode 100644 index db10be6cc482..000000000000 --- a/scripts/compare_output_images.py +++ /dev/null @@ -1,42 +0,0 @@ -import argparse -import os - -from PIL import Image, ImageChops, ImageDraw - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Create a side-by-side image comparison with diff.") - parser.add_argument("--before", required=True, help="Path to the before image.") - parser.add_argument("--after", required=True, help="Path to the after image.") - parser.add_argument("--output", default="compare_before_after_diff.png", help="Output image path.") - return parser.parse_args() - - -def main() -> None: - args = parse_args() - - before = Image.open(args.before).convert("RGB") - after = Image.open(args.after).convert("RGB") - diff = ImageChops.difference(before, after) - - width = before.width + after.width + diff.width - height = max(before.height, after.height, diff.height) + 40 - - canvas = Image.new("RGB", (width, height), (255, 255, 255)) - canvas.paste(before, (0, 40)) - canvas.paste(after, (before.width, 40)) - canvas.paste(diff, (before.width + after.width, 40)) - - draw = ImageDraw.Draw(canvas) - labels = ["Before", "After", "Diff"] - offsets = [0, before.width, before.width + after.width] - - for label, x_offset in zip(labels, offsets): - draw.text((x_offset + 10, 10), label, fill=(0, 0, 0)) - - canvas.save(args.output) - print(f"Saved comparison to {os.path.abspath(args.output)}") - - -if __name__ == "__main__": - main() diff --git a/scripts/conversion_ldm_uncond.py b/scripts/conversion_ldm_uncond.py deleted file mode 100644 index 8c22cc1ce8f2..000000000000 --- a/scripts/conversion_ldm_uncond.py +++ /dev/null @@ -1,56 +0,0 @@ -import argparse - -import torch -import yaml - -from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel - - -def convert_ldm_original(checkpoint_path, config_path, output_path): - config = yaml.safe_load(config_path) - state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] - keys = list(state_dict.keys()) - - # extract state_dict for VQVAE - first_stage_dict = {} - first_stage_key = "first_stage_model." - for key in keys: - if key.startswith(first_stage_key): - first_stage_dict[key.replace(first_stage_key, "")] = state_dict[key] - - # extract state_dict for UNetLDM - unet_state_dict = {} - unet_key = "model.diffusion_model." - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = state_dict[key] - - vqvae_init_args = config["model"]["params"]["first_stage_config"]["params"] - unet_init_args = config["model"]["params"]["unet_config"]["params"] - - vqvae = VQModel(**vqvae_init_args).eval() - vqvae.load_state_dict(first_stage_dict) - - unet = UNetLDMModel(**unet_init_args).eval() - unet.load_state_dict(unet_state_dict) - - noise_scheduler = DDIMScheduler( - timesteps=config["model"]["params"]["timesteps"], - beta_schedule="scaled_linear", - beta_start=config["model"]["params"]["linear_start"], - beta_end=config["model"]["params"]["linear_end"], - clip_sample=False, - ) - - pipeline = LDMPipeline(vqvae, unet, noise_scheduler) - pipeline.save_pretrained(output_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--checkpoint_path", type=str, required=True) - parser.add_argument("--config_path", type=str, required=True) - parser.add_argument("--output_path", type=str, required=True) - args = parser.parse_args() - - convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path) diff --git a/scripts/convert_amused.py b/scripts/convert_amused.py deleted file mode 100644 index ddd1bf508b6d..000000000000 --- a/scripts/convert_amused.py +++ /dev/null @@ -1,523 +0,0 @@ -import inspect -import os -from argparse import ArgumentParser - -import numpy as np -import torch -from muse import MaskGiTUViT, VQGANModel -from muse import PipelineMuse as OldPipelineMuse -from transformers import CLIPTextModelWithProjection, CLIPTokenizer - -from diffusers import VQModel -from diffusers.models.attention_processor import AttnProcessor -from diffusers.models.unets.uvit_2d import UVit2DModel -from diffusers.pipelines.amused.pipeline_amused import AmusedPipeline -from diffusers.schedulers import AmusedScheduler - - -torch.backends.cuda.enable_flash_sdp(False) -torch.backends.cuda.enable_mem_efficient_sdp(False) -torch.backends.cuda.enable_math_sdp(True) - -os.environ["CUDA_LAUNCH_BLOCKING"] = "1" -os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" -torch.use_deterministic_algorithms(True) - -# Enable CUDNN deterministic mode -torch.backends.cudnn.deterministic = True -torch.backends.cudnn.benchmark = False -torch.backends.cuda.matmul.allow_tf32 = False - -device = "cuda" - - -def main(): - args = ArgumentParser() - args.add_argument("--model_256", action="store_true") - args.add_argument("--write_to", type=str, required=False, default=None) - args.add_argument("--transformer_path", type=str, required=False, default=None) - args = args.parse_args() - - transformer_path = args.transformer_path - subfolder = "transformer" - - if transformer_path is None: - if args.model_256: - transformer_path = "openMUSE/muse-256" - else: - transformer_path = ( - "../research-run-512-checkpoints/research-run-512-with-downsample-checkpoint-554000/unwrapped_model/" - ) - subfolder = None - - old_transformer = MaskGiTUViT.from_pretrained(transformer_path, subfolder=subfolder) - - old_transformer.to(device) - - old_vae = VQGANModel.from_pretrained("openMUSE/muse-512", subfolder="vae") - old_vae.to(device) - - vqvae = make_vqvae(old_vae) - - tokenizer = CLIPTokenizer.from_pretrained("openMUSE/muse-512", subfolder="text_encoder") - - text_encoder = CLIPTextModelWithProjection.from_pretrained("openMUSE/muse-512", subfolder="text_encoder") - text_encoder.to(device) - - transformer = make_transformer(old_transformer, args.model_256) - - scheduler = AmusedScheduler(mask_token_id=old_transformer.config.mask_token_id) - - new_pipe = AmusedPipeline( - vqvae=vqvae, tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, scheduler=scheduler - ) - - old_pipe = OldPipelineMuse( - vae=old_vae, transformer=old_transformer, text_encoder=text_encoder, tokenizer=tokenizer - ) - old_pipe.to(device) - - if args.model_256: - transformer_seq_len = 256 - orig_size = (256, 256) - else: - transformer_seq_len = 1024 - orig_size = (512, 512) - - old_out = old_pipe( - "dog", - generator=torch.Generator(device).manual_seed(0), - transformer_seq_len=transformer_seq_len, - orig_size=orig_size, - timesteps=12, - )[0] - - new_out = new_pipe("dog", generator=torch.Generator(device).manual_seed(0)).images[0] - - old_out = np.array(old_out) - new_out = np.array(new_out) - - diff = np.abs(old_out.astype(np.float64) - new_out.astype(np.float64)) - - # assert diff diff.sum() == 0 - print("skipping pipeline full equivalence check") - - print(f"max diff: {diff.max()}, diff.sum() / diff.size {diff.sum() / diff.size}") - - if args.model_256: - assert diff.max() <= 3 - assert diff.sum() / diff.size < 0.7 - else: - assert diff.max() <= 1 - assert diff.sum() / diff.size < 0.4 - - if args.write_to is not None: - new_pipe.save_pretrained(args.write_to) - - -def make_transformer(old_transformer, model_256): - args = dict(old_transformer.config) - force_down_up_sample = args["force_down_up_sample"] - - signature = inspect.signature(UVit2DModel.__init__) - - args_ = { - "downsample": force_down_up_sample, - "upsample": force_down_up_sample, - "block_out_channels": args["block_out_channels"][0], - "sample_size": 16 if model_256 else 32, - } - - for s in list(signature.parameters.keys()): - if s in ["self", "downsample", "upsample", "sample_size", "block_out_channels"]: - continue - - args_[s] = args[s] - - new_transformer = UVit2DModel(**args_) - new_transformer.to(device) - - new_transformer.set_attn_processor(AttnProcessor()) - - state_dict = old_transformer.state_dict() - - state_dict["cond_embed.linear_1.weight"] = state_dict.pop("cond_embed.0.weight") - state_dict["cond_embed.linear_2.weight"] = state_dict.pop("cond_embed.2.weight") - - for i in range(22): - state_dict[f"transformer_layers.{i}.norm1.norm.weight"] = state_dict.pop( - f"transformer_layers.{i}.attn_layer_norm.weight" - ) - state_dict[f"transformer_layers.{i}.norm1.linear.weight"] = state_dict.pop( - f"transformer_layers.{i}.self_attn_adaLN_modulation.mapper.weight" - ) - - state_dict[f"transformer_layers.{i}.attn1.to_q.weight"] = state_dict.pop( - f"transformer_layers.{i}.attention.query.weight" - ) - state_dict[f"transformer_layers.{i}.attn1.to_k.weight"] = state_dict.pop( - f"transformer_layers.{i}.attention.key.weight" - ) - state_dict[f"transformer_layers.{i}.attn1.to_v.weight"] = state_dict.pop( - f"transformer_layers.{i}.attention.value.weight" - ) - state_dict[f"transformer_layers.{i}.attn1.to_out.0.weight"] = state_dict.pop( - f"transformer_layers.{i}.attention.out.weight" - ) - - state_dict[f"transformer_layers.{i}.norm2.norm.weight"] = state_dict.pop( - f"transformer_layers.{i}.crossattn_layer_norm.weight" - ) - state_dict[f"transformer_layers.{i}.norm2.linear.weight"] = state_dict.pop( - f"transformer_layers.{i}.cross_attn_adaLN_modulation.mapper.weight" - ) - - state_dict[f"transformer_layers.{i}.attn2.to_q.weight"] = state_dict.pop( - f"transformer_layers.{i}.crossattention.query.weight" - ) - state_dict[f"transformer_layers.{i}.attn2.to_k.weight"] = state_dict.pop( - f"transformer_layers.{i}.crossattention.key.weight" - ) - state_dict[f"transformer_layers.{i}.attn2.to_v.weight"] = state_dict.pop( - f"transformer_layers.{i}.crossattention.value.weight" - ) - state_dict[f"transformer_layers.{i}.attn2.to_out.0.weight"] = state_dict.pop( - f"transformer_layers.{i}.crossattention.out.weight" - ) - - state_dict[f"transformer_layers.{i}.norm3.norm.weight"] = state_dict.pop( - f"transformer_layers.{i}.ffn.pre_mlp_layer_norm.weight" - ) - state_dict[f"transformer_layers.{i}.norm3.linear.weight"] = state_dict.pop( - f"transformer_layers.{i}.ffn.adaLN_modulation.mapper.weight" - ) - - wi_0_weight = state_dict.pop(f"transformer_layers.{i}.ffn.wi_0.weight") - wi_1_weight = state_dict.pop(f"transformer_layers.{i}.ffn.wi_1.weight") - proj_weight = torch.concat([wi_1_weight, wi_0_weight], dim=0) - state_dict[f"transformer_layers.{i}.ff.net.0.proj.weight"] = proj_weight - - state_dict[f"transformer_layers.{i}.ff.net.2.weight"] = state_dict.pop(f"transformer_layers.{i}.ffn.wo.weight") - - if force_down_up_sample: - state_dict["down_block.downsample.norm.weight"] = state_dict.pop("down_blocks.0.downsample.0.norm.weight") - state_dict["down_block.downsample.conv.weight"] = state_dict.pop("down_blocks.0.downsample.1.weight") - - state_dict["up_block.upsample.norm.weight"] = state_dict.pop("up_blocks.0.upsample.0.norm.weight") - state_dict["up_block.upsample.conv.weight"] = state_dict.pop("up_blocks.0.upsample.1.weight") - - state_dict["mlm_layer.layer_norm.weight"] = state_dict.pop("mlm_layer.layer_norm.norm.weight") - - for i in range(3): - state_dict[f"down_block.res_blocks.{i}.norm.weight"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.norm.norm.weight" - ) - state_dict[f"down_block.res_blocks.{i}.channelwise_linear_1.weight"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.channelwise.0.weight" - ) - state_dict[f"down_block.res_blocks.{i}.channelwise_norm.gamma"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.channelwise.2.gamma" - ) - state_dict[f"down_block.res_blocks.{i}.channelwise_norm.beta"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.channelwise.2.beta" - ) - state_dict[f"down_block.res_blocks.{i}.channelwise_linear_2.weight"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.channelwise.4.weight" - ) - state_dict[f"down_block.res_blocks.{i}.cond_embeds_mapper.weight"] = state_dict.pop( - f"down_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight" - ) - - state_dict[f"down_block.attention_blocks.{i}.norm1.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.attn_layer_norm.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn1.to_q.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.attention.query.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn1.to_k.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.attention.key.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn1.to_v.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.attention.value.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn1.to_out.0.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.attention.out.weight" - ) - - state_dict[f"down_block.attention_blocks.{i}.norm2.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn2.to_q.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.crossattention.query.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn2.to_k.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.crossattention.key.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn2.to_v.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.crossattention.value.weight" - ) - state_dict[f"down_block.attention_blocks.{i}.attn2.to_out.0.weight"] = state_dict.pop( - f"down_blocks.0.attention_blocks.{i}.crossattention.out.weight" - ) - - state_dict[f"up_block.res_blocks.{i}.norm.weight"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.norm.norm.weight" - ) - state_dict[f"up_block.res_blocks.{i}.channelwise_linear_1.weight"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.channelwise.0.weight" - ) - state_dict[f"up_block.res_blocks.{i}.channelwise_norm.gamma"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.channelwise.2.gamma" - ) - state_dict[f"up_block.res_blocks.{i}.channelwise_norm.beta"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.channelwise.2.beta" - ) - state_dict[f"up_block.res_blocks.{i}.channelwise_linear_2.weight"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.channelwise.4.weight" - ) - state_dict[f"up_block.res_blocks.{i}.cond_embeds_mapper.weight"] = state_dict.pop( - f"up_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight" - ) - - state_dict[f"up_block.attention_blocks.{i}.norm1.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.attn_layer_norm.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn1.to_q.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.attention.query.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn1.to_k.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.attention.key.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn1.to_v.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.attention.value.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn1.to_out.0.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.attention.out.weight" - ) - - state_dict[f"up_block.attention_blocks.{i}.norm2.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn2.to_q.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.crossattention.query.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn2.to_k.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.crossattention.key.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn2.to_v.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.crossattention.value.weight" - ) - state_dict[f"up_block.attention_blocks.{i}.attn2.to_out.0.weight"] = state_dict.pop( - f"up_blocks.0.attention_blocks.{i}.crossattention.out.weight" - ) - - for key in list(state_dict.keys()): - if key.startswith("up_blocks.0"): - key_ = "up_block." + ".".join(key.split(".")[2:]) - state_dict[key_] = state_dict.pop(key) - - if key.startswith("down_blocks.0"): - key_ = "down_block." + ".".join(key.split(".")[2:]) - state_dict[key_] = state_dict.pop(key) - - new_transformer.load_state_dict(state_dict) - - input_ids = torch.randint(0, 10, (1, 32, 32), device=old_transformer.device) - encoder_hidden_states = torch.randn((1, 77, 768), device=old_transformer.device) - cond_embeds = torch.randn((1, 768), device=old_transformer.device) - micro_conds = torch.tensor([[512, 512, 0, 0, 6]], dtype=torch.float32, device=old_transformer.device) - - old_out = old_transformer(input_ids.reshape(1, -1), encoder_hidden_states, cond_embeds, micro_conds) - old_out = old_out.reshape(1, 32, 32, 8192).permute(0, 3, 1, 2) - - new_out = new_transformer(input_ids, encoder_hidden_states, cond_embeds, micro_conds) - - # NOTE: these differences are solely due to using the geglu block that has a single linear layer of - # double output dimension instead of two different linear layers - max_diff = (old_out - new_out).abs().max() - total_diff = (old_out - new_out).abs().sum() - print(f"Transformer max_diff: {max_diff} total_diff: {total_diff}") - assert max_diff < 0.01 - assert total_diff < 1500 - - return new_transformer - - -def make_vqvae(old_vae): - new_vae = VQModel( - act_fn="silu", - block_out_channels=[128, 256, 256, 512, 768], - down_block_types=[ - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - ], - in_channels=3, - latent_channels=64, - layers_per_block=2, - norm_num_groups=32, - num_vq_embeddings=8192, - out_channels=3, - sample_size=32, - up_block_types=[ - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - ], - mid_block_add_attention=False, - lookup_from_codebook=True, - ) - new_vae.to(device) - - # fmt: off - - new_state_dict = {} - - old_state_dict = old_vae.state_dict() - - new_state_dict["encoder.conv_in.weight"] = old_state_dict.pop("encoder.conv_in.weight") - new_state_dict["encoder.conv_in.bias"] = old_state_dict.pop("encoder.conv_in.bias") - - convert_vae_block_state_dict(old_state_dict, "encoder.down.0", new_state_dict, "encoder.down_blocks.0") - convert_vae_block_state_dict(old_state_dict, "encoder.down.1", new_state_dict, "encoder.down_blocks.1") - convert_vae_block_state_dict(old_state_dict, "encoder.down.2", new_state_dict, "encoder.down_blocks.2") - convert_vae_block_state_dict(old_state_dict, "encoder.down.3", new_state_dict, "encoder.down_blocks.3") - convert_vae_block_state_dict(old_state_dict, "encoder.down.4", new_state_dict, "encoder.down_blocks.4") - - new_state_dict["encoder.mid_block.resnets.0.norm1.weight"] = old_state_dict.pop("encoder.mid.block_1.norm1.weight") - new_state_dict["encoder.mid_block.resnets.0.norm1.bias"] = old_state_dict.pop("encoder.mid.block_1.norm1.bias") - new_state_dict["encoder.mid_block.resnets.0.conv1.weight"] = old_state_dict.pop("encoder.mid.block_1.conv1.weight") - new_state_dict["encoder.mid_block.resnets.0.conv1.bias"] = old_state_dict.pop("encoder.mid.block_1.conv1.bias") - new_state_dict["encoder.mid_block.resnets.0.norm2.weight"] = old_state_dict.pop("encoder.mid.block_1.norm2.weight") - new_state_dict["encoder.mid_block.resnets.0.norm2.bias"] = old_state_dict.pop("encoder.mid.block_1.norm2.bias") - new_state_dict["encoder.mid_block.resnets.0.conv2.weight"] = old_state_dict.pop("encoder.mid.block_1.conv2.weight") - new_state_dict["encoder.mid_block.resnets.0.conv2.bias"] = old_state_dict.pop("encoder.mid.block_1.conv2.bias") - new_state_dict["encoder.mid_block.resnets.1.norm1.weight"] = old_state_dict.pop("encoder.mid.block_2.norm1.weight") - new_state_dict["encoder.mid_block.resnets.1.norm1.bias"] = old_state_dict.pop("encoder.mid.block_2.norm1.bias") - new_state_dict["encoder.mid_block.resnets.1.conv1.weight"] = old_state_dict.pop("encoder.mid.block_2.conv1.weight") - new_state_dict["encoder.mid_block.resnets.1.conv1.bias"] = old_state_dict.pop("encoder.mid.block_2.conv1.bias") - new_state_dict["encoder.mid_block.resnets.1.norm2.weight"] = old_state_dict.pop("encoder.mid.block_2.norm2.weight") - new_state_dict["encoder.mid_block.resnets.1.norm2.bias"] = old_state_dict.pop("encoder.mid.block_2.norm2.bias") - new_state_dict["encoder.mid_block.resnets.1.conv2.weight"] = old_state_dict.pop("encoder.mid.block_2.conv2.weight") - new_state_dict["encoder.mid_block.resnets.1.conv2.bias"] = old_state_dict.pop("encoder.mid.block_2.conv2.bias") - new_state_dict["encoder.conv_norm_out.weight"] = old_state_dict.pop("encoder.norm_out.weight") - new_state_dict["encoder.conv_norm_out.bias"] = old_state_dict.pop("encoder.norm_out.bias") - new_state_dict["encoder.conv_out.weight"] = old_state_dict.pop("encoder.conv_out.weight") - new_state_dict["encoder.conv_out.bias"] = old_state_dict.pop("encoder.conv_out.bias") - new_state_dict["quant_conv.weight"] = old_state_dict.pop("quant_conv.weight") - new_state_dict["quant_conv.bias"] = old_state_dict.pop("quant_conv.bias") - new_state_dict["quantize.embedding.weight"] = old_state_dict.pop("quantize.embedding.weight") - new_state_dict["post_quant_conv.weight"] = old_state_dict.pop("post_quant_conv.weight") - new_state_dict["post_quant_conv.bias"] = old_state_dict.pop("post_quant_conv.bias") - new_state_dict["decoder.conv_in.weight"] = old_state_dict.pop("decoder.conv_in.weight") - new_state_dict["decoder.conv_in.bias"] = old_state_dict.pop("decoder.conv_in.bias") - new_state_dict["decoder.mid_block.resnets.0.norm1.weight"] = old_state_dict.pop("decoder.mid.block_1.norm1.weight") - new_state_dict["decoder.mid_block.resnets.0.norm1.bias"] = old_state_dict.pop("decoder.mid.block_1.norm1.bias") - new_state_dict["decoder.mid_block.resnets.0.conv1.weight"] = old_state_dict.pop("decoder.mid.block_1.conv1.weight") - new_state_dict["decoder.mid_block.resnets.0.conv1.bias"] = old_state_dict.pop("decoder.mid.block_1.conv1.bias") - new_state_dict["decoder.mid_block.resnets.0.norm2.weight"] = old_state_dict.pop("decoder.mid.block_1.norm2.weight") - new_state_dict["decoder.mid_block.resnets.0.norm2.bias"] = old_state_dict.pop("decoder.mid.block_1.norm2.bias") - new_state_dict["decoder.mid_block.resnets.0.conv2.weight"] = old_state_dict.pop("decoder.mid.block_1.conv2.weight") - new_state_dict["decoder.mid_block.resnets.0.conv2.bias"] = old_state_dict.pop("decoder.mid.block_1.conv2.bias") - new_state_dict["decoder.mid_block.resnets.1.norm1.weight"] = old_state_dict.pop("decoder.mid.block_2.norm1.weight") - new_state_dict["decoder.mid_block.resnets.1.norm1.bias"] = old_state_dict.pop("decoder.mid.block_2.norm1.bias") - new_state_dict["decoder.mid_block.resnets.1.conv1.weight"] = old_state_dict.pop("decoder.mid.block_2.conv1.weight") - new_state_dict["decoder.mid_block.resnets.1.conv1.bias"] = old_state_dict.pop("decoder.mid.block_2.conv1.bias") - new_state_dict["decoder.mid_block.resnets.1.norm2.weight"] = old_state_dict.pop("decoder.mid.block_2.norm2.weight") - new_state_dict["decoder.mid_block.resnets.1.norm2.bias"] = old_state_dict.pop("decoder.mid.block_2.norm2.bias") - new_state_dict["decoder.mid_block.resnets.1.conv2.weight"] = old_state_dict.pop("decoder.mid.block_2.conv2.weight") - new_state_dict["decoder.mid_block.resnets.1.conv2.bias"] = old_state_dict.pop("decoder.mid.block_2.conv2.bias") - - convert_vae_block_state_dict(old_state_dict, "decoder.up.0", new_state_dict, "decoder.up_blocks.4") - convert_vae_block_state_dict(old_state_dict, "decoder.up.1", new_state_dict, "decoder.up_blocks.3") - convert_vae_block_state_dict(old_state_dict, "decoder.up.2", new_state_dict, "decoder.up_blocks.2") - convert_vae_block_state_dict(old_state_dict, "decoder.up.3", new_state_dict, "decoder.up_blocks.1") - convert_vae_block_state_dict(old_state_dict, "decoder.up.4", new_state_dict, "decoder.up_blocks.0") - - new_state_dict["decoder.conv_norm_out.weight"] = old_state_dict.pop("decoder.norm_out.weight") - new_state_dict["decoder.conv_norm_out.bias"] = old_state_dict.pop("decoder.norm_out.bias") - new_state_dict["decoder.conv_out.weight"] = old_state_dict.pop("decoder.conv_out.weight") - new_state_dict["decoder.conv_out.bias"] = old_state_dict.pop("decoder.conv_out.bias") - - # fmt: on - - assert len(old_state_dict.keys()) == 0 - - new_vae.load_state_dict(new_state_dict) - - input = torch.randn((1, 3, 512, 512), device=device) - input = input.clamp(-1, 1) - - old_encoder_output = old_vae.quant_conv(old_vae.encoder(input)) - new_encoder_output = new_vae.quant_conv(new_vae.encoder(input)) - assert (old_encoder_output == new_encoder_output).all() - - old_decoder_output = old_vae.decoder(old_vae.post_quant_conv(old_encoder_output)) - new_decoder_output = new_vae.decoder(new_vae.post_quant_conv(new_encoder_output)) - - # assert (old_decoder_output == new_decoder_output).all() - print("kipping vae decoder equivalence check") - print(f"vae decoder diff {(old_decoder_output - new_decoder_output).float().abs().sum()}") - - old_output = old_vae(input)[0] - new_output = new_vae(input)[0] - - # assert (old_output == new_output).all() - print("skipping full vae equivalence check") - print(f"vae full diff {(old_output - new_output).float().abs().sum()}") - - return new_vae - - -def convert_vae_block_state_dict(old_state_dict, prefix_from, new_state_dict, prefix_to): - # fmt: off - - new_state_dict[f"{prefix_to}.resnets.0.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.norm1.weight") - new_state_dict[f"{prefix_to}.resnets.0.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.norm1.bias") - new_state_dict[f"{prefix_to}.resnets.0.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.conv1.weight") - new_state_dict[f"{prefix_to}.resnets.0.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.conv1.bias") - new_state_dict[f"{prefix_to}.resnets.0.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.norm2.weight") - new_state_dict[f"{prefix_to}.resnets.0.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.norm2.bias") - new_state_dict[f"{prefix_to}.resnets.0.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.conv2.weight") - new_state_dict[f"{prefix_to}.resnets.0.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.conv2.bias") - - if f"{prefix_from}.block.0.nin_shortcut.weight" in old_state_dict: - new_state_dict[f"{prefix_to}.resnets.0.conv_shortcut.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.nin_shortcut.weight") - new_state_dict[f"{prefix_to}.resnets.0.conv_shortcut.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.nin_shortcut.bias") - - new_state_dict[f"{prefix_to}.resnets.1.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.norm1.weight") - new_state_dict[f"{prefix_to}.resnets.1.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.norm1.bias") - new_state_dict[f"{prefix_to}.resnets.1.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.conv1.weight") - new_state_dict[f"{prefix_to}.resnets.1.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.conv1.bias") - new_state_dict[f"{prefix_to}.resnets.1.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.norm2.weight") - new_state_dict[f"{prefix_to}.resnets.1.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.norm2.bias") - new_state_dict[f"{prefix_to}.resnets.1.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.conv2.weight") - new_state_dict[f"{prefix_to}.resnets.1.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.conv2.bias") - - if f"{prefix_from}.downsample.conv.weight" in old_state_dict: - new_state_dict[f"{prefix_to}.downsamplers.0.conv.weight"] = old_state_dict.pop(f"{prefix_from}.downsample.conv.weight") - new_state_dict[f"{prefix_to}.downsamplers.0.conv.bias"] = old_state_dict.pop(f"{prefix_from}.downsample.conv.bias") - - if f"{prefix_from}.upsample.conv.weight" in old_state_dict: - new_state_dict[f"{prefix_to}.upsamplers.0.conv.weight"] = old_state_dict.pop(f"{prefix_from}.upsample.conv.weight") - new_state_dict[f"{prefix_to}.upsamplers.0.conv.bias"] = old_state_dict.pop(f"{prefix_from}.upsample.conv.bias") - - if f"{prefix_from}.block.2.norm1.weight" in old_state_dict: - new_state_dict[f"{prefix_to}.resnets.2.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.norm1.weight") - new_state_dict[f"{prefix_to}.resnets.2.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.norm1.bias") - new_state_dict[f"{prefix_to}.resnets.2.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.conv1.weight") - new_state_dict[f"{prefix_to}.resnets.2.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.conv1.bias") - new_state_dict[f"{prefix_to}.resnets.2.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.norm2.weight") - new_state_dict[f"{prefix_to}.resnets.2.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.norm2.bias") - new_state_dict[f"{prefix_to}.resnets.2.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.conv2.weight") - new_state_dict[f"{prefix_to}.resnets.2.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.conv2.bias") - - # fmt: on - - -if __name__ == "__main__": - main() diff --git a/scripts/convert_animatediff_motion_lora_to_diffusers.py b/scripts/convert_animatediff_motion_lora_to_diffusers.py deleted file mode 100644 index 21567ffa9e7a..000000000000 --- a/scripts/convert_animatediff_motion_lora_to_diffusers.py +++ /dev/null @@ -1,69 +0,0 @@ -import argparse -import os - -import torch -from huggingface_hub import create_repo, upload_folder -from safetensors.torch import load_file, save_file - - -def convert_motion_module(original_state_dict): - converted_state_dict = {} - for k, v in original_state_dict.items(): - if "pos_encoder" in k: - continue - - else: - converted_state_dict[ - k.replace(".norms.0", ".norm1") - .replace(".norms.1", ".norm2") - .replace(".ff_norm", ".norm3") - .replace(".attention_blocks.0", ".attn1") - .replace(".attention_blocks.1", ".attn2") - .replace(".temporal_transformer", "") - ] = v - - return converted_state_dict - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--ckpt_path", type=str, required=True, help="Path to checkpoint") - parser.add_argument("--output_path", type=str, required=True, help="Path to output directory") - parser.add_argument( - "--push_to_hub", - action="store_true", - default=False, - help="Whether to push the converted model to the HF or not", - ) - - return parser.parse_args() - - -if __name__ == "__main__": - args = get_args() - - if args.ckpt_path.endswith(".safetensors"): - state_dict = load_file(args.ckpt_path) - else: - state_dict = torch.load(args.ckpt_path, map_location="cpu") - - if "state_dict" in state_dict.keys(): - state_dict = state_dict["state_dict"] - - conv_state_dict = convert_motion_module(state_dict) - - # convert to new format - output_dict = {} - for module_name, params in conv_state_dict.items(): - if type(params) is not torch.Tensor: - continue - output_dict.update({f"unet.{module_name}": params}) - - os.makedirs(args.output_path, exist_ok=True) - - filepath = os.path.join(args.output_path, "diffusion_pytorch_model.safetensors") - save_file(output_dict, filepath) - - if args.push_to_hub: - repo_id = create_repo(args.output_path, exist_ok=True).repo_id - upload_folder(repo_id=repo_id, folder_path=args.output_path, repo_type="model") diff --git a/scripts/convert_animatediff_motion_module_to_diffusers.py b/scripts/convert_animatediff_motion_module_to_diffusers.py deleted file mode 100644 index e188a6a533e8..000000000000 --- a/scripts/convert_animatediff_motion_module_to_diffusers.py +++ /dev/null @@ -1,62 +0,0 @@ -import argparse - -import torch -from safetensors.torch import load_file - -from diffusers import MotionAdapter - - -def convert_motion_module(original_state_dict): - converted_state_dict = {} - for k, v in original_state_dict.items(): - if "pos_encoder" in k: - continue - - else: - converted_state_dict[ - k.replace(".norms.0", ".norm1") - .replace(".norms.1", ".norm2") - .replace(".ff_norm", ".norm3") - .replace(".attention_blocks.0", ".attn1") - .replace(".attention_blocks.1", ".attn2") - .replace(".temporal_transformer", "") - ] = v - - return converted_state_dict - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--ckpt_path", type=str, required=True) - parser.add_argument("--output_path", type=str, required=True) - parser.add_argument("--use_motion_mid_block", action="store_true") - parser.add_argument("--motion_max_seq_length", type=int, default=32) - parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int) - parser.add_argument("--save_fp16", action="store_true") - - return parser.parse_args() - - -if __name__ == "__main__": - args = get_args() - - if args.ckpt_path.endswith(".safetensors"): - state_dict = load_file(args.ckpt_path) - else: - state_dict = torch.load(args.ckpt_path, map_location="cpu") - - if "state_dict" in state_dict.keys(): - state_dict = state_dict["state_dict"] - - conv_state_dict = convert_motion_module(state_dict) - adapter = MotionAdapter( - block_out_channels=args.block_out_channels, - use_motion_mid_block=args.use_motion_mid_block, - motion_max_seq_length=args.motion_max_seq_length, - ) - # skip loading position embeddings - adapter.load_state_dict(conv_state_dict, strict=False) - adapter.save_pretrained(args.output_path) - - if args.save_fp16: - adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16") diff --git a/scripts/convert_animatediff_sparsectrl_to_diffusers.py b/scripts/convert_animatediff_sparsectrl_to_diffusers.py deleted file mode 100644 index b96b42d60f44..000000000000 --- a/scripts/convert_animatediff_sparsectrl_to_diffusers.py +++ /dev/null @@ -1,83 +0,0 @@ -import argparse -from typing import Dict - -import torch -import torch.nn as nn - -from diffusers import SparseControlNetModel - - -KEYS_RENAME_MAPPING = { - ".attention_blocks.0": ".attn1", - ".attention_blocks.1": ".attn2", - ".attn1.pos_encoder": ".pos_embed", - ".ff_norm": ".norm3", - ".norms.0": ".norm1", - ".norms.1": ".norm2", - ".temporal_transformer": "", -} - - -def convert(original_state_dict: Dict[str, nn.Module]) -> dict[str, nn.Module]: - converted_state_dict = {} - - for key in list(original_state_dict.keys()): - renamed_key = key - for new_name, old_name in KEYS_RENAME_MAPPING.items(): - renamed_key = renamed_key.replace(new_name, old_name) - converted_state_dict[renamed_key] = original_state_dict.pop(key) - - return converted_state_dict - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--ckpt_path", type=str, required=True, help="Path to checkpoint") - parser.add_argument("--output_path", type=str, required=True, help="Path to output directory") - parser.add_argument( - "--max_motion_seq_length", - type=int, - default=32, - help="Max motion sequence length supported by the motion adapter", - ) - parser.add_argument( - "--conditioning_channels", type=int, default=4, help="Number of channels in conditioning input to controlnet" - ) - parser.add_argument( - "--use_simplified_condition_embedding", - action="store_true", - default=False, - help="Whether or not to use simplified condition embedding. When `conditioning_channels==4` i.e. latent inputs, set this to `True`. When `conditioning_channels==3` i.e. image inputs, set this to `False`", - ) - parser.add_argument( - "--save_fp16", - action="store_true", - default=False, - help="Whether or not to save model in fp16 precision along with fp32", - ) - parser.add_argument( - "--push_to_hub", action="store_true", default=False, help="Whether or not to push saved model to the HF hub" - ) - return parser.parse_args() - - -if __name__ == "__main__": - args = get_args() - - state_dict = torch.load(args.ckpt_path, map_location="cpu") - if "state_dict" in state_dict.keys(): - state_dict: dict = state_dict["state_dict"] - - controlnet = SparseControlNetModel( - conditioning_channels=args.conditioning_channels, - motion_max_seq_length=args.max_motion_seq_length, - use_simplified_condition_embedding=args.use_simplified_condition_embedding, - ) - - state_dict = convert(state_dict) - controlnet.load_state_dict(state_dict, strict=True) - - controlnet.save_pretrained(args.output_path, push_to_hub=args.push_to_hub) - if args.save_fp16: - controlnet = controlnet.to(dtype=torch.float16) - controlnet.save_pretrained(args.output_path, variant="fp16", push_to_hub=args.push_to_hub) diff --git a/scripts/convert_asymmetric_vqgan_to_diffusers.py b/scripts/convert_asymmetric_vqgan_to_diffusers.py deleted file mode 100644 index a9f83a46c2d0..000000000000 --- a/scripts/convert_asymmetric_vqgan_to_diffusers.py +++ /dev/null @@ -1,184 +0,0 @@ -import argparse -import time -from pathlib import Path -from typing import Any, Dict, Literal - -import torch - -from diffusers import AsymmetricAutoencoderKL - - -ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG = { - "in_channels": 3, - "out_channels": 3, - "down_block_types": [ - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - ], - "down_block_out_channels": [128, 256, 512, 512], - "layers_per_down_block": 2, - "up_block_types": [ - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - ], - "up_block_out_channels": [192, 384, 768, 768], - "layers_per_up_block": 3, - "act_fn": "silu", - "latent_channels": 4, - "norm_num_groups": 32, - "sample_size": 256, - "scaling_factor": 0.18215, -} - -ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG = { - "in_channels": 3, - "out_channels": 3, - "down_block_types": [ - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - ], - "down_block_out_channels": [128, 256, 512, 512], - "layers_per_down_block": 2, - "up_block_types": [ - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - "UpDecoderBlock2D", - ], - "up_block_out_channels": [256, 512, 1024, 1024], - "layers_per_up_block": 5, - "act_fn": "silu", - "latent_channels": 4, - "norm_num_groups": 32, - "sample_size": 256, - "scaling_factor": 0.18215, -} - - -def convert_asymmetric_autoencoder_kl_state_dict(original_state_dict: Dict[str, Any]) -> dict[str, Any]: - converted_state_dict = {} - for k, v in original_state_dict.items(): - if k.startswith("encoder."): - converted_state_dict[ - k.replace("encoder.down.", "encoder.down_blocks.") - .replace("encoder.mid.", "encoder.mid_block.") - .replace("encoder.norm_out.", "encoder.conv_norm_out.") - .replace(".downsample.", ".downsamplers.0.") - .replace(".nin_shortcut.", ".conv_shortcut.") - .replace(".block.", ".resnets.") - .replace(".block_1.", ".resnets.0.") - .replace(".block_2.", ".resnets.1.") - .replace(".attn_1.k.", ".attentions.0.to_k.") - .replace(".attn_1.q.", ".attentions.0.to_q.") - .replace(".attn_1.v.", ".attentions.0.to_v.") - .replace(".attn_1.proj_out.", ".attentions.0.to_out.0.") - .replace(".attn_1.norm.", ".attentions.0.group_norm.") - ] = v - elif k.startswith("decoder.") and "up_layers" not in k: - converted_state_dict[ - k.replace("decoder.encoder.", "decoder.condition_encoder.") - .replace(".norm_out.", ".conv_norm_out.") - .replace(".up.0.", ".up_blocks.3.") - .replace(".up.1.", ".up_blocks.2.") - .replace(".up.2.", ".up_blocks.1.") - .replace(".up.3.", ".up_blocks.0.") - .replace(".block.", ".resnets.") - .replace("mid", "mid_block") - .replace(".0.upsample.", ".0.upsamplers.0.") - .replace(".1.upsample.", ".1.upsamplers.0.") - .replace(".2.upsample.", ".2.upsamplers.0.") - .replace(".nin_shortcut.", ".conv_shortcut.") - .replace(".block_1.", ".resnets.0.") - .replace(".block_2.", ".resnets.1.") - .replace(".attn_1.k.", ".attentions.0.to_k.") - .replace(".attn_1.q.", ".attentions.0.to_q.") - .replace(".attn_1.v.", ".attentions.0.to_v.") - .replace(".attn_1.proj_out.", ".attentions.0.to_out.0.") - .replace(".attn_1.norm.", ".attentions.0.group_norm.") - ] = v - elif k.startswith("quant_conv."): - converted_state_dict[k] = v - elif k.startswith("post_quant_conv."): - converted_state_dict[k] = v - else: - print(f" skipping key `{k}`") - # fix weights shape - for k, v in converted_state_dict.items(): - if ( - (k.startswith("encoder.mid_block.attentions.0") or k.startswith("decoder.mid_block.attentions.0")) - and k.endswith("weight") - and ("to_q" in k or "to_k" in k or "to_v" in k or "to_out" in k) - ): - converted_state_dict[k] = converted_state_dict[k][:, :, 0, 0] - - return converted_state_dict - - -def get_asymmetric_autoencoder_kl_from_original_checkpoint( - scale: Literal["1.5", "2"], original_checkpoint_path: str, map_location: torch.device -) -> AsymmetricAutoencoderKL: - print("Loading original state_dict") - original_state_dict = torch.load(original_checkpoint_path, map_location=map_location) - original_state_dict = original_state_dict["state_dict"] - print("Converting state_dict") - converted_state_dict = convert_asymmetric_autoencoder_kl_state_dict(original_state_dict) - kwargs = ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG if scale == "1.5" else ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG - print("Initializing AsymmetricAutoencoderKL model") - asymmetric_autoencoder_kl = AsymmetricAutoencoderKL(**kwargs) - print("Loading weight from converted state_dict") - asymmetric_autoencoder_kl.load_state_dict(converted_state_dict) - asymmetric_autoencoder_kl.eval() - print("AsymmetricAutoencoderKL successfully initialized") - return asymmetric_autoencoder_kl - - -if __name__ == "__main__": - start = time.time() - parser = argparse.ArgumentParser() - parser.add_argument( - "--scale", - default=None, - type=str, - required=True, - help="Asymmetric VQGAN scale: `1.5` or `2`", - ) - parser.add_argument( - "--original_checkpoint_path", - default=None, - type=str, - required=True, - help="Path to the original Asymmetric VQGAN checkpoint", - ) - parser.add_argument( - "--output_path", - default=None, - type=str, - required=True, - help="Path to save pretrained AsymmetricAutoencoderKL model", - ) - parser.add_argument( - "--map_location", - default="cpu", - type=str, - required=False, - help="The device passed to `map_location` when loading the checkpoint", - ) - args = parser.parse_args() - - assert args.scale in ["1.5", "2"], f"{args.scale} should be `1.5` of `2`" - assert Path(args.original_checkpoint_path).is_file() - - asymmetric_autoencoder_kl = get_asymmetric_autoencoder_kl_from_original_checkpoint( - scale=args.scale, - original_checkpoint_path=args.original_checkpoint_path, - map_location=torch.device(args.map_location), - ) - print("Saving pretrained AsymmetricAutoencoderKL") - asymmetric_autoencoder_kl.save_pretrained(args.output_path) - print(f"Done in {time.time() - start:.2f} seconds") diff --git a/scripts/convert_aura_flow_to_diffusers.py b/scripts/convert_aura_flow_to_diffusers.py deleted file mode 100644 index 74c34f4851ff..000000000000 --- a/scripts/convert_aura_flow_to_diffusers.py +++ /dev/null @@ -1,131 +0,0 @@ -import argparse - -import torch -from huggingface_hub import hf_hub_download - -from diffusers.models.transformers.auraflow_transformer_2d import AuraFlowTransformer2DModel - - -def load_original_state_dict(args): - model_pt = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename="aura_diffusion_pytorch_model.bin") - state_dict = torch.load(model_pt, map_location="cpu") - return state_dict - - -def calculate_layers(state_dict_keys, key_prefix): - dit_layers = set() - for k in state_dict_keys: - if key_prefix in k: - dit_layers.add(int(k.split(".")[2])) - print(f"{key_prefix}: {len(dit_layers)}") - return len(dit_layers) - - -# similar to SD3 but only for the last norm layer -def swap_scale_shift(weight, dim): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_transformer(state_dict): - converted_state_dict = {} - state_dict_keys = list(state_dict.keys()) - - converted_state_dict["register_tokens"] = state_dict.pop("model.register_tokens") - converted_state_dict["pos_embed.pos_embed"] = state_dict.pop("model.positional_encoding") - converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("model.init_x_linear.weight") - converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("model.init_x_linear.bias") - - converted_state_dict["time_step_proj.linear_1.weight"] = state_dict.pop("model.t_embedder.mlp.0.weight") - converted_state_dict["time_step_proj.linear_1.bias"] = state_dict.pop("model.t_embedder.mlp.0.bias") - converted_state_dict["time_step_proj.linear_2.weight"] = state_dict.pop("model.t_embedder.mlp.2.weight") - converted_state_dict["time_step_proj.linear_2.bias"] = state_dict.pop("model.t_embedder.mlp.2.bias") - - converted_state_dict["context_embedder.weight"] = state_dict.pop("model.cond_seq_linear.weight") - - mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers") - single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers") - - # MMDiT blocks 🎸. - for i in range(mmdit_layers): - # feed-forward - path_mapping = {"mlpX": "ff", "mlpC": "ff_context"} - weight_mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"} - for orig_k, diffuser_k in path_mapping.items(): - for k, v in weight_mapping.items(): - converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.{v}.weight"] = state_dict.pop( - f"model.double_layers.{i}.{orig_k}.{k}.weight" - ) - - # norms - path_mapping = {"modX": "norm1", "modC": "norm1_context"} - for orig_k, diffuser_k in path_mapping.items(): - converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.linear.weight"] = state_dict.pop( - f"model.double_layers.{i}.{orig_k}.1.weight" - ) - - # attns - x_attn_mapping = {"w2q": "to_q", "w2k": "to_k", "w2v": "to_v", "w2o": "to_out.0"} - context_attn_mapping = {"w1q": "add_q_proj", "w1k": "add_k_proj", "w1v": "add_v_proj", "w1o": "to_add_out"} - for attn_mapping in [x_attn_mapping, context_attn_mapping]: - for k, v in attn_mapping.items(): - converted_state_dict[f"joint_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop( - f"model.double_layers.{i}.attn.{k}.weight" - ) - - # Single-DiT blocks. - for i in range(single_dit_layers): - # feed-forward - mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"} - for k, v in mapping.items(): - converted_state_dict[f"single_transformer_blocks.{i}.ff.{v}.weight"] = state_dict.pop( - f"model.single_layers.{i}.mlp.{k}.weight" - ) - - # norms - converted_state_dict[f"single_transformer_blocks.{i}.norm1.linear.weight"] = state_dict.pop( - f"model.single_layers.{i}.modCX.1.weight" - ) - - # attns - x_attn_mapping = {"w1q": "to_q", "w1k": "to_k", "w1v": "to_v", "w1o": "to_out.0"} - for k, v in x_attn_mapping.items(): - converted_state_dict[f"single_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop( - f"model.single_layers.{i}.attn.{k}.weight" - ) - - # Final blocks. - converted_state_dict["proj_out.weight"] = state_dict.pop("model.final_linear.weight") - converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict.pop("model.modF.1.weight"), dim=None) - - return converted_state_dict - - -@torch.no_grad() -def populate_state_dict(args): - original_state_dict = load_original_state_dict(args) - state_dict_keys = list(original_state_dict.keys()) - mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers") - single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers") - - converted_state_dict = convert_transformer(original_state_dict) - model_diffusers = AuraFlowTransformer2DModel( - num_mmdit_layers=mmdit_layers, num_single_dit_layers=single_dit_layers - ) - model_diffusers.load_state_dict(converted_state_dict, strict=True) - - return model_diffusers - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--original_state_dict_repo_id", default="AuraDiffusion/auradiffusion-v0.1a0", type=str) - parser.add_argument("--dump_path", default="aura-flow", type=str) - parser.add_argument("--hub_id", default=None, type=str) - args = parser.parse_args() - - model_diffusers = populate_state_dict(args) - model_diffusers.save_pretrained(args.dump_path) - if args.hub_id is not None: - model_diffusers.push_to_hub(args.hub_id) diff --git a/scripts/convert_bd3lm_to_diffusers.py b/scripts/convert_bd3lm_to_diffusers.py deleted file mode 100644 index 98f380637b56..000000000000 --- a/scripts/convert_bd3lm_to_diffusers.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse - -import torch -from transformers import AutoConfig, AutoModelForMaskedLM -from transformers.dynamic_module_utils import get_class_from_dynamic_module - -from diffusers import BD3LMModel - - -def convert_state_dict(hf_state_dict, config): - hidden_dim = config.hidden_dim - num_layers = config.n_blocks - adaln = config.adaln - - state_dict = {} - state_dict["token_embedding.weight"] = hf_state_dict["backbone.vocab_embed.embedding"] - state_dict["sigma_embed.mlp.0.weight"] = hf_state_dict["backbone.sigma_map.mlp.0.weight"] - state_dict["sigma_embed.mlp.0.bias"] = hf_state_dict["backbone.sigma_map.mlp.0.bias"] - state_dict["sigma_embed.mlp.2.weight"] = hf_state_dict["backbone.sigma_map.mlp.2.weight"] - state_dict["sigma_embed.mlp.2.bias"] = hf_state_dict["backbone.sigma_map.mlp.2.bias"] - - for i in range(num_layers): - prefix = f"backbone.blocks.{i}." - qkv = hf_state_dict[prefix + "attn_qkv.weight"] - state_dict[f"blocks.{i}.attn.to_q.weight"] = qkv[:hidden_dim] - state_dict[f"blocks.{i}.attn.to_k.weight"] = qkv[hidden_dim : 2 * hidden_dim] - state_dict[f"blocks.{i}.attn.to_v.weight"] = qkv[2 * hidden_dim :] - state_dict[f"blocks.{i}.attn.to_out.0.weight"] = hf_state_dict[prefix + "attn_out.weight"] - - state_dict[f"blocks.{i}.norm1.weight"] = hf_state_dict[prefix + "norm1.weight"] - state_dict[f"blocks.{i}.norm2.weight"] = hf_state_dict[prefix + "norm2.weight"] - - state_dict[f"blocks.{i}.mlp.0.weight"] = hf_state_dict[prefix + "mlp.0.weight"] - state_dict[f"blocks.{i}.mlp.0.bias"] = hf_state_dict[prefix + "mlp.0.bias"] - state_dict[f"blocks.{i}.mlp.2.weight"] = hf_state_dict[prefix + "mlp.2.weight"] - state_dict[f"blocks.{i}.mlp.2.bias"] = hf_state_dict[prefix + "mlp.2.bias"] - - if adaln: - state_dict[f"blocks.{i}.adaLN_modulation.weight"] = hf_state_dict[prefix + "adaLN_modulation.weight"] - state_dict[f"blocks.{i}.adaLN_modulation.bias"] = hf_state_dict[prefix + "adaLN_modulation.bias"] - - state_dict["final_layer.norm.weight"] = hf_state_dict["backbone.output_layer.norm_final.weight"] - state_dict["final_layer.linear.weight"] = hf_state_dict["backbone.output_layer.linear.weight"] - state_dict["final_layer.linear.bias"] = hf_state_dict["backbone.output_layer.linear.bias"] - if adaln: - state_dict["final_layer.adaLN_modulation.weight"] = hf_state_dict[ - "backbone.output_layer.adaLN_modulation.weight" - ] - state_dict["final_layer.adaLN_modulation.bias"] = hf_state_dict["backbone.output_layer.adaLN_modulation.bias"] - - return state_dict - - -def main(): - parser = argparse.ArgumentParser(description="Convert BD3LM checkpoints to diffusers format.") - parser.add_argument("--model_id", type=str, required=True, help="HF model ID or local path.") - parser.add_argument("--output_dir", type=str, required=True, help="Output directory for the diffusers model.") - parser.add_argument( - "--torch_dtype", - type=str, - default="float32", - choices=["float16", "bfloat16", "float32"], - help="Torch dtype to load the source checkpoint.", - ) - args = parser.parse_args() - - dtype_map = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32} - torch_dtype = dtype_map[args.torch_dtype] - - config = AutoConfig.from_pretrained(args.model_id, trust_remote_code=True) - try: - hf_model = AutoModelForMaskedLM.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype, - ) - except AttributeError as err: - if "all_tied_weights_keys" not in str(err): - raise - class_ref = config.auto_map.get("AutoModelForMaskedLM") - if class_ref is None: - raise ValueError("BD3LM config does not define AutoModelForMaskedLM in auto_map.") from err - model_class = get_class_from_dynamic_module(class_ref, args.model_id, revision=None, trust_remote_code=True) - if not hasattr(model_class, "all_tied_weights_keys"): - model_class.all_tied_weights_keys = {} - hf_model = model_class.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype, - ) - hf_state_dict = hf_model.state_dict() - - model = BD3LMModel( - block_size=config.block_size, - vocab_size=config.vocab_size, - model_length=config.model_length, - cross_attn=config.cross_attn, - adaln=config.adaln, - hidden_dim=config.hidden_dim, - cond_dim=config.cond_dim, - num_layers=config.n_blocks, - num_attention_heads=config.n_heads, - dropout=config.dropout, - time_conditioning=config.time_conditioning, - var_min=config.var_min, - sampling_eps_min=config.sampling_eps_min, - sampling_eps_max=config.sampling_eps_max, - ) - - converted_state = convert_state_dict(hf_state_dict, config) - missing, unexpected = model.load_state_dict(converted_state, strict=False) - if missing: - raise ValueError(f"Missing keys when loading converted BD3LM weights: {missing}") - if unexpected: - raise ValueError(f"Unexpected keys when loading converted BD3LM weights: {unexpected}") - - model.save_pretrained(args.output_dir) - - -if __name__ == "__main__": - main() diff --git a/scripts/convert_blipdiffusion_to_diffusers.py b/scripts/convert_blipdiffusion_to_diffusers.py deleted file mode 100644 index 2c286ea0fdc7..000000000000 --- a/scripts/convert_blipdiffusion_to_diffusers.py +++ /dev/null @@ -1,344 +0,0 @@ -""" -This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main. -""" - -import argparse -import os -import tempfile - -import torch -from lavis.models import load_model_and_preprocess -from transformers import CLIPTokenizer -from transformers.models.blip_2.configuration_blip_2 import Blip2Config - -from diffusers import ( - AutoencoderKL, - PNDMScheduler, - UNet2DConditionModel, -) -from diffusers.pipelines import BlipDiffusionPipeline -from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor -from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel -from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel - - -BLIP2_CONFIG = { - "vision_config": { - "hidden_size": 1024, - "num_hidden_layers": 23, - "num_attention_heads": 16, - "image_size": 224, - "patch_size": 14, - "intermediate_size": 4096, - "hidden_act": "quick_gelu", - }, - "qformer_config": { - "cross_attention_frequency": 1, - "encoder_hidden_size": 1024, - "vocab_size": 30523, - }, - "num_query_tokens": 16, -} -blip2config = Blip2Config(**BLIP2_CONFIG) - - -def qformer_model_from_original_config(): - qformer = Blip2QFormerModel(blip2config) - return qformer - - -def embeddings_from_original_checkpoint(model, diffuser_embeddings_prefix, original_embeddings_prefix): - embeddings = {} - embeddings.update( - { - f"{diffuser_embeddings_prefix}.word_embeddings.weight": model[ - f"{original_embeddings_prefix}.word_embeddings.weight" - ] - } - ) - embeddings.update( - { - f"{diffuser_embeddings_prefix}.position_embeddings.weight": model[ - f"{original_embeddings_prefix}.position_embeddings.weight" - ] - } - ) - embeddings.update( - {f"{diffuser_embeddings_prefix}.LayerNorm.weight": model[f"{original_embeddings_prefix}.LayerNorm.weight"]} - ) - embeddings.update( - {f"{diffuser_embeddings_prefix}.LayerNorm.bias": model[f"{original_embeddings_prefix}.LayerNorm.bias"]} - ) - return embeddings - - -def proj_layer_from_original_checkpoint(model, diffuser_proj_prefix, original_proj_prefix): - proj_layer = {} - proj_layer.update({f"{diffuser_proj_prefix}.dense1.weight": model[f"{original_proj_prefix}.dense1.weight"]}) - proj_layer.update({f"{diffuser_proj_prefix}.dense1.bias": model[f"{original_proj_prefix}.dense1.bias"]}) - proj_layer.update({f"{diffuser_proj_prefix}.dense2.weight": model[f"{original_proj_prefix}.dense2.weight"]}) - proj_layer.update({f"{diffuser_proj_prefix}.dense2.bias": model[f"{original_proj_prefix}.dense2.bias"]}) - proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.weight": model[f"{original_proj_prefix}.LayerNorm.weight"]}) - proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.bias": model[f"{original_proj_prefix}.LayerNorm.bias"]}) - return proj_layer - - -def attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix): - attention = {} - attention.update( - { - f"{diffuser_attention_prefix}.attention.query.weight": model[ - f"{original_attention_prefix}.self.query.weight" - ] - } - ) - attention.update( - {f"{diffuser_attention_prefix}.attention.query.bias": model[f"{original_attention_prefix}.self.query.bias"]} - ) - attention.update( - {f"{diffuser_attention_prefix}.attention.key.weight": model[f"{original_attention_prefix}.self.key.weight"]} - ) - attention.update( - {f"{diffuser_attention_prefix}.attention.key.bias": model[f"{original_attention_prefix}.self.key.bias"]} - ) - attention.update( - { - f"{diffuser_attention_prefix}.attention.value.weight": model[ - f"{original_attention_prefix}.self.value.weight" - ] - } - ) - attention.update( - {f"{diffuser_attention_prefix}.attention.value.bias": model[f"{original_attention_prefix}.self.value.bias"]} - ) - attention.update( - {f"{diffuser_attention_prefix}.output.dense.weight": model[f"{original_attention_prefix}.output.dense.weight"]} - ) - attention.update( - {f"{diffuser_attention_prefix}.output.dense.bias": model[f"{original_attention_prefix}.output.dense.bias"]} - ) - attention.update( - { - f"{diffuser_attention_prefix}.output.LayerNorm.weight": model[ - f"{original_attention_prefix}.output.LayerNorm.weight" - ] - } - ) - attention.update( - { - f"{diffuser_attention_prefix}.output.LayerNorm.bias": model[ - f"{original_attention_prefix}.output.LayerNorm.bias" - ] - } - ) - return attention - - -def output_layers_from_original_checkpoint(model, diffuser_output_prefix, original_output_prefix): - output_layers = {} - output_layers.update({f"{diffuser_output_prefix}.dense.weight": model[f"{original_output_prefix}.dense.weight"]}) - output_layers.update({f"{diffuser_output_prefix}.dense.bias": model[f"{original_output_prefix}.dense.bias"]}) - output_layers.update( - {f"{diffuser_output_prefix}.LayerNorm.weight": model[f"{original_output_prefix}.LayerNorm.weight"]} - ) - output_layers.update( - {f"{diffuser_output_prefix}.LayerNorm.bias": model[f"{original_output_prefix}.LayerNorm.bias"]} - ) - return output_layers - - -def encoder_from_original_checkpoint(model, diffuser_encoder_prefix, original_encoder_prefix): - encoder = {} - for i in range(blip2config.qformer_config.num_hidden_layers): - encoder.update( - attention_from_original_checkpoint( - model, f"{diffuser_encoder_prefix}.{i}.attention", f"{original_encoder_prefix}.{i}.attention" - ) - ) - encoder.update( - attention_from_original_checkpoint( - model, f"{diffuser_encoder_prefix}.{i}.crossattention", f"{original_encoder_prefix}.{i}.crossattention" - ) - ) - - encoder.update( - { - f"{diffuser_encoder_prefix}.{i}.intermediate.dense.weight": model[ - f"{original_encoder_prefix}.{i}.intermediate.dense.weight" - ] - } - ) - encoder.update( - { - f"{diffuser_encoder_prefix}.{i}.intermediate.dense.bias": model[ - f"{original_encoder_prefix}.{i}.intermediate.dense.bias" - ] - } - ) - encoder.update( - { - f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.weight": model[ - f"{original_encoder_prefix}.{i}.intermediate_query.dense.weight" - ] - } - ) - encoder.update( - { - f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.bias": model[ - f"{original_encoder_prefix}.{i}.intermediate_query.dense.bias" - ] - } - ) - - encoder.update( - output_layers_from_original_checkpoint( - model, f"{diffuser_encoder_prefix}.{i}.output", f"{original_encoder_prefix}.{i}.output" - ) - ) - encoder.update( - output_layers_from_original_checkpoint( - model, f"{diffuser_encoder_prefix}.{i}.output_query", f"{original_encoder_prefix}.{i}.output_query" - ) - ) - return encoder - - -def visual_encoder_layer_from_original_checkpoint(model, diffuser_prefix, original_prefix): - visual_encoder_layer = {} - - visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.weight": model[f"{original_prefix}.ln_1.weight"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.bias": model[f"{original_prefix}.ln_1.bias"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.weight": model[f"{original_prefix}.ln_2.weight"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.bias": model[f"{original_prefix}.ln_2.bias"]}) - visual_encoder_layer.update( - {f"{diffuser_prefix}.self_attn.qkv.weight": model[f"{original_prefix}.attn.in_proj_weight"]} - ) - visual_encoder_layer.update( - {f"{diffuser_prefix}.self_attn.qkv.bias": model[f"{original_prefix}.attn.in_proj_bias"]} - ) - visual_encoder_layer.update( - {f"{diffuser_prefix}.self_attn.projection.weight": model[f"{original_prefix}.attn.out_proj.weight"]} - ) - visual_encoder_layer.update( - {f"{diffuser_prefix}.self_attn.projection.bias": model[f"{original_prefix}.attn.out_proj.bias"]} - ) - visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.weight": model[f"{original_prefix}.mlp.c_fc.weight"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.bias": model[f"{original_prefix}.mlp.c_fc.bias"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.weight": model[f"{original_prefix}.mlp.c_proj.weight"]}) - visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.bias": model[f"{original_prefix}.mlp.c_proj.bias"]}) - - return visual_encoder_layer - - -def visual_encoder_from_original_checkpoint(model, diffuser_prefix, original_prefix): - visual_encoder = {} - - visual_encoder.update( - { - f"{diffuser_prefix}.embeddings.class_embedding": model[f"{original_prefix}.class_embedding"] - .unsqueeze(0) - .unsqueeze(0) - } - ) - visual_encoder.update( - { - f"{diffuser_prefix}.embeddings.position_embedding": model[ - f"{original_prefix}.positional_embedding" - ].unsqueeze(0) - } - ) - visual_encoder.update( - {f"{diffuser_prefix}.embeddings.patch_embedding.weight": model[f"{original_prefix}.conv1.weight"]} - ) - visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.weight": model[f"{original_prefix}.ln_pre.weight"]}) - visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.bias": model[f"{original_prefix}.ln_pre.bias"]}) - - for i in range(blip2config.vision_config.num_hidden_layers): - visual_encoder.update( - visual_encoder_layer_from_original_checkpoint( - model, f"{diffuser_prefix}.encoder.layers.{i}", f"{original_prefix}.transformer.resblocks.{i}" - ) - ) - - visual_encoder.update({f"{diffuser_prefix}.post_layernorm.weight": model["blip.ln_vision.weight"]}) - visual_encoder.update({f"{diffuser_prefix}.post_layernorm.bias": model["blip.ln_vision.bias"]}) - - return visual_encoder - - -def qformer_original_checkpoint_to_diffusers_checkpoint(model): - qformer_checkpoint = {} - qformer_checkpoint.update(embeddings_from_original_checkpoint(model, "embeddings", "blip.Qformer.bert.embeddings")) - qformer_checkpoint.update({"query_tokens": model["blip.query_tokens"]}) - qformer_checkpoint.update(proj_layer_from_original_checkpoint(model, "proj_layer", "proj_layer")) - qformer_checkpoint.update( - encoder_from_original_checkpoint(model, "encoder.layer", "blip.Qformer.bert.encoder.layer") - ) - qformer_checkpoint.update(visual_encoder_from_original_checkpoint(model, "visual_encoder", "blip.visual_encoder")) - return qformer_checkpoint - - -def get_qformer(model): - print("loading qformer") - - qformer = qformer_model_from_original_config() - qformer_diffusers_checkpoint = qformer_original_checkpoint_to_diffusers_checkpoint(model) - - load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer) - - print("done loading qformer") - return qformer - - -def load_checkpoint_to_model(checkpoint, model): - with tempfile.NamedTemporaryFile(delete=False) as file: - torch.save(checkpoint, file.name) - del checkpoint - model.load_state_dict(torch.load(file.name), strict=False) - - os.remove(file.name) - - -def save_blip_diffusion_model(model, args): - qformer = get_qformer(model) - qformer.eval() - - text_encoder = ContextCLIPTextModel.from_pretrained( - "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="text_encoder" - ) - vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae") - unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet") - vae.eval() - text_encoder.eval() - scheduler = PNDMScheduler( - beta_start=0.00085, - beta_end=0.012, - beta_schedule="scaled_linear", - set_alpha_to_one=False, - skip_prk_steps=True, - ) - tokenizer = CLIPTokenizer.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="tokenizer") - image_processor = BlipImageProcessor() - blip_diffusion = BlipDiffusionPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - unet=unet, - scheduler=scheduler, - qformer=qformer, - image_processor=image_processor, - ) - blip_diffusion.save_pretrained(args.checkpoint_path) - - -def main(args): - model, _, _ = load_model_and_preprocess("blip_diffusion", "base", device="cpu", is_eval=True) - save_blip_diffusion_model(model.state_dict(), args) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - args = parser.parse_args() - - main(args) diff --git a/scripts/convert_cogvideox_to_diffusers.py b/scripts/convert_cogvideox_to_diffusers.py deleted file mode 100644 index 5220aa0bde9d..000000000000 --- a/scripts/convert_cogvideox_to_diffusers.py +++ /dev/null @@ -1,346 +0,0 @@ -import argparse -from typing import Any, Dict - -import torch -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import ( - AutoencoderKLCogVideoX, - CogVideoXDDIMScheduler, - CogVideoXImageToVideoPipeline, - CogVideoXPipeline, - CogVideoXTransformer3DModel, -) - - -def reassign_query_key_value_inplace(key: str, state_dict: Dict[str, Any]): - to_q_key = key.replace("query_key_value", "to_q") - to_k_key = key.replace("query_key_value", "to_k") - to_v_key = key.replace("query_key_value", "to_v") - to_q, to_k, to_v = torch.chunk(state_dict[key], chunks=3, dim=0) - state_dict[to_q_key] = to_q - state_dict[to_k_key] = to_k - state_dict[to_v_key] = to_v - state_dict.pop(key) - - -def reassign_query_key_layernorm_inplace(key: str, state_dict: Dict[str, Any]): - layer_id, weight_or_bias = key.split(".")[-2:] - - if "query" in key: - new_key = f"transformer_blocks.{layer_id}.attn1.norm_q.{weight_or_bias}" - elif "key" in key: - new_key = f"transformer_blocks.{layer_id}.attn1.norm_k.{weight_or_bias}" - - state_dict[new_key] = state_dict.pop(key) - - -def reassign_adaln_norm_inplace(key: str, state_dict: Dict[str, Any]): - layer_id, _, weight_or_bias = key.split(".")[-3:] - - weights_or_biases = state_dict[key].chunk(12, dim=0) - norm1_weights_or_biases = torch.cat(weights_or_biases[0:3] + weights_or_biases[6:9]) - norm2_weights_or_biases = torch.cat(weights_or_biases[3:6] + weights_or_biases[9:12]) - - norm1_key = f"transformer_blocks.{layer_id}.norm1.linear.{weight_or_bias}" - state_dict[norm1_key] = norm1_weights_or_biases - - norm2_key = f"transformer_blocks.{layer_id}.norm2.linear.{weight_or_bias}" - state_dict[norm2_key] = norm2_weights_or_biases - - state_dict.pop(key) - - -def remove_keys_inplace(key: str, state_dict: Dict[str, Any]): - state_dict.pop(key) - - -def replace_up_keys_inplace(key: str, state_dict: Dict[str, Any]): - key_split = key.split(".") - layer_index = int(key_split[2]) - replace_layer_index = 4 - 1 - layer_index - - key_split[1] = "up_blocks" - key_split[2] = str(replace_layer_index) - new_key = ".".join(key_split) - - state_dict[new_key] = state_dict.pop(key) - - -TRANSFORMER_KEYS_RENAME_DICT = { - "transformer.final_layernorm": "norm_final", - "transformer": "transformer_blocks", - "attention": "attn1", - "mlp": "ff.net", - "dense_h_to_4h": "0.proj", - "dense_4h_to_h": "2", - ".layers": "", - "dense": "to_out.0", - "input_layernorm": "norm1.norm", - "post_attn1_layernorm": "norm2.norm", - "time_embed.0": "time_embedding.linear_1", - "time_embed.2": "time_embedding.linear_2", - "ofs_embed.0": "ofs_embedding.linear_1", - "ofs_embed.2": "ofs_embedding.linear_2", - "mixins.patch_embed": "patch_embed", - "mixins.final_layer.norm_final": "norm_out.norm", - "mixins.final_layer.linear": "proj_out", - "mixins.final_layer.adaLN_modulation.1": "norm_out.linear", - "mixins.pos_embed.pos_embedding": "patch_embed.pos_embedding", # Specific to CogVideoX-5b-I2V -} - -TRANSFORMER_SPECIAL_KEYS_REMAP = { - "query_key_value": reassign_query_key_value_inplace, - "query_layernorm_list": reassign_query_key_layernorm_inplace, - "key_layernorm_list": reassign_query_key_layernorm_inplace, - "adaln_layer.adaLN_modulations": reassign_adaln_norm_inplace, - "embed_tokens": remove_keys_inplace, - "freqs_sin": remove_keys_inplace, - "freqs_cos": remove_keys_inplace, - "position_embedding": remove_keys_inplace, -} - -VAE_KEYS_RENAME_DICT = { - "block.": "resnets.", - "down.": "down_blocks.", - "downsample": "downsamplers.0", - "upsample": "upsamplers.0", - "nin_shortcut": "conv_shortcut", - "encoder.mid.block_1": "encoder.mid_block.resnets.0", - "encoder.mid.block_2": "encoder.mid_block.resnets.1", - "decoder.mid.block_1": "decoder.mid_block.resnets.0", - "decoder.mid.block_2": "decoder.mid_block.resnets.1", -} - -VAE_SPECIAL_KEYS_REMAP = { - "loss": remove_keys_inplace, - "up.": replace_up_keys_inplace, -} - -TOKENIZER_MAX_LENGTH = 226 - - -def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: - state_dict = saved_dict - if "model" in saved_dict.keys(): - state_dict = state_dict["model"] - if "module" in saved_dict.keys(): - state_dict = state_dict["module"] - if "state_dict" in saved_dict.keys(): - state_dict = state_dict["state_dict"] - return state_dict - - -def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def convert_transformer( - ckpt_path: str, - num_layers: int, - num_attention_heads: int, - use_rotary_positional_embeddings: bool, - i2v: bool, - dtype: torch.dtype, - init_kwargs: Dict[str, Any], -): - PREFIX_KEY = "model.diffusion_model." - - original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) - transformer = CogVideoXTransformer3DModel( - in_channels=32 if i2v else 16, - num_layers=num_layers, - num_attention_heads=num_attention_heads, - use_rotary_positional_embeddings=use_rotary_positional_embeddings, - ofs_embed_dim=512 if (i2v and init_kwargs["patch_size_t"] is not None) else None, # CogVideoX1.5-5B-I2V - use_learned_positional_embeddings=i2v and init_kwargs["patch_size_t"] is None, # CogVideoX-5B-I2V - **init_kwargs, - ).to(dtype=dtype) - - for key in list(original_state_dict.keys()): - new_key = key[len(PREFIX_KEY) :] - for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - transformer.load_state_dict(original_state_dict, strict=True) - return transformer - - -def convert_vae(ckpt_path: str, scaling_factor: float, version: str, dtype: torch.dtype): - init_kwargs = {"scaling_factor": scaling_factor} - if version == "1.5": - init_kwargs.update({"invert_scale_latents": True}) - - original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) - vae = AutoencoderKLCogVideoX(**init_kwargs).to(dtype=dtype) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True) - return vae - - -def get_transformer_init_kwargs(version: str): - if version == "1.0": - vae_scale_factor_spatial = 8 - init_kwargs = { - "patch_size": 2, - "patch_size_t": None, - "patch_bias": True, - "sample_height": 480 // vae_scale_factor_spatial, - "sample_width": 720 // vae_scale_factor_spatial, - "sample_frames": 49, - } - - elif version == "1.5": - vae_scale_factor_spatial = 8 - init_kwargs = { - "patch_size": 2, - "patch_size_t": 2, - "patch_bias": False, - "sample_height": 300, - "sample_width": 300, - "sample_frames": 81, - } - else: - raise ValueError("Unsupported version of CogVideoX.") - - return init_kwargs - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" - ) - parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint") - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument("--fp16", action="store_true", default=False, help="Whether to save the model weights in fp16") - parser.add_argument("--bf16", action="store_true", default=False, help="Whether to save the model weights in bf16") - parser.add_argument( - "--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving" - ) - parser.add_argument( - "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory" - ) - parser.add_argument( - "--typecast_text_encoder", - action="store_true", - default=False, - help="Whether or not to apply fp16/bf16 precision to text_encoder", - ) - # For CogVideoX-2B, num_layers is 30. For 5B, it is 42 - parser.add_argument("--num_layers", type=int, default=30, help="Number of transformer blocks") - # For CogVideoX-2B, num_attention_heads is 30. For 5B, it is 48 - parser.add_argument("--num_attention_heads", type=int, default=30, help="Number of attention heads") - # For CogVideoX-2B, use_rotary_positional_embeddings is False. For 5B, it is True - parser.add_argument( - "--use_rotary_positional_embeddings", action="store_true", default=False, help="Whether to use RoPE or not" - ) - # For CogVideoX-2B, scaling_factor is 1.15258426. For 5B, it is 0.7 - parser.add_argument("--scaling_factor", type=float, default=1.15258426, help="Scaling factor in the VAE") - # For CogVideoX-2B, snr_shift_scale is 3.0. For 5B, it is 1.0 - parser.add_argument("--snr_shift_scale", type=float, default=3.0, help="Scaling factor in the VAE") - parser.add_argument( - "--i2v", - action="store_true", - default=False, - help="Whether the model to be converted is the Image-to-Video version of CogVideoX.", - ) - parser.add_argument( - "--version", - choices=["1.0", "1.5"], - default="1.0", - help="Which version of CogVideoX to use for initializing default modeling parameters.", - ) - return parser.parse_args() - - -if __name__ == "__main__": - args = get_args() - - transformer = None - vae = None - - if args.fp16 and args.bf16: - raise ValueError("You cannot pass both --fp16 and --bf16 at the same time.") - - dtype = torch.float16 if args.fp16 else torch.bfloat16 if args.bf16 else torch.float32 - - if args.transformer_ckpt_path is not None: - init_kwargs = get_transformer_init_kwargs(args.version) - transformer = convert_transformer( - args.transformer_ckpt_path, - args.num_layers, - args.num_attention_heads, - args.use_rotary_positional_embeddings, - args.i2v, - dtype, - init_kwargs, - ) - if args.vae_ckpt_path is not None: - # Keep VAE in float32 for better quality - vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, args.version, torch.float32) - - text_encoder_id = "google/t5-v1_1-xxl" - tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) - text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) - - if args.typecast_text_encoder: - text_encoder = text_encoder.to(dtype=dtype) - - # Apparently, the conversion does not work anymore without this :shrug: - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - scheduler = CogVideoXDDIMScheduler.from_config( - { - "snr_shift_scale": args.snr_shift_scale, - "beta_end": 0.012, - "beta_schedule": "scaled_linear", - "beta_start": 0.00085, - "clip_sample": False, - "num_train_timesteps": 1000, - "prediction_type": "v_prediction", - "rescale_betas_zero_snr": True, - "set_alpha_to_one": True, - "timestep_spacing": "trailing", - } - ) - if args.i2v: - pipeline_cls = CogVideoXImageToVideoPipeline - else: - pipeline_cls = CogVideoXPipeline - - pipe = pipeline_cls( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - transformer=transformer, - scheduler=scheduler, - ) - - # We don't use variant here because the model must be run in fp16 (2B) or bf16 (5B). It would be weird - # for users to specify variant when the default is not fp32 and they want to run with the correct default (which - # is either fp16/bf16 here). - - # This is necessary This is necessary for users with insufficient memory, - # such as those using Colab and notebooks, as it can save some memory used for model loading. - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) diff --git a/scripts/convert_cogview3_to_diffusers.py b/scripts/convert_cogview3_to_diffusers.py deleted file mode 100644 index 605555ebdbef..000000000000 --- a/scripts/convert_cogview3_to_diffusers.py +++ /dev/null @@ -1,242 +0,0 @@ -""" -Convert a CogView3 checkpoint to the Diffusers format. - -This script converts a CogView3 checkpoint to the Diffusers format, which can then be used -with the Diffusers library. - -Example usage: - python scripts/convert_cogview3_to_diffusers.py \ - --transformer_checkpoint_path 'your path/cogview3plus_3b/1/mp_rank_00_model_states.pt' \ - --vae_checkpoint_path 'your path/3plus_ae/imagekl_ch16.pt' \ - --output_path "/raid/yiyi/cogview3_diffusers" \ - --dtype "bf16" - -Arguments: - --transformer_checkpoint_path: Path to Transformer state dict. - --vae_checkpoint_path: Path to VAE state dict. - --output_path: The path to save the converted model. - --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. - --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used - --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. - - Default is "bf16" because CogView3 uses bfloat16 for Training. - -Note: You must provide either --original_state_dict_repo_id or --checkpoint_path. -""" - -import argparse -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import AutoencoderKL, CogVideoXDDIMScheduler, CogView3PlusPipeline, CogView3PlusTransformer2DModel -from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -TOKENIZER_MAX_LENGTH = 224 - -parser = argparse.ArgumentParser() -parser.add_argument("--transformer_checkpoint_path", default=None, type=str) -parser.add_argument("--vae_checkpoint_path", default=None, type=str) -parser.add_argument("--output_path", required=True, type=str) -parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") -parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") -parser.add_argument("--dtype", type=str, default="bf16") - -args = parser.parse_args() - - -# this is specific to `AdaLayerNormContinuous`: -# diffusers implementation split the linear projection into the scale, shift while CogView3 split it tino shift, scale -def swap_scale_shift(weight, dim): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_cogview3_transformer_checkpoint_to_diffusers(ckpt_path): - original_state_dict = torch.load(ckpt_path, map_location="cpu") - original_state_dict = original_state_dict["module"] - original_state_dict = {k.replace("model.diffusion_model.", ""): v for k, v in original_state_dict.items()} - - new_state_dict = {} - - # Convert patch_embed - new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("mixins.patch_embed.proj.weight") - new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("mixins.patch_embed.proj.bias") - new_state_dict["patch_embed.text_proj.weight"] = original_state_dict.pop("mixins.patch_embed.text_proj.weight") - new_state_dict["patch_embed.text_proj.bias"] = original_state_dict.pop("mixins.patch_embed.text_proj.bias") - - # Convert time_condition_embed - new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "time_embed.0.weight" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "time_embed.0.bias" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "time_embed.2.weight" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "time_embed.2.bias" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = original_state_dict.pop( - "label_emb.0.0.weight" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = original_state_dict.pop( - "label_emb.0.0.bias" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = original_state_dict.pop( - "label_emb.0.2.weight" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = original_state_dict.pop( - "label_emb.0.2.bias" - ) - - # Convert transformer blocks - for i in range(30): - block_prefix = f"transformer_blocks.{i}." - old_prefix = f"transformer.layers.{i}." - adaln_prefix = f"mixins.adaln.adaln_modules.{i}." - - new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(adaln_prefix + "1.weight") - new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(adaln_prefix + "1.bias") - - qkv_weight = original_state_dict.pop(old_prefix + "attention.query_key_value.weight") - qkv_bias = original_state_dict.pop(old_prefix + "attention.query_key_value.bias") - q, k, v = qkv_weight.chunk(3, dim=0) - q_bias, k_bias, v_bias = qkv_bias.chunk(3, dim=0) - - new_state_dict[block_prefix + "attn1.to_q.weight"] = q - new_state_dict[block_prefix + "attn1.to_q.bias"] = q_bias - new_state_dict[block_prefix + "attn1.to_k.weight"] = k - new_state_dict[block_prefix + "attn1.to_k.bias"] = k_bias - new_state_dict[block_prefix + "attn1.to_v.weight"] = v - new_state_dict[block_prefix + "attn1.to_v.bias"] = v_bias - - new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( - old_prefix + "attention.dense.weight" - ) - new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop( - old_prefix + "attention.dense.bias" - ) - - new_state_dict[block_prefix + "ff.net.0.proj.weight"] = original_state_dict.pop( - old_prefix + "mlp.dense_h_to_4h.weight" - ) - new_state_dict[block_prefix + "ff.net.0.proj.bias"] = original_state_dict.pop( - old_prefix + "mlp.dense_h_to_4h.bias" - ) - new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop( - old_prefix + "mlp.dense_4h_to_h.weight" - ) - new_state_dict[block_prefix + "ff.net.2.bias"] = original_state_dict.pop(old_prefix + "mlp.dense_4h_to_h.bias") - - # Convert final norm and projection - new_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("mixins.final_layer.adaln.1.weight"), dim=0 - ) - new_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("mixins.final_layer.adaln.1.bias"), dim=0 - ) - new_state_dict["proj_out.weight"] = original_state_dict.pop("mixins.final_layer.linear.weight") - new_state_dict["proj_out.bias"] = original_state_dict.pop("mixins.final_layer.linear.bias") - - return new_state_dict - - -def convert_cogview3_vae_checkpoint_to_diffusers(ckpt_path, vae_config): - original_state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"] - return convert_ldm_vae_checkpoint(original_state_dict, vae_config) - - -def main(args): - if args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}") - - transformer = None - vae = None - - if args.transformer_checkpoint_path is not None: - converted_transformer_state_dict = convert_cogview3_transformer_checkpoint_to_diffusers( - args.transformer_checkpoint_path - ) - transformer = CogView3PlusTransformer2DModel() - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - if dtype is not None: - # Original checkpoint data type will be preserved - transformer = transformer.to(dtype=dtype) - - if args.vae_checkpoint_path is not None: - vae_config = { - "in_channels": 3, - "out_channels": 3, - "down_block_types": ("DownEncoderBlock2D",) * 4, - "up_block_types": ("UpDecoderBlock2D",) * 4, - "block_out_channels": (128, 512, 1024, 1024), - "layers_per_block": 3, - "act_fn": "silu", - "latent_channels": 16, - "norm_num_groups": 32, - "sample_size": 1024, - "scaling_factor": 1.0, - "force_upcast": True, - "use_quant_conv": False, - "use_post_quant_conv": False, - "mid_block_add_attention": False, - } - converted_vae_state_dict = convert_cogview3_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_state_dict, strict=True) - if dtype is not None: - vae = vae.to(dtype=dtype) - - text_encoder_id = "google/t5-v1_1-xxl" - tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) - text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) - - # Apparently, the conversion does not work anymore without this :shrug: - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - scheduler = CogVideoXDDIMScheduler.from_config( - { - "snr_shift_scale": 4.0, - "beta_end": 0.012, - "beta_schedule": "scaled_linear", - "beta_start": 0.00085, - "clip_sample": False, - "num_train_timesteps": 1000, - "prediction_type": "v_prediction", - "rescale_betas_zero_snr": True, - "set_alpha_to_one": True, - "timestep_spacing": "trailing", - } - ) - - pipe = CogView3PlusPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - transformer=transformer, - scheduler=scheduler, - ) - - # This is necessary for users with insufficient memory, such as those using Colab and notebooks, as it can - # save some memory used for model loading. - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_cogview4_to_diffusers.py b/scripts/convert_cogview4_to_diffusers.py deleted file mode 100644 index b6d01c797aeb..000000000000 --- a/scripts/convert_cogview4_to_diffusers.py +++ /dev/null @@ -1,254 +0,0 @@ -""" -Convert a CogView4 checkpoint from SAT(https://github.com/THUDM/SwissArmyTransformer) to the Diffusers format. -(deprecated Since 2025-02-07 and will remove it in later CogView4 version) - -This script converts a CogView4 checkpoint to the Diffusers format, which can then be used -with the Diffusers library. - -Example usage: - python scripts/convert_cogview4_to_diffusers.py \ - --transformer_checkpoint_path 'your path/cogview4_6b/1/mp_rank_00_model_states.pt' \ - --vae_checkpoint_path 'your path/cogview4_6b/imagekl_ch16.pt' \ - --output_path "THUDM/CogView4-6B" \ - --dtype "bf16" - -Arguments: - --transformer_checkpoint_path: Path to Transformer state dict. - --vae_checkpoint_path: Path to VAE state dict. - --output_path: The path to save the converted model. - --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. - --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used - --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. - - Default is "bf16" because CogView4 uses bfloat16 for Training. - -Note: You must provide either --original_state_dict_repo_id or --checkpoint_path. -""" - -import argparse -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights -from transformers import GlmForCausalLM, PreTrainedTokenizerFast - -from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler -from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--transformer_checkpoint_path", default=None, type=str) -parser.add_argument("--vae_checkpoint_path", default=None, type=str) -parser.add_argument("--output_path", required=True, type=str) -parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") -parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") -parser.add_argument("--dtype", type=str, default="bf16") - -args = parser.parse_args() - - -# this is specific to `AdaLayerNormContinuous`: -# diffusers implementation split the linear projection into the scale, shift while CogView4 split it tino shift, scale -def swap_scale_shift(weight, dim): - """ - Swap the scale and shift components in the weight tensor. - - Args: - weight (torch.Tensor): The original weight tensor. - dim (int): The dimension along which to split. - - Returns: - torch.Tensor: The modified weight tensor with scale and shift swapped. - """ - shift, scale = weight.chunk(2, dim=dim) - new_weight = torch.cat([scale, shift], dim=dim) - return new_weight - - -def convert_cogview4_transformer_checkpoint_to_diffusers(ckpt_path): - original_state_dict = torch.load(ckpt_path, map_location="cpu") - original_state_dict = original_state_dict["module"] - original_state_dict = {k.replace("model.diffusion_model.", ""): v for k, v in original_state_dict.items()} - - new_state_dict = {} - - # Convert patch_embed - new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("mixins.patch_embed.proj.weight") - new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("mixins.patch_embed.proj.bias") - new_state_dict["patch_embed.text_proj.weight"] = original_state_dict.pop("mixins.patch_embed.text_proj.weight") - new_state_dict["patch_embed.text_proj.bias"] = original_state_dict.pop("mixins.patch_embed.text_proj.bias") - - # Convert time_condition_embed - new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "time_embed.0.weight" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "time_embed.0.bias" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "time_embed.2.weight" - ) - new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "time_embed.2.bias" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = original_state_dict.pop( - "label_emb.0.0.weight" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = original_state_dict.pop( - "label_emb.0.0.bias" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = original_state_dict.pop( - "label_emb.0.2.weight" - ) - new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = original_state_dict.pop( - "label_emb.0.2.bias" - ) - - # Convert transformer blocks, for cogview4 is 28 blocks - for i in range(28): - block_prefix = f"transformer_blocks.{i}." - old_prefix = f"transformer.layers.{i}." - adaln_prefix = f"mixins.adaln.adaln_modules.{i}." - new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(adaln_prefix + "1.weight") - new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(adaln_prefix + "1.bias") - - qkv_weight = original_state_dict.pop(old_prefix + "attention.query_key_value.weight") - qkv_bias = original_state_dict.pop(old_prefix + "attention.query_key_value.bias") - q, k, v = qkv_weight.chunk(3, dim=0) - q_bias, k_bias, v_bias = qkv_bias.chunk(3, dim=0) - - new_state_dict[block_prefix + "attn1.to_q.weight"] = q - new_state_dict[block_prefix + "attn1.to_q.bias"] = q_bias - new_state_dict[block_prefix + "attn1.to_k.weight"] = k - new_state_dict[block_prefix + "attn1.to_k.bias"] = k_bias - new_state_dict[block_prefix + "attn1.to_v.weight"] = v - new_state_dict[block_prefix + "attn1.to_v.bias"] = v_bias - - new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( - old_prefix + "attention.dense.weight" - ) - new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop( - old_prefix + "attention.dense.bias" - ) - - new_state_dict[block_prefix + "ff.net.0.proj.weight"] = original_state_dict.pop( - old_prefix + "mlp.dense_h_to_4h.weight" - ) - new_state_dict[block_prefix + "ff.net.0.proj.bias"] = original_state_dict.pop( - old_prefix + "mlp.dense_h_to_4h.bias" - ) - new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop( - old_prefix + "mlp.dense_4h_to_h.weight" - ) - new_state_dict[block_prefix + "ff.net.2.bias"] = original_state_dict.pop(old_prefix + "mlp.dense_4h_to_h.bias") - - # Convert final norm and projection - new_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("mixins.final_layer.adaln.1.weight"), dim=0 - ) - new_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("mixins.final_layer.adaln.1.bias"), dim=0 - ) - new_state_dict["proj_out.weight"] = original_state_dict.pop("mixins.final_layer.linear.weight") - new_state_dict["proj_out.bias"] = original_state_dict.pop("mixins.final_layer.linear.bias") - - return new_state_dict - - -def convert_cogview4_vae_checkpoint_to_diffusers(ckpt_path, vae_config): - original_state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"] - return convert_ldm_vae_checkpoint(original_state_dict, vae_config) - - -def main(args): - if args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}") - - transformer = None - vae = None - - if args.transformer_checkpoint_path is not None: - converted_transformer_state_dict = convert_cogview4_transformer_checkpoint_to_diffusers( - args.transformer_checkpoint_path - ) - transformer = CogView4Transformer2DModel( - patch_size=2, - in_channels=16, - num_layers=28, - attention_head_dim=128, - num_attention_heads=32, - out_channels=16, - text_embed_dim=4096, - time_embed_dim=512, - condition_dim=256, - pos_embed_max_size=128, - ) - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - if dtype is not None: - # Original checkpoint data type will be preserved - transformer = transformer.to(dtype=dtype) - - if args.vae_checkpoint_path is not None: - vae_config = { - "in_channels": 3, - "out_channels": 3, - "down_block_types": ("DownEncoderBlock2D",) * 4, - "up_block_types": ("UpDecoderBlock2D",) * 4, - "block_out_channels": (128, 512, 1024, 1024), - "layers_per_block": 3, - "act_fn": "silu", - "latent_channels": 16, - "norm_num_groups": 32, - "sample_size": 1024, - "scaling_factor": 1.0, - "shift_factor": 0.0, - "force_upcast": True, - "use_quant_conv": False, - "use_post_quant_conv": False, - "mid_block_add_attention": False, - } - converted_vae_state_dict = convert_cogview4_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_state_dict, strict=True) - if dtype is not None: - vae = vae.to(dtype=dtype) - - text_encoder_id = "THUDM/glm-4-9b-hf" - tokenizer = PreTrainedTokenizerFast.from_pretrained(text_encoder_id) - text_encoder = GlmForCausalLM.from_pretrained( - text_encoder_id, - cache_dir=args.text_encoder_cache_dir, - torch_dtype=torch.bfloat16 if args.dtype == "bf16" else torch.float32, - ) - - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - scheduler = FlowMatchEulerDiscreteScheduler( - base_shift=0.25, max_shift=0.75, base_image_seq_len=256, use_dynamic_shifting=True, time_shift_type="linear" - ) - - pipe = CogView4Pipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - transformer=transformer, - scheduler=scheduler, - ) - - # This is necessary for users with insufficient memory, such as those using Colab and notebooks, as it can - # save some memory used for model loading. - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_cogview4_to_diffusers_megatron.py b/scripts/convert_cogview4_to_diffusers_megatron.py deleted file mode 100644 index 8faeccb13888..000000000000 --- a/scripts/convert_cogview4_to_diffusers_megatron.py +++ /dev/null @@ -1,384 +0,0 @@ -""" -Convert a CogView4 checkpoint from Megatron to the Diffusers format. - -Example usage: - python scripts/convert_cogview4_to_diffusers.py \ - --transformer_checkpoint_path 'your path/cogview4_6b/mp_rank_00/model_optim_rng.pt' \ - --vae_checkpoint_path 'your path/cogview4_6b/imagekl_ch16.pt' \ - --output_path "THUDM/CogView4-6B" \ - --dtype "bf16" - -Arguments: - --transformer_checkpoint_path: Path to Transformer state dict. - --vae_checkpoint_path: Path to VAE state dict. - --output_path: The path to save the converted model. - --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. - --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used. - --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. - - Default is "bf16" because CogView4 uses bfloat16 for training. - -Note: You must provide either --transformer_checkpoint_path or --vae_checkpoint_path. -""" - -import argparse - -import torch -from tqdm import tqdm -from transformers import GlmModel, PreTrainedTokenizerFast - -from diffusers import ( - AutoencoderKL, - CogView4ControlPipeline, - CogView4Pipeline, - CogView4Transformer2DModel, - FlowMatchEulerDiscreteScheduler, -) -from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint - - -parser = argparse.ArgumentParser() -parser.add_argument( - "--transformer_checkpoint_path", - default=None, - type=str, - help="Path to Megatron (not SAT) Transformer checkpoint, e.g., 'model_optim_rng.pt'.", -) -parser.add_argument( - "--vae_checkpoint_path", - default=None, - type=str, - help="(Optional) Path to VAE checkpoint, e.g., 'imagekl_ch16.pt'.", -) -parser.add_argument( - "--output_path", - required=True, - type=str, - help="Directory to save the final Diffusers format pipeline.", -) -parser.add_argument( - "--push_to_hub", - action="store_true", - default=False, - help="Whether to push the converted model to the HuggingFace Hub.", -) -parser.add_argument( - "--text_encoder_cache_dir", - type=str, - default=None, - help="Specify the cache directory for the text encoder.", -) -parser.add_argument( - "--dtype", - type=str, - default="bf16", - choices=["fp16", "bf16", "fp32"], - help="Data type to save the model in.", -) - -parser.add_argument( - "--num_layers", - type=int, - default=28, - help="Number of Transformer layers (e.g., 28, 48...).", -) -parser.add_argument( - "--num_heads", - type=int, - default=32, - help="Number of attention heads.", -) -parser.add_argument( - "--hidden_size", - type=int, - default=4096, - help="Transformer hidden dimension size.", -) -parser.add_argument( - "--attention_head_dim", - type=int, - default=128, - help="Dimension of each attention head.", -) -parser.add_argument( - "--time_embed_dim", - type=int, - default=512, - help="Dimension of time embeddings.", -) -parser.add_argument( - "--condition_dim", - type=int, - default=256, - help="Dimension of condition embeddings.", -) -parser.add_argument( - "--pos_embed_max_size", - type=int, - default=128, - help="Maximum size for positional embeddings.", -) -parser.add_argument( - "--control", - action="store_true", - default=False, - help="Whether to use control model.", -) - -args = parser.parse_args() - - -def swap_scale_shift(weight, dim): - """ - Swap the scale and shift components in the weight tensor. - - Args: - weight (torch.Tensor): The original weight tensor. - dim (int): The dimension along which to split. - - Returns: - torch.Tensor: The modified weight tensor with scale and shift swapped. - """ - shift, scale = weight.chunk(2, dim=dim) - new_weight = torch.cat([scale, shift], dim=dim) - return new_weight - - -def convert_megatron_transformer_checkpoint_to_diffusers( - ckpt_path: str, - num_layers: int, - num_heads: int, - hidden_size: int, -): - """ - Convert a Megatron Transformer checkpoint to Diffusers format. - - Args: - ckpt_path (str): Path to the Megatron Transformer checkpoint. - num_layers (int): Number of Transformer layers. - num_heads (int): Number of attention heads. - hidden_size (int): Hidden size of the Transformer. - - Returns: - dict: The converted state dictionary compatible with Diffusers. - """ - ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) - mega = ckpt["model"] - - new_state_dict = {} - - # Patch Embedding - new_state_dict["patch_embed.proj.weight"] = mega["encoder_expand_linear.weight"].reshape( - hidden_size, 128 if args.control else 64 - ) - new_state_dict["patch_embed.proj.bias"] = mega["encoder_expand_linear.bias"] - new_state_dict["patch_embed.text_proj.weight"] = mega["text_projector.weight"] - new_state_dict["patch_embed.text_proj.bias"] = mega["text_projector.bias"] - - # Time Condition Embedding - new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = mega[ - "time_embedding.time_embed.0.weight" - ] - new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = mega["time_embedding.time_embed.0.bias"] - new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = mega[ - "time_embedding.time_embed.2.weight" - ] - new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = mega["time_embedding.time_embed.2.bias"] - - new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = mega[ - "label_embedding.label_embed.0.weight" - ] - new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = mega[ - "label_embedding.label_embed.0.bias" - ] - new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = mega[ - "label_embedding.label_embed.2.weight" - ] - new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = mega[ - "label_embedding.label_embed.2.bias" - ] - - # Convert each Transformer layer - for i in tqdm(range(num_layers), desc="Converting layers (Megatron->Diffusers)"): - block_prefix = f"transformer_blocks.{i}." - - # AdaLayerNorm - new_state_dict[block_prefix + "norm1.linear.weight"] = mega[f"decoder.layers.{i}.adaln.weight"] - new_state_dict[block_prefix + "norm1.linear.bias"] = mega[f"decoder.layers.{i}.adaln.bias"] - qkv_weight = mega[f"decoder.layers.{i}.self_attention.linear_qkv.weight"] - qkv_bias = mega[f"decoder.layers.{i}.self_attention.linear_qkv.bias"] - - # Reshape to match SAT logic - qkv_weight = qkv_weight.view(num_heads, 3, hidden_size // num_heads, hidden_size) - qkv_weight = qkv_weight.permute(1, 0, 2, 3).reshape(3 * hidden_size, hidden_size) - - qkv_bias = qkv_bias.view(num_heads, 3, hidden_size // num_heads) - qkv_bias = qkv_bias.permute(1, 0, 2).reshape(3 * hidden_size) - - # Assign to Diffusers keys - q, k, v = torch.chunk(qkv_weight, 3, dim=0) - qb, kb, vb = torch.chunk(qkv_bias, 3, dim=0) - - new_state_dict[block_prefix + "attn1.to_q.weight"] = q - new_state_dict[block_prefix + "attn1.to_q.bias"] = qb - new_state_dict[block_prefix + "attn1.to_k.weight"] = k - new_state_dict[block_prefix + "attn1.to_k.bias"] = kb - new_state_dict[block_prefix + "attn1.to_v.weight"] = v - new_state_dict[block_prefix + "attn1.to_v.bias"] = vb - - # Attention Output - new_state_dict[block_prefix + "attn1.to_out.0.weight"] = mega[ - f"decoder.layers.{i}.self_attention.linear_proj.weight" - ] - new_state_dict[block_prefix + "attn1.to_out.0.bias"] = mega[ - f"decoder.layers.{i}.self_attention.linear_proj.bias" - ] - - # MLP - new_state_dict[block_prefix + "ff.net.0.proj.weight"] = mega[f"decoder.layers.{i}.mlp.linear_fc1.weight"] - new_state_dict[block_prefix + "ff.net.0.proj.bias"] = mega[f"decoder.layers.{i}.mlp.linear_fc1.bias"] - new_state_dict[block_prefix + "ff.net.2.weight"] = mega[f"decoder.layers.{i}.mlp.linear_fc2.weight"] - new_state_dict[block_prefix + "ff.net.2.bias"] = mega[f"decoder.layers.{i}.mlp.linear_fc2.bias"] - - # Final Layers - new_state_dict["norm_out.linear.weight"] = swap_scale_shift(mega["adaln_final.weight"], dim=0) - new_state_dict["norm_out.linear.bias"] = swap_scale_shift(mega["adaln_final.bias"], dim=0) - new_state_dict["proj_out.weight"] = mega["output_projector.weight"] - new_state_dict["proj_out.bias"] = mega["output_projector.bias"] - - return new_state_dict - - -def convert_cogview4_vae_checkpoint_to_diffusers(ckpt_path, vae_config): - """ - Convert a CogView4 VAE checkpoint to Diffusers format. - - Args: - ckpt_path (str): Path to the VAE checkpoint. - vae_config (dict): Configuration dictionary for the VAE. - - Returns: - dict: The converted VAE state dictionary compatible with Diffusers. - """ - original_state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=False)["state_dict"] - return convert_ldm_vae_checkpoint(original_state_dict, vae_config) - - -def main(args): - """ - Main function to convert CogView4 checkpoints to Diffusers format. - - Args: - args (argparse.Namespace): Parsed command-line arguments. - """ - # Determine the desired data type - if args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}") - - transformer = None - vae = None - - # Convert Transformer checkpoint if provided - if args.transformer_checkpoint_path is not None: - converted_transformer_state_dict = convert_megatron_transformer_checkpoint_to_diffusers( - ckpt_path=args.transformer_checkpoint_path, - num_layers=args.num_layers, - num_heads=args.num_heads, - hidden_size=args.hidden_size, - ) - transformer = CogView4Transformer2DModel( - patch_size=2, - in_channels=32 if args.control else 16, - num_layers=args.num_layers, - attention_head_dim=args.attention_head_dim, - num_attention_heads=args.num_heads, - out_channels=16, - text_embed_dim=args.hidden_size, - time_embed_dim=args.time_embed_dim, - condition_dim=args.condition_dim, - pos_embed_max_size=args.pos_embed_max_size, - ) - - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - # Convert to the specified dtype - if dtype is not None: - transformer = transformer.to(dtype=dtype) - - # Convert VAE checkpoint if provided - if args.vae_checkpoint_path is not None: - vae_config = { - "in_channels": 3, - "out_channels": 3, - "down_block_types": ("DownEncoderBlock2D",) * 4, - "up_block_types": ("UpDecoderBlock2D",) * 4, - "block_out_channels": (128, 512, 1024, 1024), - "layers_per_block": 3, - "act_fn": "silu", - "latent_channels": 16, - "norm_num_groups": 32, - "sample_size": 1024, - "scaling_factor": 1.0, - "shift_factor": 0.0, - "force_upcast": True, - "use_quant_conv": False, - "use_post_quant_conv": False, - "mid_block_add_attention": False, - } - converted_vae_state_dict = convert_cogview4_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_state_dict, strict=True) - if dtype is not None: - vae = vae.to(dtype=dtype) - - # Load the text encoder and tokenizer - text_encoder_id = "THUDM/glm-4-9b-hf" - tokenizer = PreTrainedTokenizerFast.from_pretrained(text_encoder_id) - text_encoder = GlmModel.from_pretrained( - text_encoder_id, - cache_dir=args.text_encoder_cache_dir, - torch_dtype=torch.bfloat16 if args.dtype == "bf16" else torch.float32, - ) - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - # Initialize the scheduler - scheduler = FlowMatchEulerDiscreteScheduler( - base_shift=0.25, max_shift=0.75, base_image_seq_len=256, use_dynamic_shifting=True, time_shift_type="linear" - ) - - # Create the pipeline - if args.control: - pipe = CogView4ControlPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - transformer=transformer, - scheduler=scheduler, - ) - else: - pipe = CogView4Pipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - vae=vae, - transformer=transformer, - scheduler=scheduler, - ) - - # Save the converted pipeline - pipe.save_pretrained( - args.output_path, - safe_serialization=True, - max_shard_size="5GB", - push_to_hub=args.push_to_hub, - ) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_consistency_decoder.py b/scripts/convert_consistency_decoder.py deleted file mode 100644 index 9e289457752b..000000000000 --- a/scripts/convert_consistency_decoder.py +++ /dev/null @@ -1,1131 +0,0 @@ -import math -import os -import urllib -import warnings -from argparse import ArgumentParser - -import torch -import torch.nn as nn -import torch.nn.functional as F -from huggingface_hub.utils import insecure_hashlib -from safetensors.torch import load_file as stl -from tqdm import tqdm - -from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel -from diffusers.models.autoencoders.vae import Encoder -from diffusers.models.embeddings import TimestepEmbedding -from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D - - -args = ArgumentParser() -args.add_argument("--save_pretrained", required=False, default=None, type=str) -args.add_argument("--test_image", required=True, type=str) -args = args.parse_args() - - -def _extract_into_tensor(arr, timesteps, broadcast_shape): - # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895 - # """ - res = arr[timesteps].float() - dims_to_append = len(broadcast_shape) - len(res.shape) - return res[(...,) + (None,) * dims_to_append] - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45 - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) - return torch.tensor(betas) - - -def _download(url: str, root: str): - os.makedirs(root, exist_ok=True) - filename = os.path.basename(url) - - expected_sha256 = url.split("/")[-2] - download_target = os.path.join(root, filename) - - if os.path.exists(download_target) and not os.path.isfile(download_target): - raise RuntimeError(f"{download_target} exists and is not a regular file") - - if os.path.isfile(download_target): - if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: - return download_target - else: - warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") - - with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: - with tqdm( - total=int(source.info().get("Content-Length")), - ncols=80, - unit="iB", - unit_scale=True, - unit_divisor=1024, - ) as loop: - while True: - buffer = source.read(8192) - if not buffer: - break - - output.write(buffer) - loop.update(len(buffer)) - - if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: - raise RuntimeError("Model has been downloaded but the SHA256 checksum does not match") - - return download_target - - -class ConsistencyDecoder: - def __init__(self, device="cuda:0", download_root=os.path.expanduser("~/.cache/clip")): - self.n_distilled_steps = 64 - download_target = _download( - "https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", - download_root, - ) - self.ckpt = torch.jit.load(download_target).to(device) - self.device = device - sigma_data = 0.5 - betas = betas_for_alpha_bar(1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2).to(device) - alphas = 1.0 - betas - alphas_cumprod = torch.cumprod(alphas, dim=0) - self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) - self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod) - sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod) - sigmas = torch.sqrt(1.0 / alphas_cumprod - 1) - self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2) - self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5 - self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5 - - @staticmethod - def round_timesteps(timesteps, total_timesteps, n_distilled_steps, truncate_start=True): - with torch.no_grad(): - space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor") - rounded_timesteps = (torch.div(timesteps, space, rounding_mode="floor") + 1) * space - if truncate_start: - rounded_timesteps[rounded_timesteps == total_timesteps] -= space - else: - rounded_timesteps[rounded_timesteps == total_timesteps] -= space - rounded_timesteps[rounded_timesteps == 0] += space - return rounded_timesteps - - @staticmethod - def ldm_transform_latent(z, extra_scale_factor=1): - channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294] - channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034] - - if len(z.shape) != 4: - raise ValueError() - - z = z * 0.18215 - channels = [z[:, i] for i in range(z.shape[1])] - - channels = [extra_scale_factor * (c - channel_means[i]) / channel_stds[i] for i, c in enumerate(channels)] - return torch.stack(channels, dim=1) - - @torch.no_grad() - def __call__( - self, - features: torch.Tensor, - schedule=[1.0, 0.5], - generator=None, - ): - features = self.ldm_transform_latent(features) - ts = self.round_timesteps( - torch.arange(0, 1024), - 1024, - self.n_distilled_steps, - truncate_start=False, - ) - shape = ( - features.size(0), - 3, - 8 * features.size(2), - 8 * features.size(3), - ) - x_start = torch.zeros(shape, device=features.device, dtype=features.dtype) - schedule_timesteps = [int((1024 - 1) * s) for s in schedule] - for i in schedule_timesteps: - t = ts[i].item() - t_ = torch.tensor([t] * features.shape[0]).to(self.device) - # noise = torch.randn_like(x_start) - noise = torch.randn(x_start.shape, dtype=x_start.dtype, generator=generator).to(device=x_start.device) - x_start = ( - _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape) * x_start - + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape) * noise - ) - c_in = _extract_into_tensor(self.c_in, t_, x_start.shape) - - import torch.nn.functional as F - - from diffusers import UNet2DModel - - if isinstance(self.ckpt, UNet2DModel): - input = torch.concat([c_in * x_start, F.upsample_nearest(features, scale_factor=8)], dim=1) - model_output = self.ckpt(input, t_).sample - else: - model_output = self.ckpt(c_in * x_start, t_, features=features) - - B, C = x_start.shape[:2] - model_output, _ = torch.split(model_output, C, dim=1) - pred_xstart = ( - _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output - + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start - ).clamp(-1, 1) - x_start = pred_xstart - return x_start - - -def save_image(image, name): - import numpy as np - from PIL import Image - - image = image[0].cpu().numpy() - image = (image + 1.0) * 127.5 - image = image.clip(0, 255).astype(np.uint8) - image = Image.fromarray(image.transpose(1, 2, 0)) - image.save(name) - - -def load_image(uri, size=None, center_crop=False): - import numpy as np - from PIL import Image - - image = Image.open(uri) - if center_crop: - image = image.crop( - ( - (image.width - min(image.width, image.height)) // 2, - (image.height - min(image.width, image.height)) // 2, - (image.width + min(image.width, image.height)) // 2, - (image.height + min(image.width, image.height)) // 2, - ) - ) - if size is not None: - image = image.resize(size) - image = torch.tensor(np.array(image).transpose(2, 0, 1)).unsqueeze(0).float() - image = image / 127.5 - 1.0 - return image - - -class TimestepEmbedding_(nn.Module): - def __init__(self, n_time=1024, n_emb=320, n_out=1280) -> None: - super().__init__() - self.emb = nn.Embedding(n_time, n_emb) - self.f_1 = nn.Linear(n_emb, n_out) - self.f_2 = nn.Linear(n_out, n_out) - - def forward(self, x) -> torch.Tensor: - x = self.emb(x) - x = self.f_1(x) - x = F.silu(x) - return self.f_2(x) - - -class ImageEmbedding(nn.Module): - def __init__(self, in_channels=7, out_channels=320) -> None: - super().__init__() - self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) - - def forward(self, x) -> torch.Tensor: - return self.f(x) - - -class ImageUnembedding(nn.Module): - def __init__(self, in_channels=320, out_channels=6) -> None: - super().__init__() - self.gn = nn.GroupNorm(32, in_channels) - self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) - - def forward(self, x) -> torch.Tensor: - return self.f(F.silu(self.gn(x))) - - -class ConvResblock(nn.Module): - def __init__(self, in_features=320, out_features=320) -> None: - super().__init__() - self.f_t = nn.Linear(1280, out_features * 2) - - self.gn_1 = nn.GroupNorm(32, in_features) - self.f_1 = nn.Conv2d(in_features, out_features, kernel_size=3, padding=1) - - self.gn_2 = nn.GroupNorm(32, out_features) - self.f_2 = nn.Conv2d(out_features, out_features, kernel_size=3, padding=1) - - skip_conv = in_features != out_features - self.f_s = nn.Conv2d(in_features, out_features, kernel_size=1, padding=0) if skip_conv else nn.Identity() - - def forward(self, x, t): - x_skip = x - t = self.f_t(F.silu(t)) - t = t.chunk(2, dim=1) - t_1 = t[0].unsqueeze(dim=2).unsqueeze(dim=3) + 1 - t_2 = t[1].unsqueeze(dim=2).unsqueeze(dim=3) - - gn_1 = F.silu(self.gn_1(x)) - f_1 = self.f_1(gn_1) - - gn_2 = self.gn_2(f_1) - - return self.f_s(x_skip) + self.f_2(F.silu(gn_2 * t_1 + t_2)) - - -# Also ConvResblock -class Downsample(nn.Module): - def __init__(self, in_channels=320) -> None: - super().__init__() - self.f_t = nn.Linear(1280, in_channels * 2) - - self.gn_1 = nn.GroupNorm(32, in_channels) - self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) - self.gn_2 = nn.GroupNorm(32, in_channels) - - self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) - - def forward(self, x, t) -> torch.Tensor: - x_skip = x - - t = self.f_t(F.silu(t)) - t_1, t_2 = t.chunk(2, dim=1) - t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1 - t_2 = t_2.unsqueeze(2).unsqueeze(3) - - gn_1 = F.silu(self.gn_1(x)) - avg_pool2d = F.avg_pool2d(gn_1, kernel_size=(2, 2), stride=None) - - f_1 = self.f_1(avg_pool2d) - gn_2 = self.gn_2(f_1) - - f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2))) - - return f_2 + F.avg_pool2d(x_skip, kernel_size=(2, 2), stride=None) - - -# Also ConvResblock -class Upsample(nn.Module): - def __init__(self, in_channels=1024) -> None: - super().__init__() - self.f_t = nn.Linear(1280, in_channels * 2) - - self.gn_1 = nn.GroupNorm(32, in_channels) - self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) - self.gn_2 = nn.GroupNorm(32, in_channels) - - self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) - - def forward(self, x, t) -> torch.Tensor: - x_skip = x - - t = self.f_t(F.silu(t)) - t_1, t_2 = t.chunk(2, dim=1) - t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1 - t_2 = t_2.unsqueeze(2).unsqueeze(3) - - gn_1 = F.silu(self.gn_1(x)) - upsample = F.upsample_nearest(gn_1, scale_factor=2) - f_1 = self.f_1(upsample) - gn_2 = self.gn_2(f_1) - - f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2))) - - return f_2 + F.upsample_nearest(x_skip, scale_factor=2) - - -class ConvUNetVAE(nn.Module): - def __init__(self) -> None: - super().__init__() - self.embed_image = ImageEmbedding() - self.embed_time = TimestepEmbedding_() - - down_0 = nn.ModuleList( - [ - ConvResblock(320, 320), - ConvResblock(320, 320), - ConvResblock(320, 320), - Downsample(320), - ] - ) - down_1 = nn.ModuleList( - [ - ConvResblock(320, 640), - ConvResblock(640, 640), - ConvResblock(640, 640), - Downsample(640), - ] - ) - down_2 = nn.ModuleList( - [ - ConvResblock(640, 1024), - ConvResblock(1024, 1024), - ConvResblock(1024, 1024), - Downsample(1024), - ] - ) - down_3 = nn.ModuleList( - [ - ConvResblock(1024, 1024), - ConvResblock(1024, 1024), - ConvResblock(1024, 1024), - ] - ) - self.down = nn.ModuleList( - [ - down_0, - down_1, - down_2, - down_3, - ] - ) - - self.mid = nn.ModuleList( - [ - ConvResblock(1024, 1024), - ConvResblock(1024, 1024), - ] - ) - - up_3 = nn.ModuleList( - [ - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 * 2, 1024), - Upsample(1024), - ] - ) - up_2 = nn.ModuleList( - [ - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 * 2, 1024), - ConvResblock(1024 + 640, 1024), - Upsample(1024), - ] - ) - up_1 = nn.ModuleList( - [ - ConvResblock(1024 + 640, 640), - ConvResblock(640 * 2, 640), - ConvResblock(640 * 2, 640), - ConvResblock(320 + 640, 640), - Upsample(640), - ] - ) - up_0 = nn.ModuleList( - [ - ConvResblock(320 + 640, 320), - ConvResblock(320 * 2, 320), - ConvResblock(320 * 2, 320), - ConvResblock(320 * 2, 320), - ] - ) - self.up = nn.ModuleList( - [ - up_0, - up_1, - up_2, - up_3, - ] - ) - - self.output = ImageUnembedding() - - def forward(self, x, t, features) -> torch.Tensor: - converted = hasattr(self, "converted") and self.converted - - x = torch.cat([x, F.upsample_nearest(features, scale_factor=8)], dim=1) - - if converted: - t = self.time_embedding(self.time_proj(t)) - else: - t = self.embed_time(t) - - x = self.embed_image(x) - - skips = [x] - for i, down in enumerate(self.down): - if converted and i in [0, 1, 2, 3]: - x, skips_ = down(x, t) - for skip in skips_: - skips.append(skip) - else: - for block in down: - x = block(x, t) - skips.append(x) - print(x.float().abs().sum()) - - if converted: - x = self.mid(x, t) - else: - for i in range(2): - x = self.mid[i](x, t) - print(x.float().abs().sum()) - - for i, up in enumerate(self.up[::-1]): - if converted and i in [0, 1, 2, 3]: - skip_4 = skips.pop() - skip_3 = skips.pop() - skip_2 = skips.pop() - skip_1 = skips.pop() - skips_ = (skip_1, skip_2, skip_3, skip_4) - x = up(x, skips_, t) - else: - for block in up: - if isinstance(block, ConvResblock): - x = torch.concat([x, skips.pop()], dim=1) - x = block(x, t) - - return self.output(x) - - -def rename_state_dict_key(k): - k = k.replace("blocks.", "") - for i in range(5): - k = k.replace(f"down_{i}_", f"down.{i}.") - k = k.replace(f"conv_{i}.", f"{i}.") - k = k.replace(f"up_{i}_", f"up.{i}.") - k = k.replace(f"mid_{i}", f"mid.{i}") - k = k.replace("upsamp.", "4.") - k = k.replace("downsamp.", "3.") - k = k.replace("f_t.w", "f_t.weight").replace("f_t.b", "f_t.bias") - k = k.replace("f_1.w", "f_1.weight").replace("f_1.b", "f_1.bias") - k = k.replace("f_2.w", "f_2.weight").replace("f_2.b", "f_2.bias") - k = k.replace("f_s.w", "f_s.weight").replace("f_s.b", "f_s.bias") - k = k.replace("f.w", "f.weight").replace("f.b", "f.bias") - k = k.replace("gn_1.g", "gn_1.weight").replace("gn_1.b", "gn_1.bias") - k = k.replace("gn_2.g", "gn_2.weight").replace("gn_2.b", "gn_2.bias") - k = k.replace("gn.g", "gn.weight").replace("gn.b", "gn.bias") - return k - - -def rename_state_dict(sd, embedding): - sd = {rename_state_dict_key(k): v for k, v in sd.items()} - sd["embed_time.emb.weight"] = embedding["weight"] - return sd - - -# encode with stable diffusion vae -pipe = StableDiffusionPipeline.from_pretrained( - "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16 -) -pipe.vae.cuda() - -# construct original decoder with jitted model -decoder_consistency = ConsistencyDecoder(device="cuda:0") - -# construct UNet code, overwrite the decoder with conv_unet_vae -model = ConvUNetVAE() -model.load_state_dict( - rename_state_dict( - stl("consistency_decoder.safetensors"), - stl("embedding.safetensors"), - ) -) -model = model.cuda() - -decoder_consistency.ckpt = model - -image = load_image(args.test_image, size=(256, 256), center_crop=True) -latent = pipe.vae.encode(image.half().cuda()).latent_dist.sample() - -# decode with gan -sample_gan = pipe.vae.decode(latent).sample.detach() -save_image(sample_gan, "gan.png") - -# decode with conv_unet_vae -sample_consistency_orig = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) -save_image(sample_consistency_orig, "con_orig.png") - - -########### conversion - -print("CONVERSION") - -print("DOWN BLOCK ONE") - -block_one_sd_orig = model.down[0].state_dict() -block_one_sd_new = {} - -for i in range(3): - block_one_sd_new[f"resnets.{i}.norm1.weight"] = block_one_sd_orig.pop(f"{i}.gn_1.weight") - block_one_sd_new[f"resnets.{i}.norm1.bias"] = block_one_sd_orig.pop(f"{i}.gn_1.bias") - block_one_sd_new[f"resnets.{i}.conv1.weight"] = block_one_sd_orig.pop(f"{i}.f_1.weight") - block_one_sd_new[f"resnets.{i}.conv1.bias"] = block_one_sd_orig.pop(f"{i}.f_1.bias") - block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_one_sd_orig.pop(f"{i}.f_t.weight") - block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_one_sd_orig.pop(f"{i}.f_t.bias") - block_one_sd_new[f"resnets.{i}.norm2.weight"] = block_one_sd_orig.pop(f"{i}.gn_2.weight") - block_one_sd_new[f"resnets.{i}.norm2.bias"] = block_one_sd_orig.pop(f"{i}.gn_2.bias") - block_one_sd_new[f"resnets.{i}.conv2.weight"] = block_one_sd_orig.pop(f"{i}.f_2.weight") - block_one_sd_new[f"resnets.{i}.conv2.bias"] = block_one_sd_orig.pop(f"{i}.f_2.bias") - -block_one_sd_new["downsamplers.0.norm1.weight"] = block_one_sd_orig.pop("3.gn_1.weight") -block_one_sd_new["downsamplers.0.norm1.bias"] = block_one_sd_orig.pop("3.gn_1.bias") -block_one_sd_new["downsamplers.0.conv1.weight"] = block_one_sd_orig.pop("3.f_1.weight") -block_one_sd_new["downsamplers.0.conv1.bias"] = block_one_sd_orig.pop("3.f_1.bias") -block_one_sd_new["downsamplers.0.time_emb_proj.weight"] = block_one_sd_orig.pop("3.f_t.weight") -block_one_sd_new["downsamplers.0.time_emb_proj.bias"] = block_one_sd_orig.pop("3.f_t.bias") -block_one_sd_new["downsamplers.0.norm2.weight"] = block_one_sd_orig.pop("3.gn_2.weight") -block_one_sd_new["downsamplers.0.norm2.bias"] = block_one_sd_orig.pop("3.gn_2.bias") -block_one_sd_new["downsamplers.0.conv2.weight"] = block_one_sd_orig.pop("3.f_2.weight") -block_one_sd_new["downsamplers.0.conv2.bias"] = block_one_sd_orig.pop("3.f_2.bias") - -assert len(block_one_sd_orig) == 0 - -block_one = ResnetDownsampleBlock2D( - in_channels=320, - out_channels=320, - temb_channels=1280, - num_layers=3, - add_downsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -block_one.load_state_dict(block_one_sd_new) - -print("DOWN BLOCK TWO") - -block_two_sd_orig = model.down[1].state_dict() -block_two_sd_new = {} - -for i in range(3): - block_two_sd_new[f"resnets.{i}.norm1.weight"] = block_two_sd_orig.pop(f"{i}.gn_1.weight") - block_two_sd_new[f"resnets.{i}.norm1.bias"] = block_two_sd_orig.pop(f"{i}.gn_1.bias") - block_two_sd_new[f"resnets.{i}.conv1.weight"] = block_two_sd_orig.pop(f"{i}.f_1.weight") - block_two_sd_new[f"resnets.{i}.conv1.bias"] = block_two_sd_orig.pop(f"{i}.f_1.bias") - block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_two_sd_orig.pop(f"{i}.f_t.weight") - block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_two_sd_orig.pop(f"{i}.f_t.bias") - block_two_sd_new[f"resnets.{i}.norm2.weight"] = block_two_sd_orig.pop(f"{i}.gn_2.weight") - block_two_sd_new[f"resnets.{i}.norm2.bias"] = block_two_sd_orig.pop(f"{i}.gn_2.bias") - block_two_sd_new[f"resnets.{i}.conv2.weight"] = block_two_sd_orig.pop(f"{i}.f_2.weight") - block_two_sd_new[f"resnets.{i}.conv2.bias"] = block_two_sd_orig.pop(f"{i}.f_2.bias") - - if i == 0: - block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_two_sd_orig.pop(f"{i}.f_s.weight") - block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_two_sd_orig.pop(f"{i}.f_s.bias") - -block_two_sd_new["downsamplers.0.norm1.weight"] = block_two_sd_orig.pop("3.gn_1.weight") -block_two_sd_new["downsamplers.0.norm1.bias"] = block_two_sd_orig.pop("3.gn_1.bias") -block_two_sd_new["downsamplers.0.conv1.weight"] = block_two_sd_orig.pop("3.f_1.weight") -block_two_sd_new["downsamplers.0.conv1.bias"] = block_two_sd_orig.pop("3.f_1.bias") -block_two_sd_new["downsamplers.0.time_emb_proj.weight"] = block_two_sd_orig.pop("3.f_t.weight") -block_two_sd_new["downsamplers.0.time_emb_proj.bias"] = block_two_sd_orig.pop("3.f_t.bias") -block_two_sd_new["downsamplers.0.norm2.weight"] = block_two_sd_orig.pop("3.gn_2.weight") -block_two_sd_new["downsamplers.0.norm2.bias"] = block_two_sd_orig.pop("3.gn_2.bias") -block_two_sd_new["downsamplers.0.conv2.weight"] = block_two_sd_orig.pop("3.f_2.weight") -block_two_sd_new["downsamplers.0.conv2.bias"] = block_two_sd_orig.pop("3.f_2.bias") - -assert len(block_two_sd_orig) == 0 - -block_two = ResnetDownsampleBlock2D( - in_channels=320, - out_channels=640, - temb_channels=1280, - num_layers=3, - add_downsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -block_two.load_state_dict(block_two_sd_new) - -print("DOWN BLOCK THREE") - -block_three_sd_orig = model.down[2].state_dict() -block_three_sd_new = {} - -for i in range(3): - block_three_sd_new[f"resnets.{i}.norm1.weight"] = block_three_sd_orig.pop(f"{i}.gn_1.weight") - block_three_sd_new[f"resnets.{i}.norm1.bias"] = block_three_sd_orig.pop(f"{i}.gn_1.bias") - block_three_sd_new[f"resnets.{i}.conv1.weight"] = block_three_sd_orig.pop(f"{i}.f_1.weight") - block_three_sd_new[f"resnets.{i}.conv1.bias"] = block_three_sd_orig.pop(f"{i}.f_1.bias") - block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_three_sd_orig.pop(f"{i}.f_t.weight") - block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_three_sd_orig.pop(f"{i}.f_t.bias") - block_three_sd_new[f"resnets.{i}.norm2.weight"] = block_three_sd_orig.pop(f"{i}.gn_2.weight") - block_three_sd_new[f"resnets.{i}.norm2.bias"] = block_three_sd_orig.pop(f"{i}.gn_2.bias") - block_three_sd_new[f"resnets.{i}.conv2.weight"] = block_three_sd_orig.pop(f"{i}.f_2.weight") - block_three_sd_new[f"resnets.{i}.conv2.bias"] = block_three_sd_orig.pop(f"{i}.f_2.bias") - - if i == 0: - block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_three_sd_orig.pop(f"{i}.f_s.weight") - block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_three_sd_orig.pop(f"{i}.f_s.bias") - -block_three_sd_new["downsamplers.0.norm1.weight"] = block_three_sd_orig.pop("3.gn_1.weight") -block_three_sd_new["downsamplers.0.norm1.bias"] = block_three_sd_orig.pop("3.gn_1.bias") -block_three_sd_new["downsamplers.0.conv1.weight"] = block_three_sd_orig.pop("3.f_1.weight") -block_three_sd_new["downsamplers.0.conv1.bias"] = block_three_sd_orig.pop("3.f_1.bias") -block_three_sd_new["downsamplers.0.time_emb_proj.weight"] = block_three_sd_orig.pop("3.f_t.weight") -block_three_sd_new["downsamplers.0.time_emb_proj.bias"] = block_three_sd_orig.pop("3.f_t.bias") -block_three_sd_new["downsamplers.0.norm2.weight"] = block_three_sd_orig.pop("3.gn_2.weight") -block_three_sd_new["downsamplers.0.norm2.bias"] = block_three_sd_orig.pop("3.gn_2.bias") -block_three_sd_new["downsamplers.0.conv2.weight"] = block_three_sd_orig.pop("3.f_2.weight") -block_three_sd_new["downsamplers.0.conv2.bias"] = block_three_sd_orig.pop("3.f_2.bias") - -assert len(block_three_sd_orig) == 0 - -block_three = ResnetDownsampleBlock2D( - in_channels=640, - out_channels=1024, - temb_channels=1280, - num_layers=3, - add_downsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -block_three.load_state_dict(block_three_sd_new) - -print("DOWN BLOCK FOUR") - -block_four_sd_orig = model.down[3].state_dict() -block_four_sd_new = {} - -for i in range(3): - block_four_sd_new[f"resnets.{i}.norm1.weight"] = block_four_sd_orig.pop(f"{i}.gn_1.weight") - block_four_sd_new[f"resnets.{i}.norm1.bias"] = block_four_sd_orig.pop(f"{i}.gn_1.bias") - block_four_sd_new[f"resnets.{i}.conv1.weight"] = block_four_sd_orig.pop(f"{i}.f_1.weight") - block_four_sd_new[f"resnets.{i}.conv1.bias"] = block_four_sd_orig.pop(f"{i}.f_1.bias") - block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_four_sd_orig.pop(f"{i}.f_t.weight") - block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_four_sd_orig.pop(f"{i}.f_t.bias") - block_four_sd_new[f"resnets.{i}.norm2.weight"] = block_four_sd_orig.pop(f"{i}.gn_2.weight") - block_four_sd_new[f"resnets.{i}.norm2.bias"] = block_four_sd_orig.pop(f"{i}.gn_2.bias") - block_four_sd_new[f"resnets.{i}.conv2.weight"] = block_four_sd_orig.pop(f"{i}.f_2.weight") - block_four_sd_new[f"resnets.{i}.conv2.bias"] = block_four_sd_orig.pop(f"{i}.f_2.bias") - -assert len(block_four_sd_orig) == 0 - -block_four = ResnetDownsampleBlock2D( - in_channels=1024, - out_channels=1024, - temb_channels=1280, - num_layers=3, - add_downsample=False, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -block_four.load_state_dict(block_four_sd_new) - - -print("MID BLOCK 1") - -mid_block_one_sd_orig = model.mid.state_dict() -mid_block_one_sd_new = {} - -for i in range(2): - mid_block_one_sd_new[f"resnets.{i}.norm1.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.weight") - mid_block_one_sd_new[f"resnets.{i}.norm1.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.bias") - mid_block_one_sd_new[f"resnets.{i}.conv1.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_1.weight") - mid_block_one_sd_new[f"resnets.{i}.conv1.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_1.bias") - mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_t.weight") - mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_t.bias") - mid_block_one_sd_new[f"resnets.{i}.norm2.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.weight") - mid_block_one_sd_new[f"resnets.{i}.norm2.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.bias") - mid_block_one_sd_new[f"resnets.{i}.conv2.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_2.weight") - mid_block_one_sd_new[f"resnets.{i}.conv2.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_2.bias") - -assert len(mid_block_one_sd_orig) == 0 - -mid_block_one = UNetMidBlock2D( - in_channels=1024, - temb_channels=1280, - num_layers=1, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, - add_attention=False, -) - -mid_block_one.load_state_dict(mid_block_one_sd_new) - -print("UP BLOCK ONE") - -up_block_one_sd_orig = model.up[-1].state_dict() -up_block_one_sd_new = {} - -for i in range(4): - up_block_one_sd_new[f"resnets.{i}.norm1.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_1.weight") - up_block_one_sd_new[f"resnets.{i}.norm1.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_1.bias") - up_block_one_sd_new[f"resnets.{i}.conv1.weight"] = up_block_one_sd_orig.pop(f"{i}.f_1.weight") - up_block_one_sd_new[f"resnets.{i}.conv1.bias"] = up_block_one_sd_orig.pop(f"{i}.f_1.bias") - up_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_one_sd_orig.pop(f"{i}.f_t.weight") - up_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_one_sd_orig.pop(f"{i}.f_t.bias") - up_block_one_sd_new[f"resnets.{i}.norm2.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_2.weight") - up_block_one_sd_new[f"resnets.{i}.norm2.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_2.bias") - up_block_one_sd_new[f"resnets.{i}.conv2.weight"] = up_block_one_sd_orig.pop(f"{i}.f_2.weight") - up_block_one_sd_new[f"resnets.{i}.conv2.bias"] = up_block_one_sd_orig.pop(f"{i}.f_2.bias") - up_block_one_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_one_sd_orig.pop(f"{i}.f_s.weight") - up_block_one_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_one_sd_orig.pop(f"{i}.f_s.bias") - -up_block_one_sd_new["upsamplers.0.norm1.weight"] = up_block_one_sd_orig.pop("4.gn_1.weight") -up_block_one_sd_new["upsamplers.0.norm1.bias"] = up_block_one_sd_orig.pop("4.gn_1.bias") -up_block_one_sd_new["upsamplers.0.conv1.weight"] = up_block_one_sd_orig.pop("4.f_1.weight") -up_block_one_sd_new["upsamplers.0.conv1.bias"] = up_block_one_sd_orig.pop("4.f_1.bias") -up_block_one_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_one_sd_orig.pop("4.f_t.weight") -up_block_one_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_one_sd_orig.pop("4.f_t.bias") -up_block_one_sd_new["upsamplers.0.norm2.weight"] = up_block_one_sd_orig.pop("4.gn_2.weight") -up_block_one_sd_new["upsamplers.0.norm2.bias"] = up_block_one_sd_orig.pop("4.gn_2.bias") -up_block_one_sd_new["upsamplers.0.conv2.weight"] = up_block_one_sd_orig.pop("4.f_2.weight") -up_block_one_sd_new["upsamplers.0.conv2.bias"] = up_block_one_sd_orig.pop("4.f_2.bias") - -assert len(up_block_one_sd_orig) == 0 - -up_block_one = ResnetUpsampleBlock2D( - in_channels=1024, - prev_output_channel=1024, - out_channels=1024, - temb_channels=1280, - num_layers=4, - add_upsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -up_block_one.load_state_dict(up_block_one_sd_new) - -print("UP BLOCK TWO") - -up_block_two_sd_orig = model.up[-2].state_dict() -up_block_two_sd_new = {} - -for i in range(4): - up_block_two_sd_new[f"resnets.{i}.norm1.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_1.weight") - up_block_two_sd_new[f"resnets.{i}.norm1.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_1.bias") - up_block_two_sd_new[f"resnets.{i}.conv1.weight"] = up_block_two_sd_orig.pop(f"{i}.f_1.weight") - up_block_two_sd_new[f"resnets.{i}.conv1.bias"] = up_block_two_sd_orig.pop(f"{i}.f_1.bias") - up_block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_two_sd_orig.pop(f"{i}.f_t.weight") - up_block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_two_sd_orig.pop(f"{i}.f_t.bias") - up_block_two_sd_new[f"resnets.{i}.norm2.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_2.weight") - up_block_two_sd_new[f"resnets.{i}.norm2.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_2.bias") - up_block_two_sd_new[f"resnets.{i}.conv2.weight"] = up_block_two_sd_orig.pop(f"{i}.f_2.weight") - up_block_two_sd_new[f"resnets.{i}.conv2.bias"] = up_block_two_sd_orig.pop(f"{i}.f_2.bias") - up_block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_two_sd_orig.pop(f"{i}.f_s.weight") - up_block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_two_sd_orig.pop(f"{i}.f_s.bias") - -up_block_two_sd_new["upsamplers.0.norm1.weight"] = up_block_two_sd_orig.pop("4.gn_1.weight") -up_block_two_sd_new["upsamplers.0.norm1.bias"] = up_block_two_sd_orig.pop("4.gn_1.bias") -up_block_two_sd_new["upsamplers.0.conv1.weight"] = up_block_two_sd_orig.pop("4.f_1.weight") -up_block_two_sd_new["upsamplers.0.conv1.bias"] = up_block_two_sd_orig.pop("4.f_1.bias") -up_block_two_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_two_sd_orig.pop("4.f_t.weight") -up_block_two_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_two_sd_orig.pop("4.f_t.bias") -up_block_two_sd_new["upsamplers.0.norm2.weight"] = up_block_two_sd_orig.pop("4.gn_2.weight") -up_block_two_sd_new["upsamplers.0.norm2.bias"] = up_block_two_sd_orig.pop("4.gn_2.bias") -up_block_two_sd_new["upsamplers.0.conv2.weight"] = up_block_two_sd_orig.pop("4.f_2.weight") -up_block_two_sd_new["upsamplers.0.conv2.bias"] = up_block_two_sd_orig.pop("4.f_2.bias") - -assert len(up_block_two_sd_orig) == 0 - -up_block_two = ResnetUpsampleBlock2D( - in_channels=640, - prev_output_channel=1024, - out_channels=1024, - temb_channels=1280, - num_layers=4, - add_upsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -up_block_two.load_state_dict(up_block_two_sd_new) - -print("UP BLOCK THREE") - -up_block_three_sd_orig = model.up[-3].state_dict() -up_block_three_sd_new = {} - -for i in range(4): - up_block_three_sd_new[f"resnets.{i}.norm1.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_1.weight") - up_block_three_sd_new[f"resnets.{i}.norm1.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_1.bias") - up_block_three_sd_new[f"resnets.{i}.conv1.weight"] = up_block_three_sd_orig.pop(f"{i}.f_1.weight") - up_block_three_sd_new[f"resnets.{i}.conv1.bias"] = up_block_three_sd_orig.pop(f"{i}.f_1.bias") - up_block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_three_sd_orig.pop(f"{i}.f_t.weight") - up_block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_three_sd_orig.pop(f"{i}.f_t.bias") - up_block_three_sd_new[f"resnets.{i}.norm2.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_2.weight") - up_block_three_sd_new[f"resnets.{i}.norm2.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_2.bias") - up_block_three_sd_new[f"resnets.{i}.conv2.weight"] = up_block_three_sd_orig.pop(f"{i}.f_2.weight") - up_block_three_sd_new[f"resnets.{i}.conv2.bias"] = up_block_three_sd_orig.pop(f"{i}.f_2.bias") - up_block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_three_sd_orig.pop(f"{i}.f_s.weight") - up_block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_three_sd_orig.pop(f"{i}.f_s.bias") - -up_block_three_sd_new["upsamplers.0.norm1.weight"] = up_block_three_sd_orig.pop("4.gn_1.weight") -up_block_three_sd_new["upsamplers.0.norm1.bias"] = up_block_three_sd_orig.pop("4.gn_1.bias") -up_block_three_sd_new["upsamplers.0.conv1.weight"] = up_block_three_sd_orig.pop("4.f_1.weight") -up_block_three_sd_new["upsamplers.0.conv1.bias"] = up_block_three_sd_orig.pop("4.f_1.bias") -up_block_three_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_three_sd_orig.pop("4.f_t.weight") -up_block_three_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_three_sd_orig.pop("4.f_t.bias") -up_block_three_sd_new["upsamplers.0.norm2.weight"] = up_block_three_sd_orig.pop("4.gn_2.weight") -up_block_three_sd_new["upsamplers.0.norm2.bias"] = up_block_three_sd_orig.pop("4.gn_2.bias") -up_block_three_sd_new["upsamplers.0.conv2.weight"] = up_block_three_sd_orig.pop("4.f_2.weight") -up_block_three_sd_new["upsamplers.0.conv2.bias"] = up_block_three_sd_orig.pop("4.f_2.bias") - -assert len(up_block_three_sd_orig) == 0 - -up_block_three = ResnetUpsampleBlock2D( - in_channels=320, - prev_output_channel=1024, - out_channels=640, - temb_channels=1280, - num_layers=4, - add_upsample=True, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -up_block_three.load_state_dict(up_block_three_sd_new) - -print("UP BLOCK FOUR") - -up_block_four_sd_orig = model.up[-4].state_dict() -up_block_four_sd_new = {} - -for i in range(4): - up_block_four_sd_new[f"resnets.{i}.norm1.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_1.weight") - up_block_four_sd_new[f"resnets.{i}.norm1.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_1.bias") - up_block_four_sd_new[f"resnets.{i}.conv1.weight"] = up_block_four_sd_orig.pop(f"{i}.f_1.weight") - up_block_four_sd_new[f"resnets.{i}.conv1.bias"] = up_block_four_sd_orig.pop(f"{i}.f_1.bias") - up_block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_four_sd_orig.pop(f"{i}.f_t.weight") - up_block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_four_sd_orig.pop(f"{i}.f_t.bias") - up_block_four_sd_new[f"resnets.{i}.norm2.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_2.weight") - up_block_four_sd_new[f"resnets.{i}.norm2.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_2.bias") - up_block_four_sd_new[f"resnets.{i}.conv2.weight"] = up_block_four_sd_orig.pop(f"{i}.f_2.weight") - up_block_four_sd_new[f"resnets.{i}.conv2.bias"] = up_block_four_sd_orig.pop(f"{i}.f_2.bias") - up_block_four_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_four_sd_orig.pop(f"{i}.f_s.weight") - up_block_four_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_four_sd_orig.pop(f"{i}.f_s.bias") - -assert len(up_block_four_sd_orig) == 0 - -up_block_four = ResnetUpsampleBlock2D( - in_channels=320, - prev_output_channel=640, - out_channels=320, - temb_channels=1280, - num_layers=4, - add_upsample=False, - resnet_time_scale_shift="scale_shift", - resnet_eps=1e-5, -) - -up_block_four.load_state_dict(up_block_four_sd_new) - -print("initial projection (conv_in)") - -conv_in_sd_orig = model.embed_image.state_dict() -conv_in_sd_new = {} - -conv_in_sd_new["weight"] = conv_in_sd_orig.pop("f.weight") -conv_in_sd_new["bias"] = conv_in_sd_orig.pop("f.bias") - -assert len(conv_in_sd_orig) == 0 - -block_out_channels = [320, 640, 1024, 1024] - -in_channels = 7 -conv_in_kernel = 3 -conv_in_padding = (conv_in_kernel - 1) // 2 -conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding) - -conv_in.load_state_dict(conv_in_sd_new) - -print("out projection (conv_out) (conv_norm_out)") -out_channels = 6 -norm_num_groups = 32 -norm_eps = 1e-5 -act_fn = "silu" -conv_out_kernel = 3 -conv_out_padding = (conv_out_kernel - 1) // 2 -conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) -# uses torch.functional in orig -# conv_act = get_activation(act_fn) -conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding) - -conv_norm_out.load_state_dict(model.output.gn.state_dict()) -conv_out.load_state_dict(model.output.f.state_dict()) - -print("timestep projection (time_proj) (time_embedding)") - -f1_sd = model.embed_time.f_1.state_dict() -f2_sd = model.embed_time.f_2.state_dict() - -time_embedding_sd = { - "linear_1.weight": f1_sd.pop("weight"), - "linear_1.bias": f1_sd.pop("bias"), - "linear_2.weight": f2_sd.pop("weight"), - "linear_2.bias": f2_sd.pop("bias"), -} - -assert len(f1_sd) == 0 -assert len(f2_sd) == 0 - -time_embedding_type = "learned" -num_train_timesteps = 1024 -time_embedding_dim = 1280 - -time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0]) -timestep_input_dim = block_out_channels[0] - -time_embedding = TimestepEmbedding(timestep_input_dim, time_embedding_dim) - -time_proj.load_state_dict(model.embed_time.emb.state_dict()) -time_embedding.load_state_dict(time_embedding_sd) - -print("CONVERT") - -time_embedding.to("cuda") -time_proj.to("cuda") -conv_in.to("cuda") - -block_one.to("cuda") -block_two.to("cuda") -block_three.to("cuda") -block_four.to("cuda") - -mid_block_one.to("cuda") - -up_block_one.to("cuda") -up_block_two.to("cuda") -up_block_three.to("cuda") -up_block_four.to("cuda") - -conv_norm_out.to("cuda") -conv_out.to("cuda") - -model.time_proj = time_proj -model.time_embedding = time_embedding -model.embed_image = conv_in - -model.down[0] = block_one -model.down[1] = block_two -model.down[2] = block_three -model.down[3] = block_four - -model.mid = mid_block_one - -model.up[-1] = up_block_one -model.up[-2] = up_block_two -model.up[-3] = up_block_three -model.up[-4] = up_block_four - -model.output.gn = conv_norm_out -model.output.f = conv_out - -model.converted = True - -sample_consistency_new = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) -save_image(sample_consistency_new, "con_new.png") - -assert (sample_consistency_orig == sample_consistency_new).all() - -print("making unet") - -unet = UNet2DModel( - in_channels=in_channels, - out_channels=out_channels, - down_block_types=( - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - ), - up_block_types=( - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - ), - block_out_channels=block_out_channels, - layers_per_block=3, - norm_num_groups=norm_num_groups, - norm_eps=norm_eps, - resnet_time_scale_shift="scale_shift", - time_embedding_type="learned", - num_train_timesteps=num_train_timesteps, - add_attention=False, -) - -unet_state_dict = {} - - -def add_state_dict(prefix, mod): - for k, v in mod.state_dict().items(): - unet_state_dict[f"{prefix}.{k}"] = v - - -add_state_dict("conv_in", conv_in) -add_state_dict("time_proj", time_proj) -add_state_dict("time_embedding", time_embedding) -add_state_dict("down_blocks.0", block_one) -add_state_dict("down_blocks.1", block_two) -add_state_dict("down_blocks.2", block_three) -add_state_dict("down_blocks.3", block_four) -add_state_dict("mid_block", mid_block_one) -add_state_dict("up_blocks.0", up_block_one) -add_state_dict("up_blocks.1", up_block_two) -add_state_dict("up_blocks.2", up_block_three) -add_state_dict("up_blocks.3", up_block_four) -add_state_dict("conv_norm_out", conv_norm_out) -add_state_dict("conv_out", conv_out) - -unet.load_state_dict(unet_state_dict) - -print("running with diffusers unet") - -unet.to("cuda") - -decoder_consistency.ckpt = unet - -sample_consistency_new_2 = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) -save_image(sample_consistency_new_2, "con_new_2.png") - -assert (sample_consistency_orig == sample_consistency_new_2).all() - -print("running with diffusers model") - -Encoder.old_constructor = Encoder.__init__ - - -def new_constructor(self, **kwargs): - self.old_constructor(**kwargs) - self.constructor_arguments = kwargs - - -Encoder.__init__ = new_constructor - - -vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae") -consistency_vae = ConsistencyDecoderVAE( - encoder_args=vae.encoder.constructor_arguments, - decoder_args=unet.config, - scaling_factor=vae.config.scaling_factor, - block_out_channels=vae.config.block_out_channels, - latent_channels=vae.config.latent_channels, -) -consistency_vae.encoder.load_state_dict(vae.encoder.state_dict()) -consistency_vae.quant_conv.load_state_dict(vae.quant_conv.state_dict()) -consistency_vae.decoder_unet.load_state_dict(unet.state_dict()) - -consistency_vae.to(dtype=torch.float16, device="cuda") - -sample_consistency_new_3 = consistency_vae.decode( - 0.18215 * latent, generator=torch.Generator("cpu").manual_seed(0) -).sample - -print("max difference") -print((sample_consistency_orig - sample_consistency_new_3).abs().max()) -print("total difference") -print((sample_consistency_orig - sample_consistency_new_3).abs().sum()) -# assert (sample_consistency_orig == sample_consistency_new_3).all() - -print("running with diffusers pipeline") - -pipe = DiffusionPipeline.from_pretrained( - "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16 -) -pipe.to("cuda") - -pipe("horse", generator=torch.Generator("cpu").manual_seed(0)).images[0].save("horse.png") - - -if args.save_pretrained is not None: - consistency_vae.save_pretrained(args.save_pretrained) diff --git a/scripts/convert_consistency_to_diffusers.py b/scripts/convert_consistency_to_diffusers.py deleted file mode 100644 index 2b918280ca05..000000000000 --- a/scripts/convert_consistency_to_diffusers.py +++ /dev/null @@ -1,315 +0,0 @@ -import argparse -import os - -import torch - -from diffusers import ( - CMStochasticIterativeScheduler, - ConsistencyModelPipeline, - UNet2DModel, -) - - -TEST_UNET_CONFIG = { - "sample_size": 32, - "in_channels": 3, - "out_channels": 3, - "layers_per_block": 2, - "num_class_embeds": 1000, - "block_out_channels": [32, 64], - "attention_head_dim": 8, - "down_block_types": [ - "ResnetDownsampleBlock2D", - "AttnDownBlock2D", - ], - "up_block_types": [ - "AttnUpBlock2D", - "ResnetUpsampleBlock2D", - ], - "resnet_time_scale_shift": "scale_shift", - "attn_norm_num_groups": 32, - "upsample_type": "resnet", - "downsample_type": "resnet", -} - -IMAGENET_64_UNET_CONFIG = { - "sample_size": 64, - "in_channels": 3, - "out_channels": 3, - "layers_per_block": 3, - "num_class_embeds": 1000, - "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4], - "attention_head_dim": 64, - "down_block_types": [ - "ResnetDownsampleBlock2D", - "AttnDownBlock2D", - "AttnDownBlock2D", - "AttnDownBlock2D", - ], - "up_block_types": [ - "AttnUpBlock2D", - "AttnUpBlock2D", - "AttnUpBlock2D", - "ResnetUpsampleBlock2D", - ], - "resnet_time_scale_shift": "scale_shift", - "attn_norm_num_groups": 32, - "upsample_type": "resnet", - "downsample_type": "resnet", -} - -LSUN_256_UNET_CONFIG = { - "sample_size": 256, - "in_channels": 3, - "out_channels": 3, - "layers_per_block": 2, - "num_class_embeds": None, - "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4], - "attention_head_dim": 64, - "down_block_types": [ - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "AttnDownBlock2D", - "AttnDownBlock2D", - "AttnDownBlock2D", - ], - "up_block_types": [ - "AttnUpBlock2D", - "AttnUpBlock2D", - "AttnUpBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - ], - "resnet_time_scale_shift": "default", - "upsample_type": "resnet", - "downsample_type": "resnet", -} - -CD_SCHEDULER_CONFIG = { - "num_train_timesteps": 40, - "sigma_min": 0.002, - "sigma_max": 80.0, -} - -CT_IMAGENET_64_SCHEDULER_CONFIG = { - "num_train_timesteps": 201, - "sigma_min": 0.002, - "sigma_max": 80.0, -} - -CT_LSUN_256_SCHEDULER_CONFIG = { - "num_train_timesteps": 151, - "sigma_min": 0.002, - "sigma_max": 80.0, -} - - -def str2bool(v): - """ - https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse - """ - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("boolean value expected") - - -def convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False): - new_checkpoint[f"{new_prefix}.norm1.weight"] = checkpoint[f"{old_prefix}.in_layers.0.weight"] - new_checkpoint[f"{new_prefix}.norm1.bias"] = checkpoint[f"{old_prefix}.in_layers.0.bias"] - new_checkpoint[f"{new_prefix}.conv1.weight"] = checkpoint[f"{old_prefix}.in_layers.2.weight"] - new_checkpoint[f"{new_prefix}.conv1.bias"] = checkpoint[f"{old_prefix}.in_layers.2.bias"] - new_checkpoint[f"{new_prefix}.time_emb_proj.weight"] = checkpoint[f"{old_prefix}.emb_layers.1.weight"] - new_checkpoint[f"{new_prefix}.time_emb_proj.bias"] = checkpoint[f"{old_prefix}.emb_layers.1.bias"] - new_checkpoint[f"{new_prefix}.norm2.weight"] = checkpoint[f"{old_prefix}.out_layers.0.weight"] - new_checkpoint[f"{new_prefix}.norm2.bias"] = checkpoint[f"{old_prefix}.out_layers.0.bias"] - new_checkpoint[f"{new_prefix}.conv2.weight"] = checkpoint[f"{old_prefix}.out_layers.3.weight"] - new_checkpoint[f"{new_prefix}.conv2.bias"] = checkpoint[f"{old_prefix}.out_layers.3.bias"] - - if has_skip: - new_checkpoint[f"{new_prefix}.conv_shortcut.weight"] = checkpoint[f"{old_prefix}.skip_connection.weight"] - new_checkpoint[f"{new_prefix}.conv_shortcut.bias"] = checkpoint[f"{old_prefix}.skip_connection.bias"] - - return new_checkpoint - - -def convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None): - weight_q, weight_k, weight_v = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3, dim=0) - bias_q, bias_k, bias_v = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3, dim=0) - - new_checkpoint[f"{new_prefix}.group_norm.weight"] = checkpoint[f"{old_prefix}.norm.weight"] - new_checkpoint[f"{new_prefix}.group_norm.bias"] = checkpoint[f"{old_prefix}.norm.bias"] - - new_checkpoint[f"{new_prefix}.to_q.weight"] = weight_q.squeeze(-1).squeeze(-1) - new_checkpoint[f"{new_prefix}.to_q.bias"] = bias_q.squeeze(-1).squeeze(-1) - new_checkpoint[f"{new_prefix}.to_k.weight"] = weight_k.squeeze(-1).squeeze(-1) - new_checkpoint[f"{new_prefix}.to_k.bias"] = bias_k.squeeze(-1).squeeze(-1) - new_checkpoint[f"{new_prefix}.to_v.weight"] = weight_v.squeeze(-1).squeeze(-1) - new_checkpoint[f"{new_prefix}.to_v.bias"] = bias_v.squeeze(-1).squeeze(-1) - - new_checkpoint[f"{new_prefix}.to_out.0.weight"] = ( - checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1) - ) - new_checkpoint[f"{new_prefix}.to_out.0.bias"] = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1) - - return new_checkpoint - - -def con_pt_to_diffuser(checkpoint_path: str, unet_config): - checkpoint = torch.load(checkpoint_path, map_location="cpu") - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] - - if unet_config["num_class_embeds"] is not None: - new_checkpoint["class_embedding.weight"] = checkpoint["label_emb.weight"] - - new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] - - down_block_types = unet_config["down_block_types"] - layers_per_block = unet_config["layers_per_block"] - attention_head_dim = unet_config["attention_head_dim"] - channels_list = unet_config["block_out_channels"] - current_layer = 1 - prev_channels = channels_list[0] - - for i, layer_type in enumerate(down_block_types): - current_channels = channels_list[i] - downsample_block_has_skip = current_channels != prev_channels - if layer_type == "ResnetDownsampleBlock2D": - for j in range(layers_per_block): - new_prefix = f"down_blocks.{i}.resnets.{j}" - old_prefix = f"input_blocks.{current_layer}.0" - has_skip = True if j == 0 and downsample_block_has_skip else False - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) - current_layer += 1 - - elif layer_type == "AttnDownBlock2D": - for j in range(layers_per_block): - new_prefix = f"down_blocks.{i}.resnets.{j}" - old_prefix = f"input_blocks.{current_layer}.0" - has_skip = True if j == 0 and downsample_block_has_skip else False - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) - new_prefix = f"down_blocks.{i}.attentions.{j}" - old_prefix = f"input_blocks.{current_layer}.1" - new_checkpoint = convert_attention( - checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim - ) - current_layer += 1 - - if i != len(down_block_types) - 1: - new_prefix = f"down_blocks.{i}.downsamplers.0" - old_prefix = f"input_blocks.{current_layer}.0" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) - current_layer += 1 - - prev_channels = current_channels - - # hardcoded the mid-block for now - new_prefix = "mid_block.resnets.0" - old_prefix = "middle_block.0" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) - new_prefix = "mid_block.attentions.0" - old_prefix = "middle_block.1" - new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim) - new_prefix = "mid_block.resnets.1" - old_prefix = "middle_block.2" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) - - current_layer = 0 - up_block_types = unet_config["up_block_types"] - - for i, layer_type in enumerate(up_block_types): - if layer_type == "ResnetUpsampleBlock2D": - for j in range(layers_per_block + 1): - new_prefix = f"up_blocks.{i}.resnets.{j}" - old_prefix = f"output_blocks.{current_layer}.0" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) - current_layer += 1 - - if i != len(up_block_types) - 1: - new_prefix = f"up_blocks.{i}.upsamplers.0" - old_prefix = f"output_blocks.{current_layer - 1}.1" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) - elif layer_type == "AttnUpBlock2D": - for j in range(layers_per_block + 1): - new_prefix = f"up_blocks.{i}.resnets.{j}" - old_prefix = f"output_blocks.{current_layer}.0" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) - new_prefix = f"up_blocks.{i}.attentions.{j}" - old_prefix = f"output_blocks.{current_layer}.1" - new_checkpoint = convert_attention( - checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim - ) - current_layer += 1 - - if i != len(up_block_types) - 1: - new_prefix = f"up_blocks.{i}.upsamplers.0" - old_prefix = f"output_blocks.{current_layer - 1}.2" - new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) - - new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] - new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] - new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] - - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.") - parser.add_argument( - "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model." - ) - parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.") - - args = parser.parse_args() - args.class_cond = str2bool(args.class_cond) - - ckpt_name = os.path.basename(args.unet_path) - print(f"Checkpoint: {ckpt_name}") - - # Get U-Net config - if "imagenet64" in ckpt_name: - unet_config = IMAGENET_64_UNET_CONFIG - elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): - unet_config = LSUN_256_UNET_CONFIG - elif "test" in ckpt_name: - unet_config = TEST_UNET_CONFIG - else: - raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") - - if not args.class_cond: - unet_config["num_class_embeds"] = None - - converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config) - - image_unet = UNet2DModel(**unet_config) - image_unet.load_state_dict(converted_unet_ckpt) - - # Get scheduler config - if "cd" in ckpt_name or "test" in ckpt_name: - scheduler_config = CD_SCHEDULER_CONFIG - elif "ct" in ckpt_name and "imagenet64" in ckpt_name: - scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG - elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): - scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG - else: - raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") - - cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config) - - consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler) - consistency_model.save_pretrained(args.dump_path) diff --git a/scripts/convert_cosmos_to_diffusers.py b/scripts/convert_cosmos_to_diffusers.py deleted file mode 100644 index b6f321b5b2fb..000000000000 --- a/scripts/convert_cosmos_to_diffusers.py +++ /dev/null @@ -1,976 +0,0 @@ -""" -# Cosmos 2 Predict - -Download checkpoint -```bash -hf download nvidia/Cosmos-Predict2-2B-Text2Image -``` - -convert checkpoint -```bash -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2-2B-Text2Image/snapshots/acdb5fde992a73ef0355f287977d002cbfd127e0/model.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_ckpt_path $transformer_ckpt_path \ - --transformer_type Cosmos-2.0-Diffusion-2B-Text2Image \ - --text_encoder_path google-t5/t5-11b \ - --tokenizer_path google-t5/t5-11b \ - --vae_type wan2.1 \ - --output_path converted/cosmos-p2-t2i-2b \ - --save_pipeline -``` - -# Cosmos 2.5 Predict - -Download checkpoint -```bash -hf download nvidia/Cosmos-Predict2.5-2B -``` - -Convert checkpoint -```bash -# pre-trained -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/pre-trained/d20b7120-df3e-4911-919d-db6e08bad31c_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Predict-Base-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/2b/d20b7120-df3e-4911-919d-db6e08bad31c \ - --save_pipeline - -# post-trained -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/post-trained/81edfebe-bd6a-4039-8c1d-737df1a790bf_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Predict-Base-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/2b/81edfebe-bd6a-4039-8c1d-737df1a790bf \ - --save_pipeline -``` - -## 14B - -```bash -hf download nvidia/Cosmos-Predict2.5-14B -``` - -```bash -# pre-trained -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/pre-trained/54937b8c-29de-4f04-862c-e67b04ec41e8_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Predict-Base-14B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/14b/54937b8c-29de-4f04-862c-e67b04ec41e8/ \ - --save_pipeline - -# post-trained -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/post-trained/e21d2a49-4747-44c8-ba44-9f6f9243715f_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Predict-Base-14B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/14b/e21d2a49-4747-44c8-ba44-9f6f9243715f/ \ - --save_pipeline -``` - -# Cosmos 2.5 Transfer - -Download checkpoint -```bash -hf download nvidia/Cosmos-Transfer2.5-2B -``` - -Convert checkpoint -```bash -# depth -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/depth/626e6618-bfcd-4d9a-a077-1409e2ce353f_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/depth/pipeline \ - --save_pipeline - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/depth/models - -# edge -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/edge/61f5694b-0ad5-4ecd-8ad7-c8545627d125_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/edge/pipeline \ - --save_pipeline - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/edge/models - -# blur -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/blur/ba2f44f2-c726-4fe7-949f-597069d9b91c_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/blur/pipeline \ - --save_pipeline - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/blur/models - -# seg -transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/seg/5136ef49-6d8d-42e8-8abf-7dac722a304a_ema_bf16.pt - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/seg/pipeline \ - --save_pipeline - -python scripts/convert_cosmos_to_diffusers.py \ - --transformer_type Cosmos-2.5-Transfer-General-2B \ - --transformer_ckpt_path $transformer_ckpt_path \ - --vae_type wan2.1 \ - --output_path converted/transfer/2b/general/seg/models -``` -""" - -import argparse -import pathlib -import sys -from typing import Any, Dict, Optional - -import torch -from accelerate import init_empty_weights -from huggingface_hub import snapshot_download -from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration, T5EncoderModel, T5TokenizerFast - -from diffusers import ( - AutoencoderKLCosmos, - AutoencoderKLWan, - Cosmos2TextToImagePipeline, - Cosmos2VideoToWorldPipeline, - CosmosControlNetModel, - CosmosTextToWorldPipeline, - CosmosTransformer3DModel, - CosmosVideoToWorldPipeline, - EDMEulerScheduler, - FlowMatchEulerDiscreteScheduler, - UniPCMultistepScheduler, -) -from diffusers.pipelines.cosmos.pipeline_cosmos2_5_predict import Cosmos2_5_PredictBasePipeline -from diffusers.pipelines.cosmos.pipeline_cosmos2_5_transfer import Cosmos2_5_TransferPipeline - - -def remove_keys_(key: str, state_dict: Dict[str, Any]): - state_dict.pop(key) - - -def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def rename_transformer_blocks_(key: str, state_dict: Dict[str, Any]): - block_index = int(key.split(".")[1].removeprefix("block")) - new_key = key - - old_prefix = f"blocks.block{block_index}" - new_prefix = f"transformer_blocks.{block_index}" - new_key = new_prefix + new_key.removeprefix(old_prefix) - - state_dict[new_key] = state_dict.pop(key) - - -TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 = { - "t_embedder.1": "time_embed.t_embedder", - "affline_norm": "time_embed.norm", - ".blocks.0.block.attn": ".attn1", - ".blocks.1.block.attn": ".attn2", - ".blocks.2.block": ".ff", - ".blocks.0.adaLN_modulation.1": ".norm1.linear_1", - ".blocks.0.adaLN_modulation.2": ".norm1.linear_2", - ".blocks.1.adaLN_modulation.1": ".norm2.linear_1", - ".blocks.1.adaLN_modulation.2": ".norm2.linear_2", - ".blocks.2.adaLN_modulation.1": ".norm3.linear_1", - ".blocks.2.adaLN_modulation.2": ".norm3.linear_2", - "to_q.0": "to_q", - "to_q.1": "norm_q", - "to_k.0": "to_k", - "to_k.1": "norm_k", - "to_v.0": "to_v", - "layer1": "net.0.proj", - "layer2": "net.2", - "proj.1": "proj", - "x_embedder": "patch_embed", - "extra_pos_embedder": "learnable_pos_embed", - "final_layer.adaLN_modulation.1": "norm_out.linear_1", - "final_layer.adaLN_modulation.2": "norm_out.linear_2", - "final_layer.linear": "proj_out", -} - -TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 = { - "blocks.block": rename_transformer_blocks_, - "logvar.0.freqs": remove_keys_, - "logvar.0.phases": remove_keys_, - "logvar.1.weight": remove_keys_, - "pos_embedder.seq": remove_keys_, -} - -TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 = { - "t_embedder.1": "time_embed.t_embedder", - "t_embedding_norm": "time_embed.norm", - "blocks": "transformer_blocks", - "adaln_modulation_self_attn.1": "norm1.linear_1", - "adaln_modulation_self_attn.2": "norm1.linear_2", - "adaln_modulation_cross_attn.1": "norm2.linear_1", - "adaln_modulation_cross_attn.2": "norm2.linear_2", - "adaln_modulation_mlp.1": "norm3.linear_1", - "adaln_modulation_mlp.2": "norm3.linear_2", - "self_attn": "attn1", - "cross_attn": "attn2", - "q_proj": "to_q", - "k_proj": "to_k", - "v_proj": "to_v", - "output_proj": "to_out.0", - "q_norm": "norm_q", - "k_norm": "norm_k", - "mlp.layer1": "ff.net.0.proj", - "mlp.layer2": "ff.net.2", - "x_embedder.proj.1": "patch_embed.proj", - "final_layer.adaln_modulation.1": "norm_out.linear_1", - "final_layer.adaln_modulation.2": "norm_out.linear_2", - "final_layer.linear": "proj_out", -} - -TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 = { - "accum_video_sample_counter": remove_keys_, - "accum_image_sample_counter": remove_keys_, - "accum_iteration": remove_keys_, - "accum_train_in_hours": remove_keys_, - "pos_embedder.seq": remove_keys_, - "pos_embedder.dim_spatial_range": remove_keys_, - "pos_embedder.dim_temporal_range": remove_keys_, - "_extra_state": remove_keys_, -} - - -TRANSFORMER_CONFIGS = { - "Cosmos-1.0-Diffusion-7B-Text2World": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 32, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (2.0, 1.0, 1.0), - "concat_padding_mask": True, - "extra_pos_embed_type": "learnable", - }, - "Cosmos-1.0-Diffusion-7B-Video2World": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 32, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (2.0, 1.0, 1.0), - "concat_padding_mask": True, - "extra_pos_embed_type": "learnable", - }, - "Cosmos-1.0-Diffusion-14B-Text2World": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 40, - "attention_head_dim": 128, - "num_layers": 36, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (2.0, 2.0, 2.0), - "concat_padding_mask": True, - "extra_pos_embed_type": "learnable", - }, - "Cosmos-1.0-Diffusion-14B-Video2World": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 40, - "attention_head_dim": 128, - "num_layers": 36, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (2.0, 2.0, 2.0), - "concat_padding_mask": True, - "extra_pos_embed_type": "learnable", - }, - "Cosmos-2.0-Diffusion-2B-Text2Image": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 16, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 4.0, 4.0), - "concat_padding_mask": True, - "extra_pos_embed_type": None, - }, - "Cosmos-2.0-Diffusion-14B-Text2Image": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 40, - "attention_head_dim": 128, - "num_layers": 36, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 4.0, 4.0), - "concat_padding_mask": True, - "extra_pos_embed_type": None, - }, - "Cosmos-2.0-Diffusion-2B-Video2World": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 16, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 3.0, 3.0), - "concat_padding_mask": True, - "extra_pos_embed_type": None, - }, - "Cosmos-2.0-Diffusion-14B-Video2World": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 40, - "attention_head_dim": 128, - "num_layers": 36, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (20 / 24, 2.0, 2.0), - "concat_padding_mask": True, - "extra_pos_embed_type": None, - }, - "Cosmos-2.5-Predict-Base-2B": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 16, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 3.0, 3.0), - "concat_padding_mask": True, - # NOTE: source config has pos_emb_learnable: 'True' - but params are missing - "extra_pos_embed_type": None, - "use_crossattn_projection": True, - "crossattn_proj_in_channels": 100352, - "encoder_hidden_states_channels": 1024, - }, - "Cosmos-2.5-Predict-Base-14B": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 40, - "attention_head_dim": 128, - "num_layers": 36, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 3.0, 3.0), - "concat_padding_mask": True, - # NOTE: source config has pos_emb_learnable: 'True' - but params are missing - "extra_pos_embed_type": None, - "use_crossattn_projection": True, - "crossattn_proj_in_channels": 100352, - "encoder_hidden_states_channels": 1024, - }, - "Cosmos-2.5-Transfer-General-2B": { - "in_channels": 16 + 1, - "out_channels": 16, - "num_attention_heads": 16, - "attention_head_dim": 128, - "num_layers": 28, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "max_size": (128, 240, 240), - "patch_size": (1, 2, 2), - "rope_scale": (1.0, 3.0, 3.0), - "concat_padding_mask": True, - "extra_pos_embed_type": None, - "use_crossattn_projection": True, - "crossattn_proj_in_channels": 100352, - "encoder_hidden_states_channels": 1024, - "controlnet_block_every_n": 7, - "img_context_dim_in": 1152, - "img_context_dim_out": 2048, - "img_context_num_tokens": 256, - }, -} - -CONTROLNET_CONFIGS = { - "Cosmos-2.5-Transfer-General-2B": { - "n_controlnet_blocks": 4, - "model_channels": 2048, - "in_channels": 130, - "latent_channels": 18, # (16 latent + 1 condition_mask) + 1 padding_mask = 18 - "num_attention_heads": 16, - "attention_head_dim": 128, - "mlp_ratio": 4.0, - "text_embed_dim": 1024, - "adaln_lora_dim": 256, - "patch_size": (1, 2, 2), - "max_size": (128, 240, 240), - "rope_scale": (1.0, 3.0, 3.0), - "extra_pos_embed_type": None, - "img_context_dim_in": 1152, - "img_context_dim_out": 2048, - "use_crossattn_projection": True, - "crossattn_proj_in_channels": 100352, - "encoder_hidden_states_channels": 1024, - }, -} - -CONTROLNET_KEYS_RENAME_DICT = { - **TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0, - "blocks": "blocks", - "control_embedder.proj.1": "patch_embed.proj", -} - - -CONTROLNET_SPECIAL_KEYS_REMAP = {**TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0} - -VAE_KEYS_RENAME_DICT = { - "down.0": "down_blocks.0", - "down.1": "down_blocks.1", - "down.2": "down_blocks.2", - "up.0": "up_blocks.2", - "up.1": "up_blocks.1", - "up.2": "up_blocks.0", - ".block.": ".resnets.", - "downsample": "downsamplers.0", - "upsample": "upsamplers.0", - "mid.block_1": "mid_block.resnets.0", - "mid.attn_1.0": "mid_block.attentions.0", - "mid.attn_1.1": "mid_block.temp_attentions.0", - "mid.block_2": "mid_block.resnets.1", - ".q.conv3d": ".to_q", - ".k.conv3d": ".to_k", - ".v.conv3d": ".to_v", - ".proj_out.conv3d": ".to_out.0", - ".0.conv3d": ".conv_s", - ".1.conv3d": ".conv_t", - "conv1.conv3d": "conv1", - "conv2.conv3d": "conv2", - "conv3.conv3d": "conv3", - "nin_shortcut.conv3d": "conv_shortcut", - "quant_conv.conv3d": "quant_conv", - "post_quant_conv.conv3d": "post_quant_conv", -} - -VAE_SPECIAL_KEYS_REMAP = { - "wavelets": remove_keys_, - "_arange": remove_keys_, - "patch_size_buffer": remove_keys_, -} - -VAE_CONFIGS = { - "CV8x8x8-0.1": { - "name": "nvidia/Cosmos-0.1-Tokenizer-CV8x8x8", - "diffusers_config": { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 16, - "encoder_block_out_channels": (128, 256, 512, 512), - "decode_block_out_channels": (256, 512, 512, 512), - "attention_resolutions": (32,), - "resolution": 1024, - "num_layers": 2, - "patch_size": 4, - "patch_type": "haar", - "scaling_factor": 1.0, - "spatial_compression_ratio": 8, - "temporal_compression_ratio": 8, - "latents_mean": None, - "latents_std": None, - }, - }, - "CV8x8x8-1.0": { - "name": "nvidia/Cosmos-1.0-Tokenizer-CV8x8x8", - "diffusers_config": { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 16, - "encoder_block_out_channels": (128, 256, 512, 512), - "decode_block_out_channels": (256, 512, 512, 512), - "attention_resolutions": (32,), - "resolution": 1024, - "num_layers": 2, - "patch_size": 4, - "patch_type": "haar", - "scaling_factor": 1.0, - "spatial_compression_ratio": 8, - "temporal_compression_ratio": 8, - "latents_mean": None, - "latents_std": None, - }, - }, -} - - -def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: - state_dict = saved_dict - if "model" in saved_dict.keys(): - state_dict = state_dict["model"] - if "module" in saved_dict.keys(): - state_dict = state_dict["module"] - if "state_dict" in saved_dict.keys(): - state_dict = state_dict["state_dict"] - return state_dict - - -def convert_transformer( - transformer_type: str, - state_dict: Optional[Dict[str, Any]] = None, - weights_only: bool = True, -): - PREFIX_KEY = "net." - - if "Cosmos-1.0" in transformer_type: - TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 - TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 - elif "Cosmos-2.0" in transformer_type: - TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 - TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 - elif "Cosmos-2.5" in transformer_type: - TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 - TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 - else: - assert False - - with init_empty_weights(): - config = TRANSFORMER_CONFIGS[transformer_type] - transformer = CosmosTransformer3DModel(**config) - - old2new = {} - new2old = {} - for key in list(state_dict.keys()): - new_key = key[:] - if new_key.startswith(PREFIX_KEY): - new_key = new_key.removeprefix(PREFIX_KEY) - for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - print(key, "->", new_key, flush=True) - assert new_key not in new2old, f"new key {new_key} already mapped" - assert key not in old2new, f"old key {key} already mapped" - old2new[key] = new_key - new2old[new_key] = key - update_state_dict_(state_dict, key, new_key) - - for key in list(state_dict.keys()): - for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, state_dict) - - expected_keys = set(transformer.state_dict().keys()) - mapped_keys = set(state_dict.keys()) - missing_keys = expected_keys - mapped_keys - unexpected_keys = mapped_keys - expected_keys - if missing_keys: - print(f"ERROR: missing keys ({len(missing_keys)} from state_dict:", flush=True, file=sys.stderr) - for k in missing_keys: - print(k) - sys.exit(1) - if unexpected_keys: - print(f"ERROR: unexpected keys ({len(unexpected_keys)}) from state_dict:", flush=True, file=sys.stderr) - for k in unexpected_keys: - print(k) - sys.exit(2) - - transformer.load_state_dict(state_dict, strict=True, assign=True) - return transformer - - -def convert_controlnet( - transformer_type: str, - control_state_dict: Dict[str, Any], - base_state_dict: Dict[str, Any], - weights_only: bool = True, -): - """ - Convert controlnet weights. - - Args: - transformer_type: The type of transformer/controlnet - control_state_dict: State dict containing controlnet-specific weights - base_state_dict: State dict containing base transformer weights (for shared modules) - weights_only: Whether to use weights_only loading - """ - if transformer_type not in CONTROLNET_CONFIGS: - raise AssertionError(f"{transformer_type} does not define a ControlNet config") - - PREFIX_KEY = "net." - - # Process control-specific keys - for key in list(control_state_dict.keys()): - new_key = key[:] - if new_key.startswith(PREFIX_KEY): - new_key = new_key.removeprefix(PREFIX_KEY) - for replace_key, rename_key in CONTROLNET_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(control_state_dict, key, new_key) - - for key in list(control_state_dict.keys()): - for special_key, handler_fn_inplace in CONTROLNET_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, control_state_dict) - - # Copy shared weights from base transformer to controlnet - # These are the duplicated modules: patch_embed_base, time_embed, learnable_pos_embed, img_context_proj, crossattn_proj - shared_module_mappings = { - # transformer key prefix -> controlnet key prefix - "patch_embed.": "patch_embed_base.", - "time_embed.": "time_embed.", - "learnable_pos_embed.": "learnable_pos_embed.", - "img_context_proj.": "img_context_proj.", - "crossattn_proj.": "crossattn_proj.", - } - - for key in list(base_state_dict.keys()): - for transformer_prefix, controlnet_prefix in shared_module_mappings.items(): - if key.startswith(transformer_prefix): - controlnet_key = controlnet_prefix + key[len(transformer_prefix) :] - control_state_dict[controlnet_key] = base_state_dict[key].clone() - print(f"Copied shared weight: {key} -> {controlnet_key}", flush=True) - break - - cfg = CONTROLNET_CONFIGS[transformer_type] - controlnet = CosmosControlNetModel(**cfg) - - expected_keys = set(controlnet.state_dict().keys()) - mapped_keys = set(control_state_dict.keys()) - missing_keys = expected_keys - mapped_keys - unexpected_keys = mapped_keys - expected_keys - if missing_keys: - print(f"WARNING: missing controlnet keys ({len(missing_keys)}):", file=sys.stderr, flush=True) - for k in sorted(missing_keys): - print(k, file=sys.stderr) - sys.exit(3) - if unexpected_keys: - print(f"WARNING: unexpected controlnet keys ({len(unexpected_keys)}):", file=sys.stderr, flush=True) - for k in sorted(unexpected_keys): - print(k, file=sys.stderr) - sys.exit(4) - - controlnet.load_state_dict(control_state_dict, strict=True, assign=True) - return controlnet - - -def convert_vae(vae_type: str): - model_name = VAE_CONFIGS[vae_type]["name"] - snapshot_directory = snapshot_download(model_name, repo_type="model") - directory = pathlib.Path(snapshot_directory) - - autoencoder_file = directory / "autoencoder.jit" - mean_std_file = directory / "mean_std.pt" - - original_state_dict = torch.jit.load(autoencoder_file.as_posix()).state_dict() - if mean_std_file.exists(): - mean_std = torch.load(mean_std_file, map_location="cpu", weights_only=True) - else: - mean_std = (None, None) - - config = VAE_CONFIGS[vae_type]["diffusers_config"] - config.update( - { - "latents_mean": mean_std[0].detach().cpu().numpy().tolist(), - "latents_std": mean_std[1].detach().cpu().numpy().tolist(), - } - ) - vae = AutoencoderKLCosmos(**config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True, assign=True) - return vae - - -def save_pipeline_cosmos_1_0(args, transformer, vae): - text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) - tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) - # The original code initializes EDM config with sigma_min=0.0002, but does not make use of it anywhere directly. - # So, the sigma_min values that is used is the default value of 0.002. - scheduler = EDMEulerScheduler( - sigma_min=0.002, - sigma_max=80, - sigma_data=0.5, - sigma_schedule="karras", - num_train_timesteps=1000, - prediction_type="epsilon", - rho=7.0, - final_sigmas_type="sigma_min", - ) - - pipe_cls = CosmosTextToWorldPipeline if "Text2World" in args.transformer_type else CosmosVideoToWorldPipeline - pipe = pipe_cls( - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - vae=vae, - scheduler=scheduler, - safety_checker=lambda *args, **kwargs: None, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - -def save_pipeline_cosmos_2_0(args, transformer, vae): - text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) - tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) - - scheduler = FlowMatchEulerDiscreteScheduler(use_karras_sigmas=True) - - pipe_cls = Cosmos2TextToImagePipeline if "Text2Image" in args.transformer_type else Cosmos2VideoToWorldPipeline - pipe = pipe_cls( - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - vae=vae, - scheduler=scheduler, - safety_checker=lambda *args, **kwargs: None, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - -def save_pipeline_cosmos2_5_predict(args, transformer, vae): - text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B" - tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct" - - text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained( - text_encoder_path, torch_dtype="auto", device_map="cpu" - ) - tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) - - scheduler = UniPCMultistepScheduler( - use_karras_sigmas=True, - use_flow_sigmas=True, - prediction_type="flow_prediction", - sigma_max=200.0, - sigma_min=0.01, - ) - - pipe = Cosmos2_5_PredictBasePipeline( - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - vae=vae, - scheduler=scheduler, - safety_checker=lambda *args, **kwargs: None, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - -def save_pipeline_cosmos2_5_transfer(args, transformer, controlnet, vae): - text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B" - tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct" - - text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained( - text_encoder_path, torch_dtype="auto", device_map="cpu" - ) - tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) - - scheduler = UniPCMultistepScheduler( - use_karras_sigmas=True, - use_flow_sigmas=True, - prediction_type="flow_prediction", - sigma_max=200.0, - sigma_min=0.01, - ) - - pipe = Cosmos2_5_TransferPipeline( - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - controlnet=controlnet, - vae=vae, - scheduler=scheduler, - safety_checker=lambda *args, **kwargs: None, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--transformer_type", type=str, default=None, choices=list(TRANSFORMER_CONFIGS.keys())) - parser.add_argument( - "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" - ) - parser.add_argument( - "--vae_type", type=str, default="wan2.1", choices=["wan2.1", *list(VAE_CONFIGS.keys())], help="Type of VAE" - ) - parser.add_argument("--text_encoder_path", type=str, default=None) - parser.add_argument("--tokenizer_path", type=str, default=None) - parser.add_argument("--save_pipeline", action="store_true") - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.") - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - args = get_args() - - transformer = None - controlnet = None - dtype = DTYPE_MAPPING[args.dtype] - - if args.save_pipeline: - assert args.transformer_ckpt_path is not None - assert args.vae_type is not None - - raw_state_dict = None - if args.transformer_ckpt_path is not None: - weights_only = "Cosmos-1.0" in args.transformer_type - raw_state_dict = get_state_dict( - torch.load(args.transformer_ckpt_path, map_location="cpu", weights_only=weights_only) - ) - - if raw_state_dict is not None: - if "Transfer" in args.transformer_type: - base_state_dict = {} - control_state_dict = {} - for k, v in raw_state_dict.items(): - plain_key = k.removeprefix("net.") if k.startswith("net.") else k - if "control" in plain_key.lower(): - control_state_dict[k] = v - else: - base_state_dict[k] = v - assert len(base_state_dict.keys() & control_state_dict.keys()) == 0 - - # Convert transformer first to get the processed base state dict - transformer = convert_transformer( - args.transformer_type, state_dict=base_state_dict, weights_only=weights_only - ) - transformer = transformer.to(dtype=dtype) - - # Get converted transformer state dict to copy shared weights to controlnet - converted_base_state_dict = transformer.state_dict() - - # Convert controlnet with both control-specific and shared weights from transformer - controlnet = convert_controlnet( - args.transformer_type, control_state_dict, converted_base_state_dict, weights_only=weights_only - ) - controlnet = controlnet.to(dtype=dtype) - - if not args.save_pipeline: - transformer.save_pretrained( - pathlib.Path(args.output_path) / "transformer", safe_serialization=True, max_shard_size="5GB" - ) - controlnet.save_pretrained( - pathlib.Path(args.output_path) / "controlnet", safe_serialization=True, max_shard_size="5GB" - ) - else: - transformer = convert_transformer( - args.transformer_type, state_dict=raw_state_dict, weights_only=weights_only - ) - transformer = transformer.to(dtype=dtype) - if not args.save_pipeline: - transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - if args.vae_type is not None: - if "Cosmos-1.0" in args.transformer_type: - vae = convert_vae(args.vae_type) - elif "Cosmos-2.0" in args.transformer_type or "Cosmos-2.5" in args.transformer_type: - vae = AutoencoderKLWan.from_pretrained( - "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32 - ) - else: - raise AssertionError(f"{args.transformer_type} not supported") - - if not args.save_pipeline: - vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - else: - vae = None - - if args.save_pipeline: - if "Cosmos-1.0" in args.transformer_type: - assert args.text_encoder_path is not None - assert args.tokenizer_path is not None - save_pipeline_cosmos_1_0(args, transformer, vae) - elif "Cosmos-2.0" in args.transformer_type: - assert args.text_encoder_path is not None - assert args.tokenizer_path is not None - save_pipeline_cosmos_2_0(args, transformer, vae) - elif "Cosmos-2.5" in args.transformer_type: - if "Predict" in args.transformer_type: - save_pipeline_cosmos2_5_predict(args, transformer, vae) - elif "Transfer" in args.transformer_type: - save_pipeline_cosmos2_5_transfer(args, transformer, None, vae) - else: - raise AssertionError(f"{args.transformer_type} not supported") - else: - raise AssertionError(f"{args.transformer_type} not supported") diff --git a/scripts/convert_dance_diffusion_to_diffusers.py b/scripts/convert_dance_diffusion_to_diffusers.py deleted file mode 100755 index e269a49070cc..000000000000 --- a/scripts/convert_dance_diffusion_to_diffusers.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import math -import os -from copy import deepcopy - -import requests -import torch -from audio_diffusion.models import DiffusionAttnUnet1D -from diffusion import sampling -from torch import nn - -from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel -from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT - - -MODELS_MAP = { - "gwf-440k": { - "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt", - "sample_rate": 48000, - "sample_size": 65536, - }, - "jmann-small-190k": { - "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt", - "sample_rate": 48000, - "sample_size": 65536, - }, - "jmann-large-580k": { - "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt", - "sample_rate": 48000, - "sample_size": 131072, - }, - "maestro-uncond-150k": { - "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt", - "sample_rate": 16000, - "sample_size": 65536, - }, - "unlocked-uncond-250k": { - "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt", - "sample_rate": 16000, - "sample_size": 65536, - }, - "honk-140k": { - "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt", - "sample_rate": 16000, - "sample_size": 65536, - }, -} - - -def alpha_sigma_to_t(alpha, sigma): - """Returns a timestep, given the scaling factors for the clean image and for - the noise.""" - return torch.atan2(sigma, alpha) / math.pi * 2 - - -def get_crash_schedule(t): - sigma = torch.sin(t * math.pi / 2) ** 2 - alpha = (1 - sigma**2) ** 0.5 - return alpha_sigma_to_t(alpha, sigma) - - -class Object(object): - pass - - -class DiffusionUncond(nn.Module): - def __init__(self, global_args): - super().__init__() - - self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4) - self.diffusion_ema = deepcopy(self.diffusion) - self.rng = torch.quasirandom.SobolEngine(1, scramble=True) - - -def download(model_name): - url = MODELS_MAP[model_name]["url"] - r = requests.get(url, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT) - - local_filename = f"./{model_name}.ckpt" - with open(local_filename, "wb") as fp: - for chunk in r.iter_content(chunk_size=8192): - fp.write(chunk) - - return local_filename - - -DOWN_NUM_TO_LAYER = { - "1": "resnets.0", - "2": "attentions.0", - "3": "resnets.1", - "4": "attentions.1", - "5": "resnets.2", - "6": "attentions.2", -} -UP_NUM_TO_LAYER = { - "8": "resnets.0", - "9": "attentions.0", - "10": "resnets.1", - "11": "attentions.1", - "12": "resnets.2", - "13": "attentions.2", -} -MID_NUM_TO_LAYER = { - "1": "resnets.0", - "2": "attentions.0", - "3": "resnets.1", - "4": "attentions.1", - "5": "resnets.2", - "6": "attentions.2", - "8": "resnets.3", - "9": "attentions.3", - "10": "resnets.4", - "11": "attentions.4", - "12": "resnets.5", - "13": "attentions.5", -} -DEPTH_0_TO_LAYER = { - "0": "resnets.0", - "1": "resnets.1", - "2": "resnets.2", - "4": "resnets.0", - "5": "resnets.1", - "6": "resnets.2", -} - -RES_CONV_MAP = { - "skip": "conv_skip", - "main.0": "conv_1", - "main.1": "group_norm_1", - "main.3": "conv_2", - "main.4": "group_norm_2", -} - -ATTN_MAP = { - "norm": "group_norm", - "qkv_proj": ["query", "key", "value"], - "out_proj": ["proj_attn"], -} - - -def convert_resconv_naming(name): - if name.startswith("skip"): - return name.replace("skip", RES_CONV_MAP["skip"]) - - # name has to be of format main.{digit} - if not name.startswith("main."): - raise ValueError(f"ResConvBlock error with {name}") - - return name.replace(name[:6], RES_CONV_MAP[name[:6]]) - - -def convert_attn_naming(name): - for key, value in ATTN_MAP.items(): - if name.startswith(key) and not isinstance(value, list): - return name.replace(key, value) - elif name.startswith(key): - return [name.replace(key, v) for v in value] - raise ValueError(f"Attn error with {name}") - - -def rename(input_string, max_depth=13): - string = input_string - - if string.split(".")[0] == "timestep_embed": - return string.replace("timestep_embed", "time_proj") - - depth = 0 - if string.startswith("net.3."): - depth += 1 - string = string[6:] - elif string.startswith("net."): - string = string[4:] - - while string.startswith("main.7."): - depth += 1 - string = string[7:] - - if string.startswith("main."): - string = string[5:] - - # mid block - if string[:2].isdigit(): - layer_num = string[:2] - string_left = string[2:] - else: - layer_num = string[0] - string_left = string[1:] - - if depth == max_depth: - new_layer = MID_NUM_TO_LAYER[layer_num] - prefix = "mid_block" - elif depth > 0 and int(layer_num) < 7: - new_layer = DOWN_NUM_TO_LAYER[layer_num] - prefix = f"down_blocks.{depth}" - elif depth > 0 and int(layer_num) > 7: - new_layer = UP_NUM_TO_LAYER[layer_num] - prefix = f"up_blocks.{max_depth - depth - 1}" - elif depth == 0: - new_layer = DEPTH_0_TO_LAYER[layer_num] - prefix = f"up_blocks.{max_depth - 1}" if int(layer_num) > 3 else "down_blocks.0" - - if not string_left.startswith("."): - raise ValueError(f"Naming error with {input_string} and string_left: {string_left}.") - - string_left = string_left[1:] - - if "resnets" in new_layer: - string_left = convert_resconv_naming(string_left) - elif "attentions" in new_layer: - new_string_left = convert_attn_naming(string_left) - string_left = new_string_left - - if not isinstance(string_left, list): - new_string = prefix + "." + new_layer + "." + string_left - else: - new_string = [prefix + "." + new_layer + "." + s for s in string_left] - return new_string - - -def rename_orig_weights(state_dict): - new_state_dict = {} - for k, v in state_dict.items(): - if k.endswith("kernel"): - # up- and downsample layers, don't have trainable weights - continue - - new_k = rename(k) - - # check if we need to transform from Conv => Linear for attention - if isinstance(new_k, list): - new_state_dict = transform_conv_attns(new_state_dict, new_k, v) - else: - new_state_dict[new_k] = v - - return new_state_dict - - -def transform_conv_attns(new_state_dict, new_k, v): - if len(new_k) == 1: - if len(v.shape) == 3: - # weight - new_state_dict[new_k[0]] = v[:, :, 0] - else: - # bias - new_state_dict[new_k[0]] = v - else: - # qkv matrices - trippled_shape = v.shape[0] - single_shape = trippled_shape // 3 - for i in range(3): - if len(v.shape) == 3: - new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0] - else: - new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape] - return new_state_dict - - -def main(args): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - model_name = args.model_path.split("/")[-1].split(".")[0] - if not os.path.isfile(args.model_path): - assert model_name == args.model_path, ( - f"Make sure to provide one of the official model names {MODELS_MAP.keys()}" - ) - args.model_path = download(model_name) - - sample_rate = MODELS_MAP[model_name]["sample_rate"] - sample_size = MODELS_MAP[model_name]["sample_size"] - - config = Object() - config.sample_size = sample_size - config.sample_rate = sample_rate - config.latent_dim = 0 - - diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate) - diffusers_state_dict = diffusers_model.state_dict() - - orig_model = DiffusionUncond(config) - orig_model.load_state_dict(torch.load(args.model_path, map_location=device)["state_dict"]) - orig_model = orig_model.diffusion_ema.eval() - orig_model_state_dict = orig_model.state_dict() - renamed_state_dict = rename_orig_weights(orig_model_state_dict) - - renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys()) - diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys()) - - assert len(renamed_minus_diffusers) == 0, f"Problem with {renamed_minus_diffusers}" - assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}" - - for key, value in renamed_state_dict.items(): - assert diffusers_state_dict[key].squeeze().shape == value.squeeze().shape, ( - f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}" - ) - if key == "time_proj.weight": - value = value.squeeze() - - diffusers_state_dict[key] = value - - diffusers_model.load_state_dict(diffusers_state_dict) - - steps = 100 - seed = 33 - - diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps) - - generator = torch.manual_seed(seed) - noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device) - - t = torch.linspace(1, 0, steps + 1, device=device)[:-1] - step_list = get_crash_schedule(t) - - pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler) - - generator = torch.manual_seed(33) - audio = pipe(num_inference_steps=steps, generator=generator).audios - - generated = sampling.iplms_sample(orig_model, noise, step_list, {}) - generated = generated.clamp(-1, 1) - - diff_sum = (generated - audio).abs().sum() - diff_max = (generated - audio).abs().max() - - if args.save: - pipe.save_pretrained(args.checkpoint_path) - - print("Diff sum", diff_sum) - print("Diff max", diff_max) - - assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/" - - print(f"Conversion for {model_name} successful!") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument( - "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not." - ) - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - args = parser.parse_args() - - main(args) diff --git a/scripts/convert_dcae_to_diffusers.py b/scripts/convert_dcae_to_diffusers.py deleted file mode 100644 index 1a94c4b06640..000000000000 --- a/scripts/convert_dcae_to_diffusers.py +++ /dev/null @@ -1,323 +0,0 @@ -import argparse -from typing import Any, Dict - -import torch -from huggingface_hub import hf_hub_download -from safetensors.torch import load_file - -from diffusers import AutoencoderDC - - -def remap_qkv_(key: str, state_dict: Dict[str, Any]): - qkv = state_dict.pop(key) - q, k, v = torch.chunk(qkv, 3, dim=0) - parent_module, _, _ = key.rpartition(".qkv.conv.weight") - state_dict[f"{parent_module}.to_q.weight"] = q.squeeze() - state_dict[f"{parent_module}.to_k.weight"] = k.squeeze() - state_dict[f"{parent_module}.to_v.weight"] = v.squeeze() - - -def remap_proj_conv_(key: str, state_dict: Dict[str, Any]): - parent_module, _, _ = key.rpartition(".proj.conv.weight") - state_dict[f"{parent_module}.to_out.weight"] = state_dict.pop(key).squeeze() - - -AE_KEYS_RENAME_DICT = { - # common - "main.": "", - "op_list.": "", - "context_module": "attn", - "local_module": "conv_out", - # NOTE: The below two lines work because scales in the available configs only have a tuple length of 1 - # If there were more scales, there would be more layers, so a loop would be better to handle this - "aggreg.0.0": "to_qkv_multiscale.0.proj_in", - "aggreg.0.1": "to_qkv_multiscale.0.proj_out", - "depth_conv.conv": "conv_depth", - "inverted_conv.conv": "conv_inverted", - "point_conv.conv": "conv_point", - "point_conv.norm": "norm", - "conv.conv.": "conv.", - "conv1.conv": "conv1", - "conv2.conv": "conv2", - "conv2.norm": "norm", - "proj.norm": "norm_out", - # encoder - "encoder.project_in.conv": "encoder.conv_in", - "encoder.project_out.0.conv": "encoder.conv_out", - "encoder.stages": "encoder.down_blocks", - # decoder - "decoder.project_in.conv": "decoder.conv_in", - "decoder.project_out.0": "decoder.norm_out", - "decoder.project_out.2.conv": "decoder.conv_out", - "decoder.stages": "decoder.up_blocks", -} - -AE_F32C32_KEYS = { - # encoder - "encoder.project_in.conv": "encoder.conv_in.conv", - # decoder - "decoder.project_out.2.conv": "decoder.conv_out.conv", -} - -AE_F64C128_KEYS = { - # encoder - "encoder.project_in.conv": "encoder.conv_in.conv", - # decoder - "decoder.project_out.2.conv": "decoder.conv_out.conv", -} - -AE_F128C512_KEYS = { - # encoder - "encoder.project_in.conv": "encoder.conv_in.conv", - # decoder - "decoder.project_out.2.conv": "decoder.conv_out.conv", -} - -AE_SPECIAL_KEYS_REMAP = { - "qkv.conv.weight": remap_qkv_, - "proj.conv.weight": remap_proj_conv_, -} - - -def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: - state_dict = saved_dict - if "model" in saved_dict.keys(): - state_dict = state_dict["model"] - if "module" in saved_dict.keys(): - state_dict = state_dict["module"] - if "state_dict" in saved_dict.keys(): - state_dict = state_dict["state_dict"] - return state_dict - - -def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def convert_ae(config_name: str, dtype: torch.dtype): - config = get_ae_config(config_name) - hub_id = f"mit-han-lab/{config_name}" - ckpt_path = hf_hub_download(hub_id, "model.safetensors") - original_state_dict = get_state_dict(load_file(ckpt_path)) - - ae = AutoencoderDC(**config).to(dtype=dtype) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in AE_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in AE_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - ae.load_state_dict(original_state_dict, strict=True) - return ae - - -def get_ae_config(name: str): - if name in ["dc-ae-f32c32-sana-1.0"]: - config = { - "latent_channels": 32, - "encoder_block_types": ( - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ), - "decoder_block_types": ( - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ), - "encoder_block_out_channels": (128, 256, 512, 512, 1024, 1024), - "decoder_block_out_channels": (128, 256, 512, 512, 1024, 1024), - "encoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)), - "decoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)), - "encoder_layers_per_block": (2, 2, 2, 3, 3, 3), - "decoder_layers_per_block": [3, 3, 3, 3, 3, 3], - "downsample_block_type": "conv", - "upsample_block_type": "interpolate", - "decoder_norm_types": "rms_norm", - "decoder_act_fns": "silu", - "scaling_factor": 0.41407, - } - elif name in ["dc-ae-f32c32-in-1.0", "dc-ae-f32c32-mix-1.0"]: - AE_KEYS_RENAME_DICT.update(AE_F32C32_KEYS) - config = { - "latent_channels": 32, - "encoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "decoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024], - "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024], - "encoder_layers_per_block": [0, 4, 8, 2, 2, 2], - "decoder_layers_per_block": [0, 5, 10, 2, 2, 2], - "encoder_qkv_multiscales": ((), (), (), (), (), ()), - "decoder_qkv_multiscales": ((), (), (), (), (), ()), - "decoder_norm_types": ["batch_norm", "batch_norm", "batch_norm", "rms_norm", "rms_norm", "rms_norm"], - "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu"], - } - if name == "dc-ae-f32c32-in-1.0": - config["scaling_factor"] = 0.3189 - elif name == "dc-ae-f32c32-mix-1.0": - config["scaling_factor"] = 0.4552 - elif name in ["dc-ae-f64c128-in-1.0", "dc-ae-f64c128-mix-1.0"]: - AE_KEYS_RENAME_DICT.update(AE_F64C128_KEYS) - config = { - "latent_channels": 128, - "encoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "decoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048], - "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048], - "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2], - "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2], - "encoder_qkv_multiscales": ((), (), (), (), (), (), ()), - "decoder_qkv_multiscales": ((), (), (), (), (), (), ()), - "decoder_norm_types": [ - "batch_norm", - "batch_norm", - "batch_norm", - "rms_norm", - "rms_norm", - "rms_norm", - "rms_norm", - ], - "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu"], - } - if name == "dc-ae-f64c128-in-1.0": - config["scaling_factor"] = 0.2889 - elif name == "dc-ae-f64c128-mix-1.0": - config["scaling_factor"] = 0.4538 - elif name in ["dc-ae-f128c512-in-1.0", "dc-ae-f128c512-mix-1.0"]: - AE_KEYS_RENAME_DICT.update(AE_F128C512_KEYS) - config = { - "latent_channels": 512, - "encoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "decoder_block_types": [ - "ResBlock", - "ResBlock", - "ResBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - "EfficientViTBlock", - ], - "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048], - "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048], - "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2, 2], - "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2, 2], - "encoder_qkv_multiscales": ((), (), (), (), (), (), (), ()), - "decoder_qkv_multiscales": ((), (), (), (), (), (), (), ()), - "decoder_norm_types": [ - "batch_norm", - "batch_norm", - "batch_norm", - "rms_norm", - "rms_norm", - "rms_norm", - "rms_norm", - "rms_norm", - ], - "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu", "silu"], - } - if name == "dc-ae-f128c512-in-1.0": - config["scaling_factor"] = 0.4883 - elif name == "dc-ae-f128c512-mix-1.0": - config["scaling_factor"] = 0.3620 - else: - raise ValueError("Invalid config name provided.") - - return config - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--config_name", - type=str, - default="dc-ae-f32c32-sana-1.0", - choices=[ - "dc-ae-f32c32-sana-1.0", - "dc-ae-f32c32-in-1.0", - "dc-ae-f32c32-mix-1.0", - "dc-ae-f64c128-in-1.0", - "dc-ae-f64c128-mix-1.0", - "dc-ae-f128c512-in-1.0", - "dc-ae-f128c512-mix-1.0", - ], - help="The DCAE checkpoint to convert", - ) - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.") - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - -VARIANT_MAPPING = { - "fp32": None, - "fp16": "fp16", - "bf16": "bf16", -} - - -if __name__ == "__main__": - args = get_args() - - dtype = DTYPE_MAPPING[args.dtype] - variant = VARIANT_MAPPING[args.dtype] - - ae = convert_ae(args.config_name, dtype) - ae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", variant=variant) diff --git a/scripts/convert_ddpm_original_checkpoint_to_diffusers.py b/scripts/convert_ddpm_original_checkpoint_to_diffusers.py deleted file mode 100644 index 46595784b0ba..000000000000 --- a/scripts/convert_ddpm_original_checkpoint_to_diffusers.py +++ /dev/null @@ -1,431 +0,0 @@ -import argparse -import json - -import torch - -from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - mapping = [] - for old_item in old_list: - new_item = old_item - new_item = new_item.replace("block.", "resnets.") - new_item = new_item.replace("conv_shorcut", "conv1") - new_item = new_item.replace("in_shortcut", "conv_shortcut") - new_item = new_item.replace("temb_proj", "time_emb_proj") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False): - mapping = [] - for old_item in old_list: - new_item = old_item - - # In `model.mid`, the layer is called `attn`. - if not in_mid: - new_item = new_item.replace("attn", "attentions") - new_item = new_item.replace(".k.", ".key.") - new_item = new_item.replace(".v.", ".value.") - new_item = new_item.replace(".q.", ".query.") - - new_item = new_item.replace("proj_out", "proj_attn") - new_item = new_item.replace("norm", "group_norm") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - if attention_paths_to_split is not None: - if config is None: - raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'.") - - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config.get("num_head_channels", 1) // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape).squeeze() - checkpoint[path_map["key"]] = key.reshape(target_shape).squeeze() - checkpoint[path_map["value"]] = value.reshape(target_shape).squeeze() - - for path in paths: - new_path = path["new"] - - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - new_path = new_path.replace("down.", "down_blocks.") - new_path = new_path.replace("up.", "up_blocks.") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - if "attentions" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]].squeeze() - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def convert_ddpm_checkpoint(checkpoint, config): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["temb.dense.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["temb.dense.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["temb.dense.1.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["temb.dense.1.bias"] - - new_checkpoint["conv_norm_out.weight"] = checkpoint["norm_out.weight"] - new_checkpoint["conv_norm_out.bias"] = checkpoint["norm_out.bias"] - - new_checkpoint["conv_in.weight"] = checkpoint["conv_in.weight"] - new_checkpoint["conv_in.bias"] = checkpoint["conv_in.bias"] - new_checkpoint["conv_out.weight"] = checkpoint["conv_out.weight"] - new_checkpoint["conv_out.bias"] = checkpoint["conv_out.bias"] - - num_down_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer}) - down_blocks = { - layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - num_up_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer}) - up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} - - for i in range(num_down_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - - if any("downsample" in layer for layer in down_blocks[i]): - new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ - f"down.{i}.downsample.op.weight" - ] - new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[f"down.{i}.downsample.op.bias"] - # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight'] - # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias'] - - if any("block" in layer for layer in down_blocks[i]): - num_blocks = len( - {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer} - ) - blocks = { - layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] - for layer_id in range(num_blocks) - } - - if num_blocks > 0: - for j in range(config["layers_per_block"]): - paths = renew_resnet_paths(blocks[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint) - - if any("attn" in layer for layer in down_blocks[i]): - num_attn = len( - {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer} - ) - attns = { - layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] - for layer_id in range(num_blocks) - } - - if num_attn > 0: - for j in range(config["layers_per_block"]): - paths = renew_attention_paths(attns[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) - - mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] - mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] - mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] - - # Mid new 2 - paths = renew_resnet_paths(mid_block_1_layers) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], - ) - - paths = renew_resnet_paths(mid_block_2_layers) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], - ) - - paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], - ) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - - if any("upsample" in layer for layer in up_blocks[i]): - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ - f"up.{i}.upsample.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[f"up.{i}.upsample.conv.bias"] - - if any("block" in layer for layer in up_blocks[i]): - num_blocks = len( - {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer} - ) - blocks = { - layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) - } - - if num_blocks > 0: - for j in range(config["layers_per_block"] + 1): - replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} - paths = renew_resnet_paths(blocks[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) - - if any("attn" in layer for layer in up_blocks[i]): - num_attn = len( - {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer} - ) - attns = { - layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) - } - - if num_attn > 0: - for j in range(config["layers_per_block"] + 1): - replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} - paths = renew_attention_paths(attns[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) - - new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} - return new_checkpoint - - -def convert_vq_autoenc_checkpoint(checkpoint, config): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - new_checkpoint = {} - - new_checkpoint["encoder.conv_norm_out.weight"] = checkpoint["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = checkpoint["encoder.norm_out.bias"] - - new_checkpoint["encoder.conv_in.weight"] = checkpoint["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = checkpoint["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = checkpoint["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = checkpoint["encoder.conv_out.bias"] - - new_checkpoint["decoder.conv_norm_out.weight"] = checkpoint["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = checkpoint["decoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = checkpoint["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = checkpoint["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = checkpoint["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = checkpoint["decoder.conv_out.bias"] - - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "down" in layer}) - down_blocks = { - layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer}) - up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} - - for i in range(num_down_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - - if any("downsample" in layer for layer in down_blocks[i]): - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ - f"encoder.down.{i}.downsample.conv.weight" - ] - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[ - f"encoder.down.{i}.downsample.conv.bias" - ] - - if any("block" in layer for layer in down_blocks[i]): - num_blocks = len( - {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer} - ) - blocks = { - layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] - for layer_id in range(num_blocks) - } - - if num_blocks > 0: - for j in range(config["layers_per_block"]): - paths = renew_resnet_paths(blocks[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint) - - if any("attn" in layer for layer in down_blocks[i]): - num_attn = len( - {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer} - ) - attns = { - layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] - for layer_id in range(num_blocks) - } - - if num_attn > 0: - for j in range(config["layers_per_block"]): - paths = renew_attention_paths(attns[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) - - mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] - mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] - mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] - - # Mid new 2 - paths = renew_resnet_paths(mid_block_1_layers) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], - ) - - paths = renew_resnet_paths(mid_block_2_layers) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], - ) - - paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], - ) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - - if any("upsample" in layer for layer in up_blocks[i]): - new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ - f"decoder.up.{i}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ - f"decoder.up.{i}.upsample.conv.bias" - ] - - if any("block" in layer for layer in up_blocks[i]): - num_blocks = len( - {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer} - ) - blocks = { - layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) - } - - if num_blocks > 0: - for j in range(config["layers_per_block"] + 1): - replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} - paths = renew_resnet_paths(blocks[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) - - if any("attn" in layer for layer in up_blocks[i]): - num_attn = len( - {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer} - ) - attns = { - layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) - } - - if num_attn > 0: - for j in range(config["layers_per_block"] + 1): - replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} - paths = renew_attention_paths(attns[j]) - assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) - - new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} - new_checkpoint["quant_conv.weight"] = checkpoint["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = checkpoint["quant_conv.bias"] - if "quantize.embedding.weight" in checkpoint: - new_checkpoint["quantize.embedding.weight"] = checkpoint["quantize.embedding.weight"] - new_checkpoint["post_quant_conv.weight"] = checkpoint["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = checkpoint["post_quant_conv.bias"] - - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - - parser.add_argument( - "--config_file", - default=None, - type=str, - required=True, - help="The config json file corresponding to the architecture.", - ) - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - args = parser.parse_args() - checkpoint = torch.load(args.checkpoint_path) - - with open(args.config_file) as f: - config = json.loads(f.read()) - - # unet case - key_prefix_set = {key.split(".")[0] for key in checkpoint.keys()} - if "encoder" in key_prefix_set and "decoder" in key_prefix_set: - converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config) - else: - converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config) - - if "ddpm" in config: - del config["ddpm"] - - if config["_class_name"] == "VQModel": - model = VQModel(**config) - model.load_state_dict(converted_checkpoint) - model.save_pretrained(args.dump_path) - elif config["_class_name"] == "AutoencoderKL": - model = AutoencoderKL(**config) - model.load_state_dict(converted_checkpoint) - model.save_pretrained(args.dump_path) - else: - model = UNet2DModel(**config) - model.load_state_dict(converted_checkpoint) - - scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) - - pipe = DDPMPipeline(unet=model, scheduler=scheduler) - pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_diffusers_sdxl_lora_to_webui.py b/scripts/convert_diffusers_sdxl_lora_to_webui.py deleted file mode 100644 index dfb3871275cb..000000000000 --- a/scripts/convert_diffusers_sdxl_lora_to_webui.py +++ /dev/null @@ -1,56 +0,0 @@ -# Script for converting a Hugging Face Diffusers trained SDXL LoRAs to Kohya format -# This means that you can input your diffusers-trained LoRAs and -# Get the output to work with WebUIs such as AUTOMATIC1111, ComfyUI, SD.Next and others. - -# To get started you can find some cool `diffusers` trained LoRAs such as this cute Corgy -# https://huggingface.co/ignasbud/corgy_dog_LoRA/, download its `pytorch_lora_weights.safetensors` file -# and run the script: -# python convert_diffusers_sdxl_lora_to_webui.py --input_lora pytorch_lora_weights.safetensors --output_lora corgy.safetensors -# now you can use corgy.safetensors in your WebUI of choice! - -# To train your own, here are some diffusers training scripts and utils that you can use and then convert: -# LoRA Ease - no code SDXL Dreambooth LoRA trainer: https://huggingface.co/spaces/multimodalart/lora-ease -# Dreambooth Advanced Training Script - state of the art techniques such as pivotal tuning and prodigy optimizer: -# - Script: https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py -# - Colab (only on Pro): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb -# Canonical diffusers training scripts: -# - Script: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py -# - Colab (runs on free tier): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb - -import argparse -import os - -from safetensors.torch import load_file, save_file - -from diffusers.utils import convert_all_state_dict_to_peft, convert_state_dict_to_kohya - - -def convert_and_save(input_lora, output_lora=None): - if output_lora is None: - base_name = os.path.splitext(input_lora)[0] - output_lora = f"{base_name}_webui.safetensors" - - diffusers_state_dict = load_file(input_lora) - peft_state_dict = convert_all_state_dict_to_peft(diffusers_state_dict) - kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict) - save_file(kohya_state_dict, output_lora) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Convert LoRA model to PEFT and then to Kohya format.") - parser.add_argument( - "--input_lora", - type=str, - required=True, - help="Path to the input LoRA model file in the diffusers format.", - ) - parser.add_argument( - "--output_lora", - type=str, - required=False, - help="Path for the converted LoRA (safetensors format for AUTOMATIC1111, ComfyUI, etc.). Optional, defaults to input name with a _webui suffix.", - ) - - args = parser.parse_args() - - convert_and_save(args.input_lora, args.output_lora) diff --git a/scripts/convert_diffusers_to_original_sdxl.py b/scripts/convert_diffusers_to_original_sdxl.py deleted file mode 100644 index 1aa792b3f06a..000000000000 --- a/scripts/convert_diffusers_to_original_sdxl.py +++ /dev/null @@ -1,350 +0,0 @@ -# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. -# *Only* converts the UNet, VAE, and Text Encoder. -# Does not convert optimizer state or any other thing. - -import argparse -import os.path as osp -import re - -import torch -from safetensors.torch import load_file, save_file - - -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), - # the following are for sdxl - ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(3): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i > 0: - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(4): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i < 2: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) -unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv.")) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2 * j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name in mapping.items()} - return new_state_dict - - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3 - i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i + 1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - # the following are for SDXL - ("q.", "to_q."), - ("k.", "to_k."), - ("v.", "to_v."), - ("proj_out.", "to_out.0."), -] - - -def reshape_weight_for_sd(w): - # convert HF linear weights to SD conv2d weights - if not w.ndim == 1: - return w.reshape(*w.shape, 1, 1) - else: - return w - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v) - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# - - -textenc_conversion_lst = [ - # (stable-diffusion, HF Diffusers) - ("transformer.resblocks.", "text_model.encoder.layers."), - ("ln_1", "layer_norm1"), - ("ln_2", "layer_norm2"), - (".c_fc.", ".fc1."), - (".c_proj.", ".fc2."), - (".attn", ".self_attn"), - ("ln_final.", "text_model.final_layer_norm."), - ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"), - ("positional_embedding", "text_model.embeddings.position_embedding.weight"), -] -protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} -textenc_pattern = re.compile("|".join(protected.keys())) - -# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp -code2idx = {"q": 0, "k": 1, "v": 2} - - -def convert_openclip_text_enc_state_dict(text_enc_dict): - new_state_dict = {} - capture_qkv_weight = {} - capture_qkv_bias = {} - for k, v in text_enc_dict.items(): - if ( - k.endswith(".self_attn.q_proj.weight") - or k.endswith(".self_attn.k_proj.weight") - or k.endswith(".self_attn.v_proj.weight") - ): - k_pre = k[: -len(".q_proj.weight")] - k_code = k[-len("q_proj.weight")] - if k_pre not in capture_qkv_weight: - capture_qkv_weight[k_pre] = [None, None, None] - capture_qkv_weight[k_pre][code2idx[k_code]] = v - continue - - if ( - k.endswith(".self_attn.q_proj.bias") - or k.endswith(".self_attn.k_proj.bias") - or k.endswith(".self_attn.v_proj.bias") - ): - k_pre = k[: -len(".q_proj.bias")] - k_code = k[-len("q_proj.bias")] - if k_pre not in capture_qkv_bias: - capture_qkv_bias[k_pre] = [None, None, None] - capture_qkv_bias[k_pre][code2idx[k_code]] = v - continue - - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v - - for k_pre, tensors in capture_qkv_weight.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) - - for k_pre, tensors in capture_qkv_bias.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) - - return new_state_dict - - -def convert_openai_text_enc_state_dict(text_enc_dict): - return text_enc_dict - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." - ) - - args = parser.parse_args() - - assert args.model_path is not None, "Must provide a model path!" - - assert args.checkpoint_path is not None, "Must provide a checkpoint path!" - - # Path for safetensors - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") - text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") - text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "model.safetensors") - - # Load models from safetensors if it exists, if it doesn't pytorch - if osp.exists(unet_path): - unet_state_dict = load_file(unet_path, device="cpu") - else: - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") - unet_state_dict = torch.load(unet_path, map_location="cpu") - - if osp.exists(vae_path): - vae_state_dict = load_file(vae_path, device="cpu") - else: - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") - vae_state_dict = torch.load(vae_path, map_location="cpu") - - if osp.exists(text_enc_path): - text_enc_dict = load_file(text_enc_path, device="cpu") - else: - text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - - if osp.exists(text_enc_2_path): - text_enc_2_dict = load_file(text_enc_2_path, device="cpu") - else: - text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "pytorch_model.bin") - text_enc_2_dict = torch.load(text_enc_2_path, map_location="cpu") - - # Convert the UNet model - unet_state_dict = convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - # Convert text encoder 1 - text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"conditioner.embedders.0.transformer." + k: v for k, v in text_enc_dict.items()} - - # Convert text encoder 2 - text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict) - text_enc_2_dict = {"conditioner.embedders.1.model." + k: v for k, v in text_enc_2_dict.items()} - # We call the `.T.contiguous()` to match what's done in - # https://github.com/huggingface/diffusers/blob/84905ca7287876b925b6bf8e9bb92fec21c78764/src/diffusers/loaders/single_file_utils.py#L1085 - text_enc_2_dict["conditioner.embedders.1.model.text_projection"] = text_enc_2_dict.pop( - "conditioner.embedders.1.model.text_projection.weight" - ).T.contiguous() - - # Put together new checkpoint - state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict} - - if args.half: - state_dict = {k: v.half() for k, v in state_dict.items()} - - if args.use_safetensors: - save_file(state_dict, args.checkpoint_path) - else: - state_dict = {"state_dict": state_dict} - torch.save(state_dict, args.checkpoint_path) diff --git a/scripts/convert_diffusers_to_original_stable_diffusion.py b/scripts/convert_diffusers_to_original_stable_diffusion.py deleted file mode 100644 index 049dda7d42a7..000000000000 --- a/scripts/convert_diffusers_to_original_stable_diffusion.py +++ /dev/null @@ -1,353 +0,0 @@ -# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. -# *Only* converts the UNet, VAE, and Text Encoder. -# Does not convert optimizer state or any other thing. - -import argparse -import os.path as osp -import re - -import torch -from safetensors.torch import load_file, save_file - - -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(4): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i < 3: - # no attention layers in down_blocks.3 - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(3): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i > 0: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) - -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2 * j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} - return new_state_dict - - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3 - i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i + 1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - ("q.", "query."), - ("k.", "key."), - ("v.", "value."), - ("proj_out.", "proj_attn."), -] - -# This is probably not the most ideal solution, but it does work. -vae_extra_conversion_map = [ - ("to_q", "q"), - ("to_k", "k"), - ("to_v", "v"), - ("to_out.0", "proj_out"), -] - - -def reshape_weight_for_sd(w): - # convert HF linear weights to SD conv2d weights - if not w.ndim == 1: - return w.reshape(*w.shape, 1, 1) - else: - return w - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - keys_to_rename = {} - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v) - for weight_name, real_weight_name in vae_extra_conversion_map: - if f"mid.attn_1.{weight_name}.weight" in k or f"mid.attn_1.{weight_name}.bias" in k: - keys_to_rename[k] = k.replace(weight_name, real_weight_name) - for k, v in keys_to_rename.items(): - if k in new_state_dict: - print(f"Renaming {k} to {v}") - new_state_dict[v] = reshape_weight_for_sd(new_state_dict[k]) - del new_state_dict[k] - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# - - -textenc_conversion_lst = [ - # (stable-diffusion, HF Diffusers) - ("resblocks.", "text_model.encoder.layers."), - ("ln_1", "layer_norm1"), - ("ln_2", "layer_norm2"), - (".c_fc.", ".fc1."), - (".c_proj.", ".fc2."), - (".attn", ".self_attn"), - ("ln_final.", "transformer.text_model.final_layer_norm."), - ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), - ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), -] -protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} -textenc_pattern = re.compile("|".join(protected.keys())) - -# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp -code2idx = {"q": 0, "k": 1, "v": 2} - - -def convert_text_enc_state_dict_v20(text_enc_dict): - new_state_dict = {} - capture_qkv_weight = {} - capture_qkv_bias = {} - for k, v in text_enc_dict.items(): - if ( - k.endswith(".self_attn.q_proj.weight") - or k.endswith(".self_attn.k_proj.weight") - or k.endswith(".self_attn.v_proj.weight") - ): - k_pre = k[: -len(".q_proj.weight")] - k_code = k[-len("q_proj.weight")] - if k_pre not in capture_qkv_weight: - capture_qkv_weight[k_pre] = [None, None, None] - capture_qkv_weight[k_pre][code2idx[k_code]] = v - continue - - if ( - k.endswith(".self_attn.q_proj.bias") - or k.endswith(".self_attn.k_proj.bias") - or k.endswith(".self_attn.v_proj.bias") - ): - k_pre = k[: -len(".q_proj.bias")] - k_code = k[-len("q_proj.bias")] - if k_pre not in capture_qkv_bias: - capture_qkv_bias[k_pre] = [None, None, None] - capture_qkv_bias[k_pre][code2idx[k_code]] = v - continue - - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v - - for k_pre, tensors in capture_qkv_weight.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) - - for k_pre, tensors in capture_qkv_bias.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) - - return new_state_dict - - -def convert_text_enc_state_dict(text_enc_dict): - return text_enc_dict - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." - ) - - args = parser.parse_args() - - assert args.model_path is not None, "Must provide a model path!" - - assert args.checkpoint_path is not None, "Must provide a checkpoint path!" - - # Path for safetensors - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") - text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") - - # Load models from safetensors if it exists, if it doesn't pytorch - if osp.exists(unet_path): - unet_state_dict = load_file(unet_path, device="cpu") - else: - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") - unet_state_dict = torch.load(unet_path, map_location="cpu") - - if osp.exists(vae_path): - vae_state_dict = load_file(vae_path, device="cpu") - else: - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") - vae_state_dict = torch.load(vae_path, map_location="cpu") - - if osp.exists(text_enc_path): - text_enc_dict = load_file(text_enc_path, device="cpu") - else: - text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - - # Convert the UNet model - unet_state_dict = convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper - is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict - - if is_v20_model: - # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm - text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} - text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) - text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} - else: - text_enc_dict = convert_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} - - # Put together new checkpoint - state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} - if args.half: - state_dict = {k: v.half() for k, v in state_dict.items()} - - if args.use_safetensors: - save_file(state_dict, args.checkpoint_path) - else: - state_dict = {"state_dict": state_dict} - torch.save(state_dict, args.checkpoint_path) diff --git a/scripts/convert_dit_to_diffusers.py b/scripts/convert_dit_to_diffusers.py deleted file mode 100644 index dc127f69555c..000000000000 --- a/scripts/convert_dit_to_diffusers.py +++ /dev/null @@ -1,162 +0,0 @@ -import argparse -import os - -import torch -from torchvision.datasets.utils import download_url - -from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel - - -pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"} - - -def download_model(model_name): - """ - Downloads a pre-trained DiT model from the web. - """ - local_path = f"pretrained_models/{model_name}" - if not os.path.isfile(local_path): - os.makedirs("pretrained_models", exist_ok=True) - web_path = f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}" - download_url(web_path, "pretrained_models") - model = torch.load(local_path, map_location=lambda storage, loc: storage) - return model - - -def main(args): - state_dict = download_model(pretrained_models[args.image_size]) - - state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] - state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] - state_dict.pop("x_embedder.proj.weight") - state_dict.pop("x_embedder.proj.bias") - - for depth in range(28): - state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight"] = state_dict[ - "t_embedder.mlp.0.weight" - ] - state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias"] = state_dict[ - "t_embedder.mlp.0.bias" - ] - state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight"] = state_dict[ - "t_embedder.mlp.2.weight" - ] - state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias"] = state_dict[ - "t_embedder.mlp.2.bias" - ] - state_dict[f"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight"] = state_dict[ - "y_embedder.embedding_table.weight" - ] - - state_dict[f"transformer_blocks.{depth}.norm1.linear.weight"] = state_dict[ - f"blocks.{depth}.adaLN_modulation.1.weight" - ] - state_dict[f"transformer_blocks.{depth}.norm1.linear.bias"] = state_dict[ - f"blocks.{depth}.adaLN_modulation.1.bias" - ] - - q, k, v = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"], 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"], 3, dim=0) - - state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias - state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias - state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias - - state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict[ - f"blocks.{depth}.attn.proj.weight" - ] - state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict[f"blocks.{depth}.attn.proj.bias"] - - state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict[f"blocks.{depth}.mlp.fc1.weight"] - state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict[f"blocks.{depth}.mlp.fc1.bias"] - state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict[f"blocks.{depth}.mlp.fc2.weight"] - state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict[f"blocks.{depth}.mlp.fc2.bias"] - - state_dict.pop(f"blocks.{depth}.attn.qkv.weight") - state_dict.pop(f"blocks.{depth}.attn.qkv.bias") - state_dict.pop(f"blocks.{depth}.attn.proj.weight") - state_dict.pop(f"blocks.{depth}.attn.proj.bias") - state_dict.pop(f"blocks.{depth}.mlp.fc1.weight") - state_dict.pop(f"blocks.{depth}.mlp.fc1.bias") - state_dict.pop(f"blocks.{depth}.mlp.fc2.weight") - state_dict.pop(f"blocks.{depth}.mlp.fc2.bias") - state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.weight") - state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.bias") - - state_dict.pop("t_embedder.mlp.0.weight") - state_dict.pop("t_embedder.mlp.0.bias") - state_dict.pop("t_embedder.mlp.2.weight") - state_dict.pop("t_embedder.mlp.2.bias") - state_dict.pop("y_embedder.embedding_table.weight") - - state_dict["proj_out_1.weight"] = state_dict["final_layer.adaLN_modulation.1.weight"] - state_dict["proj_out_1.bias"] = state_dict["final_layer.adaLN_modulation.1.bias"] - state_dict["proj_out_2.weight"] = state_dict["final_layer.linear.weight"] - state_dict["proj_out_2.bias"] = state_dict["final_layer.linear.bias"] - - state_dict.pop("final_layer.linear.weight") - state_dict.pop("final_layer.linear.bias") - state_dict.pop("final_layer.adaLN_modulation.1.weight") - state_dict.pop("final_layer.adaLN_modulation.1.bias") - - # DiT XL/2 - transformer = Transformer2DModel( - sample_size=args.image_size // 8, - num_layers=28, - attention_head_dim=72, - in_channels=4, - out_channels=8, - patch_size=2, - attention_bias=True, - num_attention_heads=16, - activation_fn="gelu-approximate", - num_embeds_ada_norm=1000, - norm_type="ada_norm_zero", - norm_elementwise_affine=False, - ) - transformer.load_state_dict(state_dict, strict=True) - - scheduler = DDIMScheduler( - num_train_timesteps=1000, - beta_schedule="linear", - prediction_type="epsilon", - clip_sample=False, - ) - - vae = AutoencoderKL.from_pretrained(args.vae_model) - - pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler) - - if args.save: - pipeline.save_pretrained(args.checkpoint_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--image_size", - default=256, - type=int, - required=False, - help="Image size of pretrained model, either 256 or 512.", - ) - parser.add_argument( - "--vae_model", - default="stabilityai/sd-vae-ft-ema", - type=str, - required=False, - help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.", - ) - parser.add_argument( - "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." - ) - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline." - ) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_flux2_to_diffusers.py b/scripts/convert_flux2_to_diffusers.py deleted file mode 100644 index a8fa6f87eee1..000000000000 --- a/scripts/convert_flux2_to_diffusers.py +++ /dev/null @@ -1,536 +0,0 @@ -import argparse -from contextlib import nullcontext -from typing import Any, Dict, Tuple - -import safetensors.torch -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download -from transformers import AutoProcessor, GenerationConfig, Mistral3ForConditionalGeneration - -from diffusers import AutoencoderKLFlux2, FlowMatchEulerDiscreteScheduler, Flux2Pipeline, Flux2Transformer2DModel -from diffusers.utils.import_utils import is_accelerate_available - - -""" -# VAE - -python scripts/convert_flux2_to_diffusers.py \ ---original_state_dict_repo_id "diffusers-internal-dev/new-model-image" \ ---vae_filename "flux2-vae.sft" \ ---output_path "/raid/yiyi/dummy-flux2-diffusers" \ ---vae - -# DiT - -python scripts/convert_flux2_to_diffusers.py \ - --original_state_dict_repo_id diffusers-internal-dev/new-model-image \ - --dit_filename flux-dev-dummy.sft \ - --dit \ - --output_path . - -# Full pipe - -python scripts/convert_flux2_to_diffusers.py \ - --original_state_dict_repo_id diffusers-internal-dev/new-model-image \ - --dit_filename flux-dev-dummy.sft \ - --vae_filename "flux2-vae.sft" \ - --dit --vae --full_pipe \ - --output_path . -""" - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--original_state_dict_repo_id", default=None, type=str) -parser.add_argument("--vae_filename", default="flux2-vae.sft", type=str) -parser.add_argument("--dit_filename", default="flux2-dev.safetensors", type=str) -parser.add_argument("--vae", action="store_true") -parser.add_argument("--dit", action="store_true") -parser.add_argument("--vae_dtype", type=str, default="fp32") -parser.add_argument("--dit_dtype", type=str, default="bf16") -parser.add_argument("--checkpoint_path", default=None, type=str) -parser.add_argument("--full_pipe", action="store_true") -parser.add_argument("--output_path", type=str) - -args = parser.parse_args() - - -def load_original_checkpoint(args, filename): - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=filename) - elif args.checkpoint_path is not None: - ckpt_path = args.checkpoint_path - else: - raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -DIFFUSERS_VAE_TO_FLUX2_MAPPING = { - "encoder.conv_in.weight": "encoder.conv_in.weight", - "encoder.conv_in.bias": "encoder.conv_in.bias", - "encoder.conv_out.weight": "encoder.conv_out.weight", - "encoder.conv_out.bias": "encoder.conv_out.bias", - "encoder.conv_norm_out.weight": "encoder.norm_out.weight", - "encoder.conv_norm_out.bias": "encoder.norm_out.bias", - "decoder.conv_in.weight": "decoder.conv_in.weight", - "decoder.conv_in.bias": "decoder.conv_in.bias", - "decoder.conv_out.weight": "decoder.conv_out.weight", - "decoder.conv_out.bias": "decoder.conv_out.bias", - "decoder.conv_norm_out.weight": "decoder.norm_out.weight", - "decoder.conv_norm_out.bias": "decoder.norm_out.bias", - "quant_conv.weight": "encoder.quant_conv.weight", - "quant_conv.bias": "encoder.quant_conv.bias", - "post_quant_conv.weight": "decoder.post_quant_conv.weight", - "post_quant_conv.bias": "decoder.post_quant_conv.bias", - "bn.running_mean": "bn.running_mean", - "bn.running_var": "bn.running_var", -} - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def update_vae_resnet_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping): - for ldm_key in keys: - diffusers_key = ldm_key.replace(mapping["old"], mapping["new"]).replace("nin_shortcut", "conv_shortcut") - new_checkpoint[diffusers_key] = checkpoint.get(ldm_key) - - -def update_vae_attentions_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping): - for ldm_key in keys: - diffusers_key = ( - ldm_key.replace(mapping["old"], mapping["new"]) - .replace("norm.weight", "group_norm.weight") - .replace("norm.bias", "group_norm.bias") - .replace("q.weight", "to_q.weight") - .replace("q.bias", "to_q.bias") - .replace("k.weight", "to_k.weight") - .replace("k.bias", "to_k.bias") - .replace("v.weight", "to_v.weight") - .replace("v.bias", "to_v.bias") - .replace("proj_out.weight", "to_out.0.weight") - .replace("proj_out.bias", "to_out.0.bias") - ) - new_checkpoint[diffusers_key] = checkpoint.get(ldm_key) - - # proj_attn.weight has to be converted from conv 1D to linear - shape = new_checkpoint[diffusers_key].shape - - if len(shape) == 3: - new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0] - elif len(shape) == 4: - new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0, 0] - - -def convert_flux2_vae_checkpoint_to_diffusers(vae_state_dict, config): - new_checkpoint = {} - for diffusers_key, ldm_key in DIFFUSERS_VAE_TO_FLUX2_MAPPING.items(): - if ldm_key not in vae_state_dict: - continue - new_checkpoint[diffusers_key] = vae_state_dict[ldm_key] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len(config["down_block_types"]) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - update_vae_resnet_ldm_to_diffusers( - resnets, - new_checkpoint, - vae_state_dict, - mapping={"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}, - ) - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.get( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.get( - f"encoder.down.{i}.downsample.conv.bias" - ) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - update_vae_resnet_ldm_to_diffusers( - resnets, - new_checkpoint, - vae_state_dict, - mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}, - ) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - update_vae_attentions_ldm_to_diffusers( - mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"} - ) - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len(config["up_block_types"]) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - update_vae_resnet_ldm_to_diffusers( - resnets, - new_checkpoint, - vae_state_dict, - mapping={"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}, - ) - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - update_vae_resnet_ldm_to_diffusers( - resnets, - new_checkpoint, - vae_state_dict, - mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}, - ) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - update_vae_attentions_ldm_to_diffusers( - mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"} - ) - conv_attn_to_linear(new_checkpoint) - - return new_checkpoint - - -FLUX2_TRANSFORMER_KEYS_RENAME_DICT = { - # Image and text input projections - "img_in": "x_embedder", - "txt_in": "context_embedder", - # Timestep and guidance embeddings - "time_in.in_layer": "time_guidance_embed.timestep_embedder.linear_1", - "time_in.out_layer": "time_guidance_embed.timestep_embedder.linear_2", - "guidance_in.in_layer": "time_guidance_embed.guidance_embedder.linear_1", - "guidance_in.out_layer": "time_guidance_embed.guidance_embedder.linear_2", - # Modulation parameters - "double_stream_modulation_img.lin": "double_stream_modulation_img.linear", - "double_stream_modulation_txt.lin": "double_stream_modulation_txt.linear", - "single_stream_modulation.lin": "single_stream_modulation.linear", - # Final output layer - # "final_layer.adaLN_modulation.1": "norm_out.linear", # Handle separately since we need to swap mod params - "final_layer.linear": "proj_out", -} - - -FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP = { - "final_layer.adaLN_modulation.1": "norm_out.linear", -} - - -FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP = { - # Handle fused QKV projections separately as we need to break into Q, K, V projections - "img_attn.norm.query_norm": "attn.norm_q", - "img_attn.norm.key_norm": "attn.norm_k", - "img_attn.proj": "attn.to_out.0", - "img_mlp.0": "ff.linear_in", - "img_mlp.2": "ff.linear_out", - "txt_attn.norm.query_norm": "attn.norm_added_q", - "txt_attn.norm.key_norm": "attn.norm_added_k", - "txt_attn.proj": "attn.to_add_out", - "txt_mlp.0": "ff_context.linear_in", - "txt_mlp.2": "ff_context.linear_out", -} - - -FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP = { - "linear1": "attn.to_qkv_mlp_proj", - "linear2": "attn.to_out", - "norm.query_norm": "attn.norm_q", - "norm.key_norm": "attn.norm_k", -} - - -# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; -# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use -# diffusers implementation -def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_ada_layer_norm_weights(key: str, state_dict: Dict[str, Any]) -> None: - # Skip if not a weight - if ".weight" not in key: - return - - # If adaLN_modulation is in the key, swap scale and shift parameters - # Original implementation is (shift, scale); diffusers implementation is (scale, shift) - if "adaLN_modulation" in key: - key_without_param_type, param_type = key.rsplit(".", maxsplit=1) - # Assume all such keys are in the AdaLayerNorm key map - new_key_without_param_type = FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP[key_without_param_type] - new_key = ".".join([new_key_without_param_type, param_type]) - - swapped_weight = swap_scale_shift(state_dict.pop(key)) - state_dict[new_key] = swapped_weight - return - - -def convert_flux2_double_stream_blocks(key: str, state_dict: Dict[str, Any]) -> None: - # Skip if not a weight, bias, or scale - if ".weight" not in key and ".bias" not in key and ".scale" not in key: - return - - new_prefix = "transformer_blocks" - if "double_blocks." in key: - parts = key.split(".") - block_idx = parts[1] - modality_block_name = parts[2] # img_attn, img_mlp, txt_attn, txt_mlp - within_block_name = ".".join(parts[2:-1]) - param_type = parts[-1] - - if param_type == "scale": - param_type = "weight" - - if "qkv" in within_block_name: - fused_qkv_weight = state_dict.pop(key) - to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) - if "img" in modality_block_name: - # double_blocks.{N}.img_attn.qkv --> transformer_blocks.{N}.attn.{to_q|to_k|to_v} - to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) - new_q_name = "attn.to_q" - new_k_name = "attn.to_k" - new_v_name = "attn.to_v" - elif "txt" in modality_block_name: - # double_blocks.{N}.txt_attn.qkv --> transformer_blocks.{N}.attn.{add_q_proj|add_k_proj|add_v_proj} - to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) - new_q_name = "attn.add_q_proj" - new_k_name = "attn.add_k_proj" - new_v_name = "attn.add_v_proj" - new_q_key = ".".join([new_prefix, block_idx, new_q_name, param_type]) - new_k_key = ".".join([new_prefix, block_idx, new_k_name, param_type]) - new_v_key = ".".join([new_prefix, block_idx, new_v_name, param_type]) - state_dict[new_q_key] = to_q_weight - state_dict[new_k_key] = to_k_weight - state_dict[new_v_key] = to_v_weight - else: - new_within_block_name = FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP[within_block_name] - new_key = ".".join([new_prefix, block_idx, new_within_block_name, param_type]) - - param = state_dict.pop(key) - state_dict[new_key] = param - return - - -def convert_flux2_single_stream_blocks(key: str, state_dict: Dict[str, Any]) -> None: - # Skip if not a weight, bias, or scale - if ".weight" not in key and ".bias" not in key and ".scale" not in key: - return - - # Mapping: - # - single_blocks.{N}.linear1 --> single_transformer_blocks.{N}.attn.to_qkv_mlp_proj - # - single_blocks.{N}.linear2 --> single_transformer_blocks.{N}.attn.to_out - # - single_blocks.{N}.norm.query_norm.scale --> single_transformer_blocks.{N}.attn.norm_q.weight - # - single_blocks.{N}.norm.key_norm.scale --> single_transformer_blocks.{N}.attn.norm_k.weight - new_prefix = "single_transformer_blocks" - if "single_blocks." in key: - parts = key.split(".") - block_idx = parts[1] - within_block_name = ".".join(parts[2:-1]) - param_type = parts[-1] - - if param_type == "scale": - param_type = "weight" - - new_within_block_name = FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP[within_block_name] - new_key = ".".join([new_prefix, block_idx, new_within_block_name, param_type]) - - param = state_dict.pop(key) - state_dict[new_key] = param - return - - -TRANSFORMER_SPECIAL_KEYS_REMAP = { - "adaLN_modulation": convert_ada_layer_norm_weights, - "double_blocks": convert_flux2_double_stream_blocks, - "single_blocks": convert_flux2_single_stream_blocks, -} - - -def update_state_dict(state_dict: Dict[str, Any], old_key: str, new_key: str) -> None: - state_dict[new_key] = state_dict.pop(old_key) - - -def get_flux2_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]: - if model_type == "flux2-dev": - config = { - "model_id": "black-forest-labs/FLUX.2-dev", - "diffusers_config": { - "patch_size": 1, - "in_channels": 128, - "num_layers": 8, - "num_single_layers": 48, - "attention_head_dim": 128, - "num_attention_heads": 48, - "joint_attention_dim": 15360, - "timestep_guidance_channels": 256, - "mlp_ratio": 3.0, - "axes_dims_rope": (32, 32, 32, 32), - "rope_theta": 2000, - "eps": 1e-6, - }, - } - rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "klein-4b": - config = { - "model_id": "diffusers-internal-dev/dummy0115", - "diffusers_config": { - "patch_size": 1, - "in_channels": 128, - "num_layers": 5, - "num_single_layers": 20, - "attention_head_dim": 128, - "num_attention_heads": 24, - "joint_attention_dim": 7680, - "timestep_guidance_channels": 256, - "mlp_ratio": 3.0, - "axes_dims_rope": (32, 32, 32, 32), - "rope_theta": 2000, - "eps": 1e-6, - "guidance_embeds": False, - }, - } - rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP - - elif model_type == "klein-9b": - config = { - "model_id": "diffusers-internal-dev/dummy0115", - "diffusers_config": { - "patch_size": 1, - "in_channels": 128, - "num_layers": 8, - "num_single_layers": 24, - "attention_head_dim": 128, - "num_attention_heads": 32, - "joint_attention_dim": 12288, - "timestep_guidance_channels": 256, - "mlp_ratio": 3.0, - "axes_dims_rope": (32, 32, 32, 32), - "rope_theta": 2000, - "eps": 1e-6, - "guidance_embeds": False, - }, - } - rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP - - else: - raise ValueError(f"Unknown model_type: {model_type}. Choose from: flux2-dev, klein-4b, klein-9b") - - return config, rename_dict, special_keys_remap - - -def convert_flux2_transformer_to_diffusers(original_state_dict: Dict[str, torch.Tensor], model_type: str): - config, rename_dict, special_keys_remap = get_flux2_transformer_config(model_type) - - diffusers_config = config["diffusers_config"] - - with init_empty_weights(): - transformer = Flux2Transformer2DModel.from_config(diffusers_config) - - # Handle official code --> diffusers key remapping via the remap dict - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict(original_state_dict, key, new_key) - - # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in - # special_keys_remap - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - transformer.load_state_dict(original_state_dict, strict=True, assign=True) - return transformer - - -def main(args): - if args.vae: - original_vae_ckpt = load_original_checkpoint(args, filename=args.vae_filename) - vae = AutoencoderKLFlux2() - converted_vae_state_dict = convert_flux2_vae_checkpoint_to_diffusers(original_vae_ckpt, vae.config) - vae.load_state_dict(converted_vae_state_dict, strict=True) - if not args.full_pipe: - vae_dtype = torch.bfloat16 if args.vae_dtype == "bf16" else torch.float32 - vae.to(vae_dtype).save_pretrained(f"{args.output_path}/vae") - - if args.dit: - original_dit_ckpt = load_original_checkpoint(args, filename=args.dit_filename) - - if "klein-4b" in args.dit_filename: - model_type = "klein-4b" - elif "klein-9b" in args.dit_filename: - model_type = "klein-9b" - else: - model_type = "flux2-dev" - transformer = convert_flux2_transformer_to_diffusers(original_dit_ckpt, model_type) - if not args.full_pipe: - dit_dtype = torch.bfloat16 if args.dit_dtype == "bf16" else torch.float32 - transformer.to(dit_dtype).save_pretrained(f"{args.output_path}/transformer") - - if args.full_pipe: - tokenizer_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" - text_encoder_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" - generate_config = GenerationConfig.from_pretrained(text_encoder_id) - generate_config.do_sample = True - text_encoder = Mistral3ForConditionalGeneration.from_pretrained( - text_encoder_id, generation_config=generate_config, torch_dtype=torch.bfloat16 - ) - tokenizer = AutoProcessor.from_pretrained(tokenizer_id) - scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( - "black-forest-labs/FLUX.1-dev", subfolder="scheduler" - ) - - if_distilled = "base" not in args.dit_filename - - pipe = Flux2Pipeline( - vae=vae, - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - scheduler=scheduler, - if_distilled=if_distilled, - ) - pipe.save_pretrained(args.output_path) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_flux_to_diffusers.py b/scripts/convert_flux_to_diffusers.py deleted file mode 100644 index ec31d842d4db..000000000000 --- a/scripts/convert_flux_to_diffusers.py +++ /dev/null @@ -1,308 +0,0 @@ -import argparse -from contextlib import nullcontext - -import safetensors.torch -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download - -from diffusers import AutoencoderKL, FluxTransformer2DModel -from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint -from diffusers.utils.import_utils import is_accelerate_available - - -""" -# Transformer - -python scripts/convert_flux_to_diffusers.py \ ---original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \ ---filename "flux1-schnell.sft" ---output_path "flux-schnell" \ ---transformer -""" - -""" -# VAE - -python scripts/convert_flux_to_diffusers.py \ ---original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \ ---filename "ae.sft" ---output_path "flux-schnell" \ ---vae -""" - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--original_state_dict_repo_id", default=None, type=str) -parser.add_argument("--filename", default="flux.safetensors", type=str) -parser.add_argument("--checkpoint_path", default=None, type=str) -parser.add_argument("--in_channels", type=int, default=64) -parser.add_argument("--out_channels", type=int, default=None) -parser.add_argument("--vae", action="store_true") -parser.add_argument("--transformer", action="store_true") -parser.add_argument("--output_path", type=str) -parser.add_argument("--dtype", type=str, default="bf16") - -args = parser.parse_args() -dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 - - -def load_original_checkpoint(args): - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) - elif args.checkpoint_path is not None: - ckpt_path = args.checkpoint_path - else: - raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; -# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation -def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_flux_transformer_checkpoint_to_diffusers( - original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0 -): - converted_state_dict = {} - - ## time_text_embed.timestep_embedder <- time_in - converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "time_in.in_layer.weight" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "time_in.in_layer.bias" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "time_in.out_layer.weight" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "time_in.out_layer.bias" - ) - - ## time_text_embed.text_embedder <- vector_in - converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( - "vector_in.in_layer.weight" - ) - converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( - "vector_in.in_layer.bias" - ) - converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( - "vector_in.out_layer.weight" - ) - converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( - "vector_in.out_layer.bias" - ) - - # guidance - has_guidance = any("guidance" in k for k in original_state_dict) - if has_guidance: - converted_state_dict["time_text_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop( - "guidance_in.in_layer.weight" - ) - converted_state_dict["time_text_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop( - "guidance_in.in_layer.bias" - ) - converted_state_dict["time_text_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop( - "guidance_in.out_layer.weight" - ) - converted_state_dict["time_text_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop( - "guidance_in.out_layer.bias" - ) - - # context_embedder - converted_state_dict["context_embedder.weight"] = original_state_dict.pop("txt_in.weight") - converted_state_dict["context_embedder.bias"] = original_state_dict.pop("txt_in.bias") - - # x_embedder - converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight") - converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias") - - # double transformer blocks - for i in range(num_layers): - block_prefix = f"transformer_blocks.{i}." - # norms. - ## norm1 - converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.lin.bias" - ) - ## norm1_context - converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.lin.bias" - ) - # Q, K, V - sample_q, sample_k, sample_v = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0 - ) - context_q, context_k, context_v = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0 - ) - sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0 - ) - context_q_bias, context_k_bias, context_v_bias = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0 - ) - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q]) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias]) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k]) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias]) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v]) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias]) - converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q]) - converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias]) - converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k]) - converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias]) - converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v]) - converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias]) - # qk_norm - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.norm.query_norm.scale" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.norm.key_norm.scale" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.norm.query_norm.scale" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.norm.key_norm.scale" - ) - # ff img_mlp - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.0.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.0.bias" - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.2.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.2.bias" - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.0.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.0.bias" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.2.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.2.bias" - ) - # output projections. - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.proj.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.proj.bias" - ) - - # single transformer blocks - for i in range(num_single_layers): - block_prefix = f"single_transformer_blocks.{i}." - # norm.linear <- single_blocks.0.modulation.lin - converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.lin.bias" - ) - # Q, K, V, mlp - mlp_hidden_dim = int(inner_dim * mlp_ratio) - split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim) - q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0) - q_bias, k_bias, v_bias, mlp_bias = torch.split( - original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0 - ) - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q]) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias]) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k]) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias]) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v]) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias]) - converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp]) - converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias]) - # qk norm - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"single_blocks.{i}.norm.query_norm.scale" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"single_blocks.{i}.norm.key_norm.scale" - ) - # output projections. - converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.weight" - ) - converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.bias" - ) - - converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.weight") - ) - converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.bias") - ) - - return converted_state_dict - - -def main(args): - original_ckpt = load_original_checkpoint(args) - has_guidance = any("guidance" in k for k in original_ckpt) - - if args.transformer: - num_layers = 19 - num_single_layers = 38 - inner_dim = 3072 - mlp_ratio = 4.0 - - converted_transformer_state_dict = convert_flux_transformer_checkpoint_to_diffusers( - original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio - ) - transformer = FluxTransformer2DModel( - in_channels=args.in_channels, out_channels=args.out_channels, guidance_embeds=has_guidance - ) - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - print( - f"Saving Flux Transformer in Diffusers format. Variant: {'guidance-distilled' if has_guidance else 'timestep-distilled'}" - ) - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - if args.vae: - config = AutoencoderKL.load_config("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="vae") - vae = AutoencoderKL.from_config(config, scaling_factor=0.3611, shift_factor=0.1159).to(torch.bfloat16) - - converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config) - vae.load_state_dict(converted_vae_state_dict, strict=True) - vae.to(dtype).save_pretrained(f"{args.output_path}/vae") - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py b/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py deleted file mode 100644 index b701b7fb40b1..000000000000 --- a/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py +++ /dev/null @@ -1,97 +0,0 @@ -import argparse -from contextlib import nullcontext - -import safetensors.torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download - -from diffusers.utils.import_utils import is_accelerate_available, is_transformers_available - - -if is_transformers_available(): - from transformers import CLIPVisionModelWithProjection - - vision = True -else: - vision = False - -""" -python scripts/convert_flux_xlabs_ipadapter_to_diffusers.py \ ---original_state_dict_repo_id "XLabs-AI/flux-ip-adapter" \ ---filename "flux-ip-adapter.safetensors" ---output_path "flux-ip-adapter-hf/" -""" - - -CTX = init_empty_weights if is_accelerate_available else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--original_state_dict_repo_id", default=None, type=str) -parser.add_argument("--filename", default="flux.safetensors", type=str) -parser.add_argument("--checkpoint_path", default=None, type=str) -parser.add_argument("--output_path", type=str) -parser.add_argument("--vision_pretrained_or_path", default="openai/clip-vit-large-patch14", type=str) - -args = parser.parse_args() - - -def load_original_checkpoint(args): - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) - elif args.checkpoint_path is not None: - ckpt_path = args.checkpoint_path - else: - raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -def convert_flux_ipadapter_checkpoint_to_diffusers(original_state_dict, num_layers): - converted_state_dict = {} - - # image_proj - ## norm - converted_state_dict["image_proj.norm.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight") - converted_state_dict["image_proj.norm.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias") - ## proj - converted_state_dict["image_proj.proj.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight") - converted_state_dict["image_proj.proj.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias") - - # double transformer blocks - for i in range(num_layers): - block_prefix = f"ip_adapter.{i}." - # to_k_ip - converted_state_dict[f"{block_prefix}to_k_ip.bias"] = original_state_dict.pop( - f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.bias" - ) - converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop( - f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.weight" - ) - # to_v_ip - converted_state_dict[f"{block_prefix}to_v_ip.bias"] = original_state_dict.pop( - f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.bias" - ) - converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop( - f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.weight" - ) - - return converted_state_dict - - -def main(args): - original_ckpt = load_original_checkpoint(args) - - num_layers = 19 - converted_ip_adapter_state_dict = convert_flux_ipadapter_checkpoint_to_diffusers(original_ckpt, num_layers) - - print("Saving Flux IP-Adapter in Diffusers format.") - safetensors.torch.save_file(converted_ip_adapter_state_dict, f"{args.output_path}/model.safetensors") - - if vision: - model = CLIPVisionModelWithProjection.from_pretrained(args.vision_pretrained_or_path) - model.save_pretrained(f"{args.output_path}/image_encoder") - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_gligen_to_diffusers.py b/scripts/convert_gligen_to_diffusers.py deleted file mode 100644 index 83c1f928e407..000000000000 --- a/scripts/convert_gligen_to_diffusers.py +++ /dev/null @@ -1,581 +0,0 @@ -import argparse -import re - -import torch -import yaml -from transformers import ( - CLIPProcessor, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - StableDiffusionGLIGENPipeline, - StableDiffusionGLIGENTextImagePipeline, - UNet2DConditionModel, -) -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( - assign_to_checkpoint, - conv_attn_to_linear, - protected, - renew_attention_paths, - renew_resnet_paths, - renew_vae_attention_paths, - renew_vae_resnet_paths, - shave_segments, - textenc_conversion_map, - textenc_pattern, -) - - -def convert_open_clip_checkpoint(checkpoint): - checkpoint = checkpoint["text_encoder"] - text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") - - keys = list(checkpoint.keys()) - - text_model_dict = {} - - if "cond_stage_model.model.text_projection" in checkpoint: - d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) - else: - d_model = 1024 - - for key in keys: - if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer - continue - if key in textenc_conversion_map: - text_model_dict[textenc_conversion_map[key]] = checkpoint[key] - # if key.startswith("cond_stage_model.model.transformer."): - new_key = key[len("transformer.") :] - if new_key.endswith(".in_proj_weight"): - new_key = new_key[: -len(".in_proj_weight")] - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] - text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] - text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] - elif new_key.endswith(".in_proj_bias"): - new_key = new_key[: -len(".in_proj_bias")] - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] - text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] - text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] - else: - if key != "transformer.text_model.embeddings.position_ids": - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - - text_model_dict[new_key] = checkpoint[key] - - if key == "transformer.text_model.embeddings.token_embedding.weight": - text_model_dict["text_model.embeddings.token_embedding.weight"] = checkpoint[key] - - text_model_dict.pop("text_model.embeddings.transformer.text_model.embeddings.token_embedding.weight") - - text_model.load_state_dict(text_model_dict) - - return text_model - - -def convert_gligen_vae_checkpoint(checkpoint, config): - checkpoint = checkpoint["autoencoder"] - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for key in new_checkpoint.keys(): - if "encoder.mid_block.attentions.0" in key or "decoder.mid_block.attentions.0" in key: - if "query" in key: - new_checkpoint[key.replace("query", "to_q")] = new_checkpoint.pop(key) - if "value" in key: - new_checkpoint[key.replace("value", "to_v")] = new_checkpoint.pop(key) - if "key" in key: - new_checkpoint[key.replace("key", "to_k")] = new_checkpoint.pop(key) - if "proj_attn" in key: - new_checkpoint[key.replace("proj_attn", "to_out.0")] = new_checkpoint.pop(key) - - return new_checkpoint - - -def convert_gligen_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - unet_state_dict = {} - checkpoint = checkpoint["model"] - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has bot EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - for key in keys: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - for key in keys: - if "position_net" in key: - new_checkpoint[key] = unet_state_dict[key] - - return new_checkpoint - - -def create_vae_config(original_config, image_size: int): - vae_params = original_config["autoencoder"]["params"]["ddconfig"] - _ = original_config["autoencoder"]["params"]["embed_dim"] - - block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - config = { - "sample_size": image_size, - "in_channels": vae_params["in_channels"], - "out_channels": vae_params["out_ch"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params["z_channels"], - "layers_per_block": vae_params["num_res_blocks"], - } - - return config - - -def create_unet_config(original_config, image_size: int, attention_type): - unet_params = original_config["model"]["params"] - vae_params = original_config["autoencoder"]["params"]["ddconfig"] - - block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) - - head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None - use_linear_projection = ( - unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False - ) - if use_linear_projection: - if head_dim is None: - head_dim = [5, 10, 20, 20] - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params["in_channels"], - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params["num_res_blocks"], - "cross_attention_dim": unet_params["context_dim"], - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "attention_type": attention_type, - } - - return config - - -def convert_gligen_to_diffusers( - checkpoint_path: str, - original_config_file: str, - attention_type: str, - image_size: int = 512, - extract_ema: bool = False, - num_in_channels: int = None, - device: str = None, -): - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load(checkpoint_path, map_location=device) - else: - checkpoint = torch.load(checkpoint_path, map_location=device) - - if "global_step" in checkpoint: - checkpoint["global_step"] - else: - print("global_step key not found in model") - - original_config = yaml.safe_load(original_config_file) - - if num_in_channels is not None: - original_config["model"]["params"]["in_channels"] = num_in_channels - - num_train_timesteps = original_config["diffusion"]["params"]["timesteps"] - beta_start = original_config["diffusion"]["params"]["linear_start"] - beta_end = original_config["diffusion"]["params"]["linear_end"] - - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type="epsilon", - ) - - # Convert the UNet2DConditionalModel model - unet_config = create_unet_config(original_config, image_size, attention_type) - unet = UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_gligen_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # Convert the VAE model - vae_config = create_vae_config(original_config, image_size) - converted_vae_checkpoint = convert_gligen_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - # Convert the text model - text_encoder = convert_open_clip_checkpoint(checkpoint) - tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - - if attention_type == "gated-text-image": - image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") - - pipe = StableDiffusionGLIGENTextImagePipeline( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - image_encoder=image_encoder, - processor=processor, - unet=unet, - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - ) - elif attention_type == "gated": - pipe = StableDiffusionGLIGENPipeline( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - required=True, - help="The YAML config file corresponding to the gligen architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--attention_type", - default=None, - type=str, - required=True, - help="Type of attention ex: gated or gated-text-image", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - - args = parser.parse_args() - - pipe = convert_gligen_to_diffusers( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - attention_type=args.attention_type, - extract_ema=args.extract_ema, - num_in_channels=args.num_in_channels, - device=args.device, - ) - - if args.half: - pipe.to(dtype=torch.float16) - - pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_hunyuan_image_to_diffusers.py b/scripts/convert_hunyuan_image_to_diffusers.py deleted file mode 100644 index c41e934cc3d4..000000000000 --- a/scripts/convert_hunyuan_image_to_diffusers.py +++ /dev/null @@ -1,1044 +0,0 @@ -import argparse -import logging - -import torch -from safetensors import safe_open - -from diffusers import AutoencoderKLHunyuanImage, AutoencoderKLHunyuanImageRefiner, HunyuanImageTransformer2DModel - - -logger = logging.getLogger(__name__) # pylint: disable=invalid-name - - -""" -Usage examples -============== - -python scripts/convert_hunyuan_image_to_diffusers.py \ - --model_type hunyuanimage2.1 \ - --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage2.1.safetensors" \ - --vae_checkpoint_path "HunyuanImage-2.1/ckpts/vae/vae_2_1/pytorch_model.ckpt" \ - --output_path "/raid/yiyi/test-hy21-diffusers" \ - --dtype fp32 - -python scripts/convert_hunyuan_image_to_diffusers.py \ - --model_type hunyuanimage2.1-distilled \ - --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage2.1-distilled.safetensors" \ - --vae_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/vae/vae_2_1/pytorch_model.ckpt" \ - --output_path "/raid/yiyi/test-hy21-distilled-diffusers" \ - --dtype fp32 - - -python scripts/convert_hunyuan_image_to_diffusers.py \ - --model_type hunyuanimage-refiner \ - --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage-refiner.safetensors" \ - --vae_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/vae/vae_refiner/pytorch_model.pt" \ - --output_path "/raid/yiyi/test-hy2-refiner-diffusers" \ - --dtype fp32 -""" - -parser = argparse.ArgumentParser() -parser.add_argument( - "--model_type", type=str, default=None -) # hunyuanimage2.1, hunyuanimage2.1-distilled, hunyuanimage-refiner -parser.add_argument("--transformer_checkpoint_path", default=None, type=str) # ckpts/dit/hunyuanimage2.1.safetensors -parser.add_argument("--vae_checkpoint_path", default=None, type=str) # ckpts/vae/vae_2_1/pytorch_model.ckpt -parser.add_argument("--output_path", type=str) -parser.add_argument("--dtype", type=str, default="fp32") - -args = parser.parse_args() -dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 - - -# copied from https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/hyimage/models/hunyuan/modules/hunyuanimage_dit.py#L21 -def convert_hunyuan_dict_for_tensor_parallel(state_dict): - """ - Convert a Hunyuan model state dict to be compatible with tensor parallel architectures. - - Args: - state_dict: Original state dict - - Returns: - new_dict: Converted state dict - """ - new_dict = {} - for k, w in state_dict.items(): - if k.startswith("double_blocks") and "attn_qkv.weight" in k: - hidden_size = w.shape[1] - k1 = k.replace("attn_qkv.weight", "attn_q.weight") - w1 = w[:hidden_size, :] - new_dict[k1] = w1 - k2 = k.replace("attn_qkv.weight", "attn_k.weight") - w2 = w[hidden_size : 2 * hidden_size, :] - new_dict[k2] = w2 - k3 = k.replace("attn_qkv.weight", "attn_v.weight") - w3 = w[-hidden_size:, :] - new_dict[k3] = w3 - elif k.startswith("double_blocks") and "attn_qkv.bias" in k: - hidden_size = w.shape[0] // 3 - k1 = k.replace("attn_qkv.bias", "attn_q.bias") - w1 = w[:hidden_size] - new_dict[k1] = w1 - k2 = k.replace("attn_qkv.bias", "attn_k.bias") - w2 = w[hidden_size : 2 * hidden_size] - new_dict[k2] = w2 - k3 = k.replace("attn_qkv.bias", "attn_v.bias") - w3 = w[-hidden_size:] - new_dict[k3] = w3 - elif k.startswith("single_blocks") and "linear1" in k: - hidden_size = state_dict[k.replace("linear1", "linear2")].shape[0] - k1 = k.replace("linear1", "linear1_q") - w1 = w[:hidden_size] - new_dict[k1] = w1 - k2 = k.replace("linear1", "linear1_k") - w2 = w[hidden_size : 2 * hidden_size] - new_dict[k2] = w2 - k3 = k.replace("linear1", "linear1_v") - w3 = w[2 * hidden_size : 3 * hidden_size] - new_dict[k3] = w3 - k4 = k.replace("linear1", "linear1_mlp") - w4 = w[3 * hidden_size :] - new_dict[k4] = w4 - elif k.startswith("single_blocks") and "linear2" in k: - k1 = k.replace("linear2", "linear2.fc") - new_dict[k1] = w - else: - new_dict[k] = w - return new_dict - - -def load_original_vae_checkpoint(args): - # "ckpts/vae/vae_2_1/pytorch_model.ckpt" - state_dict = torch.load(args.vae_checkpoint_path) - - if "state_dict" in state_dict: - state_dict = state_dict["state_dict"] - vae_state_dict = {} - for k, v in state_dict.items(): - if k.startswith("vae."): - vae_state_dict[k.replace("vae.", "")] = v - - for k, v in vae_state_dict.items(): - if "weight" in k: - if len(v.shape) == 5 and v.shape[2] == 1: - vae_state_dict[k] = v.squeeze(2) - else: - vae_state_dict[k] = v - else: - vae_state_dict[k] = v - return vae_state_dict - - -def load_original_refiner_vae_checkpoint(args): - # "ckpts/vae/vae_refiner/pytorch_model.pt" - state_dict = torch.load(args.vae_checkpoint_path) - - if "state_dict" in state_dict: - state_dict = state_dict["state_dict"] - vae_state_dict = {} - for k, v in state_dict.items(): - if k.startswith("vae."): - vae_state_dict[k.replace("vae.", "")] = v - return vae_state_dict - - -def load_original_transformer_checkpoint(args): - # ckpts/dit/hunyuanimage-refiner.safetensors" - # ckpts/dit/hunyuanimage2.1.safetensors" - state_dict = {} - with safe_open(args.transformer_checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - state_dict[key] = f.get_tensor(key) - if args.model_type == "hunyuanimage-2.1": - state_dict = convert_hunyuan_dict_for_tensor_parallel(state_dict) - return state_dict - - -def convert_hunyuan_image_transformer_checkpoint_to_diffusers( - original_state_dict, use_byt5=True, guidance_distilled=False, use_meanflow=False -): - converted_state_dict = {} - - # 1. byt5_in -> context_embedder_2 - if use_byt5: - converted_state_dict["context_embedder_2.norm.weight"] = original_state_dict.pop("byt5_in.layernorm.weight") - converted_state_dict["context_embedder_2.norm.bias"] = original_state_dict.pop("byt5_in.layernorm.bias") - converted_state_dict["context_embedder_2.linear_1.weight"] = original_state_dict.pop("byt5_in.fc1.weight") - converted_state_dict["context_embedder_2.linear_1.bias"] = original_state_dict.pop("byt5_in.fc1.bias") - converted_state_dict["context_embedder_2.linear_2.weight"] = original_state_dict.pop("byt5_in.fc2.weight") - converted_state_dict["context_embedder_2.linear_2.bias"] = original_state_dict.pop("byt5_in.fc2.bias") - converted_state_dict["context_embedder_2.linear_3.weight"] = original_state_dict.pop("byt5_in.fc3.weight") - converted_state_dict["context_embedder_2.linear_3.bias"] = original_state_dict.pop("byt5_in.fc3.bias") - - # 2. img_in -> x_embedder - converted_state_dict["x_embedder.proj.weight"] = original_state_dict.pop("img_in.proj.weight") - converted_state_dict["x_embedder.proj.bias"] = original_state_dict.pop("img_in.proj.bias") - - # 3. txt_in -> context_embedder (complex mapping) - # txt_in.input_embedder -> context_embedder.proj_in - converted_state_dict["context_embedder.proj_in.weight"] = original_state_dict.pop("txt_in.input_embedder.weight") - converted_state_dict["context_embedder.proj_in.bias"] = original_state_dict.pop("txt_in.input_embedder.bias") - - # txt_in.t_embedder -> context_embedder.time_text_embed.timestep_embedder - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.weight"] = ( - original_state_dict.pop("txt_in.t_embedder.mlp.0.weight") - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "txt_in.t_embedder.mlp.0.bias" - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.weight"] = ( - original_state_dict.pop("txt_in.t_embedder.mlp.2.weight") - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "txt_in.t_embedder.mlp.2.bias" - ) - - # txt_in.c_embedder -> context_embedder.time_text_embed.text_embedder - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( - "txt_in.c_embedder.linear_1.weight" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( - "txt_in.c_embedder.linear_1.bias" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( - "txt_in.c_embedder.linear_2.weight" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( - "txt_in.c_embedder.linear_2.bias" - ) - - # txt_in.individual_token_refiner -> context_embedder.token_refiner - for i in range(2): # 2 refiner blocks - block_prefix = f"context_embedder.token_refiner.refiner_blocks.{i}." - # norm1 - converted_state_dict[f"{block_prefix}norm1.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.norm1.weight" - ) - converted_state_dict[f"{block_prefix}norm1.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.norm1.bias" - ) - # norm2 - converted_state_dict[f"{block_prefix}norm2.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.norm2.weight" - ) - converted_state_dict[f"{block_prefix}norm2.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.norm2.bias" - ) - - # Split QKV - qkv_weight = original_state_dict.pop(f"txt_in.individual_token_refiner.blocks.{i}.self_attn_qkv.weight") - qkv_bias = original_state_dict.pop(f"txt_in.individual_token_refiner.blocks.{i}.self_attn_qkv.bias") - q_weight, k_weight, v_weight = torch.chunk(qkv_weight, 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) - - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = q_weight - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = q_bias - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = k_weight - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = k_bias - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = v_weight - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = v_bias - - # attn projection - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.self_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.self_attn_proj.bias" - ) - - # MLP - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc2.bias" - ) - - # norm_out - converted_state_dict[f"{block_prefix}norm_out.linear.weight"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.adaLN_modulation.1.weight" - ) - converted_state_dict[f"{block_prefix}norm_out.linear.bias"] = original_state_dict.pop( - f"txt_in.individual_token_refiner.blocks.{i}.adaLN_modulation.1.bias" - ) - - # 4. time_in -> time_text_embed.timestep_embedder - converted_state_dict["time_guidance_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "time_in.mlp.0.weight" - ) - converted_state_dict["time_guidance_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "time_in.mlp.0.bias" - ) - converted_state_dict["time_guidance_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "time_in.mlp.2.weight" - ) - converted_state_dict["time_guidance_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "time_in.mlp.2.bias" - ) - - # time_r_in -> time_guidance_embed.timestep_r_embedder - if use_meanflow: - converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_1.weight"] = original_state_dict.pop( - "time_r_in.mlp.0.weight" - ) - converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_1.bias"] = original_state_dict.pop( - "time_r_in.mlp.0.bias" - ) - converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_2.weight"] = original_state_dict.pop( - "time_r_in.mlp.2.weight" - ) - converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_2.bias"] = original_state_dict.pop( - "time_r_in.mlp.2.bias" - ) - - # guidance_in -> time_guidance_embed.guidance_embedder - if guidance_distilled: - converted_state_dict["time_guidance_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop( - "guidance_in.mlp.0.weight" - ) - converted_state_dict["time_guidance_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop( - "guidance_in.mlp.0.bias" - ) - converted_state_dict["time_guidance_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop( - "guidance_in.mlp.2.weight" - ) - converted_state_dict["time_guidance_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop( - "guidance_in.mlp.2.bias" - ) - - # 5. double_blocks -> transformer_blocks - for i in range(20): # 20 double blocks - block_prefix = f"transformer_blocks.{i}." - - # norm1 (img_mod) - converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.linear.weight" - ) - converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.linear.bias" - ) - - # norm1_context (txt_mod) - converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.linear.weight" - ) - converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.linear.bias" - ) - - # img attention - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_q.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_q.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_k.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_k.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_v.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_v.bias" - ) - - # img attention norms - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_q_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_k_norm.weight" - ) - - # img attention projection - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn_proj.bias" - ) - - # img MLP - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.fc2.bias" - ) - - # txt attention (additional projections) - converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_q.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_q.bias" - ) - converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_k.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_k.bias" - ) - converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_v.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_v.bias" - ) - - # txt attention norms - converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_q_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_k_norm.weight" - ) - - # txt attention projection - converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn_proj.bias" - ) - - # txt MLP (ff_context) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.fc2.bias" - ) - - # 6. single_blocks -> single_transformer_blocks - for i in range(40): # 40 single blocks - block_prefix = f"single_transformer_blocks.{i}." - - # norm - converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.linear.weight" - ) - converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.linear.bias" - ) - - # attention Q, K, V - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_q.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_q.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_k.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_k.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_v.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_v.bias" - ) - - # attention norms - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"single_blocks.{i}.q_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"single_blocks.{i}.k_norm.weight" - ) - - # MLP projection - converted_state_dict[f"{block_prefix}proj_mlp.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_mlp.weight" - ) - converted_state_dict[f"{block_prefix}proj_mlp.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear1_mlp.bias" - ) - - # output projection - converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.fc.weight" - ) - converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.fc.bias" - ) - - # 7. final_layer -> norm_out + proj_out - converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - shift_w, scale_w = original_state_dict.pop("final_layer.adaLN_modulation.1.weight").chunk(2, dim=0) - shift_b, scale_b = original_state_dict.pop("final_layer.adaLN_modulation.1.bias").chunk(2, dim=0) - converted_state_dict["norm_out.linear.weight"] = torch.cat([scale_w, shift_w], dim=0) - converted_state_dict["norm_out.linear.bias"] = torch.cat([scale_b, shift_b], dim=0) - - return converted_state_dict, original_state_dict - - -def convert_hunyuan_image_vae_checkpoint_to_diffusers( - original_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 -): - """Convert original VAE state dict to Diffusers format.""" - converted = {} - - # 1. Encoder - # 1.1 conv_in - converted["encoder.conv_in.weight"] = original_state_dict.pop("encoder.conv_in.weight") - converted["encoder.conv_in.bias"] = original_state_dict.pop("encoder.conv_in.bias") - - # 1.2 down blocks - diffusers_block_idx = 0 - - for block_index in range(len(block_out_channels)): - for resnet_block_index in range(layers_per_block): - orig_prefix = f"encoder.down.{block_index}.block.{resnet_block_index}" - diff_prefix = f"encoder.down_blocks.{diffusers_block_idx}" - - # resnet blocks - converted[f"{diff_prefix}.norm1.weight"] = original_state_dict.pop(f"{orig_prefix}.norm1.weight") - converted[f"{diff_prefix}.norm1.bias"] = original_state_dict.pop(f"{orig_prefix}.norm1.bias") - converted[f"{diff_prefix}.conv1.weight"] = original_state_dict.pop(f"{orig_prefix}.conv1.weight") - converted[f"{diff_prefix}.conv1.bias"] = original_state_dict.pop(f"{orig_prefix}.conv1.bias") - converted[f"{diff_prefix}.norm2.weight"] = original_state_dict.pop(f"{orig_prefix}.norm2.weight") - converted[f"{diff_prefix}.norm2.bias"] = original_state_dict.pop(f"{orig_prefix}.norm2.bias") - converted[f"{diff_prefix}.conv2.weight"] = original_state_dict.pop(f"{orig_prefix}.conv2.weight") - converted[f"{diff_prefix}.conv2.bias"] = original_state_dict.pop(f"{orig_prefix}.conv2.bias") - - diffusers_block_idx += 1 - - # downsample blocks - if f"encoder.down.{block_index}.downsample.conv.weight" in original_state_dict: - converted[f"encoder.down_blocks.{diffusers_block_idx}.conv.weight"] = original_state_dict.pop( - f"encoder.down.{block_index}.downsample.conv.weight" - ) - converted[f"encoder.down_blocks.{diffusers_block_idx}.conv.bias"] = original_state_dict.pop( - f"encoder.down.{block_index}.downsample.conv.bias" - ) - diffusers_block_idx += 1 - - # 1.3 mid block - converted["encoder.mid_block.resnets.0.norm1.weight"] = original_state_dict.pop("encoder.mid.block_1.norm1.weight") - converted["encoder.mid_block.resnets.0.norm1.bias"] = original_state_dict.pop("encoder.mid.block_1.norm1.bias") - converted["encoder.mid_block.resnets.0.conv1.weight"] = original_state_dict.pop("encoder.mid.block_1.conv1.weight") - converted["encoder.mid_block.resnets.0.conv1.bias"] = original_state_dict.pop("encoder.mid.block_1.conv1.bias") - converted["encoder.mid_block.resnets.0.norm2.weight"] = original_state_dict.pop("encoder.mid.block_1.norm2.weight") - converted["encoder.mid_block.resnets.0.norm2.bias"] = original_state_dict.pop("encoder.mid.block_1.norm2.bias") - converted["encoder.mid_block.resnets.0.conv2.weight"] = original_state_dict.pop("encoder.mid.block_1.conv2.weight") - converted["encoder.mid_block.resnets.0.conv2.bias"] = original_state_dict.pop("encoder.mid.block_1.conv2.bias") - - converted["encoder.mid_block.resnets.1.norm1.weight"] = original_state_dict.pop("encoder.mid.block_2.norm1.weight") - converted["encoder.mid_block.resnets.1.norm1.bias"] = original_state_dict.pop("encoder.mid.block_2.norm1.bias") - converted["encoder.mid_block.resnets.1.conv1.weight"] = original_state_dict.pop("encoder.mid.block_2.conv1.weight") - converted["encoder.mid_block.resnets.1.conv1.bias"] = original_state_dict.pop("encoder.mid.block_2.conv1.bias") - converted["encoder.mid_block.resnets.1.norm2.weight"] = original_state_dict.pop("encoder.mid.block_2.norm2.weight") - converted["encoder.mid_block.resnets.1.norm2.bias"] = original_state_dict.pop("encoder.mid.block_2.norm2.bias") - converted["encoder.mid_block.resnets.1.conv2.weight"] = original_state_dict.pop("encoder.mid.block_2.conv2.weight") - converted["encoder.mid_block.resnets.1.conv2.bias"] = original_state_dict.pop("encoder.mid.block_2.conv2.bias") - - converted["encoder.mid_block.attentions.0.norm.weight"] = original_state_dict.pop("encoder.mid.attn_1.norm.weight") - converted["encoder.mid_block.attentions.0.norm.bias"] = original_state_dict.pop("encoder.mid.attn_1.norm.bias") - converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") - converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") - converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") - converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") - converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") - converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") - converted["encoder.mid_block.attentions.0.proj.weight"] = original_state_dict.pop( - "encoder.mid.attn_1.proj_out.weight" - ) - converted["encoder.mid_block.attentions.0.proj.bias"] = original_state_dict.pop("encoder.mid.attn_1.proj_out.bias") - - # 1.4 encoder output - converted["encoder.norm_out.weight"] = original_state_dict.pop("encoder.norm_out.weight") - converted["encoder.norm_out.bias"] = original_state_dict.pop("encoder.norm_out.bias") - converted["encoder.conv_out.weight"] = original_state_dict.pop("encoder.conv_out.weight") - converted["encoder.conv_out.bias"] = original_state_dict.pop("encoder.conv_out.bias") - - # 2. Decoder - # 2.1 conv_in - converted["decoder.conv_in.weight"] = original_state_dict.pop("decoder.conv_in.weight") - converted["decoder.conv_in.bias"] = original_state_dict.pop("decoder.conv_in.bias") - - # 2.2 mid block - converted["decoder.mid_block.resnets.0.norm1.weight"] = original_state_dict.pop("decoder.mid.block_1.norm1.weight") - converted["decoder.mid_block.resnets.0.norm1.bias"] = original_state_dict.pop("decoder.mid.block_1.norm1.bias") - converted["decoder.mid_block.resnets.0.conv1.weight"] = original_state_dict.pop("decoder.mid.block_1.conv1.weight") - converted["decoder.mid_block.resnets.0.conv1.bias"] = original_state_dict.pop("decoder.mid.block_1.conv1.bias") - converted["decoder.mid_block.resnets.0.norm2.weight"] = original_state_dict.pop("decoder.mid.block_1.norm2.weight") - converted["decoder.mid_block.resnets.0.norm2.bias"] = original_state_dict.pop("decoder.mid.block_1.norm2.bias") - converted["decoder.mid_block.resnets.0.conv2.weight"] = original_state_dict.pop("decoder.mid.block_1.conv2.weight") - converted["decoder.mid_block.resnets.0.conv2.bias"] = original_state_dict.pop("decoder.mid.block_1.conv2.bias") - - converted["decoder.mid_block.resnets.1.norm1.weight"] = original_state_dict.pop("decoder.mid.block_2.norm1.weight") - converted["decoder.mid_block.resnets.1.norm1.bias"] = original_state_dict.pop("decoder.mid.block_2.norm1.bias") - converted["decoder.mid_block.resnets.1.conv1.weight"] = original_state_dict.pop("decoder.mid.block_2.conv1.weight") - converted["decoder.mid_block.resnets.1.conv1.bias"] = original_state_dict.pop("decoder.mid.block_2.conv1.bias") - converted["decoder.mid_block.resnets.1.norm2.weight"] = original_state_dict.pop("decoder.mid.block_2.norm2.weight") - converted["decoder.mid_block.resnets.1.norm2.bias"] = original_state_dict.pop("decoder.mid.block_2.norm2.bias") - converted["decoder.mid_block.resnets.1.conv2.weight"] = original_state_dict.pop("decoder.mid.block_2.conv2.weight") - converted["decoder.mid_block.resnets.1.conv2.bias"] = original_state_dict.pop("decoder.mid.block_2.conv2.bias") - - converted["decoder.mid_block.attentions.0.norm.weight"] = original_state_dict.pop("decoder.mid.attn_1.norm.weight") - converted["decoder.mid_block.attentions.0.norm.bias"] = original_state_dict.pop("decoder.mid.attn_1.norm.bias") - converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") - converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") - converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") - converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") - converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") - converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") - converted["decoder.mid_block.attentions.0.proj.weight"] = original_state_dict.pop( - "decoder.mid.attn_1.proj_out.weight" - ) - converted["decoder.mid_block.attentions.0.proj.bias"] = original_state_dict.pop("decoder.mid.attn_1.proj_out.bias") - - # 2.3 up blocks - diffusers_block_idx = 0 - for up_block_index in range(len(block_out_channels)): - # resnet blocks - for resnet_block_index in range(layers_per_block + 1): - orig_prefix = f"decoder.up.{up_block_index}.block.{resnet_block_index}" - diff_prefix = f"decoder.up_blocks.{diffusers_block_idx}" - - converted[f"{diff_prefix}.norm1.weight"] = original_state_dict.pop(f"{orig_prefix}.norm1.weight") - converted[f"{diff_prefix}.norm1.bias"] = original_state_dict.pop(f"{orig_prefix}.norm1.bias") - converted[f"{diff_prefix}.conv1.weight"] = original_state_dict.pop(f"{orig_prefix}.conv1.weight") - converted[f"{diff_prefix}.conv1.bias"] = original_state_dict.pop(f"{orig_prefix}.conv1.bias") - converted[f"{diff_prefix}.norm2.weight"] = original_state_dict.pop(f"{orig_prefix}.norm2.weight") - converted[f"{diff_prefix}.norm2.bias"] = original_state_dict.pop(f"{orig_prefix}.norm2.bias") - converted[f"{diff_prefix}.conv2.weight"] = original_state_dict.pop(f"{orig_prefix}.conv2.weight") - converted[f"{diff_prefix}.conv2.bias"] = original_state_dict.pop(f"{orig_prefix}.conv2.bias") - - diffusers_block_idx += 1 - - # upsample blocks - if f"decoder.up.{up_block_index}.upsample.conv.weight" in original_state_dict: - converted[f"decoder.up_blocks.{diffusers_block_idx}.conv.weight"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.weight" - ) - converted[f"decoder.up_blocks.{diffusers_block_idx}.conv.bias"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.bias" - ) - diffusers_block_idx += 1 - - # 2.4 decoder output - converted["decoder.norm_out.weight"] = original_state_dict.pop("decoder.norm_out.weight") - converted["decoder.norm_out.bias"] = original_state_dict.pop("decoder.norm_out.bias") - converted["decoder.conv_out.weight"] = original_state_dict.pop("decoder.conv_out.weight") - converted["decoder.conv_out.bias"] = original_state_dict.pop("decoder.conv_out.bias") - - return converted, original_state_dict - - -def convert_hunyuan_image_refiner_vae_checkpoint_to_diffusers( - original_state_dict, block_out_channels=[128, 256, 512, 1024, 1024], layers_per_block=2 -): - converted = {} - - # 1. Encoder - # 1.1 conv_in - converted["encoder.conv_in.conv.weight"] = original_state_dict.pop("encoder.conv_in.conv.weight") - converted["encoder.conv_in.conv.bias"] = original_state_dict.pop("encoder.conv_in.conv.bias") - - # 1.2 Down blocks - for down_block_index in range(len(block_out_channels)): # 0 to 4 - # ResNet blocks - for resnet_block_index in range(layers_per_block): # 0 to 1 - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm1.gamma") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( - original_state_dict.pop( - f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.weight" - ) - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.bias") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm2.gamma") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( - original_state_dict.pop( - f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.weight" - ) - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.bias") - ) - - # Downsample (if exists) - if f"encoder.down.{down_block_index}.downsample.conv.conv.weight" in original_state_dict: - converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.weight"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.weight") - ) - converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.bias") - ) - - # 1.3 Mid block - converted["encoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm1.gamma") - converted["encoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_1.conv1.conv.weight" - ) - converted["encoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_1.conv1.conv.bias" - ) - converted["encoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm2.gamma") - converted["encoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_1.conv2.conv.weight" - ) - converted["encoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_1.conv2.conv.bias" - ) - - converted["encoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm1.gamma") - converted["encoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_2.conv1.conv.weight" - ) - converted["encoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_2.conv1.conv.bias" - ) - converted["encoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm2.gamma") - converted["encoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_2.conv2.conv.weight" - ) - converted["encoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_2.conv2.conv.bias" - ) - - # Attention block - converted["encoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("encoder.mid.attn_1.norm.gamma") - converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") - converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") - converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") - converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") - converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") - converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") - converted["encoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( - "encoder.mid.attn_1.proj_out.weight" - ) - converted["encoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( - "encoder.mid.attn_1.proj_out.bias" - ) - - # 1.4 Encoder output - converted["encoder.norm_out.gamma"] = original_state_dict.pop("encoder.norm_out.gamma") - converted["encoder.conv_out.conv.weight"] = original_state_dict.pop("encoder.conv_out.conv.weight") - converted["encoder.conv_out.conv.bias"] = original_state_dict.pop("encoder.conv_out.conv.bias") - - # 2. Decoder - # 2.1 conv_in - converted["decoder.conv_in.conv.weight"] = original_state_dict.pop("decoder.conv_in.conv.weight") - converted["decoder.conv_in.conv.bias"] = original_state_dict.pop("decoder.conv_in.conv.bias") - - # 2.2 Mid block - converted["decoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm1.gamma") - converted["decoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_1.conv1.conv.weight" - ) - converted["decoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_1.conv1.conv.bias" - ) - converted["decoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm2.gamma") - converted["decoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_1.conv2.conv.weight" - ) - converted["decoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_1.conv2.conv.bias" - ) - - converted["decoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm1.gamma") - converted["decoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_2.conv1.conv.weight" - ) - converted["decoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_2.conv1.conv.bias" - ) - converted["decoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm2.gamma") - converted["decoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_2.conv2.conv.weight" - ) - converted["decoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_2.conv2.conv.bias" - ) - - # Decoder attention block - converted["decoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("decoder.mid.attn_1.norm.gamma") - converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") - converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") - converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") - converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") - converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") - converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") - converted["decoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( - "decoder.mid.attn_1.proj_out.weight" - ) - converted["decoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( - "decoder.mid.attn_1.proj_out.bias" - ) - - # 2.3 Up blocks - for up_block_index in range(len(block_out_channels)): # 0 to 5 - # ResNet blocks - for resnet_block_index in range(layers_per_block + 1): # 0 to 2 (decoder has 3 resnets per level) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm1.gamma") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.weight") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.bias") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm2.gamma") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.weight") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.bias") - ) - - # Upsample (if exists) - if f"decoder.up.{up_block_index}.upsample.conv.conv.weight" in original_state_dict: - converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.weight"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.conv.weight" - ) - converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.bias"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.conv.bias" - ) - - # 2.4 Decoder output - converted["decoder.norm_out.gamma"] = original_state_dict.pop("decoder.norm_out.gamma") - converted["decoder.conv_out.conv.weight"] = original_state_dict.pop("decoder.conv_out.conv.weight") - converted["decoder.conv_out.conv.bias"] = original_state_dict.pop("decoder.conv_out.conv.bias") - - return converted, original_state_dict - - -def main(args): - if args.model_type == "hunyuanimage2.1": - original_transformer_state_dict = load_original_transformer_checkpoint(args) - original_vae_state_dict = load_original_vae_checkpoint(args) - - transformer_config = { - "in_channels": 64, - "out_channels": 64, - "num_attention_heads": 28, - "attention_head_dim": 128, - "num_layers": 20, - "num_single_layers": 40, - "num_refiner_layers": 2, - "patch_size": (1, 1), - "qk_norm": "rms_norm", - "guidance_embeds": False, - "text_embed_dim": 3584, - "text_embed_2_dim": 1472, - "rope_theta": 256.0, - "rope_axes_dim": (64, 64), - } - - converted_transformer_state_dict, original_transformer_state_dict = ( - convert_hunyuan_image_transformer_checkpoint_to_diffusers( - original_transformer_state_dict, use_byt5=True, guidance_distilled=False - ) - ) - - if original_transformer_state_dict: - logger.warning( - f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" - ) - - transformer = HunyuanImageTransformer2DModel(**transformer_config) - missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - if missing_keys: - logger.warning(f"Missing keys for transformer: {missing_keys}") - if unexpected_key: - logger.warning(f"Unexpected keys for transformer: {unexpected_key}") - - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - vae_config_diffusers = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 64, - "block_out_channels": [128, 256, 512, 512, 1024, 1024], - "layers_per_block": 2, - "spatial_compression_ratio": 32, - "sample_size": 384, - "scaling_factor": 0.75289, - "downsample_match_channel": True, - "upsample_match_channel": True, - } - converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_vae_checkpoint_to_diffusers( - original_vae_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 - ) - if original_vae_state_dict: - logger.warning( - f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" - ) - - vae = AutoencoderKLHunyuanImage(**vae_config_diffusers) - missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) - - if missing_keys: - logger.warning(f"Missing keys for vae: {missing_keys}") - if unexpected_key: - logger.warning(f"Unexpected keys for vae: {unexpected_key}") - - vae.to(dtype).save_pretrained(f"{args.output_path}/vae") - - elif args.model_type == "hunyuanimage2.1-distilled": - original_transformer_state_dict = load_original_transformer_checkpoint(args) - original_vae_state_dict = load_original_vae_checkpoint(args) - - transformer_config = { - "in_channels": 64, - "out_channels": 64, - "num_attention_heads": 28, - "attention_head_dim": 128, - "num_layers": 20, - "num_single_layers": 40, - "num_refiner_layers": 2, - "patch_size": (1, 1), - "qk_norm": "rms_norm", - "guidance_embeds": True, - "text_embed_dim": 3584, - "text_embed_2_dim": 1472, - "rope_theta": 256.0, - "rope_axes_dim": (64, 64), - "use_meanflow": True, - } - - converted_transformer_state_dict, original_transformer_state_dict = ( - convert_hunyuan_image_transformer_checkpoint_to_diffusers( - original_transformer_state_dict, use_byt5=True, guidance_distilled=True, use_meanflow=True - ) - ) - - if original_transformer_state_dict: - logger.warning( - f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" - ) - - transformer = HunyuanImageTransformer2DModel(**transformer_config) - missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - if missing_keys: - logger.warning(f"Missing keys for transformer: {missing_keys}") - if unexpected_key: - logger.warning(f"Unexpected keys for transformer: {unexpected_key}") - - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - vae_config_diffusers = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 64, - "block_out_channels": [128, 256, 512, 512, 1024, 1024], - "layers_per_block": 2, - "spatial_compression_ratio": 32, - "sample_size": 384, - "scaling_factor": 0.75289, - "downsample_match_channel": True, - "upsample_match_channel": True, - } - converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_vae_checkpoint_to_diffusers( - original_vae_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 - ) - if original_vae_state_dict: - logger.warning( - f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" - ) - - vae = AutoencoderKLHunyuanImage(**vae_config_diffusers) - missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) - - if missing_keys: - logger.warning(f"Missing keys for vae: {missing_keys}") - if unexpected_key: - logger.warning(f"Unexpected keys for vae: {unexpected_key}") - - vae.to(dtype).save_pretrained(f"{args.output_path}/vae") - - elif args.model_type == "hunyuanimage-refiner": - original_transformer_state_dict = load_original_transformer_checkpoint(args) - original_vae_state_dict = load_original_refiner_vae_checkpoint(args) - - transformer_config = { - "in_channels": 128, - "out_channels": 64, - "num_layers": 20, - "num_single_layers": 40, - "rope_axes_dim": [16, 56, 56], - "num_attention_heads": 26, - "attention_head_dim": 128, - "mlp_ratio": 4, - "patch_size": (1, 1, 1), - "text_embed_dim": 3584, - "guidance_embeds": True, - } - converted_transformer_state_dict, original_transformer_state_dict = ( - convert_hunyuan_image_transformer_checkpoint_to_diffusers( - original_transformer_state_dict, use_byt5=False, guidance_distilled=True - ) - ) - if original_transformer_state_dict: - logger.warning( - f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" - ) - - transformer = HunyuanImageTransformer2DModel(**transformer_config) - missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) - if missing_keys: - logger.warning(f"Missing keys for transformer: {missing_keys}") - if unexpected_key: - logger.warning(f"Unexpected keys for transformer: {unexpected_key}") - - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - vae = AutoencoderKLHunyuanImageRefiner() - - converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_refiner_vae_checkpoint_to_diffusers( - original_vae_state_dict - ) - if original_vae_state_dict: - logger.warning( - f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" - ) - - missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) - logger.warning(f"Missing keys for vae: {missing_keys}") - logger.warning(f"Unexpected keys for vae: {unexpected_key}") - - vae.to(dtype).save_pretrained(f"{args.output_path}/vae") - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_hunyuan_video1_5_to_diffusers.py b/scripts/convert_hunyuan_video1_5_to_diffusers.py deleted file mode 100644 index 89e5cdb16956..000000000000 --- a/scripts/convert_hunyuan_video1_5_to_diffusers.py +++ /dev/null @@ -1,875 +0,0 @@ -import argparse -import json -import os -import pathlib - -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download, snapshot_download -from safetensors.torch import load_file -from transformers import ( - AutoModel, - AutoTokenizer, - SiglipImageProcessor, - SiglipVisionModel, - T5EncoderModel, -) - -from diffusers import ( - AutoencoderKLHunyuanVideo15, - ClassifierFreeGuidance, - FlowMatchEulerDiscreteScheduler, - HunyuanVideo15ImageToVideoPipeline, - HunyuanVideo15Pipeline, - HunyuanVideo15Transformer3DModel, -) - - -# to convert only transformer -""" -python scripts/convert_hunyuan_video1_5_to_diffusers.py \ - --original_state_dict_repo_id tencent/HunyuanVideo-1.5\ - --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers/transformer\ - --transformer_type 480p_t2v -""" - -# to convert full pipeline -""" -python scripts/convert_hunyuan_video1_5_to_diffusers.py \ - --original_state_dict_repo_id tencent/HunyuanVideo-1.5\ - --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers \ - --save_pipeline \ - --byt5_path /fsx/yiyi/hy15/text_encoder/Glyph-SDXL-v2\ - --transformer_type 480p_t2v -""" - - -TRANSFORMER_CONFIGS = { - "480p_t2v": { - "target_size": 640, - "task_type": "i2v", - }, - "720p_t2v": { - "target_size": 960, - "task_type": "t2v", - }, - "720p_i2v": { - "target_size": 960, - "task_type": "i2v", - }, - "480p_t2v_distilled": { - "target_size": 640, - "task_type": "t2v", - }, - "480p_i2v_distilled": { - "target_size": 640, - "task_type": "i2v", - }, - "720p_i2v_distilled": { - "target_size": 960, - "task_type": "i2v", - }, - "480p_i2v_step_distilled": { - "target_size": 640, - "task_type": "i2v", - "use_meanflow": True, - }, -} - -SCHEDULER_CONFIGS = { - "480p_t2v": { - "shift": 5.0, - }, - "480p_i2v": { - "shift": 5.0, - }, - "720p_t2v": { - "shift": 9.0, - }, - "720p_i2v": { - "shift": 7.0, - }, - "480p_t2v_distilled": { - "shift": 5.0, - }, - "480p_i2v_distilled": { - "shift": 5.0, - }, - "720p_i2v_distilled": { - "shift": 7.0, - }, - "480p_i2v_step_distilled": { - "shift": 7.0, - }, -} - -GUIDANCE_CONFIGS = { - "480p_t2v": { - "guidance_scale": 6.0, - }, - "480p_i2v": { - "guidance_scale": 6.0, - }, - "720p_t2v": { - "guidance_scale": 6.0, - }, - "720p_i2v": { - "guidance_scale": 6.0, - }, - "480p_t2v_distilled": { - "guidance_scale": 1.0, - }, - "480p_i2v_distilled": { - "guidance_scale": 1.0, - }, - "720p_i2v_distilled": { - "guidance_scale": 1.0, - }, - "480p_i2v_step_distilled": { - "guidance_scale": 1.0, - }, -} - - -def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=None): - """ - Convert HunyuanVideo 1.5 original checkpoint to Diffusers format. - """ - converted_state_dict = {} - - # 1. time_embed.timestep_embedder <- time_in - converted_state_dict["time_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "time_in.mlp.0.weight" - ) - converted_state_dict["time_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop("time_in.mlp.0.bias") - converted_state_dict["time_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "time_in.mlp.2.weight" - ) - converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("time_in.mlp.2.bias") - - if config.use_meanflow: - converted_state_dict["time_embed.timestep_embedder_r.linear_1.weight"] = original_state_dict.pop( - "time_r_in.mlp.0.weight" - ) - converted_state_dict["time_embed.timestep_embedder_r.linear_1.bias"] = original_state_dict.pop( - "time_r_in.mlp.0.bias" - ) - converted_state_dict["time_embed.timestep_embedder_r.linear_2.weight"] = original_state_dict.pop( - "time_r_in.mlp.2.weight" - ) - converted_state_dict["time_embed.timestep_embedder_r.linear_2.bias"] = original_state_dict.pop( - "time_r_in.mlp.2.bias" - ) - - # 2. context_embedder.time_text_embed.timestep_embedder <- txt_in.t_embedder - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.weight"] = ( - original_state_dict.pop("txt_in.t_embedder.mlp.0.weight") - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "txt_in.t_embedder.mlp.0.bias" - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.weight"] = ( - original_state_dict.pop("txt_in.t_embedder.mlp.2.weight") - ) - converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "txt_in.t_embedder.mlp.2.bias" - ) - - # 3. context_embedder.time_text_embed.text_embedder <- txt_in.c_embedder - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( - "txt_in.c_embedder.linear_1.weight" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( - "txt_in.c_embedder.linear_1.bias" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( - "txt_in.c_embedder.linear_2.weight" - ) - converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( - "txt_in.c_embedder.linear_2.bias" - ) - - # 4. context_embedder.proj_in <- txt_in.input_embedder - converted_state_dict["context_embedder.proj_in.weight"] = original_state_dict.pop("txt_in.input_embedder.weight") - converted_state_dict["context_embedder.proj_in.bias"] = original_state_dict.pop("txt_in.input_embedder.bias") - - # 5. context_embedder.token_refiner <- txt_in.individual_token_refiner - num_refiner_blocks = 2 - for i in range(num_refiner_blocks): - block_prefix = f"context_embedder.token_refiner.refiner_blocks.{i}." - orig_prefix = f"txt_in.individual_token_refiner.blocks.{i}." - - # norm1 - converted_state_dict[f"{block_prefix}norm1.weight"] = original_state_dict.pop(f"{orig_prefix}norm1.weight") - converted_state_dict[f"{block_prefix}norm1.bias"] = original_state_dict.pop(f"{orig_prefix}norm1.bias") - - # Split self_attn_qkv into to_q, to_k, to_v - qkv_weight = original_state_dict.pop(f"{orig_prefix}self_attn_qkv.weight") - qkv_bias = original_state_dict.pop(f"{orig_prefix}self_attn_qkv.bias") - q, k, v = torch.chunk(qkv_weight, 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) - - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = q - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = q_bias - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = k - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = k_bias - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = v - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = v_bias - - # self_attn_proj -> attn.to_out.0 - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"{orig_prefix}self_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"{orig_prefix}self_attn_proj.bias" - ) - - # norm2 - converted_state_dict[f"{block_prefix}norm2.weight"] = original_state_dict.pop(f"{orig_prefix}norm2.weight") - converted_state_dict[f"{block_prefix}norm2.bias"] = original_state_dict.pop(f"{orig_prefix}norm2.bias") - - # mlp -> ff - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( - f"{orig_prefix}mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( - f"{orig_prefix}mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"{orig_prefix}mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop(f"{orig_prefix}mlp.fc2.bias") - - # adaLN_modulation -> norm_out - converted_state_dict[f"{block_prefix}norm_out.linear.weight"] = original_state_dict.pop( - f"{orig_prefix}adaLN_modulation.1.weight" - ) - converted_state_dict[f"{block_prefix}norm_out.linear.bias"] = original_state_dict.pop( - f"{orig_prefix}adaLN_modulation.1.bias" - ) - - # 6. context_embedder_2 <- byt5_in - converted_state_dict["context_embedder_2.norm.weight"] = original_state_dict.pop("byt5_in.layernorm.weight") - converted_state_dict["context_embedder_2.norm.bias"] = original_state_dict.pop("byt5_in.layernorm.bias") - converted_state_dict["context_embedder_2.linear_1.weight"] = original_state_dict.pop("byt5_in.fc1.weight") - converted_state_dict["context_embedder_2.linear_1.bias"] = original_state_dict.pop("byt5_in.fc1.bias") - converted_state_dict["context_embedder_2.linear_2.weight"] = original_state_dict.pop("byt5_in.fc2.weight") - converted_state_dict["context_embedder_2.linear_2.bias"] = original_state_dict.pop("byt5_in.fc2.bias") - converted_state_dict["context_embedder_2.linear_3.weight"] = original_state_dict.pop("byt5_in.fc3.weight") - converted_state_dict["context_embedder_2.linear_3.bias"] = original_state_dict.pop("byt5_in.fc3.bias") - - # 7. image_embedder <- vision_in - converted_state_dict["image_embedder.norm_in.weight"] = original_state_dict.pop("vision_in.proj.0.weight") - converted_state_dict["image_embedder.norm_in.bias"] = original_state_dict.pop("vision_in.proj.0.bias") - converted_state_dict["image_embedder.linear_1.weight"] = original_state_dict.pop("vision_in.proj.1.weight") - converted_state_dict["image_embedder.linear_1.bias"] = original_state_dict.pop("vision_in.proj.1.bias") - converted_state_dict["image_embedder.linear_2.weight"] = original_state_dict.pop("vision_in.proj.3.weight") - converted_state_dict["image_embedder.linear_2.bias"] = original_state_dict.pop("vision_in.proj.3.bias") - converted_state_dict["image_embedder.norm_out.weight"] = original_state_dict.pop("vision_in.proj.4.weight") - converted_state_dict["image_embedder.norm_out.bias"] = original_state_dict.pop("vision_in.proj.4.bias") - - # 8. x_embedder <- img_in - converted_state_dict["x_embedder.proj.weight"] = original_state_dict.pop("img_in.proj.weight") - converted_state_dict["x_embedder.proj.bias"] = original_state_dict.pop("img_in.proj.bias") - - # 9. cond_type_embed <- cond_type_embedding - converted_state_dict["cond_type_embed.weight"] = original_state_dict.pop("cond_type_embedding.weight") - - # 10. transformer_blocks <- double_blocks - num_layers = 54 - for i in range(num_layers): - block_prefix = f"transformer_blocks.{i}." - orig_prefix = f"double_blocks.{i}." - - # norm1 (img_mod) - converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( - f"{orig_prefix}img_mod.linear.weight" - ) - converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( - f"{orig_prefix}img_mod.linear.bias" - ) - - # norm1_context (txt_mod) - converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_mod.linear.weight" - ) - converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_mod.linear.bias" - ) - - # img attention (to_q, to_k, to_v) - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_q.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( - f"{orig_prefix}img_attn_q.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_k.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( - f"{orig_prefix}img_attn_k.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_v.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( - f"{orig_prefix}img_attn_v.bias" - ) - - # img attention qk norm - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_q_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_k_norm.weight" - ) - - # img attention output projection - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"{orig_prefix}img_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"{orig_prefix}img_attn_proj.bias" - ) - - # txt attention (add_q_proj, add_k_proj, add_v_proj) - converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_q.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_q.bias" - ) - converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_k.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_k.bias" - ) - converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_v.weight" - ) - converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_v.bias" - ) - - # txt attention qk norm - converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_q_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_k_norm.weight" - ) - - # txt attention output projection - converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_attn_proj.bias" - ) - - # norm2 and norm2_context (these don't have weights in the original, they're LayerNorm with elementwise_affine=False) - # So we skip them - - # img_mlp -> ff - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( - f"{orig_prefix}img_mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( - f"{orig_prefix}img_mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"{orig_prefix}img_mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( - f"{orig_prefix}img_mlp.fc2.bias" - ) - - # txt_mlp -> ff_context - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_mlp.fc1.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_mlp.fc1.bias" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( - f"{orig_prefix}txt_mlp.fc2.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( - f"{orig_prefix}txt_mlp.fc2.bias" - ) - - # 11. norm_out and proj_out <- final_layer - converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.weight") - ) - converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.bias") - ) - converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - - return converted_state_dict - - -def convert_hunyuan_video_15_vae_checkpoint_to_diffusers( - original_state_dict, block_out_channels=[128, 256, 512, 1024, 1024], layers_per_block=2 -): - converted = {} - - # 1. Encoder - # 1.1 conv_in - converted["encoder.conv_in.conv.weight"] = original_state_dict.pop("encoder.conv_in.conv.weight") - converted["encoder.conv_in.conv.bias"] = original_state_dict.pop("encoder.conv_in.conv.bias") - - # 1.2 Down blocks - for down_block_index in range(len(block_out_channels)): # 0 to 4 - # ResNet blocks - for resnet_block_index in range(layers_per_block): # 0 to 1 - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm1.gamma") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( - original_state_dict.pop( - f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.weight" - ) - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.bias") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm2.gamma") - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( - original_state_dict.pop( - f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.weight" - ) - ) - converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.bias") - ) - - # Downsample (if exists) - if f"encoder.down.{down_block_index}.downsample.conv.conv.weight" in original_state_dict: - converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.weight"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.weight") - ) - converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.bias"] = ( - original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.bias") - ) - - # 1.3 Mid block - converted["encoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm1.gamma") - converted["encoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_1.conv1.conv.weight" - ) - converted["encoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_1.conv1.conv.bias" - ) - converted["encoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm2.gamma") - converted["encoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_1.conv2.conv.weight" - ) - converted["encoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_1.conv2.conv.bias" - ) - - converted["encoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm1.gamma") - converted["encoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_2.conv1.conv.weight" - ) - converted["encoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_2.conv1.conv.bias" - ) - converted["encoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm2.gamma") - converted["encoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( - "encoder.mid.block_2.conv2.conv.weight" - ) - converted["encoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( - "encoder.mid.block_2.conv2.conv.bias" - ) - - # Attention block - converted["encoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("encoder.mid.attn_1.norm.gamma") - converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") - converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") - converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") - converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") - converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") - converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") - converted["encoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( - "encoder.mid.attn_1.proj_out.weight" - ) - converted["encoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( - "encoder.mid.attn_1.proj_out.bias" - ) - - # 1.4 Encoder output - converted["encoder.norm_out.gamma"] = original_state_dict.pop("encoder.norm_out.gamma") - converted["encoder.conv_out.conv.weight"] = original_state_dict.pop("encoder.conv_out.conv.weight") - converted["encoder.conv_out.conv.bias"] = original_state_dict.pop("encoder.conv_out.conv.bias") - - # 2. Decoder - # 2.1 conv_in - converted["decoder.conv_in.conv.weight"] = original_state_dict.pop("decoder.conv_in.conv.weight") - converted["decoder.conv_in.conv.bias"] = original_state_dict.pop("decoder.conv_in.conv.bias") - - # 2.2 Mid block - converted["decoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm1.gamma") - converted["decoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_1.conv1.conv.weight" - ) - converted["decoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_1.conv1.conv.bias" - ) - converted["decoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm2.gamma") - converted["decoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_1.conv2.conv.weight" - ) - converted["decoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_1.conv2.conv.bias" - ) - - converted["decoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm1.gamma") - converted["decoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_2.conv1.conv.weight" - ) - converted["decoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_2.conv1.conv.bias" - ) - converted["decoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm2.gamma") - converted["decoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( - "decoder.mid.block_2.conv2.conv.weight" - ) - converted["decoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( - "decoder.mid.block_2.conv2.conv.bias" - ) - - # Decoder attention block - converted["decoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("decoder.mid.attn_1.norm.gamma") - converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") - converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") - converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") - converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") - converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") - converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") - converted["decoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( - "decoder.mid.attn_1.proj_out.weight" - ) - converted["decoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( - "decoder.mid.attn_1.proj_out.bias" - ) - - # 2.3 Up blocks - for up_block_index in range(len(block_out_channels)): # 0 to 5 - # ResNet blocks - for resnet_block_index in range(layers_per_block + 1): # 0 to 2 (decoder has 3 resnets per level) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm1.gamma") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.weight") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.bias") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm2.gamma") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.weight") - ) - converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( - original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.bias") - ) - - # Upsample (if exists) - if f"decoder.up.{up_block_index}.upsample.conv.conv.weight" in original_state_dict: - converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.weight"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.conv.weight" - ) - converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.bias"] = original_state_dict.pop( - f"decoder.up.{up_block_index}.upsample.conv.conv.bias" - ) - - # 2.4 Decoder output - converted["decoder.norm_out.gamma"] = original_state_dict.pop("decoder.norm_out.gamma") - converted["decoder.conv_out.conv.weight"] = original_state_dict.pop("decoder.conv_out.conv.weight") - converted["decoder.conv_out.conv.bias"] = original_state_dict.pop("decoder.conv_out.conv.bias") - - return converted - - -def load_sharded_safetensors(dir: pathlib.Path): - file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) - state_dict = {} - for path in file_paths: - state_dict.update(load_file(path)) - return state_dict - - -def load_original_transformer_state_dict(args): - if args.original_state_dict_repo_id is not None: - model_dir = snapshot_download( - args.original_state_dict_repo_id, - repo_type="model", - allow_patterns="transformer/" + args.transformer_type + "/*", - ) - elif args.original_state_dict_folder is not None: - model_dir = pathlib.Path(args.original_state_dict_folder) - else: - raise ValueError("Please provide either `original_state_dict_repo_id` or `original_state_dict_folder`") - model_dir = pathlib.Path(model_dir) - model_dir = model_dir / "transformer" / args.transformer_type - return load_sharded_safetensors(model_dir) - - -def load_original_vae_state_dict(args): - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download( - repo_id=args.original_state_dict_repo_id, filename="vae/diffusion_pytorch_model.safetensors" - ) - elif args.original_state_dict_folder is not None: - model_dir = pathlib.Path(args.original_state_dict_folder) - ckpt_path = model_dir / "vae/diffusion_pytorch_model.safetensors" - else: - raise ValueError("Please provide either `original_state_dict_repo_id` or `original_state_dict_folder`") - - original_state_dict = load_file(ckpt_path) - return original_state_dict - - -def convert_transformer(args): - original_state_dict = load_original_transformer_state_dict(args) - - config = TRANSFORMER_CONFIGS[args.transformer_type] - with init_empty_weights(): - transformer = HunyuanVideo15Transformer3DModel(**config) - state_dict = convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=transformer.config) - transformer.load_state_dict(state_dict, strict=True, assign=True) - - return transformer - - -def convert_vae(args): - original_state_dict = load_original_vae_state_dict(args) - with init_empty_weights(): - vae = AutoencoderKLHunyuanVideo15() - state_dict = convert_hunyuan_video_15_vae_checkpoint_to_diffusers(original_state_dict) - vae.load_state_dict(state_dict, strict=True, assign=True) - return vae - - -def load_mllm(): - print(" loading from Qwen/Qwen2.5-VL-7B-Instruct") - text_encoder = AutoModel.from_pretrained( - "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True - ) - if hasattr(text_encoder, "language_model"): - text_encoder = text_encoder.language_model - tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", padding_side="right") - return text_encoder, tokenizer - - -# copied from https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/910da2a829c484ea28982e8cff3bbc2cacdf1681/hyvideo/models/text_encoders/byT5/__init__.py#L89 -def add_special_token( - tokenizer, - text_encoder, - add_color=True, - add_font=True, - multilingual=True, - color_ann_path="assets/color_idx.json", - font_ann_path="assets/multilingual_10-lang_idx.json", -): - """ - Add special tokens for color and font to tokenizer and text encoder. - - Args: - tokenizer: Huggingface tokenizer. - text_encoder: Huggingface T5 encoder. - add_color (bool): Whether to add color tokens. - add_font (bool): Whether to add font tokens. - color_ann_path (str): Path to color annotation JSON. - font_ann_path (str): Path to font annotation JSON. - multilingual (bool): Whether to use multilingual font tokens. - """ - with open(font_ann_path, "r") as f: - idx_font_dict = json.load(f) - with open(color_ann_path, "r") as f: - idx_color_dict = json.load(f) - - if multilingual: - font_token = [f"<{font_code[:2]}-font-{idx_font_dict[font_code]}>" for font_code in idx_font_dict] - else: - font_token = [f"<font-{i}>" for i in range(len(idx_font_dict))] - color_token = [f"<color-{i}>" for i in range(len(idx_color_dict))] - additional_special_tokens = [] - if add_color: - additional_special_tokens += color_token - if add_font: - additional_special_tokens += font_token - - tokenizer.add_tokens(additional_special_tokens, special_tokens=True) - # Set mean_resizing=False to avoid PyTorch LAPACK dependency - text_encoder.resize_token_embeddings(len(tokenizer), mean_resizing=False) - - -def load_byt5(args): - """ - Load ByT5 encoder with Glyph-SDXL-v2 weights and save in HuggingFace format. - """ - - # 1. Load base tokenizer and encoder - tokenizer = AutoTokenizer.from_pretrained("google/byt5-small") - - # Load as T5EncoderModel - encoder = T5EncoderModel.from_pretrained("google/byt5-small") - - byt5_checkpoint_path = os.path.join(args.byt5_path, "checkpoints/byt5_model.pt") - color_ann_path = os.path.join(args.byt5_path, "assets/color_idx.json") - font_ann_path = os.path.join(args.byt5_path, "assets/multilingual_10-lang_idx.json") - - # 2. Add special tokens - add_special_token( - tokenizer=tokenizer, - text_encoder=encoder, - add_color=True, - add_font=True, - color_ann_path=color_ann_path, - font_ann_path=font_ann_path, - multilingual=True, - ) - - # 3. Load Glyph-SDXL-v2 checkpoint - print(f"\n3. Loading Glyph-SDXL-v2 checkpoint: {byt5_checkpoint_path}") - checkpoint = torch.load(byt5_checkpoint_path, map_location="cpu") - - # Handle different checkpoint formats - if "state_dict" in checkpoint: - state_dict = checkpoint["state_dict"] - else: - state_dict = checkpoint - - # add 'encoder.' prefix to the keys - # Remove 'module.text_tower.encoder.' prefix if present - cleaned_state_dict = {} - for key, value in state_dict.items(): - if key.startswith("module.text_tower.encoder."): - new_key = "encoder." + key[len("module.text_tower.encoder.") :] - cleaned_state_dict[new_key] = value - else: - new_key = "encoder." + key - cleaned_state_dict[new_key] = value - - # 4. Load weights - missing_keys, unexpected_keys = encoder.load_state_dict(cleaned_state_dict, strict=False) - if unexpected_keys: - raise ValueError(f"Unexpected keys: {unexpected_keys}") - if "shared.weight" in missing_keys: - print(" Missing shared.weight as expected") - missing_keys.remove("shared.weight") - if missing_keys: - raise ValueError(f"Missing keys: {missing_keys}") - - return encoder, tokenizer - - -def load_siglip(): - image_encoder = SiglipVisionModel.from_pretrained( - "black-forest-labs/FLUX.1-Redux-dev", subfolder="image_encoder", torch_dtype=torch.bfloat16 - ) - feature_extractor = SiglipImageProcessor.from_pretrained( - "black-forest-labs/FLUX.1-Redux-dev", subfolder="feature_extractor" - ) - return image_encoder, feature_extractor - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--original_state_dict_repo_id", type=str, default=None, help="Path to original hub_id for the model" - ) - parser.add_argument( - "--original_state_dict_folder", type=str, default=None, help="Local folder name of the original state dict" - ) - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model(s) should be saved") - parser.add_argument("--transformer_type", type=str, default="480p_i2v", choices=list(TRANSFORMER_CONFIGS.keys())) - parser.add_argument( - "--byt5_path", - type=str, - default=None, - help=( - "path to the downloaded byt5 checkpoint & assets. " - "Note: They use Glyph-SDXL-v2 as byt5 encoder. You can download from modelscope like: " - "`modelscope download --model AI-ModelScope/Glyph-SDXL-v2 --local_dir ./ckpts/text_encoder/Glyph-SDXL-v2` " - "or manually download following the instructions on " - "https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/910da2a829c484ea28982e8cff3bbc2cacdf1681/checkpoints-download.md. " - "The path should point to the Glyph-SDXL-v2 folder which should contain an `assets` folder and a `checkpoints` folder, " - "like: Glyph-SDXL-v2/assets/... and Glyph-SDXL-v2/checkpoints/byt5_model.pt" - ), - ) - parser.add_argument("--save_pipeline", action="store_true") - return parser.parse_args() - - -if __name__ == "__main__": - args = get_args() - - if args.save_pipeline and args.byt5_path is None: - raise ValueError("Please provide --byt5_path when saving pipeline") - - transformer = None - - transformer = convert_transformer(args) - if not args.save_pipeline: - transformer.save_pretrained(args.output_path, safe_serialization=True) - else: - task_type = transformer.config.task_type - - vae = convert_vae(args) - - text_encoder, tokenizer = load_mllm() - text_encoder_2, tokenizer_2 = load_byt5(args) - - flow_shift = SCHEDULER_CONFIGS[args.transformer_type]["shift"] - scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) - - guidance_scale = GUIDANCE_CONFIGS[args.transformer_type]["guidance_scale"] - guider = ClassifierFreeGuidance(guidance_scale=guidance_scale) - - if task_type == "i2v": - image_encoder, feature_extractor = load_siglip() - pipeline = HunyuanVideo15ImageToVideoPipeline( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - transformer=transformer, - guider=guider, - scheduler=scheduler, - image_encoder=image_encoder, - feature_extractor=feature_extractor, - ) - elif task_type == "t2v": - pipeline = HunyuanVideo15Pipeline( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - transformer=transformer, - guider=guider, - scheduler=scheduler, - ) - else: - raise ValueError(f"Task type {task_type} is not supported") - - pipeline.save_pretrained(args.output_path, safe_serialization=True) diff --git a/scripts/convert_hunyuan_video_to_diffusers.py b/scripts/convert_hunyuan_video_to_diffusers.py deleted file mode 100644 index fb604c98b65b..000000000000 --- a/scripts/convert_hunyuan_video_to_diffusers.py +++ /dev/null @@ -1,353 +0,0 @@ -import argparse -from typing import Any, Dict - -import torch -from accelerate import init_empty_weights -from transformers import ( - AutoModel, - AutoTokenizer, - CLIPImageProcessor, - CLIPTextModel, - CLIPTokenizer, - LlavaForConditionalGeneration, -) - -from diffusers import ( - AutoencoderKLHunyuanVideo, - FlowMatchEulerDiscreteScheduler, - HunyuanVideoImageToVideoPipeline, - HunyuanVideoPipeline, - HunyuanVideoTransformer3DModel, -) - - -def remap_norm_scale_shift_(key, state_dict): - weight = state_dict.pop(key) - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - state_dict[key.replace("final_layer.adaLN_modulation.1", "norm_out.linear")] = new_weight - - -def remap_txt_in_(key, state_dict): - def rename_key(key): - new_key = key.replace("individual_token_refiner.blocks", "token_refiner.refiner_blocks") - new_key = new_key.replace("adaLN_modulation.1", "norm_out.linear") - new_key = new_key.replace("txt_in", "context_embedder") - new_key = new_key.replace("t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1") - new_key = new_key.replace("t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2") - new_key = new_key.replace("c_embedder", "time_text_embed.text_embedder") - new_key = new_key.replace("mlp", "ff") - return new_key - - if "self_attn_qkv" in key: - weight = state_dict.pop(key) - to_q, to_k, to_v = weight.chunk(3, dim=0) - state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_q"))] = to_q - state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_k"))] = to_k - state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_v"))] = to_v - else: - state_dict[rename_key(key)] = state_dict.pop(key) - - -def remap_img_attn_qkv_(key, state_dict): - weight = state_dict.pop(key) - to_q, to_k, to_v = weight.chunk(3, dim=0) - state_dict[key.replace("img_attn_qkv", "attn.to_q")] = to_q - state_dict[key.replace("img_attn_qkv", "attn.to_k")] = to_k - state_dict[key.replace("img_attn_qkv", "attn.to_v")] = to_v - - -def remap_txt_attn_qkv_(key, state_dict): - weight = state_dict.pop(key) - to_q, to_k, to_v = weight.chunk(3, dim=0) - state_dict[key.replace("txt_attn_qkv", "attn.add_q_proj")] = to_q - state_dict[key.replace("txt_attn_qkv", "attn.add_k_proj")] = to_k - state_dict[key.replace("txt_attn_qkv", "attn.add_v_proj")] = to_v - - -def remap_single_transformer_blocks_(key, state_dict): - hidden_size = 3072 - - if "linear1.weight" in key: - linear1_weight = state_dict.pop(key) - split_size = (hidden_size, hidden_size, hidden_size, linear1_weight.size(0) - 3 * hidden_size) - q, k, v, mlp = torch.split(linear1_weight, split_size, dim=0) - new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.weight") - state_dict[f"{new_key}.attn.to_q.weight"] = q - state_dict[f"{new_key}.attn.to_k.weight"] = k - state_dict[f"{new_key}.attn.to_v.weight"] = v - state_dict[f"{new_key}.proj_mlp.weight"] = mlp - - elif "linear1.bias" in key: - linear1_bias = state_dict.pop(key) - split_size = (hidden_size, hidden_size, hidden_size, linear1_bias.size(0) - 3 * hidden_size) - q_bias, k_bias, v_bias, mlp_bias = torch.split(linear1_bias, split_size, dim=0) - new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.bias") - state_dict[f"{new_key}.attn.to_q.bias"] = q_bias - state_dict[f"{new_key}.attn.to_k.bias"] = k_bias - state_dict[f"{new_key}.attn.to_v.bias"] = v_bias - state_dict[f"{new_key}.proj_mlp.bias"] = mlp_bias - - else: - new_key = key.replace("single_blocks", "single_transformer_blocks") - new_key = new_key.replace("linear2", "proj_out") - new_key = new_key.replace("q_norm", "attn.norm_q") - new_key = new_key.replace("k_norm", "attn.norm_k") - state_dict[new_key] = state_dict.pop(key) - - -TRANSFORMER_KEYS_RENAME_DICT = { - "img_in": "x_embedder", - "time_in.mlp.0": "time_text_embed.timestep_embedder.linear_1", - "time_in.mlp.2": "time_text_embed.timestep_embedder.linear_2", - "guidance_in.mlp.0": "time_text_embed.guidance_embedder.linear_1", - "guidance_in.mlp.2": "time_text_embed.guidance_embedder.linear_2", - "vector_in.in_layer": "time_text_embed.text_embedder.linear_1", - "vector_in.out_layer": "time_text_embed.text_embedder.linear_2", - "double_blocks": "transformer_blocks", - "img_attn_q_norm": "attn.norm_q", - "img_attn_k_norm": "attn.norm_k", - "img_attn_proj": "attn.to_out.0", - "txt_attn_q_norm": "attn.norm_added_q", - "txt_attn_k_norm": "attn.norm_added_k", - "txt_attn_proj": "attn.to_add_out", - "img_mod.linear": "norm1.linear", - "img_norm1": "norm1.norm", - "img_norm2": "norm2", - "img_mlp": "ff", - "txt_mod.linear": "norm1_context.linear", - "txt_norm1": "norm1.norm", - "txt_norm2": "norm2_context", - "txt_mlp": "ff_context", - "self_attn_proj": "attn.to_out.0", - "modulation.linear": "norm.linear", - "pre_norm": "norm.norm", - "final_layer.norm_final": "norm_out.norm", - "final_layer.linear": "proj_out", - "fc1": "net.0.proj", - "fc2": "net.2", - "input_embedder": "proj_in", -} - -TRANSFORMER_SPECIAL_KEYS_REMAP = { - "txt_in": remap_txt_in_, - "img_attn_qkv": remap_img_attn_qkv_, - "txt_attn_qkv": remap_txt_attn_qkv_, - "single_blocks": remap_single_transformer_blocks_, - "final_layer.adaLN_modulation.1": remap_norm_scale_shift_, -} - -VAE_KEYS_RENAME_DICT = {} - -VAE_SPECIAL_KEYS_REMAP = {} - - -TRANSFORMER_CONFIGS = { - "HYVideo-T/2-cfgdistill": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 24, - "attention_head_dim": 128, - "num_layers": 20, - "num_single_layers": 40, - "num_refiner_layers": 2, - "mlp_ratio": 4.0, - "patch_size": 2, - "patch_size_t": 1, - "qk_norm": "rms_norm", - "guidance_embeds": True, - "text_embed_dim": 4096, - "pooled_projection_dim": 768, - "rope_theta": 256.0, - "rope_axes_dim": (16, 56, 56), - "image_condition_type": None, - }, - "HYVideo-T/2-I2V-33ch": { - "in_channels": 16 * 2 + 1, - "out_channels": 16, - "num_attention_heads": 24, - "attention_head_dim": 128, - "num_layers": 20, - "num_single_layers": 40, - "num_refiner_layers": 2, - "mlp_ratio": 4.0, - "patch_size": 2, - "patch_size_t": 1, - "qk_norm": "rms_norm", - "guidance_embeds": False, - "text_embed_dim": 4096, - "pooled_projection_dim": 768, - "rope_theta": 256.0, - "rope_axes_dim": (16, 56, 56), - "image_condition_type": "latent_concat", - }, - "HYVideo-T/2-I2V-16ch": { - "in_channels": 16, - "out_channels": 16, - "num_attention_heads": 24, - "attention_head_dim": 128, - "num_layers": 20, - "num_single_layers": 40, - "num_refiner_layers": 2, - "mlp_ratio": 4.0, - "patch_size": 2, - "patch_size_t": 1, - "qk_norm": "rms_norm", - "guidance_embeds": True, - "text_embed_dim": 4096, - "pooled_projection_dim": 768, - "rope_theta": 256.0, - "rope_axes_dim": (16, 56, 56), - "image_condition_type": "token_replace", - }, -} - - -def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: - state_dict = saved_dict - if "model" in saved_dict.keys(): - state_dict = state_dict["model"] - if "module" in saved_dict.keys(): - state_dict = state_dict["module"] - if "state_dict" in saved_dict.keys(): - state_dict = state_dict["state_dict"] - return state_dict - - -def convert_transformer(ckpt_path: str, transformer_type: str): - original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True)) - config = TRANSFORMER_CONFIGS[transformer_type] - - with init_empty_weights(): - transformer = HunyuanVideoTransformer3DModel(**config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - transformer.load_state_dict(original_state_dict, strict=True, assign=True) - return transformer - - -def convert_vae(ckpt_path: str): - original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True)) - - with init_empty_weights(): - vae = AutoencoderKLHunyuanVideo() - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True, assign=True) - return vae - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" - ) - parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original VAE checkpoint") - parser.add_argument("--text_encoder_path", type=str, default=None, help="Path to original llama checkpoint") - parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to original llama tokenizer") - parser.add_argument("--text_encoder_2_path", type=str, default=None, help="Path to original clip checkpoint") - parser.add_argument("--save_pipeline", action="store_true") - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.") - parser.add_argument( - "--transformer_type", type=str, default="HYVideo-T/2-cfgdistill", choices=list(TRANSFORMER_CONFIGS.keys()) - ) - parser.add_argument("--flow_shift", type=float, default=7.0) - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - args = get_args() - - transformer = None - dtype = DTYPE_MAPPING[args.dtype] - - if args.save_pipeline: - assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None - assert args.text_encoder_path is not None - assert args.tokenizer_path is not None - assert args.text_encoder_2_path is not None - - if args.transformer_ckpt_path is not None: - transformer = convert_transformer(args.transformer_ckpt_path, args.transformer_type) - transformer = transformer.to(dtype=dtype) - if not args.save_pipeline: - transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - if args.vae_ckpt_path is not None: - vae = convert_vae(args.vae_ckpt_path) - if not args.save_pipeline: - vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - if args.save_pipeline: - if args.transformer_type == "HYVideo-T/2-cfgdistill": - text_encoder = AutoModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.float16) - tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right") - text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16) - tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path) - scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift) - - pipe = HunyuanVideoPipeline( - transformer=transformer, - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - text_encoder_2=text_encoder_2, - tokenizer_2=tokenizer_2, - scheduler=scheduler, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - else: - text_encoder = LlavaForConditionalGeneration.from_pretrained( - args.text_encoder_path, torch_dtype=torch.float16 - ) - tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right") - text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16) - tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path) - scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift) - image_processor = CLIPImageProcessor.from_pretrained(args.text_encoder_path) - - pipe = HunyuanVideoImageToVideoPipeline( - transformer=transformer, - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - text_encoder_2=text_encoder_2, - tokenizer_2=tokenizer_2, - scheduler=scheduler, - image_processor=image_processor, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") diff --git a/scripts/convert_hunyuandit_controlnet_to_diffusers.py b/scripts/convert_hunyuandit_controlnet_to_diffusers.py deleted file mode 100644 index 5cef46c98983..000000000000 --- a/scripts/convert_hunyuandit_controlnet_to_diffusers.py +++ /dev/null @@ -1,241 +0,0 @@ -import argparse - -import torch - -from diffusers import HunyuanDiT2DControlNetModel - - -def main(args): - state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu") - - if args.load_key != "none": - try: - state_dict = state_dict[args.load_key] - except KeyError: - raise KeyError( - f"{args.load_key} not found in the checkpoint." - "Please load from the following keys:{state_dict.keys()}" - ) - device = "cuda" - - model_config = HunyuanDiT2DControlNetModel.load_config( - "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", subfolder="transformer" - ) - model_config["use_style_cond_and_image_meta_size"] = ( - args.use_style_cond_and_image_meta_size - ) ### version <= v1.1: True; version >= v1.2: False - print(model_config) - - for key in state_dict: - print("local:", key) - - model = HunyuanDiT2DControlNetModel.from_config(model_config).to(device) - - for key in model.state_dict(): - print("diffusers:", key) - - num_layers = 19 - for i in range(num_layers): - # attn1 - # Wkqv -> to_q, to_k, to_v - q, k, v = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.weight"], 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.bias"], 3, dim=0) - state_dict[f"blocks.{i}.attn1.to_q.weight"] = q - state_dict[f"blocks.{i}.attn1.to_q.bias"] = q_bias - state_dict[f"blocks.{i}.attn1.to_k.weight"] = k - state_dict[f"blocks.{i}.attn1.to_k.bias"] = k_bias - state_dict[f"blocks.{i}.attn1.to_v.weight"] = v - state_dict[f"blocks.{i}.attn1.to_v.bias"] = v_bias - state_dict.pop(f"blocks.{i}.attn1.Wqkv.weight") - state_dict.pop(f"blocks.{i}.attn1.Wqkv.bias") - - # q_norm, k_norm -> norm_q, norm_k - state_dict[f"blocks.{i}.attn1.norm_q.weight"] = state_dict[f"blocks.{i}.attn1.q_norm.weight"] - state_dict[f"blocks.{i}.attn1.norm_q.bias"] = state_dict[f"blocks.{i}.attn1.q_norm.bias"] - state_dict[f"blocks.{i}.attn1.norm_k.weight"] = state_dict[f"blocks.{i}.attn1.k_norm.weight"] - state_dict[f"blocks.{i}.attn1.norm_k.bias"] = state_dict[f"blocks.{i}.attn1.k_norm.bias"] - - state_dict.pop(f"blocks.{i}.attn1.q_norm.weight") - state_dict.pop(f"blocks.{i}.attn1.q_norm.bias") - state_dict.pop(f"blocks.{i}.attn1.k_norm.weight") - state_dict.pop(f"blocks.{i}.attn1.k_norm.bias") - - # out_proj -> to_out - state_dict[f"blocks.{i}.attn1.to_out.0.weight"] = state_dict[f"blocks.{i}.attn1.out_proj.weight"] - state_dict[f"blocks.{i}.attn1.to_out.0.bias"] = state_dict[f"blocks.{i}.attn1.out_proj.bias"] - state_dict.pop(f"blocks.{i}.attn1.out_proj.weight") - state_dict.pop(f"blocks.{i}.attn1.out_proj.bias") - - # attn2 - # kq_proj -> to_k, to_v - k, v = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.weight"], 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.bias"], 2, dim=0) - state_dict[f"blocks.{i}.attn2.to_k.weight"] = k - state_dict[f"blocks.{i}.attn2.to_k.bias"] = k_bias - state_dict[f"blocks.{i}.attn2.to_v.weight"] = v - state_dict[f"blocks.{i}.attn2.to_v.bias"] = v_bias - state_dict.pop(f"blocks.{i}.attn2.kv_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.kv_proj.bias") - - # q_proj -> to_q - state_dict[f"blocks.{i}.attn2.to_q.weight"] = state_dict[f"blocks.{i}.attn2.q_proj.weight"] - state_dict[f"blocks.{i}.attn2.to_q.bias"] = state_dict[f"blocks.{i}.attn2.q_proj.bias"] - state_dict.pop(f"blocks.{i}.attn2.q_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.q_proj.bias") - - # q_norm, k_norm -> norm_q, norm_k - state_dict[f"blocks.{i}.attn2.norm_q.weight"] = state_dict[f"blocks.{i}.attn2.q_norm.weight"] - state_dict[f"blocks.{i}.attn2.norm_q.bias"] = state_dict[f"blocks.{i}.attn2.q_norm.bias"] - state_dict[f"blocks.{i}.attn2.norm_k.weight"] = state_dict[f"blocks.{i}.attn2.k_norm.weight"] - state_dict[f"blocks.{i}.attn2.norm_k.bias"] = state_dict[f"blocks.{i}.attn2.k_norm.bias"] - - state_dict.pop(f"blocks.{i}.attn2.q_norm.weight") - state_dict.pop(f"blocks.{i}.attn2.q_norm.bias") - state_dict.pop(f"blocks.{i}.attn2.k_norm.weight") - state_dict.pop(f"blocks.{i}.attn2.k_norm.bias") - - # out_proj -> to_out - state_dict[f"blocks.{i}.attn2.to_out.0.weight"] = state_dict[f"blocks.{i}.attn2.out_proj.weight"] - state_dict[f"blocks.{i}.attn2.to_out.0.bias"] = state_dict[f"blocks.{i}.attn2.out_proj.bias"] - state_dict.pop(f"blocks.{i}.attn2.out_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.out_proj.bias") - - # switch norm 2 and norm 3 - norm2_weight = state_dict[f"blocks.{i}.norm2.weight"] - norm2_bias = state_dict[f"blocks.{i}.norm2.bias"] - state_dict[f"blocks.{i}.norm2.weight"] = state_dict[f"blocks.{i}.norm3.weight"] - state_dict[f"blocks.{i}.norm2.bias"] = state_dict[f"blocks.{i}.norm3.bias"] - state_dict[f"blocks.{i}.norm3.weight"] = norm2_weight - state_dict[f"blocks.{i}.norm3.bias"] = norm2_bias - - # norm1 -> norm1.norm - # default_modulation.1 -> norm1.linear - state_dict[f"blocks.{i}.norm1.norm.weight"] = state_dict[f"blocks.{i}.norm1.weight"] - state_dict[f"blocks.{i}.norm1.norm.bias"] = state_dict[f"blocks.{i}.norm1.bias"] - state_dict[f"blocks.{i}.norm1.linear.weight"] = state_dict[f"blocks.{i}.default_modulation.1.weight"] - state_dict[f"blocks.{i}.norm1.linear.bias"] = state_dict[f"blocks.{i}.default_modulation.1.bias"] - state_dict.pop(f"blocks.{i}.norm1.weight") - state_dict.pop(f"blocks.{i}.norm1.bias") - state_dict.pop(f"blocks.{i}.default_modulation.1.weight") - state_dict.pop(f"blocks.{i}.default_modulation.1.bias") - - # mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2 - state_dict[f"blocks.{i}.ff.net.0.proj.weight"] = state_dict[f"blocks.{i}.mlp.fc1.weight"] - state_dict[f"blocks.{i}.ff.net.0.proj.bias"] = state_dict[f"blocks.{i}.mlp.fc1.bias"] - state_dict[f"blocks.{i}.ff.net.2.weight"] = state_dict[f"blocks.{i}.mlp.fc2.weight"] - state_dict[f"blocks.{i}.ff.net.2.bias"] = state_dict[f"blocks.{i}.mlp.fc2.bias"] - state_dict.pop(f"blocks.{i}.mlp.fc1.weight") - state_dict.pop(f"blocks.{i}.mlp.fc1.bias") - state_dict.pop(f"blocks.{i}.mlp.fc2.weight") - state_dict.pop(f"blocks.{i}.mlp.fc2.bias") - - # after_proj_list -> controlnet_blocks - state_dict[f"controlnet_blocks.{i}.weight"] = state_dict[f"after_proj_list.{i}.weight"] - state_dict[f"controlnet_blocks.{i}.bias"] = state_dict[f"after_proj_list.{i}.bias"] - state_dict.pop(f"after_proj_list.{i}.weight") - state_dict.pop(f"after_proj_list.{i}.bias") - - # before_proj -> input_block - state_dict["input_block.weight"] = state_dict["before_proj.weight"] - state_dict["input_block.bias"] = state_dict["before_proj.bias"] - state_dict.pop("before_proj.weight") - state_dict.pop("before_proj.bias") - - # pooler -> time_extra_emb - state_dict["time_extra_emb.pooler.positional_embedding"] = state_dict["pooler.positional_embedding"] - state_dict["time_extra_emb.pooler.k_proj.weight"] = state_dict["pooler.k_proj.weight"] - state_dict["time_extra_emb.pooler.k_proj.bias"] = state_dict["pooler.k_proj.bias"] - state_dict["time_extra_emb.pooler.q_proj.weight"] = state_dict["pooler.q_proj.weight"] - state_dict["time_extra_emb.pooler.q_proj.bias"] = state_dict["pooler.q_proj.bias"] - state_dict["time_extra_emb.pooler.v_proj.weight"] = state_dict["pooler.v_proj.weight"] - state_dict["time_extra_emb.pooler.v_proj.bias"] = state_dict["pooler.v_proj.bias"] - state_dict["time_extra_emb.pooler.c_proj.weight"] = state_dict["pooler.c_proj.weight"] - state_dict["time_extra_emb.pooler.c_proj.bias"] = state_dict["pooler.c_proj.bias"] - state_dict.pop("pooler.k_proj.weight") - state_dict.pop("pooler.k_proj.bias") - state_dict.pop("pooler.q_proj.weight") - state_dict.pop("pooler.q_proj.bias") - state_dict.pop("pooler.v_proj.weight") - state_dict.pop("pooler.v_proj.bias") - state_dict.pop("pooler.c_proj.weight") - state_dict.pop("pooler.c_proj.bias") - state_dict.pop("pooler.positional_embedding") - - # t_embedder -> time_embedding (`TimestepEmbedding`) - state_dict["time_extra_emb.timestep_embedder.linear_1.bias"] = state_dict["t_embedder.mlp.0.bias"] - state_dict["time_extra_emb.timestep_embedder.linear_1.weight"] = state_dict["t_embedder.mlp.0.weight"] - state_dict["time_extra_emb.timestep_embedder.linear_2.bias"] = state_dict["t_embedder.mlp.2.bias"] - state_dict["time_extra_emb.timestep_embedder.linear_2.weight"] = state_dict["t_embedder.mlp.2.weight"] - - state_dict.pop("t_embedder.mlp.0.bias") - state_dict.pop("t_embedder.mlp.0.weight") - state_dict.pop("t_embedder.mlp.2.bias") - state_dict.pop("t_embedder.mlp.2.weight") - - # x_embedder -> pos_embd (`PatchEmbed`) - state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] - state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] - state_dict.pop("x_embedder.proj.weight") - state_dict.pop("x_embedder.proj.bias") - - # mlp_t5 -> text_embedder - state_dict["text_embedder.linear_1.bias"] = state_dict["mlp_t5.0.bias"] - state_dict["text_embedder.linear_1.weight"] = state_dict["mlp_t5.0.weight"] - state_dict["text_embedder.linear_2.bias"] = state_dict["mlp_t5.2.bias"] - state_dict["text_embedder.linear_2.weight"] = state_dict["mlp_t5.2.weight"] - state_dict.pop("mlp_t5.0.bias") - state_dict.pop("mlp_t5.0.weight") - state_dict.pop("mlp_t5.2.bias") - state_dict.pop("mlp_t5.2.weight") - - # extra_embedder -> extra_embedder - state_dict["time_extra_emb.extra_embedder.linear_1.bias"] = state_dict["extra_embedder.0.bias"] - state_dict["time_extra_emb.extra_embedder.linear_1.weight"] = state_dict["extra_embedder.0.weight"] - state_dict["time_extra_emb.extra_embedder.linear_2.bias"] = state_dict["extra_embedder.2.bias"] - state_dict["time_extra_emb.extra_embedder.linear_2.weight"] = state_dict["extra_embedder.2.weight"] - state_dict.pop("extra_embedder.0.bias") - state_dict.pop("extra_embedder.0.weight") - state_dict.pop("extra_embedder.2.bias") - state_dict.pop("extra_embedder.2.weight") - - # style_embedder - if model_config["use_style_cond_and_image_meta_size"]: - print(state_dict["style_embedder.weight"]) - print(state_dict["style_embedder.weight"].shape) - state_dict["time_extra_emb.style_embedder.weight"] = state_dict["style_embedder.weight"][0:1] - state_dict.pop("style_embedder.weight") - - model.load_state_dict(state_dict) - - if args.save: - model.save_pretrained(args.output_checkpoint_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." - ) - parser.add_argument( - "--pt_checkpoint_path", default=None, type=str, required=True, help="Path to the .pt pretrained model." - ) - parser.add_argument( - "--output_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the output converted diffusers pipeline.", - ) - parser.add_argument( - "--load_key", default="none", type=str, required=False, help="The key to load from the pretrained .pt file" - ) - parser.add_argument( - "--use_style_cond_and_image_meta_size", - type=bool, - default=False, - help="version <= v1.1: True; version >= v1.2: False", - ) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_hunyuandit_to_diffusers.py b/scripts/convert_hunyuandit_to_diffusers.py deleted file mode 100644 index 65fcccb22a1a..000000000000 --- a/scripts/convert_hunyuandit_to_diffusers.py +++ /dev/null @@ -1,266 +0,0 @@ -import argparse - -import torch - -from diffusers import HunyuanDiT2DModel - - -def main(args): - state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu") - - if args.load_key != "none": - try: - state_dict = state_dict[args.load_key] - except KeyError: - raise KeyError( - f"{args.load_key} not found in the checkpoint.Please load from the following keys:{state_dict.keys()}" - ) - - device = "cuda" - model_config = HunyuanDiT2DModel.load_config("Tencent-Hunyuan/HunyuanDiT-Diffusers", subfolder="transformer") - model_config["use_style_cond_and_image_meta_size"] = ( - args.use_style_cond_and_image_meta_size - ) ### version <= v1.1: True; version >= v1.2: False - - # input_size -> sample_size, text_dim -> cross_attention_dim - for key in state_dict: - print("local:", key) - - model = HunyuanDiT2DModel.from_config(model_config).to(device) - - for key in model.state_dict(): - print("diffusers:", key) - - num_layers = 40 - for i in range(num_layers): - # attn1 - # Wkqv -> to_q, to_k, to_v - q, k, v = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.weight"], 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.bias"], 3, dim=0) - state_dict[f"blocks.{i}.attn1.to_q.weight"] = q - state_dict[f"blocks.{i}.attn1.to_q.bias"] = q_bias - state_dict[f"blocks.{i}.attn1.to_k.weight"] = k - state_dict[f"blocks.{i}.attn1.to_k.bias"] = k_bias - state_dict[f"blocks.{i}.attn1.to_v.weight"] = v - state_dict[f"blocks.{i}.attn1.to_v.bias"] = v_bias - state_dict.pop(f"blocks.{i}.attn1.Wqkv.weight") - state_dict.pop(f"blocks.{i}.attn1.Wqkv.bias") - - # q_norm, k_norm -> norm_q, norm_k - state_dict[f"blocks.{i}.attn1.norm_q.weight"] = state_dict[f"blocks.{i}.attn1.q_norm.weight"] - state_dict[f"blocks.{i}.attn1.norm_q.bias"] = state_dict[f"blocks.{i}.attn1.q_norm.bias"] - state_dict[f"blocks.{i}.attn1.norm_k.weight"] = state_dict[f"blocks.{i}.attn1.k_norm.weight"] - state_dict[f"blocks.{i}.attn1.norm_k.bias"] = state_dict[f"blocks.{i}.attn1.k_norm.bias"] - - state_dict.pop(f"blocks.{i}.attn1.q_norm.weight") - state_dict.pop(f"blocks.{i}.attn1.q_norm.bias") - state_dict.pop(f"blocks.{i}.attn1.k_norm.weight") - state_dict.pop(f"blocks.{i}.attn1.k_norm.bias") - - # out_proj -> to_out - state_dict[f"blocks.{i}.attn1.to_out.0.weight"] = state_dict[f"blocks.{i}.attn1.out_proj.weight"] - state_dict[f"blocks.{i}.attn1.to_out.0.bias"] = state_dict[f"blocks.{i}.attn1.out_proj.bias"] - state_dict.pop(f"blocks.{i}.attn1.out_proj.weight") - state_dict.pop(f"blocks.{i}.attn1.out_proj.bias") - - # attn2 - # kq_proj -> to_k, to_v - k, v = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.weight"], 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.bias"], 2, dim=0) - state_dict[f"blocks.{i}.attn2.to_k.weight"] = k - state_dict[f"blocks.{i}.attn2.to_k.bias"] = k_bias - state_dict[f"blocks.{i}.attn2.to_v.weight"] = v - state_dict[f"blocks.{i}.attn2.to_v.bias"] = v_bias - state_dict.pop(f"blocks.{i}.attn2.kv_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.kv_proj.bias") - - # q_proj -> to_q - state_dict[f"blocks.{i}.attn2.to_q.weight"] = state_dict[f"blocks.{i}.attn2.q_proj.weight"] - state_dict[f"blocks.{i}.attn2.to_q.bias"] = state_dict[f"blocks.{i}.attn2.q_proj.bias"] - state_dict.pop(f"blocks.{i}.attn2.q_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.q_proj.bias") - - # q_norm, k_norm -> norm_q, norm_k - state_dict[f"blocks.{i}.attn2.norm_q.weight"] = state_dict[f"blocks.{i}.attn2.q_norm.weight"] - state_dict[f"blocks.{i}.attn2.norm_q.bias"] = state_dict[f"blocks.{i}.attn2.q_norm.bias"] - state_dict[f"blocks.{i}.attn2.norm_k.weight"] = state_dict[f"blocks.{i}.attn2.k_norm.weight"] - state_dict[f"blocks.{i}.attn2.norm_k.bias"] = state_dict[f"blocks.{i}.attn2.k_norm.bias"] - - state_dict.pop(f"blocks.{i}.attn2.q_norm.weight") - state_dict.pop(f"blocks.{i}.attn2.q_norm.bias") - state_dict.pop(f"blocks.{i}.attn2.k_norm.weight") - state_dict.pop(f"blocks.{i}.attn2.k_norm.bias") - - # out_proj -> to_out - state_dict[f"blocks.{i}.attn2.to_out.0.weight"] = state_dict[f"blocks.{i}.attn2.out_proj.weight"] - state_dict[f"blocks.{i}.attn2.to_out.0.bias"] = state_dict[f"blocks.{i}.attn2.out_proj.bias"] - state_dict.pop(f"blocks.{i}.attn2.out_proj.weight") - state_dict.pop(f"blocks.{i}.attn2.out_proj.bias") - - # switch norm 2 and norm 3 - norm2_weight = state_dict[f"blocks.{i}.norm2.weight"] - norm2_bias = state_dict[f"blocks.{i}.norm2.bias"] - state_dict[f"blocks.{i}.norm2.weight"] = state_dict[f"blocks.{i}.norm3.weight"] - state_dict[f"blocks.{i}.norm2.bias"] = state_dict[f"blocks.{i}.norm3.bias"] - state_dict[f"blocks.{i}.norm3.weight"] = norm2_weight - state_dict[f"blocks.{i}.norm3.bias"] = norm2_bias - - # norm1 -> norm1.norm - # default_modulation.1 -> norm1.linear - state_dict[f"blocks.{i}.norm1.norm.weight"] = state_dict[f"blocks.{i}.norm1.weight"] - state_dict[f"blocks.{i}.norm1.norm.bias"] = state_dict[f"blocks.{i}.norm1.bias"] - state_dict[f"blocks.{i}.norm1.linear.weight"] = state_dict[f"blocks.{i}.default_modulation.1.weight"] - state_dict[f"blocks.{i}.norm1.linear.bias"] = state_dict[f"blocks.{i}.default_modulation.1.bias"] - state_dict.pop(f"blocks.{i}.norm1.weight") - state_dict.pop(f"blocks.{i}.norm1.bias") - state_dict.pop(f"blocks.{i}.default_modulation.1.weight") - state_dict.pop(f"blocks.{i}.default_modulation.1.bias") - - # mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2 - state_dict[f"blocks.{i}.ff.net.0.proj.weight"] = state_dict[f"blocks.{i}.mlp.fc1.weight"] - state_dict[f"blocks.{i}.ff.net.0.proj.bias"] = state_dict[f"blocks.{i}.mlp.fc1.bias"] - state_dict[f"blocks.{i}.ff.net.2.weight"] = state_dict[f"blocks.{i}.mlp.fc2.weight"] - state_dict[f"blocks.{i}.ff.net.2.bias"] = state_dict[f"blocks.{i}.mlp.fc2.bias"] - state_dict.pop(f"blocks.{i}.mlp.fc1.weight") - state_dict.pop(f"blocks.{i}.mlp.fc1.bias") - state_dict.pop(f"blocks.{i}.mlp.fc2.weight") - state_dict.pop(f"blocks.{i}.mlp.fc2.bias") - - # pooler -> time_extra_emb - state_dict["time_extra_emb.pooler.positional_embedding"] = state_dict["pooler.positional_embedding"] - state_dict["time_extra_emb.pooler.k_proj.weight"] = state_dict["pooler.k_proj.weight"] - state_dict["time_extra_emb.pooler.k_proj.bias"] = state_dict["pooler.k_proj.bias"] - state_dict["time_extra_emb.pooler.q_proj.weight"] = state_dict["pooler.q_proj.weight"] - state_dict["time_extra_emb.pooler.q_proj.bias"] = state_dict["pooler.q_proj.bias"] - state_dict["time_extra_emb.pooler.v_proj.weight"] = state_dict["pooler.v_proj.weight"] - state_dict["time_extra_emb.pooler.v_proj.bias"] = state_dict["pooler.v_proj.bias"] - state_dict["time_extra_emb.pooler.c_proj.weight"] = state_dict["pooler.c_proj.weight"] - state_dict["time_extra_emb.pooler.c_proj.bias"] = state_dict["pooler.c_proj.bias"] - state_dict.pop("pooler.k_proj.weight") - state_dict.pop("pooler.k_proj.bias") - state_dict.pop("pooler.q_proj.weight") - state_dict.pop("pooler.q_proj.bias") - state_dict.pop("pooler.v_proj.weight") - state_dict.pop("pooler.v_proj.bias") - state_dict.pop("pooler.c_proj.weight") - state_dict.pop("pooler.c_proj.bias") - state_dict.pop("pooler.positional_embedding") - - # t_embedder -> time_embedding (`TimestepEmbedding`) - state_dict["time_extra_emb.timestep_embedder.linear_1.bias"] = state_dict["t_embedder.mlp.0.bias"] - state_dict["time_extra_emb.timestep_embedder.linear_1.weight"] = state_dict["t_embedder.mlp.0.weight"] - state_dict["time_extra_emb.timestep_embedder.linear_2.bias"] = state_dict["t_embedder.mlp.2.bias"] - state_dict["time_extra_emb.timestep_embedder.linear_2.weight"] = state_dict["t_embedder.mlp.2.weight"] - - state_dict.pop("t_embedder.mlp.0.bias") - state_dict.pop("t_embedder.mlp.0.weight") - state_dict.pop("t_embedder.mlp.2.bias") - state_dict.pop("t_embedder.mlp.2.weight") - - # x_embedder -> pos_embd (`PatchEmbed`) - state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] - state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] - state_dict.pop("x_embedder.proj.weight") - state_dict.pop("x_embedder.proj.bias") - - # mlp_t5 -> text_embedder - state_dict["text_embedder.linear_1.bias"] = state_dict["mlp_t5.0.bias"] - state_dict["text_embedder.linear_1.weight"] = state_dict["mlp_t5.0.weight"] - state_dict["text_embedder.linear_2.bias"] = state_dict["mlp_t5.2.bias"] - state_dict["text_embedder.linear_2.weight"] = state_dict["mlp_t5.2.weight"] - state_dict.pop("mlp_t5.0.bias") - state_dict.pop("mlp_t5.0.weight") - state_dict.pop("mlp_t5.2.bias") - state_dict.pop("mlp_t5.2.weight") - - # extra_embedder -> extra_embedder - state_dict["time_extra_emb.extra_embedder.linear_1.bias"] = state_dict["extra_embedder.0.bias"] - state_dict["time_extra_emb.extra_embedder.linear_1.weight"] = state_dict["extra_embedder.0.weight"] - state_dict["time_extra_emb.extra_embedder.linear_2.bias"] = state_dict["extra_embedder.2.bias"] - state_dict["time_extra_emb.extra_embedder.linear_2.weight"] = state_dict["extra_embedder.2.weight"] - state_dict.pop("extra_embedder.0.bias") - state_dict.pop("extra_embedder.0.weight") - state_dict.pop("extra_embedder.2.bias") - state_dict.pop("extra_embedder.2.weight") - - # model.final_adaLN_modulation.1 -> norm_out.linear - def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.weight"]) - state_dict["norm_out.linear.bias"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.bias"]) - state_dict.pop("final_layer.adaLN_modulation.1.weight") - state_dict.pop("final_layer.adaLN_modulation.1.bias") - - # final_linear -> proj_out - state_dict["proj_out.weight"] = state_dict["final_layer.linear.weight"] - state_dict["proj_out.bias"] = state_dict["final_layer.linear.bias"] - state_dict.pop("final_layer.linear.weight") - state_dict.pop("final_layer.linear.bias") - - # style_embedder - if model_config["use_style_cond_and_image_meta_size"]: - print(state_dict["style_embedder.weight"]) - print(state_dict["style_embedder.weight"].shape) - state_dict["time_extra_emb.style_embedder.weight"] = state_dict["style_embedder.weight"][0:1] - state_dict.pop("style_embedder.weight") - - model.load_state_dict(state_dict) - - from diffusers import HunyuanDiTPipeline - - if args.use_style_cond_and_image_meta_size: - pipe = HunyuanDiTPipeline.from_pretrained( - "Tencent-Hunyuan/HunyuanDiT-Diffusers", transformer=model, torch_dtype=torch.float32 - ) - else: - pipe = HunyuanDiTPipeline.from_pretrained( - "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", transformer=model, torch_dtype=torch.float32 - ) - pipe.to("cuda") - pipe.to(dtype=torch.float32) - - if args.save: - pipe.save_pretrained(args.output_checkpoint_path) - - # ### NOTE: HunyuanDiT supports both Chinese and English inputs - prompt = "一个宇航员在骑马" - # prompt = "An astronaut riding a horse" - generator = torch.Generator(device="cuda").manual_seed(0) - image = pipe( - height=1024, width=1024, prompt=prompt, generator=generator, num_inference_steps=25, guidance_scale=5.0 - ).images[0] - - image.save("img.png") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." - ) - parser.add_argument( - "--pt_checkpoint_path", default=None, type=str, required=True, help="Path to the .pt pretrained model." - ) - parser.add_argument( - "--output_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the output converted diffusers pipeline.", - ) - parser.add_argument( - "--load_key", default="none", type=str, required=False, help="The key to load from the pretrained .pt file" - ) - parser.add_argument( - "--use_style_cond_and_image_meta_size", - type=bool, - default=False, - help="version <= v1.1: True; version >= v1.2: False", - ) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_i2vgen_to_diffusers.py b/scripts/convert_i2vgen_to_diffusers.py deleted file mode 100644 index 643780caac2d..000000000000 --- a/scripts/convert_i2vgen_to_diffusers.py +++ /dev/null @@ -1,510 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the LDM checkpoints.""" - -import argparse - -import torch -from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection - -from diffusers import DDIMScheduler, I2VGenXLPipeline, I2VGenXLUNet, StableDiffusionPipeline - - -CLIP_ID = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - weight = old_checkpoint[path["old"]] - names = ["proj_attn.weight"] - names_2 = ["proj_out.weight", "proj_in.weight"] - if any(k in new_path for k in names): - checkpoint[new_path] = weight[:, :, 0] - elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and ".attentions." not in new_path: - checkpoint[new_path] = weight[:, :, 0] - else: - checkpoint[new_path] = weight - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_temp_conv_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - mapping.append({"old": old_item, "new": old_item}) - - return mapping - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - if "temopral_conv" not in old_item: - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has both EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - additional_embedding_substrings = [ - "local_image_concat", - "context_embedding", - "local_image_embedding", - "fps_embedding", - ] - for k in unet_state_dict: - if any(substring in k for substring in additional_embedding_substrings): - diffusers_key = k.replace("local_image_concat", "image_latents_proj_in").replace( - "local_image_embedding", "image_latents_context_embedding" - ) - new_checkpoint[diffusers_key] = unet_state_dict[k] - - # temporal encoder. - new_checkpoint["image_latents_temporal_encoder.norm1.weight"] = unet_state_dict[ - "local_temporal_encoder.layers.0.0.norm.weight" - ] - new_checkpoint["image_latents_temporal_encoder.norm1.bias"] = unet_state_dict[ - "local_temporal_encoder.layers.0.0.norm.bias" - ] - - # attention - qkv = unet_state_dict["local_temporal_encoder.layers.0.0.fn.to_qkv.weight"] - q, k, v = torch.chunk(qkv, 3, dim=0) - new_checkpoint["image_latents_temporal_encoder.attn1.to_q.weight"] = q - new_checkpoint["image_latents_temporal_encoder.attn1.to_k.weight"] = k - new_checkpoint["image_latents_temporal_encoder.attn1.to_v.weight"] = v - new_checkpoint["image_latents_temporal_encoder.attn1.to_out.0.weight"] = unet_state_dict[ - "local_temporal_encoder.layers.0.0.fn.to_out.0.weight" - ] - new_checkpoint["image_latents_temporal_encoder.attn1.to_out.0.bias"] = unet_state_dict[ - "local_temporal_encoder.layers.0.0.fn.to_out.0.bias" - ] - - # feedforward - new_checkpoint["image_latents_temporal_encoder.ff.net.0.proj.weight"] = unet_state_dict[ - "local_temporal_encoder.layers.0.1.net.0.0.weight" - ] - new_checkpoint["image_latents_temporal_encoder.ff.net.0.proj.bias"] = unet_state_dict[ - "local_temporal_encoder.layers.0.1.net.0.0.bias" - ] - new_checkpoint["image_latents_temporal_encoder.ff.net.2.weight"] = unet_state_dict[ - "local_temporal_encoder.layers.0.1.net.2.weight" - ] - new_checkpoint["image_latents_temporal_encoder.ff.net.2.bias"] = unet_state_dict[ - "local_temporal_encoder.layers.0.1.net.2.bias" - ] - - if "class_embed_type" in config: - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - first_temp_attention = [v for v in unet_state_dict if v.startswith("input_blocks.0.1")] - paths = renew_attention_paths(first_temp_attention) - meta_path = {"old": "input_blocks.0.1", "new": "transformer_in"} - assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - temp_attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key] - - if f"input_blocks.{i}.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temporal_convs = [key for key in resnets if "temopral_conv" in key] - paths = renew_temp_conv_paths(temporal_convs) - meta_path = { - "old": f"input_blocks.{i}.0.temopral_conv", - "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(temp_attentions): - paths = renew_attention_paths(temp_attentions) - meta_path = { - "old": f"input_blocks.{i}.2", - "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - temporal_convs_0 = [key for key in resnet_0 if "temopral_conv" in key] - attentions = middle_blocks[1] - temp_attentions = middle_blocks[2] - resnet_1 = middle_blocks[3] - temporal_convs_1 = [key for key in resnet_1 if "temopral_conv" in key] - - resnet_0_paths = renew_resnet_paths(resnet_0) - meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} - assign_to_checkpoint( - resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0) - meta_path = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"} - assign_to_checkpoint( - temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - resnet_1_paths = renew_resnet_paths(resnet_1) - meta_path = {"old": "middle_block.3", "new": "mid_block.resnets.1"} - assign_to_checkpoint( - resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1) - meta_path = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"} - assign_to_checkpoint( - temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temp_attentions_paths = renew_attention_paths(temp_attentions) - meta_path = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"} - assign_to_checkpoint( - temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - temp_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temporal_convs = [key for key in resnets if "temopral_conv" in key] - paths = renew_temp_conv_paths(temporal_convs) - meta_path = { - "old": f"output_blocks.{i}.0.temopral_conv", - "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(temp_attentions): - paths = renew_attention_paths(temp_attentions) - meta_path = { - "old": f"output_blocks.{i}.2", - "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - new_checkpoint[new_path] = unet_state_dict[old_path] - - temopral_conv_paths = [l for l in output_block_layers if "temopral_conv" in l] - for path in temopral_conv_paths: - pruned_path = path.split("temopral_conv.")[-1] - old_path = ".".join(["output_blocks", str(i), str(block_id), "temopral_conv", pruned_path]) - new_path = ".".join(["up_blocks", str(block_id), "temp_convs", str(layer_in_block_id), pruned_path]) - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--unet_checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--push_to_hub", action="store_true") - args = parser.parse_args() - - # UNet - unet_checkpoint = torch.load(args.unet_checkpoint_path, map_location="cpu") - unet_checkpoint = unet_checkpoint["state_dict"] - unet = I2VGenXLUNet(sample_size=32) - - converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config) - - diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys()) - diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys()) - - assert len(diff_0) == len(diff_1) == 0, "Converted weights don't match" - - unet.load_state_dict(converted_ckpt, strict=True) - - # vae - temp_pipe = StableDiffusionPipeline.from_single_file( - "https://huggingface.co/ali-vilab/i2vgen-xl/blob/main/models/v2-1_512-ema-pruned.ckpt" - ) - vae = temp_pipe.vae - del temp_pipe - - # text encoder and tokenizer - text_encoder = CLIPTextModel.from_pretrained(CLIP_ID) - tokenizer = CLIPTokenizer.from_pretrained(CLIP_ID) - - # image encoder and feature extractor - image_encoder = CLIPVisionModelWithProjection.from_pretrained(CLIP_ID) - feature_extractor = CLIPImageProcessor.from_pretrained(CLIP_ID) - - # scheduler - # https://github.com/ali-vilab/i2vgen-xl/blob/main/configs/i2vgen_xl_train.yaml - scheduler = DDIMScheduler( - beta_schedule="squaredcos_cap_v2", - rescale_betas_zero_snr=True, - set_alpha_to_one=True, - clip_sample=False, - steps_offset=1, - timestep_spacing="leading", - prediction_type="v_prediction", - ) - - # final - pipeline = I2VGenXLPipeline( - unet=unet, - vae=vae, - image_encoder=image_encoder, - feature_extractor=feature_extractor, - text_encoder=text_encoder, - tokenizer=tokenizer, - scheduler=scheduler, - ) - - pipeline.save_pretrained(args.dump_path, push_to_hub=args.push_to_hub) diff --git a/scripts/convert_if.py b/scripts/convert_if.py deleted file mode 100644 index 85c739ca92f0..000000000000 --- a/scripts/convert_if.py +++ /dev/null @@ -1,1250 +0,0 @@ -import argparse -import inspect -import os - -import numpy as np -import torch -import yaml -from torch.nn import functional as F -from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer - -from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel -from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker - - -def parse_args(): - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", required=False, default=None, type=str) - - parser.add_argument("--dump_path_stage_2", required=False, default=None, type=str) - - parser.add_argument("--dump_path_stage_3", required=False, default=None, type=str) - - parser.add_argument("--unet_config", required=False, default=None, type=str, help="Path to unet config file") - - parser.add_argument( - "--unet_checkpoint_path", required=False, default=None, type=str, help="Path to unet checkpoint file" - ) - - parser.add_argument( - "--unet_checkpoint_path_stage_2", - required=False, - default=None, - type=str, - help="Path to stage 2 unet checkpoint file", - ) - - parser.add_argument( - "--unet_checkpoint_path_stage_3", - required=False, - default=None, - type=str, - help="Path to stage 3 unet checkpoint file", - ) - - parser.add_argument("--p_head_path", type=str, required=True) - - parser.add_argument("--w_head_path", type=str, required=True) - - args = parser.parse_args() - - return args - - -def main(args): - tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-xxl") - text_encoder = T5EncoderModel.from_pretrained("google/t5-v1_1-xxl") - - feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14") - safety_checker = convert_safety_checker(p_head_path=args.p_head_path, w_head_path=args.w_head_path) - - if args.unet_config is not None and args.unet_checkpoint_path is not None and args.dump_path is not None: - convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args) - - if args.unet_checkpoint_path_stage_2 is not None and args.dump_path_stage_2 is not None: - convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=2) - - if args.unet_checkpoint_path_stage_3 is not None and args.dump_path_stage_3 is not None: - convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=3) - - -def convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args): - unet = get_stage_1_unet(args.unet_config, args.unet_checkpoint_path) - - scheduler = DDPMScheduler( - variance_type="learned_range", - beta_schedule="squaredcos_cap_v2", - prediction_type="epsilon", - thresholding=True, - dynamic_thresholding_ratio=0.95, - sample_max_value=1.5, - ) - - pipe = IFPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - unet=unet, - scheduler=scheduler, - safety_checker=safety_checker, - feature_extractor=feature_extractor, - requires_safety_checker=True, - ) - - pipe.save_pretrained(args.dump_path) - - -def convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage): - if stage == 2: - unet_checkpoint_path = args.unet_checkpoint_path_stage_2 - sample_size = None - dump_path = args.dump_path_stage_2 - elif stage == 3: - unet_checkpoint_path = args.unet_checkpoint_path_stage_3 - sample_size = 1024 - dump_path = args.dump_path_stage_3 - else: - assert False - - unet = get_super_res_unet(unet_checkpoint_path, verify_param_count=False, sample_size=sample_size) - - image_noising_scheduler = DDPMScheduler( - beta_schedule="squaredcos_cap_v2", - ) - - scheduler = DDPMScheduler( - variance_type="learned_range", - beta_schedule="squaredcos_cap_v2", - prediction_type="epsilon", - thresholding=True, - dynamic_thresholding_ratio=0.95, - sample_max_value=1.0, - ) - - pipe = IFSuperResolutionPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - unet=unet, - scheduler=scheduler, - image_noising_scheduler=image_noising_scheduler, - safety_checker=safety_checker, - feature_extractor=feature_extractor, - requires_safety_checker=True, - ) - - pipe.save_pretrained(dump_path) - - -def get_stage_1_unet(unet_config, unet_checkpoint_path): - original_unet_config = yaml.safe_load(unet_config) - original_unet_config = original_unet_config["params"] - - unet_diffusers_config = create_unet_diffusers_config(original_unet_config) - - unet = UNet2DConditionModel(**unet_diffusers_config) - - device = "cuda" if torch.cuda.is_available() else "cpu" - unet_checkpoint = torch.load(unet_checkpoint_path, map_location=device) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path - ) - - unet.load_state_dict(converted_unet_checkpoint) - - return unet - - -def convert_safety_checker(p_head_path, w_head_path): - state_dict = {} - - # p head - - p_head = np.load(p_head_path) - - p_head_weights = p_head["weights"] - p_head_weights = torch.from_numpy(p_head_weights) - p_head_weights = p_head_weights.unsqueeze(0) - - p_head_biases = p_head["biases"] - p_head_biases = torch.from_numpy(p_head_biases) - p_head_biases = p_head_biases.unsqueeze(0) - - state_dict["p_head.weight"] = p_head_weights - state_dict["p_head.bias"] = p_head_biases - - # w head - - w_head = np.load(w_head_path) - - w_head_weights = w_head["weights"] - w_head_weights = torch.from_numpy(w_head_weights) - w_head_weights = w_head_weights.unsqueeze(0) - - w_head_biases = w_head["biases"] - w_head_biases = torch.from_numpy(w_head_biases) - w_head_biases = w_head_biases.unsqueeze(0) - - state_dict["w_head.weight"] = w_head_weights - state_dict["w_head.bias"] = w_head_biases - - # vision model - - vision_model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - vision_model_state_dict = vision_model.state_dict() - - for key, value in vision_model_state_dict.items(): - key = f"vision_model.{key}" - state_dict[key] = value - - # full model - - config = CLIPConfig.from_pretrained("openai/clip-vit-large-patch14") - safety_checker = IFSafetyChecker(config) - - safety_checker.load_state_dict(state_dict) - - return safety_checker - - -def create_unet_diffusers_config(original_unet_config, class_embed_type=None): - attention_resolutions = parse_list(original_unet_config["attention_resolutions"]) - attention_resolutions = [original_unet_config["image_size"] // int(res) for res in attention_resolutions] - - channel_mult = parse_list(original_unet_config["channel_mult"]) - block_out_channels = [original_unet_config["model_channels"] * mult for mult in channel_mult] - - down_block_types = [] - resolution = 1 - - for i in range(len(block_out_channels)): - if resolution in attention_resolutions: - block_type = "SimpleCrossAttnDownBlock2D" - elif original_unet_config["resblock_updown"]: - block_type = "ResnetDownsampleBlock2D" - else: - block_type = "DownBlock2D" - - down_block_types.append(block_type) - - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - if resolution in attention_resolutions: - block_type = "SimpleCrossAttnUpBlock2D" - elif original_unet_config["resblock_updown"]: - block_type = "ResnetUpsampleBlock2D" - else: - block_type = "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - head_dim = original_unet_config["num_head_channels"] - - use_linear_projection = ( - original_unet_config["use_linear_in_transformer"] - if "use_linear_in_transformer" in original_unet_config - else False - ) - if use_linear_projection: - # stable diffusion 2-base-512 and 2-768 - if head_dim is None: - head_dim = [5, 10, 20, 20] - - projection_class_embeddings_input_dim = None - - if class_embed_type is None: - if "num_classes" in original_unet_config: - if original_unet_config["num_classes"] == "sequential": - class_embed_type = "projection" - assert "adm_in_channels" in original_unet_config - projection_class_embeddings_input_dim = original_unet_config["adm_in_channels"] - else: - raise NotImplementedError( - f"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}" - ) - - config = { - "sample_size": original_unet_config["image_size"], - "in_channels": original_unet_config["in_channels"], - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": original_unet_config["num_res_blocks"], - "cross_attention_dim": original_unet_config["encoder_channels"], - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "class_embed_type": class_embed_type, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "out_channels": original_unet_config["out_channels"], - "up_block_types": tuple(up_block_types), - "upcast_attention": False, # TODO: guessing - "cross_attention_norm": "group_norm", - "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", - "addition_embed_type": "text", - "act_fn": "gelu", - } - - if original_unet_config["use_scale_shift_norm"]: - config["resnet_time_scale_shift"] = "scale_shift" - - if "encoder_dim" in original_unet_config: - config["encoder_hid_dim"] = original_unet_config["encoder_dim"] - - return config - - -def convert_ldm_unet_checkpoint(unet_state_dict, config, path=None): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] in [None, "identity"]: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - - # TODO need better check than i in [4, 8, 12, 16] - block_type = config["down_block_types"][block_id] - if (block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D") and i in [ - 4, - 8, - 12, - 16, - ]: - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"} - else: - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - old_path = f"input_blocks.{i}.1" - new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - paths = renew_attention_paths(attentions) - meta_path = {"old": old_path, "new": new_path} - assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - old_path = "middle_block.1" - new_path = "mid_block.attentions.0" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - # len(output_block_list) == 1 -> resnet - # len(output_block_list) == 2 -> resnet, attention - # len(output_block_list) == 3 -> resnet, attention, upscale resnet - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - old_path = f"output_blocks.{i}.1" - new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - paths = renew_attention_paths(attentions) - meta_path = { - "old": old_path, - "new": new_path, - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(output_block_list) == 3: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"output_blocks.{i}.2", "new": f"up_blocks.{block_id}.upsamplers.0"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - if "encoder_proj.weight" in unet_state_dict: - new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict.pop("encoder_proj.weight") - new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict.pop("encoder_proj.bias") - - if "encoder_pooling.0.weight" in unet_state_dict: - new_checkpoint["add_embedding.norm1.weight"] = unet_state_dict.pop("encoder_pooling.0.weight") - new_checkpoint["add_embedding.norm1.bias"] = unet_state_dict.pop("encoder_pooling.0.bias") - - new_checkpoint["add_embedding.pool.positional_embedding"] = unet_state_dict.pop( - "encoder_pooling.1.positional_embedding" - ) - new_checkpoint["add_embedding.pool.k_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.k_proj.weight") - new_checkpoint["add_embedding.pool.k_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.k_proj.bias") - new_checkpoint["add_embedding.pool.q_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.q_proj.weight") - new_checkpoint["add_embedding.pool.q_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.q_proj.bias") - new_checkpoint["add_embedding.pool.v_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.v_proj.weight") - new_checkpoint["add_embedding.pool.v_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.v_proj.bias") - - new_checkpoint["add_embedding.proj.weight"] = unet_state_dict.pop("encoder_pooling.2.weight") - new_checkpoint["add_embedding.proj.bias"] = unet_state_dict.pop("encoder_pooling.2.bias") - - new_checkpoint["add_embedding.norm2.weight"] = unet_state_dict.pop("encoder_pooling.3.weight") - new_checkpoint["add_embedding.norm2.bias"] = unet_state_dict.pop("encoder_pooling.3.bias") - - return new_checkpoint - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - if "qkv" in new_item: - continue - - if "encoder_kv" in new_item: - continue - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = new_item.replace("norm_encoder.weight", "norm_cross.weight") - new_item = new_item.replace("norm_encoder.bias", "norm_cross.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def assign_attention_to_checkpoint(new_checkpoint, unet_state_dict, old_path, new_path, config): - qkv_weight = unet_state_dict.pop(f"{old_path}.qkv.weight") - qkv_weight = qkv_weight[:, :, 0] - - qkv_bias = unet_state_dict.pop(f"{old_path}.qkv.bias") - - is_cross_attn_only = "only_cross_attention" in config and config["only_cross_attention"] - - split = 1 if is_cross_attn_only else 3 - - weights, bias = split_attentions( - weight=qkv_weight, - bias=qkv_bias, - split=split, - chunk_size=config["attention_head_dim"], - ) - - if is_cross_attn_only: - query_weight, q_bias = weights, bias - new_checkpoint[f"{new_path}.to_q.weight"] = query_weight[0] - new_checkpoint[f"{new_path}.to_q.bias"] = q_bias[0] - else: - [query_weight, key_weight, value_weight], [q_bias, k_bias, v_bias] = weights, bias - new_checkpoint[f"{new_path}.to_q.weight"] = query_weight - new_checkpoint[f"{new_path}.to_q.bias"] = q_bias - new_checkpoint[f"{new_path}.to_k.weight"] = key_weight - new_checkpoint[f"{new_path}.to_k.bias"] = k_bias - new_checkpoint[f"{new_path}.to_v.weight"] = value_weight - new_checkpoint[f"{new_path}.to_v.bias"] = v_bias - - encoder_kv_weight = unet_state_dict.pop(f"{old_path}.encoder_kv.weight") - encoder_kv_weight = encoder_kv_weight[:, :, 0] - - encoder_kv_bias = unet_state_dict.pop(f"{old_path}.encoder_kv.bias") - - [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( - weight=encoder_kv_weight, - bias=encoder_kv_bias, - split=2, - chunk_size=config["attention_head_dim"], - ) - - new_checkpoint[f"{new_path}.add_k_proj.weight"] = encoder_k_weight - new_checkpoint[f"{new_path}.add_k_proj.bias"] = encoder_k_bias - new_checkpoint[f"{new_path}.add_v_proj.weight"] = encoder_v_weight - new_checkpoint[f"{new_path}.add_v_proj.bias"] = encoder_v_bias - - -def assign_to_checkpoint(paths, checkpoint, old_checkpoint, additional_replacements=None, config=None): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - for path in paths: - new_path = path["new"] - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path or "to_out.0.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) -def split_attentions(*, weight, bias, split, chunk_size): - weights = [None] * split - biases = [None] * split - - weights_biases_idx = 0 - - for starting_row_index in range(0, weight.shape[0], chunk_size): - row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) - - weight_rows = weight[row_indices, :] - bias_rows = bias[row_indices] - - if weights[weights_biases_idx] is None: - weights[weights_biases_idx] = weight_rows - biases[weights_biases_idx] = bias_rows - else: - assert weights[weights_biases_idx] is not None - weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) - biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) - - weights_biases_idx = (weights_biases_idx + 1) % split - - return weights, biases - - -def parse_list(value): - if isinstance(value, str): - value = value.split(",") - value = [int(v) for v in value] - elif isinstance(value, list): - pass - else: - raise ValueError(f"Can't parse list for type: {type(value)}") - - return value - - -# below is copy and pasted from original convert_if_stage_2.py script - - -def get_super_res_unet(unet_checkpoint_path, verify_param_count=True, sample_size=None): - orig_path = unet_checkpoint_path - - original_unet_config = yaml.safe_load(os.path.join(orig_path, "config.yml")) - original_unet_config = original_unet_config["params"] - - unet_diffusers_config = superres_create_unet_diffusers_config(original_unet_config) - unet_diffusers_config["time_embedding_dim"] = original_unet_config["model_channels"] * int( - original_unet_config["channel_mult"].split(",")[-1] - ) - if original_unet_config["encoder_dim"] != original_unet_config["encoder_channels"]: - unet_diffusers_config["encoder_hid_dim"] = original_unet_config["encoder_dim"] - unet_diffusers_config["class_embed_type"] = "timestep" - unet_diffusers_config["addition_embed_type"] = "text" - - unet_diffusers_config["time_embedding_act_fn"] = "gelu" - unet_diffusers_config["resnet_skip_time_act"] = True - unet_diffusers_config["resnet_out_scale_factor"] = 1 / 0.7071 - unet_diffusers_config["mid_block_scale_factor"] = 1 / 0.7071 - unet_diffusers_config["only_cross_attention"] = ( - bool(original_unet_config["disable_self_attentions"]) - if ( - "disable_self_attentions" in original_unet_config - and isinstance(original_unet_config["disable_self_attentions"], int) - ) - else True - ) - - if sample_size is None: - unet_diffusers_config["sample_size"] = original_unet_config["image_size"] - else: - # The second upscaler unet's sample size is incorrectly specified - # in the config and is instead hardcoded in source - unet_diffusers_config["sample_size"] = sample_size - - unet_checkpoint = torch.load(os.path.join(unet_checkpoint_path, "pytorch_model.bin"), map_location="cpu") - - if verify_param_count: - # check that architecture matches - is a bit slow - verify_param_count(orig_path, unet_diffusers_config) - - converted_unet_checkpoint = superres_convert_ldm_unet_checkpoint( - unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path - ) - converted_keys = converted_unet_checkpoint.keys() - - model = UNet2DConditionModel(**unet_diffusers_config) - expected_weights = model.state_dict().keys() - - diff_c_e = set(converted_keys) - set(expected_weights) - diff_e_c = set(expected_weights) - set(converted_keys) - - assert len(diff_e_c) == 0, f"Expected, but not converted: {diff_e_c}" - assert len(diff_c_e) == 0, f"Converted, but not expected: {diff_c_e}" - - model.load_state_dict(converted_unet_checkpoint) - - return model - - -def superres_create_unet_diffusers_config(original_unet_config): - attention_resolutions = parse_list(original_unet_config["attention_resolutions"]) - attention_resolutions = [original_unet_config["image_size"] // int(res) for res in attention_resolutions] - - channel_mult = parse_list(original_unet_config["channel_mult"]) - block_out_channels = [original_unet_config["model_channels"] * mult for mult in channel_mult] - - down_block_types = [] - resolution = 1 - - for i in range(len(block_out_channels)): - if resolution in attention_resolutions: - block_type = "SimpleCrossAttnDownBlock2D" - elif original_unet_config["resblock_updown"]: - block_type = "ResnetDownsampleBlock2D" - else: - block_type = "DownBlock2D" - - down_block_types.append(block_type) - - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - if resolution in attention_resolutions: - block_type = "SimpleCrossAttnUpBlock2D" - elif original_unet_config["resblock_updown"]: - block_type = "ResnetUpsampleBlock2D" - else: - block_type = "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - head_dim = original_unet_config["num_head_channels"] - use_linear_projection = ( - original_unet_config["use_linear_in_transformer"] - if "use_linear_in_transformer" in original_unet_config - else False - ) - if use_linear_projection: - # stable diffusion 2-base-512 and 2-768 - if head_dim is None: - head_dim = [5, 10, 20, 20] - - class_embed_type = None - projection_class_embeddings_input_dim = None - - if "num_classes" in original_unet_config: - if original_unet_config["num_classes"] == "sequential": - class_embed_type = "projection" - assert "adm_in_channels" in original_unet_config - projection_class_embeddings_input_dim = original_unet_config["adm_in_channels"] - else: - raise NotImplementedError( - f"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}" - ) - - config = { - "in_channels": original_unet_config["in_channels"], - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": tuple(original_unet_config["num_res_blocks"]), - "cross_attention_dim": original_unet_config["encoder_channels"], - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "class_embed_type": class_embed_type, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "out_channels": original_unet_config["out_channels"], - "up_block_types": tuple(up_block_types), - "upcast_attention": False, # TODO: guessing - "cross_attention_norm": "group_norm", - "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", - "act_fn": "gelu", - } - - if original_unet_config["use_scale_shift_norm"]: - config["resnet_time_scale_shift"] = "scale_shift" - - return config - - -def superres_convert_ldm_unet_checkpoint(unet_state_dict, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["aug_proj.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["aug_proj.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["aug_proj.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["aug_proj.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - if "encoder_proj.weight" in unet_state_dict: - new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict["encoder_proj.weight"] - new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict["encoder_proj.bias"] - - if "encoder_pooling.0.weight" in unet_state_dict: - mapping = { - "encoder_pooling.0": "add_embedding.norm1", - "encoder_pooling.1": "add_embedding.pool", - "encoder_pooling.2": "add_embedding.proj", - "encoder_pooling.3": "add_embedding.norm2", - } - for key in unet_state_dict.keys(): - if key.startswith("encoder_pooling"): - prefix = key[: len("encoder_pooling.0")] - new_key = key.replace(prefix, mapping[prefix]) - new_checkpoint[new_key] = unet_state_dict[key] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] - for layer_id in range(num_output_blocks) - } - if not isinstance(config["layers_per_block"], int): - layers_per_block_list = [e + 1 for e in config["layers_per_block"]] - layers_per_block_cumsum = list(np.cumsum(layers_per_block_list)) - downsampler_ids = layers_per_block_cumsum - else: - # TODO need better check than i in [4, 8, 12, 16] - downsampler_ids = [4, 8, 12, 16] - - for i in range(1, num_input_blocks): - if isinstance(config["layers_per_block"], int): - layers_per_block = config["layers_per_block"] - block_id = (i - 1) // (layers_per_block + 1) - layer_in_block_id = (i - 1) % (layers_per_block + 1) - else: - block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if (i - 1) < n) - passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0 - layer_in_block_id = (i - 1) - passed_blocks - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - - block_type = config["down_block_types"][block_id] - if ( - block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D" - ) and i in downsampler_ids: - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"} - else: - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - old_path = f"input_blocks.{i}.1" - new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - paths = renew_attention_paths(attentions) - meta_path = {"old": old_path, "new": new_path} - assign_to_checkpoint( - paths, - new_checkpoint, - unet_state_dict, - additional_replacements=[meta_path], - config=config, - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - old_path = "middle_block.1" - new_path = "mid_block.attentions.0" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - if not isinstance(config["layers_per_block"], int): - layers_per_block_list = list(reversed([e + 1 for e in config["layers_per_block"]])) - layers_per_block_cumsum = list(np.cumsum(layers_per_block_list)) - - for i in range(num_output_blocks): - if isinstance(config["layers_per_block"], int): - layers_per_block = config["layers_per_block"] - block_id = i // (layers_per_block + 1) - layer_in_block_id = i % (layers_per_block + 1) - else: - block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if i < n) - passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0 - layer_in_block_id = i - passed_blocks - - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - # len(output_block_list) == 1 -> resnet - # len(output_block_list) == 2 -> resnet, attention or resnet, upscale resnet - # len(output_block_list) == 3 -> resnet, attention, upscale resnet - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - - has_attention = True - if len(output_block_list) == 2 and any("in_layers" in k for k in output_block_list["1"]): - has_attention = False - - maybe_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # this layer was no attention - has_attention = False - maybe_attentions = [] - - if has_attention: - old_path = f"output_blocks.{i}.1" - new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}" - - assign_attention_to_checkpoint( - new_checkpoint=new_checkpoint, - unet_state_dict=unet_state_dict, - old_path=old_path, - new_path=new_path, - config=config, - ) - - paths = renew_attention_paths(maybe_attentions) - meta_path = { - "old": old_path, - "new": new_path, - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(output_block_list) == 3 or (not has_attention and len(maybe_attentions) > 0): - layer_id = len(output_block_list) - 1 - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.{layer_id}" in key] - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"output_blocks.{i}.{layer_id}", "new": f"up_blocks.{block_id}.upsamplers.0"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -def verify_param_count(orig_path, unet_diffusers_config): - if "-II-" in orig_path: - from deepfloyd_if.modules import IFStageII - - if_II = IFStageII(device="cpu", dir_or_name=orig_path) - elif "-III-" in orig_path: - from deepfloyd_if.modules import IFStageIII - - if_II = IFStageIII(device="cpu", dir_or_name=orig_path) - else: - assert f"Weird name. Should have -II- or -III- in path: {orig_path}" - - unet = UNet2DConditionModel(**unet_diffusers_config) - - # in params - assert_param_count(unet.time_embedding, if_II.model.time_embed) - assert_param_count(unet.conv_in, if_II.model.input_blocks[:1]) - - # downblocks - assert_param_count(unet.down_blocks[0], if_II.model.input_blocks[1:4]) - assert_param_count(unet.down_blocks[1], if_II.model.input_blocks[4:7]) - assert_param_count(unet.down_blocks[2], if_II.model.input_blocks[7:11]) - - if "-II-" in orig_path: - assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:17]) - assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[17:]) - if "-III-" in orig_path: - assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:15]) - assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[15:20]) - assert_param_count(unet.down_blocks[5], if_II.model.input_blocks[20:]) - - # mid block - assert_param_count(unet.mid_block, if_II.model.middle_block) - - # up block - if "-II-" in orig_path: - assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:6]) - assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[6:12]) - assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[12:16]) - assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[16:19]) - assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[19:]) - if "-III-" in orig_path: - assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:5]) - assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[5:10]) - assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[10:14]) - assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[14:18]) - assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[18:21]) - assert_param_count(unet.up_blocks[5], if_II.model.output_blocks[21:24]) - - # out params - assert_param_count(unet.conv_norm_out, if_II.model.out[0]) - assert_param_count(unet.conv_out, if_II.model.out[2]) - - # make sure all model architecture has same param count - assert_param_count(unet, if_II.model) - - -def assert_param_count(model_1, model_2): - count_1 = sum(p.numel() for p in model_1.parameters()) - count_2 = sum(p.numel() for p in model_2.parameters()) - assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}" - - -def superres_check_against_original(dump_path, unet_checkpoint_path): - model_path = dump_path - model = UNet2DConditionModel.from_pretrained(model_path) - model.to("cuda") - orig_path = unet_checkpoint_path - - if "-II-" in orig_path: - from deepfloyd_if.modules import IFStageII - - if_II_model = IFStageII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model - elif "-III-" in orig_path: - from deepfloyd_if.modules import IFStageIII - - if_II_model = IFStageIII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model - - batch_size = 1 - channels = model.config.in_channels // 2 - height = model.config.sample_size - width = model.config.sample_size - height = 1024 - width = 1024 - - torch.manual_seed(0) - - latents = torch.randn((batch_size, channels, height, width), device=model.device) - image_small = torch.randn((batch_size, channels, height // 4, width // 4), device=model.device) - - interpolate_antialias = {} - if "antialias" in inspect.signature(F.interpolate).parameters: - interpolate_antialias["antialias"] = True - image_upscaled = F.interpolate( - image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias - ) - - latent_model_input = torch.cat([latents, image_upscaled], dim=1).to(model.dtype) - t = torch.tensor([5], device=model.device).to(model.dtype) - - seq_len = 64 - encoder_hidden_states = torch.randn((batch_size, seq_len, model.config.encoder_hid_dim), device=model.device).to( - model.dtype - ) - - fake_class_labels = torch.tensor([t], device=model.device).to(model.dtype) - - with torch.no_grad(): - out = if_II_model(latent_model_input, t, aug_steps=fake_class_labels, text_emb=encoder_hidden_states) - - if_II_model.to("cpu") - del if_II_model - import gc - - torch.cuda.empty_cache() - gc.collect() - print(50 * "=") - - with torch.no_grad(): - noise_pred = model( - sample=latent_model_input, - encoder_hidden_states=encoder_hidden_states, - class_labels=fake_class_labels, - timestep=t, - ).sample - - print("Out shape", noise_pred.shape) - print("Diff", (out - noise_pred).abs().sum()) - - -if __name__ == "__main__": - main(parse_args()) diff --git a/scripts/convert_k_upscaler_to_diffusers.py b/scripts/convert_k_upscaler_to_diffusers.py deleted file mode 100644 index cff845ef8099..000000000000 --- a/scripts/convert_k_upscaler_to_diffusers.py +++ /dev/null @@ -1,297 +0,0 @@ -import argparse - -import huggingface_hub -import k_diffusion as K -import torch - -from diffusers import UNet2DConditionModel - - -UPSCALER_REPO = "pcuenq/k-upscaler" - - -def resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - rv = { - # norm1 - f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"], - f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"], - # conv1 - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"], - # norm2 - f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"], - f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"], - # conv2 - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"], - } - - if resnet.conv_shortcut is not None: - rv.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"], - } - ) - - return rv - - -def self_attn_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): - weight_q, weight_k, weight_v = checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3, dim=0) - bias_q, bias_k, bias_v = checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3, dim=0) - rv = { - # norm - f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"], - f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"], - # to_q - f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1).squeeze(-1), - f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q, - # to_k - f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1).squeeze(-1), - f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k, - # to_v - f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1).squeeze(-1), - f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v, - # to_out - f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"] - .squeeze(-1) - .squeeze(-1), - f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"], - } - - return rv - - -def cross_attn_to_diffusers_checkpoint( - checkpoint, *, diffusers_attention_prefix, diffusers_attention_index, attention_prefix -): - weight_k, weight_v = checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2, dim=0) - bias_k, bias_v = checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2, dim=0) - - rv = { - # norm2 (ada groupnorm) - f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[ - f"{attention_prefix}.norm_dec.mapper.weight" - ], - f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[ - f"{attention_prefix}.norm_dec.mapper.bias" - ], - # layernorm on encoder_hidden_state - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[ - f"{attention_prefix}.norm_enc.weight" - ], - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[ - f"{attention_prefix}.norm_enc.bias" - ], - # to_q - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[ - f"{attention_prefix}.q_proj.weight" - ] - .squeeze(-1) - .squeeze(-1), - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[ - f"{attention_prefix}.q_proj.bias" - ], - # to_k - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1).squeeze(-1), - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k, - # to_v - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1).squeeze(-1), - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v, - # to_out - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[ - f"{attention_prefix}.out_proj.weight" - ] - .squeeze(-1) - .squeeze(-1), - f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[ - f"{attention_prefix}.out_proj.bias" - ], - } - - return rv - - -def block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type): - block_prefix = "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks" - block_prefix = f"{block_prefix}.{block_idx}" - - diffusers_checkpoint = {} - - if not hasattr(block, "attentions"): - n = 1 # resnet only - elif not block.attentions[0].add_self_attention: - n = 2 # resnet -> cross-attention - else: - n = 3 # resnet -> self-attention -> cross-attention) - - for resnet_idx, resnet in enumerate(block.resnets): - # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" - diffusers_resnet_prefix = f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}" - idx = n * resnet_idx if block_type == "up" else n * resnet_idx + 1 - resnet_prefix = f"{block_prefix}.{idx}" if block_type == "up" else f"{block_prefix}.{idx}" - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - if hasattr(block, "attentions"): - for attention_idx, attention in enumerate(block.attentions): - diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}" - idx = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2 - self_attention_prefix = f"{block_prefix}.{idx}" - cross_attention_prefix = f"{block_prefix}.{idx}" - cross_attention_index = 1 if not attention.add_self_attention else 2 - idx = ( - n * attention_idx + cross_attention_index - if block_type == "up" - else n * attention_idx + cross_attention_index + 1 - ) - cross_attention_prefix = f"{block_prefix}.{idx}" - - diffusers_checkpoint.update( - cross_attn_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - diffusers_attention_index=2, - attention_prefix=cross_attention_prefix, - ) - ) - - if attention.add_self_attention is True: - diffusers_checkpoint.update( - self_attn_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - attention_prefix=self_attention_prefix, - ) - ) - - return diffusers_checkpoint - - -def unet_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # pre-processing - diffusers_checkpoint.update( - { - "conv_in.weight": checkpoint["inner_model.proj_in.weight"], - "conv_in.bias": checkpoint["inner_model.proj_in.bias"], - } - ) - - # timestep and class embedding - diffusers_checkpoint.update( - { - "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1), - "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"], - "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"], - "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"], - "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"], - "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"], - } - ) - - # down_blocks - for down_block_idx, down_block in enumerate(model.down_blocks): - diffusers_checkpoint.update(block_to_diffusers_checkpoint(down_block, checkpoint, down_block_idx, "down")) - - # up_blocks - for up_block_idx, up_block in enumerate(model.up_blocks): - diffusers_checkpoint.update(block_to_diffusers_checkpoint(up_block, checkpoint, up_block_idx, "up")) - - # post-processing - diffusers_checkpoint.update( - { - "conv_out.weight": checkpoint["inner_model.proj_out.weight"], - "conv_out.bias": checkpoint["inner_model.proj_out.bias"], - } - ) - - return diffusers_checkpoint - - -def unet_model_from_original_config(original_config): - in_channels = original_config["input_channels"] + original_config["unet_cond_dim"] - out_channels = original_config["input_channels"] + (1 if original_config["has_variance"] else 0) - - block_out_channels = original_config["channels"] - - assert len(set(original_config["depths"])) == 1, ( - "UNet2DConditionModel currently do not support blocks with different number of layers" - ) - layers_per_block = original_config["depths"][0] - - class_labels_dim = original_config["mapping_cond_dim"] - cross_attention_dim = original_config["cross_cond_dim"] - - attn1_types = [] - attn2_types = [] - for s, c in zip(original_config["self_attn_depths"], original_config["cross_attn_depths"]): - if s: - a1 = "self" - a2 = "cross" if c else None - elif c: - a1 = "cross" - a2 = None - else: - a1 = None - a2 = None - attn1_types.append(a1) - attn2_types.append(a2) - - unet = UNet2DConditionModel( - in_channels=in_channels, - out_channels=out_channels, - down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D"), - mid_block_type=None, - up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"), - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - act_fn="gelu", - norm_num_groups=None, - cross_attention_dim=cross_attention_dim, - attention_head_dim=64, - time_cond_proj_dim=class_labels_dim, - resnet_time_scale_shift="scale_shift", - time_embedding_type="fourier", - timestep_post_act="gelu", - conv_in_kernel=1, - conv_out_kernel=1, - ) - - return unet - - -def main(args): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - orig_config_path = huggingface_hub.hf_hub_download(UPSCALER_REPO, "config_laion_text_cond_latent_upscaler_2.json") - orig_weights_path = huggingface_hub.hf_hub_download( - UPSCALER_REPO, "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth" - ) - print(f"loading original model configuration from {orig_config_path}") - print(f"loading original model checkpoint from {orig_weights_path}") - - print("converting to diffusers unet") - orig_config = K.config.load_config(open(orig_config_path))["model"] - model = unet_model_from_original_config(orig_config) - - orig_checkpoint = torch.load(orig_weights_path, map_location=device)["model_ema"] - converted_checkpoint = unet_to_diffusers_checkpoint(model, orig_checkpoint) - - model.load_state_dict(converted_checkpoint, strict=True) - model.save_pretrained(args.dump_path) - print(f"saving converted unet model in {args.dump_path}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - args = parser.parse_args() - - main(args) diff --git a/scripts/convert_kakao_brain_unclip_to_diffusers.py b/scripts/convert_kakao_brain_unclip_to_diffusers.py deleted file mode 100644 index 5135eaed5b98..000000000000 --- a/scripts/convert_kakao_brain_unclip_to_diffusers.py +++ /dev/null @@ -1,1159 +0,0 @@ -import argparse -import tempfile - -import torch -from accelerate import load_checkpoint_and_dispatch -from transformers import CLIPTextModelWithProjection, CLIPTokenizer - -from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel -from diffusers.models.transformers.prior_transformer import PriorTransformer -from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel -from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler - - -r""" -Example - From the diffusers root directory: - -Download weights: -```sh -$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt -$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt -$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt -$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th -``` - -Convert the model: -```sh -$ python scripts/convert_kakao_brain_unclip_to_diffusers.py \ - --decoder_checkpoint_path ./decoder-ckpt-step\=01000000-of-01000000.ckpt \ - --super_res_unet_checkpoint_path ./improved-sr-ckpt-step\=1.2M.ckpt \ - --prior_checkpoint_path ./prior-ckpt-step\=01000000-of-01000000.ckpt \ - --clip_stat_path ./ViT-L-14_stats.th \ - --dump_path <path where to save model> -``` -""" - - -# prior - -PRIOR_ORIGINAL_PREFIX = "model" - -# Uses default arguments -PRIOR_CONFIG = {} - - -def prior_model_from_original_config(): - model = PriorTransformer(**PRIOR_CONFIG) - - return model - - -def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint): - diffusers_checkpoint = {} - - # <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1 - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight"], - "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias"], - } - ) - - # <original>.clip_img_proj -> <diffusers>.proj_in - diffusers_checkpoint.update( - { - "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight"], - "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias"], - } - ) - - # <original>.text_emb_proj -> <diffusers>.embedding_proj - diffusers_checkpoint.update( - { - "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight"], - "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias"], - } - ) - - # <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj - diffusers_checkpoint.update( - { - "encoder_hidden_states_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight"], - "encoder_hidden_states_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias"], - } - ) - - # <original>.positional_embedding -> <diffusers>.positional_embedding - diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.positional_embedding"]}) - - # <original>.prd_emb -> <diffusers>.prd_embedding - diffusers_checkpoint.update({"prd_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.prd_emb"]}) - - # <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2 - diffusers_checkpoint.update( - { - "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight"], - "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias"], - } - ) - - # <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x> - for idx in range(len(model.transformer_blocks)): - diffusers_transformer_prefix = f"transformer_blocks.{idx}" - original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}" - - # <original>.attn -> <diffusers>.attn1 - diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" - original_attention_prefix = f"{original_transformer_prefix}.attn" - diffusers_checkpoint.update( - prior_attention_to_diffusers( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - original_attention_prefix=original_attention_prefix, - attention_head_dim=model.attention_head_dim, - ) - ) - - # <original>.mlp -> <diffusers>.ff - diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" - original_ff_prefix = f"{original_transformer_prefix}.mlp" - diffusers_checkpoint.update( - prior_ff_to_diffusers( - checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix - ) - ) - - # <original>.ln_1 -> <diffusers>.norm1 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ - f"{original_transformer_prefix}.ln_1.weight" - ], - f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], - } - ) - - # <original>.ln_2 -> <diffusers>.norm3 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ - f"{original_transformer_prefix}.ln_2.weight" - ], - f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], - } - ) - - # <original>.final_ln -> <diffusers>.norm_out - diffusers_checkpoint.update( - { - "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight"], - "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias"], - } - ) - - # <original>.out_proj -> <diffusers>.proj_to_clip_embeddings - diffusers_checkpoint.update( - { - "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight"], - "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias"], - } - ) - - # clip stats - clip_mean, clip_std = clip_stats_checkpoint - clip_mean = clip_mean[None, :] - clip_std = clip_std[None, :] - - diffusers_checkpoint.update({"clip_mean": clip_mean, "clip_std": clip_std}) - - return diffusers_checkpoint - - -def prior_attention_to_diffusers( - checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim -): - diffusers_checkpoint = {} - - # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} - [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( - weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], - bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], - split=3, - chunk_size=attention_head_dim, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_q.weight": q_weight, - f"{diffusers_attention_prefix}.to_q.bias": q_bias, - f"{diffusers_attention_prefix}.to_k.weight": k_weight, - f"{diffusers_attention_prefix}.to_k.bias": k_bias, - f"{diffusers_attention_prefix}.to_v.weight": v_weight, - f"{diffusers_attention_prefix}.to_v.bias": v_bias, - } - ) - - # <original>.c_proj -> <diffusers>.to_out.0 - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], - } - ) - - return diffusers_checkpoint - - -def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): - diffusers_checkpoint = { - # <original>.c_fc -> <diffusers>.net.0.proj - f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], - f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], - # <original>.c_proj -> <diffusers>.net.2 - f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], - f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], - } - - return diffusers_checkpoint - - -# done prior - - -# decoder - -DECODER_ORIGINAL_PREFIX = "model" - -# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can -# update then. -DECODER_CONFIG = { - "sample_size": 64, - "layers_per_block": 3, - "down_block_types": ( - "ResnetDownsampleBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - ), - "up_block_types": ( - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "ResnetUpsampleBlock2D", - ), - "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", - "block_out_channels": (320, 640, 960, 1280), - "in_channels": 3, - "out_channels": 6, - "cross_attention_dim": 1536, - "class_embed_type": "identity", - "attention_head_dim": 64, - "resnet_time_scale_shift": "scale_shift", -} - - -def decoder_model_from_original_config(): - model = UNet2DConditionModel(**DECODER_CONFIG) - - return model - - -def decoder_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - original_unet_prefix = DECODER_ORIGINAL_PREFIX - num_head_channels = DECODER_CONFIG["attention_head_dim"] - - diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) - - # <original>.input_blocks -> <diffusers>.down_blocks - - original_down_block_idx = 1 - - for diffusers_down_block_idx in range(len(model.down_blocks)): - checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_down_block_idx=diffusers_down_block_idx, - original_down_block_idx=original_down_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=num_head_channels, - ) - - original_down_block_idx += num_original_down_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.input_blocks -> <diffusers>.down_blocks - - diffusers_checkpoint.update( - unet_midblock_to_diffusers_checkpoint( - model, - checkpoint, - original_unet_prefix=original_unet_prefix, - num_head_channels=num_head_channels, - ) - ) - - # <original>.output_blocks -> <diffusers>.up_blocks - - original_up_block_idx = 0 - - for diffusers_up_block_idx in range(len(model.up_blocks)): - checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_up_block_idx=diffusers_up_block_idx, - original_up_block_idx=original_up_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=num_head_channels, - ) - - original_up_block_idx += num_original_up_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.output_blocks -> <diffusers>.up_blocks - - diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) - - return diffusers_checkpoint - - -# done decoder - -# text proj - - -def text_proj_from_original_config(): - # From the conditional unet constructor where the dimension of the projected time embeddings is - # constructed - time_embed_dim = DECODER_CONFIG["block_out_channels"][0] * 4 - - cross_attention_dim = DECODER_CONFIG["cross_attention_dim"] - - model = UnCLIPTextProjModel(time_embed_dim=time_embed_dim, cross_attention_dim=cross_attention_dim) - - return model - - -# Note that the input checkpoint is the original decoder checkpoint -def text_proj_original_checkpoint_to_diffusers_checkpoint(checkpoint): - diffusers_checkpoint = { - # <original>.text_seq_proj.0 -> <diffusers>.encoder_hidden_states_proj - "encoder_hidden_states_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.weight"], - "encoder_hidden_states_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.bias"], - # <original>.text_seq_proj.1 -> <diffusers>.text_encoder_hidden_states_norm - "text_encoder_hidden_states_norm.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.weight"], - "text_encoder_hidden_states_norm.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.bias"], - # <original>.clip_tok_proj -> <diffusers>.clip_extra_context_tokens_proj - "clip_extra_context_tokens_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.weight"], - "clip_extra_context_tokens_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.bias"], - # <original>.text_feat_proj -> <diffusers>.embedding_proj - "embedding_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.weight"], - "embedding_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.bias"], - # <original>.cf_param -> <diffusers>.learned_classifier_free_guidance_embeddings - "learned_classifier_free_guidance_embeddings": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.cf_param"], - # <original>.clip_emb -> <diffusers>.clip_image_embeddings_project_to_time_embeddings - "clip_image_embeddings_project_to_time_embeddings.weight": checkpoint[ - f"{DECODER_ORIGINAL_PREFIX}.clip_emb.weight" - ], - "clip_image_embeddings_project_to_time_embeddings.bias": checkpoint[ - f"{DECODER_ORIGINAL_PREFIX}.clip_emb.bias" - ], - } - - return diffusers_checkpoint - - -# done text proj - -# super res unet first steps - -SUPER_RES_UNET_FIRST_STEPS_PREFIX = "model_first_steps" - -SUPER_RES_UNET_FIRST_STEPS_CONFIG = { - "sample_size": 256, - "layers_per_block": 3, - "down_block_types": ( - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - ), - "up_block_types": ( - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - ), - "block_out_channels": (320, 640, 960, 1280), - "in_channels": 6, - "out_channels": 3, - "add_attention": False, -} - - -def super_res_unet_first_steps_model_from_original_config(): - model = UNet2DModel(**SUPER_RES_UNET_FIRST_STEPS_CONFIG) - - return model - - -def super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - original_unet_prefix = SUPER_RES_UNET_FIRST_STEPS_PREFIX - - diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) - - # <original>.input_blocks -> <diffusers>.down_blocks - - original_down_block_idx = 1 - - for diffusers_down_block_idx in range(len(model.down_blocks)): - checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_down_block_idx=diffusers_down_block_idx, - original_down_block_idx=original_down_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - - original_down_block_idx += num_original_down_blocks - - diffusers_checkpoint.update(checkpoint_update) - - diffusers_checkpoint.update( - unet_midblock_to_diffusers_checkpoint( - model, - checkpoint, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - ) - - # <original>.output_blocks -> <diffusers>.up_blocks - - original_up_block_idx = 0 - - for diffusers_up_block_idx in range(len(model.up_blocks)): - checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_up_block_idx=diffusers_up_block_idx, - original_up_block_idx=original_up_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - - original_up_block_idx += num_original_up_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.output_blocks -> <diffusers>.up_blocks - - diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) - - return diffusers_checkpoint - - -# done super res unet first steps - -# super res unet last step - -SUPER_RES_UNET_LAST_STEP_PREFIX = "model_last_step" - -SUPER_RES_UNET_LAST_STEP_CONFIG = { - "sample_size": 256, - "layers_per_block": 3, - "down_block_types": ( - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - "ResnetDownsampleBlock2D", - ), - "up_block_types": ( - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - "ResnetUpsampleBlock2D", - ), - "block_out_channels": (320, 640, 960, 1280), - "in_channels": 6, - "out_channels": 3, - "add_attention": False, -} - - -def super_res_unet_last_step_model_from_original_config(): - model = UNet2DModel(**SUPER_RES_UNET_LAST_STEP_CONFIG) - - return model - - -def super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - original_unet_prefix = SUPER_RES_UNET_LAST_STEP_PREFIX - - diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) - - # <original>.input_blocks -> <diffusers>.down_blocks - - original_down_block_idx = 1 - - for diffusers_down_block_idx in range(len(model.down_blocks)): - checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_down_block_idx=diffusers_down_block_idx, - original_down_block_idx=original_down_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - - original_down_block_idx += num_original_down_blocks - - diffusers_checkpoint.update(checkpoint_update) - - diffusers_checkpoint.update( - unet_midblock_to_diffusers_checkpoint( - model, - checkpoint, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - ) - - # <original>.output_blocks -> <diffusers>.up_blocks - - original_up_block_idx = 0 - - for diffusers_up_block_idx in range(len(model.up_blocks)): - checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_up_block_idx=diffusers_up_block_idx, - original_up_block_idx=original_up_block_idx, - original_unet_prefix=original_unet_prefix, - num_head_channels=None, - ) - - original_up_block_idx += num_original_up_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.output_blocks -> <diffusers>.up_blocks - - diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) - diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) - - return diffusers_checkpoint - - -# done super res unet last step - - -# unet utils - - -# <original>.time_embed -> <diffusers>.time_embedding -def unet_time_embeddings(checkpoint, original_unet_prefix): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint[f"{original_unet_prefix}.time_embed.0.weight"], - "time_embedding.linear_1.bias": checkpoint[f"{original_unet_prefix}.time_embed.0.bias"], - "time_embedding.linear_2.weight": checkpoint[f"{original_unet_prefix}.time_embed.2.weight"], - "time_embedding.linear_2.bias": checkpoint[f"{original_unet_prefix}.time_embed.2.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.input_blocks.0 -> <diffusers>.conv_in -def unet_conv_in(checkpoint, original_unet_prefix): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_in.weight": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.weight"], - "conv_in.bias": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.out.0 -> <diffusers>.conv_norm_out -def unet_conv_norm_out(checkpoint, original_unet_prefix): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_norm_out.weight": checkpoint[f"{original_unet_prefix}.out.0.weight"], - "conv_norm_out.bias": checkpoint[f"{original_unet_prefix}.out.0.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.out.2 -> <diffusers>.conv_out -def unet_conv_out(checkpoint, original_unet_prefix): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_out.weight": checkpoint[f"{original_unet_prefix}.out.2.weight"], - "conv_out.bias": checkpoint[f"{original_unet_prefix}.out.2.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.input_blocks -> <diffusers>.down_blocks -def unet_downblock_to_diffusers_checkpoint( - model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, original_unet_prefix, num_head_channels -): - diffusers_checkpoint = {} - - diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.resnets" - original_down_block_prefix = f"{original_unet_prefix}.input_blocks" - - down_block = model.down_blocks[diffusers_down_block_idx] - - num_resnets = len(down_block.resnets) - - if down_block.downsamplers is None: - downsampler = False - else: - assert len(down_block.downsamplers) == 1 - downsampler = True - # The downsample block is also a resnet - num_resnets += 1 - - for resnet_idx_inc in range(num_resnets): - full_resnet_prefix = f"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0" - - if downsampler and resnet_idx_inc == num_resnets - 1: - # this is a downsample block - full_diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.downsamplers.0" - else: - # this is a regular resnet block - full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix - ) - ) - - if hasattr(down_block, "attentions"): - num_attentions = len(down_block.attentions) - diffusers_attention_prefix = f"down_blocks.{diffusers_down_block_idx}.attentions" - - for attention_idx_inc in range(num_attentions): - full_attention_prefix = f"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1" - full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" - - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - attention_prefix=full_attention_prefix, - diffusers_attention_prefix=full_diffusers_attention_prefix, - num_head_channels=num_head_channels, - ) - ) - - num_original_down_blocks = num_resnets - - return diffusers_checkpoint, num_original_down_blocks - - -# <original>.middle_block -> <diffusers>.mid_block -def unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, original_unet_prefix, num_head_channels): - diffusers_checkpoint = {} - - # block 0 - - original_block_idx = 0 - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, - diffusers_resnet_prefix="mid_block.resnets.0", - resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", - ) - ) - - original_block_idx += 1 - - # optional block 1 - - if hasattr(model.mid_block, "attentions") and model.mid_block.attentions[0] is not None: - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix="mid_block.attentions.0", - attention_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", - num_head_channels=num_head_channels, - ) - ) - original_block_idx += 1 - - # block 1 or block 2 - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, - diffusers_resnet_prefix="mid_block.resnets.1", - resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", - ) - ) - - return diffusers_checkpoint - - -# <original>.output_blocks -> <diffusers>.up_blocks -def unet_upblock_to_diffusers_checkpoint( - model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, original_unet_prefix, num_head_channels -): - diffusers_checkpoint = {} - - diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.resnets" - original_up_block_prefix = f"{original_unet_prefix}.output_blocks" - - up_block = model.up_blocks[diffusers_up_block_idx] - - num_resnets = len(up_block.resnets) - - if up_block.upsamplers is None: - upsampler = False - else: - assert len(up_block.upsamplers) == 1 - upsampler = True - # The upsample block is also a resnet - num_resnets += 1 - - has_attentions = hasattr(up_block, "attentions") - - for resnet_idx_inc in range(num_resnets): - if upsampler and resnet_idx_inc == num_resnets - 1: - # this is an upsample block - if has_attentions: - # There is a middle attention block that we skip - original_resnet_block_idx = 2 - else: - original_resnet_block_idx = 1 - - # we add the `minus 1` because the last two resnets are stuck together in the same output block - full_resnet_prefix = ( - f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}" - ) - - full_diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.upsamplers.0" - else: - # this is a regular resnet block - full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0" - full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix - ) - ) - - if has_attentions: - num_attentions = len(up_block.attentions) - diffusers_attention_prefix = f"up_blocks.{diffusers_up_block_idx}.attentions" - - for attention_idx_inc in range(num_attentions): - full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1" - full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" - - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - attention_prefix=full_attention_prefix, - diffusers_attention_prefix=full_diffusers_attention_prefix, - num_head_channels=num_head_channels, - ) - ) - - num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets - - return diffusers_checkpoint, num_original_down_blocks - - -def resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - diffusers_checkpoint = { - f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.in_layers.0.weight"], - f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.in_layers.0.bias"], - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.in_layers.2.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.in_layers.2.bias"], - f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[f"{resnet_prefix}.emb_layers.1.weight"], - f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[f"{resnet_prefix}.emb_layers.1.bias"], - f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.out_layers.0.weight"], - f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.out_layers.0.bias"], - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.out_layers.3.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.out_layers.3.bias"], - } - - skip_connection_prefix = f"{resnet_prefix}.skip_connection" - - if f"{skip_connection_prefix}.weight" in checkpoint: - diffusers_checkpoint.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{skip_connection_prefix}.weight"], - f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{skip_connection_prefix}.bias"], - } - ) - - return diffusers_checkpoint - - -def attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels): - diffusers_checkpoint = {} - - # <original>.norm -> <diffusers>.group_norm - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], - f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], - } - ) - - # <original>.qkv -> <diffusers>.{query, key, value} - [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( - weight=checkpoint[f"{attention_prefix}.qkv.weight"][:, :, 0], - bias=checkpoint[f"{attention_prefix}.qkv.bias"], - split=3, - chunk_size=num_head_channels, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_q.weight": q_weight, - f"{diffusers_attention_prefix}.to_q.bias": q_bias, - f"{diffusers_attention_prefix}.to_k.weight": k_weight, - f"{diffusers_attention_prefix}.to_k.bias": k_bias, - f"{diffusers_attention_prefix}.to_v.weight": v_weight, - f"{diffusers_attention_prefix}.to_v.bias": v_bias, - } - ) - - # <original>.encoder_kv -> <diffusers>.{context_key, context_value} - [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( - weight=checkpoint[f"{attention_prefix}.encoder_kv.weight"][:, :, 0], - bias=checkpoint[f"{attention_prefix}.encoder_kv.bias"], - split=2, - chunk_size=num_head_channels, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.add_k_proj.weight": encoder_k_weight, - f"{diffusers_attention_prefix}.add_k_proj.bias": encoder_k_bias, - f"{diffusers_attention_prefix}.add_v_proj.weight": encoder_v_weight, - f"{diffusers_attention_prefix}.add_v_proj.bias": encoder_v_bias, - } - ) - - # <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear) - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ - :, :, 0 - ], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], - } - ) - - return diffusers_checkpoint - - -# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) -def split_attentions(*, weight, bias, split, chunk_size): - weights = [None] * split - biases = [None] * split - - weights_biases_idx = 0 - - for starting_row_index in range(0, weight.shape[0], chunk_size): - row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) - - weight_rows = weight[row_indices, :] - bias_rows = bias[row_indices] - - if weights[weights_biases_idx] is None: - assert weights[weights_biases_idx] is None - weights[weights_biases_idx] = weight_rows - biases[weights_biases_idx] = bias_rows - else: - assert weights[weights_biases_idx] is not None - weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) - biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) - - weights_biases_idx = (weights_biases_idx + 1) % split - - return weights, biases - - -# done unet utils - - -# Driver functions - - -def text_encoder(): - print("loading CLIP text encoder") - - clip_name = "openai/clip-vit-large-patch14" - - # sets pad_value to 0 - pad_token = "!" - - tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto") - - assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0 - - text_encoder_model = CLIPTextModelWithProjection.from_pretrained( - clip_name, - # `CLIPTextModel` does not support device_map="auto" - # device_map="auto" - ) - - print("done loading CLIP text encoder") - - return text_encoder_model, tokenizer_model - - -def prior(*, args, checkpoint_map_location): - print("loading prior") - - prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) - prior_checkpoint = prior_checkpoint["state_dict"] - - clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location) - - prior_model = prior_model_from_original_config() - - prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint( - prior_model, prior_checkpoint, clip_stats_checkpoint - ) - - del prior_checkpoint - del clip_stats_checkpoint - - load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True) - - print("done loading prior") - - return prior_model - - -def decoder(*, args, checkpoint_map_location): - print("loading decoder") - - decoder_checkpoint = torch.load(args.decoder_checkpoint_path, map_location=checkpoint_map_location) - decoder_checkpoint = decoder_checkpoint["state_dict"] - - decoder_model = decoder_model_from_original_config() - - decoder_diffusers_checkpoint = decoder_original_checkpoint_to_diffusers_checkpoint( - decoder_model, decoder_checkpoint - ) - - # text proj interlude - - # The original decoder implementation includes a set of parameters that are used - # for creating the `encoder_hidden_states` which are what the U-net is conditioned - # on. The diffusers conditional unet directly takes the encoder_hidden_states. We pull - # the parameters into the UnCLIPTextProjModel class - text_proj_model = text_proj_from_original_config() - - text_proj_checkpoint = text_proj_original_checkpoint_to_diffusers_checkpoint(decoder_checkpoint) - - load_checkpoint_to_model(text_proj_checkpoint, text_proj_model, strict=True) - - # done text proj interlude - - del decoder_checkpoint - - load_checkpoint_to_model(decoder_diffusers_checkpoint, decoder_model, strict=True) - - print("done loading decoder") - - return decoder_model, text_proj_model - - -def super_res_unet(*, args, checkpoint_map_location): - print("loading super resolution unet") - - super_res_checkpoint = torch.load(args.super_res_unet_checkpoint_path, map_location=checkpoint_map_location) - super_res_checkpoint = super_res_checkpoint["state_dict"] - - # model_first_steps - - super_res_first_model = super_res_unet_first_steps_model_from_original_config() - - super_res_first_steps_checkpoint = super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint( - super_res_first_model, super_res_checkpoint - ) - - # model_last_step - super_res_last_model = super_res_unet_last_step_model_from_original_config() - - super_res_last_step_checkpoint = super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint( - super_res_last_model, super_res_checkpoint - ) - - del super_res_checkpoint - - load_checkpoint_to_model(super_res_first_steps_checkpoint, super_res_first_model, strict=True) - - load_checkpoint_to_model(super_res_last_step_checkpoint, super_res_last_model, strict=True) - - print("done loading super resolution unet") - - return super_res_first_model, super_res_last_model - - -def load_checkpoint_to_model(checkpoint, model, strict=False): - with tempfile.NamedTemporaryFile() as file: - torch.save(checkpoint, file.name) - del checkpoint - if strict: - model.load_state_dict(torch.load(file.name), strict=True) - else: - load_checkpoint_and_dispatch(model, file.name, device_map="auto") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--prior_checkpoint_path", - default=None, - type=str, - required=True, - help="Path to the prior checkpoint to convert.", - ) - - parser.add_argument( - "--decoder_checkpoint_path", - default=None, - type=str, - required=True, - help="Path to the decoder checkpoint to convert.", - ) - - parser.add_argument( - "--super_res_unet_checkpoint_path", - default=None, - type=str, - required=True, - help="Path to the super resolution checkpoint to convert.", - ) - - parser.add_argument( - "--clip_stat_path", default=None, type=str, required=True, help="Path to the clip stats checkpoint to convert." - ) - - parser.add_argument( - "--checkpoint_load_device", - default="cpu", - type=str, - required=False, - help="The device passed to `map_location` when loading checkpoints.", - ) - - parser.add_argument( - "--debug", - default=None, - type=str, - required=False, - help="Only run a specific stage of the convert script. Used for debugging", - ) - - args = parser.parse_args() - - print(f"loading checkpoints to {args.checkpoint_load_device}") - - checkpoint_map_location = torch.device(args.checkpoint_load_device) - - if args.debug is not None: - print(f"debug: only executing {args.debug}") - - if args.debug is None: - text_encoder_model, tokenizer_model = text_encoder() - - prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) - - decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location) - - super_res_first_model, super_res_last_model = super_res_unet( - args=args, checkpoint_map_location=checkpoint_map_location - ) - - prior_scheduler = UnCLIPScheduler( - variance_type="fixed_small_log", - prediction_type="sample", - num_train_timesteps=1000, - clip_sample_range=5.0, - ) - - decoder_scheduler = UnCLIPScheduler( - variance_type="learned_range", - prediction_type="epsilon", - num_train_timesteps=1000, - ) - - super_res_scheduler = UnCLIPScheduler( - variance_type="fixed_small_log", - prediction_type="epsilon", - num_train_timesteps=1000, - ) - - print(f"saving Kakao Brain unCLIP to {args.dump_path}") - - pipe = UnCLIPPipeline( - prior=prior_model, - decoder=decoder_model, - text_proj=text_proj_model, - tokenizer=tokenizer_model, - text_encoder=text_encoder_model, - super_res_first=super_res_first_model, - super_res_last=super_res_last_model, - prior_scheduler=prior_scheduler, - decoder_scheduler=decoder_scheduler, - super_res_scheduler=super_res_scheduler, - ) - pipe.save_pretrained(args.dump_path) - - print("done writing Kakao Brain unCLIP") - elif args.debug == "text_encoder": - text_encoder_model, tokenizer_model = text_encoder() - elif args.debug == "prior": - prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) - elif args.debug == "decoder": - decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location) - elif args.debug == "super_res_unet": - super_res_first_model, super_res_last_model = super_res_unet( - args=args, checkpoint_map_location=checkpoint_map_location - ) - else: - raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_kandinsky3_unet.py b/scripts/convert_kandinsky3_unet.py deleted file mode 100644 index 4fe8c54eb7fc..000000000000 --- a/scripts/convert_kandinsky3_unet.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import fnmatch - -from safetensors.torch import load_file - -from diffusers import Kandinsky3UNet - - -MAPPING = { - "to_time_embed.1": "time_embedding.linear_1", - "to_time_embed.3": "time_embedding.linear_2", - "in_layer": "conv_in", - "out_layer.0": "conv_norm_out", - "out_layer.2": "conv_out", - "down_samples": "down_blocks", - "up_samples": "up_blocks", - "projection_lin": "encoder_hid_proj.projection_linear", - "projection_ln": "encoder_hid_proj.projection_norm", - "feature_pooling": "add_time_condition", - "to_query": "to_q", - "to_key": "to_k", - "to_value": "to_v", - "output_layer": "to_out.0", - "self_attention_block": "attentions.0", -} - -DYNAMIC_MAP = { - "resnet_attn_blocks.*.0": "resnets_in.*", - "resnet_attn_blocks.*.1": ("attentions.*", 1), - "resnet_attn_blocks.*.2": "resnets_out.*", -} -# MAPPING = {} - - -def convert_state_dict(unet_state_dict): - """ - Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model. - Args: - unet_model (torch.nn.Module): The original U-Net model. - unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with. - - Returns: - OrderedDict: The converted state dictionary. - """ - # Example of renaming logic (this will vary based on your model's architecture) - converted_state_dict = {} - for key in unet_state_dict: - new_key = key - for pattern, new_pattern in MAPPING.items(): - new_key = new_key.replace(pattern, new_pattern) - - for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items(): - has_matched = False - if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched: - star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1]) - - if isinstance(dyn_new_pattern, tuple): - new_star = star + dyn_new_pattern[-1] - dyn_new_pattern = dyn_new_pattern[0] - else: - new_star = star - - pattern = dyn_pattern.replace("*", str(star)) - new_pattern = dyn_new_pattern.replace("*", str(new_star)) - - new_key = new_key.replace(pattern, new_pattern) - has_matched = True - - converted_state_dict[new_key] = unet_state_dict[key] - - return converted_state_dict - - -def main(model_path, output_path): - # Load your original U-Net model - unet_state_dict = load_file(model_path) - - # Initialize your Kandinsky3UNet model - config = {} - - # Convert the state dict - converted_state_dict = convert_state_dict(unet_state_dict) - - unet = Kandinsky3UNet(config) - unet.load_state_dict(converted_state_dict) - - unet.save_pretrained(output_path) - print(f"Converted model saved to {output_path}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format") - parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model") - parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model") - - args = parser.parse_args() - main(args.model_path, args.output_path) diff --git a/scripts/convert_kandinsky_to_diffusers.py b/scripts/convert_kandinsky_to_diffusers.py deleted file mode 100644 index 8d3f7b63d0c1..000000000000 --- a/scripts/convert_kandinsky_to_diffusers.py +++ /dev/null @@ -1,1411 +0,0 @@ -import argparse -import os -import tempfile - -import torch -from accelerate import load_checkpoint_and_dispatch - -from diffusers import UNet2DConditionModel -from diffusers.models.transformers.prior_transformer import PriorTransformer -from diffusers.models.vq_model import VQModel - - -""" -Example - From the diffusers root directory: - -Download weights: -```sh -$ wget https://huggingface.co/ai-forever/Kandinsky_2.1/blob/main/prior_fp16.ckpt -``` - -Convert the model: -```sh -python scripts/convert_kandinsky_to_diffusers.py \ - --prior_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/prior_fp16.ckpt \ - --clip_stat_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/ViT-L-14_stats.th \ - --text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/decoder_fp16.ckpt \ - --inpaint_text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/inpainting_fp16.ckpt \ - --movq_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/movq_final.ckpt \ - --dump_path /home/yiyi_huggingface_co/dump \ - --debug decoder -``` -""" - - -# prior - -PRIOR_ORIGINAL_PREFIX = "model" - -# Uses default arguments -PRIOR_CONFIG = {} - - -def prior_model_from_original_config(): - model = PriorTransformer(**PRIOR_CONFIG) - - return model - - -def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint): - diffusers_checkpoint = {} - - # <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1 - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight"], - "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias"], - } - ) - - # <original>.clip_img_proj -> <diffusers>.proj_in - diffusers_checkpoint.update( - { - "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight"], - "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias"], - } - ) - - # <original>.text_emb_proj -> <diffusers>.embedding_proj - diffusers_checkpoint.update( - { - "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight"], - "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias"], - } - ) - - # <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj - diffusers_checkpoint.update( - { - "encoder_hidden_states_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight"], - "encoder_hidden_states_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias"], - } - ) - - # <original>.positional_embedding -> <diffusers>.positional_embedding - diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.positional_embedding"]}) - - # <original>.prd_emb -> <diffusers>.prd_embedding - diffusers_checkpoint.update({"prd_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.prd_emb"]}) - - # <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2 - diffusers_checkpoint.update( - { - "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight"], - "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias"], - } - ) - - # <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x> - for idx in range(len(model.transformer_blocks)): - diffusers_transformer_prefix = f"transformer_blocks.{idx}" - original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}" - - # <original>.attn -> <diffusers>.attn1 - diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" - original_attention_prefix = f"{original_transformer_prefix}.attn" - diffusers_checkpoint.update( - prior_attention_to_diffusers( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - original_attention_prefix=original_attention_prefix, - attention_head_dim=model.attention_head_dim, - ) - ) - - # <original>.mlp -> <diffusers>.ff - diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" - original_ff_prefix = f"{original_transformer_prefix}.mlp" - diffusers_checkpoint.update( - prior_ff_to_diffusers( - checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix - ) - ) - - # <original>.ln_1 -> <diffusers>.norm1 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ - f"{original_transformer_prefix}.ln_1.weight" - ], - f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], - } - ) - - # <original>.ln_2 -> <diffusers>.norm3 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ - f"{original_transformer_prefix}.ln_2.weight" - ], - f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], - } - ) - - # <original>.final_ln -> <diffusers>.norm_out - diffusers_checkpoint.update( - { - "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight"], - "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias"], - } - ) - - # <original>.out_proj -> <diffusers>.proj_to_clip_embeddings - diffusers_checkpoint.update( - { - "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight"], - "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias"], - } - ) - - # clip stats - clip_mean, clip_std = clip_stats_checkpoint - clip_mean = clip_mean[None, :] - clip_std = clip_std[None, :] - - diffusers_checkpoint.update({"clip_mean": clip_mean, "clip_std": clip_std}) - - return diffusers_checkpoint - - -def prior_attention_to_diffusers( - checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim -): - diffusers_checkpoint = {} - - # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} - [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( - weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], - bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], - split=3, - chunk_size=attention_head_dim, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_q.weight": q_weight, - f"{diffusers_attention_prefix}.to_q.bias": q_bias, - f"{diffusers_attention_prefix}.to_k.weight": k_weight, - f"{diffusers_attention_prefix}.to_k.bias": k_bias, - f"{diffusers_attention_prefix}.to_v.weight": v_weight, - f"{diffusers_attention_prefix}.to_v.bias": v_bias, - } - ) - - # <original>.c_proj -> <diffusers>.to_out.0 - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], - } - ) - - return diffusers_checkpoint - - -def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): - diffusers_checkpoint = { - # <original>.c_fc -> <diffusers>.net.0.proj - f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], - f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], - # <original>.c_proj -> <diffusers>.net.2 - f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], - f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], - } - - return diffusers_checkpoint - - -# done prior - -# unet - -# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can -# update then. - -UNET_CONFIG = { - "act_fn": "silu", - "addition_embed_type": "text_image", - "addition_embed_type_num_heads": 64, - "attention_head_dim": 64, - "block_out_channels": [384, 768, 1152, 1536], - "center_input_sample": False, - "class_embed_type": None, - "class_embeddings_concat": False, - "conv_in_kernel": 3, - "conv_out_kernel": 3, - "cross_attention_dim": 768, - "cross_attention_norm": None, - "down_block_types": [ - "ResnetDownsampleBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - ], - "downsample_padding": 1, - "dual_cross_attention": False, - "encoder_hid_dim": 1024, - "encoder_hid_dim_type": "text_image_proj", - "flip_sin_to_cos": True, - "freq_shift": 0, - "in_channels": 4, - "layers_per_block": 3, - "mid_block_only_cross_attention": None, - "mid_block_scale_factor": 1, - "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", - "norm_eps": 1e-05, - "norm_num_groups": 32, - "num_class_embeds": None, - "only_cross_attention": False, - "out_channels": 8, - "projection_class_embeddings_input_dim": None, - "resnet_out_scale_factor": 1.0, - "resnet_skip_time_act": False, - "resnet_time_scale_shift": "scale_shift", - "sample_size": 64, - "time_cond_proj_dim": None, - "time_embedding_act_fn": None, - "time_embedding_dim": None, - "time_embedding_type": "positional", - "timestep_post_act": None, - "up_block_types": [ - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "ResnetUpsampleBlock2D", - ], - "upcast_attention": False, - "use_linear_projection": False, -} - - -def unet_model_from_original_config(): - model = UNet2DConditionModel(**UNET_CONFIG) - - return model - - -def unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - num_head_channels = UNET_CONFIG["attention_head_dim"] - - diffusers_checkpoint.update(unet_time_embeddings(checkpoint)) - diffusers_checkpoint.update(unet_conv_in(checkpoint)) - diffusers_checkpoint.update(unet_add_embedding(checkpoint)) - diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint)) - - # <original>.input_blocks -> <diffusers>.down_blocks - - original_down_block_idx = 1 - - for diffusers_down_block_idx in range(len(model.down_blocks)): - checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_down_block_idx=diffusers_down_block_idx, - original_down_block_idx=original_down_block_idx, - num_head_channels=num_head_channels, - ) - - original_down_block_idx += num_original_down_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.input_blocks -> <diffusers>.down_blocks - - diffusers_checkpoint.update( - unet_midblock_to_diffusers_checkpoint( - model, - checkpoint, - num_head_channels=num_head_channels, - ) - ) - - # <original>.output_blocks -> <diffusers>.up_blocks - - original_up_block_idx = 0 - - for diffusers_up_block_idx in range(len(model.up_blocks)): - checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_up_block_idx=diffusers_up_block_idx, - original_up_block_idx=original_up_block_idx, - num_head_channels=num_head_channels, - ) - - original_up_block_idx += num_original_up_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.output_blocks -> <diffusers>.up_blocks - - diffusers_checkpoint.update(unet_conv_norm_out(checkpoint)) - diffusers_checkpoint.update(unet_conv_out(checkpoint)) - - return diffusers_checkpoint - - -# done unet - -# inpaint unet - -# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can -# update then. - -INPAINT_UNET_CONFIG = { - "act_fn": "silu", - "addition_embed_type": "text_image", - "addition_embed_type_num_heads": 64, - "attention_head_dim": 64, - "block_out_channels": [384, 768, 1152, 1536], - "center_input_sample": False, - "class_embed_type": None, - "class_embeddings_concat": None, - "conv_in_kernel": 3, - "conv_out_kernel": 3, - "cross_attention_dim": 768, - "cross_attention_norm": None, - "down_block_types": [ - "ResnetDownsampleBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - "SimpleCrossAttnDownBlock2D", - ], - "downsample_padding": 1, - "dual_cross_attention": False, - "encoder_hid_dim": 1024, - "encoder_hid_dim_type": "text_image_proj", - "flip_sin_to_cos": True, - "freq_shift": 0, - "in_channels": 9, - "layers_per_block": 3, - "mid_block_only_cross_attention": None, - "mid_block_scale_factor": 1, - "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", - "norm_eps": 1e-05, - "norm_num_groups": 32, - "num_class_embeds": None, - "only_cross_attention": False, - "out_channels": 8, - "projection_class_embeddings_input_dim": None, - "resnet_out_scale_factor": 1.0, - "resnet_skip_time_act": False, - "resnet_time_scale_shift": "scale_shift", - "sample_size": 64, - "time_cond_proj_dim": None, - "time_embedding_act_fn": None, - "time_embedding_dim": None, - "time_embedding_type": "positional", - "timestep_post_act": None, - "up_block_types": [ - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "SimpleCrossAttnUpBlock2D", - "ResnetUpsampleBlock2D", - ], - "upcast_attention": False, - "use_linear_projection": False, -} - - -def inpaint_unet_model_from_original_config(): - model = UNet2DConditionModel(**INPAINT_UNET_CONFIG) - - return model - - -def inpaint_unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - num_head_channels = INPAINT_UNET_CONFIG["attention_head_dim"] - - diffusers_checkpoint.update(unet_time_embeddings(checkpoint)) - diffusers_checkpoint.update(unet_conv_in(checkpoint)) - diffusers_checkpoint.update(unet_add_embedding(checkpoint)) - diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint)) - - # <original>.input_blocks -> <diffusers>.down_blocks - - original_down_block_idx = 1 - - for diffusers_down_block_idx in range(len(model.down_blocks)): - checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_down_block_idx=diffusers_down_block_idx, - original_down_block_idx=original_down_block_idx, - num_head_channels=num_head_channels, - ) - - original_down_block_idx += num_original_down_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.input_blocks -> <diffusers>.down_blocks - - diffusers_checkpoint.update( - unet_midblock_to_diffusers_checkpoint( - model, - checkpoint, - num_head_channels=num_head_channels, - ) - ) - - # <original>.output_blocks -> <diffusers>.up_blocks - - original_up_block_idx = 0 - - for diffusers_up_block_idx in range(len(model.up_blocks)): - checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( - model, - checkpoint, - diffusers_up_block_idx=diffusers_up_block_idx, - original_up_block_idx=original_up_block_idx, - num_head_channels=num_head_channels, - ) - - original_up_block_idx += num_original_up_blocks - - diffusers_checkpoint.update(checkpoint_update) - - # done <original>.output_blocks -> <diffusers>.up_blocks - - diffusers_checkpoint.update(unet_conv_norm_out(checkpoint)) - diffusers_checkpoint.update(unet_conv_out(checkpoint)) - - return diffusers_checkpoint - - -# done inpaint unet - - -# unet utils - - -# <original>.time_embed -> <diffusers>.time_embedding -def unet_time_embeddings(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint["time_embed.0.weight"], - "time_embedding.linear_1.bias": checkpoint["time_embed.0.bias"], - "time_embedding.linear_2.weight": checkpoint["time_embed.2.weight"], - "time_embedding.linear_2.bias": checkpoint["time_embed.2.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.input_blocks.0 -> <diffusers>.conv_in -def unet_conv_in(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_in.weight": checkpoint["input_blocks.0.0.weight"], - "conv_in.bias": checkpoint["input_blocks.0.0.bias"], - } - ) - - return diffusers_checkpoint - - -def unet_add_embedding(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "add_embedding.text_norm.weight": checkpoint["ln_model_n.weight"], - "add_embedding.text_norm.bias": checkpoint["ln_model_n.bias"], - "add_embedding.text_proj.weight": checkpoint["proj_n.weight"], - "add_embedding.text_proj.bias": checkpoint["proj_n.bias"], - "add_embedding.image_proj.weight": checkpoint["img_layer.weight"], - "add_embedding.image_proj.bias": checkpoint["img_layer.bias"], - } - ) - - return diffusers_checkpoint - - -def unet_encoder_hid_proj(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "encoder_hid_proj.image_embeds.weight": checkpoint["clip_to_seq.weight"], - "encoder_hid_proj.image_embeds.bias": checkpoint["clip_to_seq.bias"], - "encoder_hid_proj.text_proj.weight": checkpoint["to_model_dim_n.weight"], - "encoder_hid_proj.text_proj.bias": checkpoint["to_model_dim_n.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.out.0 -> <diffusers>.conv_norm_out -def unet_conv_norm_out(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_norm_out.weight": checkpoint["out.0.weight"], - "conv_norm_out.bias": checkpoint["out.0.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.out.2 -> <diffusers>.conv_out -def unet_conv_out(checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update( - { - "conv_out.weight": checkpoint["out.2.weight"], - "conv_out.bias": checkpoint["out.2.bias"], - } - ) - - return diffusers_checkpoint - - -# <original>.input_blocks -> <diffusers>.down_blocks -def unet_downblock_to_diffusers_checkpoint( - model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, num_head_channels -): - diffusers_checkpoint = {} - - diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.resnets" - original_down_block_prefix = "input_blocks" - - down_block = model.down_blocks[diffusers_down_block_idx] - - num_resnets = len(down_block.resnets) - - if down_block.downsamplers is None: - downsampler = False - else: - assert len(down_block.downsamplers) == 1 - downsampler = True - # The downsample block is also a resnet - num_resnets += 1 - - for resnet_idx_inc in range(num_resnets): - full_resnet_prefix = f"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0" - - if downsampler and resnet_idx_inc == num_resnets - 1: - # this is a downsample block - full_diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.downsamplers.0" - else: - # this is a regular resnet block - full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix - ) - ) - - if hasattr(down_block, "attentions"): - num_attentions = len(down_block.attentions) - diffusers_attention_prefix = f"down_blocks.{diffusers_down_block_idx}.attentions" - - for attention_idx_inc in range(num_attentions): - full_attention_prefix = f"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1" - full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" - - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - attention_prefix=full_attention_prefix, - diffusers_attention_prefix=full_diffusers_attention_prefix, - num_head_channels=num_head_channels, - ) - ) - - num_original_down_blocks = num_resnets - - return diffusers_checkpoint, num_original_down_blocks - - -# <original>.middle_block -> <diffusers>.mid_block -def unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, num_head_channels): - diffusers_checkpoint = {} - - # block 0 - - original_block_idx = 0 - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, - diffusers_resnet_prefix="mid_block.resnets.0", - resnet_prefix=f"middle_block.{original_block_idx}", - ) - ) - - original_block_idx += 1 - - # optional block 1 - - if hasattr(model.mid_block, "attentions") and model.mid_block.attentions[0] is not None: - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix="mid_block.attentions.0", - attention_prefix=f"middle_block.{original_block_idx}", - num_head_channels=num_head_channels, - ) - ) - original_block_idx += 1 - - # block 1 or block 2 - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, - diffusers_resnet_prefix="mid_block.resnets.1", - resnet_prefix=f"middle_block.{original_block_idx}", - ) - ) - - return diffusers_checkpoint - - -# <original>.output_blocks -> <diffusers>.up_blocks -def unet_upblock_to_diffusers_checkpoint( - model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, num_head_channels -): - diffusers_checkpoint = {} - - diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.resnets" - original_up_block_prefix = "output_blocks" - - up_block = model.up_blocks[diffusers_up_block_idx] - - num_resnets = len(up_block.resnets) - - if up_block.upsamplers is None: - upsampler = False - else: - assert len(up_block.upsamplers) == 1 - upsampler = True - # The upsample block is also a resnet - num_resnets += 1 - - has_attentions = hasattr(up_block, "attentions") - - for resnet_idx_inc in range(num_resnets): - if upsampler and resnet_idx_inc == num_resnets - 1: - # this is an upsample block - if has_attentions: - # There is a middle attention block that we skip - original_resnet_block_idx = 2 - else: - original_resnet_block_idx = 1 - - # we add the `minus 1` because the last two resnets are stuck together in the same output block - full_resnet_prefix = ( - f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}" - ) - - full_diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.upsamplers.0" - else: - # this is a regular resnet block - full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0" - full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" - - diffusers_checkpoint.update( - resnet_to_diffusers_checkpoint( - checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix - ) - ) - - if has_attentions: - num_attentions = len(up_block.attentions) - diffusers_attention_prefix = f"up_blocks.{diffusers_up_block_idx}.attentions" - - for attention_idx_inc in range(num_attentions): - full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1" - full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" - - diffusers_checkpoint.update( - attention_to_diffusers_checkpoint( - checkpoint, - attention_prefix=full_attention_prefix, - diffusers_attention_prefix=full_diffusers_attention_prefix, - num_head_channels=num_head_channels, - ) - ) - - num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets - - return diffusers_checkpoint, num_original_down_blocks - - -def resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - diffusers_checkpoint = { - f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.in_layers.0.weight"], - f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.in_layers.0.bias"], - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.in_layers.2.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.in_layers.2.bias"], - f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[f"{resnet_prefix}.emb_layers.1.weight"], - f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[f"{resnet_prefix}.emb_layers.1.bias"], - f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.out_layers.0.weight"], - f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.out_layers.0.bias"], - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.out_layers.3.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.out_layers.3.bias"], - } - - skip_connection_prefix = f"{resnet_prefix}.skip_connection" - - if f"{skip_connection_prefix}.weight" in checkpoint: - diffusers_checkpoint.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{skip_connection_prefix}.weight"], - f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{skip_connection_prefix}.bias"], - } - ) - - return diffusers_checkpoint - - -def attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels): - diffusers_checkpoint = {} - - # <original>.norm -> <diffusers>.group_norm - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], - f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], - } - ) - - # <original>.qkv -> <diffusers>.{query, key, value} - [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( - weight=checkpoint[f"{attention_prefix}.qkv.weight"][:, :, 0], - bias=checkpoint[f"{attention_prefix}.qkv.bias"], - split=3, - chunk_size=num_head_channels, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_q.weight": q_weight, - f"{diffusers_attention_prefix}.to_q.bias": q_bias, - f"{diffusers_attention_prefix}.to_k.weight": k_weight, - f"{diffusers_attention_prefix}.to_k.bias": k_bias, - f"{diffusers_attention_prefix}.to_v.weight": v_weight, - f"{diffusers_attention_prefix}.to_v.bias": v_bias, - } - ) - - # <original>.encoder_kv -> <diffusers>.{context_key, context_value} - [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( - weight=checkpoint[f"{attention_prefix}.encoder_kv.weight"][:, :, 0], - bias=checkpoint[f"{attention_prefix}.encoder_kv.bias"], - split=2, - chunk_size=num_head_channels, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.add_k_proj.weight": encoder_k_weight, - f"{diffusers_attention_prefix}.add_k_proj.bias": encoder_k_bias, - f"{diffusers_attention_prefix}.add_v_proj.weight": encoder_v_weight, - f"{diffusers_attention_prefix}.add_v_proj.bias": encoder_v_bias, - } - ) - - # <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear) - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ - :, :, 0 - ], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], - } - ) - - return diffusers_checkpoint - - -# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) -def split_attentions(*, weight, bias, split, chunk_size): - weights = [None] * split - biases = [None] * split - - weights_biases_idx = 0 - - for starting_row_index in range(0, weight.shape[0], chunk_size): - row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) - - weight_rows = weight[row_indices, :] - bias_rows = bias[row_indices] - - if weights[weights_biases_idx] is None: - assert weights[weights_biases_idx] is None - weights[weights_biases_idx] = weight_rows - biases[weights_biases_idx] = bias_rows - else: - assert weights[weights_biases_idx] is not None - weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) - biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) - - weights_biases_idx = (weights_biases_idx + 1) % split - - return weights, biases - - -# done unet utils - - -def prior(*, args, checkpoint_map_location): - print("loading prior") - - prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) - - clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location) - - prior_model = prior_model_from_original_config() - - prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint( - prior_model, prior_checkpoint, clip_stats_checkpoint - ) - - del prior_checkpoint - del clip_stats_checkpoint - - load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True) - - print("done loading prior") - - return prior_model - - -def text2img(*, args, checkpoint_map_location): - print("loading text2img") - - text2img_checkpoint = torch.load(args.text2img_checkpoint_path, map_location=checkpoint_map_location) - - unet_model = unet_model_from_original_config() - - unet_diffusers_checkpoint = unet_original_checkpoint_to_diffusers_checkpoint(unet_model, text2img_checkpoint) - - del text2img_checkpoint - - load_checkpoint_to_model(unet_diffusers_checkpoint, unet_model, strict=True) - - print("done loading text2img") - - return unet_model - - -def inpaint_text2img(*, args, checkpoint_map_location): - print("loading inpaint text2img") - - inpaint_text2img_checkpoint = torch.load( - args.inpaint_text2img_checkpoint_path, map_location=checkpoint_map_location - ) - - inpaint_unet_model = inpaint_unet_model_from_original_config() - - inpaint_unet_diffusers_checkpoint = inpaint_unet_original_checkpoint_to_diffusers_checkpoint( - inpaint_unet_model, inpaint_text2img_checkpoint - ) - - del inpaint_text2img_checkpoint - - load_checkpoint_to_model(inpaint_unet_diffusers_checkpoint, inpaint_unet_model, strict=True) - - print("done loading inpaint text2img") - - return inpaint_unet_model - - -# movq - -MOVQ_CONFIG = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 4, - "down_block_types": ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "AttnDownEncoderBlock2D"), - "up_block_types": ("AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"), - "num_vq_embeddings": 16384, - "block_out_channels": (128, 256, 256, 512), - "vq_embed_dim": 4, - "layers_per_block": 2, - "norm_type": "spatial", -} - - -def movq_model_from_original_config(): - movq = VQModel(**MOVQ_CONFIG) - return movq - - -def movq_encoder_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # conv_in - diffusers_checkpoint.update( - { - "encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"], - "encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"], - } - ) - - # down_blocks - for down_block_idx, down_block in enumerate(model.encoder.down_blocks): - diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}" - down_block_prefix = f"encoder.down.{down_block_idx}" - - # resnets - for resnet_idx, resnet in enumerate(down_block.resnets): - diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}" - resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}" - - diffusers_checkpoint.update( - movq_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - # downsample - - # do not include the downsample when on the last down block - # There is no downsample on the last down block - if down_block_idx != len(model.encoder.down_blocks) - 1: - # There's a single downsample in the original checkpoint but a list of downsamples - # in the diffusers model. - diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv" - downsample_prefix = f"{down_block_prefix}.downsample.conv" - diffusers_checkpoint.update( - { - f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], - f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], - } - ) - - # attentions - - if hasattr(down_block, "attentions"): - for attention_idx, _ in enumerate(down_block.attentions): - diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}" - attention_prefix = f"{down_block_prefix}.attn.{attention_idx}" - diffusers_checkpoint.update( - movq_attention_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - attention_prefix=attention_prefix, - ) - ) - - # mid block - - # mid block attentions - - # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder - diffusers_attention_prefix = "encoder.mid_block.attentions.0" - attention_prefix = "encoder.mid.attn_1" - diffusers_checkpoint.update( - movq_attention_to_diffusers_checkpoint( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # mid block resnets - - for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): - diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}" - - # the hardcoded prefixes to `block_` are 1 and 2 - orig_resnet_idx = diffusers_resnet_idx + 1 - # There are two hardcoded resnets in the middle of the VQ-diffusion encoder - resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}" - - diffusers_checkpoint.update( - movq_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - diffusers_checkpoint.update( - { - # conv_norm_out - "encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"], - "encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"], - # conv_out - "encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"], - "encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"], - } - ) - - return diffusers_checkpoint - - -def movq_decoder_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # conv in - diffusers_checkpoint.update( - { - "decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"], - "decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"], - } - ) - - # up_blocks - - for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks): - # up_blocks are stored in reverse order in the VQ-diffusion checkpoint - orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx - - diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}" - up_block_prefix = f"decoder.up.{orig_up_block_idx}" - - # resnets - for resnet_idx, resnet in enumerate(up_block.resnets): - diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" - resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}" - - diffusers_checkpoint.update( - movq_resnet_to_diffusers_checkpoint_spatial_norm( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - # upsample - - # there is no up sample on the last up block - if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1: - # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples - # in the diffusers model. - diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv" - downsample_prefix = f"{up_block_prefix}.upsample.conv" - diffusers_checkpoint.update( - { - f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], - f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], - } - ) - - # attentions - - if hasattr(up_block, "attentions"): - for attention_idx, _ in enumerate(up_block.attentions): - diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}" - attention_prefix = f"{up_block_prefix}.attn.{attention_idx}" - diffusers_checkpoint.update( - movq_attention_to_diffusers_checkpoint_spatial_norm( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - attention_prefix=attention_prefix, - ) - ) - - # mid block - - # mid block attentions - - # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder - diffusers_attention_prefix = "decoder.mid_block.attentions.0" - attention_prefix = "decoder.mid.attn_1" - diffusers_checkpoint.update( - movq_attention_to_diffusers_checkpoint_spatial_norm( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # mid block resnets - - for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): - diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}" - - # the hardcoded prefixes to `block_` are 1 and 2 - orig_resnet_idx = diffusers_resnet_idx + 1 - # There are two hardcoded resnets in the middle of the VQ-diffusion decoder - resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}" - - diffusers_checkpoint.update( - movq_resnet_to_diffusers_checkpoint_spatial_norm( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - diffusers_checkpoint.update( - { - # conv_norm_out - "decoder.conv_norm_out.norm_layer.weight": checkpoint["decoder.norm_out.norm_layer.weight"], - "decoder.conv_norm_out.norm_layer.bias": checkpoint["decoder.norm_out.norm_layer.bias"], - "decoder.conv_norm_out.conv_y.weight": checkpoint["decoder.norm_out.conv_y.weight"], - "decoder.conv_norm_out.conv_y.bias": checkpoint["decoder.norm_out.conv_y.bias"], - "decoder.conv_norm_out.conv_b.weight": checkpoint["decoder.norm_out.conv_b.weight"], - "decoder.conv_norm_out.conv_b.bias": checkpoint["decoder.norm_out.conv_b.bias"], - # conv_out - "decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"], - "decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"], - } - ) - - return diffusers_checkpoint - - -def movq_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - rv = { - # norm1 - f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"], - f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"], - # conv1 - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], - # norm2 - f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"], - f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"], - # conv2 - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], - } - - if resnet.conv_shortcut is not None: - rv.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], - f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], - } - ) - - return rv - - -def movq_resnet_to_diffusers_checkpoint_spatial_norm(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - rv = { - # norm1 - f"{diffusers_resnet_prefix}.norm1.norm_layer.weight": checkpoint[f"{resnet_prefix}.norm1.norm_layer.weight"], - f"{diffusers_resnet_prefix}.norm1.norm_layer.bias": checkpoint[f"{resnet_prefix}.norm1.norm_layer.bias"], - f"{diffusers_resnet_prefix}.norm1.conv_y.weight": checkpoint[f"{resnet_prefix}.norm1.conv_y.weight"], - f"{diffusers_resnet_prefix}.norm1.conv_y.bias": checkpoint[f"{resnet_prefix}.norm1.conv_y.bias"], - f"{diffusers_resnet_prefix}.norm1.conv_b.weight": checkpoint[f"{resnet_prefix}.norm1.conv_b.weight"], - f"{diffusers_resnet_prefix}.norm1.conv_b.bias": checkpoint[f"{resnet_prefix}.norm1.conv_b.bias"], - # conv1 - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], - # norm2 - f"{diffusers_resnet_prefix}.norm2.norm_layer.weight": checkpoint[f"{resnet_prefix}.norm2.norm_layer.weight"], - f"{diffusers_resnet_prefix}.norm2.norm_layer.bias": checkpoint[f"{resnet_prefix}.norm2.norm_layer.bias"], - f"{diffusers_resnet_prefix}.norm2.conv_y.weight": checkpoint[f"{resnet_prefix}.norm2.conv_y.weight"], - f"{diffusers_resnet_prefix}.norm2.conv_y.bias": checkpoint[f"{resnet_prefix}.norm2.conv_y.bias"], - f"{diffusers_resnet_prefix}.norm2.conv_b.weight": checkpoint[f"{resnet_prefix}.norm2.conv_b.weight"], - f"{diffusers_resnet_prefix}.norm2.conv_b.bias": checkpoint[f"{resnet_prefix}.norm2.conv_b.bias"], - # conv2 - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], - } - - if resnet.conv_shortcut is not None: - rv.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], - f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], - } - ) - - return rv - - -def movq_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): - return { - # norm - f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], - f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], - # query - f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.q.bias"], - # key - f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.k.bias"], - # value - f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.v.bias"], - # proj_attn - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], - } - - -def movq_attention_to_diffusers_checkpoint_spatial_norm(checkpoint, *, diffusers_attention_prefix, attention_prefix): - return { - # norm - f"{diffusers_attention_prefix}.spatial_norm.norm_layer.weight": checkpoint[ - f"{attention_prefix}.norm.norm_layer.weight" - ], - f"{diffusers_attention_prefix}.spatial_norm.norm_layer.bias": checkpoint[ - f"{attention_prefix}.norm.norm_layer.bias" - ], - f"{diffusers_attention_prefix}.spatial_norm.conv_y.weight": checkpoint[ - f"{attention_prefix}.norm.conv_y.weight" - ], - f"{diffusers_attention_prefix}.spatial_norm.conv_y.bias": checkpoint[f"{attention_prefix}.norm.conv_y.bias"], - f"{diffusers_attention_prefix}.spatial_norm.conv_b.weight": checkpoint[ - f"{attention_prefix}.norm.conv_b.weight" - ], - f"{diffusers_attention_prefix}.spatial_norm.conv_b.bias": checkpoint[f"{attention_prefix}.norm.conv_b.bias"], - # query - f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.q.bias"], - # key - f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.k.bias"], - # value - f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.v.bias"], - # proj_attn - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], - } - - -def movq_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - diffusers_checkpoint.update(movq_encoder_to_diffusers_checkpoint(model, checkpoint)) - - # quant_conv - - diffusers_checkpoint.update( - { - "quant_conv.weight": checkpoint["quant_conv.weight"], - "quant_conv.bias": checkpoint["quant_conv.bias"], - } - ) - - # quantize - diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding.weight"]}) - - # post_quant_conv - diffusers_checkpoint.update( - { - "post_quant_conv.weight": checkpoint["post_quant_conv.weight"], - "post_quant_conv.bias": checkpoint["post_quant_conv.bias"], - } - ) - - # decoder - diffusers_checkpoint.update(movq_decoder_to_diffusers_checkpoint(model, checkpoint)) - - return diffusers_checkpoint - - -def movq(*, args, checkpoint_map_location): - print("loading movq") - - movq_checkpoint = torch.load(args.movq_checkpoint_path, map_location=checkpoint_map_location) - - movq_model = movq_model_from_original_config() - - movq_diffusers_checkpoint = movq_original_checkpoint_to_diffusers_checkpoint(movq_model, movq_checkpoint) - - del movq_checkpoint - - load_checkpoint_to_model(movq_diffusers_checkpoint, movq_model, strict=True) - - print("done loading movq") - - return movq_model - - -def load_checkpoint_to_model(checkpoint, model, strict=False): - with tempfile.NamedTemporaryFile(delete=False) as file: - torch.save(checkpoint, file.name) - del checkpoint - if strict: - model.load_state_dict(torch.load(file.name), strict=True) - else: - load_checkpoint_and_dispatch(model, file.name, device_map="auto") - os.remove(file.name) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--prior_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the prior checkpoint to convert.", - ) - parser.add_argument( - "--clip_stat_path", - default=None, - type=str, - required=False, - help="Path to the clip stats checkpoint to convert.", - ) - parser.add_argument( - "--text2img_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the text2img checkpoint to convert.", - ) - parser.add_argument( - "--movq_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the text2img checkpoint to convert.", - ) - parser.add_argument( - "--inpaint_text2img_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the inpaint text2img checkpoint to convert.", - ) - parser.add_argument( - "--checkpoint_load_device", - default="cpu", - type=str, - required=False, - help="The device passed to `map_location` when loading checkpoints.", - ) - - parser.add_argument( - "--debug", - default=None, - type=str, - required=False, - help="Only run a specific stage of the convert script. Used for debugging", - ) - - args = parser.parse_args() - - print(f"loading checkpoints to {args.checkpoint_load_device}") - - checkpoint_map_location = torch.device(args.checkpoint_load_device) - - if args.debug is not None: - print(f"debug: only executing {args.debug}") - - if args.debug is None: - print("to-do") - elif args.debug == "prior": - prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) - prior_model.save_pretrained(args.dump_path) - elif args.debug == "text2img": - unet_model = text2img(args=args, checkpoint_map_location=checkpoint_map_location) - unet_model.save_pretrained(f"{args.dump_path}/unet") - elif args.debug == "inpaint_text2img": - inpaint_unet_model = inpaint_text2img(args=args, checkpoint_map_location=checkpoint_map_location) - inpaint_unet_model.save_pretrained(f"{args.dump_path}/inpaint_unet") - elif args.debug == "decoder": - decoder = movq(args=args, checkpoint_map_location=checkpoint_map_location) - decoder.save_pretrained(f"{args.dump_path}/decoder") - else: - raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_ldm_original_checkpoint_to_diffusers.py b/scripts/convert_ldm_original_checkpoint_to_diffusers.py deleted file mode 100644 index cdaf317af752..000000000000 --- a/scripts/convert_ldm_original_checkpoint_to_diffusers.py +++ /dev/null @@ -1,359 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the LDM checkpoints.""" - -import argparse -import json - -import torch - -from diffusers import DDPMScheduler, LDMPipeline, UNet2DModel, VQModel - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("proj_out.weight", "proj_attn.weight") - new_item = new_item.replace("proj_out.bias", "proj_attn.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming - to them. It splits attention layers, and takes into account additional replacements - that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def convert_ldm_checkpoint(checkpoint, config): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] - - new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] - new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] - new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["num_res_blocks"] + 1) - layer_in_block_id = (i - 1) % (config["num_res_blocks"] + 1) - - resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in checkpoint: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = checkpoint[ - f"input_blocks.{i}.0.op.weight" - ] - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[ - f"input_blocks.{i}.0.op.bias" - ] - continue - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - resnet_op = {"old": "resnets.2.op", "new": "downsamplers.0.op"} - assign_to_checkpoint( - paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"input_blocks.{i}.1", - "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}", - } - to_split = { - f"input_blocks.{i}.1.qkv.bias": { - "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", - "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", - "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", - }, - f"input_blocks.{i}.1.qkv.weight": { - "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", - "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", - "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", - }, - } - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[meta_path], - attention_paths_to_split=to_split, - config=config, - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config) - - attentions_paths = renew_attention_paths(attentions) - to_split = { - "middle_block.1.qkv.bias": { - "key": "mid_block.attentions.0.key.bias", - "query": "mid_block.attentions.0.query.bias", - "value": "mid_block.attentions.0.value.bias", - }, - "middle_block.1.qkv.weight": { - "key": "mid_block.attentions.0.key.weight", - "query": "mid_block.attentions.0.query.weight", - "value": "mid_block.attentions.0.value.weight", - }, - } - assign_to_checkpoint( - attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["num_res_blocks"] + 1) - layer_in_block_id = i % (config["num_res_blocks"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config) - - if ["conv.weight", "conv.bias"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - to_split = { - f"output_blocks.{i}.1.qkv.bias": { - "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", - "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", - "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", - }, - f"output_blocks.{i}.1.qkv.weight": { - "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", - "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", - "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", - }, - } - assign_to_checkpoint( - paths, - new_checkpoint, - checkpoint, - additional_replacements=[meta_path], - attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None, - config=config, - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = checkpoint[old_path] - - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - - parser.add_argument( - "--config_file", - default=None, - type=str, - required=True, - help="The config json file corresponding to the architecture.", - ) - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - args = parser.parse_args() - - checkpoint = torch.load(args.checkpoint_path) - - with open(args.config_file) as f: - config = json.loads(f.read()) - - converted_checkpoint = convert_ldm_checkpoint(checkpoint, config) - - if "ldm" in config: - del config["ldm"] - - model = UNet2DModel(**config) - model.load_state_dict(converted_checkpoint) - - try: - scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) - vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1])) - - pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae) - pipe.save_pretrained(args.dump_path) - except: # noqa: E722 - model.save_pretrained(args.dump_path) diff --git a/scripts/convert_lora_safetensor_to_diffusers.py b/scripts/convert_lora_safetensor_to_diffusers.py deleted file mode 100644 index 4237452e2ed1..000000000000 --- a/scripts/convert_lora_safetensor_to_diffusers.py +++ /dev/null @@ -1,128 +0,0 @@ -# coding=utf-8 -# Copyright 2024, Haofan Wang, Qixun Wang, All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Conversion script for the LoRA's safetensors checkpoints.""" - -import argparse - -import torch -from safetensors.torch import load_file - -from diffusers import StableDiffusionPipeline - - -def convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha): - # load base model - pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32) - - # load LoRA weight from .safetensors - state_dict = load_file(checkpoint_path) - - visited = [] - - # directly update weight in diffusers model - for key in state_dict: - # it is suggested to print out the key, it usually will be something like below - # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" - - # as we have set the alpha beforehand, so just skip - if ".alpha" in key or key in visited: - continue - - if "text" in key: - layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") - curr_layer = pipeline.text_encoder - else: - layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_") - curr_layer = pipeline.unet - - # find the target layer - temp_name = layer_infos.pop(0) - while len(layer_infos) > -1: - try: - curr_layer = curr_layer.__getattr__(temp_name) - if len(layer_infos) > 0: - temp_name = layer_infos.pop(0) - elif len(layer_infos) == 0: - break - except Exception: - if len(temp_name) > 0: - temp_name += "_" + layer_infos.pop(0) - else: - temp_name = layer_infos.pop(0) - - pair_keys = [] - if "lora_down" in key: - pair_keys.append(key.replace("lora_down", "lora_up")) - pair_keys.append(key) - else: - pair_keys.append(key) - pair_keys.append(key.replace("lora_up", "lora_down")) - - # update weight - if len(state_dict[pair_keys[0]].shape) == 4: - weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) - weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) - else: - weight_up = state_dict[pair_keys[0]].to(torch.float32) - weight_down = state_dict[pair_keys[1]].to(torch.float32) - curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down) - - # update visited list - for item in pair_keys: - visited.append(item) - - return pipeline - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format." - ) - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument( - "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors" - ) - parser.add_argument( - "--lora_prefix_text_encoder", - default="lora_te", - type=str, - help="The prefix of text encoder weight in safetensors", - ) - parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW") - parser.add_argument( - "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not." - ) - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - - args = parser.parse_args() - - base_model_path = args.base_model_path - checkpoint_path = args.checkpoint_path - dump_path = args.dump_path - lora_prefix_unet = args.lora_prefix_unet - lora_prefix_text_encoder = args.lora_prefix_text_encoder - alpha = args.alpha - - pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) - - pipe = pipe.to(args.device) - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_ltx2_to_diffusers.py b/scripts/convert_ltx2_to_diffusers.py deleted file mode 100644 index f1556557889f..000000000000 --- a/scripts/convert_ltx2_to_diffusers.py +++ /dev/null @@ -1,1194 +0,0 @@ -import argparse -import os -from contextlib import nullcontext -from typing import Any - -import safetensors.torch -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download -from transformers import AutoTokenizer, Gemma3ForConditionalGeneration, Gemma3Processor - -from diffusers import ( - AutoencoderKLLTX2Audio, - AutoencoderKLLTX2Video, - FlowMatchEulerDiscreteScheduler, - LTX2LatentUpsamplePipeline, - LTX2Pipeline, - LTX2VideoTransformer3DModel, -) -from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel, LTX2TextConnectors, LTX2Vocoder, LTX2VocoderWithBWE -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - - -LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT = { - # Input Patchify Projections - "patchify_proj": "proj_in", - "audio_patchify_proj": "audio_proj_in", - # Modulation Parameters - # Handle adaln_single --> time_embed, audioln_single --> audio_time_embed separately as the original keys are - # substrings of the other modulation parameters below - "av_ca_video_scale_shift_adaln_single": "av_cross_attn_video_scale_shift", - "av_ca_a2v_gate_adaln_single": "av_cross_attn_video_a2v_gate", - "av_ca_audio_scale_shift_adaln_single": "av_cross_attn_audio_scale_shift", - "av_ca_v2a_gate_adaln_single": "av_cross_attn_audio_v2a_gate", - # Transformer Blocks - # Per-Block Cross Attention Modulatin Parameters - "scale_shift_table_a2v_ca_video": "video_a2v_cross_attn_scale_shift_table", - "scale_shift_table_a2v_ca_audio": "audio_a2v_cross_attn_scale_shift_table", - # Attention QK Norms - "q_norm": "norm_q", - "k_norm": "norm_k", -} - -LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT = { - **LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT, - "audio_prompt_adaln_single": "audio_prompt_adaln", - "prompt_adaln_single": "prompt_adaln", -} - -LTX_2_0_VIDEO_VAE_RENAME_DICT = { - # Encoder - "down_blocks.0": "down_blocks.0", - "down_blocks.1": "down_blocks.0.downsamplers.0", - "down_blocks.2": "down_blocks.1", - "down_blocks.3": "down_blocks.1.downsamplers.0", - "down_blocks.4": "down_blocks.2", - "down_blocks.5": "down_blocks.2.downsamplers.0", - "down_blocks.6": "down_blocks.3", - "down_blocks.7": "down_blocks.3.downsamplers.0", - "down_blocks.8": "mid_block", - # Decoder - "up_blocks.0": "mid_block", - "up_blocks.1": "up_blocks.0.upsamplers.0", - "up_blocks.2": "up_blocks.0", - "up_blocks.3": "up_blocks.1.upsamplers.0", - "up_blocks.4": "up_blocks.1", - "up_blocks.5": "up_blocks.2.upsamplers.0", - "up_blocks.6": "up_blocks.2", - "last_time_embedder": "time_embedder", - "last_scale_shift_table": "scale_shift_table", - # Common - # For all 3D ResNets - "res_blocks": "resnets", - "per_channel_statistics.mean-of-means": "latents_mean", - "per_channel_statistics.std-of-means": "latents_std", -} - -LTX_2_3_VIDEO_VAE_RENAME_DICT = { - **LTX_2_0_VIDEO_VAE_RENAME_DICT, - # Decoder extra blocks - "up_blocks.7": "up_blocks.3.upsamplers.0", - "up_blocks.8": "up_blocks.3", -} - -LTX_2_0_AUDIO_VAE_RENAME_DICT = { - "per_channel_statistics.mean-of-means": "latents_mean", - "per_channel_statistics.std-of-means": "latents_std", -} - -LTX_2_0_VOCODER_RENAME_DICT = { - "ups": "upsamplers", - "resblocks": "resnets", - "conv_pre": "conv_in", - "conv_post": "conv_out", -} - -LTX_2_3_VOCODER_RENAME_DICT = { - # Handle upsamplers ("ups" --> "upsamplers") due to name clash - "resblocks": "resnets", - "conv_pre": "conv_in", - "conv_post": "conv_out", - "act_post": "act_out", - "downsample.lowpass": "downsample", -} - -LTX_2_0_CONNECTORS_KEYS_RENAME_DICT = { - "connectors.": "", - "video_embeddings_connector": "video_connector", - "audio_embeddings_connector": "audio_connector", - "transformer_1d_blocks": "transformer_blocks", - "text_embedding_projection.aggregate_embed": "text_proj_in", - # Attention QK Norms - "q_norm": "norm_q", - "k_norm": "norm_k", -} - -LTX_2_3_CONNECTORS_KEYS_RENAME_DICT = { - "connectors.": "", - "video_embeddings_connector": "video_connector", - "audio_embeddings_connector": "audio_connector", - "transformer_1d_blocks": "transformer_blocks", - # LTX-2.3 uses per-modality embedding projections - "text_embedding_projection.audio_aggregate_embed": "audio_text_proj_in", - "text_embedding_projection.video_aggregate_embed": "video_text_proj_in", - # Attention QK Norms - "q_norm": "norm_q", - "k_norm": "norm_k", -} - - -def update_state_dict_inplace(state_dict: dict[str, Any], old_key: str, new_key: str) -> None: - state_dict[new_key] = state_dict.pop(old_key) - - -def remove_keys_inplace(key: str, state_dict: dict[str, Any]) -> None: - state_dict.pop(key) - - -def convert_ltx2_transformer_adaln_single(key: str, state_dict: dict[str, Any]) -> None: - # Skip if not a weight, bias - if ".weight" not in key and ".bias" not in key: - return - - if key.startswith("adaln_single."): - new_key = key.replace("adaln_single.", "time_embed.") - param = state_dict.pop(key) - state_dict[new_key] = param - - if key.startswith("audio_adaln_single."): - new_key = key.replace("audio_adaln_single.", "audio_time_embed.") - param = state_dict.pop(key) - state_dict[new_key] = param - - return - - -def convert_ltx2_audio_vae_per_channel_statistics(key: str, state_dict: dict[str, Any]) -> None: - if key.startswith("per_channel_statistics"): - new_key = ".".join(["decoder", key]) - param = state_dict.pop(key) - state_dict[new_key] = param - - return - - -def convert_ltx2_3_vocoder_upsamplers(key: str, state_dict: dict[str, Any]) -> None: - # Skip if not a weight, bias - if ".weight" not in key and ".bias" not in key: - return - - if ".ups." in key: - new_key = key.replace(".ups.", ".upsamplers.") - param = state_dict.pop(key) - state_dict[new_key] = param - return - - -LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP = { - "video_embeddings_connector": remove_keys_inplace, - "audio_embeddings_connector": remove_keys_inplace, - "adaln_single": convert_ltx2_transformer_adaln_single, -} - -LTX_2_0_VAE_SPECIAL_KEYS_REMAP = { - "per_channel_statistics.channel": remove_keys_inplace, - "per_channel_statistics.mean-of-stds": remove_keys_inplace, -} - -LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP = {} - -LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP = {} - -LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP = { - ".ups.": convert_ltx2_3_vocoder_upsamplers, -} - -LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP = {} - - -def split_transformer_and_connector_state_dict(state_dict: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]: - connector_prefixes = ( - "video_embeddings_connector", - "audio_embeddings_connector", - "transformer_1d_blocks", - "text_embedding_projection", - "connectors.", - "video_connector", - "audio_connector", - "text_proj_in", - ) - - transformer_state_dict, connector_state_dict = {}, {} - for key, value in state_dict.items(): - if key.startswith(connector_prefixes): - connector_state_dict[key] = value - else: - transformer_state_dict[key] = value - - return transformer_state_dict, connector_state_dict - - -def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: - if version == "test": - # Produces a transformer of the same size as used in test_models_transformer_ltx2.py - config = { - "model_id": "diffusers-internal-dev/dummy-ltx2", - "diffusers_config": { - "in_channels": 4, - "out_channels": 4, - "patch_size": 1, - "patch_size_t": 1, - "num_attention_heads": 2, - "attention_head_dim": 8, - "cross_attention_dim": 16, - "vae_scale_factors": (8, 32, 32), - "pos_embed_max_pos": 20, - "base_height": 2048, - "base_width": 2048, - "audio_in_channels": 4, - "audio_out_channels": 4, - "audio_patch_size": 1, - "audio_patch_size_t": 1, - "audio_num_attention_heads": 2, - "audio_attention_head_dim": 4, - "audio_cross_attention_dim": 8, - "audio_scale_factor": 4, - "audio_pos_embed_max_pos": 20, - "audio_sampling_rate": 16000, - "audio_hop_length": 160, - "num_layers": 2, - "activation_fn": "gelu-approximate", - "qk_norm": "rms_norm_across_heads", - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "caption_channels": 16, - "attention_bias": True, - "attention_out_bias": True, - "rope_theta": 10000.0, - "rope_double_precision": False, - "causal_offset": 1, - "timestep_scale_multiplier": 1000, - "cross_attn_timestep_scale_multiplier": 1, - }, - } - rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP - elif version == "2.0": - config = { - "model_id": "Lightricks/LTX-2", - "diffusers_config": { - "in_channels": 128, - "out_channels": 128, - "patch_size": 1, - "patch_size_t": 1, - "num_attention_heads": 32, - "attention_head_dim": 128, - "cross_attention_dim": 4096, - "vae_scale_factors": (8, 32, 32), - "pos_embed_max_pos": 20, - "base_height": 2048, - "base_width": 2048, - "gated_attn": False, - "cross_attn_mod": False, - "audio_in_channels": 128, - "audio_out_channels": 128, - "audio_patch_size": 1, - "audio_patch_size_t": 1, - "audio_num_attention_heads": 32, - "audio_attention_head_dim": 64, - "audio_cross_attention_dim": 2048, - "audio_scale_factor": 4, - "audio_pos_embed_max_pos": 20, - "audio_sampling_rate": 16000, - "audio_hop_length": 160, - "audio_gated_attn": False, - "audio_cross_attn_mod": False, - "num_layers": 48, - "activation_fn": "gelu-approximate", - "qk_norm": "rms_norm_across_heads", - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "caption_channels": 3840, - "attention_bias": True, - "attention_out_bias": True, - "rope_theta": 10000.0, - "rope_double_precision": True, - "causal_offset": 1, - "timestep_scale_multiplier": 1000, - "cross_attn_timestep_scale_multiplier": 1000, - "rope_type": "split", - "use_prompt_embeddings": True, - "perturbed_attn": False, - }, - } - rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP - elif version == "2.3": - config = { - "model_id": "Lightricks/LTX-2.3", - "diffusers_config": { - "in_channels": 128, - "out_channels": 128, - "patch_size": 1, - "patch_size_t": 1, - "num_attention_heads": 32, - "attention_head_dim": 128, - "cross_attention_dim": 4096, - "vae_scale_factors": (8, 32, 32), - "pos_embed_max_pos": 20, - "base_height": 2048, - "base_width": 2048, - "gated_attn": True, - "cross_attn_mod": True, - "audio_in_channels": 128, - "audio_out_channels": 128, - "audio_patch_size": 1, - "audio_patch_size_t": 1, - "audio_num_attention_heads": 32, - "audio_attention_head_dim": 64, - "audio_cross_attention_dim": 2048, - "audio_scale_factor": 4, - "audio_pos_embed_max_pos": 20, - "audio_sampling_rate": 16000, - "audio_hop_length": 160, - "audio_gated_attn": True, - "audio_cross_attn_mod": True, - "num_layers": 48, - "activation_fn": "gelu-approximate", - "qk_norm": "rms_norm_across_heads", - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "caption_channels": 3840, - "attention_bias": True, - "attention_out_bias": True, - "rope_theta": 10000.0, - "rope_double_precision": True, - "causal_offset": 1, - "timestep_scale_multiplier": 1000, - "cross_attn_timestep_scale_multiplier": 1000, - "rope_type": "split", - "use_prompt_embeddings": False, - "perturbed_attn": True, - }, - } - rename_dict = LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT - special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP - return config, rename_dict, special_keys_remap - - -def get_ltx2_connectors_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: - if version == "test": - config = { - "model_id": "diffusers-internal-dev/dummy-ltx2", - "diffusers_config": { - "caption_channels": 16, - "text_proj_in_factor": 3, - "video_connector_num_attention_heads": 4, - "video_connector_attention_head_dim": 8, - "video_connector_num_layers": 1, - "video_connector_num_learnable_registers": None, - "audio_connector_num_attention_heads": 4, - "audio_connector_attention_head_dim": 8, - "audio_connector_num_layers": 1, - "audio_connector_num_learnable_registers": None, - "connector_rope_base_seq_len": 32, - "rope_theta": 10000.0, - "rope_double_precision": False, - "causal_temporal_positioning": False, - }, - } - elif version == "2.0": - config = { - "model_id": "Lightricks/LTX-2", - "diffusers_config": { - "caption_channels": 3840, - "text_proj_in_factor": 49, - "video_connector_num_attention_heads": 30, - "video_connector_attention_head_dim": 128, - "video_connector_num_layers": 2, - "video_connector_num_learnable_registers": 128, - "video_gated_attn": False, - "audio_connector_num_attention_heads": 30, - "audio_connector_attention_head_dim": 128, - "audio_connector_num_layers": 2, - "audio_connector_num_learnable_registers": 128, - "audio_gated_attn": False, - "connector_rope_base_seq_len": 4096, - "rope_theta": 10000.0, - "rope_double_precision": True, - "causal_temporal_positioning": False, - "rope_type": "split", - "per_modality_projections": False, - "proj_bias": False, - }, - } - rename_dict = LTX_2_0_CONNECTORS_KEYS_RENAME_DICT - special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP - elif version == "2.3": - config = { - "model_id": "Lightricks/LTX-2.3", - "diffusers_config": { - "caption_channels": 3840, - "text_proj_in_factor": 49, - "video_connector_num_attention_heads": 32, - "video_connector_attention_head_dim": 128, - "video_connector_num_layers": 8, - "video_connector_num_learnable_registers": 128, - "video_gated_attn": True, - "audio_connector_num_attention_heads": 32, - "audio_connector_attention_head_dim": 64, - "audio_connector_num_layers": 8, - "audio_connector_num_learnable_registers": 128, - "audio_gated_attn": True, - "connector_rope_base_seq_len": 4096, - "rope_theta": 10000.0, - "rope_double_precision": True, - "causal_temporal_positioning": False, - "rope_type": "split", - "per_modality_projections": True, - "video_hidden_dim": 4096, - "audio_hidden_dim": 2048, - "proj_bias": True, - }, - } - rename_dict = LTX_2_3_CONNECTORS_KEYS_RENAME_DICT - special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP - - return config, rename_dict, special_keys_remap - - -def convert_ltx2_transformer(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: - config, rename_dict, special_keys_remap = get_ltx2_transformer_config(version) - diffusers_config = config["diffusers_config"] - - transformer_state_dict, _ = split_transformer_and_connector_state_dict(original_state_dict) - - with init_empty_weights(): - transformer = LTX2VideoTransformer3DModel.from_config(diffusers_config) - - # Handle official code --> diffusers key remapping via the remap dict - for key in list(transformer_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(transformer_state_dict, key, new_key) - - # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in - # special_keys_remap - for key in list(transformer_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, transformer_state_dict) - - transformer.load_state_dict(transformer_state_dict, strict=True, assign=True) - return transformer - - -def convert_ltx2_connectors(original_state_dict: dict[str, Any], version: str) -> LTX2TextConnectors: - config, rename_dict, special_keys_remap = get_ltx2_connectors_config(version) - diffusers_config = config["diffusers_config"] - - _, connector_state_dict = split_transformer_and_connector_state_dict(original_state_dict) - if len(connector_state_dict) == 0: - raise ValueError("No connector weights found in the provided state dict.") - - with init_empty_weights(): - connectors = LTX2TextConnectors.from_config(diffusers_config) - - for key in list(connector_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(connector_state_dict, key, new_key) - - for key in list(connector_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, connector_state_dict) - - connectors.load_state_dict(connector_state_dict, strict=True, assign=True) - return connectors - - -def get_ltx2_video_vae_config( - version: str, timestep_conditioning: bool = False -) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: - if version == "test": - config = { - "model_id": "diffusers-internal-dev/dummy-ltx2", - "diffusers_config": { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (256, 512, 1024, 2048), - "down_block_types": ( - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 1024), - "layers_per_block": (4, 6, 6, 2, 2), - "decoder_layers_per_block": (5, 5, 5, 5), - "spatio_temporal_scaling": (True, True, True, True), - "decoder_spatio_temporal_scaling": (True, True, True), - "decoder_inject_noise": (False, False, False, False), - "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), - "upsample_residual": (True, True, True), - "upsample_factor": (2, 2, 2), - "timestep_conditioning": timestep_conditioning, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "encoder_causal": True, - "decoder_causal": False, - "encoder_spatial_padding_mode": "zeros", - "decoder_spatial_padding_mode": "reflect", - "spatial_compression_ratio": 32, - "temporal_compression_ratio": 8, - }, - } - rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT - special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP - elif version == "2.0": - config = { - "model_id": "Lightricks/LTX-2", - "diffusers_config": { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (256, 512, 1024, 2048), - "down_block_types": ( - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 1024), - "layers_per_block": (4, 6, 6, 2, 2), - "decoder_layers_per_block": (5, 5, 5, 5), - "spatio_temporal_scaling": (True, True, True, True), - "decoder_spatio_temporal_scaling": (True, True, True), - "decoder_inject_noise": (False, False, False, False), - "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), - "upsample_type": ("spatiotemporal", "spatiotemporal", "spatiotemporal"), - "upsample_residual": (True, True, True), - "upsample_factor": (2, 2, 2), - "timestep_conditioning": timestep_conditioning, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "encoder_causal": True, - "decoder_causal": False, - "encoder_spatial_padding_mode": "zeros", - "decoder_spatial_padding_mode": "reflect", - "spatial_compression_ratio": 32, - "temporal_compression_ratio": 8, - }, - } - rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT - special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP - elif version == "2.3": - config = { - "model_id": "Lightricks/LTX-2.3", - "diffusers_config": { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (256, 512, 1024, 1024), - "down_block_types": ( - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - "LTX2VideoDownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 512, 1024), - "layers_per_block": (4, 6, 4, 2, 2), - "decoder_layers_per_block": (4, 6, 4, 2, 2), - "spatio_temporal_scaling": (True, True, True, True), - "decoder_spatio_temporal_scaling": (True, True, True, True), - "decoder_inject_noise": (False, False, False, False, False), - "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), - "upsample_type": ("spatiotemporal", "spatiotemporal", "temporal", "spatial"), - "upsample_residual": (False, False, False, False), - "upsample_factor": (2, 2, 1, 2), - "timestep_conditioning": timestep_conditioning, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "encoder_causal": True, - "decoder_causal": False, - "encoder_spatial_padding_mode": "zeros", - "decoder_spatial_padding_mode": "zeros", - "spatial_compression_ratio": 32, - "temporal_compression_ratio": 8, - }, - } - rename_dict = LTX_2_3_VIDEO_VAE_RENAME_DICT - special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP - return config, rename_dict, special_keys_remap - - -def convert_ltx2_video_vae( - original_state_dict: dict[str, Any], version: str, timestep_conditioning: bool -) -> dict[str, Any]: - config, rename_dict, special_keys_remap = get_ltx2_video_vae_config(version, timestep_conditioning) - diffusers_config = config["diffusers_config"] - - with init_empty_weights(): - vae = AutoencoderKLLTX2Video.from_config(diffusers_config) - - # Handle official code --> diffusers key remapping via the remap dict - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in - # special_keys_remap - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True, assign=True) - return vae - - -def get_ltx2_audio_vae_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: - if version == "2.0": - config = { - "model_id": "Lightricks/LTX-2", - "diffusers_config": { - "base_channels": 128, - "output_channels": 2, - "ch_mult": (1, 2, 4), - "num_res_blocks": 2, - "attn_resolutions": None, - "in_channels": 2, - "resolution": 256, - "latent_channels": 8, - "norm_type": "pixel", - "causality_axis": "height", - "dropout": 0.0, - "mid_block_add_attention": False, - "sample_rate": 16000, - "mel_hop_length": 160, - "is_causal": True, - "mel_bins": 64, - "double_z": True, - }, - } - rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT - special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP - elif version == "2.3": - config = { - "model_id": "Lightricks/LTX-2.3", - "diffusers_config": { - "base_channels": 128, - "output_channels": 2, - "ch_mult": (1, 2, 4), - "num_res_blocks": 2, - "attn_resolutions": None, - "in_channels": 2, - "resolution": 256, - "latent_channels": 8, - "norm_type": "pixel", - "causality_axis": "height", - "dropout": 0.0, - "mid_block_add_attention": False, - "sample_rate": 16000, - "mel_hop_length": 160, - "is_causal": True, - "mel_bins": 64, - "double_z": True, - }, # Same config as LTX-2.0 - } - rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT - special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP - return config, rename_dict, special_keys_remap - - -def convert_ltx2_audio_vae(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: - config, rename_dict, special_keys_remap = get_ltx2_audio_vae_config(version) - diffusers_config = config["diffusers_config"] - - with init_empty_weights(): - vae = AutoencoderKLLTX2Audio.from_config(diffusers_config) - - # Handle official code --> diffusers key remapping via the remap dict - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in - # special_keys_remap - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True, assign=True) - return vae - - -def get_ltx2_vocoder_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: - if version == "2.0": - config = { - "model_id": "Lightricks/LTX-2", - "diffusers_config": { - "in_channels": 128, - "hidden_channels": 1024, - "out_channels": 2, - "upsample_kernel_sizes": [16, 15, 8, 4, 4], - "upsample_factors": [6, 5, 2, 2, 2], - "resnet_kernel_sizes": [3, 7, 11], - "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "act_fn": "leaky_relu", - "leaky_relu_negative_slope": 0.1, - "antialias": False, - "final_act_fn": "tanh", - "final_bias": True, - "output_sampling_rate": 24000, - }, - } - rename_dict = LTX_2_0_VOCODER_RENAME_DICT - special_keys_remap = LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP - elif version == "2.3": - config = { - "model_id": "Lightricks/LTX-2.3", - "diffusers_config": { - "in_channels": 128, - "hidden_channels": 1536, - "out_channels": 2, - "upsample_kernel_sizes": [11, 4, 4, 4, 4, 4], - "upsample_factors": [5, 2, 2, 2, 2, 2], - "resnet_kernel_sizes": [3, 7, 11], - "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "act_fn": "snakebeta", - "leaky_relu_negative_slope": 0.1, - "antialias": True, - "antialias_ratio": 2, - "antialias_kernel_size": 12, - "final_act_fn": None, - "final_bias": False, - "bwe_in_channels": 128, - "bwe_hidden_channels": 512, - "bwe_out_channels": 2, - "bwe_upsample_kernel_sizes": [12, 11, 4, 4, 4], - "bwe_upsample_factors": [6, 5, 2, 2, 2], - "bwe_resnet_kernel_sizes": [3, 7, 11], - "bwe_resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "bwe_act_fn": "snakebeta", - "bwe_leaky_relu_negative_slope": 0.1, - "bwe_antialias": True, - "bwe_antialias_ratio": 2, - "bwe_antialias_kernel_size": 12, - "bwe_final_act_fn": None, - "bwe_final_bias": False, - "filter_length": 512, - "hop_length": 80, - "window_length": 512, - "num_mel_channels": 64, - "input_sampling_rate": 16000, - "output_sampling_rate": 48000, - }, - } - rename_dict = LTX_2_3_VOCODER_RENAME_DICT - special_keys_remap = LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP - return config, rename_dict, special_keys_remap - - -def convert_ltx2_vocoder(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: - config, rename_dict, special_keys_remap = get_ltx2_vocoder_config(version) - diffusers_config = config["diffusers_config"] - if version == "2.3": - vocoder_cls = LTX2VocoderWithBWE - else: - vocoder_cls = LTX2Vocoder - - with init_empty_weights(): - vocoder = vocoder_cls.from_config(diffusers_config) - - # Handle official code --> diffusers key remapping via the remap dict - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in rename_dict.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in - # special_keys_remap - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in special_keys_remap.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vocoder.load_state_dict(original_state_dict, strict=True, assign=True) - return vocoder - - -def get_ltx2_spatial_latent_upsampler_config(version: str): - if version == "2.0": - config = { - "in_channels": 128, - "mid_channels": 1024, - "num_blocks_per_stage": 4, - "dims": 3, - "spatial_upsample": True, - "temporal_upsample": False, - "rational_spatial_scale": 2.0, - "use_rational_resampler": True, - } - elif version == "2.3": - config = { - "in_channels": 128, - "mid_channels": 1024, - "num_blocks_per_stage": 4, - "dims": 3, - "spatial_upsample": True, - "temporal_upsample": False, - "rational_spatial_scale": 2.0, - "use_rational_resampler": False, - } - else: - raise ValueError(f"Unsupported version: {version}") - return config - - -def convert_ltx2_spatial_latent_upsampler( - original_state_dict: dict[str, Any], config: dict[str, Any], dtype: torch.dtype -): - with init_empty_weights(): - latent_upsampler = LTX2LatentUpsamplerModel(**config) - - latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True) - latent_upsampler.to(dtype) - return latent_upsampler - - -def load_original_checkpoint(args, filename: str | None) -> dict[str, Any]: - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=filename) - elif args.checkpoint_path is not None: - ckpt_path = args.checkpoint_path - else: - raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -def load_hub_or_local_checkpoint(repo_id: str | None = None, filename: str | None = None) -> dict[str, Any]: - if repo_id is None and filename is None: - raise ValueError("Please supply at least one of `repo_id` or `filename`") - - if repo_id is not None: - if filename is None: - raise ValueError("If repo_id is specified, filename must also be specified.") - ckpt_path = hf_hub_download(repo_id=repo_id, filename=filename) - else: - ckpt_path = filename - - _, ext = os.path.splitext(ckpt_path) - if ext in [".safetensors", ".sft"]: - state_dict = safetensors.torch.load_file(ckpt_path) - else: - state_dict = torch.load(ckpt_path, map_location="cpu") - - return state_dict - - -def get_model_state_dict_from_combined_ckpt(combined_ckpt: dict[str, Any], prefix: str) -> dict[str, Any]: - # Ensure that the key prefix ends with a dot (.) - if not prefix.endswith("."): - prefix = prefix + "." - - model_state_dict = {} - for param_name, param in combined_ckpt.items(): - if param_name.startswith(prefix): - model_state_dict[param_name.removeprefix(prefix)] = param - - if prefix == "model.diffusion_model.": - # Some checkpoints store the text connector projection outside the diffusion model prefix. - connector_prefixes = ["text_embedding_projection"] - for param_name, param in combined_ckpt.items(): - for prefix in connector_prefixes: - if param_name.startswith(prefix): - # Check to make sure we're not overwriting an existing key - if param_name not in model_state_dict: - model_state_dict[param_name] = combined_ckpt[param_name] - - return model_state_dict - - -def get_args(): - parser = argparse.ArgumentParser() - - def none_or_str(value: str): - if isinstance(value, str) and value.lower() == "none": - return None - return value - - parser.add_argument( - "--original_state_dict_repo_id", - default="Lightricks/LTX-2", - type=none_or_str, - help="HF Hub repo id with LTX 2.0 checkpoint", - ) - parser.add_argument( - "--checkpoint_path", - default=None, - type=str, - help="Local checkpoint path for LTX 2.0. Will be used if `original_state_dict_repo_id` is not specified.", - ) - parser.add_argument( - "--version", - type=str, - default="2.0", - choices=["test", "2.0", "2.3"], - help="Version of the LTX 2.0 model", - ) - - parser.add_argument( - "--combined_filename", - default="ltx-2-19b-dev.safetensors", - type=none_or_str, - help="Filename for combined checkpoint with all LTX 2.0 models (VAE, DiT, etc.)", - ) - parser.add_argument("--vae_prefix", default="vae.", type=str) - parser.add_argument("--audio_vae_prefix", default="audio_vae.", type=str) - parser.add_argument("--dit_prefix", default="model.diffusion_model.", type=str) - parser.add_argument("--vocoder_prefix", default="vocoder.", type=str) - - parser.add_argument("--vae_filename", default=None, type=str, help="VAE filename; overrides combined ckpt if set") - parser.add_argument( - "--audio_vae_filename", default=None, type=str, help="Audio VAE filename; overrides combined ckpt if set" - ) - parser.add_argument("--dit_filename", default=None, type=str, help="DiT filename; overrides combined ckpt if set") - parser.add_argument( - "--vocoder_filename", default=None, type=str, help="Vocoder filename; overrides combined ckpt if set" - ) - parser.add_argument( - "--text_encoder_model_id", - default="google/gemma-3-12b-it-qat-q4_0-unquantized", - type=none_or_str, - help="HF Hub id for the LTX 2.0 base text encoder model", - ) - parser.add_argument( - "--tokenizer_id", - default="google/gemma-3-12b-it-qat-q4_0-unquantized", - type=none_or_str, - help="HF Hub id for the LTX 2.0 text tokenizer", - ) - parser.add_argument( - "--latent_upsampler_filename", - default="ltx-2-spatial-upscaler-x2-1.0.safetensors", - type=none_or_str, - help="Latent upsampler filename", - ) - - parser.add_argument( - "--timestep_conditioning", action="store_true", help="Whether to add timestep condition to the video VAE model" - ) - parser.add_argument("--vae", action="store_true", help="Whether to convert the video VAE model") - parser.add_argument("--audio_vae", action="store_true", help="Whether to convert the audio VAE model") - parser.add_argument("--dit", action="store_true", help="Whether to convert the DiT model") - parser.add_argument("--connectors", action="store_true", help="Whether to convert the connector model") - parser.add_argument("--vocoder", action="store_true", help="Whether to convert the vocoder model") - parser.add_argument("--text_encoder", action="store_true", help="Whether to conver the text encoder") - parser.add_argument("--latent_upsampler", action="store_true", help="Whether to convert the latent upsampler") - parser.add_argument( - "--full_pipeline", - action="store_true", - help="Whether to save the pipeline. This will attempt to convert all models (e.g. vae, dit, etc.)", - ) - parser.add_argument( - "--upsample_pipeline", - action="store_true", - help="Whether to save a latent upsampling pipeline", - ) - parser.add_argument( - "--add_processor", - action="store_true", - help="Whether to add a Gemma3Processor to the pipeline for prompt enhancement.", - ) - - parser.add_argument("--vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--audio_vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--dit_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--vocoder_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) - parser.add_argument("--text_encoder_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) - - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument( - "--upsample_output_path", - type=str, - default=None, - help="Path where converted upsampling pipeline should be saved", - ) - - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - -VARIANT_MAPPING = { - "fp32": None, - "fp16": "fp16", - "bf16": "bf16", -} - - -def main(args): - vae_dtype = DTYPE_MAPPING[args.vae_dtype] - audio_vae_dtype = DTYPE_MAPPING[args.audio_vae_dtype] - dit_dtype = DTYPE_MAPPING[args.dit_dtype] - vocoder_dtype = DTYPE_MAPPING[args.vocoder_dtype] - text_encoder_dtype = DTYPE_MAPPING[args.text_encoder_dtype] - - combined_ckpt = None - load_combined_models = any( - [ - args.vae, - args.audio_vae, - args.dit, - args.vocoder, - args.connectors, - args.full_pipeline, - args.upsample_pipeline, - ] - ) - if args.combined_filename is not None and load_combined_models: - combined_ckpt = load_original_checkpoint(args, filename=args.combined_filename) - - if args.vae or args.full_pipeline or args.upsample_pipeline: - if args.vae_filename is not None: - original_vae_ckpt = load_hub_or_local_checkpoint(filename=args.vae_filename) - elif combined_ckpt is not None: - original_vae_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.vae_prefix) - vae = convert_ltx2_video_vae( - original_vae_ckpt, version=args.version, timestep_conditioning=args.timestep_conditioning - ) - if not args.full_pipeline and not args.upsample_pipeline: - vae.to(vae_dtype).save_pretrained(os.path.join(args.output_path, "vae")) - - if args.audio_vae or args.full_pipeline: - if args.audio_vae_filename is not None: - original_audio_vae_ckpt = load_hub_or_local_checkpoint(filename=args.audio_vae_filename) - elif combined_ckpt is not None: - original_audio_vae_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.audio_vae_prefix) - audio_vae = convert_ltx2_audio_vae(original_audio_vae_ckpt, version=args.version) - if not args.full_pipeline: - audio_vae.to(audio_vae_dtype).save_pretrained(os.path.join(args.output_path, "audio_vae")) - - if args.dit or args.full_pipeline: - if args.dit_filename is not None: - original_dit_ckpt = load_hub_or_local_checkpoint(filename=args.dit_filename) - elif combined_ckpt is not None: - original_dit_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.dit_prefix) - transformer = convert_ltx2_transformer(original_dit_ckpt, version=args.version) - if not args.full_pipeline: - transformer.to(dit_dtype).save_pretrained(os.path.join(args.output_path, "transformer")) - - if args.connectors or args.full_pipeline: - if args.dit_filename is not None: - original_connectors_ckpt = load_hub_or_local_checkpoint(filename=args.dit_filename) - elif combined_ckpt is not None: - original_connectors_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.dit_prefix) - connectors = convert_ltx2_connectors(original_connectors_ckpt, version=args.version) - if not args.full_pipeline: - connectors.to(dit_dtype).save_pretrained(os.path.join(args.output_path, "connectors")) - - if args.vocoder or args.full_pipeline: - if args.vocoder_filename is not None: - original_vocoder_ckpt = load_hub_or_local_checkpoint(filename=args.vocoder_filename) - elif combined_ckpt is not None: - original_vocoder_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.vocoder_prefix) - vocoder = convert_ltx2_vocoder(original_vocoder_ckpt, version=args.version) - if not args.full_pipeline: - vocoder.to(vocoder_dtype).save_pretrained(os.path.join(args.output_path, "vocoder")) - - if args.text_encoder or args.full_pipeline: - # text_encoder = AutoModel.from_pretrained(args.text_encoder_model_id) - text_encoder = Gemma3ForConditionalGeneration.from_pretrained(args.text_encoder_model_id) - if not args.full_pipeline: - text_encoder.to(text_encoder_dtype).save_pretrained(os.path.join(args.output_path, "text_encoder")) - - tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_id) - if not args.full_pipeline: - tokenizer.save_pretrained(os.path.join(args.output_path, "tokenizer")) - - if args.add_processor: - processor = Gemma3Processor.from_pretrained(args.text_encoder_model_id) - if not args.full_pipeline: - processor.save_pretrained(os.path.join(args.output_path, "processor")) - - if args.latent_upsampler or args.upsample_pipeline: - original_latent_upsampler_ckpt = load_hub_or_local_checkpoint( - repo_id=args.original_state_dict_repo_id, filename=args.latent_upsampler_filename - ) - latent_upsampler_config = get_ltx2_spatial_latent_upsampler_config(args.version) - latent_upsampler = convert_ltx2_spatial_latent_upsampler( - original_latent_upsampler_ckpt, - latent_upsampler_config, - dtype=vae_dtype, - ) - if not args.full_pipeline and not args.upsample_pipeline: - latent_upsampler.save_pretrained(os.path.join(args.output_path, "latent_upsampler")) - - if args.full_pipeline: - is_distilled_ckpt = "distilled" in args.combined_filename - if is_distilled_ckpt: - # Disable dynamic shifting and terminal shift so that distilled sigmas are used as-is - scheduler = FlowMatchEulerDiscreteScheduler( - use_dynamic_shifting=False, - base_shift=0.95, - max_shift=2.05, - base_image_seq_len=1024, - max_image_seq_len=4096, - shift_terminal=None, - ) - else: - scheduler = FlowMatchEulerDiscreteScheduler( - use_dynamic_shifting=True, - base_shift=0.95, - max_shift=2.05, - base_image_seq_len=1024, - max_image_seq_len=4096, - shift_terminal=0.1, - ) - - pipe = LTX2Pipeline( - scheduler=scheduler, - vae=vae, - audio_vae=audio_vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - connectors=connectors, - transformer=transformer, - vocoder=vocoder, - ) - - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") - - if args.upsample_pipeline: - pipe = LTX2LatentUpsamplePipeline(vae=vae, latent_upsampler=latent_upsampler) - - # As two diffusers pipelines cannot be in the same directory, save the upsampling pipeline to its own directory - if args.upsample_output_path: - upsample_output_path = args.upsample_output_path - else: - upsample_output_path = args.output_path - pipe.save_pretrained(upsample_output_path, safe_serialization=True, max_shard_size="5GB") - - -if __name__ == "__main__": - args = get_args() - main(args) diff --git a/scripts/convert_ltx_to_diffusers.py b/scripts/convert_ltx_to_diffusers.py deleted file mode 100644 index b4ae57f9c8f0..000000000000 --- a/scripts/convert_ltx_to_diffusers.py +++ /dev/null @@ -1,525 +0,0 @@ -import argparse -from pathlib import Path -from typing import Any, Dict - -import torch -from accelerate import init_empty_weights -from safetensors.torch import load_file -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import ( - AutoencoderKLLTXVideo, - FlowMatchEulerDiscreteScheduler, - LTXConditionPipeline, - LTXLatentUpsamplePipeline, - LTXPipeline, - LTXVideoTransformer3DModel, -) -from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel - - -def remove_keys_(key: str, state_dict: Dict[str, Any]): - state_dict.pop(key) - - -TOKENIZER_MAX_LENGTH = 128 - -TRANSFORMER_KEYS_RENAME_DICT = { - "patchify_proj": "proj_in", - "adaln_single": "time_embed", - "q_norm": "norm_q", - "k_norm": "norm_k", -} - -TRANSFORMER_SPECIAL_KEYS_REMAP = { - "vae": remove_keys_, -} - -VAE_KEYS_RENAME_DICT = { - # decoder - "up_blocks.0": "mid_block", - "up_blocks.1": "up_blocks.0", - "up_blocks.2": "up_blocks.1.upsamplers.0", - "up_blocks.3": "up_blocks.1", - "up_blocks.4": "up_blocks.2.conv_in", - "up_blocks.5": "up_blocks.2.upsamplers.0", - "up_blocks.6": "up_blocks.2", - "up_blocks.7": "up_blocks.3.conv_in", - "up_blocks.8": "up_blocks.3.upsamplers.0", - "up_blocks.9": "up_blocks.3", - # encoder - "down_blocks.0": "down_blocks.0", - "down_blocks.1": "down_blocks.0.downsamplers.0", - "down_blocks.2": "down_blocks.0.conv_out", - "down_blocks.3": "down_blocks.1", - "down_blocks.4": "down_blocks.1.downsamplers.0", - "down_blocks.5": "down_blocks.1.conv_out", - "down_blocks.6": "down_blocks.2", - "down_blocks.7": "down_blocks.2.downsamplers.0", - "down_blocks.8": "down_blocks.3", - "down_blocks.9": "mid_block", - # common - "conv_shortcut": "conv_shortcut.conv", - "res_blocks": "resnets", - "norm3.norm": "norm3", - "per_channel_statistics.mean-of-means": "latents_mean", - "per_channel_statistics.std-of-means": "latents_std", -} - -VAE_091_RENAME_DICT = { - # decoder - "up_blocks.0": "mid_block", - "up_blocks.1": "up_blocks.0.upsamplers.0", - "up_blocks.2": "up_blocks.0", - "up_blocks.3": "up_blocks.1.upsamplers.0", - "up_blocks.4": "up_blocks.1", - "up_blocks.5": "up_blocks.2.upsamplers.0", - "up_blocks.6": "up_blocks.2", - "up_blocks.7": "up_blocks.3.upsamplers.0", - "up_blocks.8": "up_blocks.3", - # common - "last_time_embedder": "time_embedder", - "last_scale_shift_table": "scale_shift_table", -} - -VAE_095_RENAME_DICT = { - # decoder - "up_blocks.0": "mid_block", - "up_blocks.1": "up_blocks.0.upsamplers.0", - "up_blocks.2": "up_blocks.0", - "up_blocks.3": "up_blocks.1.upsamplers.0", - "up_blocks.4": "up_blocks.1", - "up_blocks.5": "up_blocks.2.upsamplers.0", - "up_blocks.6": "up_blocks.2", - "up_blocks.7": "up_blocks.3.upsamplers.0", - "up_blocks.8": "up_blocks.3", - # encoder - "down_blocks.0": "down_blocks.0", - "down_blocks.1": "down_blocks.0.downsamplers.0", - "down_blocks.2": "down_blocks.1", - "down_blocks.3": "down_blocks.1.downsamplers.0", - "down_blocks.4": "down_blocks.2", - "down_blocks.5": "down_blocks.2.downsamplers.0", - "down_blocks.6": "down_blocks.3", - "down_blocks.7": "down_blocks.3.downsamplers.0", - "down_blocks.8": "mid_block", - # common - "last_time_embedder": "time_embedder", - "last_scale_shift_table": "scale_shift_table", -} - -VAE_SPECIAL_KEYS_REMAP = { - "per_channel_statistics.channel": remove_keys_, - "per_channel_statistics.mean-of-means": remove_keys_, - "per_channel_statistics.mean-of-stds": remove_keys_, - "model.diffusion_model": remove_keys_, -} - - -def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: - state_dict = saved_dict - if "model" in saved_dict.keys(): - state_dict = state_dict["model"] - if "module" in saved_dict.keys(): - state_dict = state_dict["module"] - if "state_dict" in saved_dict.keys(): - state_dict = state_dict["state_dict"] - return state_dict - - -def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def convert_transformer(ckpt_path: str, config, dtype: torch.dtype): - PREFIX_KEY = "model.diffusion_model." - - original_state_dict = get_state_dict(load_file(ckpt_path)) - with init_empty_weights(): - transformer = LTXVideoTransformer3DModel(**config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - if new_key.startswith(PREFIX_KEY): - new_key = key[len(PREFIX_KEY) :] - for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - transformer.load_state_dict(original_state_dict, strict=True, assign=True) - return transformer - - -def convert_vae(ckpt_path: str, config, dtype: torch.dtype): - PREFIX_KEY = "vae." - - original_state_dict = get_state_dict(load_file(ckpt_path)) - with init_empty_weights(): - vae = AutoencoderKLLTXVideo(**config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - if new_key.startswith(PREFIX_KEY): - new_key = key[len(PREFIX_KEY) :] - for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_inplace(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - vae.load_state_dict(original_state_dict, strict=True, assign=True) - return vae - - -def convert_spatial_latent_upsampler(ckpt_path: str, config, dtype: torch.dtype): - original_state_dict = get_state_dict(load_file(ckpt_path)) - - with init_empty_weights(): - latent_upsampler = LTXLatentUpsamplerModel(**config) - - latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True) - latent_upsampler.to(dtype) - return latent_upsampler - - -def get_transformer_config(version: str) -> dict[str, Any]: - if version == "0.9.7": - config = { - "in_channels": 128, - "out_channels": 128, - "patch_size": 1, - "patch_size_t": 1, - "num_attention_heads": 32, - "attention_head_dim": 128, - "cross_attention_dim": 4096, - "num_layers": 48, - "activation_fn": "gelu-approximate", - "qk_norm": "rms_norm_across_heads", - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "caption_channels": 4096, - "attention_bias": True, - "attention_out_bias": True, - } - else: - config = { - "in_channels": 128, - "out_channels": 128, - "patch_size": 1, - "patch_size_t": 1, - "num_attention_heads": 32, - "attention_head_dim": 64, - "cross_attention_dim": 2048, - "num_layers": 28, - "activation_fn": "gelu-approximate", - "qk_norm": "rms_norm_across_heads", - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "caption_channels": 4096, - "attention_bias": True, - "attention_out_bias": True, - } - return config - - -def get_vae_config(version: str) -> dict[str, Any]: - if version in ["0.9.0"]: - config = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (128, 256, 512, 512), - "down_block_types": ( - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - ), - "decoder_block_out_channels": (128, 256, 512, 512), - "layers_per_block": (4, 3, 3, 3, 4), - "decoder_layers_per_block": (4, 3, 3, 3, 4), - "spatio_temporal_scaling": (True, True, True, False), - "decoder_spatio_temporal_scaling": (True, True, True, False), - "decoder_inject_noise": (False, False, False, False, False), - "downsample_type": ("conv", "conv", "conv", "conv"), - "upsample_residual": (False, False, False, False), - "upsample_factor": (1, 1, 1, 1), - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "scaling_factor": 1.0, - "encoder_causal": True, - "decoder_causal": False, - "timestep_conditioning": False, - } - elif version in ["0.9.1"]: - config = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (128, 256, 512, 512), - "down_block_types": ( - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - "LTXVideoDownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 1024), - "layers_per_block": (4, 3, 3, 3, 4), - "decoder_layers_per_block": (5, 6, 7, 8), - "spatio_temporal_scaling": (True, True, True, False), - "decoder_spatio_temporal_scaling": (True, True, True), - "decoder_inject_noise": (True, True, True, False), - "downsample_type": ("conv", "conv", "conv", "conv"), - "upsample_residual": (True, True, True), - "upsample_factor": (2, 2, 2), - "timestep_conditioning": True, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "scaling_factor": 1.0, - "encoder_causal": True, - "decoder_causal": False, - } - VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT) - elif version in ["0.9.5"]: - config = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (128, 256, 512, 1024, 2048), - "down_block_types": ( - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 1024), - "layers_per_block": (4, 6, 6, 2, 2), - "decoder_layers_per_block": (5, 5, 5, 5), - "spatio_temporal_scaling": (True, True, True, True), - "decoder_spatio_temporal_scaling": (True, True, True), - "decoder_inject_noise": (False, False, False, False), - "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), - "upsample_residual": (True, True, True), - "upsample_factor": (2, 2, 2), - "timestep_conditioning": True, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "scaling_factor": 1.0, - "encoder_causal": True, - "decoder_causal": False, - "spatial_compression_ratio": 32, - "temporal_compression_ratio": 8, - } - VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT) - elif version in ["0.9.7"]: - config = { - "in_channels": 3, - "out_channels": 3, - "latent_channels": 128, - "block_out_channels": (128, 256, 512, 1024, 2048), - "down_block_types": ( - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - "LTXVideo095DownBlock3D", - ), - "decoder_block_out_channels": (256, 512, 1024), - "layers_per_block": (4, 6, 6, 2, 2), - "decoder_layers_per_block": (5, 5, 5, 5), - "spatio_temporal_scaling": (True, True, True, True), - "decoder_spatio_temporal_scaling": (True, True, True), - "decoder_inject_noise": (False, False, False, False), - "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), - "upsample_residual": (True, True, True), - "upsample_factor": (2, 2, 2), - "timestep_conditioning": True, - "patch_size": 4, - "patch_size_t": 1, - "resnet_norm_eps": 1e-6, - "scaling_factor": 1.0, - "encoder_causal": True, - "decoder_causal": False, - "spatial_compression_ratio": 32, - "temporal_compression_ratio": 8, - } - VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT) - return config - - -def get_spatial_latent_upsampler_config(version: str) -> dict[str, Any]: - if version == "0.9.7": - config = { - "in_channels": 128, - "mid_channels": 512, - "num_blocks_per_stage": 4, - "dims": 3, - "spatial_upsample": True, - "temporal_upsample": False, - } - elif version == "0.9.8": - config = { - "in_channels": 128, - "mid_channels": 512, - "num_blocks_per_stage": 4, - "dims": 3, - "spatial_upsample": True, - "temporal_upsample": False, - } - else: - raise ValueError(f"Unsupported version: {version}") - return config - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" - ) - parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint") - parser.add_argument( - "--spatial_latent_upsampler_path", - type=str, - default=None, - help="Path to original spatial latent upsampler checkpoint", - ) - parser.add_argument( - "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory" - ) - parser.add_argument( - "--typecast_text_encoder", - action="store_true", - default=False, - help="Whether or not to apply fp16/bf16 precision to text_encoder", - ) - parser.add_argument("--save_pipeline", action="store_true") - parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") - parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.") - parser.add_argument( - "--version", - type=str, - default="0.9.0", - choices=["0.9.0", "0.9.1", "0.9.5", "0.9.7", "0.9.8"], - help="Version of the LTX model", - ) - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - -VARIANT_MAPPING = { - "fp32": None, - "fp16": "fp16", - "bf16": "bf16", -} - - -if __name__ == "__main__": - args = get_args() - - transformer = None - dtype = DTYPE_MAPPING[args.dtype] - variant = VARIANT_MAPPING[args.dtype] - output_path = Path(args.output_path) - - if args.transformer_ckpt_path is not None: - config = get_transformer_config(args.version) - transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, config, dtype) - if not args.save_pipeline: - transformer.save_pretrained( - output_path / "transformer", safe_serialization=True, max_shard_size="5GB", variant=variant - ) - - if args.vae_ckpt_path is not None: - config = get_vae_config(args.version) - vae: AutoencoderKLLTXVideo = convert_vae(args.vae_ckpt_path, config, dtype) - if not args.save_pipeline: - vae.save_pretrained(output_path / "vae", safe_serialization=True, max_shard_size="5GB", variant=variant) - - if args.spatial_latent_upsampler_path is not None: - config = get_spatial_latent_upsampler_config(args.version) - latent_upsampler: LTXLatentUpsamplerModel = convert_spatial_latent_upsampler( - args.spatial_latent_upsampler_path, config, dtype - ) - if not args.save_pipeline: - latent_upsampler.save_pretrained( - output_path / "latent_upsampler", safe_serialization=True, max_shard_size="5GB", variant=variant - ) - - if args.save_pipeline: - text_encoder_id = "google/t5-v1_1-xxl" - tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) - text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) - - if args.typecast_text_encoder: - text_encoder = text_encoder.to(dtype=dtype) - - # Apparently, the conversion does not work anymore without this :shrug: - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - if args.version in ["0.9.5", "0.9.7"]: - scheduler = FlowMatchEulerDiscreteScheduler(use_dynamic_shifting=False) - else: - scheduler = FlowMatchEulerDiscreteScheduler( - use_dynamic_shifting=True, - base_shift=0.95, - max_shift=2.05, - base_image_seq_len=1024, - max_image_seq_len=4096, - shift_terminal=0.1, - ) - - if args.version in ["0.9.0", "0.9.1", "0.9.5"]: - pipe = LTXPipeline( - scheduler=scheduler, - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - ) - pipe.save_pretrained( - output_path.as_posix(), safe_serialization=True, variant=variant, max_shard_size="5GB" - ) - elif args.version in ["0.9.7"]: - pipe = LTXConditionPipeline( - scheduler=scheduler, - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - ) - pipe_upsample = LTXLatentUpsamplePipeline( - vae=vae, - latent_upsampler=latent_upsampler, - ) - pipe.save_pretrained( - (output_path / "ltx_pipeline").as_posix(), - safe_serialization=True, - variant=variant, - max_shard_size="5GB", - ) - pipe_upsample.save_pretrained( - (output_path / "ltx_upsample_pipeline").as_posix(), - safe_serialization=True, - variant=variant, - max_shard_size="5GB", - ) - else: - raise ValueError(f"Unsupported version: {args.version}") diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py deleted file mode 100644 index c14aad3c6bf2..000000000000 --- a/scripts/convert_lumina_to_diffusers.py +++ /dev/null @@ -1,142 +0,0 @@ -import argparse -import os - -import torch -from safetensors.torch import load_file -from transformers import AutoModel, AutoTokenizer - -from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaPipeline - - -def main(args): - # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I - all_sd = load_file(args.origin_ckpt_path, device="cpu") - converted_state_dict = {} - # pad token - converted_state_dict["pad_token"] = all_sd["pad_token"] - - # patch embed - converted_state_dict["patch_embedder.weight"] = all_sd["x_embedder.weight"] - converted_state_dict["patch_embedder.bias"] = all_sd["x_embedder.bias"] - - # time and caption embed - converted_state_dict["time_caption_embed.timestep_embedder.linear_1.weight"] = all_sd["t_embedder.mlp.0.weight"] - converted_state_dict["time_caption_embed.timestep_embedder.linear_1.bias"] = all_sd["t_embedder.mlp.0.bias"] - converted_state_dict["time_caption_embed.timestep_embedder.linear_2.weight"] = all_sd["t_embedder.mlp.2.weight"] - converted_state_dict["time_caption_embed.timestep_embedder.linear_2.bias"] = all_sd["t_embedder.mlp.2.bias"] - converted_state_dict["time_caption_embed.caption_embedder.0.weight"] = all_sd["cap_embedder.0.weight"] - converted_state_dict["time_caption_embed.caption_embedder.0.bias"] = all_sd["cap_embedder.0.bias"] - converted_state_dict["time_caption_embed.caption_embedder.1.weight"] = all_sd["cap_embedder.1.weight"] - converted_state_dict["time_caption_embed.caption_embedder.1.bias"] = all_sd["cap_embedder.1.bias"] - - for i in range(24): - # adaln - converted_state_dict[f"layers.{i}.gate"] = all_sd[f"layers.{i}.attention.gate"] - converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] - converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] - - # qkv - converted_state_dict[f"layers.{i}.attn1.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.attn1.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] - converted_state_dict[f"layers.{i}.attn1.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] - - # cap - converted_state_dict[f"layers.{i}.attn2.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.attn2.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] - converted_state_dict[f"layers.{i}.attn2.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] - - # output - converted_state_dict[f"layers.{i}.attn2.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] - - # attention - # qk norm - converted_state_dict[f"layers.{i}.attn1.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.attn1.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - - converted_state_dict[f"layers.{i}.attn1.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] - converted_state_dict[f"layers.{i}.attn1.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - - converted_state_dict[f"layers.{i}.attn2.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.attn2.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - - converted_state_dict[f"layers.{i}.attn2.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] - converted_state_dict[f"layers.{i}.attn2.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] - - # attention norm - converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] - converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] - - # feed forward - converted_state_dict[f"layers.{i}.feed_forward.linear_1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] - converted_state_dict[f"layers.{i}.feed_forward.linear_2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] - converted_state_dict[f"layers.{i}.feed_forward.linear_3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] - - # feed forward norm - converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] - converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] - - # final layer - converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] - converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] - - converted_state_dict["final_layer.adaLN_modulation.1.weight"] = all_sd["final_layer.adaLN_modulation.1.weight"] - converted_state_dict["final_layer.adaLN_modulation.1.bias"] = all_sd["final_layer.adaLN_modulation.1.bias"] - - # Lumina-Next-SFT 2B - transformer = LuminaNextDiT2DModel( - sample_size=128, - patch_size=2, - in_channels=4, - hidden_size=2304, - num_layers=24, - num_attention_heads=32, - num_kv_heads=8, - multiple_of=256, - ffn_dim_multiplier=None, - norm_eps=1e-5, - learn_sigma=True, - qk_norm=True, - cross_attention_dim=2048, - scaling_factor=1.0, - ) - transformer.load_state_dict(converted_state_dict, strict=True) - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - if args.only_transformer: - transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) - else: - scheduler = FlowMatchEulerDiscreteScheduler() - - vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32) - - tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") - text_encoder = AutoModel.from_pretrained("google/gemma-2b") - - pipeline = LuminaPipeline( - tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler - ) - pipeline.save_pretrained(args.dump_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--origin_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--image_size", - default=1024, - type=int, - choices=[256, 512, 1024], - required=False, - help="Image size of pretrained model, either 512 or 1024.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--only_transformer", default=True, type=bool, required=True) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_mochi_to_diffusers.py b/scripts/convert_mochi_to_diffusers.py deleted file mode 100644 index 64e4f69eac17..000000000000 --- a/scripts/convert_mochi_to_diffusers.py +++ /dev/null @@ -1,463 +0,0 @@ -import argparse -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights -from safetensors.torch import load_file -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -TOKENIZER_MAX_LENGTH = 256 - -parser = argparse.ArgumentParser() -parser.add_argument("--transformer_checkpoint_path", default=None, type=str) -parser.add_argument("--vae_encoder_checkpoint_path", default=None, type=str) -parser.add_argument("--vae_decoder_checkpoint_path", default=None, type=str) -parser.add_argument("--output_path", required=True, type=str) -parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") -parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") -parser.add_argument("--dtype", type=str, default=None) - -args = parser.parse_args() - - -# This is specific to `AdaLayerNormContinuous`: -# Diffusers implementation split the linear projection into the scale, shift while Mochi split it into shift, scale -def swap_scale_shift(weight, dim): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def swap_proj_gate(weight): - proj, gate = weight.chunk(2, dim=0) - new_weight = torch.cat([gate, proj], dim=0) - return new_weight - - -def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path): - original_state_dict = load_file(ckpt_path, device="cpu") - new_state_dict = {} - - # Convert patch_embed - new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight") - new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias") - - # Convert time_embed - new_state_dict["time_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop("t_embedder.mlp.0.weight") - new_state_dict["time_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop("t_embedder.mlp.0.bias") - new_state_dict["time_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop("t_embedder.mlp.2.weight") - new_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("t_embedder.mlp.2.bias") - new_state_dict["time_embed.pooler.to_kv.weight"] = original_state_dict.pop("t5_y_embedder.to_kv.weight") - new_state_dict["time_embed.pooler.to_kv.bias"] = original_state_dict.pop("t5_y_embedder.to_kv.bias") - new_state_dict["time_embed.pooler.to_q.weight"] = original_state_dict.pop("t5_y_embedder.to_q.weight") - new_state_dict["time_embed.pooler.to_q.bias"] = original_state_dict.pop("t5_y_embedder.to_q.bias") - new_state_dict["time_embed.pooler.to_out.weight"] = original_state_dict.pop("t5_y_embedder.to_out.weight") - new_state_dict["time_embed.pooler.to_out.bias"] = original_state_dict.pop("t5_y_embedder.to_out.bias") - new_state_dict["time_embed.caption_proj.weight"] = original_state_dict.pop("t5_yproj.weight") - new_state_dict["time_embed.caption_proj.bias"] = original_state_dict.pop("t5_yproj.bias") - - # Convert transformer blocks - num_layers = 48 - for i in range(num_layers): - block_prefix = f"transformer_blocks.{i}." - old_prefix = f"blocks.{i}." - - # norm1 - new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(old_prefix + "mod_x.weight") - new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(old_prefix + "mod_x.bias") - if i < num_layers - 1: - new_state_dict[block_prefix + "norm1_context.linear.weight"] = original_state_dict.pop( - old_prefix + "mod_y.weight" - ) - new_state_dict[block_prefix + "norm1_context.linear.bias"] = original_state_dict.pop( - old_prefix + "mod_y.bias" - ) - else: - new_state_dict[block_prefix + "norm1_context.linear_1.weight"] = original_state_dict.pop( - old_prefix + "mod_y.weight" - ) - new_state_dict[block_prefix + "norm1_context.linear_1.bias"] = original_state_dict.pop( - old_prefix + "mod_y.bias" - ) - - # Visual attention - qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_x.weight") - q, k, v = qkv_weight.chunk(3, dim=0) - - new_state_dict[block_prefix + "attn1.to_q.weight"] = q - new_state_dict[block_prefix + "attn1.to_k.weight"] = k - new_state_dict[block_prefix + "attn1.to_v.weight"] = v - new_state_dict[block_prefix + "attn1.norm_q.weight"] = original_state_dict.pop( - old_prefix + "attn.q_norm_x.weight" - ) - new_state_dict[block_prefix + "attn1.norm_k.weight"] = original_state_dict.pop( - old_prefix + "attn.k_norm_x.weight" - ) - new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( - old_prefix + "attn.proj_x.weight" - ) - new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop(old_prefix + "attn.proj_x.bias") - - # Context attention - qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_y.weight") - q, k, v = qkv_weight.chunk(3, dim=0) - - new_state_dict[block_prefix + "attn1.add_q_proj.weight"] = q - new_state_dict[block_prefix + "attn1.add_k_proj.weight"] = k - new_state_dict[block_prefix + "attn1.add_v_proj.weight"] = v - new_state_dict[block_prefix + "attn1.norm_added_q.weight"] = original_state_dict.pop( - old_prefix + "attn.q_norm_y.weight" - ) - new_state_dict[block_prefix + "attn1.norm_added_k.weight"] = original_state_dict.pop( - old_prefix + "attn.k_norm_y.weight" - ) - if i < num_layers - 1: - new_state_dict[block_prefix + "attn1.to_add_out.weight"] = original_state_dict.pop( - old_prefix + "attn.proj_y.weight" - ) - new_state_dict[block_prefix + "attn1.to_add_out.bias"] = original_state_dict.pop( - old_prefix + "attn.proj_y.bias" - ) - - # MLP - new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate( - original_state_dict.pop(old_prefix + "mlp_x.w1.weight") - ) - new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop(old_prefix + "mlp_x.w2.weight") - if i < num_layers - 1: - new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate( - original_state_dict.pop(old_prefix + "mlp_y.w1.weight") - ) - new_state_dict[block_prefix + "ff_context.net.2.weight"] = original_state_dict.pop( - old_prefix + "mlp_y.w2.weight" - ) - - # Output layers - new_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("final_layer.mod.weight"), dim=0 - ) - new_state_dict["norm_out.linear.bias"] = swap_scale_shift(original_state_dict.pop("final_layer.mod.bias"), dim=0) - new_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - new_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - - new_state_dict["pos_frequencies"] = original_state_dict.pop("pos_frequencies") - - print("Remaining Keys:", original_state_dict.keys()) - - return new_state_dict - - -def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_path): - encoder_state_dict = load_file(encoder_ckpt_path, device="cpu") - decoder_state_dict = load_file(decoder_ckpt_path, device="cpu") - new_state_dict = {} - - # ==== Decoder ===== - prefix = "decoder." - - # Convert conv_in - new_state_dict[f"{prefix}conv_in.weight"] = decoder_state_dict.pop("blocks.0.0.weight") - new_state_dict[f"{prefix}conv_in.bias"] = decoder_state_dict.pop("blocks.0.0.bias") - - # Convert block_in (MochiMidBlock3D) - for i in range(3): # layers_per_block[-1] = 3 - new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.0.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.0.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.2.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.2.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.3.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.3.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.5.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( - f"blocks.0.{i + 1}.stack.5.bias" - ) - - # Convert up_blocks (MochiUpBlock3D) - down_block_layers = [6, 4, 3] # layers_per_block[-2], layers_per_block[-3], layers_per_block[-4] - for block in range(3): - for i in range(down_block_layers[block]): - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.0.weight" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.0.bias" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.2.weight" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.2.bias" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.3.weight" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.3.bias" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.5.weight" - ) - new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( - f"blocks.{block + 1}.blocks.{i}.stack.5.bias" - ) - new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = decoder_state_dict.pop( - f"blocks.{block + 1}.proj.weight" - ) - new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop( - f"blocks.{block + 1}.proj.bias" - ) - - # Convert block_out (MochiMidBlock3D) - for i in range(3): # layers_per_block[0] = 3 - new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.0.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.0.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.2.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.2.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.3.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.3.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.5.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( - f"blocks.4.{i}.stack.5.bias" - ) - - # Convert proj_out (Conv1x1 ~= nn.Linear) - new_state_dict[f"{prefix}proj_out.weight"] = decoder_state_dict.pop("output_proj.weight") - new_state_dict[f"{prefix}proj_out.bias"] = decoder_state_dict.pop("output_proj.bias") - - print("Remaining Decoder Keys:", decoder_state_dict.keys()) - - # ==== Encoder ===== - prefix = "encoder." - - new_state_dict[f"{prefix}proj_in.weight"] = encoder_state_dict.pop("layers.0.weight") - new_state_dict[f"{prefix}proj_in.bias"] = encoder_state_dict.pop("layers.0.bias") - - # Convert block_in (MochiMidBlock3D) - for i in range(3): # layers_per_block[0] = 3 - new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.0.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.0.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.2.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.2.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.3.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.3.bias" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.5.weight" - ) - new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( - f"layers.{i + 1}.stack.5.bias" - ) - - # Convert down_blocks (MochiDownBlock3D) - down_block_layers = [3, 4, 6] # layers_per_block[1], layers_per_block[2], layers_per_block[3] - for block in range(3): - new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.weight"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.0.weight" - ) - new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.0.bias" - ) - - for i in range(down_block_layers[block]): - # Convert resnets - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = ( - encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.0.weight") - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.0.bias" - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.2.weight" - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.2.bias" - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = ( - encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.3.weight") - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.3.bias" - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.5.weight" - ) - new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.stack.5.bias" - ) - - # Convert attentions - qkv_weight = encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.qkv.weight") - q, k, v = qkv_weight.chunk(3, dim=0) - - new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"] = q - new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"] = k - new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"] = v - new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.weight" - ) - new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.bias" - ) - new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.weight" - ) - new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.bias" - ) - - # Convert block_out (MochiMidBlock3D) - for i in range(3): # layers_per_block[-1] = 3 - # Convert resnets - new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.0.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.0.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.2.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.2.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.3.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.3.bias" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.5.weight" - ) - new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.stack.5.bias" - ) - - # Convert attentions - qkv_weight = encoder_state_dict.pop(f"layers.{i + 7}.attn_block.attn.qkv.weight") - q, k, v = qkv_weight.chunk(3, dim=0) - - new_state_dict[f"{prefix}block_out.attentions.{i}.to_q.weight"] = q - new_state_dict[f"{prefix}block_out.attentions.{i}.to_k.weight"] = k - new_state_dict[f"{prefix}block_out.attentions.{i}.to_v.weight"] = v - new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.attn_block.attn.out.weight" - ) - new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.attn_block.attn.out.bias" - ) - new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop( - f"layers.{i + 7}.attn_block.norm.weight" - ) - new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop( - f"layers.{i + 7}.attn_block.norm.bias" - ) - - # Convert output layers - new_state_dict[f"{prefix}norm_out.norm_layer.weight"] = encoder_state_dict.pop("output_norm.weight") - new_state_dict[f"{prefix}norm_out.norm_layer.bias"] = encoder_state_dict.pop("output_norm.bias") - new_state_dict[f"{prefix}proj_out.weight"] = encoder_state_dict.pop("output_proj.weight") - - print("Remaining Encoder Keys:", encoder_state_dict.keys()) - - return new_state_dict - - -def main(args): - if args.dtype is None: - dtype = None - if args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}") - - transformer = None - vae = None - - if args.transformer_checkpoint_path is not None: - converted_transformer_state_dict = convert_mochi_transformer_checkpoint_to_diffusers( - args.transformer_checkpoint_path - ) - transformer = MochiTransformer3DModel() - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - if dtype is not None: - transformer = transformer.to(dtype=dtype) - - if args.vae_encoder_checkpoint_path is not None and args.vae_decoder_checkpoint_path is not None: - vae = AutoencoderKLMochi(latent_channels=12, out_channels=3) - converted_vae_state_dict = convert_mochi_vae_state_dict_to_diffusers( - args.vae_encoder_checkpoint_path, args.vae_decoder_checkpoint_path - ) - vae.load_state_dict(converted_vae_state_dict, strict=True) - if dtype is not None: - vae = vae.to(dtype=dtype) - - text_encoder_id = "google/t5-v1_1-xxl" - tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) - text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) - - # Apparently, the conversion does not work anymore without this :shrug: - for param in text_encoder.parameters(): - param.data = param.data.contiguous() - - pipe = MochiPipeline( - scheduler=FlowMatchEulerDiscreteScheduler(invert_sigmas=True), - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - transformer=transformer, - ) - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_models_diffuser_to_diffusers.py b/scripts/convert_models_diffuser_to_diffusers.py deleted file mode 100644 index cc5321e33fe0..000000000000 --- a/scripts/convert_models_diffuser_to_diffusers.py +++ /dev/null @@ -1,100 +0,0 @@ -import json -import os - -import torch - -from diffusers import UNet1DModel - - -os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True) -os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True) - -os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True) - - -def unet(hor): - if hor == 128: - down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") - block_out_channels = (32, 128, 256) - up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D") - - elif hor == 32: - down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") - block_out_channels = (32, 64, 128, 256) - up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D") - model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch") - state_dict = model.state_dict() - config = { - "down_block_types": down_block_types, - "block_out_channels": block_out_channels, - "up_block_types": up_block_types, - "layers_per_block": 1, - "use_timestep_embedding": True, - "out_block_type": "OutConv1DBlock", - "norm_num_groups": 8, - "downsample_each_block": False, - "in_channels": 14, - "out_channels": 14, - "extra_in_channels": 0, - "time_embedding_type": "positional", - "flip_sin_to_cos": False, - "freq_shift": 1, - "sample_size": 65536, - "mid_block_type": "MidResTemporalBlock1D", - "act_fn": "mish", - } - hf_value_function = UNet1DModel(**config) - print(f"length of state dict: {len(state_dict.keys())}") - print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") - mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys())) - for k, v in mapping.items(): - state_dict[v] = state_dict.pop(k) - hf_value_function.load_state_dict(state_dict) - - torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin") - with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f: - json.dump(config, f) - - -def value_function(): - config = { - "in_channels": 14, - "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"), - "up_block_types": (), - "out_block_type": "ValueFunction", - "mid_block_type": "ValueFunctionMidBlock1D", - "block_out_channels": (32, 64, 128, 256), - "layers_per_block": 1, - "downsample_each_block": True, - "sample_size": 65536, - "out_channels": 14, - "extra_in_channels": 0, - "time_embedding_type": "positional", - "use_timestep_embedding": True, - "flip_sin_to_cos": False, - "freq_shift": 1, - "norm_num_groups": 8, - "act_fn": "mish", - } - - model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch") - state_dict = model - hf_value_function = UNet1DModel(**config) - print(f"length of state dict: {len(state_dict.keys())}") - print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") - - mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys())) - for k, v in mapping.items(): - state_dict[v] = state_dict.pop(k) - - hf_value_function.load_state_dict(state_dict) - - torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin") - with open("hub/hopper-medium-v2/value_function/config.json", "w") as f: - json.dump(config, f) - - -if __name__ == "__main__": - unet(32) - # unet(128) - value_function() diff --git a/scripts/convert_ms_text_to_video_to_diffusers.py b/scripts/convert_ms_text_to_video_to_diffusers.py deleted file mode 100644 index e150a491a0b0..000000000000 --- a/scripts/convert_ms_text_to_video_to_diffusers.py +++ /dev/null @@ -1,428 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the LDM checkpoints.""" - -import argparse - -import torch - -from diffusers import UNet3DConditionModel - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - weight = old_checkpoint[path["old"]] - names = ["proj_attn.weight"] - names_2 = ["proj_out.weight", "proj_in.weight"] - if any(k in new_path for k in names): - checkpoint[new_path] = weight[:, :, 0] - elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and ".attentions." not in new_path: - checkpoint[new_path] = weight[:, :, 0] - else: - checkpoint[new_path] = weight - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_temp_conv_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - mapping.append({"old": old_item, "new": old_item}) - - return mapping - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - if "temopral_conv" not in old_item: - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has both EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - first_temp_attention = [v for v in unet_state_dict if v.startswith("input_blocks.0.1")] - paths = renew_attention_paths(first_temp_attention) - meta_path = {"old": "input_blocks.0.1", "new": "transformer_in"} - assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - temp_attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key] - - if f"input_blocks.{i}.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temporal_convs = [key for key in resnets if "temopral_conv" in key] - paths = renew_temp_conv_paths(temporal_convs) - meta_path = { - "old": f"input_blocks.{i}.0.temopral_conv", - "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(temp_attentions): - paths = renew_attention_paths(temp_attentions) - meta_path = { - "old": f"input_blocks.{i}.2", - "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - temporal_convs_0 = [key for key in resnet_0 if "temopral_conv" in key] - attentions = middle_blocks[1] - temp_attentions = middle_blocks[2] - resnet_1 = middle_blocks[3] - temporal_convs_1 = [key for key in resnet_1 if "temopral_conv" in key] - - resnet_0_paths = renew_resnet_paths(resnet_0) - meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} - assign_to_checkpoint( - resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0) - meta_path = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"} - assign_to_checkpoint( - temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - resnet_1_paths = renew_resnet_paths(resnet_1) - meta_path = {"old": "middle_block.3", "new": "mid_block.resnets.1"} - assign_to_checkpoint( - resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1) - meta_path = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"} - assign_to_checkpoint( - temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] - ) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temp_attentions_paths = renew_attention_paths(temp_attentions) - meta_path = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"} - assign_to_checkpoint( - temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - temp_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - temporal_convs = [key for key in resnets if "temopral_conv" in key] - paths = renew_temp_conv_paths(temporal_convs) - meta_path = { - "old": f"output_blocks.{i}.0.temopral_conv", - "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(temp_attentions): - paths = renew_attention_paths(temp_attentions) - meta_path = { - "old": f"output_blocks.{i}.2", - "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - new_checkpoint[new_path] = unet_state_dict[old_path] - - temopral_conv_paths = [l for l in output_block_layers if "temopral_conv" in l] - for path in temopral_conv_paths: - pruned_path = path.split("temopral_conv.")[-1] - old_path = ".".join(["output_blocks", str(i), str(block_id), "temopral_conv", pruned_path]) - new_path = ".".join(["up_blocks", str(block_id), "temp_convs", str(layer_in_block_id), pruned_path]) - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - args = parser.parse_args() - - unet_checkpoint = torch.load(args.checkpoint_path, map_location="cpu") - unet = UNet3DConditionModel() - - converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config) - - diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys()) - diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys()) - - assert len(diff_0) == len(diff_1) == 0, "Converted weights don't match" - - # load state_dict - unet.load_state_dict(converted_ckpt) - - unet.save_pretrained(args.dump_path) - - # -- finish converting the unet -- diff --git a/scripts/convert_music_spectrogram_to_diffusers.py b/scripts/convert_music_spectrogram_to_diffusers.py deleted file mode 100644 index 1ac9ceddb57e..000000000000 --- a/scripts/convert_music_spectrogram_to_diffusers.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os - -import jax as jnp -import numpy as onp -import torch -import torch.nn as nn -from music_spectrogram_diffusion import inference -from t5x import checkpoints - -from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline -from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder - - -MODEL = "base_with_context" - - -def load_notes_encoder(weights, model): - model.token_embedder.weight = nn.Parameter(torch.Tensor(weights["token_embedder"]["embedding"])) - model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) - for lyr_num, lyr in enumerate(model.encoders): - ly_weight = weights[f"layers_{lyr_num}"] - lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"])) - - attention_weights = ly_weight["attention"] - lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) - lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) - lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) - lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) - - lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) - - lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) - lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) - lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) - - model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"])) - return model - - -def load_continuous_encoder(weights, model): - model.input_proj.weight = nn.Parameter(torch.Tensor(weights["input_proj"]["kernel"].T)) - - model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) - - for lyr_num, lyr in enumerate(model.encoders): - ly_weight = weights[f"layers_{lyr_num}"] - attention_weights = ly_weight["attention"] - - lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) - lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) - lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) - lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) - lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"])) - - lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) - lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) - lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) - lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) - - model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"])) - - return model - - -def load_decoder(weights, model): - model.conditioning_emb[0].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense0"]["kernel"].T)) - model.conditioning_emb[2].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense1"]["kernel"].T)) - - model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) - - model.continuous_inputs_projection.weight = nn.Parameter( - torch.Tensor(weights["continuous_inputs_projection"]["kernel"].T) - ) - - for lyr_num, lyr in enumerate(model.decoders): - ly_weight = weights[f"layers_{lyr_num}"] - lyr.layer[0].layer_norm.weight = nn.Parameter( - torch.Tensor(ly_weight["pre_self_attention_layer_norm"]["scale"]) - ) - - lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter( - torch.Tensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T) - ) - - attention_weights = ly_weight["self_attention"] - lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) - lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) - lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) - lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) - - attention_weights = ly_weight["MultiHeadDotProductAttention_0"] - lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) - lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) - lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) - lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) - lyr.layer[1].layer_norm.weight = nn.Parameter( - torch.Tensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]) - ) - - lyr.layer[2].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) - lyr.layer[2].film.scale_bias.weight = nn.Parameter( - torch.Tensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T) - ) - lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) - lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) - lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) - - model.decoder_norm.weight = nn.Parameter(torch.Tensor(weights["decoder_norm"]["scale"])) - - model.spec_out.weight = nn.Parameter(torch.Tensor(weights["spec_out_dense"]["kernel"].T)) - - return model - - -def main(args): - t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path) - t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint) - - gin_overrides = [ - "from __gin__ import dynamic_registration", - "from music_spectrogram_diffusion.models.diffusion import diffusion_utils", - "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0", - "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()", - ] - - gin_file = os.path.join(args.checkpoint_path, "..", "config.gin") - gin_config = inference.parse_training_gin_file(gin_file, gin_overrides) - synth_model = inference.InferenceModel(args.checkpoint_path, gin_config) - - scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large") - - notes_encoder = SpectrogramNotesEncoder( - max_length=synth_model.sequence_length["inputs"], - vocab_size=synth_model.model.module.config.vocab_size, - d_model=synth_model.model.module.config.emb_dim, - dropout_rate=synth_model.model.module.config.dropout_rate, - num_layers=synth_model.model.module.config.num_encoder_layers, - num_heads=synth_model.model.module.config.num_heads, - d_kv=synth_model.model.module.config.head_dim, - d_ff=synth_model.model.module.config.mlp_dim, - feed_forward_proj="gated-gelu", - ) - - continuous_encoder = SpectrogramContEncoder( - input_dims=synth_model.audio_codec.n_dims, - targets_context_length=synth_model.sequence_length["targets_context"], - d_model=synth_model.model.module.config.emb_dim, - dropout_rate=synth_model.model.module.config.dropout_rate, - num_layers=synth_model.model.module.config.num_encoder_layers, - num_heads=synth_model.model.module.config.num_heads, - d_kv=synth_model.model.module.config.head_dim, - d_ff=synth_model.model.module.config.mlp_dim, - feed_forward_proj="gated-gelu", - ) - - decoder = T5FilmDecoder( - input_dims=synth_model.audio_codec.n_dims, - targets_length=synth_model.sequence_length["targets_context"], - max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time, - d_model=synth_model.model.module.config.emb_dim, - num_layers=synth_model.model.module.config.num_decoder_layers, - num_heads=synth_model.model.module.config.num_heads, - d_kv=synth_model.model.module.config.head_dim, - d_ff=synth_model.model.module.config.mlp_dim, - dropout_rate=synth_model.model.module.config.dropout_rate, - ) - - notes_encoder = load_notes_encoder(t5_checkpoint["target"]["token_encoder"], notes_encoder) - continuous_encoder = load_continuous_encoder(t5_checkpoint["target"]["continuous_encoder"], continuous_encoder) - decoder = load_decoder(t5_checkpoint["target"]["decoder"], decoder) - - melgan = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder") - - pipe = SpectrogramDiffusionPipeline( - notes_encoder=notes_encoder, - continuous_encoder=continuous_encoder, - decoder=decoder, - scheduler=scheduler, - melgan=melgan, - ) - if args.save: - pipe.save_pretrained(args.output_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.") - parser.add_argument( - "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not." - ) - parser.add_argument( - "--checkpoint_path", - default=f"{MODEL}/checkpoint_500000", - type=str, - required=False, - help="Path to the original jax model checkpoint.", - ) - args = parser.parse_args() - - main(args) diff --git a/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py b/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py deleted file mode 100644 index bcab90e2a3db..000000000000 --- a/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py +++ /dev/null @@ -1,185 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the NCSNPP checkpoints.""" - -import argparse -import json - -import torch - -from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel - - -def convert_ncsnpp_checkpoint(checkpoint, config): - """ - Takes a state dict and the path to - """ - new_model_architecture = UNet2DModel(**config) - new_model_architecture.time_proj.W.data = checkpoint["all_modules.0.W"].data - new_model_architecture.time_proj.weight.data = checkpoint["all_modules.0.W"].data - new_model_architecture.time_embedding.linear_1.weight.data = checkpoint["all_modules.1.weight"].data - new_model_architecture.time_embedding.linear_1.bias.data = checkpoint["all_modules.1.bias"].data - - new_model_architecture.time_embedding.linear_2.weight.data = checkpoint["all_modules.2.weight"].data - new_model_architecture.time_embedding.linear_2.bias.data = checkpoint["all_modules.2.bias"].data - - new_model_architecture.conv_in.weight.data = checkpoint["all_modules.3.weight"].data - new_model_architecture.conv_in.bias.data = checkpoint["all_modules.3.bias"].data - - new_model_architecture.conv_norm_out.weight.data = checkpoint[list(checkpoint.keys())[-4]].data - new_model_architecture.conv_norm_out.bias.data = checkpoint[list(checkpoint.keys())[-3]].data - new_model_architecture.conv_out.weight.data = checkpoint[list(checkpoint.keys())[-2]].data - new_model_architecture.conv_out.bias.data = checkpoint[list(checkpoint.keys())[-1]].data - - module_index = 4 - - def set_attention_weights(new_layer, old_checkpoint, index): - new_layer.query.weight.data = old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T - new_layer.key.weight.data = old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T - new_layer.value.weight.data = old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T - - new_layer.query.bias.data = old_checkpoint[f"all_modules.{index}.NIN_0.b"].data - new_layer.key.bias.data = old_checkpoint[f"all_modules.{index}.NIN_1.b"].data - new_layer.value.bias.data = old_checkpoint[f"all_modules.{index}.NIN_2.b"].data - - new_layer.proj_attn.weight.data = old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T - new_layer.proj_attn.bias.data = old_checkpoint[f"all_modules.{index}.NIN_3.b"].data - - new_layer.group_norm.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data - new_layer.group_norm.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data - - def set_resnet_weights(new_layer, old_checkpoint, index): - new_layer.conv1.weight.data = old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data - new_layer.conv1.bias.data = old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data - new_layer.norm1.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data - new_layer.norm1.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data - - new_layer.conv2.weight.data = old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data - new_layer.conv2.bias.data = old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data - new_layer.norm2.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data - new_layer.norm2.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data - - new_layer.time_emb_proj.weight.data = old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data - new_layer.time_emb_proj.bias.data = old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data - - if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down: - new_layer.conv_shortcut.weight.data = old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data - new_layer.conv_shortcut.bias.data = old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data - - for i, block in enumerate(new_model_architecture.downsample_blocks): - has_attentions = hasattr(block, "attentions") - for j in range(len(block.resnets)): - set_resnet_weights(block.resnets[j], checkpoint, module_index) - module_index += 1 - if has_attentions: - set_attention_weights(block.attentions[j], checkpoint, module_index) - module_index += 1 - - if hasattr(block, "downsamplers") and block.downsamplers is not None: - set_resnet_weights(block.resnet_down, checkpoint, module_index) - module_index += 1 - block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data - block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data - module_index += 1 - - set_resnet_weights(new_model_architecture.mid_block.resnets[0], checkpoint, module_index) - module_index += 1 - set_attention_weights(new_model_architecture.mid_block.attentions[0], checkpoint, module_index) - module_index += 1 - set_resnet_weights(new_model_architecture.mid_block.resnets[1], checkpoint, module_index) - module_index += 1 - - for i, block in enumerate(new_model_architecture.up_blocks): - has_attentions = hasattr(block, "attentions") - for j in range(len(block.resnets)): - set_resnet_weights(block.resnets[j], checkpoint, module_index) - module_index += 1 - if has_attentions: - set_attention_weights( - block.attentions[0], checkpoint, module_index - ) # why can there only be a single attention layer for up? - module_index += 1 - - if hasattr(block, "resnet_up") and block.resnet_up is not None: - block.skip_norm.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data - block.skip_norm.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data - module_index += 1 - block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data - block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data - module_index += 1 - set_resnet_weights(block.resnet_up, checkpoint, module_index) - module_index += 1 - - new_model_architecture.conv_norm_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data - new_model_architecture.conv_norm_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data - module_index += 1 - new_model_architecture.conv_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data - new_model_architecture.conv_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data - - return new_model_architecture.state_dict() - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", - default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin", - type=str, - required=False, - help="Path to the checkpoint to convert.", - ) - - parser.add_argument( - "--config_file", - default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json", - type=str, - required=False, - help="The config json file corresponding to the architecture.", - ) - - parser.add_argument( - "--dump_path", - default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt", - type=str, - required=False, - help="Path to the output model.", - ) - - args = parser.parse_args() - - checkpoint = torch.load(args.checkpoint_path, map_location="cpu") - - with open(args.config_file) as f: - config = json.loads(f.read()) - - converted_checkpoint = convert_ncsnpp_checkpoint( - checkpoint, - config, - ) - - if "sde" in config: - del config["sde"] - - model = UNet2DModel(**config) - model.load_state_dict(converted_checkpoint) - - try: - scheduler = ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) - - pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler) - pipe.save_pretrained(args.dump_path) - except: # noqa: E722 - model.save_pretrained(args.dump_path) diff --git a/scripts/convert_omnigen_to_diffusers.py b/scripts/convert_omnigen_to_diffusers.py deleted file mode 100644 index 96bc935633f0..000000000000 --- a/scripts/convert_omnigen_to_diffusers.py +++ /dev/null @@ -1,203 +0,0 @@ -import argparse -import os - -import torch -from huggingface_hub import snapshot_download -from safetensors.torch import load_file -from transformers import AutoTokenizer - -from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, OmniGenPipeline, OmniGenTransformer2DModel - - -def main(args): - # checkpoint from https://huggingface.co/Shitao/OmniGen-v1 - - if not os.path.exists(args.origin_ckpt_path): - print("Model not found, downloading...") - cache_folder = os.getenv("HF_HUB_CACHE") - args.origin_ckpt_path = snapshot_download( - repo_id=args.origin_ckpt_path, - cache_dir=cache_folder, - ignore_patterns=["flax_model.msgpack", "rust_model.ot", "tf_model.h5", "model.pt"], - ) - print(f"Downloaded model to {args.origin_ckpt_path}") - - ckpt = os.path.join(args.origin_ckpt_path, "model.safetensors") - ckpt = load_file(ckpt, device="cpu") - - mapping_dict = { - "pos_embed": "patch_embedding.pos_embed", - "x_embedder.proj.weight": "patch_embedding.output_image_proj.weight", - "x_embedder.proj.bias": "patch_embedding.output_image_proj.bias", - "input_x_embedder.proj.weight": "patch_embedding.input_image_proj.weight", - "input_x_embedder.proj.bias": "patch_embedding.input_image_proj.bias", - "final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", - "final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", - "final_layer.linear.weight": "proj_out.weight", - "final_layer.linear.bias": "proj_out.bias", - "time_token.mlp.0.weight": "time_token.linear_1.weight", - "time_token.mlp.0.bias": "time_token.linear_1.bias", - "time_token.mlp.2.weight": "time_token.linear_2.weight", - "time_token.mlp.2.bias": "time_token.linear_2.bias", - "t_embedder.mlp.0.weight": "t_embedder.linear_1.weight", - "t_embedder.mlp.0.bias": "t_embedder.linear_1.bias", - "t_embedder.mlp.2.weight": "t_embedder.linear_2.weight", - "t_embedder.mlp.2.bias": "t_embedder.linear_2.bias", - "llm.embed_tokens.weight": "embed_tokens.weight", - } - - converted_state_dict = {} - for k, v in ckpt.items(): - if k in mapping_dict: - converted_state_dict[mapping_dict[k]] = v - elif "qkv" in k: - to_q, to_k, to_v = v.chunk(3) - converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_q.weight"] = to_q - converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_k.weight"] = to_k - converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_v.weight"] = to_v - elif "o_proj" in k: - converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_out.0.weight"] = v - else: - converted_state_dict[k[4:]] = v - - transformer = OmniGenTransformer2DModel( - rope_scaling={ - "long_factor": [ - 1.0299999713897705, - 1.0499999523162842, - 1.0499999523162842, - 1.0799999237060547, - 1.2299998998641968, - 1.2299998998641968, - 1.2999999523162842, - 1.4499999284744263, - 1.5999999046325684, - 1.6499998569488525, - 1.8999998569488525, - 2.859999895095825, - 3.68999981880188, - 5.419999599456787, - 5.489999771118164, - 5.489999771118164, - 9.09000015258789, - 11.579999923706055, - 15.65999984741211, - 15.769999504089355, - 15.789999961853027, - 18.360000610351562, - 21.989999771118164, - 23.079999923706055, - 30.009998321533203, - 32.35000228881836, - 32.590003967285156, - 35.56000518798828, - 39.95000457763672, - 53.840003967285156, - 56.20000457763672, - 57.95000457763672, - 59.29000473022461, - 59.77000427246094, - 59.920005798339844, - 61.190006256103516, - 61.96000671386719, - 62.50000762939453, - 63.3700065612793, - 63.48000717163086, - 63.48000717163086, - 63.66000747680664, - 63.850006103515625, - 64.08000946044922, - 64.760009765625, - 64.80001068115234, - 64.81001281738281, - 64.81001281738281, - ], - "short_factor": [ - 1.05, - 1.05, - 1.05, - 1.1, - 1.1, - 1.1, - 1.2500000000000002, - 1.2500000000000002, - 1.4000000000000004, - 1.4500000000000004, - 1.5500000000000005, - 1.8500000000000008, - 1.9000000000000008, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.000000000000001, - 2.1000000000000005, - 2.1000000000000005, - 2.2, - 2.3499999999999996, - 2.3499999999999996, - 2.3499999999999996, - 2.3499999999999996, - 2.3999999999999995, - 2.3999999999999995, - 2.6499999999999986, - 2.6999999999999984, - 2.8999999999999977, - 2.9499999999999975, - 3.049999999999997, - 3.049999999999997, - 3.049999999999997, - ], - "type": "su", - }, - patch_size=2, - in_channels=4, - pos_embed_max_size=192, - ) - transformer.load_state_dict(converted_state_dict, strict=True) - transformer.to(torch.bfloat16) - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - scheduler = FlowMatchEulerDiscreteScheduler(invert_sigmas=True, num_train_timesteps=1) - - vae = AutoencoderKL.from_pretrained(os.path.join(args.origin_ckpt_path, "vae"), torch_dtype=torch.float32) - - tokenizer = AutoTokenizer.from_pretrained(args.origin_ckpt_path) - - pipeline = OmniGenPipeline(tokenizer=tokenizer, transformer=transformer, vae=vae, scheduler=scheduler) - pipeline.save_pretrained(args.dump_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--origin_ckpt_path", - default="Shitao/OmniGen-v1", - type=str, - required=False, - help="Path to the checkpoint to convert.", - ) - - parser.add_argument( - "--dump_path", default="OmniGen-v1-diffusers", type=str, required=False, help="Path to the output pipeline." - ) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_original_audioldm2_to_diffusers.py b/scripts/convert_original_audioldm2_to_diffusers.py deleted file mode 100644 index 2c0695ce5595..000000000000 --- a/scripts/convert_original_audioldm2_to_diffusers.py +++ /dev/null @@ -1,1135 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the AudioLDM2 checkpoints.""" - -import argparse -import re -from typing import List, Union - -import torch -import yaml -from transformers import ( - AutoFeatureExtractor, - AutoTokenizer, - ClapConfig, - ClapModel, - GPT2Config, - GPT2Model, - SpeechT5HifiGan, - SpeechT5HifiGanConfig, - T5Config, - T5EncoderModel, -) - -from diffusers import ( - AudioLDM2Pipeline, - AudioLDM2ProjectionModel, - AudioLDM2UNet2DConditionModel, - AutoencoderKL, - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, -) -from diffusers.utils import is_safetensors_available -from diffusers.utils.import_utils import BACKENDS_MAPPING - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths -def renew_attention_paths(old_list): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] - proj_key = "to_out.0.weight" - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys or ".".join(key.split(".")[-3:]) == proj_key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key].squeeze() - - -def create_unet_diffusers_config(original_config, image_size: int): - """ - Creates a UNet config for diffusers based on the config of the original AudioLDM2 model. - """ - unet_params = original_config["model"]["params"]["unet_config"]["params"] - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - - block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) - - cross_attention_dim = list(unet_params["context_dim"]) if "context_dim" in unet_params else block_out_channels - if len(cross_attention_dim) > 1: - # require two or more cross-attention layers per-block, each of different dimension - cross_attention_dim = [cross_attention_dim for _ in range(len(block_out_channels))] - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params["in_channels"], - "out_channels": unet_params["out_channels"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params["num_res_blocks"], - "transformer_layers_per_block": unet_params["transformer_depth"], - "cross_attention_dim": tuple(cross_attention_dim), - } - - return config - - -# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config -def create_vae_diffusers_config(original_config, checkpoint, image_size: int): - """ - Creates a VAE config for diffusers based on the config of the original AudioLDM2 model. Compared to the original - Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. - """ - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] - - block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 - - config = { - "sample_size": image_size, - "in_channels": vae_params["in_channels"], - "out_channels": vae_params["out_ch"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params["z_channels"], - "layers_per_block": vae_params["num_res_blocks"], - "scaling_factor": float(scaling_factor), - } - return config - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular -def create_diffusers_schedular(original_config): - schedular = DDIMScheduler( - num_train_timesteps=original_config["model"]["params"]["timesteps"], - beta_start=original_config["model"]["params"]["linear_start"], - beta_end=original_config["model"]["params"]["linear_end"], - beta_schedule="scaled_linear", - ) - return schedular - - -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted UNet checkpoint. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has both EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - # strip the unet prefix from the weight names - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] - for layer_id in range(num_output_blocks) - } - - # Check how many Transformer blocks we have per layer - if isinstance(config.get("cross_attention_dim"), (list, tuple)): - if isinstance(config["cross_attention_dim"][0], (list, tuple)): - # in this case we have multiple cross-attention layers per-block - num_attention_layers = len(config.get("cross_attention_dim")[0]) - else: - num_attention_layers = 1 - - if config.get("extra_self_attn_layer"): - num_attention_layers += 1 - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.0" not in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = [ - { - "old": f"input_blocks.{i}.{1 + layer_id}", - "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}", - } - for layer_id in range(num_attention_layers) - ] - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config - ) - - resnet_0 = middle_blocks[0] - resnet_1 = middle_blocks[num_middle_blocks - 1] - - resnet_0_paths = renew_resnet_paths(resnet_0) - meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} - assign_to_checkpoint( - resnet_0_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_1_paths = renew_resnet_paths(resnet_1) - meta_path = {"old": f"middle_block.{len(middle_blocks) - 1}", "new": "mid_block.resnets.1"} - assign_to_checkpoint( - resnet_1_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(1, num_middle_blocks - 1): - attentions = middle_blocks[i] - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": f"middle_block.{i}", "new": f"mid_block.attentions.{i - 1}"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.0" not in key] - - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - attentions.remove(f"output_blocks.{i}.{index}.conv.bias") - attentions.remove(f"output_blocks.{i}.{index}.conv.weight") - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = [ - { - "old": f"output_blocks.{i}.{1 + layer_id}", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}", - } - for layer_id in range(num_attention_layers) - ] - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -CLAP_KEYS_TO_MODIFY_MAPPING = { - "text_branch": "text_model", - "audio_branch": "audio_model.audio_encoder", - "attn": "attention.self", - "self.proj": "output.dense", - "attention.self_mask": "attn_mask", - "mlp.fc1": "intermediate.dense", - "mlp.fc2": "output.dense", - "norm1": "layernorm_before", - "norm2": "layernorm_after", - "bn0": "batch_norm", -} - -CLAP_KEYS_TO_IGNORE = [ - "text_transform", - "audio_transform", - "stft", - "logmel_extractor", - "tscam_conv", - "head", - "attn_mask", -] - -CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] - - -def convert_open_clap_checkpoint(checkpoint): - """ - Takes a state dict and returns a converted CLAP checkpoint. - """ - # extract state dict for CLAP text embedding model, discarding the audio component - model_state_dict = {} - model_key = "clap.model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(model_key): - model_state_dict[key.replace(model_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - sequential_layers_pattern = r".*sequential.(\d+).*" - text_projection_pattern = r".*_projection.(\d+).*" - - for key, value in model_state_dict.items(): - # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end - for key_to_ignore in CLAP_KEYS_TO_IGNORE: - if key_to_ignore in key: - key = "spectrogram" - - # check if any key needs to be modified - for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): - if key_to_modify in key: - key = key.replace(key_to_modify, new_key) - - if re.match(sequential_layers_pattern, key): - # replace sequential layers with list - sequential_layer = re.match(sequential_layers_pattern, key).group(1) - - key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") - elif re.match(text_projection_pattern, key): - projecton_layer = int(re.match(text_projection_pattern, key).group(1)) - - # Because in CLAP they use `nn.Sequential`... - transformers_projection_layer = 1 if projecton_layer == 0 else 2 - - key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") - - if "audio" and "qkv" in key: - # split qkv into query key and value - mixed_qkv = value - qkv_dim = mixed_qkv.size(0) // 3 - - query_layer = mixed_qkv[:qkv_dim] - key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] - value_layer = mixed_qkv[qkv_dim * 2 :] - - new_checkpoint[key.replace("qkv", "query")] = query_layer - new_checkpoint[key.replace("qkv", "key")] = key_layer - new_checkpoint[key.replace("qkv", "value")] = value_layer - elif key != "spectrogram": - new_checkpoint[key] = value - - return new_checkpoint - - -def create_transformers_vocoder_config(original_config): - """ - Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. - """ - vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] - - config = { - "model_in_dim": vocoder_params["num_mels"], - "sampling_rate": vocoder_params["sampling_rate"], - "upsample_initial_channel": vocoder_params["upsample_initial_channel"], - "upsample_rates": list(vocoder_params["upsample_rates"]), - "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), - "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), - "resblock_dilation_sizes": [ - list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] - ], - "normalize_before": False, - } - - return config - - -def extract_sub_model(checkpoint, key_prefix): - """ - Takes a state dict and returns the state dict for a particular sub-model. - """ - - sub_model_state_dict = {} - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(key_prefix): - sub_model_state_dict[key.replace(key_prefix, "")] = checkpoint.get(key) - - return sub_model_state_dict - - -def convert_hifigan_checkpoint(checkpoint, config): - """ - Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. - """ - # extract state dict for vocoder - vocoder_state_dict = extract_sub_model(checkpoint, key_prefix="first_stage_model.vocoder.") - - # fix upsampler keys, everything else is correct already - for i in range(len(config.upsample_rates)): - vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") - vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") - - if not config.normalize_before: - # if we don't set normalize_before then these variables are unused, so we set them to their initialised values - vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) - vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) - - return vocoder_state_dict - - -def convert_projection_checkpoint(checkpoint): - projection_state_dict = {} - conditioner_state_dict = extract_sub_model(checkpoint, key_prefix="cond_stage_models.0.") - - projection_state_dict["sos_embed"] = conditioner_state_dict["start_of_sequence_tokens.weight"][0] - projection_state_dict["sos_embed_1"] = conditioner_state_dict["start_of_sequence_tokens.weight"][1] - - projection_state_dict["eos_embed"] = conditioner_state_dict["end_of_sequence_tokens.weight"][0] - projection_state_dict["eos_embed_1"] = conditioner_state_dict["end_of_sequence_tokens.weight"][1] - - projection_state_dict["projection.weight"] = conditioner_state_dict["input_sequence_embed_linear.0.weight"] - projection_state_dict["projection.bias"] = conditioner_state_dict["input_sequence_embed_linear.0.bias"] - - projection_state_dict["projection_1.weight"] = conditioner_state_dict["input_sequence_embed_linear.1.weight"] - projection_state_dict["projection_1.bias"] = conditioner_state_dict["input_sequence_embed_linear.1.bias"] - - return projection_state_dict - - -# Adapted from https://github.com/haoheliu/AudioLDM2/blob/81ad2c6ce015c1310387695e2dae975a7d2ed6fd/audioldm2/utils.py#L143 -DEFAULT_CONFIG = { - "model": { - "params": { - "linear_start": 0.0015, - "linear_end": 0.0195, - "timesteps": 1000, - "channels": 8, - "scale_by_std": True, - "unet_config": { - "target": "audioldm2.latent_diffusion.openaimodel.UNetModel", - "params": { - "context_dim": [None, 768, 1024], - "in_channels": 8, - "out_channels": 8, - "model_channels": 128, - "attention_resolutions": [8, 4, 2], - "num_res_blocks": 2, - "channel_mult": [1, 2, 3, 5], - "num_head_channels": 32, - "transformer_depth": 1, - }, - }, - "first_stage_config": { - "target": "audioldm2.variational_autoencoder.autoencoder.AutoencoderKL", - "params": { - "embed_dim": 8, - "ddconfig": { - "z_channels": 8, - "resolution": 256, - "in_channels": 1, - "out_ch": 1, - "ch": 128, - "ch_mult": [1, 2, 4], - "num_res_blocks": 2, - }, - }, - }, - "cond_stage_config": { - "crossattn_audiomae_generated": { - "target": "audioldm2.latent_diffusion.modules.encoders.modules.SequenceGenAudioMAECond", - "params": { - "sequence_gen_length": 8, - "sequence_input_embed_dim": [512, 1024], - }, - } - }, - "vocoder_config": { - "target": "audioldm2.first_stage_model.vocoder", - "params": { - "upsample_rates": [5, 4, 2, 2, 2], - "upsample_kernel_sizes": [16, 16, 8, 4, 4], - "upsample_initial_channel": 1024, - "resblock_kernel_sizes": [3, 7, 11], - "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "num_mels": 64, - "sampling_rate": 16000, - }, - }, - }, - }, -} - - -def load_pipeline_from_original_AudioLDM2_ckpt( - checkpoint_path: str, - original_config_file: str = None, - image_size: int = 1024, - prediction_type: str = None, - extract_ema: bool = False, - scheduler_type: str = "ddim", - cross_attention_dim: Union[List, List[List]] = None, - transformer_layers_per_block: int = None, - device: str = None, - from_safetensors: bool = False, -) -> AudioLDM2Pipeline: - """ - Load an AudioLDM2 pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. - - Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the - global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is - recommended that you override the default values and/or supply an `original_config_file` wherever possible. - - Args: - checkpoint_path (`str`): Path to `.ckpt` file. - original_config_file (`str`): - Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically - set to the AudioLDM2 base config. - image_size (`int`, *optional*, defaults to 1024): - The image size that the model was trained on. - prediction_type (`str`, *optional*): - The prediction type that the model was trained on. If `None`, will be automatically - inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. - scheduler_type (`str`, *optional*, defaults to 'ddim'): - Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", - "ddim"]`. - cross_attention_dim (`list`, *optional*, defaults to `None`): - The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be - automatically inferred. Set to `[768, 1024]` for the base model, or `[768, 1024, None]` for the large model. - transformer_layers_per_block (`int`, *optional*, defaults to `None`): - The number of transformer layers in each transformer block. If `None`, number of layers will be " - "automatically inferred. Set to `1` for the base model, or `2` for the large model. - extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for - checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to - `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for - inference. Non-EMA weights are usually better to continue fine-tuning. - device (`str`, *optional*, defaults to `None`): - The device to use. Pass `None` to determine automatically. - from_safetensors (`str`, *optional*, defaults to `False`): - If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. - return: An AudioLDM2Pipeline object representing the passed-in `.ckpt`/`.safetensors` file. - """ - - if from_safetensors: - if not is_safetensors_available(): - raise ValueError(BACKENDS_MAPPING["safetensors"][1]) - - from safetensors import safe_open - - checkpoint = {} - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - checkpoint[key] = f.get_tensor(key) - else: - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load(checkpoint_path, map_location=device) - else: - checkpoint = torch.load(checkpoint_path, map_location=device) - - if "state_dict" in checkpoint: - checkpoint = checkpoint["state_dict"] - - if original_config_file is None: - original_config = DEFAULT_CONFIG - else: - original_config = yaml.safe_load(original_config_file) - - if image_size is not None: - original_config["model"]["params"]["unet_config"]["params"]["image_size"] = image_size - - if cross_attention_dim is not None: - original_config["model"]["params"]["unet_config"]["params"]["context_dim"] = cross_attention_dim - - if transformer_layers_per_block is not None: - original_config["model"]["params"]["unet_config"]["params"]["transformer_depth"] = transformer_layers_per_block - - if ( - "parameterization" in original_config["model"]["params"] - and original_config["model"]["params"]["parameterization"] == "v" - ): - if prediction_type is None: - prediction_type = "v_prediction" - else: - if prediction_type is None: - prediction_type = "epsilon" - - num_train_timesteps = original_config["model"]["params"]["timesteps"] - beta_start = original_config["model"]["params"]["linear_start"] - beta_end = original_config["model"]["params"]["linear_end"] - - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type=prediction_type, - ) - # make sure scheduler works correctly with DDIM - scheduler.register_to_config(clip_sample=False) - - if scheduler_type == "pndm": - config = dict(scheduler.config) - config["skip_prk_steps"] = True - scheduler = PNDMScheduler.from_config(config) - elif scheduler_type == "lms": - scheduler = LMSDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "heun": - scheduler = HeunDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler": - scheduler = EulerDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler-ancestral": - scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "dpm": - scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) - elif scheduler_type == "ddim": - scheduler = scheduler - else: - raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") - - # Convert the UNet2DModel - unet_config = create_unet_diffusers_config(original_config, image_size=image_size) - unet = AudioLDM2UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # Convert the VAE model - vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) - converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - # Convert the joint audio-text encoding model - clap_config = ClapConfig.from_pretrained("laion/clap-htsat-unfused") - clap_config.audio_config.update( - { - "patch_embeds_hidden_size": 128, - "hidden_size": 1024, - "depths": [2, 2, 12, 2], - } - ) - # AudioLDM2 uses the same tokenizer and feature extractor as the original CLAP model - clap_tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") - clap_feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused") - - converted_clap_model = convert_open_clap_checkpoint(checkpoint) - clap_model = ClapModel(clap_config) - - missing_keys, unexpected_keys = clap_model.load_state_dict(converted_clap_model, strict=False) - # we expect not to have token_type_ids in our original state dict so let's ignore them - missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) - - if len(unexpected_keys) > 0: - raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") - - if len(missing_keys) > 0: - raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") - - # Convert the vocoder model - vocoder_config = create_transformers_vocoder_config(original_config) - vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) - converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) - - vocoder = SpeechT5HifiGan(vocoder_config) - vocoder.load_state_dict(converted_vocoder_checkpoint) - - # Convert the Flan-T5 encoder model: AudioLDM2 uses the same configuration and tokenizer as the original Flan-T5 large model - t5_config = T5Config.from_pretrained("google/flan-t5-large") - converted_t5_checkpoint = extract_sub_model(checkpoint, key_prefix="cond_stage_models.1.model.") - - t5_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large") - # hard-coded in the original implementation (i.e. not retrievable from the config) - t5_tokenizer.model_max_length = 128 - t5_model = T5EncoderModel(t5_config) - t5_model.load_state_dict(converted_t5_checkpoint) - - # Convert the GPT2 encoder model: AudioLDM2 uses the same configuration as the original GPT2 base model - gpt2_config = GPT2Config.from_pretrained("gpt2") - gpt2_model = GPT2Model(gpt2_config) - gpt2_model.config.max_new_tokens = original_config["model"]["params"]["cond_stage_config"][ - "crossattn_audiomae_generated" - ]["params"]["sequence_gen_length"] - - converted_gpt2_checkpoint = extract_sub_model(checkpoint, key_prefix="cond_stage_models.0.model.") - gpt2_model.load_state_dict(converted_gpt2_checkpoint) - - # Convert the extra embedding / projection layers - projection_model = AudioLDM2ProjectionModel(clap_config.projection_dim, t5_config.d_model, gpt2_config.n_embd) - - converted_projection_checkpoint = convert_projection_checkpoint(checkpoint) - projection_model.load_state_dict(converted_projection_checkpoint) - - # Instantiate the diffusers pipeline - pipe = AudioLDM2Pipeline( - vae=vae, - text_encoder=clap_model, - text_encoder_2=t5_model, - projection_model=projection_model, - language_model=gpt2_model, - tokenizer=clap_tokenizer, - tokenizer_2=t5_tokenizer, - feature_extractor=clap_feature_extractor, - unet=unet, - scheduler=scheduler, - vocoder=vocoder, - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--cross_attention_dim", - default=None, - type=int, - nargs="+", - help="The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be " - "automatically inferred. Set to `768+1024` for the base model, or `768+1024+640` for the large model", - ) - parser.add_argument( - "--transformer_layers_per_block", - default=None, - type=int, - help="The number of transformer layers in each transformer block. If `None`, number of layers will be " - "automatically inferred. Set to `1` for the base model, or `2` for the large model.", - ) - parser.add_argument( - "--scheduler_type", - default="ddim", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--image_size", - default=1048, - type=int, - help="The image size that the model was trained on.", - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=("The prediction type that the model was trained on."), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - args = parser.parse_args() - - pipe = load_pipeline_from_original_AudioLDM2_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - prediction_type=args.prediction_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - cross_attention_dim=args.cross_attention_dim, - transformer_layers_per_block=args.transformer_layers_per_block, - from_safetensors=args.from_safetensors, - device=args.device, - ) - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_audioldm_to_diffusers.py b/scripts/convert_original_audioldm_to_diffusers.py deleted file mode 100644 index 44183f1aea29..000000000000 --- a/scripts/convert_original_audioldm_to_diffusers.py +++ /dev/null @@ -1,1042 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the AudioLDM checkpoints.""" - -import argparse -import re - -import torch -import yaml -from transformers import ( - AutoTokenizer, - ClapTextConfig, - ClapTextModelWithProjection, - SpeechT5HifiGan, - SpeechT5HifiGanConfig, -) - -from diffusers import ( - AudioLDMPipeline, - AutoencoderKL, - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, - UNet2DConditionModel, -) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths -def renew_attention_paths(old_list): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "query.weight") - new_item = new_item.replace("q.bias", "query.bias") - - new_item = new_item.replace("k.weight", "key.weight") - new_item = new_item.replace("k.bias", "key.bias") - - new_item = new_item.replace("v.weight", "value.weight") - new_item = new_item.replace("v.bias", "value.bias") - - new_item = new_item.replace("proj_out.weight", "proj_attn.weight") - new_item = new_item.replace("proj_out.bias", "proj_attn.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def create_unet_diffusers_config(original_config, image_size: int): - """ - Creates a UNet config for diffusers based on the config of the original AudioLDM model. - """ - unet_params = original_config["model"]["params"]["unet_config"]["params"] - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - - block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) - - cross_attention_dim = ( - unet_params["cross_attention_dim"] if "cross_attention_dim" in unet_params else block_out_channels - ) - - class_embed_type = "simple_projection" if "extra_film_condition_dim" in unet_params else None - projection_class_embeddings_input_dim = ( - unet_params["extra_film_condition_dim"] if "extra_film_condition_dim" in unet_params else None - ) - class_embeddings_concat = unet_params["extra_film_use_concat"] if "extra_film_use_concat" in unet_params else None - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params["in_channels"], - "out_channels": unet_params["out_channels"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params["num_res_blocks"], - "cross_attention_dim": cross_attention_dim, - "class_embed_type": class_embed_type, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "class_embeddings_concat": class_embeddings_concat, - } - - return config - - -# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config -def create_vae_diffusers_config(original_config, checkpoint, image_size: int): - """ - Creates a VAE config for diffusers based on the config of the original AudioLDM model. Compared to the original - Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. - """ - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] - - block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 - - config = { - "sample_size": image_size, - "in_channels": vae_params["in_channels"], - "out_channels": vae_params["out_ch"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params["z_channels"], - "layers_per_block": vae_params["num_res_blocks"], - "scaling_factor": float(scaling_factor), - } - return config - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular -def create_diffusers_schedular(original_config): - schedular = DDIMScheduler( - num_train_timesteps=original_config["model"]["params"]["timesteps"], - beta_start=original_config["model"]["params"]["linear_start"], - beta_end=original_config["model"]["params"]["linear_end"], - beta_schedule="scaled_linear", - ) - return schedular - - -# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_unet_checkpoint -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion - conversion, this function additionally converts the learnt film embedding linear layer. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has both EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - new_checkpoint["class_embedding.weight"] = unet_state_dict["film_emb.weight"] - new_checkpoint["class_embedding.bias"] = unet_state_dict["film_emb.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -CLAP_KEYS_TO_MODIFY_MAPPING = { - "text_branch": "text_model", - "attn": "attention.self", - "self.proj": "output.dense", - "attention.self_mask": "attn_mask", - "mlp.fc1": "intermediate.dense", - "mlp.fc2": "output.dense", - "norm1": "layernorm_before", - "norm2": "layernorm_after", - "bn0": "batch_norm", -} - -CLAP_KEYS_TO_IGNORE = ["text_transform"] - -CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] - - -def convert_open_clap_checkpoint(checkpoint): - """ - Takes a state dict and returns a converted CLAP checkpoint. - """ - # extract state dict for CLAP text embedding model, discarding the audio component - model_state_dict = {} - model_key = "cond_stage_model.model.text_" - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(model_key): - model_state_dict[key.replace(model_key, "text_")] = checkpoint.get(key) - - new_checkpoint = {} - - sequential_layers_pattern = r".*sequential.(\d+).*" - text_projection_pattern = r".*_projection.(\d+).*" - - for key, value in model_state_dict.items(): - # check if key should be ignored in mapping - if key.split(".")[0] in CLAP_KEYS_TO_IGNORE: - continue - - # check if any key needs to be modified - for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): - if key_to_modify in key: - key = key.replace(key_to_modify, new_key) - - if re.match(sequential_layers_pattern, key): - # replace sequential layers with list - sequential_layer = re.match(sequential_layers_pattern, key).group(1) - - key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") - elif re.match(text_projection_pattern, key): - projecton_layer = int(re.match(text_projection_pattern, key).group(1)) - - # Because in CLAP they use `nn.Sequential`... - transformers_projection_layer = 1 if projecton_layer == 0 else 2 - - key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") - - if "audio" and "qkv" in key: - # split qkv into query key and value - mixed_qkv = value - qkv_dim = mixed_qkv.size(0) // 3 - - query_layer = mixed_qkv[:qkv_dim] - key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] - value_layer = mixed_qkv[qkv_dim * 2 :] - - new_checkpoint[key.replace("qkv", "query")] = query_layer - new_checkpoint[key.replace("qkv", "key")] = key_layer - new_checkpoint[key.replace("qkv", "value")] = value_layer - else: - new_checkpoint[key] = value - - return new_checkpoint - - -def create_transformers_vocoder_config(original_config): - """ - Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. - """ - vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] - - config = { - "model_in_dim": vocoder_params["num_mels"], - "sampling_rate": vocoder_params["sampling_rate"], - "upsample_initial_channel": vocoder_params["upsample_initial_channel"], - "upsample_rates": list(vocoder_params["upsample_rates"]), - "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), - "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), - "resblock_dilation_sizes": [ - list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] - ], - "normalize_before": False, - } - - return config - - -def convert_hifigan_checkpoint(checkpoint, config): - """ - Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. - """ - # extract state dict for vocoder - vocoder_state_dict = {} - vocoder_key = "first_stage_model.vocoder." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vocoder_key): - vocoder_state_dict[key.replace(vocoder_key, "")] = checkpoint.get(key) - - # fix upsampler keys, everything else is correct already - for i in range(len(config.upsample_rates)): - vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") - vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") - - if not config.normalize_before: - # if we don't set normalize_before then these variables are unused, so we set them to their initialised values - vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) - vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) - - return vocoder_state_dict - - -# Adapted from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/audioldm/utils.py#L72-L73 -DEFAULT_CONFIG = { - "model": { - "params": { - "linear_start": 0.0015, - "linear_end": 0.0195, - "timesteps": 1000, - "channels": 8, - "scale_by_std": True, - "unet_config": { - "target": "audioldm.latent_diffusion.openaimodel.UNetModel", - "params": { - "extra_film_condition_dim": 512, - "extra_film_use_concat": True, - "in_channels": 8, - "out_channels": 8, - "model_channels": 128, - "attention_resolutions": [8, 4, 2], - "num_res_blocks": 2, - "channel_mult": [1, 2, 3, 5], - "num_head_channels": 32, - }, - }, - "first_stage_config": { - "target": "audioldm.variational_autoencoder.autoencoder.AutoencoderKL", - "params": { - "embed_dim": 8, - "ddconfig": { - "z_channels": 8, - "resolution": 256, - "in_channels": 1, - "out_ch": 1, - "ch": 128, - "ch_mult": [1, 2, 4], - "num_res_blocks": 2, - }, - }, - }, - "vocoder_config": { - "target": "audioldm.first_stage_model.vocoder", - "params": { - "upsample_rates": [5, 4, 2, 2, 2], - "upsample_kernel_sizes": [16, 16, 8, 4, 4], - "upsample_initial_channel": 1024, - "resblock_kernel_sizes": [3, 7, 11], - "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "num_mels": 64, - "sampling_rate": 16000, - }, - }, - }, - }, -} - - -def load_pipeline_from_original_audioldm_ckpt( - checkpoint_path: str, - original_config_file: str = None, - image_size: int = 512, - prediction_type: str = None, - extract_ema: bool = False, - scheduler_type: str = "ddim", - num_in_channels: int = None, - model_channels: int = None, - num_head_channels: int = None, - device: str = None, - from_safetensors: bool = False, -) -> AudioLDMPipeline: - """ - Load an AudioLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. - - Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the - global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is - recommended that you override the default values and/or supply an `original_config_file` wherever possible. - - Args: - checkpoint_path (`str`): Path to `.ckpt` file. - original_config_file (`str`): - Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically - set to the audioldm-s-full-v2 config. - image_size (`int`, *optional*, defaults to 512): - The image size that the model was trained on. - prediction_type (`str`, *optional*): - The prediction type that the model was trained on. If `None`, will be automatically - inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. - num_in_channels (`int`, *optional*, defaults to None): - The number of UNet input channels. If `None`, it will be automatically inferred from the config. - model_channels (`int`, *optional*, defaults to None): - The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override - to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large. - num_head_channels (`int`, *optional*, defaults to None): - The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override - to 32 for the small and medium checkpoints, and 64 for the large. - scheduler_type (`str`, *optional*, defaults to 'pndm'): - Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", - "ddim"]`. - extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for - checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to - `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for - inference. Non-EMA weights are usually better to continue fine-tuning. - device (`str`, *optional*, defaults to `None`): - The device to use. Pass `None` to determine automatically. - from_safetensors (`str`, *optional*, defaults to `False`): - If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. - return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file. - """ - - if from_safetensors: - from safetensors import safe_open - - checkpoint = {} - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - checkpoint[key] = f.get_tensor(key) - else: - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load(checkpoint_path, map_location=device) - else: - checkpoint = torch.load(checkpoint_path, map_location=device) - - if "state_dict" in checkpoint: - checkpoint = checkpoint["state_dict"] - - if original_config_file is None: - original_config = DEFAULT_CONFIG - else: - original_config = yaml.safe_load(original_config_file) - - if num_in_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels - - if model_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["model_channels"] = model_channels - - if num_head_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["num_head_channels"] = num_head_channels - - if ( - "parameterization" in original_config["model"]["params"] - and original_config["model"]["params"]["parameterization"] == "v" - ): - if prediction_type is None: - prediction_type = "v_prediction" - else: - if prediction_type is None: - prediction_type = "epsilon" - - if image_size is None: - image_size = 512 - - num_train_timesteps = original_config["model"]["params"]["timesteps"] - beta_start = original_config["model"]["params"]["linear_start"] - beta_end = original_config["model"]["params"]["linear_end"] - - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type=prediction_type, - ) - # make sure scheduler works correctly with DDIM - scheduler.register_to_config(clip_sample=False) - - if scheduler_type == "pndm": - config = dict(scheduler.config) - config["skip_prk_steps"] = True - scheduler = PNDMScheduler.from_config(config) - elif scheduler_type == "lms": - scheduler = LMSDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "heun": - scheduler = HeunDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler": - scheduler = EulerDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler-ancestral": - scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "dpm": - scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) - elif scheduler_type == "ddim": - scheduler = scheduler - else: - raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") - - # Convert the UNet2DModel - unet_config = create_unet_diffusers_config(original_config, image_size=image_size) - unet = UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # Convert the VAE model - vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) - converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - # Convert the text model - # AudioLDM uses the same configuration and tokenizer as the original CLAP model - config = ClapTextConfig.from_pretrained("laion/clap-htsat-unfused") - tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") - - converted_text_model = convert_open_clap_checkpoint(checkpoint) - text_model = ClapTextModelWithProjection(config) - - missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False) - # we expect not to have token_type_ids in our original state dict so let's ignore them - missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) - - if len(unexpected_keys) > 0: - raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") - - if len(missing_keys) > 0: - raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") - - # Convert the vocoder model - vocoder_config = create_transformers_vocoder_config(original_config) - vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) - converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) - - vocoder = SpeechT5HifiGan(vocoder_config) - vocoder.load_state_dict(converted_vocoder_checkpoint) - - # Instantiate the diffusers pipeline - pipe = AudioLDMPipeline( - vae=vae, - text_encoder=text_model, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - vocoder=vocoder, - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--model_channels", - default=None, - type=int, - help="The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override" - " to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.", - ) - parser.add_argument( - "--num_head_channels", - default=None, - type=int, - help="The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override" - " to 32 for the small and medium checkpoints, and 64 for the large.", - ) - parser.add_argument( - "--scheduler_type", - default="ddim", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=("The image size that the model was trained on."), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=("The prediction type that the model was trained on."), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - args = parser.parse_args() - - pipe = load_pipeline_from_original_audioldm_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - prediction_type=args.prediction_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - model_channels=args.model_channels, - num_head_channels=args.num_head_channels, - from_safetensors=args.from_safetensors, - device=args.device, - ) - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_controlnet_to_diffusers.py b/scripts/convert_original_controlnet_to_diffusers.py deleted file mode 100644 index 4c6fe90cb09f..000000000000 --- a/scripts/convert_original_controlnet_to_diffusers.py +++ /dev/null @@ -1,109 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for stable diffusion checkpoints which _only_ contain a controlnet.""" - -import argparse - -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - type=str, - required=True, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--image_size", - default=512, - type=int, - help=( - "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2" - " Base. Use 768 for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--upcast_attention", - action="store_true", - help=( - "Whether the attention computation should always be upcasted. This is necessary when running stable" - " diffusion 2.1." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - - # small workaround to get argparser to parse a boolean input as either true _or_ false - def parse_bool(string): - if string == "True": - return True - elif string == "False": - return False - else: - raise ValueError(f"could not parse string as bool {string}") - - parser.add_argument( - "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool - ) - - parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int) - - args = parser.parse_args() - - controlnet = download_controlnet_from_original_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - extract_ema=args.extract_ema, - num_in_channels=args.num_in_channels, - upcast_attention=args.upcast_attention, - from_safetensors=args.from_safetensors, - device=args.device, - use_linear_projection=args.use_linear_projection, - cross_attention_dim=args.cross_attention_dim, - ) - - controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_musicldm_to_diffusers.py b/scripts/convert_original_musicldm_to_diffusers.py deleted file mode 100644 index 00836fde2592..000000000000 --- a/scripts/convert_original_musicldm_to_diffusers.py +++ /dev/null @@ -1,1056 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the MusicLDM checkpoints.""" - -import argparse -import re - -import torch -import yaml -from transformers import ( - AutoFeatureExtractor, - AutoTokenizer, - ClapConfig, - ClapModel, - SpeechT5HifiGan, - SpeechT5HifiGanConfig, -) - -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - LMSDiscreteScheduler, - MusicLDMPipeline, - PNDMScheduler, - UNet2DConditionModel, -) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths -def renew_attention_paths(old_list): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] - proj_key = "to_out.0.weight" - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys or ".".join(key.split(".")[-3:]) == proj_key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key].squeeze() - - -def create_unet_diffusers_config(original_config, image_size: int): - """ - Creates a UNet config for diffusers based on the config of the original MusicLDM model. - """ - unet_params = original_config["model"]["params"]["unet_config"]["params"] - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - - block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) - - cross_attention_dim = ( - unet_params["cross_attention_dim"] if "cross_attention_dim" in unet_params else block_out_channels - ) - - class_embed_type = "simple_projection" if "extra_film_condition_dim" in unet_params else None - projection_class_embeddings_input_dim = ( - unet_params["extra_film_condition_dim"] if "extra_film_condition_dim" in unet_params else None - ) - class_embeddings_concat = unet_params["extra_film_use_concat"] if "extra_film_use_concat" in unet_params else None - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params["in_channels"], - "out_channels": unet_params["out_channels"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params["num_res_blocks"], - "cross_attention_dim": cross_attention_dim, - "class_embed_type": class_embed_type, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "class_embeddings_concat": class_embeddings_concat, - } - - return config - - -# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config -def create_vae_diffusers_config(original_config, checkpoint, image_size: int): - """ - Creates a VAE config for diffusers based on the config of the original MusicLDM model. Compared to the original - Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. - """ - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] - - block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 - - config = { - "sample_size": image_size, - "in_channels": vae_params["in_channels"], - "out_channels": vae_params["out_ch"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params["z_channels"], - "layers_per_block": vae_params["num_res_blocks"], - "scaling_factor": float(scaling_factor), - } - return config - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular -def create_diffusers_schedular(original_config): - schedular = DDIMScheduler( - num_train_timesteps=original_config["model"]["params"]["timesteps"], - beta_start=original_config["model"]["params"]["linear_start"], - beta_end=original_config["model"]["params"]["linear_end"], - beta_schedule="scaled_linear", - ) - return schedular - - -def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion - conversion, this function additionally converts the learnt film embedding linear layer. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has both EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - new_checkpoint["class_embedding.weight"] = unet_state_dict["film_emb.weight"] - new_checkpoint["class_embedding.bias"] = unet_state_dict["film_emb.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -CLAP_KEYS_TO_MODIFY_MAPPING = { - "text_branch": "text_model", - "audio_branch": "audio_model.audio_encoder", - "attn": "attention.self", - "self.proj": "output.dense", - "attention.self_mask": "attn_mask", - "mlp.fc1": "intermediate.dense", - "mlp.fc2": "output.dense", - "norm1": "layernorm_before", - "norm2": "layernorm_after", - "bn0": "batch_norm", -} - -CLAP_KEYS_TO_IGNORE = [ - "text_transform", - "audio_transform", - "stft", - "logmel_extractor", - "tscam_conv", - "head", - "attn_mask", -] - -CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] - - -def convert_open_clap_checkpoint(checkpoint): - """ - Takes a state dict and returns a converted CLAP checkpoint. - """ - # extract state dict for CLAP text embedding model, discarding the audio component - model_state_dict = {} - model_key = "cond_stage_model.model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(model_key): - model_state_dict[key.replace(model_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - sequential_layers_pattern = r".*sequential.(\d+).*" - text_projection_pattern = r".*_projection.(\d+).*" - - for key, value in model_state_dict.items(): - # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end - for key_to_ignore in CLAP_KEYS_TO_IGNORE: - if key_to_ignore in key: - key = "spectrogram" - - # check if any key needs to be modified - for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): - if key_to_modify in key: - key = key.replace(key_to_modify, new_key) - - if re.match(sequential_layers_pattern, key): - # replace sequential layers with list - sequential_layer = re.match(sequential_layers_pattern, key).group(1) - - key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") - elif re.match(text_projection_pattern, key): - projecton_layer = int(re.match(text_projection_pattern, key).group(1)) - - # Because in CLAP they use `nn.Sequential`... - transformers_projection_layer = 1 if projecton_layer == 0 else 2 - - key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") - - if "audio" and "qkv" in key: - # split qkv into query key and value - mixed_qkv = value - qkv_dim = mixed_qkv.size(0) // 3 - - query_layer = mixed_qkv[:qkv_dim] - key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] - value_layer = mixed_qkv[qkv_dim * 2 :] - - new_checkpoint[key.replace("qkv", "query")] = query_layer - new_checkpoint[key.replace("qkv", "key")] = key_layer - new_checkpoint[key.replace("qkv", "value")] = value_layer - elif key != "spectrogram": - new_checkpoint[key] = value - - return new_checkpoint - - -def create_transformers_vocoder_config(original_config): - """ - Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. - """ - vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] - - config = { - "model_in_dim": vocoder_params["num_mels"], - "sampling_rate": vocoder_params["sampling_rate"], - "upsample_initial_channel": vocoder_params["upsample_initial_channel"], - "upsample_rates": list(vocoder_params["upsample_rates"]), - "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), - "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), - "resblock_dilation_sizes": [ - list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] - ], - "normalize_before": False, - } - - return config - - -def convert_hifigan_checkpoint(checkpoint, config): - """ - Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. - """ - # extract state dict for vocoder - vocoder_state_dict = {} - vocoder_key = "first_stage_model.vocoder." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vocoder_key): - vocoder_state_dict[key.replace(vocoder_key, "")] = checkpoint.get(key) - - # fix upsampler keys, everything else is correct already - for i in range(len(config.upsample_rates)): - vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") - vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") - - if not config.normalize_before: - # if we don't set normalize_before then these variables are unused, so we set them to their initialised values - vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) - vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) - - return vocoder_state_dict - - -# Adapted from https://huggingface.co/spaces/haoheliu/MusicLDM-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/MusicLDM/utils.py#L72-L73 -DEFAULT_CONFIG = { - "model": { - "params": { - "linear_start": 0.0015, - "linear_end": 0.0195, - "timesteps": 1000, - "channels": 8, - "scale_by_std": True, - "unet_config": { - "target": "MusicLDM.latent_diffusion.openaimodel.UNetModel", - "params": { - "extra_film_condition_dim": 512, - "extra_film_use_concat": True, - "in_channels": 8, - "out_channels": 8, - "model_channels": 128, - "attention_resolutions": [8, 4, 2], - "num_res_blocks": 2, - "channel_mult": [1, 2, 3, 5], - "num_head_channels": 32, - }, - }, - "first_stage_config": { - "target": "MusicLDM.variational_autoencoder.autoencoder.AutoencoderKL", - "params": { - "embed_dim": 8, - "ddconfig": { - "z_channels": 8, - "resolution": 256, - "in_channels": 1, - "out_ch": 1, - "ch": 128, - "ch_mult": [1, 2, 4], - "num_res_blocks": 2, - }, - }, - }, - "vocoder_config": { - "target": "MusicLDM.first_stage_model.vocoder", - "params": { - "upsample_rates": [5, 4, 2, 2, 2], - "upsample_kernel_sizes": [16, 16, 8, 4, 4], - "upsample_initial_channel": 1024, - "resblock_kernel_sizes": [3, 7, 11], - "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], - "num_mels": 64, - "sampling_rate": 16000, - }, - }, - }, - }, -} - - -def load_pipeline_from_original_MusicLDM_ckpt( - checkpoint_path: str, - original_config_file: str = None, - image_size: int = 1024, - prediction_type: str = None, - extract_ema: bool = False, - scheduler_type: str = "ddim", - num_in_channels: int = None, - model_channels: int = None, - num_head_channels: int = None, - device: str = None, - from_safetensors: bool = False, -) -> MusicLDMPipeline: - """ - Load an MusicLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. - - Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the - global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is - recommended that you override the default values and/or supply an `original_config_file` wherever possible. - - Args: - checkpoint_path (`str`): Path to `.ckpt` file. - original_config_file (`str`): - Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically - set to the MusicLDM-s-full-v2 config. - image_size (`int`, *optional*, defaults to 1024): - The image size that the model was trained on. - prediction_type (`str`, *optional*): - The prediction type that the model was trained on. If `None`, will be automatically - inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. - num_in_channels (`int`, *optional*, defaults to None): - The number of UNet input channels. If `None`, it will be automatically inferred from the config. - model_channels (`int`, *optional*, defaults to None): - The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override - to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large. - num_head_channels (`int`, *optional*, defaults to None): - The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override - to 32 for the small and medium checkpoints, and 64 for the large. - scheduler_type (`str`, *optional*, defaults to 'pndm'): - Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", - "ddim"]`. - extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for - checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to - `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for - inference. Non-EMA weights are usually better to continue fine-tuning. - device (`str`, *optional*, defaults to `None`): - The device to use. Pass `None` to determine automatically. - from_safetensors (`str`, *optional*, defaults to `False`): - If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. - return: An MusicLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file. - """ - if from_safetensors: - from safetensors import safe_open - - checkpoint = {} - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - checkpoint[key] = f.get_tensor(key) - else: - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load(checkpoint_path, map_location=device) - else: - checkpoint = torch.load(checkpoint_path, map_location=device) - - if "state_dict" in checkpoint: - checkpoint = checkpoint["state_dict"] - - if original_config_file is None: - original_config = DEFAULT_CONFIG - else: - original_config = yaml.safe_load(original_config_file) - - if num_in_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels - - if model_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["model_channels"] = model_channels - - if num_head_channels is not None: - original_config["model"]["params"]["unet_config"]["params"]["num_head_channels"] = num_head_channels - - if ( - "parameterization" in original_config["model"]["params"] - and original_config["model"]["params"]["parameterization"] == "v" - ): - if prediction_type is None: - prediction_type = "v_prediction" - else: - if prediction_type is None: - prediction_type = "epsilon" - - if image_size is None: - image_size = 512 - - num_train_timesteps = original_config["model"]["params"]["timesteps"] - beta_start = original_config["model"]["params"]["linear_start"] - beta_end = original_config["model"]["params"]["linear_end"] - - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type=prediction_type, - ) - # make sure scheduler works correctly with DDIM - scheduler.register_to_config(clip_sample=False) - - if scheduler_type == "pndm": - config = dict(scheduler.config) - config["skip_prk_steps"] = True - scheduler = PNDMScheduler.from_config(config) - elif scheduler_type == "lms": - scheduler = LMSDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "heun": - scheduler = HeunDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler": - scheduler = EulerDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "euler-ancestral": - scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) - elif scheduler_type == "dpm": - scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) - elif scheduler_type == "ddim": - scheduler = scheduler - else: - raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") - - # Convert the UNet2DModel - unet_config = create_unet_diffusers_config(original_config, image_size=image_size) - unet = UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # Convert the VAE model - vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) - converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - # Convert the text model - # MusicLDM uses the same tokenizer as the original CLAP model, but a slightly different configuration - config = ClapConfig.from_pretrained("laion/clap-htsat-unfused") - config.audio_config.update( - { - "patch_embeds_hidden_size": 128, - "hidden_size": 1024, - "depths": [2, 2, 12, 2], - } - ) - tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") - feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused") - - converted_text_model = convert_open_clap_checkpoint(checkpoint) - text_model = ClapModel(config) - - missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False) - # we expect not to have token_type_ids in our original state dict so let's ignore them - missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) - - if len(unexpected_keys) > 0: - raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") - - if len(missing_keys) > 0: - raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") - - # Convert the vocoder model - vocoder_config = create_transformers_vocoder_config(original_config) - vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) - converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) - - vocoder = SpeechT5HifiGan(vocoder_config) - vocoder.load_state_dict(converted_vocoder_checkpoint) - - # Instantiate the diffusers pipeline - pipe = MusicLDMPipeline( - vae=vae, - text_encoder=text_model, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - vocoder=vocoder, - feature_extractor=feature_extractor, - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--model_channels", - default=None, - type=int, - help="The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override" - " to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.", - ) - parser.add_argument( - "--num_head_channels", - default=None, - type=int, - help="The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override" - " to 32 for the small and medium checkpoints, and 64 for the large.", - ) - parser.add_argument( - "--scheduler_type", - default="ddim", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=("The image size that the model was trained on."), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=("The prediction type that the model was trained on."), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - args = parser.parse_args() - - pipe = load_pipeline_from_original_MusicLDM_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - prediction_type=args.prediction_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - model_channels=args.model_channels, - num_head_channels=args.num_head_channels, - from_safetensors=args.from_safetensors, - device=args.device, - ) - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_stable_diffusion_to_diffusers.py b/scripts/convert_original_stable_diffusion_to_diffusers.py deleted file mode 100644 index 59eeeec24c79..000000000000 --- a/scripts/convert_original_stable_diffusion_to_diffusers.py +++ /dev/null @@ -1,188 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the LDM checkpoints.""" - -import argparse -import importlib - -import torch - -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--config_files", - default=None, - type=str, - help="The YAML config file corresponding to the architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--scheduler_type", - default="pndm", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--pipeline_type", - default=None, - type=str, - help=( - "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" - ". If `None` pipeline will be automatically inferred." - ), - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=( - "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2" - " Base. Use 768 for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=( - "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" - " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--upcast_attention", - action="store_true", - help=( - "Whether the attention computation should always be upcasted. This is necessary when running stable" - " diffusion 2.1." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - parser.add_argument( - "--stable_unclip", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", - ) - parser.add_argument( - "--stable_unclip_prior", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", - ) - parser.add_argument( - "--clip_stats_path", - type=str, - help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", - required=False, - ) - parser.add_argument( - "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint." - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--vae_path", - type=str, - default=None, - required=False, - help="Set to a path, hub id to an already converted vae to not convert it again.", - ) - parser.add_argument( - "--pipeline_class_name", - type=str, - default=None, - required=False, - help="Specify the pipeline class name", - ) - - args = parser.parse_args() - - if args.pipeline_class_name is not None: - library = importlib.import_module("diffusers") - class_obj = getattr(library, args.pipeline_class_name) - pipeline_class = class_obj - else: - pipeline_class = None - - pipe = download_from_original_stable_diffusion_ckpt( - checkpoint_path_or_dict=args.checkpoint_path, - original_config_file=args.original_config_file, - config_files=args.config_files, - image_size=args.image_size, - prediction_type=args.prediction_type, - model_type=args.pipeline_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - upcast_attention=args.upcast_attention, - from_safetensors=args.from_safetensors, - device=args.device, - stable_unclip=args.stable_unclip, - stable_unclip_prior=args.stable_unclip_prior, - clip_stats_path=args.clip_stats_path, - controlnet=args.controlnet, - vae_path=args.vae_path, - pipeline_class=pipeline_class, - ) - - if args.half: - pipe.to(dtype=torch.float16) - - if args.controlnet: - # only save the controlnet model - pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) - else: - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_t2i_adapter.py b/scripts/convert_original_t2i_adapter.py deleted file mode 100644 index e23a2431ce9e..000000000000 --- a/scripts/convert_original_t2i_adapter.py +++ /dev/null @@ -1,250 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" -Conversion script for the T2I-Adapter checkpoints. -""" - -import argparse - -import torch - -from diffusers import T2IAdapter - - -def convert_adapter(src_state, in_channels): - original_body_length = max([int(x.split(".")[1]) for x in src_state.keys() if "body." in x]) + 1 - - assert original_body_length == 8 - - # (0, 1) -> channels 1 - assert src_state["body.0.block1.weight"].shape == (320, 320, 3, 3) - - # (2, 3) -> channels 2 - assert src_state["body.2.in_conv.weight"].shape == (640, 320, 1, 1) - - # (4, 5) -> channels 3 - assert src_state["body.4.in_conv.weight"].shape == (1280, 640, 1, 1) - - # (6, 7) -> channels 4 - assert src_state["body.6.block1.weight"].shape == (1280, 1280, 3, 3) - - res_state = { - "adapter.conv_in.weight": src_state.pop("conv_in.weight"), - "adapter.conv_in.bias": src_state.pop("conv_in.bias"), - # 0.resnets.0 - "adapter.body.0.resnets.0.block1.weight": src_state.pop("body.0.block1.weight"), - "adapter.body.0.resnets.0.block1.bias": src_state.pop("body.0.block1.bias"), - "adapter.body.0.resnets.0.block2.weight": src_state.pop("body.0.block2.weight"), - "adapter.body.0.resnets.0.block2.bias": src_state.pop("body.0.block2.bias"), - # 0.resnets.1 - "adapter.body.0.resnets.1.block1.weight": src_state.pop("body.1.block1.weight"), - "adapter.body.0.resnets.1.block1.bias": src_state.pop("body.1.block1.bias"), - "adapter.body.0.resnets.1.block2.weight": src_state.pop("body.1.block2.weight"), - "adapter.body.0.resnets.1.block2.bias": src_state.pop("body.1.block2.bias"), - # 1 - "adapter.body.1.in_conv.weight": src_state.pop("body.2.in_conv.weight"), - "adapter.body.1.in_conv.bias": src_state.pop("body.2.in_conv.bias"), - # 1.resnets.0 - "adapter.body.1.resnets.0.block1.weight": src_state.pop("body.2.block1.weight"), - "adapter.body.1.resnets.0.block1.bias": src_state.pop("body.2.block1.bias"), - "adapter.body.1.resnets.0.block2.weight": src_state.pop("body.2.block2.weight"), - "adapter.body.1.resnets.0.block2.bias": src_state.pop("body.2.block2.bias"), - # 1.resnets.1 - "adapter.body.1.resnets.1.block1.weight": src_state.pop("body.3.block1.weight"), - "adapter.body.1.resnets.1.block1.bias": src_state.pop("body.3.block1.bias"), - "adapter.body.1.resnets.1.block2.weight": src_state.pop("body.3.block2.weight"), - "adapter.body.1.resnets.1.block2.bias": src_state.pop("body.3.block2.bias"), - # 2 - "adapter.body.2.in_conv.weight": src_state.pop("body.4.in_conv.weight"), - "adapter.body.2.in_conv.bias": src_state.pop("body.4.in_conv.bias"), - # 2.resnets.0 - "adapter.body.2.resnets.0.block1.weight": src_state.pop("body.4.block1.weight"), - "adapter.body.2.resnets.0.block1.bias": src_state.pop("body.4.block1.bias"), - "adapter.body.2.resnets.0.block2.weight": src_state.pop("body.4.block2.weight"), - "adapter.body.2.resnets.0.block2.bias": src_state.pop("body.4.block2.bias"), - # 2.resnets.1 - "adapter.body.2.resnets.1.block1.weight": src_state.pop("body.5.block1.weight"), - "adapter.body.2.resnets.1.block1.bias": src_state.pop("body.5.block1.bias"), - "adapter.body.2.resnets.1.block2.weight": src_state.pop("body.5.block2.weight"), - "adapter.body.2.resnets.1.block2.bias": src_state.pop("body.5.block2.bias"), - # 3.resnets.0 - "adapter.body.3.resnets.0.block1.weight": src_state.pop("body.6.block1.weight"), - "adapter.body.3.resnets.0.block1.bias": src_state.pop("body.6.block1.bias"), - "adapter.body.3.resnets.0.block2.weight": src_state.pop("body.6.block2.weight"), - "adapter.body.3.resnets.0.block2.bias": src_state.pop("body.6.block2.bias"), - # 3.resnets.1 - "adapter.body.3.resnets.1.block1.weight": src_state.pop("body.7.block1.weight"), - "adapter.body.3.resnets.1.block1.bias": src_state.pop("body.7.block1.bias"), - "adapter.body.3.resnets.1.block2.weight": src_state.pop("body.7.block2.weight"), - "adapter.body.3.resnets.1.block2.bias": src_state.pop("body.7.block2.bias"), - } - - assert len(src_state) == 0 - - adapter = T2IAdapter(in_channels=in_channels, adapter_type="full_adapter") - - adapter.load_state_dict(res_state) - - return adapter - - -def convert_light_adapter(src_state): - original_body_length = max([int(x.split(".")[1]) for x in src_state.keys() if "body." in x]) + 1 - - assert original_body_length == 4 - - res_state = { - # body.0.in_conv - "adapter.body.0.in_conv.weight": src_state.pop("body.0.in_conv.weight"), - "adapter.body.0.in_conv.bias": src_state.pop("body.0.in_conv.bias"), - # body.0.resnets.0 - "adapter.body.0.resnets.0.block1.weight": src_state.pop("body.0.body.0.block1.weight"), - "adapter.body.0.resnets.0.block1.bias": src_state.pop("body.0.body.0.block1.bias"), - "adapter.body.0.resnets.0.block2.weight": src_state.pop("body.0.body.0.block2.weight"), - "adapter.body.0.resnets.0.block2.bias": src_state.pop("body.0.body.0.block2.bias"), - # body.0.resnets.1 - "adapter.body.0.resnets.1.block1.weight": src_state.pop("body.0.body.1.block1.weight"), - "adapter.body.0.resnets.1.block1.bias": src_state.pop("body.0.body.1.block1.bias"), - "adapter.body.0.resnets.1.block2.weight": src_state.pop("body.0.body.1.block2.weight"), - "adapter.body.0.resnets.1.block2.bias": src_state.pop("body.0.body.1.block2.bias"), - # body.0.resnets.2 - "adapter.body.0.resnets.2.block1.weight": src_state.pop("body.0.body.2.block1.weight"), - "adapter.body.0.resnets.2.block1.bias": src_state.pop("body.0.body.2.block1.bias"), - "adapter.body.0.resnets.2.block2.weight": src_state.pop("body.0.body.2.block2.weight"), - "adapter.body.0.resnets.2.block2.bias": src_state.pop("body.0.body.2.block2.bias"), - # body.0.resnets.3 - "adapter.body.0.resnets.3.block1.weight": src_state.pop("body.0.body.3.block1.weight"), - "adapter.body.0.resnets.3.block1.bias": src_state.pop("body.0.body.3.block1.bias"), - "adapter.body.0.resnets.3.block2.weight": src_state.pop("body.0.body.3.block2.weight"), - "adapter.body.0.resnets.3.block2.bias": src_state.pop("body.0.body.3.block2.bias"), - # body.0.out_conv - "adapter.body.0.out_conv.weight": src_state.pop("body.0.out_conv.weight"), - "adapter.body.0.out_conv.bias": src_state.pop("body.0.out_conv.bias"), - # body.1.in_conv - "adapter.body.1.in_conv.weight": src_state.pop("body.1.in_conv.weight"), - "adapter.body.1.in_conv.bias": src_state.pop("body.1.in_conv.bias"), - # body.1.resnets.0 - "adapter.body.1.resnets.0.block1.weight": src_state.pop("body.1.body.0.block1.weight"), - "adapter.body.1.resnets.0.block1.bias": src_state.pop("body.1.body.0.block1.bias"), - "adapter.body.1.resnets.0.block2.weight": src_state.pop("body.1.body.0.block2.weight"), - "adapter.body.1.resnets.0.block2.bias": src_state.pop("body.1.body.0.block2.bias"), - # body.1.resnets.1 - "adapter.body.1.resnets.1.block1.weight": src_state.pop("body.1.body.1.block1.weight"), - "adapter.body.1.resnets.1.block1.bias": src_state.pop("body.1.body.1.block1.bias"), - "adapter.body.1.resnets.1.block2.weight": src_state.pop("body.1.body.1.block2.weight"), - "adapter.body.1.resnets.1.block2.bias": src_state.pop("body.1.body.1.block2.bias"), - # body.1.body.2 - "adapter.body.1.resnets.2.block1.weight": src_state.pop("body.1.body.2.block1.weight"), - "adapter.body.1.resnets.2.block1.bias": src_state.pop("body.1.body.2.block1.bias"), - "adapter.body.1.resnets.2.block2.weight": src_state.pop("body.1.body.2.block2.weight"), - "adapter.body.1.resnets.2.block2.bias": src_state.pop("body.1.body.2.block2.bias"), - # body.1.body.3 - "adapter.body.1.resnets.3.block1.weight": src_state.pop("body.1.body.3.block1.weight"), - "adapter.body.1.resnets.3.block1.bias": src_state.pop("body.1.body.3.block1.bias"), - "adapter.body.1.resnets.3.block2.weight": src_state.pop("body.1.body.3.block2.weight"), - "adapter.body.1.resnets.3.block2.bias": src_state.pop("body.1.body.3.block2.bias"), - # body.1.out_conv - "adapter.body.1.out_conv.weight": src_state.pop("body.1.out_conv.weight"), - "adapter.body.1.out_conv.bias": src_state.pop("body.1.out_conv.bias"), - # body.2.in_conv - "adapter.body.2.in_conv.weight": src_state.pop("body.2.in_conv.weight"), - "adapter.body.2.in_conv.bias": src_state.pop("body.2.in_conv.bias"), - # body.2.body.0 - "adapter.body.2.resnets.0.block1.weight": src_state.pop("body.2.body.0.block1.weight"), - "adapter.body.2.resnets.0.block1.bias": src_state.pop("body.2.body.0.block1.bias"), - "adapter.body.2.resnets.0.block2.weight": src_state.pop("body.2.body.0.block2.weight"), - "adapter.body.2.resnets.0.block2.bias": src_state.pop("body.2.body.0.block2.bias"), - # body.2.body.1 - "adapter.body.2.resnets.1.block1.weight": src_state.pop("body.2.body.1.block1.weight"), - "adapter.body.2.resnets.1.block1.bias": src_state.pop("body.2.body.1.block1.bias"), - "adapter.body.2.resnets.1.block2.weight": src_state.pop("body.2.body.1.block2.weight"), - "adapter.body.2.resnets.1.block2.bias": src_state.pop("body.2.body.1.block2.bias"), - # body.2.body.2 - "adapter.body.2.resnets.2.block1.weight": src_state.pop("body.2.body.2.block1.weight"), - "adapter.body.2.resnets.2.block1.bias": src_state.pop("body.2.body.2.block1.bias"), - "adapter.body.2.resnets.2.block2.weight": src_state.pop("body.2.body.2.block2.weight"), - "adapter.body.2.resnets.2.block2.bias": src_state.pop("body.2.body.2.block2.bias"), - # body.2.body.3 - "adapter.body.2.resnets.3.block1.weight": src_state.pop("body.2.body.3.block1.weight"), - "adapter.body.2.resnets.3.block1.bias": src_state.pop("body.2.body.3.block1.bias"), - "adapter.body.2.resnets.3.block2.weight": src_state.pop("body.2.body.3.block2.weight"), - "adapter.body.2.resnets.3.block2.bias": src_state.pop("body.2.body.3.block2.bias"), - # body.2.out_conv - "adapter.body.2.out_conv.weight": src_state.pop("body.2.out_conv.weight"), - "adapter.body.2.out_conv.bias": src_state.pop("body.2.out_conv.bias"), - # body.3.in_conv - "adapter.body.3.in_conv.weight": src_state.pop("body.3.in_conv.weight"), - "adapter.body.3.in_conv.bias": src_state.pop("body.3.in_conv.bias"), - # body.3.body.0 - "adapter.body.3.resnets.0.block1.weight": src_state.pop("body.3.body.0.block1.weight"), - "adapter.body.3.resnets.0.block1.bias": src_state.pop("body.3.body.0.block1.bias"), - "adapter.body.3.resnets.0.block2.weight": src_state.pop("body.3.body.0.block2.weight"), - "adapter.body.3.resnets.0.block2.bias": src_state.pop("body.3.body.0.block2.bias"), - # body.3.body.1 - "adapter.body.3.resnets.1.block1.weight": src_state.pop("body.3.body.1.block1.weight"), - "adapter.body.3.resnets.1.block1.bias": src_state.pop("body.3.body.1.block1.bias"), - "adapter.body.3.resnets.1.block2.weight": src_state.pop("body.3.body.1.block2.weight"), - "adapter.body.3.resnets.1.block2.bias": src_state.pop("body.3.body.1.block2.bias"), - # body.3.body.2 - "adapter.body.3.resnets.2.block1.weight": src_state.pop("body.3.body.2.block1.weight"), - "adapter.body.3.resnets.2.block1.bias": src_state.pop("body.3.body.2.block1.bias"), - "adapter.body.3.resnets.2.block2.weight": src_state.pop("body.3.body.2.block2.weight"), - "adapter.body.3.resnets.2.block2.bias": src_state.pop("body.3.body.2.block2.bias"), - # body.3.body.3 - "adapter.body.3.resnets.3.block1.weight": src_state.pop("body.3.body.3.block1.weight"), - "adapter.body.3.resnets.3.block1.bias": src_state.pop("body.3.body.3.block1.bias"), - "adapter.body.3.resnets.3.block2.weight": src_state.pop("body.3.body.3.block2.weight"), - "adapter.body.3.resnets.3.block2.bias": src_state.pop("body.3.body.3.block2.bias"), - # body.3.out_conv - "adapter.body.3.out_conv.weight": src_state.pop("body.3.out_conv.weight"), - "adapter.body.3.out_conv.bias": src_state.pop("body.3.out_conv.bias"), - } - - assert len(src_state) == 0 - - adapter = T2IAdapter(in_channels=3, channels=[320, 640, 1280], num_res_blocks=4, adapter_type="light_adapter") - - adapter.load_state_dict(res_state) - - return adapter - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--output_path", default=None, type=str, required=True, help="Path to the store the result checkpoint." - ) - parser.add_argument( - "--is_adapter_light", - action="store_true", - help="Is checkpoint come from Adapter-Light architecture. ex: color-adapter", - ) - parser.add_argument("--in_channels", required=False, type=int, help="Input channels for non-light adapter") - - args = parser.parse_args() - src_state = torch.load(args.checkpoint_path) - - if args.is_adapter_light: - adapter = convert_light_adapter(src_state) - else: - if args.in_channels is None: - raise ValueError("set `--in_channels=<n>`") - adapter = convert_adapter(src_state, args.in_channels) - - adapter.save_pretrained(args.output_path) diff --git a/scripts/convert_ovis_image_to_diffusers.py b/scripts/convert_ovis_image_to_diffusers.py deleted file mode 100644 index 0d3d9cd44bf6..000000000000 --- a/scripts/convert_ovis_image_to_diffusers.py +++ /dev/null @@ -1,263 +0,0 @@ -import argparse -from contextlib import nullcontext - -import safetensors.torch -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download - -from diffusers import OvisImageTransformer2DModel -from diffusers.utils.import_utils import is_accelerate_available - - -""" -# Transformer - -python scripts/convert_ovis_image_to_diffusers.py \ ---original_state_dict_repo_id "AIDC-AI/Ovis-Image-7B" \ ---filename "ovis_image.safetensors" ---output_path "ovis-image" \ ---transformer -""" - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--original_state_dict_repo_id", default=None, type=str) -parser.add_argument("--filename", default="ovis_image.safetensors", type=str) -parser.add_argument("--checkpoint_path", default=None, type=str) -parser.add_argument("--in_channels", type=int, default=64) -parser.add_argument("--out_channels", type=int, default=None) -parser.add_argument("--transformer", action="store_true") -parser.add_argument("--output_path", type=str) -parser.add_argument("--dtype", type=str, default="bf16") - -args = parser.parse_args() -dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 - - -def load_original_checkpoint(args): - if args.original_state_dict_repo_id is not None: - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) - elif args.checkpoint_path is not None: - ckpt_path = args.checkpoint_path - else: - raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; -# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation -def swap_scale_shift(weight): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_ovis_image_transformer_checkpoint_to_diffusers( - original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0 -): - converted_state_dict = {} - - ## time_text_embed.timestep_embedder <- time_in - converted_state_dict["timestep_embedder.linear_1.weight"] = original_state_dict.pop("time_in.in_layer.weight") - converted_state_dict["timestep_embedder.linear_1.bias"] = original_state_dict.pop("time_in.in_layer.bias") - converted_state_dict["timestep_embedder.linear_2.weight"] = original_state_dict.pop("time_in.out_layer.weight") - converted_state_dict["timestep_embedder.linear_2.bias"] = original_state_dict.pop("time_in.out_layer.bias") - - # context_embedder - converted_state_dict["context_embedder_norm.weight"] = original_state_dict.pop("semantic_txt_norm.weight") - converted_state_dict["context_embedder.weight"] = original_state_dict.pop("semantic_txt_in.weight") - converted_state_dict["context_embedder.bias"] = original_state_dict.pop("semantic_txt_in.bias") - - # x_embedder - converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight") - converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias") - - # double transformer blocks - for i in range(num_layers): - block_prefix = f"transformer_blocks.{i}." - # norms. - ## norm1 - converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mod.lin.bias" - ) - ## norm1_context - converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mod.lin.bias" - ) - # Q, K, V - sample_q, sample_k, sample_v = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0 - ) - context_q, context_k, context_v = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0 - ) - sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0 - ) - context_q_bias, context_k_bias, context_v_bias = torch.chunk( - original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0 - ) - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q]) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias]) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k]) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias]) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v]) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias]) - converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q]) - converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias]) - converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k]) - converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias]) - converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v]) - converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias]) - # qk_norm - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.norm.query_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.norm.key_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.norm.query_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.norm.key_norm.weight" - ) - # ff img_mlp - converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = torch.cat( - [ - original_state_dict.pop(f"double_blocks.{i}.img_mlp.up_proj.weight"), - original_state_dict.pop(f"double_blocks.{i}.img_mlp.gate_proj.weight"), - ], - dim=0, - ) - converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = torch.cat( - [ - original_state_dict.pop(f"double_blocks.{i}.img_mlp.up_proj.bias"), - original_state_dict.pop(f"double_blocks.{i}.img_mlp.gate_proj.bias"), - ], - dim=0, - ) - converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.down_proj.weight" - ) - converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_mlp.down_proj.bias" - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = torch.cat( - [ - original_state_dict.pop(f"double_blocks.{i}.txt_mlp.up_proj.weight"), - original_state_dict.pop(f"double_blocks.{i}.txt_mlp.gate_proj.weight"), - ], - dim=0, - ) - converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = torch.cat( - [ - original_state_dict.pop(f"double_blocks.{i}.txt_mlp.up_proj.bias"), - original_state_dict.pop(f"double_blocks.{i}.txt_mlp.gate_proj.bias"), - ], - dim=0, - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.down_proj.weight" - ) - converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_mlp.down_proj.bias" - ) - # output projections. - converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( - f"double_blocks.{i}.img_attn.proj.bias" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.proj.weight" - ) - converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( - f"double_blocks.{i}.txt_attn.proj.bias" - ) - - # single transformer blocks - for i in range(num_single_layers): - block_prefix = f"single_transformer_blocks.{i}." - # norm.linear <- single_blocks.0.modulation.lin - converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.lin.weight" - ) - converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( - f"single_blocks.{i}.modulation.lin.bias" - ) - # Q, K, V, mlp - mlp_hidden_dim = int(inner_dim * mlp_ratio) - split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim * 2) - q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0) - q_bias, k_bias, v_bias, mlp_bias = torch.split( - original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0 - ) - converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q]) - converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias]) - converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k]) - converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias]) - converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v]) - converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias]) - converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp]) - converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias]) - # qk norm - converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( - f"single_blocks.{i}.norm.query_norm.weight" - ) - converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( - f"single_blocks.{i}.norm.key_norm.weight" - ) - # output projections. - converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.weight" - ) - converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( - f"single_blocks.{i}.linear2.bias" - ) - - converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.weight") - ) - converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.bias") - ) - - return converted_state_dict - - -def main(args): - original_ckpt = load_original_checkpoint(args) - - if args.transformer: - num_layers = 6 - num_single_layers = 27 - inner_dim = 3072 - mlp_ratio = 4.0 - - converted_transformer_state_dict = convert_ovis_image_transformer_checkpoint_to_diffusers( - original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio - ) - transformer = OvisImageTransformer2DModel(in_channels=args.in_channels, out_channels=args.out_channels) - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - print("Saving Ovis-Image Transformer in Diffusers format.") - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_pixart_alpha_to_diffusers.py b/scripts/convert_pixart_alpha_to_diffusers.py deleted file mode 100644 index 228b479df090..000000000000 --- a/scripts/convert_pixart_alpha_to_diffusers.py +++ /dev/null @@ -1,198 +0,0 @@ -import argparse -import os - -import torch -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel - - -ckpt_id = "PixArt-alpha/PixArt-alpha" -# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125 -interpolation_scale = {256: 0.5, 512: 1, 1024: 2} - - -def main(args): - all_state_dict = torch.load(args.orig_ckpt_path, map_location="cpu") - state_dict = all_state_dict.pop("state_dict") - converted_state_dict = {} - - # Patch embeddings. - converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") - converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") - - # Caption projection. - converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") - converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") - converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") - converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") - - # AdaLN-single LN - converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") - converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") - - if args.image_size == 1024: - # Resolution. - converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop( - "csize_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop( - "csize_embedder.mlp.0.bias" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop( - "csize_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop( - "csize_embedder.mlp.2.bias" - ) - # Aspect ratio. - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop( - "ar_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop( - "ar_embedder.mlp.0.bias" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop( - "ar_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop( - "ar_embedder.mlp.2.bias" - ) - # Shared norm. - converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight") - converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias") - - for depth in range(28): - # Transformer blocks. - converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( - f"blocks.{depth}.scale_shift_table" - ) - - # Attention is all you need 🤘 - - # Self attention. - q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias - # Projection. - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.attn.proj.bias" - ) - - # Feed-forward. - converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.fc1.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.fc1.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.fc2.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.fc2.bias" - ) - - # Cross-attention. - q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") - q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") - k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.bias" - ) - - # Final block. - converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") - converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") - - # DiT XL/2 - transformer = Transformer2DModel( - sample_size=args.image_size // 8, - num_layers=28, - attention_head_dim=72, - in_channels=4, - out_channels=8, - patch_size=2, - attention_bias=True, - num_attention_heads=16, - cross_attention_dim=1152, - activation_fn="gelu-approximate", - num_embeds_ada_norm=1000, - norm_type="ada_norm_single", - norm_elementwise_affine=False, - norm_eps=1e-6, - caption_channels=4096, - ) - transformer.load_state_dict(converted_state_dict, strict=True) - - assert transformer.pos_embed.pos_embed is not None - state_dict.pop("pos_embed") - state_dict.pop("y_embedder.y_embedding") - assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - if args.only_transformer: - transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) - else: - scheduler = DPMSolverMultistepScheduler() - - vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="sd-vae-ft-ema") - - tokenizer = T5Tokenizer.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl") - text_encoder = T5EncoderModel.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl") - - pipeline = PixArtAlphaPipeline( - tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler - ) - - pipeline.save_pretrained(args.dump_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--image_size", - default=1024, - type=int, - choices=[256, 512, 1024], - required=False, - help="Image size of pretrained model, either 512 or 1024.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--only_transformer", default=True, type=bool, required=True) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_pixart_sigma_to_diffusers.py b/scripts/convert_pixart_sigma_to_diffusers.py deleted file mode 100644 index 9572a83c0644..000000000000 --- a/scripts/convert_pixart_sigma_to_diffusers.py +++ /dev/null @@ -1,223 +0,0 @@ -import argparse -import os - -import torch -from transformers import T5EncoderModel, T5Tokenizer - -from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtSigmaPipeline, Transformer2DModel - - -ckpt_id = "PixArt-alpha" -# https://github.com/PixArt-alpha/PixArt-sigma/blob/dd087141864e30ec44f12cb7448dd654be065e88/scripts/inference.py#L158 -interpolation_scale = {256: 0.5, 512: 1, 1024: 2, 2048: 4} - - -def main(args): - all_state_dict = torch.load(args.orig_ckpt_path) - state_dict = all_state_dict.pop("state_dict") - converted_state_dict = {} - - # Patch embeddings. - converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") - converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") - - # Caption projection. - converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") - converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") - converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") - converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") - - # AdaLN-single LN - converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") - converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") - - if args.micro_condition: - # Resolution. - converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop( - "csize_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop( - "csize_embedder.mlp.0.bias" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop( - "csize_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop( - "csize_embedder.mlp.2.bias" - ) - # Aspect ratio. - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop( - "ar_embedder.mlp.0.weight" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop( - "ar_embedder.mlp.0.bias" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop( - "ar_embedder.mlp.2.weight" - ) - converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop( - "ar_embedder.mlp.2.bias" - ) - # Shared norm. - converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight") - converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias") - - for depth in range(28): - # Transformer blocks. - converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( - f"blocks.{depth}.scale_shift_table" - ) - # Attention is all you need 🤘 - - # Self attention. - q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias - # Projection. - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.attn.proj.bias" - ) - if args.qk_norm: - converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.weight"] = state_dict.pop( - f"blocks.{depth}.attn.q_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.bias"] = state_dict.pop( - f"blocks.{depth}.attn.q_norm.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.weight"] = state_dict.pop( - f"blocks.{depth}.attn.k_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.bias"] = state_dict.pop( - f"blocks.{depth}.attn.k_norm.bias" - ) - - # Feed-forward. - converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.fc1.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.fc1.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.fc2.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.fc2.bias" - ) - - # Cross-attention. - q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") - q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") - k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.bias" - ) - - # Final block. - converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") - converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") - - # PixArt XL/2 - transformer = Transformer2DModel( - sample_size=args.image_size // 8, - num_layers=28, - attention_head_dim=72, - in_channels=4, - out_channels=8, - patch_size=2, - attention_bias=True, - num_attention_heads=16, - cross_attention_dim=1152, - activation_fn="gelu-approximate", - num_embeds_ada_norm=1000, - norm_type="ada_norm_single", - norm_elementwise_affine=False, - norm_eps=1e-6, - caption_channels=4096, - interpolation_scale=interpolation_scale[args.image_size], - use_additional_conditions=args.micro_condition, - ) - transformer.load_state_dict(converted_state_dict, strict=True) - - assert transformer.pos_embed.pos_embed is not None - try: - state_dict.pop("y_embedder.y_embedding") - state_dict.pop("pos_embed") - except Exception as e: - print(f"Skipping {str(e)}") - pass - assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - if args.only_transformer: - transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) - else: - # pixart-Sigma vae link: https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers/tree/main/vae - vae = AutoencoderKL.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="vae") - - scheduler = DPMSolverMultistepScheduler() - - tokenizer = T5Tokenizer.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="tokenizer") - text_encoder = T5EncoderModel.from_pretrained( - f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="text_encoder" - ) - - pipeline = PixArtSigmaPipeline( - tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler - ) - - pipeline.save_pretrained(args.dump_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--micro_condition", action="store_true", help="If use Micro-condition in PixArtMS structure during training." - ) - parser.add_argument("--qk_norm", action="store_true", help="If use qk norm during training.") - parser.add_argument( - "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--image_size", - default=1024, - type=int, - choices=[256, 512, 1024, 2048], - required=False, - help="Image size of pretrained model, 256, 512, 1024, or 2048.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--only_transformer", default=True, type=bool, required=True) - - args = parser.parse_args() - main(args) diff --git a/scripts/convert_prx_to_diffusers.py b/scripts/convert_prx_to_diffusers.py deleted file mode 100644 index 00bb3f6fe99e..000000000000 --- a/scripts/convert_prx_to_diffusers.py +++ /dev/null @@ -1,345 +0,0 @@ -#!/usr/bin/env python3 -""" -Script to convert PRX checkpoint from original codebase to diffusers format. -""" - -import argparse -import json -import os -import sys -from dataclasses import asdict, dataclass -from typing import Dict, Tuple - -import torch -from safetensors.torch import save_file - -from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel -from diffusers.pipelines.prx import PRXPipeline - - -DEFAULT_RESOLUTION = 512 - - -@dataclass(frozen=True) -class PRXBase: - context_in_dim: int = 2304 - hidden_size: int = 1792 - mlp_ratio: float = 3.5 - num_heads: int = 28 - depth: int = 16 - axes_dim: Tuple[int, int] = (32, 32) - theta: int = 10_000 - time_factor: float = 1000.0 - time_max_period: int = 10_000 - - -@dataclass(frozen=True) -class PRXFlux(PRXBase): - in_channels: int = 16 - patch_size: int = 2 - - -@dataclass(frozen=True) -class PRXDCAE(PRXBase): - in_channels: int = 32 - patch_size: int = 1 - - -def build_config(vae_type: str) -> Tuple[dict, int]: - if vae_type == "flux": - cfg = PRXFlux() - elif vae_type == "dc-ae": - cfg = PRXDCAE() - else: - raise ValueError(f"Unsupported VAE type: {vae_type}. Use 'flux' or 'dc-ae'") - - config_dict = asdict(cfg) - config_dict["axes_dim"] = list(config_dict["axes_dim"]) # type: ignore[index] - return config_dict - - -def create_parameter_mapping(depth: int) -> dict: - """Create mapping from old parameter names to new diffusers names.""" - - # Key mappings for structural changes - mapping = {} - - # Map old structure (layers in PRXBlock) to new structure (layers in PRXAttention) - for i in range(depth): - # QKV projections moved to attention module - mapping[f"blocks.{i}.img_qkv_proj.weight"] = f"blocks.{i}.attention.img_qkv_proj.weight" - mapping[f"blocks.{i}.txt_kv_proj.weight"] = f"blocks.{i}.attention.txt_kv_proj.weight" - - # QK norm moved to attention module and renamed to match Attention's qk_norm structure - mapping[f"blocks.{i}.qk_norm.query_norm.scale"] = f"blocks.{i}.attention.norm_q.weight" - mapping[f"blocks.{i}.qk_norm.key_norm.scale"] = f"blocks.{i}.attention.norm_k.weight" - mapping[f"blocks.{i}.qk_norm.query_norm.weight"] = f"blocks.{i}.attention.norm_q.weight" - mapping[f"blocks.{i}.qk_norm.key_norm.weight"] = f"blocks.{i}.attention.norm_k.weight" - - # K norm for text tokens moved to attention module - mapping[f"blocks.{i}.k_norm.scale"] = f"blocks.{i}.attention.norm_added_k.weight" - mapping[f"blocks.{i}.k_norm.weight"] = f"blocks.{i}.attention.norm_added_k.weight" - - # Attention output projection - mapping[f"blocks.{i}.attn_out.weight"] = f"blocks.{i}.attention.to_out.0.weight" - - return mapping - - -def convert_checkpoint_parameters(old_state_dict: Dict[str, torch.Tensor], depth: int) -> dict[str, torch.Tensor]: - """Convert old checkpoint parameters to new diffusers format.""" - - print("Converting checkpoint parameters...") - - mapping = create_parameter_mapping(depth) - converted_state_dict = {} - - for key, value in old_state_dict.items(): - new_key = key - - # Apply specific mappings if needed - if key in mapping: - new_key = mapping[key] - print(f" Mapped: {key} -> {new_key}") - - converted_state_dict[new_key] = value - - print(f"✓ Converted {len(converted_state_dict)} parameters") - return converted_state_dict - - -def create_transformer_from_checkpoint(checkpoint_path: str, config: dict) -> PRXTransformer2DModel: - """Create and load PRXTransformer2DModel from old checkpoint.""" - - print(f"Loading checkpoint from: {checkpoint_path}") - - # Load old checkpoint - if not os.path.exists(checkpoint_path): - raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}") - - old_checkpoint = torch.load(checkpoint_path, map_location="cpu") - - # Handle different checkpoint formats - if isinstance(old_checkpoint, dict): - if "model" in old_checkpoint: - state_dict = old_checkpoint["model"] - elif "state_dict" in old_checkpoint: - state_dict = old_checkpoint["state_dict"] - else: - state_dict = old_checkpoint - else: - state_dict = old_checkpoint - - print(f"✓ Loaded checkpoint with {len(state_dict)} parameters") - - # Convert parameter names if needed - model_depth = int(config.get("depth", 16)) - converted_state_dict = convert_checkpoint_parameters(state_dict, depth=model_depth) - - # Create transformer with config - print("Creating PRXTransformer2DModel...") - transformer = PRXTransformer2DModel(**config) - - # Load state dict - print("Loading converted parameters...") - missing_keys, unexpected_keys = transformer.load_state_dict(converted_state_dict, strict=False) - - if missing_keys: - print(f"⚠ Missing keys: {missing_keys}") - if unexpected_keys: - print(f"⚠ Unexpected keys: {unexpected_keys}") - - if not missing_keys and not unexpected_keys: - print("✓ All parameters loaded successfully!") - - return transformer - - -def create_scheduler_config(output_path: str, shift: float): - """Create FlowMatchEulerDiscreteScheduler config.""" - - scheduler_config = {"_class_name": "FlowMatchEulerDiscreteScheduler", "num_train_timesteps": 1000, "shift": shift} - - scheduler_path = os.path.join(output_path, "scheduler") - os.makedirs(scheduler_path, exist_ok=True) - - with open(os.path.join(scheduler_path, "scheduler_config.json"), "w") as f: - json.dump(scheduler_config, f, indent=2) - - print("✓ Created scheduler config") - - -def download_and_save_vae(vae_type: str, output_path: str): - """Download and save VAE to local directory.""" - from diffusers import AutoencoderDC, AutoencoderKL - - vae_path = os.path.join(output_path, "vae") - os.makedirs(vae_path, exist_ok=True) - - if vae_type == "flux": - print("Downloading FLUX VAE from black-forest-labs/FLUX.1-dev...") - vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae") - else: # dc-ae - print("Downloading DC-AE VAE from mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers...") - vae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers") - - vae.save_pretrained(vae_path) - print(f"✓ Saved VAE to {vae_path}") - - -def download_and_save_text_encoder(output_path: str): - """Download and save T5Gemma text encoder and tokenizer.""" - from transformers import GemmaTokenizerFast - from transformers.models.t5gemma.modeling_t5gemma import T5GemmaModel - - text_encoder_path = os.path.join(output_path, "text_encoder") - tokenizer_path = os.path.join(output_path, "tokenizer") - os.makedirs(text_encoder_path, exist_ok=True) - os.makedirs(tokenizer_path, exist_ok=True) - - print("Downloading T5Gemma model from google/t5gemma-2b-2b-ul2...") - t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2") - - # Extract and save only the encoder - t5gemma_encoder = t5gemma_model.encoder - t5gemma_encoder.save_pretrained(text_encoder_path) - print(f"✓ Saved T5GemmaEncoder to {text_encoder_path}") - - print("Downloading tokenizer from google/t5gemma-2b-2b-ul2...") - tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2") - tokenizer.model_max_length = 256 - tokenizer.save_pretrained(tokenizer_path) - print(f"✓ Saved tokenizer to {tokenizer_path}") - - -def create_model_index(vae_type: str, default_image_size: int, output_path: str): - """Create model_index.json for the pipeline.""" - - if vae_type == "flux": - vae_class = "AutoencoderKL" - else: # dc-ae - vae_class = "AutoencoderDC" - - model_index = { - "_class_name": "PRXPipeline", - "_diffusers_version": "0.31.0.dev0", - "_name_or_path": os.path.basename(output_path), - "default_sample_size": default_image_size, - "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"], - "text_encoder": ["prx", "T5GemmaEncoder"], - "tokenizer": ["transformers", "GemmaTokenizerFast"], - "transformer": ["diffusers", "PRXTransformer2DModel"], - "vae": ["diffusers", vae_class], - } - - model_index_path = os.path.join(output_path, "model_index.json") - with open(model_index_path, "w") as f: - json.dump(model_index, f, indent=2) - - -def main(args): - # Validate inputs - if not os.path.exists(args.checkpoint_path): - raise FileNotFoundError(f"Checkpoint not found: {args.checkpoint_path}") - - config = build_config(args.vae_type) - - # Create output directory - os.makedirs(args.output_path, exist_ok=True) - print(f"✓ Output directory: {args.output_path}") - - # Create transformer from checkpoint - transformer = create_transformer_from_checkpoint(args.checkpoint_path, config) - - # Save transformer - transformer_path = os.path.join(args.output_path, "transformer") - os.makedirs(transformer_path, exist_ok=True) - - # Save config - with open(os.path.join(transformer_path, "config.json"), "w") as f: - json.dump(config, f, indent=2) - - # Save model weights as safetensors - state_dict = transformer.state_dict() - save_file(state_dict, os.path.join(transformer_path, "diffusion_pytorch_model.safetensors")) - print(f"✓ Saved transformer to {transformer_path}") - - # Create scheduler config - create_scheduler_config(args.output_path, args.shift) - - download_and_save_vae(args.vae_type, args.output_path) - download_and_save_text_encoder(args.output_path) - - # Create model_index.json - create_model_index(args.vae_type, args.resolution, args.output_path) - - # Verify the pipeline can be loaded - try: - pipeline = PRXPipeline.from_pretrained(args.output_path) - print("Pipeline loaded successfully!") - print(f"Transformer: {type(pipeline.transformer).__name__}") - print(f"VAE: {type(pipeline.vae).__name__}") - print(f"Text Encoder: {type(pipeline.text_encoder).__name__}") - print(f"Scheduler: {type(pipeline.scheduler).__name__}") - - # Display model info - num_params = sum(p.numel() for p in pipeline.transformer.parameters()) - print(f"✓ Transformer parameters: {num_params:,}") - - except Exception as e: - print(f"Pipeline verification failed: {e}") - return False - - print("Conversion completed successfully!") - print(f"Converted pipeline saved to: {args.output_path}") - print(f"VAE type: {args.vae_type}") - - return True - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Convert PRX checkpoint to diffusers format") - - parser.add_argument( - "--checkpoint_path", type=str, required=True, help="Path to the original PRX checkpoint (.pth file )" - ) - - parser.add_argument( - "--output_path", type=str, required=True, help="Output directory for the converted diffusers pipeline" - ) - - parser.add_argument( - "--vae_type", - type=str, - choices=["flux", "dc-ae"], - required=True, - help="VAE type to use: 'flux' for AutoencoderKL (16 channels) or 'dc-ae' for AutoencoderDC (32 channels)", - ) - - parser.add_argument( - "--resolution", - type=int, - choices=[256, 512, 1024], - default=DEFAULT_RESOLUTION, - help="Target resolution for the model (256, 512, or 1024). Affects the transformer's sample_size.", - ) - - parser.add_argument( - "--shift", - type=float, - default=3.0, - help="Shift for the scheduler", - ) - - args = parser.parse_args() - - try: - success = main(args) - if not success: - sys.exit(1) - except Exception as e: - print(f"Conversion failed: {e}") - import traceback - - traceback.print_exc() - sys.exit(1) diff --git a/scripts/convert_rae_to_diffusers.py b/scripts/convert_rae_to_diffusers.py deleted file mode 100644 index 0502e49ef30c..000000000000 --- a/scripts/convert_rae_to_diffusers.py +++ /dev/null @@ -1,406 +0,0 @@ -import argparse -from pathlib import Path -from typing import Any - -import torch -from huggingface_hub import HfApi, hf_hub_download - -from diffusers import AutoencoderRAE - - -DECODER_CONFIGS = { - "ViTB": { - "decoder_hidden_size": 768, - "decoder_intermediate_size": 3072, - "decoder_num_attention_heads": 12, - "decoder_num_hidden_layers": 12, - }, - "ViTL": { - "decoder_hidden_size": 1024, - "decoder_intermediate_size": 4096, - "decoder_num_attention_heads": 16, - "decoder_num_hidden_layers": 24, - }, - "ViTXL": { - "decoder_hidden_size": 1152, - "decoder_intermediate_size": 4096, - "decoder_num_attention_heads": 16, - "decoder_num_hidden_layers": 28, - }, -} - -ENCODER_DEFAULT_NAME_OR_PATH = { - "dinov2": "facebook/dinov2-with-registers-base", - "siglip2": "google/siglip2-base-patch16-256", - "mae": "facebook/vit-mae-base", -} - -ENCODER_HIDDEN_SIZE = { - "dinov2": 768, - "siglip2": 768, - "mae": 768, -} - -ENCODER_PATCH_SIZE = { - "dinov2": 14, - "siglip2": 16, - "mae": 16, -} - -DEFAULT_DECODER_SUBDIR = { - "dinov2": "decoders/dinov2/wReg_base", - "mae": "decoders/mae/base_p16", - "siglip2": "decoders/siglip2/base_p16_i256", -} - -DEFAULT_STATS_SUBDIR = { - "dinov2": "stats/dinov2/wReg_base", - "mae": "stats/mae/base_p16", - "siglip2": "stats/siglip2/base_p16_i256", -} - -DECODER_FILE_CANDIDATES = ("dinov2_decoder.pt", "model.pt") -STATS_FILE_CANDIDATES = ("stat.pt",) - - -def dataset_case_candidates(name: str) -> tuple[str, ...]: - return (name, name.lower(), name.upper(), name.title(), "imagenet1k", "ImageNet1k") - - -class RepoAccessor: - def __init__(self, repo_or_path: str, cache_dir: str | None = None): - self.repo_or_path = repo_or_path - self.cache_dir = cache_dir - self.local_root: Path | None = None - self.repo_id: str | None = None - self.repo_files: set[str] | None = None - - root = Path(repo_or_path) - if root.exists() and root.is_dir(): - self.local_root = root - else: - self.repo_id = repo_or_path - self.repo_files = set(HfApi().list_repo_files(repo_or_path)) - - def exists(self, relative_path: str) -> bool: - relative_path = relative_path.replace("\\", "/") - if self.local_root is not None: - return (self.local_root / relative_path).is_file() - return relative_path in self.repo_files - - def fetch(self, relative_path: str) -> Path: - relative_path = relative_path.replace("\\", "/") - if self.local_root is not None: - return self.local_root / relative_path - downloaded = hf_hub_download(repo_id=self.repo_id, filename=relative_path, cache_dir=self.cache_dir) - return Path(downloaded) - - -def unwrap_state_dict(maybe_wrapped: dict[str, Any]) -> dict[str, Any]: - state_dict = maybe_wrapped - for k in ("model", "module", "state_dict"): - if isinstance(state_dict, dict) and k in state_dict and isinstance(state_dict[k], dict): - state_dict = state_dict[k] - - out = dict(state_dict) - if len(out) > 0 and all(key.startswith("module.") for key in out): - out = {key[len("module.") :]: value for key, value in out.items()} - if len(out) > 0 and all(key.startswith("decoder.") for key in out): - out = {key[len("decoder.") :]: value for key, value in out.items()} - return out - - -def remap_decoder_attention_keys_for_diffusers(state_dict: dict[str, Any]) -> dict[str, Any]: - """ - Map official RAE decoder attention key layout to diffusers Attention layout used by AutoencoderRAE decoder. - - Example mappings: - - `...attention.attention.query.*` -> `...attention.to_q.*` - - `...attention.attention.key.*` -> `...attention.to_k.*` - - `...attention.attention.value.*` -> `...attention.to_v.*` - - `...attention.output.dense.*` -> `...attention.to_out.0.*` - """ - remapped: dict[str, Any] = {} - for key, value in state_dict.items(): - new_key = key - new_key = new_key.replace(".attention.attention.query.", ".attention.to_q.") - new_key = new_key.replace(".attention.attention.key.", ".attention.to_k.") - new_key = new_key.replace(".attention.attention.value.", ".attention.to_v.") - new_key = new_key.replace(".attention.output.dense.", ".attention.to_out.0.") - remapped[new_key] = value - return remapped - - -def resolve_decoder_file( - accessor: RepoAccessor, encoder_type: str, variant: str, decoder_checkpoint: str | None -) -> str: - if decoder_checkpoint is not None: - if accessor.exists(decoder_checkpoint): - return decoder_checkpoint - raise FileNotFoundError(f"Decoder checkpoint not found: {decoder_checkpoint}") - - base = f"{DEFAULT_DECODER_SUBDIR[encoder_type]}/{variant}" - for name in DECODER_FILE_CANDIDATES: - candidate = f"{base}/{name}" - if accessor.exists(candidate): - return candidate - - raise FileNotFoundError( - f"Could not find decoder checkpoint under `{base}`. Tried: {list(DECODER_FILE_CANDIDATES)}" - ) - - -def resolve_stats_file( - accessor: RepoAccessor, - encoder_type: str, - dataset_name: str, - stats_checkpoint: str | None, -) -> str | None: - if stats_checkpoint is not None: - if accessor.exists(stats_checkpoint): - return stats_checkpoint - raise FileNotFoundError(f"Stats checkpoint not found: {stats_checkpoint}") - - base = DEFAULT_STATS_SUBDIR[encoder_type] - for dataset in dataset_case_candidates(dataset_name): - for name in STATS_FILE_CANDIDATES: - candidate = f"{base}/{dataset}/{name}" - if accessor.exists(candidate): - return candidate - - return None - - -def extract_latent_stats(stats_obj: Any) -> tuple[Any | None, Any | None]: - if not isinstance(stats_obj, dict): - return None, None - - if "latents_mean" in stats_obj or "latents_std" in stats_obj: - return stats_obj.get("latents_mean", None), stats_obj.get("latents_std", None) - - mean = stats_obj.get("mean", None) - var = stats_obj.get("var", None) - if mean is None and var is None: - return None, None - - latents_std = None - if var is not None: - if isinstance(var, torch.Tensor): - latents_std = torch.sqrt(var + 1e-5) - else: - latents_std = torch.sqrt(torch.tensor(var) + 1e-5) - return mean, latents_std - - -def _strip_final_layernorm_affine(state_dict: dict[str, Any], prefix: str = "") -> dict[str, Any]: - """Remove final layernorm weight/bias from encoder state dict. - - RAE uses non-affine layernorm (weight=1, bias=0 is the default identity). - Stripping these keys means the model keeps its default init values, which - is functionally equivalent to setting elementwise_affine=False. - """ - keys_to_strip = {f"{prefix}weight", f"{prefix}bias"} - return {k: v for k, v in state_dict.items() if k not in keys_to_strip} - - -def _load_hf_encoder_state_dict(encoder_type: str, encoder_name_or_path: str) -> dict[str, Any]: - """Download the HF encoder and extract the state dict for the inner model.""" - if encoder_type == "dinov2": - from transformers import Dinov2WithRegistersModel - - hf_model = Dinov2WithRegistersModel.from_pretrained(encoder_name_or_path) - sd = hf_model.state_dict() - return _strip_final_layernorm_affine(sd, prefix="layernorm.") - elif encoder_type == "siglip2": - from transformers import SiglipModel - - # SiglipModel.vision_model is a SiglipVisionTransformer. - # Our Siglip2Encoder wraps it inside SiglipVisionModel which nests it - # under .vision_model, so we add the prefix to match the diffusers key layout. - hf_model = SiglipModel.from_pretrained(encoder_name_or_path).vision_model - sd = {f"vision_model.{k}": v for k, v in hf_model.state_dict().items()} - return _strip_final_layernorm_affine(sd, prefix="vision_model.post_layernorm.") - elif encoder_type == "mae": - from transformers import ViTMAEForPreTraining - - hf_model = ViTMAEForPreTraining.from_pretrained(encoder_name_or_path).vit - sd = hf_model.state_dict() - return _strip_final_layernorm_affine(sd, prefix="layernorm.") - else: - raise ValueError(f"Unknown encoder_type: {encoder_type}") - - -def convert(args: argparse.Namespace) -> None: - accessor = RepoAccessor(args.repo_or_path, cache_dir=args.cache_dir) - encoder_name_or_path = args.encoder_name_or_path or ENCODER_DEFAULT_NAME_OR_PATH[args.encoder_type] - - decoder_relpath = resolve_decoder_file(accessor, args.encoder_type, args.variant, args.decoder_checkpoint) - stats_relpath = resolve_stats_file(accessor, args.encoder_type, args.dataset_name, args.stats_checkpoint) - - print(f"Using decoder checkpoint: {decoder_relpath}") - if stats_relpath is not None: - print(f"Using stats checkpoint: {stats_relpath}") - else: - print("No stats checkpoint found; conversion will proceed without latent stats.") - - if args.dry_run: - return - - decoder_path = accessor.fetch(decoder_relpath) - decoder_obj = torch.load(decoder_path, map_location="cpu") - decoder_state_dict = unwrap_state_dict(decoder_obj) - decoder_state_dict = remap_decoder_attention_keys_for_diffusers(decoder_state_dict) - - latents_mean, latents_std = None, None - if stats_relpath is not None: - stats_path = accessor.fetch(stats_relpath) - stats_obj = torch.load(stats_path, map_location="cpu") - latents_mean, latents_std = extract_latent_stats(stats_obj) - - decoder_cfg = DECODER_CONFIGS[args.decoder_config_name] - - # Read encoder normalization stats from the HF image processor (only place that downloads encoder info) - from transformers import AutoConfig, AutoImageProcessor - - proc = AutoImageProcessor.from_pretrained(encoder_name_or_path) - encoder_norm_mean = list(proc.image_mean) - encoder_norm_std = list(proc.image_std) - - # Read encoder hidden size and patch size from HF config - encoder_hidden_size = ENCODER_HIDDEN_SIZE[args.encoder_type] - encoder_patch_size = ENCODER_PATCH_SIZE[args.encoder_type] - try: - hf_config = AutoConfig.from_pretrained(encoder_name_or_path) - # For models like SigLIP that nest vision config - if hasattr(hf_config, "vision_config"): - hf_config = hf_config.vision_config - encoder_hidden_size = hf_config.hidden_size - encoder_patch_size = hf_config.patch_size - except Exception: - pass - - # Load the actual encoder weights from HF to include in the saved model - encoder_state_dict = _load_hf_encoder_state_dict(args.encoder_type, encoder_name_or_path) - - # Build model on meta device to avoid double init overhead - with torch.device("meta"): - model = AutoencoderRAE( - encoder_type=args.encoder_type, - encoder_hidden_size=encoder_hidden_size, - encoder_patch_size=encoder_patch_size, - encoder_input_size=args.encoder_input_size, - patch_size=args.patch_size, - image_size=args.image_size, - num_channels=args.num_channels, - encoder_norm_mean=encoder_norm_mean, - encoder_norm_std=encoder_norm_std, - decoder_hidden_size=decoder_cfg["decoder_hidden_size"], - decoder_num_hidden_layers=decoder_cfg["decoder_num_hidden_layers"], - decoder_num_attention_heads=decoder_cfg["decoder_num_attention_heads"], - decoder_intermediate_size=decoder_cfg["decoder_intermediate_size"], - latents_mean=latents_mean, - latents_std=latents_std, - scaling_factor=args.scaling_factor, - ) - - # Assemble full state dict and load with assign=True - full_state_dict = {} - - # Encoder weights (prefixed with "encoder.") - for k, v in encoder_state_dict.items(): - full_state_dict[f"encoder.{k}"] = v - - # Decoder weights (prefixed with "decoder.") - for k, v in decoder_state_dict.items(): - full_state_dict[f"decoder.{k}"] = v - - # Buffers from config - full_state_dict["encoder_mean"] = torch.tensor(encoder_norm_mean, dtype=torch.float32).view(1, 3, 1, 1) - full_state_dict["encoder_std"] = torch.tensor(encoder_norm_std, dtype=torch.float32).view(1, 3, 1, 1) - if latents_mean is not None: - latents_mean_t = latents_mean if isinstance(latents_mean, torch.Tensor) else torch.tensor(latents_mean) - full_state_dict["_latents_mean"] = latents_mean_t - else: - full_state_dict["_latents_mean"] = torch.zeros(1) - if latents_std is not None: - latents_std_t = latents_std if isinstance(latents_std, torch.Tensor) else torch.tensor(latents_std) - full_state_dict["_latents_std"] = latents_std_t - else: - full_state_dict["_latents_std"] = torch.ones(1) - - model.load_state_dict(full_state_dict, strict=False, assign=True) - - # Verify no critical keys are missing - model_keys = {name for name, _ in model.named_parameters()} - model_keys |= {name for name, _ in model.named_buffers()} - loaded_keys = set(full_state_dict.keys()) - missing = model_keys - loaded_keys - # decoder_pos_embed is initialized in-model. trainable_cls_token is only - # allowed to be missing if it was absent in the source decoder checkpoint. - allowed_missing = {"decoder.decoder_pos_embed"} - if "trainable_cls_token" not in decoder_state_dict: - allowed_missing.add("decoder.trainable_cls_token") - if missing - allowed_missing: - print(f"Warning: missing keys after conversion: {sorted(missing - allowed_missing)}") - - output_path = Path(args.output_path) - output_path.mkdir(parents=True, exist_ok=True) - model.save_pretrained(output_path) - - if args.verify_load: - print("Verifying converted checkpoint with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False)...") - loaded_model = AutoencoderRAE.from_pretrained(output_path, low_cpu_mem_usage=False) - if not isinstance(loaded_model, AutoencoderRAE): - raise RuntimeError("Verification failed: loaded object is not AutoencoderRAE.") - print("Verification passed.") - - print(f"Saved converted AutoencoderRAE to: {output_path}") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert RAE decoder checkpoints to diffusers AutoencoderRAE format") - parser.add_argument( - "--repo_or_path", type=str, required=True, help="Hub repo id (e.g. nyu-visionx/RAE-collections) or local path" - ) - parser.add_argument("--output_path", type=str, required=True, help="Directory to save converted model") - - parser.add_argument("--encoder_type", type=str, choices=["dinov2", "mae", "siglip2"], required=True) - parser.add_argument( - "--encoder_name_or_path", type=str, default=None, help="Optional encoder HF model id or local path override" - ) - - parser.add_argument("--variant", type=str, default="ViTXL_n08", help="Decoder variant folder name") - parser.add_argument("--dataset_name", type=str, default="imagenet1k", help="Stats dataset folder name") - - parser.add_argument( - "--decoder_checkpoint", type=str, default=None, help="Relative path to decoder checkpoint inside repo/path" - ) - parser.add_argument( - "--stats_checkpoint", type=str, default=None, help="Relative path to stats checkpoint inside repo/path" - ) - - parser.add_argument("--decoder_config_name", type=str, choices=list(DECODER_CONFIGS.keys()), default="ViTXL") - parser.add_argument("--encoder_input_size", type=int, default=224) - parser.add_argument("--patch_size", type=int, default=16) - parser.add_argument("--image_size", type=int, default=None) - parser.add_argument("--num_channels", type=int, default=3) - parser.add_argument("--scaling_factor", type=float, default=1.0) - - parser.add_argument("--cache_dir", type=str, default=None) - parser.add_argument("--dry_run", action="store_true", help="Only resolve and print selected files") - parser.add_argument( - "--verify_load", - action="store_true", - help="After conversion, load back with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False).", - ) - - return parser.parse_args() - - -def main() -> None: - args = parse_args() - convert(args) - - -if __name__ == "__main__": - main() diff --git a/scripts/convert_sana_controlnet_to_diffusers.py b/scripts/convert_sana_controlnet_to_diffusers.py deleted file mode 100644 index f7fcd7252576..000000000000 --- a/scripts/convert_sana_controlnet_to_diffusers.py +++ /dev/null @@ -1,216 +0,0 @@ -#!/usr/bin/env python -from __future__ import annotations - -import argparse -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights - -from diffusers import ( - SanaControlNetModel, -) -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available else nullcontext - - -def main(args): - file_path = args.orig_ckpt_path - - all_state_dict = torch.load(file_path, weights_only=True) - state_dict = all_state_dict.pop("state_dict") - converted_state_dict = {} - - # Patch embeddings. - converted_state_dict["patch_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") - converted_state_dict["patch_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") - - # Caption projection. - converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") - converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") - converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") - converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") - - # AdaLN-single LN - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") - - # Shared norm. - converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") - converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") - - # y norm - converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") - - # Positional embedding interpolation scale. - interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0} - - # ControlNet Input Projection. - converted_state_dict["input_block.weight"] = state_dict.pop("controlnet.0.before_proj.weight") - converted_state_dict["input_block.bias"] = state_dict.pop("controlnet.0.before_proj.bias") - - for depth in range(7): - # Transformer blocks. - converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( - f"controlnet.{depth}.copied_block.scale_shift_table" - ) - - # Linear Attention is all you need 🤘 - # Self attention. - q, k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.attn.qkv.weight"), 3, dim=0) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - # Projection. - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( - f"controlnet.{depth}.copied_block.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( - f"controlnet.{depth}.copied_block.attn.proj.bias" - ) - - # Feed-forward. - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( - f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( - f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( - f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( - f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( - f"controlnet.{depth}.copied_block.mlp.point_conv.conv.weight" - ) - - # Cross-attention. - q = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.weight") - q_bias = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.bias") - k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.weight"), 2, dim=0) - k_bias, v_bias = torch.chunk( - state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.bias"), 2, dim=0 - ) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( - f"controlnet.{depth}.copied_block.cross_attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( - f"controlnet.{depth}.copied_block.cross_attn.proj.bias" - ) - - # ControlNet After Projection - converted_state_dict[f"controlnet_blocks.{depth}.weight"] = state_dict.pop( - f"controlnet.{depth}.after_proj.weight" - ) - converted_state_dict[f"controlnet_blocks.{depth}.bias"] = state_dict.pop(f"controlnet.{depth}.after_proj.bias") - - # ControlNet - with CTX(): - controlnet = SanaControlNetModel( - num_attention_heads=model_kwargs[args.model_type]["num_attention_heads"], - attention_head_dim=model_kwargs[args.model_type]["attention_head_dim"], - num_layers=model_kwargs[args.model_type]["num_layers"], - num_cross_attention_heads=model_kwargs[args.model_type]["num_cross_attention_heads"], - cross_attention_head_dim=model_kwargs[args.model_type]["cross_attention_head_dim"], - cross_attention_dim=model_kwargs[args.model_type]["cross_attention_dim"], - caption_channels=2304, - sample_size=args.image_size // 32, - interpolation_scale=interpolation_scale[args.image_size], - ) - - if is_accelerate_available(): - load_model_dict_into_meta(controlnet, converted_state_dict) - else: - controlnet.load_state_dict(converted_state_dict, strict=True, assign=True) - - num_model_params = sum(p.numel() for p in controlnet.parameters()) - print(f"Total number of controlnet parameters: {num_model_params}") - - controlnet = controlnet.to(weight_dtype) - controlnet.load_state_dict(converted_state_dict, strict=True) - - print(f"Saving Sana ControlNet in Diffusers format in {args.dump_path}.") - controlnet.save_pretrained(args.dump_path) - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - -VARIANT_MAPPING = { - "fp32": None, - "fp16": "fp16", - "bf16": "bf16", -} - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--orig_ckpt_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--image_size", - default=1024, - type=int, - choices=[512, 1024, 2048, 4096], - required=False, - help="Image size of pretrained model, 512, 1024, 2048 or 4096.", - ) - parser.add_argument( - "--model_type", - default="SanaMS_1600M_P1_ControlNet_D7", - type=str, - choices=["SanaMS_1600M_P1_ControlNet_D7", "SanaMS_600M_P1_ControlNet_D7"], - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--dtype", default="fp16", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") - - args = parser.parse_args() - - model_kwargs = { - "SanaMS_1600M_P1_ControlNet_D7": { - "num_attention_heads": 70, - "attention_head_dim": 32, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "num_layers": 7, - }, - "SanaMS_600M_P1_ControlNet_D7": { - "num_attention_heads": 36, - "attention_head_dim": 32, - "num_cross_attention_heads": 16, - "cross_attention_head_dim": 72, - "cross_attention_dim": 1152, - "num_layers": 7, - }, - } - - device = "cuda" if torch.cuda.is_available() else "cpu" - weight_dtype = DTYPE_MAPPING[args.dtype] - variant = VARIANT_MAPPING[args.dtype] - - main(args) diff --git a/scripts/convert_sana_to_diffusers.py b/scripts/convert_sana_to_diffusers.py deleted file mode 100644 index 833e64ba75e7..000000000000 --- a/scripts/convert_sana_to_diffusers.py +++ /dev/null @@ -1,456 +0,0 @@ -#!/usr/bin/env python -from __future__ import annotations - -import argparse -import os -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download, snapshot_download -from termcolor import colored -from transformers import AutoModelForCausalLM, AutoTokenizer - -from diffusers import ( - AutoencoderDC, - DPMSolverMultistepScheduler, - FlowMatchEulerDiscreteScheduler, - SanaPipeline, - SanaSprintPipeline, - SanaTransformer2DModel, - SCMScheduler, -) -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available else nullcontext - -ckpt_ids = [ - "Efficient-Large-Model/Sana_Sprint_0.6B_1024px/checkpoints/Sana_Sprint_0.6B_1024px.pth" - "Efficient-Large-Model/Sana_Sprint_1.6B_1024px/checkpoints/Sana_Sprint_1.6B_1024px.pth" - "Efficient-Large-Model/SANA1.5_4.8B_1024px/checkpoints/SANA1.5_4.8B_1024px.pth", - "Efficient-Large-Model/SANA1.5_1.6B_1024px/checkpoints/SANA1.5_1.6B_1024px.pth", - "Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth", - "Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth", - "Efficient-Large-Model/Sana_1600M_1024px_MultiLing/checkpoints/Sana_1600M_1024px_MultiLing.pth", - "Efficient-Large-Model/Sana_1600M_1024px_BF16/checkpoints/Sana_1600M_1024px_BF16.pth", - "Efficient-Large-Model/Sana_1600M_512px_MultiLing/checkpoints/Sana_1600M_512px_MultiLing.pth", - "Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth", - "Efficient-Large-Model/Sana_1600M_512px/checkpoints/Sana_1600M_512px.pth", - "Efficient-Large-Model/Sana_600M_1024px/checkpoints/Sana_600M_1024px_MultiLing.pth", - "Efficient-Large-Model/Sana_600M_512px/checkpoints/Sana_600M_512px_MultiLing.pth", -] -# https://github.com/NVlabs/Sana/blob/main/scripts/inference.py - - -def main(args): - cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub") - - if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids: - ckpt_id = args.orig_ckpt_path or ckpt_ids[0] - snapshot_download( - repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", - cache_dir=cache_dir_path, - repo_type="model", - ) - file_path = hf_hub_download( - repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", - filename=f"{'/'.join(ckpt_id.split('/')[2:])}", - cache_dir=cache_dir_path, - repo_type="model", - ) - else: - file_path = args.orig_ckpt_path - - print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"])) - all_state_dict = torch.load(file_path, weights_only=True) - state_dict = all_state_dict.pop("state_dict") - converted_state_dict = {} - - # Patch embeddings. - converted_state_dict["patch_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") - converted_state_dict["patch_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") - - # Caption projection. - converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") - converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") - converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") - converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") - - # Handle different time embedding structure based on model type - - if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: - # For Sana Sprint, the time embedding structure is different - converted_state_dict["time_embed.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["time_embed.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") - converted_state_dict["time_embed.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") - - # Guidance embedder for Sana Sprint - converted_state_dict["time_embed.guidance_embedder.linear_1.weight"] = state_dict.pop( - "cfg_embedder.mlp.0.weight" - ) - converted_state_dict["time_embed.guidance_embedder.linear_1.bias"] = state_dict.pop("cfg_embedder.mlp.0.bias") - converted_state_dict["time_embed.guidance_embedder.linear_2.weight"] = state_dict.pop( - "cfg_embedder.mlp.2.weight" - ) - converted_state_dict["time_embed.guidance_embedder.linear_2.bias"] = state_dict.pop("cfg_embedder.mlp.2.bias") - else: - # Original Sana time embedding structure - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop( - "t_embedder.mlp.0.bias" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop( - "t_embedder.mlp.2.bias" - ) - - # Shared norm. - converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") - converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") - - # y norm - converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") - - # scheduler - if args.image_size == 4096: - flow_shift = 6.0 - else: - flow_shift = 3.0 - - # model config - if args.model_type in ["SanaMS_1600M_P1_D20", "SanaSprint_1600M_P1_D20", "SanaMS1.5_1600M_P1_D20"]: - layer_num = 20 - elif args.model_type in ["SanaMS_600M_P1_D28", "SanaSprint_600M_P1_D28"]: - layer_num = 28 - elif args.model_type == "SanaMS_4800M_P1_D60": - layer_num = 60 - else: - raise ValueError(f"{args.model_type} is not supported.") - # Positional embedding interpolation scale. - interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0} - qk_norm = ( - "rms_norm_across_heads" - if args.model_type - in ["SanaMS1.5_1600M_P1_D20", "SanaMS1.5_4800M_P1_D60", "SanaSprint_600M_P1_D28", "SanaSprint_1600M_P1_D20"] - else None - ) - - for depth in range(layer_num): - # Transformer blocks. - converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( - f"blocks.{depth}.scale_shift_table" - ) - - # Linear Attention is all you need 🤘 - # Self attention. - q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - if qk_norm is not None: - # Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5 - converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop( - f"blocks.{depth}.attn.q_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop( - f"blocks.{depth}.attn.k_norm.weight" - ) - # Projection. - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.attn.proj.bias" - ) - - # Feed-forward. - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.inverted_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.inverted_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.depth_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.depth_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.point_conv.conv.weight" - ) - - # Cross-attention. - q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") - q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") - k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias - if qk_norm is not None: - # Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5 - converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.q_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.k_norm.weight" - ) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.bias" - ) - - # Final block. - converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") - converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") - - # Transformer - with CTX(): - transformer_kwargs = { - "in_channels": 32, - "out_channels": 32, - "num_attention_heads": model_kwargs[args.model_type]["num_attention_heads"], - "attention_head_dim": model_kwargs[args.model_type]["attention_head_dim"], - "num_layers": model_kwargs[args.model_type]["num_layers"], - "num_cross_attention_heads": model_kwargs[args.model_type]["num_cross_attention_heads"], - "cross_attention_head_dim": model_kwargs[args.model_type]["cross_attention_head_dim"], - "cross_attention_dim": model_kwargs[args.model_type]["cross_attention_dim"], - "caption_channels": 2304, - "mlp_ratio": 2.5, - "attention_bias": False, - "sample_size": args.image_size // 32, - "patch_size": 1, - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "interpolation_scale": interpolation_scale[args.image_size], - } - - # Add qk_norm parameter for Sana Sprint - if args.model_type in [ - "SanaMS1.5_1600M_P1_D20", - "SanaMS1.5_4800M_P1_D60", - "SanaSprint_600M_P1_D28", - "SanaSprint_1600M_P1_D20", - ]: - transformer_kwargs["qk_norm"] = "rms_norm_across_heads" - if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: - transformer_kwargs["guidance_embeds"] = True - - transformer = SanaTransformer2DModel(**transformer_kwargs) - - if is_accelerate_available(): - load_model_dict_into_meta(transformer, converted_state_dict) - else: - transformer.load_state_dict(converted_state_dict, strict=True, assign=True) - - try: - state_dict.pop("y_embedder.y_embedding") - state_dict.pop("pos_embed") - state_dict.pop("logvar_linear.weight") - state_dict.pop("logvar_linear.bias") - except KeyError: - print("y_embedder.y_embedding or pos_embed not found in the state_dict") - - assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - transformer = transformer.to(weight_dtype) - - if not args.save_full_pipeline: - print( - colored( - f"Only saving transformer model of {args.model_type}. " - f"Set --save_full_pipeline to save the whole Pipeline", - "green", - attrs=["bold"], - ) - ) - transformer.save_pretrained( - os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB" - ) - else: - print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"])) - # VAE - ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers", torch_dtype=torch.float32) - - # Text Encoder - text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it" - tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path) - tokenizer.padding_side = "right" - text_encoder = AutoModelForCausalLM.from_pretrained( - text_encoder_model_path, torch_dtype=torch.bfloat16 - ).get_decoder() - - # Choose the appropriate pipeline and scheduler based on model type - if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: - # Force SCM Scheduler for Sana Sprint regardless of scheduler_type - if args.scheduler_type != "scm": - print( - colored( - f"Warning: Overriding scheduler_type '{args.scheduler_type}' to 'scm' for SanaSprint model", - "yellow", - attrs=["bold"], - ) - ) - - # SCM Scheduler for Sana Sprint - scheduler_config = { - "prediction_type": "trigflow", - "sigma_data": 0.5, - } - scheduler = SCMScheduler(**scheduler_config) - pipe = SanaSprintPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - transformer=transformer, - vae=ae, - scheduler=scheduler, - ) - else: - # Original Sana scheduler - if args.scheduler_type == "flow-dpm_solver": - scheduler = DPMSolverMultistepScheduler( - flow_shift=flow_shift, - use_flow_sigmas=True, - prediction_type="flow_prediction", - ) - elif args.scheduler_type == "flow-euler": - scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) - else: - raise ValueError(f"Scheduler type {args.scheduler_type} is not supported") - - pipe = SanaPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - transformer=transformer, - vae=ae, - scheduler=scheduler, - ) - - pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB") - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--image_size", - default=1024, - type=int, - choices=[512, 1024, 2048, 4096], - required=False, - help="Image size of pretrained model, 512, 1024, 2048 or 4096.", - ) - parser.add_argument( - "--model_type", - default="SanaMS_1600M_P1_D20", - type=str, - choices=[ - "SanaMS_1600M_P1_D20", - "SanaMS_600M_P1_D28", - "SanaMS1.5_1600M_P1_D20", - "SanaMS1.5_4800M_P1_D60", - "SanaSprint_1600M_P1_D20", - "SanaSprint_600M_P1_D28", - ], - ) - parser.add_argument( - "--scheduler_type", - default="flow-dpm_solver", - type=str, - choices=["flow-dpm_solver", "flow-euler", "scm"], - help="Scheduler type to use. Use 'scm' for Sana Sprint models.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.") - parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") - - args = parser.parse_args() - - model_kwargs = { - "SanaMS_1600M_P1_D20": { - "num_attention_heads": 70, - "attention_head_dim": 32, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "num_layers": 20, - }, - "SanaMS_600M_P1_D28": { - "num_attention_heads": 36, - "attention_head_dim": 32, - "num_cross_attention_heads": 16, - "cross_attention_head_dim": 72, - "cross_attention_dim": 1152, - "num_layers": 28, - }, - "SanaMS1.5_1600M_P1_D20": { - "num_attention_heads": 70, - "attention_head_dim": 32, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "num_layers": 20, - }, - "SanaMS1.5_4800M_P1_D60": { - "num_attention_heads": 70, - "attention_head_dim": 32, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "num_layers": 60, - }, - "SanaSprint_600M_P1_D28": { - "num_attention_heads": 36, - "attention_head_dim": 32, - "num_cross_attention_heads": 16, - "cross_attention_head_dim": 72, - "cross_attention_dim": 1152, - "num_layers": 28, - }, - "SanaSprint_1600M_P1_D20": { - "num_attention_heads": 70, - "attention_head_dim": 32, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "num_layers": 20, - }, - } - - device = "cuda" if torch.cuda.is_available() else "cpu" - weight_dtype = DTYPE_MAPPING[args.dtype] - - main(args) diff --git a/scripts/convert_sana_video_to_diffusers.py b/scripts/convert_sana_video_to_diffusers.py deleted file mode 100644 index c6be52d455b8..000000000000 --- a/scripts/convert_sana_video_to_diffusers.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/env python -from __future__ import annotations - -import argparse -import os -from contextlib import nullcontext - -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download, snapshot_download -from termcolor import colored -from transformers import AutoModelForCausalLM, AutoTokenizer - -from diffusers import ( - AutoencoderKLLTX2Video, - AutoencoderKLWan, - DPMSolverMultistepScheduler, - FlowMatchEulerDiscreteScheduler, - SanaVideoPipeline, - SanaVideoTransformer3DModel, - UniPCMultistepScheduler, -) -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available else nullcontext - -ckpt_ids = [ - "Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth", - "Efficient-Large-Model/SANA-Video_2B_720p/checkpoints/SANA_Video_2B_720p_LTXVAE.pth", -] -# https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py - - -def main(args): - cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub") - - if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids: - ckpt_id = args.orig_ckpt_path or ckpt_ids[0] - snapshot_download( - repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", - cache_dir=cache_dir_path, - repo_type="model", - ) - file_path = hf_hub_download( - repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", - filename=f"{'/'.join(ckpt_id.split('/')[2:])}", - cache_dir=cache_dir_path, - repo_type="model", - ) - else: - file_path = args.orig_ckpt_path - - print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"])) - all_state_dict = torch.load(file_path, weights_only=True) - state_dict = all_state_dict.pop("state_dict") - converted_state_dict = {} - - # Patch embeddings. - converted_state_dict["patch_embedding.weight"] = state_dict.pop("x_embedder.proj.weight") - converted_state_dict["patch_embedding.bias"] = state_dict.pop("x_embedder.proj.bias") - - # Caption projection. - converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") - converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") - converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") - converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") - - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") - - # Shared norm. - converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") - converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") - - # y norm - converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") - - # scheduler - flow_shift = 8.0 - if args.task == "i2v": - assert args.scheduler_type == "flow-euler", "Scheduler type must be flow-euler for i2v task." - - # model config - layer_num = 20 - # Positional embedding interpolation scale. - qk_norm = True - - # sample size - if args.video_size == 480: - sample_size = 30 # Wan-VAE: 8xp2 downsample factor - patch_size = (1, 2, 2) - in_channels = 16 - out_channels = 16 - elif args.video_size == 720: - sample_size = 22 # DC-AE-V: 32xp1 downsample factor - patch_size = (1, 1, 1) - in_channels = 32 - out_channels = 32 - else: - raise ValueError(f"Video size {args.video_size} is not supported.") - - if args.vae_type == "ltx2": - sample_size = 22 - patch_size = (1, 1, 1) - in_channels = 128 - out_channels = 128 - - for depth in range(layer_num): - # Transformer blocks. - converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( - f"blocks.{depth}.scale_shift_table" - ) - - # Linear Attention is all you need 🤘 - # Self attention. - q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v - if qk_norm is not None: - # Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5 - converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop( - f"blocks.{depth}.attn.q_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop( - f"blocks.{depth}.attn.k_norm.weight" - ) - # Projection. - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.attn.proj.bias" - ) - - # Feed-forward. - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.inverted_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.inverted_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.depth_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( - f"blocks.{depth}.mlp.depth_conv.conv.bias" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.point_conv.conv.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.ff.conv_temp.weight"] = state_dict.pop( - f"blocks.{depth}.mlp.t_conv.weight" - ) - - # Cross-attention. - q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") - q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") - k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) - k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias - if qk_norm is not None: - # Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5 - converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.q_norm.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.k_norm.weight" - ) - - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( - f"blocks.{depth}.cross_attn.proj.bias" - ) - - # Final block. - converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") - converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") - - # Transformer - with CTX(): - transformer_kwargs = { - "in_channels": in_channels, - "out_channels": out_channels, - "num_attention_heads": 20, - "attention_head_dim": 112, - "num_layers": 20, - "num_cross_attention_heads": 20, - "cross_attention_head_dim": 112, - "cross_attention_dim": 2240, - "caption_channels": 2304, - "mlp_ratio": 3.0, - "attention_bias": False, - "sample_size": sample_size, - "patch_size": patch_size, - "norm_elementwise_affine": False, - "norm_eps": 1e-6, - "qk_norm": "rms_norm_across_heads", - "rope_max_seq_len": 1024, - } - - transformer = SanaVideoTransformer3DModel(**transformer_kwargs) - - transformer.load_state_dict(converted_state_dict, strict=True, assign=True) - - try: - state_dict.pop("y_embedder.y_embedding") - state_dict.pop("pos_embed") - state_dict.pop("logvar_linear.weight") - state_dict.pop("logvar_linear.bias") - except KeyError: - print("y_embedder.y_embedding or pos_embed not found in the state_dict") - - assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" - - num_model_params = sum(p.numel() for p in transformer.parameters()) - print(f"Total number of transformer parameters: {num_model_params}") - - transformer = transformer.to(weight_dtype) - - if not args.save_full_pipeline: - print( - colored( - f"Only saving transformer model of {args.model_type}. " - f"Set --save_full_pipeline to save the whole Pipeline", - "green", - attrs=["bold"], - ) - ) - transformer.save_pretrained( - os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB" - ) - else: - print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"])) - # VAE - if args.vae_type == "ltx2": - vae_path = args.vae_path or "Lightricks/LTX-2" - vae = AutoencoderKLLTX2Video.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32) - else: - vae_path = args.vae_path or "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" - vae = AutoencoderKLWan.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32) - - # Text Encoder - text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it" - tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path) - tokenizer.padding_side = "right" - text_encoder = AutoModelForCausalLM.from_pretrained( - text_encoder_model_path, torch_dtype=torch.bfloat16 - ).get_decoder() - - # Choose the appropriate pipeline and scheduler based on model type - # Original Sana scheduler - if args.scheduler_type == "flow-dpm_solver": - scheduler = DPMSolverMultistepScheduler( - flow_shift=flow_shift, - use_flow_sigmas=True, - prediction_type="flow_prediction", - ) - elif args.scheduler_type == "flow-euler": - scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) - elif args.scheduler_type == "uni-pc": - scheduler = UniPCMultistepScheduler( - prediction_type="flow_prediction", - use_flow_sigmas=True, - num_train_timesteps=1000, - flow_shift=flow_shift, - ) - else: - raise ValueError(f"Scheduler type {args.scheduler_type} is not supported") - - pipe = SanaVideoPipeline( - tokenizer=tokenizer, - text_encoder=text_encoder, - transformer=transformer, - vae=vae, - scheduler=scheduler, - ) - - pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB") - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--video_size", - default=480, - type=int, - choices=[480, 720], - required=False, - help="Video size of pretrained model, 480 or 720.", - ) - parser.add_argument( - "--model_type", - default="SanaVideo", - type=str, - choices=[ - "SanaVideo", - ], - ) - parser.add_argument( - "--scheduler_type", - default="flow-dpm_solver", - type=str, - choices=["flow-dpm_solver", "flow-euler", "uni-pc"], - help="Scheduler type to use.", - ) - parser.add_argument( - "--vae_type", - default="wan", - type=str, - choices=["wan", "ltx2"], - help="VAE type to use for saving full pipeline (ltx2 uses patchify 1x1x1).", - ) - parser.add_argument( - "--vae_path", - default=None, - type=str, - required=False, - help="Optional VAE path or repo id. If not set, a default is used per VAE type.", - ) - parser.add_argument( - "--task", default="t2v", type=str, required=True, choices=["t2v", "i2v"], help="Task to convert, t2v or i2v." - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") - parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.") - parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") - - args = parser.parse_args() - - device = "cuda" if torch.cuda.is_available() else "cpu" - weight_dtype = DTYPE_MAPPING[args.dtype] - - main(args) diff --git a/scripts/convert_sd3_controlnet_to_diffusers.py b/scripts/convert_sd3_controlnet_to_diffusers.py deleted file mode 100644 index 171f40a7aa06..000000000000 --- a/scripts/convert_sd3_controlnet_to_diffusers.py +++ /dev/null @@ -1,185 +0,0 @@ -""" -A script to convert Stable Diffusion 3.5 ControlNet checkpoints to the Diffusers format. - -Example: - Convert a SD3.5 ControlNet checkpoint to Diffusers format using local file: - ```bash - python scripts/convert_sd3_controlnet_to_diffusers.py \ - --checkpoint_path "path/to/local/sd3.5_large_controlnet_canny.safetensors" \ - --output_path "output/sd35-controlnet-canny" \ - --dtype "fp16" # optional, defaults to fp32 - ``` - - Or download and convert from HuggingFace repository: - ```bash - python scripts/convert_sd3_controlnet_to_diffusers.py \ - --original_state_dict_repo_id "stabilityai/stable-diffusion-3.5-controlnets" \ - --filename "sd3.5_large_controlnet_canny.safetensors" \ - --output_path "/raid/yiyi/sd35-controlnet-canny-diffusers" \ - --dtype "fp32" # optional, defaults to fp32 - ``` - -Note: - The script supports the following ControlNet types from SD3.5: - - Canny edge detection - - Depth estimation - - Blur detection - - The checkpoint files can be downloaded from: - https://huggingface.co/stabilityai/stable-diffusion-3.5-controlnets -""" - -import argparse - -import safetensors.torch -import torch -from huggingface_hub import hf_hub_download - -from diffusers import SD3ControlNetModel - - -parser = argparse.ArgumentParser() -parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to local checkpoint file") -parser.add_argument( - "--original_state_dict_repo_id", type=str, default=None, help="HuggingFace repo ID containing the checkpoint" -) -parser.add_argument("--filename", type=str, default=None, help="Filename of the checkpoint in the HF repo") -parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model") -parser.add_argument( - "--dtype", type=str, default="fp32", help="Data type for the converted model (fp16, bf16, or fp32)" -) - -args = parser.parse_args() - - -def load_original_checkpoint(args): - if args.original_state_dict_repo_id is not None: - if args.filename is None: - raise ValueError("When using `original_state_dict_repo_id`, `filename` must also be specified") - print(f"Downloading checkpoint from {args.original_state_dict_repo_id}/{args.filename}") - ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) - elif args.checkpoint_path is not None: - print(f"Loading checkpoint from local path: {args.checkpoint_path}") - ckpt_path = args.checkpoint_path - else: - raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") - - original_state_dict = safetensors.torch.load_file(ckpt_path) - return original_state_dict - - -def convert_sd3_controlnet_checkpoint_to_diffusers(original_state_dict): - converted_state_dict = {} - - # Direct mappings for controlnet blocks - for i in range(19): # 19 controlnet blocks - converted_state_dict[f"controlnet_blocks.{i}.weight"] = original_state_dict[f"controlnet_blocks.{i}.weight"] - converted_state_dict[f"controlnet_blocks.{i}.bias"] = original_state_dict[f"controlnet_blocks.{i}.bias"] - - # Positional embeddings - converted_state_dict["pos_embed_input.proj.weight"] = original_state_dict["pos_embed_input.proj.weight"] - converted_state_dict["pos_embed_input.proj.bias"] = original_state_dict["pos_embed_input.proj.bias"] - - # Time and text embeddings - time_text_mappings = { - "time_text_embed.timestep_embedder.linear_1.weight": "time_text_embed.timestep_embedder.linear_1.weight", - "time_text_embed.timestep_embedder.linear_1.bias": "time_text_embed.timestep_embedder.linear_1.bias", - "time_text_embed.timestep_embedder.linear_2.weight": "time_text_embed.timestep_embedder.linear_2.weight", - "time_text_embed.timestep_embedder.linear_2.bias": "time_text_embed.timestep_embedder.linear_2.bias", - "time_text_embed.text_embedder.linear_1.weight": "time_text_embed.text_embedder.linear_1.weight", - "time_text_embed.text_embedder.linear_1.bias": "time_text_embed.text_embedder.linear_1.bias", - "time_text_embed.text_embedder.linear_2.weight": "time_text_embed.text_embedder.linear_2.weight", - "time_text_embed.text_embedder.linear_2.bias": "time_text_embed.text_embedder.linear_2.bias", - } - - for new_key, old_key in time_text_mappings.items(): - if old_key in original_state_dict: - converted_state_dict[new_key] = original_state_dict[old_key] - - # Transformer blocks - for i in range(19): - # Split QKV into separate Q, K, V - qkv_weight = original_state_dict[f"transformer_blocks.{i}.attn.qkv.weight"] - qkv_bias = original_state_dict[f"transformer_blocks.{i}.attn.qkv.bias"] - q, k, v = torch.chunk(qkv_weight, 3, dim=0) - q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) - - block_mappings = { - f"transformer_blocks.{i}.attn.to_q.weight": q, - f"transformer_blocks.{i}.attn.to_q.bias": q_bias, - f"transformer_blocks.{i}.attn.to_k.weight": k, - f"transformer_blocks.{i}.attn.to_k.bias": k_bias, - f"transformer_blocks.{i}.attn.to_v.weight": v, - f"transformer_blocks.{i}.attn.to_v.bias": v_bias, - # Output projections - f"transformer_blocks.{i}.attn.to_out.0.weight": original_state_dict[ - f"transformer_blocks.{i}.attn.proj.weight" - ], - f"transformer_blocks.{i}.attn.to_out.0.bias": original_state_dict[ - f"transformer_blocks.{i}.attn.proj.bias" - ], - # Feed forward - f"transformer_blocks.{i}.ff.net.0.proj.weight": original_state_dict[ - f"transformer_blocks.{i}.mlp.fc1.weight" - ], - f"transformer_blocks.{i}.ff.net.0.proj.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc1.bias"], - f"transformer_blocks.{i}.ff.net.2.weight": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.weight"], - f"transformer_blocks.{i}.ff.net.2.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.bias"], - # Norms - f"transformer_blocks.{i}.norm1.linear.weight": original_state_dict[ - f"transformer_blocks.{i}.adaLN_modulation.1.weight" - ], - f"transformer_blocks.{i}.norm1.linear.bias": original_state_dict[ - f"transformer_blocks.{i}.adaLN_modulation.1.bias" - ], - } - converted_state_dict.update(block_mappings) - - return converted_state_dict - - -def main(args): - original_ckpt = load_original_checkpoint(args) - original_dtype = next(iter(original_ckpt.values())).dtype - - # Initialize dtype with fp32 as default - if args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}. Must be one of: fp16, bf16, fp32") - - if dtype != original_dtype: - print( - f"Converting checkpoint from {original_dtype} to {dtype}. This can lead to unexpected results, proceed with caution." - ) - - converted_controlnet_state_dict = convert_sd3_controlnet_checkpoint_to_diffusers(original_ckpt) - - controlnet = SD3ControlNetModel( - patch_size=2, - in_channels=16, - num_layers=19, - attention_head_dim=64, - num_attention_heads=38, - joint_attention_dim=None, - caption_projection_dim=2048, - pooled_projection_dim=2048, - out_channels=16, - pos_embed_max_size=None, - pos_embed_type=None, - use_pos_embed=False, - force_zeros_for_pooled_projection=False, - ) - - controlnet.load_state_dict(converted_controlnet_state_dict, strict=True) - - print(f"Saving SD3 ControlNet in Diffusers format in {args.output_path}.") - controlnet.to(dtype).save_pretrained(args.output_path) - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_sd3_to_diffusers.py b/scripts/convert_sd3_to_diffusers.py deleted file mode 100644 index 83cb436e6e32..000000000000 --- a/scripts/convert_sd3_to_diffusers.py +++ /dev/null @@ -1,351 +0,0 @@ -import argparse -from contextlib import nullcontext - -import safetensors.torch -import torch -from accelerate import init_empty_weights - -from diffusers import AutoencoderKL, SD3Transformer2DModel -from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.utils.import_utils import is_accelerate_available - - -CTX = init_empty_weights if is_accelerate_available() else nullcontext - -parser = argparse.ArgumentParser() -parser.add_argument("--checkpoint_path", type=str) -parser.add_argument("--output_path", type=str) -parser.add_argument("--dtype", type=str) - -args = parser.parse_args() - - -def load_original_checkpoint(ckpt_path): - original_state_dict = safetensors.torch.load_file(ckpt_path) - keys = list(original_state_dict.keys()) - for k in keys: - if "model.diffusion_model." in k: - original_state_dict[k.replace("model.diffusion_model.", "")] = original_state_dict.pop(k) - - return original_state_dict - - -# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; -# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation -def swap_scale_shift(weight, dim): - shift, scale = weight.chunk(2, dim=0) - new_weight = torch.cat([scale, shift], dim=0) - return new_weight - - -def convert_sd3_transformer_checkpoint_to_diffusers( - original_state_dict, num_layers, caption_projection_dim, dual_attention_layers, has_qk_norm -): - converted_state_dict = {} - - # Positional and patch embeddings. - converted_state_dict["pos_embed.pos_embed"] = original_state_dict.pop("pos_embed") - converted_state_dict["pos_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight") - converted_state_dict["pos_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias") - - # Timestep embeddings. - converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( - "t_embedder.mlp.0.weight" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( - "t_embedder.mlp.0.bias" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( - "t_embedder.mlp.2.weight" - ) - converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( - "t_embedder.mlp.2.bias" - ) - - # Context projections. - converted_state_dict["context_embedder.weight"] = original_state_dict.pop("context_embedder.weight") - converted_state_dict["context_embedder.bias"] = original_state_dict.pop("context_embedder.bias") - - # Pooled context projection. - converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( - "y_embedder.mlp.0.weight" - ) - converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( - "y_embedder.mlp.0.bias" - ) - converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( - "y_embedder.mlp.2.weight" - ) - converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( - "y_embedder.mlp.2.bias" - ) - - # Transformer blocks 🎸. - for i in range(num_layers): - # Q, K, V - sample_q, sample_k, sample_v = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.weight"), 3, dim=0 - ) - context_q, context_k, context_v = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.weight"), 3, dim=0 - ) - sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.bias"), 3, dim=0 - ) - context_q_bias, context_k_bias, context_v_bias = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.bias"), 3, dim=0 - ) - - converted_state_dict[f"transformer_blocks.{i}.attn.to_q.weight"] = torch.cat([sample_q]) - converted_state_dict[f"transformer_blocks.{i}.attn.to_q.bias"] = torch.cat([sample_q_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn.to_k.weight"] = torch.cat([sample_k]) - converted_state_dict[f"transformer_blocks.{i}.attn.to_k.bias"] = torch.cat([sample_k_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn.to_v.weight"] = torch.cat([sample_v]) - converted_state_dict[f"transformer_blocks.{i}.attn.to_v.bias"] = torch.cat([sample_v_bias]) - - converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.weight"] = torch.cat([context_q]) - converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.bias"] = torch.cat([context_q_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.weight"] = torch.cat([context_k]) - converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.bias"] = torch.cat([context_k_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.weight"] = torch.cat([context_v]) - converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.bias"] = torch.cat([context_v_bias]) - - # qk norm - if has_qk_norm: - converted_state_dict[f"transformer_blocks.{i}.attn.norm_q.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn.ln_q.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn.norm_k.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn.ln_k.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_q.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.attn.ln_q.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_k.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.attn.ln_k.weight" - ) - - # output projections. - converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn.proj.bias" - ) - if not (i == num_layers - 1): - converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.attn.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.attn.proj.bias" - ) - - # attn2 - if i in dual_attention_layers: - # Q, K, V - sample_q2, sample_k2, sample_v2 = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.weight"), 3, dim=0 - ) - sample_q2_bias, sample_k2_bias, sample_v2_bias = torch.chunk( - original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.bias"), 3, dim=0 - ) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.weight"] = torch.cat([sample_q2]) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.bias"] = torch.cat([sample_q2_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.weight"] = torch.cat([sample_k2]) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.bias"] = torch.cat([sample_k2_bias]) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.weight"] = torch.cat([sample_v2]) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.bias"] = torch.cat([sample_v2_bias]) - - # qk norm - if has_qk_norm: - converted_state_dict[f"transformer_blocks.{i}.attn2.norm_q.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn2.ln_q.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn2.norm_k.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn2.ln_k.weight" - ) - - # output projections. - converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn2.proj.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.attn2.proj.bias" - ) - - # norms. - converted_state_dict[f"transformer_blocks.{i}.norm1.linear.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.adaLN_modulation.1.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.norm1.linear.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.adaLN_modulation.1.bias" - ) - if not (i == num_layers - 1): - converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias" - ) - else: - converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = swap_scale_shift( - original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"), - dim=caption_projection_dim, - ) - converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = swap_scale_shift( - original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"), - dim=caption_projection_dim, - ) - - # ffs. - converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.mlp.fc1.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.mlp.fc1.bias" - ) - converted_state_dict[f"transformer_blocks.{i}.ff.net.2.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.mlp.fc2.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.ff.net.2.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.x_block.mlp.fc2.bias" - ) - if not (i == num_layers - 1): - converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.mlp.fc1.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.mlp.fc1.bias" - ) - converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.weight"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.mlp.fc2.weight" - ) - converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.bias"] = original_state_dict.pop( - f"joint_blocks.{i}.context_block.mlp.fc2.bias" - ) - - # Final blocks. - converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") - converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") - converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.weight"), dim=caption_projection_dim - ) - converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( - original_state_dict.pop("final_layer.adaLN_modulation.1.bias"), dim=caption_projection_dim - ) - - return converted_state_dict - - -def is_vae_in_checkpoint(original_state_dict): - return ("first_stage_model.decoder.conv_in.weight" in original_state_dict) and ( - "first_stage_model.encoder.conv_in.weight" in original_state_dict - ) - - -def get_attn2_layers(state_dict): - attn2_layers = [] - for key in state_dict.keys(): - if "attn2." in key: - # Extract the layer number from the key - layer_num = int(key.split(".")[1]) - attn2_layers.append(layer_num) - return tuple(sorted(set(attn2_layers))) - - -def get_pos_embed_max_size(state_dict): - num_patches = state_dict["pos_embed"].shape[1] - pos_embed_max_size = int(num_patches**0.5) - return pos_embed_max_size - - -def get_caption_projection_dim(state_dict): - caption_projection_dim = state_dict["context_embedder.weight"].shape[0] - return caption_projection_dim - - -def main(args): - original_ckpt = load_original_checkpoint(args.checkpoint_path) - original_dtype = next(iter(original_ckpt.values())).dtype - - # Initialize dtype with a default value - dtype = None - - if args.dtype is None: - dtype = original_dtype - elif args.dtype == "fp16": - dtype = torch.float16 - elif args.dtype == "bf16": - dtype = torch.bfloat16 - elif args.dtype == "fp32": - dtype = torch.float32 - else: - raise ValueError(f"Unsupported dtype: {args.dtype}") - - if dtype != original_dtype: - print( - f"Checkpoint dtype {original_dtype} does not match requested dtype {dtype}. This can lead to unexpected results, proceed with caution." - ) - - num_layers = list(set(int(k.split(".", 2)[1]) for k in original_ckpt if "joint_blocks" in k))[-1] + 1 # noqa: C401 - - caption_projection_dim = get_caption_projection_dim(original_ckpt) - - # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5 - attn2_layers = get_attn2_layers(original_ckpt) - - # sd3.5 use qk norm("rms_norm") - has_qk_norm = any("ln_q" in key for key in original_ckpt.keys()) - - # sd3.5 2b use pox_embed_max_size=384 and sd3.0 and sd3.5 8b use 192 - pos_embed_max_size = get_pos_embed_max_size(original_ckpt) - - converted_transformer_state_dict = convert_sd3_transformer_checkpoint_to_diffusers( - original_ckpt, num_layers, caption_projection_dim, attn2_layers, has_qk_norm - ) - - with CTX(): - transformer = SD3Transformer2DModel( - sample_size=128, - patch_size=2, - in_channels=16, - joint_attention_dim=4096, - num_layers=num_layers, - caption_projection_dim=caption_projection_dim, - num_attention_heads=num_layers, - pos_embed_max_size=pos_embed_max_size, - qk_norm="rms_norm" if has_qk_norm else None, - dual_attention_layers=attn2_layers, - ) - if is_accelerate_available(): - load_model_dict_into_meta(transformer, converted_transformer_state_dict) - else: - transformer.load_state_dict(converted_transformer_state_dict, strict=True) - - print("Saving SD3 Transformer in Diffusers format.") - transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") - - if is_vae_in_checkpoint(original_ckpt): - with CTX(): - vae = AutoencoderKL.from_config( - "stabilityai/stable-diffusion-xl-base-1.0", - subfolder="vae", - latent_channels=16, - use_post_quant_conv=False, - use_quant_conv=False, - scaling_factor=1.5305, - shift_factor=0.0609, - ) - converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config) - if is_accelerate_available(): - load_model_dict_into_meta(vae, converted_vae_state_dict) - else: - vae.load_state_dict(converted_vae_state_dict, strict=True) - - print("Saving SD3 Autoencoder in Diffusers format.") - vae.to(dtype).save_pretrained(f"{args.output_path}/vae") - - -if __name__ == "__main__": - main(args) diff --git a/scripts/convert_shap_e_to_diffusers.py b/scripts/convert_shap_e_to_diffusers.py deleted file mode 100644 index ac6543667af9..000000000000 --- a/scripts/convert_shap_e_to_diffusers.py +++ /dev/null @@ -1,1080 +0,0 @@ -import argparse -import tempfile - -import torch -from accelerate import load_checkpoint_and_dispatch - -from diffusers.models.transformers.prior_transformer import PriorTransformer -from diffusers.pipelines.shap_e import ShapERenderer - - -""" -Example - From the diffusers root directory: - -Download weights: -```sh -$ wget "https://openaipublic.azureedge.net/main/shap-e/text_cond.pt" -``` - -Convert the model: -```sh -$ python scripts/convert_shap_e_to_diffusers.py \ - --prior_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/text_cond.pt \ - --prior_image_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/image_cond.pt \ - --transmitter_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/transmitter.pt\ - --dump_path /home/yiyi_huggingface_co/model_repo/shap-e-img2img/shap_e_renderer\ - --debug renderer -``` -""" - - -# prior - -PRIOR_ORIGINAL_PREFIX = "wrapped" - -PRIOR_CONFIG = { - "num_attention_heads": 16, - "attention_head_dim": 1024 // 16, - "num_layers": 24, - "embedding_dim": 1024, - "num_embeddings": 1024, - "additional_embeddings": 0, - "time_embed_act_fn": "gelu", - "norm_in_type": "layer", - "encoder_hid_proj_type": None, - "added_emb_type": None, - "time_embed_dim": 1024 * 4, - "embedding_proj_dim": 768, - "clip_embed_dim": 1024 * 2, -} - - -def prior_model_from_original_config(): - model = PriorTransformer(**PRIOR_CONFIG) - - return model - - -def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1 - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.weight"], - "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.bias"], - } - ) - - # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2 - diffusers_checkpoint.update( - { - "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.weight"], - "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.bias"], - } - ) - - # <original>.input_proj -> <diffusers>.proj_in - diffusers_checkpoint.update( - { - "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.weight"], - "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.bias"], - } - ) - - # <original>.clip_emb -> <diffusers>.embedding_proj - diffusers_checkpoint.update( - { - "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.weight"], - "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.bias"], - } - ) - - # <original>.pos_emb -> <diffusers>.positional_embedding - diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.pos_emb"][None, :]}) - - # <original>.ln_pre -> <diffusers>.norm_in - diffusers_checkpoint.update( - { - "norm_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.weight"], - "norm_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.bias"], - } - ) - - # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x> - for idx in range(len(model.transformer_blocks)): - diffusers_transformer_prefix = f"transformer_blocks.{idx}" - original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.backbone.resblocks.{idx}" - - # <original>.attn -> <diffusers>.attn1 - diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" - original_attention_prefix = f"{original_transformer_prefix}.attn" - diffusers_checkpoint.update( - prior_attention_to_diffusers( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - original_attention_prefix=original_attention_prefix, - attention_head_dim=model.attention_head_dim, - ) - ) - - # <original>.mlp -> <diffusers>.ff - diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" - original_ff_prefix = f"{original_transformer_prefix}.mlp" - diffusers_checkpoint.update( - prior_ff_to_diffusers( - checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix - ) - ) - - # <original>.ln_1 -> <diffusers>.norm1 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ - f"{original_transformer_prefix}.ln_1.weight" - ], - f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], - } - ) - - # <original>.ln_2 -> <diffusers>.norm3 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ - f"{original_transformer_prefix}.ln_2.weight" - ], - f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], - } - ) - - # <original>.ln_post -> <diffusers>.norm_out - diffusers_checkpoint.update( - { - "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.weight"], - "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.bias"], - } - ) - - # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings - diffusers_checkpoint.update( - { - "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.weight"], - "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.bias"], - } - ) - - return diffusers_checkpoint - - -def prior_attention_to_diffusers( - checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim -): - diffusers_checkpoint = {} - - # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} - [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( - weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], - bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], - split=3, - chunk_size=attention_head_dim, - ) - - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_q.weight": q_weight, - f"{diffusers_attention_prefix}.to_q.bias": q_bias, - f"{diffusers_attention_prefix}.to_k.weight": k_weight, - f"{diffusers_attention_prefix}.to_k.bias": k_bias, - f"{diffusers_attention_prefix}.to_v.weight": v_weight, - f"{diffusers_attention_prefix}.to_v.bias": v_bias, - } - ) - - # <original>.c_proj -> <diffusers>.to_out.0 - diffusers_checkpoint.update( - { - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], - } - ) - - return diffusers_checkpoint - - -def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): - diffusers_checkpoint = { - # <original>.c_fc -> <diffusers>.net.0.proj - f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], - f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], - # <original>.c_proj -> <diffusers>.net.2 - f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], - f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], - } - - return diffusers_checkpoint - - -# done prior - - -# prior_image (only slightly different from prior) - - -PRIOR_IMAGE_ORIGINAL_PREFIX = "wrapped" - -# Uses default arguments -PRIOR_IMAGE_CONFIG = { - "num_attention_heads": 8, - "attention_head_dim": 1024 // 8, - "num_layers": 24, - "embedding_dim": 1024, - "num_embeddings": 1024, - "additional_embeddings": 0, - "time_embed_act_fn": "gelu", - "norm_in_type": "layer", - "embedding_proj_norm_type": "layer", - "encoder_hid_proj_type": None, - "added_emb_type": None, - "time_embed_dim": 1024 * 4, - "embedding_proj_dim": 1024, - "clip_embed_dim": 1024 * 2, -} - - -def prior_image_model_from_original_config(): - model = PriorTransformer(**PRIOR_IMAGE_CONFIG) - - return model - - -def prior_image_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1 - diffusers_checkpoint.update( - { - "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.weight"], - "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.bias"], - } - ) - - # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2 - diffusers_checkpoint.update( - { - "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.weight"], - "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.bias"], - } - ) - - # <original>.input_proj -> <diffusers>.proj_in - diffusers_checkpoint.update( - { - "proj_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.weight"], - "proj_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.bias"], - } - ) - - # <original>.clip_embed.0 -> <diffusers>.embedding_proj_norm - diffusers_checkpoint.update( - { - "embedding_proj_norm.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.weight"], - "embedding_proj_norm.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.bias"], - } - ) - - # <original>..clip_embed.1 -> <diffusers>.embedding_proj - diffusers_checkpoint.update( - { - "embedding_proj.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.weight"], - "embedding_proj.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.bias"], - } - ) - - # <original>.pos_emb -> <diffusers>.positional_embedding - diffusers_checkpoint.update( - {"positional_embedding": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.pos_emb"][None, :]} - ) - - # <original>.ln_pre -> <diffusers>.norm_in - diffusers_checkpoint.update( - { - "norm_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.weight"], - "norm_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.bias"], - } - ) - - # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x> - for idx in range(len(model.transformer_blocks)): - diffusers_transformer_prefix = f"transformer_blocks.{idx}" - original_transformer_prefix = f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.backbone.resblocks.{idx}" - - # <original>.attn -> <diffusers>.attn1 - diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" - original_attention_prefix = f"{original_transformer_prefix}.attn" - diffusers_checkpoint.update( - prior_attention_to_diffusers( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - original_attention_prefix=original_attention_prefix, - attention_head_dim=model.attention_head_dim, - ) - ) - - # <original>.mlp -> <diffusers>.ff - diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" - original_ff_prefix = f"{original_transformer_prefix}.mlp" - diffusers_checkpoint.update( - prior_ff_to_diffusers( - checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix - ) - ) - - # <original>.ln_1 -> <diffusers>.norm1 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ - f"{original_transformer_prefix}.ln_1.weight" - ], - f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], - } - ) - - # <original>.ln_2 -> <diffusers>.norm3 - diffusers_checkpoint.update( - { - f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ - f"{original_transformer_prefix}.ln_2.weight" - ], - f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], - } - ) - - # <original>.ln_post -> <diffusers>.norm_out - diffusers_checkpoint.update( - { - "norm_out.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.weight"], - "norm_out.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.bias"], - } - ) - - # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings - diffusers_checkpoint.update( - { - "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.weight"], - "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.bias"], - } - ) - - return diffusers_checkpoint - - -# done prior_image - - -# renderer - -## create the lookup table for marching cubes method used in MeshDecoder - -MC_TABLE = [ - [], - [[0, 1, 0, 2, 0, 4]], - [[1, 0, 1, 5, 1, 3]], - [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2]], - [[2, 0, 2, 3, 2, 6]], - [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4]], - [[1, 0, 1, 5, 1, 3], [2, 6, 0, 2, 3, 2]], - [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4]], - [[3, 1, 3, 7, 3, 2]], - [[0, 2, 0, 4, 0, 1], [3, 7, 2, 3, 1, 3]], - [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0]], - [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5]], - [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6]], - [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6]], - [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7]], - [[0, 4, 1, 5, 3, 7], [0, 4, 3, 7, 2, 6]], - [[4, 0, 4, 6, 4, 5]], - [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1]], - [[1, 5, 1, 3, 1, 0], [4, 6, 5, 4, 0, 4]], - [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2]], - [[2, 0, 2, 3, 2, 6], [4, 5, 0, 4, 6, 4]], - [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1]], - [[2, 6, 2, 0, 3, 2], [1, 0, 1, 5, 3, 1], [6, 4, 5, 4, 0, 4]], - [[1, 3, 5, 4, 1, 5], [1, 3, 4, 6, 5, 4], [1, 3, 3, 2, 4, 6], [3, 2, 2, 6, 4, 6]], - [[3, 1, 3, 7, 3, 2], [6, 4, 5, 4, 0, 4]], - [[4, 5, 0, 1, 4, 6], [0, 1, 0, 2, 4, 6], [7, 3, 2, 3, 1, 3]], - [[3, 2, 1, 0, 3, 7], [1, 0, 1, 5, 3, 7], [6, 4, 5, 4, 0, 4]], - [[3, 7, 3, 2, 1, 5], [3, 2, 6, 4, 1, 5], [1, 5, 6, 4, 5, 4], [3, 2, 2, 0, 6, 4]], - [[3, 7, 2, 6, 3, 1], [2, 6, 2, 0, 3, 1], [5, 4, 0, 4, 6, 4]], - [[1, 0, 1, 3, 5, 4], [1, 3, 2, 6, 5, 4], [1, 3, 3, 7, 2, 6], [5, 4, 2, 6, 4, 6]], - [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7], [4, 5, 0, 4, 4, 6]], - [[6, 2, 4, 6, 4, 5], [4, 5, 5, 1, 6, 2], [6, 2, 5, 1, 7, 3]], - [[5, 1, 5, 4, 5, 7]], - [[0, 1, 0, 2, 0, 4], [5, 7, 1, 5, 4, 5]], - [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3]], - [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3]], - [[2, 0, 2, 3, 2, 6], [7, 5, 1, 5, 4, 5]], - [[2, 6, 0, 4, 2, 3], [0, 4, 0, 1, 2, 3], [7, 5, 1, 5, 4, 5]], - [[5, 7, 1, 3, 5, 4], [1, 3, 1, 0, 5, 4], [6, 2, 0, 2, 3, 2]], - [[3, 1, 3, 2, 7, 5], [3, 2, 0, 4, 7, 5], [3, 2, 2, 6, 0, 4], [7, 5, 0, 4, 5, 4]], - [[3, 7, 3, 2, 3, 1], [5, 4, 7, 5, 1, 5]], - [[0, 4, 0, 1, 2, 0], [3, 1, 3, 7, 2, 3], [4, 5, 7, 5, 1, 5]], - [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0]], - [[0, 4, 2, 3, 0, 2], [0, 4, 3, 7, 2, 3], [0, 4, 4, 5, 3, 7], [4, 5, 5, 7, 3, 7]], - [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6], [4, 5, 7, 5, 1, 5]], - [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6], [5, 7, 1, 5, 5, 4]], - [[2, 6, 2, 0, 3, 7], [2, 0, 4, 5, 3, 7], [3, 7, 4, 5, 7, 5], [2, 0, 0, 1, 4, 5]], - [[4, 0, 5, 4, 5, 7], [5, 7, 7, 3, 4, 0], [4, 0, 7, 3, 6, 2]], - [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0]], - [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6]], - [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7]], - [[0, 2, 4, 6, 5, 7], [0, 2, 5, 7, 1, 3]], - [[5, 1, 4, 0, 5, 7], [4, 0, 4, 6, 5, 7], [3, 2, 6, 2, 0, 2]], - [[2, 3, 2, 6, 0, 1], [2, 6, 7, 5, 0, 1], [0, 1, 7, 5, 1, 5], [2, 6, 6, 4, 7, 5]], - [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7], [2, 6, 0, 2, 2, 3]], - [[3, 1, 2, 3, 2, 6], [2, 6, 6, 4, 3, 1], [3, 1, 6, 4, 7, 5]], - [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0], [2, 3, 1, 3, 7, 3]], - [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6], [3, 2, 1, 3, 3, 7]], - [[0, 1, 0, 4, 2, 3], [0, 4, 5, 7, 2, 3], [0, 4, 4, 6, 5, 7], [2, 3, 5, 7, 3, 7]], - [[7, 5, 3, 7, 3, 2], [3, 2, 2, 0, 7, 5], [7, 5, 2, 0, 6, 4]], - [[0, 4, 4, 6, 5, 7], [0, 4, 5, 7, 1, 5], [0, 2, 1, 3, 3, 7], [3, 7, 2, 6, 0, 2]], - [ - [3, 1, 7, 3, 6, 2], - [6, 2, 0, 1, 3, 1], - [6, 4, 0, 1, 6, 2], - [6, 4, 5, 1, 0, 1], - [6, 4, 7, 5, 5, 1], - ], - [ - [4, 0, 6, 4, 7, 5], - [7, 5, 1, 0, 4, 0], - [7, 3, 1, 0, 7, 5], - [7, 3, 2, 0, 1, 0], - [7, 3, 6, 2, 2, 0], - ], - [[7, 3, 6, 2, 6, 4], [7, 5, 7, 3, 6, 4]], - [[6, 2, 6, 7, 6, 4]], - [[0, 4, 0, 1, 0, 2], [6, 7, 4, 6, 2, 6]], - [[1, 0, 1, 5, 1, 3], [7, 6, 4, 6, 2, 6]], - [[1, 3, 0, 2, 1, 5], [0, 2, 0, 4, 1, 5], [7, 6, 4, 6, 2, 6]], - [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0]], - [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3]], - [[6, 4, 2, 0, 6, 7], [2, 0, 2, 3, 6, 7], [5, 1, 3, 1, 0, 1]], - [[1, 5, 1, 3, 0, 4], [1, 3, 7, 6, 0, 4], [0, 4, 7, 6, 4, 6], [1, 3, 3, 2, 7, 6]], - [[3, 2, 3, 1, 3, 7], [6, 4, 2, 6, 7, 6]], - [[3, 7, 3, 2, 1, 3], [0, 2, 0, 4, 1, 0], [7, 6, 4, 6, 2, 6]], - [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0], [4, 6, 2, 6, 7, 6]], - [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5], [6, 4, 2, 6, 6, 7]], - [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0]], - [[0, 1, 4, 6, 0, 4], [0, 1, 6, 7, 4, 6], [0, 1, 1, 3, 6, 7], [1, 3, 3, 7, 6, 7]], - [[0, 2, 0, 1, 4, 6], [0, 1, 3, 7, 4, 6], [0, 1, 1, 5, 3, 7], [4, 6, 3, 7, 6, 7]], - [[7, 3, 6, 7, 6, 4], [6, 4, 4, 0, 7, 3], [7, 3, 4, 0, 5, 1]], - [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5]], - [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5]], - [[6, 7, 4, 5, 6, 2], [4, 5, 4, 0, 6, 2], [3, 1, 0, 1, 5, 1]], - [[2, 0, 2, 6, 3, 1], [2, 6, 4, 5, 3, 1], [2, 6, 6, 7, 4, 5], [3, 1, 4, 5, 1, 5]], - [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7]], - [[0, 1, 2, 3, 6, 7], [0, 1, 6, 7, 4, 5]], - 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], - [[2, 0, 3, 7, 2, 3], [2, 0, 7, 5, 3, 7], [2, 0, 6, 7, 7, 5], [2, 6, 6, 7, 2, 0]], - [ - [5, 7, 1, 5, 0, 4], - [0, 4, 6, 7, 5, 7], - [0, 2, 6, 7, 0, 4], - [0, 2, 3, 7, 6, 7], - [0, 2, 1, 3, 3, 7], - ], - [[1, 0, 5, 7, 1, 5], [1, 0, 7, 6, 5, 7], [1, 0, 3, 7, 7, 6], [1, 3, 3, 7, 1, 0]], - [[0, 2, 0, 1, 0, 4], [3, 7, 6, 7, 5, 7]], - [[7, 5, 7, 3, 7, 6]], - [[7, 3, 7, 5, 7, 6]], - [[0, 1, 0, 2, 0, 4], [6, 7, 3, 7, 5, 7]], - [[1, 3, 1, 0, 1, 5], [7, 6, 3, 7, 5, 7]], - [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2], [6, 7, 3, 7, 5, 7]], - [[2, 6, 2, 0, 2, 3], [7, 5, 6, 7, 3, 7]], - [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4], [5, 7, 6, 7, 3, 7]], - [[1, 5, 1, 3, 0, 1], [2, 3, 2, 6, 0, 2], [5, 7, 6, 7, 3, 7]], - [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4], [7, 6, 3, 7, 7, 5]], - [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2]], - [[7, 6, 3, 2, 7, 5], [3, 2, 3, 1, 7, 5], [4, 0, 1, 0, 2, 0]], - [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2]], - [[2, 3, 2, 0, 6, 7], [2, 0, 1, 5, 6, 7], [2, 0, 0, 4, 1, 5], [6, 7, 1, 5, 7, 5]], - [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1]], - [[0, 4, 0, 1, 2, 6], [0, 1, 5, 7, 2, 6], [2, 6, 5, 7, 6, 7], [0, 1, 1, 3, 5, 7]], - [[1, 5, 0, 2, 1, 0], [1, 5, 2, 6, 0, 2], [1, 5, 5, 7, 2, 6], [5, 7, 7, 6, 2, 6]], - [[5, 1, 7, 5, 7, 6], [7, 6, 6, 2, 5, 1], [5, 1, 6, 2, 4, 0]], - [[4, 5, 4, 0, 4, 6], [7, 3, 5, 7, 6, 7]], - [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1], [3, 7, 5, 7, 6, 7]], - [[4, 6, 4, 5, 0, 4], [1, 5, 1, 3, 0, 1], [6, 7, 3, 7, 5, 7]], - [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2], [7, 3, 5, 7, 7, 6]], - [[2, 3, 2, 6, 0, 2], [4, 6, 4, 5, 0, 4], [3, 7, 5, 7, 6, 7]], - [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [7, 5, 6, 7, 7, 3]], - [[0, 1, 1, 5, 1, 3], [0, 2, 2, 3, 2, 6], [4, 5, 0, 4, 4, 6], [5, 7, 6, 7, 3, 7]], - [ - [1, 3, 5, 4, 1, 5], - [1, 3, 4, 6, 5, 4], - [1, 3, 3, 2, 4, 6], - [3, 2, 2, 6, 4, 6], - [7, 6, 3, 7, 5, 7], - ], - [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2], [0, 4, 6, 4, 5, 4]], - [[1, 0, 0, 2, 4, 6], [1, 0, 4, 6, 5, 4], [1, 3, 5, 7, 7, 6], [7, 6, 3, 2, 1, 3]], - [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2], [4, 6, 5, 4, 4, 0]], - [ - [7, 5, 6, 7, 2, 3], - [2, 3, 1, 5, 7, 5], - [2, 0, 1, 5, 2, 3], - [2, 0, 4, 5, 1, 5], - [2, 0, 6, 4, 4, 5], - ], - [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1], [4, 0, 6, 4, 4, 5]], - [ - [4, 6, 5, 4, 1, 0], - [1, 0, 2, 6, 4, 6], - [1, 3, 2, 6, 1, 0], - [1, 3, 7, 6, 2, 6], - [1, 3, 5, 7, 7, 6], - ], - [ - [1, 5, 0, 2, 1, 0], - [1, 5, 2, 6, 0, 2], - [1, 5, 5, 7, 2, 6], - [5, 7, 7, 6, 2, 6], - [4, 6, 5, 4, 0, 4], - ], - [[5, 1, 4, 6, 5, 4], [5, 1, 6, 2, 4, 6], [5, 1, 7, 6, 6, 2], [5, 7, 7, 6, 5, 1]], - [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1]], - [[7, 3, 5, 1, 7, 6], [5, 1, 5, 4, 7, 6], [2, 0, 4, 0, 1, 0]], - [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4]], - [[0, 2, 0, 4, 1, 3], [0, 4, 6, 7, 1, 3], [1, 3, 6, 7, 3, 7], [0, 4, 4, 5, 6, 7]], - [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1], [0, 2, 3, 2, 6, 2]], - [[1, 5, 5, 4, 7, 6], [1, 5, 7, 6, 3, 7], [1, 0, 3, 2, 2, 6], [2, 6, 0, 4, 1, 0]], - [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4], [2, 0, 3, 2, 2, 6]], - [ - [2, 3, 6, 2, 4, 0], - [4, 0, 1, 3, 2, 3], - [4, 5, 1, 3, 4, 0], - [4, 5, 7, 3, 1, 3], - [4, 5, 6, 7, 7, 3], - ], - [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6]], - [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6], [0, 4, 1, 0, 0, 2]], - [[1, 0, 5, 4, 7, 6], [1, 0, 7, 6, 3, 2]], - [[2, 3, 0, 2, 0, 4], [0, 4, 4, 5, 2, 3], [2, 3, 4, 5, 6, 7]], - [[1, 3, 1, 5, 0, 2], [1, 5, 7, 6, 0, 2], [1, 5, 5, 4, 7, 6], [0, 2, 7, 6, 2, 6]], - [ - [5, 1, 4, 5, 6, 7], - [6, 7, 3, 1, 5, 1], - [6, 2, 3, 1, 6, 7], - [6, 2, 0, 1, 3, 1], - [6, 2, 4, 0, 0, 1], - ], - [[6, 7, 2, 6, 2, 0], [2, 0, 0, 1, 6, 7], [6, 7, 0, 1, 4, 5]], - [[6, 2, 4, 0, 4, 5], [6, 7, 6, 2, 4, 5]], - [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1]], - [[1, 5, 1, 0, 3, 7], [1, 0, 4, 6, 3, 7], [1, 0, 0, 2, 4, 6], [3, 7, 4, 6, 7, 6]], - [[1, 0, 3, 7, 1, 3], [1, 0, 7, 6, 3, 7], [1, 0, 0, 4, 7, 6], [0, 4, 4, 6, 7, 6]], - [[6, 4, 7, 6, 7, 3], [7, 3, 3, 1, 6, 4], [6, 4, 3, 1, 2, 0]], - [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1], [2, 3, 6, 2, 2, 0]], - [ - [7, 6, 3, 7, 1, 5], - [1, 5, 4, 6, 7, 6], - [1, 0, 4, 6, 1, 5], - [1, 0, 2, 6, 4, 6], - [1, 0, 3, 2, 2, 6], - ], - [ - [1, 0, 3, 7, 1, 3], - [1, 0, 7, 6, 3, 7], - [1, 0, 0, 4, 7, 6], - [0, 4, 4, 6, 7, 6], - [2, 6, 0, 2, 3, 2], - ], - [[3, 1, 7, 6, 3, 7], [3, 1, 6, 4, 7, 6], [3, 1, 2, 6, 6, 4], [3, 2, 2, 6, 3, 1]], - [[3, 2, 3, 1, 7, 6], [3, 1, 0, 4, 7, 6], [7, 6, 0, 4, 6, 4], [3, 1, 1, 5, 0, 4]], - [ - [0, 1, 2, 0, 6, 4], - [6, 4, 5, 1, 0, 1], - [6, 7, 5, 1, 6, 4], - [6, 7, 3, 1, 5, 1], - [6, 7, 2, 3, 3, 1], - ], - [[0, 1, 4, 0, 4, 6], [4, 6, 6, 7, 0, 1], [0, 1, 6, 7, 2, 3]], - [[6, 7, 2, 3, 2, 0], [6, 4, 6, 7, 2, 0]], - [ - [2, 6, 0, 2, 1, 3], - [1, 3, 7, 6, 2, 6], - [1, 5, 7, 6, 1, 3], - [1, 5, 4, 6, 7, 6], - [1, 5, 0, 4, 4, 6], - ], - [[1, 5, 1, 0, 1, 3], [4, 6, 7, 6, 2, 6]], - [[0, 1, 2, 6, 0, 2], [0, 1, 6, 7, 2, 6], [0, 1, 4, 6, 6, 7], [0, 4, 4, 6, 0, 1]], - [[6, 7, 6, 2, 6, 4]], - [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4]], - [[7, 5, 6, 4, 7, 3], [6, 4, 6, 2, 7, 3], [1, 0, 2, 0, 4, 0]], - [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4], [0, 1, 5, 1, 3, 1]], - [[2, 0, 0, 4, 1, 5], [2, 0, 1, 5, 3, 1], [2, 6, 3, 7, 7, 5], [7, 5, 6, 4, 2, 6]], - [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4]], - [[3, 2, 3, 7, 1, 0], [3, 7, 6, 4, 1, 0], [3, 7, 7, 5, 6, 4], [1, 0, 6, 4, 0, 4]], - [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4], [1, 5, 3, 1, 1, 0]], - [ - [7, 3, 5, 7, 4, 6], - [4, 6, 2, 3, 7, 3], - [4, 0, 2, 3, 4, 6], - [4, 0, 1, 3, 2, 3], - [4, 0, 5, 1, 1, 3], - ], - [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5]], - [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5], [0, 1, 2, 0, 0, 4]], - [[1, 0, 1, 5, 3, 2], [1, 5, 4, 6, 3, 2], [3, 2, 4, 6, 2, 6], [1, 5, 5, 7, 4, 6]], - [ - [0, 2, 4, 0, 5, 1], - [5, 1, 3, 2, 0, 2], - [5, 7, 3, 2, 5, 1], - [5, 7, 6, 2, 3, 2], - [5, 7, 4, 6, 6, 2], - ], - [[2, 0, 3, 1, 7, 5], [2, 0, 7, 5, 6, 4]], - [[4, 6, 0, 4, 0, 1], [0, 1, 1, 3, 4, 6], [4, 6, 1, 3, 5, 7]], - [[0, 2, 1, 0, 1, 5], [1, 5, 5, 7, 0, 2], [0, 2, 5, 7, 4, 6]], - [[5, 7, 4, 6, 4, 0], [5, 1, 5, 7, 4, 0]], - [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2]], - [[0, 1, 0, 2, 4, 5], [0, 2, 3, 7, 4, 5], [4, 5, 3, 7, 5, 7], [0, 2, 2, 6, 3, 7]], - [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2], [1, 0, 5, 1, 1, 3]], - [ - [1, 5, 3, 1, 2, 0], - [2, 0, 4, 5, 1, 5], - [2, 6, 4, 5, 2, 0], - [2, 6, 7, 5, 4, 5], - [2, 6, 3, 7, 7, 5], - ], - [[2, 3, 0, 4, 2, 0], [2, 3, 4, 5, 0, 4], [2, 3, 3, 7, 4, 5], [3, 7, 7, 5, 4, 5]], - [[3, 2, 7, 3, 7, 5], [7, 5, 5, 4, 3, 2], [3, 2, 5, 4, 1, 0]], - [ - [2, 3, 0, 4, 2, 0], - [2, 3, 4, 5, 0, 4], - [2, 3, 3, 7, 4, 5], - [3, 7, 7, 5, 4, 5], - [1, 5, 3, 1, 0, 1], - ], - [[3, 2, 1, 5, 3, 1], [3, 2, 5, 4, 1, 5], [3, 2, 7, 5, 5, 4], [3, 7, 7, 5, 3, 2]], - [[2, 6, 2, 3, 0, 4], [2, 3, 7, 5, 0, 4], [2, 3, 3, 1, 7, 5], [0, 4, 7, 5, 4, 5]], - [ - [3, 2, 1, 3, 5, 7], - [5, 7, 6, 2, 3, 2], - [5, 4, 6, 2, 5, 7], - [5, 4, 0, 2, 6, 2], - [5, 4, 1, 0, 0, 2], - ], - [ - [4, 5, 0, 4, 2, 6], - [2, 6, 7, 5, 4, 5], - [2, 3, 7, 5, 2, 6], - [2, 3, 1, 5, 7, 5], - [2, 3, 0, 1, 1, 5], - ], - [[2, 3, 2, 0, 2, 6], [1, 5, 7, 5, 4, 5]], - [[5, 7, 4, 5, 4, 0], [4, 0, 0, 2, 5, 7], [5, 7, 0, 2, 1, 3]], - [[5, 4, 1, 0, 1, 3], [5, 7, 5, 4, 1, 3]], - [[0, 2, 4, 5, 0, 4], [0, 2, 5, 7, 4, 5], [0, 2, 1, 5, 5, 7], [0, 1, 1, 5, 0, 2]], - [[5, 4, 5, 1, 5, 7]], - [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3]], - [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3], [0, 2, 4, 0, 0, 1]], - [[3, 7, 3, 1, 2, 6], [3, 1, 5, 4, 2, 6], [3, 1, 1, 0, 5, 4], [2, 6, 5, 4, 6, 4]], - [ - [6, 4, 2, 6, 3, 7], - [3, 7, 5, 4, 6, 4], - [3, 1, 5, 4, 3, 7], - [3, 1, 0, 4, 5, 4], - [3, 1, 2, 0, 0, 4], - ], - [[2, 0, 2, 3, 6, 4], [2, 3, 1, 5, 6, 4], [6, 4, 1, 5, 4, 5], [2, 3, 3, 7, 1, 5]], - [ - [0, 4, 1, 0, 3, 2], - [3, 2, 6, 4, 0, 4], - [3, 7, 6, 4, 3, 2], - [3, 7, 5, 4, 6, 4], - [3, 7, 1, 5, 5, 4], - ], - [ - [1, 3, 0, 1, 4, 5], - [4, 5, 7, 3, 1, 3], - [4, 6, 7, 3, 4, 5], - [4, 6, 2, 3, 7, 3], - [4, 6, 0, 2, 2, 3], - ], - [[3, 7, 3, 1, 3, 2], [5, 4, 6, 4, 0, 4]], - [[3, 1, 2, 6, 3, 2], [3, 1, 6, 4, 2, 6], [3, 1, 1, 5, 6, 4], [1, 5, 5, 4, 6, 4]], - [ - [3, 1, 2, 6, 3, 2], - [3, 1, 6, 4, 2, 6], - [3, 1, 1, 5, 6, 4], - [1, 5, 5, 4, 6, 4], - [0, 4, 1, 0, 2, 0], - ], - [[4, 5, 6, 4, 6, 2], [6, 2, 2, 3, 4, 5], [4, 5, 2, 3, 0, 1]], - [[2, 3, 6, 4, 2, 6], [2, 3, 4, 5, 6, 4], [2, 3, 0, 4, 4, 5], [2, 0, 0, 4, 2, 3]], - [[1, 3, 5, 1, 5, 4], [5, 4, 4, 6, 1, 3], [1, 3, 4, 6, 0, 2]], - [[1, 3, 0, 4, 1, 0], [1, 3, 4, 6, 0, 4], [1, 3, 5, 4, 4, 6], [1, 5, 5, 4, 1, 3]], - [[4, 6, 0, 2, 0, 1], [4, 5, 4, 6, 0, 1]], - [[4, 6, 4, 0, 4, 5]], - [[4, 0, 6, 2, 7, 3], [4, 0, 7, 3, 5, 1]], - [[1, 5, 0, 1, 0, 2], [0, 2, 2, 6, 1, 5], [1, 5, 2, 6, 3, 7]], - [[3, 7, 1, 3, 1, 0], [1, 0, 0, 4, 3, 7], [3, 7, 0, 4, 2, 6]], - [[3, 1, 2, 0, 2, 6], [3, 7, 3, 1, 2, 6]], - [[0, 4, 2, 0, 2, 3], [2, 3, 3, 7, 0, 4], [0, 4, 3, 7, 1, 5]], - [[3, 7, 1, 5, 1, 0], [3, 2, 3, 7, 1, 0]], - [[0, 4, 1, 3, 0, 1], [0, 4, 3, 7, 1, 3], [0, 4, 2, 3, 3, 7], [0, 2, 2, 3, 0, 4]], - [[3, 7, 3, 1, 3, 2]], - [[2, 6, 3, 2, 3, 1], [3, 1, 1, 5, 2, 6], [2, 6, 1, 5, 0, 4]], - [[1, 5, 3, 2, 1, 3], [1, 5, 2, 6, 3, 2], [1, 5, 0, 2, 2, 6], [1, 0, 0, 2, 1, 5]], - [[2, 3, 0, 1, 0, 4], [2, 6, 2, 3, 0, 4]], - [[2, 3, 2, 0, 2, 6]], - [[1, 5, 0, 4, 0, 2], [1, 3, 1, 5, 0, 2]], - [[1, 5, 1, 0, 1, 3]], - [[0, 2, 0, 1, 0, 4]], - [], -] - - -def create_mc_lookup_table(): - cases = torch.zeros(256, 5, 3, dtype=torch.long) - masks = torch.zeros(256, 5, dtype=torch.bool) - - edge_to_index = { - (0, 1): 0, - (2, 3): 1, - (4, 5): 2, - (6, 7): 3, - (0, 2): 4, - (1, 3): 5, - (4, 6): 6, - (5, 7): 7, - (0, 4): 8, - (1, 5): 9, - (2, 6): 10, - (3, 7): 11, - } - - for i, case in enumerate(MC_TABLE): - for j, tri in enumerate(case): - for k, (c1, c2) in enumerate(zip(tri[::2], tri[1::2])): - cases[i, j, k] = edge_to_index[(c1, c2) if c1 < c2 else (c2, c1)] - masks[i, j] = True - return cases, masks - - -RENDERER_CONFIG = {} - - -def renderer_model_from_original_config(): - model = ShapERenderer(**RENDERER_CONFIG) - - return model - - -RENDERER_MLP_ORIGINAL_PREFIX = "renderer.nerstf" - -RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX = "encoder.params_proj" - - -def renderer_model_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - diffusers_checkpoint.update( - {f"mlp.{k}": checkpoint[f"{RENDERER_MLP_ORIGINAL_PREFIX}.{k}"] for k in model.mlp.state_dict().keys()} - ) - - diffusers_checkpoint.update( - { - f"params_proj.{k}": checkpoint[f"{RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX}.{k}"] - for k in model.params_proj.state_dict().keys() - } - ) - - diffusers_checkpoint.update({"void.background": model.state_dict()["void.background"]}) - - cases, masks = create_mc_lookup_table() - - diffusers_checkpoint.update({"mesh_decoder.cases": cases}) - diffusers_checkpoint.update({"mesh_decoder.masks": masks}) - - return diffusers_checkpoint - - -# done renderer - - -# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) -def split_attentions(*, weight, bias, split, chunk_size): - weights = [None] * split - biases = [None] * split - - weights_biases_idx = 0 - - for starting_row_index in range(0, weight.shape[0], chunk_size): - row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) - - weight_rows = weight[row_indices, :] - bias_rows = bias[row_indices] - - if weights[weights_biases_idx] is None: - assert weights[weights_biases_idx] is None - weights[weights_biases_idx] = weight_rows - biases[weights_biases_idx] = bias_rows - else: - assert weights[weights_biases_idx] is not None - weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) - biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) - - weights_biases_idx = (weights_biases_idx + 1) % split - - return weights, biases - - -# done unet utils - - -# Driver functions - - -def prior(*, args, checkpoint_map_location): - print("loading prior") - - prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) - - prior_model = prior_model_from_original_config() - - prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint) - - del prior_checkpoint - - load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model) - - print("done loading prior") - - return prior_model - - -def prior_image(*, args, checkpoint_map_location): - print("loading prior_image") - - print(f"load checkpoint from {args.prior_image_checkpoint_path}") - prior_checkpoint = torch.load(args.prior_image_checkpoint_path, map_location=checkpoint_map_location) - - prior_model = prior_image_model_from_original_config() - - prior_diffusers_checkpoint = prior_image_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint) - - del prior_checkpoint - - load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model) - - print("done loading prior_image") - - return prior_model - - -def renderer(*, args, checkpoint_map_location): - print(" loading renderer") - - renderer_checkpoint = torch.load(args.transmitter_checkpoint_path, map_location=checkpoint_map_location) - - renderer_model = renderer_model_from_original_config() - - renderer_diffusers_checkpoint = renderer_model_original_checkpoint_to_diffusers_checkpoint( - renderer_model, renderer_checkpoint - ) - - del renderer_checkpoint - - load_checkpoint_to_model(renderer_diffusers_checkpoint, renderer_model, strict=True) - - print("done loading renderer") - - return renderer_model - - -# prior model will expect clip_mean and clip_std, which are missing from the state_dict -PRIOR_EXPECTED_MISSING_KEYS = ["clip_mean", "clip_std"] - - -def load_prior_checkpoint_to_model(checkpoint, model): - with tempfile.NamedTemporaryFile() as file: - torch.save(checkpoint, file.name) - del checkpoint - missing_keys, unexpected_keys = model.load_state_dict(torch.load(file.name), strict=False) - missing_keys = list(set(missing_keys) - set(PRIOR_EXPECTED_MISSING_KEYS)) - - if len(unexpected_keys) > 0: - raise ValueError(f"Unexpected keys when loading prior model: {unexpected_keys}") - if len(missing_keys) > 0: - raise ValueError(f"Missing keys when loading prior model: {missing_keys}") - - -def load_checkpoint_to_model(checkpoint, model, strict=False): - with tempfile.NamedTemporaryFile() as file: - torch.save(checkpoint, file.name) - del checkpoint - if strict: - model.load_state_dict(torch.load(file.name), strict=True) - else: - load_checkpoint_and_dispatch(model, file.name, device_map="auto") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--prior_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the prior checkpoint to convert.", - ) - - parser.add_argument( - "--prior_image_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the prior_image checkpoint to convert.", - ) - - parser.add_argument( - "--transmitter_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to the transmitter checkpoint to convert.", - ) - - parser.add_argument( - "--checkpoint_load_device", - default="cpu", - type=str, - required=False, - help="The device passed to `map_location` when loading checkpoints.", - ) - - parser.add_argument( - "--debug", - default=None, - type=str, - required=False, - help="Only run a specific stage of the convert script. Used for debugging", - ) - - args = parser.parse_args() - - print(f"loading checkpoints to {args.checkpoint_load_device}") - - checkpoint_map_location = torch.device(args.checkpoint_load_device) - - if args.debug is not None: - print(f"debug: only executing {args.debug}") - - if args.debug is None: - print("YiYi TO-DO") - elif args.debug == "prior": - prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) - prior_model.save_pretrained(args.dump_path) - elif args.debug == "prior_image": - prior_model = prior_image(args=args, checkpoint_map_location=checkpoint_map_location) - prior_model.save_pretrained(args.dump_path) - elif args.debug == "renderer": - renderer_model = renderer(args=args, checkpoint_map_location=checkpoint_map_location) - renderer_model.save_pretrained(args.dump_path) - else: - raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_skyreelsv2_to_diffusers.py b/scripts/convert_skyreelsv2_to_diffusers.py deleted file mode 100644 index 29d5a7666a6e..000000000000 --- a/scripts/convert_skyreelsv2_to_diffusers.py +++ /dev/null @@ -1,637 +0,0 @@ -import argparse -import os -import pathlib -from typing import Any, Dict - -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download -from safetensors.torch import load_file -from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel - -from diffusers import ( - AutoencoderKLWan, - SkyReelsV2DiffusionForcingPipeline, - SkyReelsV2ImageToVideoPipeline, - SkyReelsV2Pipeline, - SkyReelsV2Transformer3DModel, - UniPCMultistepScheduler, -) - - -TRANSFORMER_KEYS_RENAME_DICT = { - "time_embedding.0": "condition_embedder.time_embedder.linear_1", - "time_embedding.2": "condition_embedder.time_embedder.linear_2", - "text_embedding.0": "condition_embedder.text_embedder.linear_1", - "text_embedding.2": "condition_embedder.text_embedder.linear_2", - "time_projection.1": "condition_embedder.time_proj", - "head.modulation": "scale_shift_table", - "head.head": "proj_out", - "modulation": "scale_shift_table", - "ffn.0": "ffn.net.0.proj", - "ffn.2": "ffn.net.2", - "fps_projection.0": "fps_projection.net.0.proj", - "fps_projection.2": "fps_projection.net.2", - # Hack to swap the layer names - # The original model calls the norms in following order: norm1, norm3, norm2 - # We convert it to: norm1, norm2, norm3 - "norm2": "norm__placeholder", - "norm3": "norm2", - "norm__placeholder": "norm3", - # For the I2V model - "img_emb.proj.0": "condition_embedder.image_embedder.norm1", - "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", - "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", - "img_emb.proj.4": "condition_embedder.image_embedder.norm2", - # for the FLF2V model - "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", - # Add attention component mappings - "self_attn.q": "attn1.to_q", - "self_attn.k": "attn1.to_k", - "self_attn.v": "attn1.to_v", - "self_attn.o": "attn1.to_out.0", - "self_attn.norm_q": "attn1.norm_q", - "self_attn.norm_k": "attn1.norm_k", - "cross_attn.q": "attn2.to_q", - "cross_attn.k": "attn2.to_k", - "cross_attn.v": "attn2.to_v", - "cross_attn.o": "attn2.to_out.0", - "cross_attn.norm_q": "attn2.norm_q", - "cross_attn.norm_k": "attn2.norm_k", - "attn2.to_k_img": "attn2.add_k_proj", - "attn2.to_v_img": "attn2.add_v_proj", - "attn2.norm_k_img": "attn2.norm_added_k", -} - -TRANSFORMER_SPECIAL_KEYS_REMAP = {} - - -def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def load_sharded_safetensors(dir: pathlib.Path): - if "720P" in str(dir): - file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) - else: - file_paths = list(dir.glob("model*.safetensors")) - state_dict = {} - for path in file_paths: - state_dict.update(load_file(path)) - return state_dict - - -def get_transformer_config(model_type: str) -> dict[str, Any]: - if model_type == "SkyReels-V2-DF-1.3B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-DF-1.3B-540P", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 8960, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 12, - "inject_sample_info": True, - "num_layers": 30, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - elif model_type == "SkyReels-V2-DF-14B-720P": - config = { - "model_id": "Skywork/SkyReels-V2-DF-14B-720P", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - elif model_type == "SkyReels-V2-DF-14B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-DF-14B-540P", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - elif model_type == "SkyReels-V2-T2V-14B-720P": - config = { - "model_id": "Skywork/SkyReels-V2-T2V-14B-720P", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - elif model_type == "SkyReels-V2-T2V-14B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-T2V-14B-540P", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - elif model_type == "SkyReels-V2-I2V-1.3B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P", - "diffusers_config": { - "added_kv_proj_dim": 1536, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 8960, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 12, - "inject_sample_info": False, - "num_layers": 30, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - }, - } - elif model_type == "SkyReels-V2-I2V-14B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-14B-540P", - "diffusers_config": { - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - }, - } - elif model_type == "SkyReels-V2-I2V-14B-720P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-14B-720P", - "diffusers_config": { - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - }, - } - elif model_type == "SkyReels-V2-FLF2V-1.3B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P", - "diffusers_config": { - "added_kv_proj_dim": 1536, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 8960, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 12, - "inject_sample_info": False, - "num_layers": 30, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - "pos_embed_seq_len": 514, - }, - } - elif model_type == "SkyReels-V2-FLF2V-14B-540P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-14B-540P", - "diffusers_config": { - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - "pos_embed_seq_len": 514, - }, - } - elif model_type == "SkyReels-V2-FLF2V-14B-720P": - config = { - "model_id": "Skywork/SkyReels-V2-I2V-14B-720P", - "diffusers_config": { - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "inject_sample_info": False, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "image_dim": 1280, - "pos_embed_seq_len": 514, - }, - } - return config - - -def convert_transformer(model_type: str): - config = get_transformer_config(model_type) - diffusers_config = config["diffusers_config"] - model_id = config["model_id"] - - if "1.3B" in model_type: - original_state_dict = load_file(hf_hub_download(model_id, "model.safetensors")) - else: - os.makedirs(model_type, exist_ok=True) - model_dir = pathlib.Path(model_type) - if "720P" in model_type: - top_shard = 7 if "I2V" in model_type else 6 - zeros = "0" * (4 if "I2V" or "T2V" in model_type else 3) - model_name = "diffusion_pytorch_model" - elif "540P" in model_type: - top_shard = 14 if "I2V" in model_type else 12 - model_name = "model" - - for i in range(1, top_shard + 1): - shard_path = f"{model_name}-{i:05d}-of-{zeros}{top_shard}.safetensors" - hf_hub_download(model_id, shard_path, local_dir=model_dir) - original_state_dict = load_sharded_safetensors(model_dir) - - with init_empty_weights(): - transformer = SkyReelsV2Transformer3DModel.from_config(diffusers_config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - if "FLF2V" in model_type: - if ( - hasattr(transformer.condition_embedder, "image_embedder") - and hasattr(transformer.condition_embedder.image_embedder, "pos_embed") - and transformer.condition_embedder.image_embedder.pos_embed is not None - ): - pos_embed_shape = transformer.condition_embedder.image_embedder.pos_embed.shape - original_state_dict["condition_embedder.image_embedder.pos_embed"] = torch.zeros(pos_embed_shape) - - transformer.load_state_dict(original_state_dict, strict=True, assign=True) - return transformer - - -def convert_vae(): - vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth") - old_state_dict = torch.load(vae_ckpt_path, weights_only=True) - new_state_dict = {} - - # Create mappings for specific components - middle_key_mapping = { - # Encoder middle block - "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", - "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", - "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", - "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", - "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", - "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", - "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", - "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", - "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", - "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", - "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", - "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", - # Decoder middle block - "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", - "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", - "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", - "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", - "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", - "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", - "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", - "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", - "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", - "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", - "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", - "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", - } - - # Create a mapping for attention blocks - attention_mapping = { - # Encoder middle attention - "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", - "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", - "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", - "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", - "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", - # Decoder middle attention - "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", - "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", - "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", - "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", - "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", - } - - # Create a mapping for the head components - head_mapping = { - # Encoder head - "encoder.head.0.gamma": "encoder.norm_out.gamma", - "encoder.head.2.bias": "encoder.conv_out.bias", - "encoder.head.2.weight": "encoder.conv_out.weight", - # Decoder head - "decoder.head.0.gamma": "decoder.norm_out.gamma", - "decoder.head.2.bias": "decoder.conv_out.bias", - "decoder.head.2.weight": "decoder.conv_out.weight", - } - - # Create a mapping for the quant components - quant_mapping = { - "conv1.weight": "quant_conv.weight", - "conv1.bias": "quant_conv.bias", - "conv2.weight": "post_quant_conv.weight", - "conv2.bias": "post_quant_conv.bias", - } - - # Process each key in the state dict - for key, value in old_state_dict.items(): - # Handle middle block keys using the mapping - if key in middle_key_mapping: - new_key = middle_key_mapping[key] - new_state_dict[new_key] = value - # Handle attention blocks using the mapping - elif key in attention_mapping: - new_key = attention_mapping[key] - new_state_dict[new_key] = value - # Handle head keys using the mapping - elif key in head_mapping: - new_key = head_mapping[key] - new_state_dict[new_key] = value - # Handle quant keys using the mapping - elif key in quant_mapping: - new_key = quant_mapping[key] - new_state_dict[new_key] = value - # Handle encoder conv1 - elif key == "encoder.conv1.weight": - new_state_dict["encoder.conv_in.weight"] = value - elif key == "encoder.conv1.bias": - new_state_dict["encoder.conv_in.bias"] = value - # Handle decoder conv1 - elif key == "decoder.conv1.weight": - new_state_dict["decoder.conv_in.weight"] = value - elif key == "decoder.conv1.bias": - new_state_dict["decoder.conv_in.bias"] = value - # Handle encoder downsamples - elif key.startswith("encoder.downsamples."): - # Convert to down_blocks - new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") - - # Convert residual block naming but keep the original structure - if ".residual.0.gamma" in new_key: - new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") - elif ".residual.2.bias" in new_key: - new_key = new_key.replace(".residual.2.bias", ".conv1.bias") - elif ".residual.2.weight" in new_key: - new_key = new_key.replace(".residual.2.weight", ".conv1.weight") - elif ".residual.3.gamma" in new_key: - new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") - elif ".residual.6.bias" in new_key: - new_key = new_key.replace(".residual.6.bias", ".conv2.bias") - elif ".residual.6.weight" in new_key: - new_key = new_key.replace(".residual.6.weight", ".conv2.weight") - elif ".shortcut.bias" in new_key: - new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") - elif ".shortcut.weight" in new_key: - new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") - - new_state_dict[new_key] = value - - # Handle decoder upsamples - elif key.startswith("decoder.upsamples."): - # Convert to up_blocks - parts = key.split(".") - block_idx = int(parts[2]) - - # Group residual blocks - if "residual" in key: - if block_idx in [0, 1, 2]: - new_block_idx = 0 - resnet_idx = block_idx - elif block_idx in [4, 5, 6]: - new_block_idx = 1 - resnet_idx = block_idx - 4 - elif block_idx in [8, 9, 10]: - new_block_idx = 2 - resnet_idx = block_idx - 8 - elif block_idx in [12, 13, 14]: - new_block_idx = 3 - resnet_idx = block_idx - 12 - else: - # Keep as is for other blocks - new_state_dict[key] = value - continue - - # Convert residual block naming - if ".residual.0.gamma" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma" - elif ".residual.2.bias" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias" - elif ".residual.2.weight" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight" - elif ".residual.3.gamma" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma" - elif ".residual.6.bias" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias" - elif ".residual.6.weight" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight" - else: - new_key = key - - new_state_dict[new_key] = value - - # Handle shortcut connections - elif ".shortcut." in key: - if block_idx == 4: - new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.") - new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1") - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - new_key = new_key.replace(".shortcut.", ".conv_shortcut.") - - new_state_dict[new_key] = value - - # Handle upsamplers - elif ".resample." in key or ".time_conv." in key: - if block_idx == 3: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0") - elif block_idx == 7: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0") - elif block_idx == 11: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0") - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - - new_state_dict[new_key] = value - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - new_state_dict[new_key] = value - else: - # Keep other keys unchanged - new_state_dict[key] = value - - with init_empty_weights(): - vae = AutoencoderKLWan() - vae.load_state_dict(new_state_dict, strict=True, assign=True) - return vae - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--model_type", type=str, default=None) - parser.add_argument("--output_path", type=str, required=True) - parser.add_argument("--dtype", default="fp32") - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - args = get_args() - - transformer = None - dtype = DTYPE_MAPPING[args.dtype] - - transformer = convert_transformer(args.model_type).to(dtype=dtype) - vae = convert_vae() - text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl") - tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") - scheduler = UniPCMultistepScheduler( - prediction_type="flow_prediction", - num_train_timesteps=1000, - use_flow_sigmas=True, - ) - - if "I2V" in args.model_type or "FLF2V" in args.model_type: - image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") - image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") - pipe = SkyReelsV2ImageToVideoPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - image_encoder=image_encoder, - image_processor=image_processor, - ) - elif "T2V" in args.model_type: - pipe = SkyReelsV2Pipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - ) - elif "DF" in args.model_type: - pipe = SkyReelsV2DiffusionForcingPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - ) - - pipe.save_pretrained( - args.output_path, - safe_serialization=True, - max_shard_size="5GB", - # push_to_hub=True, - # repo_id=f"<place_holder>/{args.model_type}-Diffusers", - ) diff --git a/scripts/convert_stable_audio.py b/scripts/convert_stable_audio.py deleted file mode 100644 index c3479fc6b2bb..000000000000 --- a/scripts/convert_stable_audio.py +++ /dev/null @@ -1,279 +0,0 @@ -# Run this script to convert the Stable Audio model weights to a diffusers pipeline. -import argparse -import json -import os -from contextlib import nullcontext - -import torch -from safetensors.torch import load_file -from transformers import ( - AutoTokenizer, - T5EncoderModel, -) - -from diffusers import ( - AutoencoderOobleck, - CosineDPMSolverMultistepScheduler, - StableAudioDiTModel, - StableAudioPipeline, - StableAudioProjectionModel, -) -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.utils import is_accelerate_available - - -if is_accelerate_available(): - from accelerate import init_empty_weights - - -def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_layers=5): - projection_model_state_dict = { - k.replace("conditioner.conditioners.", "").replace("embedder.embedding", "time_positional_embedding"): v - for (k, v) in state_dict.items() - if "conditioner.conditioners" in k - } - - # NOTE: we assume here that there's no projection layer from the text encoder to the latent space, script should be adapted a bit if there is. - for key, value in list(projection_model_state_dict.items()): - new_key = key.replace("seconds_start", "start_number_conditioner").replace( - "seconds_total", "end_number_conditioner" - ) - projection_model_state_dict[new_key] = projection_model_state_dict.pop(key) - - model_state_dict = {k.replace("model.model.", ""): v for (k, v) in state_dict.items() if "model.model." in k} - for key, value in list(model_state_dict.items()): - # attention layers - new_key = ( - key.replace("transformer.", "") - .replace("layers", "transformer_blocks") - .replace("self_attn", "attn1") - .replace("cross_attn", "attn2") - .replace("ff.ff", "ff.net") - ) - new_key = ( - new_key.replace("pre_norm", "norm1") - .replace("cross_attend_norm", "norm2") - .replace("ff_norm", "norm3") - .replace("to_out", "to_out.0") - ) - new_key = new_key.replace("gamma", "weight").replace("beta", "bias") # replace layernorm - - # other layers - new_key = ( - new_key.replace("project", "proj") - .replace("to_timestep_embed", "timestep_proj") - .replace("timestep_features", "time_proj") - .replace("to_global_embed", "global_proj") - .replace("to_cond_embed", "cross_attention_proj") - ) - - # we're using diffusers implementation of time_proj (GaussianFourierProjection) which creates a 1D tensor - if new_key == "time_proj.weight": - model_state_dict[key] = model_state_dict[key].squeeze(1) - - if "to_qkv" in new_key: - q, k, v = torch.chunk(model_state_dict.pop(key), 3, dim=0) - model_state_dict[new_key.replace("qkv", "q")] = q - model_state_dict[new_key.replace("qkv", "k")] = k - model_state_dict[new_key.replace("qkv", "v")] = v - elif "to_kv" in new_key: - k, v = torch.chunk(model_state_dict.pop(key), 2, dim=0) - model_state_dict[new_key.replace("kv", "k")] = k - model_state_dict[new_key.replace("kv", "v")] = v - else: - model_state_dict[new_key] = model_state_dict.pop(key) - - autoencoder_state_dict = { - k.replace("pretransform.model.", "").replace("coder.layers.0", "coder.conv1"): v - for (k, v) in state_dict.items() - if "pretransform.model." in k - } - - for key, _ in list(autoencoder_state_dict.items()): - new_key = key - if "coder.layers" in new_key: - # get idx of the layer - idx = int(new_key.split("coder.layers.")[1].split(".")[0]) - - new_key = new_key.replace(f"coder.layers.{idx}", f"coder.block.{idx - 1}") - - if "encoder" in new_key: - for i in range(3): - new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i + 1}") - new_key = new_key.replace(f"block.{idx - 1}.layers.3", f"block.{idx - 1}.snake1") - new_key = new_key.replace(f"block.{idx - 1}.layers.4", f"block.{idx - 1}.conv1") - else: - for i in range(2, 5): - new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i - 1}") - new_key = new_key.replace(f"block.{idx - 1}.layers.0", f"block.{idx - 1}.snake1") - new_key = new_key.replace(f"block.{idx - 1}.layers.1", f"block.{idx - 1}.conv_t1") - - new_key = new_key.replace("layers.0.beta", "snake1.beta") - new_key = new_key.replace("layers.0.alpha", "snake1.alpha") - new_key = new_key.replace("layers.2.beta", "snake2.beta") - new_key = new_key.replace("layers.2.alpha", "snake2.alpha") - new_key = new_key.replace("layers.1.bias", "conv1.bias") - new_key = new_key.replace("layers.1.weight_", "conv1.weight_") - new_key = new_key.replace("layers.3.bias", "conv2.bias") - new_key = new_key.replace("layers.3.weight_", "conv2.weight_") - - if idx == num_autoencoder_layers + 1: - new_key = new_key.replace(f"block.{idx - 1}", "snake1") - elif idx == num_autoencoder_layers + 2: - new_key = new_key.replace(f"block.{idx - 1}", "conv2") - - else: - new_key = new_key - - value = autoencoder_state_dict.pop(key) - if "snake" in new_key: - value = value.unsqueeze(0).unsqueeze(-1) - if new_key in autoencoder_state_dict: - raise ValueError(f"{new_key} already in state dict.") - autoencoder_state_dict[new_key] = value - - return model_state_dict, projection_model_state_dict, autoencoder_state_dict - - -parser = argparse.ArgumentParser(description="Convert Stable Audio 1.0 model weights to a diffusers pipeline") -parser.add_argument("--model_folder_path", type=str, help="Location of Stable Audio weights and config") -parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") -parser.add_argument( - "--save_directory", - type=str, - default="./tmp/stable-audio-1.0", - help="Directory to save a pipeline to. Will be created if it doesn't exist.", -) -parser.add_argument( - "--repo_id", - type=str, - default="stable-audio-1.0", - help="Hub organization to save the pipelines to", -) -parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") -parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") - -args = parser.parse_args() - -checkpoint_path = ( - os.path.join(args.model_folder_path, "model.safetensors") - if args.use_safetensors - else os.path.join(args.model_folder_path, "model.ckpt") -) -config_path = os.path.join(args.model_folder_path, "model_config.json") - -device = "cpu" -if args.variant == "bf16": - dtype = torch.bfloat16 -else: - dtype = torch.float32 - -with open(config_path) as f_in: - config_dict = json.load(f_in) - -conditioning_dict = { - conditioning["id"]: conditioning["config"] for conditioning in config_dict["model"]["conditioning"]["configs"] -} - -t5_model_config = conditioning_dict["prompt"] - -# T5 Text encoder -text_encoder = T5EncoderModel.from_pretrained(t5_model_config["t5_model_name"]) -tokenizer = AutoTokenizer.from_pretrained( - t5_model_config["t5_model_name"], truncation=True, model_max_length=t5_model_config["max_length"] -) - - -# scheduler -scheduler = CosineDPMSolverMultistepScheduler( - sigma_min=0.3, - sigma_max=500, - solver_order=2, - prediction_type="v_prediction", - sigma_data=1.0, - sigma_schedule="exponential", -) -ctx = init_empty_weights if is_accelerate_available() else nullcontext - - -if args.use_safetensors: - orig_state_dict = load_file(checkpoint_path, device=device) -else: - orig_state_dict = torch.load(checkpoint_path, map_location=device) - - -model_config = config_dict["model"]["diffusion"]["config"] - -model_state_dict, projection_model_state_dict, autoencoder_state_dict = convert_stable_audio_state_dict_to_diffusers( - orig_state_dict -) - - -with ctx(): - projection_model = StableAudioProjectionModel( - text_encoder_dim=text_encoder.config.d_model, - conditioning_dim=config_dict["model"]["conditioning"]["cond_dim"], - min_value=conditioning_dict["seconds_start"][ - "min_val" - ], # assume `seconds_start` and `seconds_total` have the same min / max values. - max_value=conditioning_dict["seconds_start"][ - "max_val" - ], # assume `seconds_start` and `seconds_total` have the same min / max values. - ) -if is_accelerate_available(): - load_model_dict_into_meta(projection_model, projection_model_state_dict) -else: - projection_model.load_state_dict(projection_model_state_dict) - -attention_head_dim = model_config["embed_dim"] // model_config["num_heads"] -with ctx(): - model = StableAudioDiTModel( - sample_size=int(config_dict["sample_size"]) - / int(config_dict["model"]["pretransform"]["config"]["downsampling_ratio"]), - in_channels=model_config["io_channels"], - num_layers=model_config["depth"], - attention_head_dim=attention_head_dim, - num_key_value_attention_heads=model_config["cond_token_dim"] // attention_head_dim, - num_attention_heads=model_config["num_heads"], - out_channels=model_config["io_channels"], - cross_attention_dim=model_config["cond_token_dim"], - time_proj_dim=256, - global_states_input_dim=model_config["global_cond_dim"], - cross_attention_input_dim=model_config["cond_token_dim"], - ) -if is_accelerate_available(): - load_model_dict_into_meta(model, model_state_dict) -else: - model.load_state_dict(model_state_dict) - - -autoencoder_config = config_dict["model"]["pretransform"]["config"] -with ctx(): - autoencoder = AutoencoderOobleck( - encoder_hidden_size=autoencoder_config["encoder"]["config"]["channels"], - downsampling_ratios=autoencoder_config["encoder"]["config"]["strides"], - decoder_channels=autoencoder_config["decoder"]["config"]["channels"], - decoder_input_channels=autoencoder_config["decoder"]["config"]["latent_dim"], - audio_channels=autoencoder_config["io_channels"], - channel_multiples=autoencoder_config["encoder"]["config"]["c_mults"], - sampling_rate=config_dict["sample_rate"], - ) - -if is_accelerate_available(): - load_model_dict_into_meta(autoencoder, autoencoder_state_dict) -else: - autoencoder.load_state_dict(autoencoder_state_dict) - - -# Prior pipeline -pipeline = StableAudioPipeline( - transformer=model, - tokenizer=tokenizer, - text_encoder=text_encoder, - scheduler=scheduler, - vae=autoencoder, - projection_model=projection_model, -) -pipeline.to(dtype).save_pretrained( - args.save_directory, repo_id=args.repo_id, push_to_hub=args.push_to_hub, variant=args.variant -) diff --git a/scripts/convert_stable_cascade.py b/scripts/convert_stable_cascade.py deleted file mode 100644 index 97ed18d9b4d4..000000000000 --- a/scripts/convert_stable_cascade.py +++ /dev/null @@ -1,218 +0,0 @@ -# Run this script to convert the Stable Cascade model weights to a diffusers pipeline. -import argparse -from contextlib import nullcontext - -import torch -from safetensors.torch import load_file -from transformers import ( - AutoTokenizer, - CLIPConfig, - CLIPImageProcessor, - CLIPTextModelWithProjection, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - DDPMWuerstchenScheduler, - StableCascadeCombinedPipeline, - StableCascadeDecoderPipeline, - StableCascadePriorPipeline, -) -from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers -from diffusers.models import StableCascadeUNet -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.pipelines.wuerstchen import PaellaVQModel -from diffusers.utils import is_accelerate_available - - -if is_accelerate_available(): - from accelerate import init_empty_weights - -parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline") -parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights") -parser.add_argument("--stage_c_name", type=str, default="stage_c.safetensors", help="Name of stage c checkpoint file") -parser.add_argument("--stage_b_name", type=str, default="stage_b.safetensors", help="Name of stage b checkpoint file") -parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c") -parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b") -parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") -parser.add_argument( - "--prior_output_path", default="stable-cascade-prior", type=str, help="Hub organization to save the pipelines to" -) -parser.add_argument( - "--decoder_output_path", - type=str, - default="stable-cascade-decoder", - help="Hub organization to save the pipelines to", -) -parser.add_argument( - "--combined_output_path", - type=str, - default="stable-cascade-combined", - help="Hub organization to save the pipelines to", -) -parser.add_argument("--save_combined", action="store_true") -parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") -parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") - -args = parser.parse_args() - -if args.skip_stage_b and args.skip_stage_c: - raise ValueError("At least one stage should be converted") -if (args.skip_stage_b or args.skip_stage_c) and args.save_combined: - raise ValueError("Cannot skip stages when creating a combined pipeline") - -model_path = args.model_path - -device = "cpu" -if args.variant == "bf16": - dtype = torch.bfloat16 -else: - dtype = torch.float32 - -# set paths to model weights -prior_checkpoint_path = f"{model_path}/{args.stage_c_name}" -decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}" - -# Clip Text encoder and tokenizer -config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") -config.text_config.projection_dim = config.projection_dim -text_encoder = CLIPTextModelWithProjection.from_pretrained( - "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config -) -tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") - -# image processor -feature_extractor = CLIPImageProcessor() -image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - -# scheduler for prior and decoder -scheduler = DDPMWuerstchenScheduler() -ctx = init_empty_weights if is_accelerate_available() else nullcontext - -if not args.skip_stage_c: - # Prior - if args.use_safetensors: - prior_orig_state_dict = load_file(prior_checkpoint_path, device=device) - else: - prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device) - - prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict) - - with ctx(): - prior_model = StableCascadeUNet( - in_channels=16, - out_channels=16, - timestep_ratio_embedding_dim=64, - patch_size=1, - conditioning_dim=2048, - block_out_channels=[2048, 2048], - num_attention_heads=[32, 32], - down_num_layers_per_block=[8, 24], - up_num_layers_per_block=[24, 8], - down_blocks_repeat_mappers=[1, 1], - up_blocks_repeat_mappers=[1, 1], - block_types_per_layer=[ - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ], - clip_text_in_channels=1280, - clip_text_pooled_in_channels=1280, - clip_image_in_channels=768, - clip_seq=4, - kernel_size=3, - dropout=[0.1, 0.1], - self_attn=True, - timestep_conditioning_type=["sca", "crp"], - switch_level=[False], - ) - if is_accelerate_available(): - load_model_dict_into_meta(prior_model, prior_state_dict) - else: - prior_model.load_state_dict(prior_state_dict) - - # Prior pipeline - prior_pipeline = StableCascadePriorPipeline( - prior=prior_model, - tokenizer=tokenizer, - text_encoder=text_encoder, - image_encoder=image_encoder, - scheduler=scheduler, - feature_extractor=feature_extractor, - ) - prior_pipeline.to(dtype).save_pretrained( - args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) - -if not args.skip_stage_b: - # Decoder - if args.use_safetensors: - decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device) - else: - decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device) - - decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict) - with ctx(): - decoder = StableCascadeUNet( - in_channels=4, - out_channels=4, - timestep_ratio_embedding_dim=64, - patch_size=2, - conditioning_dim=1280, - block_out_channels=[320, 640, 1280, 1280], - down_num_layers_per_block=[2, 6, 28, 6], - up_num_layers_per_block=[6, 28, 6, 2], - down_blocks_repeat_mappers=[1, 1, 1, 1], - up_blocks_repeat_mappers=[3, 3, 2, 2], - num_attention_heads=[0, 0, 20, 20], - block_types_per_layer=[ - ["SDCascadeResBlock", "SDCascadeTimestepBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ], - clip_text_pooled_in_channels=1280, - clip_seq=4, - effnet_in_channels=16, - pixel_mapper_in_channels=3, - kernel_size=3, - dropout=[0, 0, 0.1, 0.1], - self_attn=True, - timestep_conditioning_type=["sca"], - ) - - if is_accelerate_available(): - load_model_dict_into_meta(decoder, decoder_state_dict) - else: - decoder.load_state_dict(decoder_state_dict) - - # VQGAN from Wuerstchen-V2 - vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan") - - # Decoder pipeline - decoder_pipeline = StableCascadeDecoderPipeline( - decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler - ) - decoder_pipeline.to(dtype).save_pretrained( - args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) - -if args.save_combined: - # Stable Cascade combined pipeline - stable_cascade_pipeline = StableCascadeCombinedPipeline( - # Decoder - text_encoder=text_encoder, - tokenizer=tokenizer, - decoder=decoder, - scheduler=scheduler, - vqgan=vqmodel, - # Prior - prior_text_encoder=text_encoder, - prior_tokenizer=tokenizer, - prior_prior=prior_model, - prior_scheduler=scheduler, - prior_image_encoder=image_encoder, - prior_feature_extractor=feature_extractor, - ) - stable_cascade_pipeline.to(dtype).save_pretrained( - args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) diff --git a/scripts/convert_stable_cascade_lite.py b/scripts/convert_stable_cascade_lite.py deleted file mode 100644 index 5f4804e30f74..000000000000 --- a/scripts/convert_stable_cascade_lite.py +++ /dev/null @@ -1,226 +0,0 @@ -# Run this script to convert the Stable Cascade model weights to a diffusers pipeline. -import argparse -from contextlib import nullcontext - -import torch -from safetensors.torch import load_file -from transformers import ( - AutoTokenizer, - CLIPConfig, - CLIPImageProcessor, - CLIPTextModelWithProjection, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - DDPMWuerstchenScheduler, - StableCascadeCombinedPipeline, - StableCascadeDecoderPipeline, - StableCascadePriorPipeline, -) -from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers -from diffusers.models import StableCascadeUNet -from diffusers.models.model_loading_utils import load_model_dict_into_meta -from diffusers.pipelines.wuerstchen import PaellaVQModel -from diffusers.utils import is_accelerate_available - - -if is_accelerate_available(): - from accelerate import init_empty_weights - -parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline") -parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights") -parser.add_argument( - "--stage_c_name", type=str, default="stage_c_lite.safetensors", help="Name of stage c checkpoint file" -) -parser.add_argument( - "--stage_b_name", type=str, default="stage_b_lite.safetensors", help="Name of stage b checkpoint file" -) -parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c") -parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b") -parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") -parser.add_argument( - "--prior_output_path", - default="stable-cascade-prior-lite", - type=str, - help="Hub organization to save the pipelines to", -) -parser.add_argument( - "--decoder_output_path", - type=str, - default="stable-cascade-decoder-lite", - help="Hub organization to save the pipelines to", -) -parser.add_argument( - "--combined_output_path", - type=str, - default="stable-cascade-combined-lite", - help="Hub organization to save the pipelines to", -) -parser.add_argument("--save_combined", action="store_true") -parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") -parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") - -args = parser.parse_args() - -if args.skip_stage_b and args.skip_stage_c: - raise ValueError("At least one stage should be converted") -if (args.skip_stage_b or args.skip_stage_c) and args.save_combined: - raise ValueError("Cannot skip stages when creating a combined pipeline") - -model_path = args.model_path - -device = "cpu" -if args.variant == "bf16": - dtype = torch.bfloat16 -else: - dtype = torch.float32 - -# set paths to model weights -prior_checkpoint_path = f"{model_path}/{args.stage_c_name}" -decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}" - -# Clip Text encoder and tokenizer -config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") -config.text_config.projection_dim = config.projection_dim -text_encoder = CLIPTextModelWithProjection.from_pretrained( - "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config -) -tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") - -# image processor -feature_extractor = CLIPImageProcessor() -image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") -# scheduler for prior and decoder -scheduler = DDPMWuerstchenScheduler() - -ctx = init_empty_weights if is_accelerate_available() else nullcontext - -if not args.skip_stage_c: - # Prior - if args.use_safetensors: - prior_orig_state_dict = load_file(prior_checkpoint_path, device=device) - else: - prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device) - - prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict) - with ctx(): - prior_model = StableCascadeUNet( - in_channels=16, - out_channels=16, - timestep_ratio_embedding_dim=64, - patch_size=1, - conditioning_dim=1536, - block_out_channels=[1536, 1536], - num_attention_heads=[24, 24], - down_num_layers_per_block=[4, 12], - up_num_layers_per_block=[12, 4], - down_blocks_repeat_mappers=[1, 1], - up_blocks_repeat_mappers=[1, 1], - block_types_per_layer=[ - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ], - clip_text_in_channels=1280, - clip_text_pooled_in_channels=1280, - clip_image_in_channels=768, - clip_seq=4, - kernel_size=3, - dropout=[0.1, 0.1], - self_attn=True, - timestep_conditioning_type=["sca", "crp"], - switch_level=[False], - ) - - if is_accelerate_available(): - load_model_dict_into_meta(prior_model, prior_state_dict) - else: - prior_model.load_state_dict(prior_state_dict) - - # Prior pipeline - prior_pipeline = StableCascadePriorPipeline( - prior=prior_model, - tokenizer=tokenizer, - text_encoder=text_encoder, - image_encoder=image_encoder, - scheduler=scheduler, - feature_extractor=feature_extractor, - ) - prior_pipeline.to(dtype).save_pretrained( - args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) - -if not args.skip_stage_b: - # Decoder - if args.use_safetensors: - decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device) - else: - decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device) - - decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict) - - with ctx(): - decoder = StableCascadeUNet( - in_channels=4, - out_channels=4, - timestep_ratio_embedding_dim=64, - patch_size=2, - conditioning_dim=1280, - block_out_channels=[320, 576, 1152, 1152], - down_num_layers_per_block=[2, 4, 14, 4], - up_num_layers_per_block=[4, 14, 4, 2], - down_blocks_repeat_mappers=[1, 1, 1, 1], - up_blocks_repeat_mappers=[2, 2, 2, 2], - num_attention_heads=[0, 9, 18, 18], - block_types_per_layer=[ - ["SDCascadeResBlock", "SDCascadeTimestepBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], - ], - clip_text_pooled_in_channels=1280, - clip_seq=4, - effnet_in_channels=16, - pixel_mapper_in_channels=3, - kernel_size=3, - dropout=[0, 0, 0.1, 0.1], - self_attn=True, - timestep_conditioning_type=["sca"], - ) - - if is_accelerate_available(): - load_model_dict_into_meta(decoder, decoder_state_dict) - else: - decoder.load_state_dict(decoder_state_dict) - - # VQGAN from Wuerstchen-V2 - vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan") - - # Decoder pipeline - decoder_pipeline = StableCascadeDecoderPipeline( - decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler - ) - decoder_pipeline.to(dtype).save_pretrained( - args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) - -if args.save_combined: - # Stable Cascade combined pipeline - stable_cascade_pipeline = StableCascadeCombinedPipeline( - # Decoder - text_encoder=text_encoder, - tokenizer=tokenizer, - decoder=decoder, - scheduler=scheduler, - vqgan=vqmodel, - # Prior - prior_text_encoder=text_encoder, - prior_tokenizer=tokenizer, - prior_prior=prior_model, - prior_scheduler=scheduler, - prior_image_encoder=image_encoder, - prior_feature_extractor=feature_extractor, - ) - stable_cascade_pipeline.to(dtype).save_pretrained( - args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant - ) diff --git a/scripts/convert_stable_diffusion_checkpoint_to_onnx.py b/scripts/convert_stable_diffusion_checkpoint_to_onnx.py deleted file mode 100644 index 1fb9f8d35bae..000000000000 --- a/scripts/convert_stable_diffusion_checkpoint_to_onnx.py +++ /dev/null @@ -1,265 +0,0 @@ -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -import os -import shutil -from pathlib import Path - -import onnx -import torch -from packaging import version -from torch.onnx import export - -from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline - - -is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") - - -def onnx_export( - model, - model_args: tuple, - output_path: Path, - ordered_input_names, - output_names, - dynamic_axes, - opset, - use_external_data_format=False, -): - output_path.parent.mkdir(parents=True, exist_ok=True) - # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, - # so we check the torch version for backwards compatibility - if is_torch_less_than_1_11: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - use_external_data_format=use_external_data_format, - enable_onnx_checker=True, - opset_version=opset, - ) - else: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - opset_version=opset, - ) - - -@torch.no_grad() -def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False): - dtype = torch.float16 if fp16 else torch.float32 - if fp16 and torch.cuda.is_available(): - device = "cuda" - elif fp16 and not torch.cuda.is_available(): - raise ValueError("`float16` model export is only supported on GPUs with CUDA") - else: - device = "cpu" - pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device) - output_path = Path(output_path) - - # TEXT ENCODER - num_tokens = pipeline.text_encoder.config.max_position_embeddings - text_hidden_size = pipeline.text_encoder.config.hidden_size - text_input = pipeline.tokenizer( - "A sample prompt", - padding="max_length", - max_length=pipeline.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - onnx_export( - pipeline.text_encoder, - # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files - model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)), - output_path=output_path / "text_encoder" / "model.onnx", - ordered_input_names=["input_ids"], - output_names=["last_hidden_state", "pooler_output"], - dynamic_axes={ - "input_ids": {0: "batch", 1: "sequence"}, - }, - opset=opset, - ) - del pipeline.text_encoder - - # UNET - unet_in_channels = pipeline.unet.config.in_channels - unet_sample_size = pipeline.unet.config.sample_size - unet_path = output_path / "unet" / "model.onnx" - onnx_export( - pipeline.unet, - model_args=( - torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), - torch.randn(2).to(device=device, dtype=dtype), - torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), - False, - ), - output_path=unet_path, - ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"], - output_names=["out_sample"], # has to be different from "sample" for correct tracing - dynamic_axes={ - "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - "timestep": {0: "batch"}, - "encoder_hidden_states": {0: "batch", 1: "sequence"}, - }, - opset=opset, - use_external_data_format=True, # UNet is > 2GB, so the weights need to be split - ) - unet_model_path = str(unet_path.absolute().as_posix()) - unet_dir = os.path.dirname(unet_model_path) - unet = onnx.load(unet_model_path) - # clean up existing tensor files - shutil.rmtree(unet_dir) - os.mkdir(unet_dir) - # collate external tensor files into one - onnx.save_model( - unet, - unet_model_path, - save_as_external_data=True, - all_tensors_to_one_file=True, - location="weights.pb", - convert_attribute=False, - ) - del pipeline.unet - - # VAE ENCODER - vae_encoder = pipeline.vae - vae_in_channels = vae_encoder.config.in_channels - vae_sample_size = vae_encoder.config.sample_size - # need to get the raw tensor output (sample) from the encoder - vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample() - onnx_export( - vae_encoder, - model_args=( - torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype), - False, - ), - output_path=output_path / "vae_encoder" / "model.onnx", - ordered_input_names=["sample", "return_dict"], - output_names=["latent_sample"], - dynamic_axes={ - "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - }, - opset=opset, - ) - - # VAE DECODER - vae_decoder = pipeline.vae - vae_latent_channels = vae_decoder.config.latent_channels - vae_out_channels = vae_decoder.config.out_channels - # forward only through the decoder part - vae_decoder.forward = vae_encoder.decode - onnx_export( - vae_decoder, - model_args=( - torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), - False, - ), - output_path=output_path / "vae_decoder" / "model.onnx", - ordered_input_names=["latent_sample", "return_dict"], - output_names=["sample"], - dynamic_axes={ - "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - }, - opset=opset, - ) - del pipeline.vae - - # SAFETY CHECKER - if pipeline.safety_checker is not None: - safety_checker = pipeline.safety_checker - clip_num_channels = safety_checker.config.vision_config.num_channels - clip_image_size = safety_checker.config.vision_config.image_size - safety_checker.forward = safety_checker.forward_onnx - onnx_export( - pipeline.safety_checker, - model_args=( - torch.randn( - 1, - clip_num_channels, - clip_image_size, - clip_image_size, - ).to(device=device, dtype=dtype), - torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype), - ), - output_path=output_path / "safety_checker" / "model.onnx", - ordered_input_names=["clip_input", "images"], - output_names=["out_images", "has_nsfw_concepts"], - dynamic_axes={ - "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - "images": {0: "batch", 1: "height", 2: "width", 3: "channels"}, - }, - opset=opset, - ) - del pipeline.safety_checker - safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker") - feature_extractor = pipeline.feature_extractor - else: - safety_checker = None - feature_extractor = None - - onnx_pipeline = OnnxStableDiffusionPipeline( - vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"), - vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"), - text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"), - tokenizer=pipeline.tokenizer, - unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"), - scheduler=pipeline.scheduler, - safety_checker=safety_checker, - feature_extractor=feature_extractor, - requires_safety_checker=safety_checker is not None, - ) - - onnx_pipeline.save_pretrained(output_path) - print("ONNX pipeline saved to", output_path) - - del pipeline - del onnx_pipeline - _ = OnnxStableDiffusionPipeline.from_pretrained(output_path, provider="CPUExecutionProvider") - print("ONNX pipeline is loadable") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--model_path", - type=str, - required=True, - help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", - ) - - parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--opset", - default=14, - type=int, - help="The version of the ONNX operator set to use.", - ) - parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") - - args = parser.parse_args() - - convert_models(args.model_path, args.output_path, args.opset, args.fp16) diff --git a/scripts/convert_stable_diffusion_controlnet_to_onnx.py b/scripts/convert_stable_diffusion_controlnet_to_onnx.py deleted file mode 100644 index 4af39b287836..000000000000 --- a/scripts/convert_stable_diffusion_controlnet_to_onnx.py +++ /dev/null @@ -1,505 +0,0 @@ -import argparse -import os -import shutil -from pathlib import Path - -import onnx -import onnx_graphsurgeon as gs -import torch -from onnx import shape_inference -from packaging import version -from polygraphy.backend.onnx.loader import fold_constants -from torch.onnx import export - -from diffusers import ( - ControlNetModel, - StableDiffusionControlNetImg2ImgPipeline, -) -from diffusers.models.attention_processor import AttnProcessor -from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline - - -is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") -is_torch_2_0_1 = version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.1") - - -class Optimizer: - def __init__(self, onnx_graph, verbose=False): - self.graph = gs.import_onnx(onnx_graph) - self.verbose = verbose - - def info(self, prefix): - if self.verbose: - print( - f"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs" - ) - - def cleanup(self, return_onnx=False): - self.graph.cleanup().toposort() - if return_onnx: - return gs.export_onnx(self.graph) - - def select_outputs(self, keep, names=None): - self.graph.outputs = [self.graph.outputs[o] for o in keep] - if names: - for i, name in enumerate(names): - self.graph.outputs[i].name = name - - def fold_constants(self, return_onnx=False): - onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True) - self.graph = gs.import_onnx(onnx_graph) - if return_onnx: - return onnx_graph - - def infer_shapes(self, return_onnx=False): - onnx_graph = gs.export_onnx(self.graph) - if onnx_graph.ByteSize() > 2147483648: - raise TypeError("ERROR: model size exceeds supported 2GB limit") - else: - onnx_graph = shape_inference.infer_shapes(onnx_graph) - - self.graph = gs.import_onnx(onnx_graph) - if return_onnx: - return onnx_graph - - -def optimize(onnx_graph, name, verbose): - opt = Optimizer(onnx_graph, verbose=verbose) - opt.info(name + ": original") - opt.cleanup() - opt.info(name + ": cleanup") - opt.fold_constants() - opt.info(name + ": fold constants") - # opt.infer_shapes() - # opt.info(name + ': shape inference') - onnx_opt_graph = opt.cleanup(return_onnx=True) - opt.info(name + ": finished") - return onnx_opt_graph - - -class UNet2DConditionControlNetModel(torch.nn.Module): - def __init__( - self, - unet, - controlnets: ControlNetModel, - ): - super().__init__() - self.unet = unet - self.controlnets = controlnets - - def forward( - self, - sample, - timestep, - encoder_hidden_states, - controlnet_conds, - controlnet_scales, - ): - for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate( - zip(controlnet_conds, controlnet_scales, self.controlnets) - ): - down_samples, mid_sample = controlnet( - sample, - timestep, - encoder_hidden_states=encoder_hidden_states, - controlnet_cond=controlnet_cond, - conditioning_scale=conditioning_scale, - return_dict=False, - ) - - # merge samples - if i == 0: - down_block_res_samples, mid_block_res_sample = down_samples, mid_sample - else: - down_block_res_samples = [ - samples_prev + samples_curr - for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) - ] - mid_block_res_sample += mid_sample - - noise_pred = self.unet( - sample, - timestep, - encoder_hidden_states=encoder_hidden_states, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - return_dict=False, - )[0] - return noise_pred - - -class UNet2DConditionXLControlNetModel(torch.nn.Module): - def __init__( - self, - unet, - controlnets: ControlNetModel, - ): - super().__init__() - self.unet = unet - self.controlnets = controlnets - - def forward( - self, - sample, - timestep, - encoder_hidden_states, - controlnet_conds, - controlnet_scales, - text_embeds, - time_ids, - ): - added_cond_kwargs = {"text_embeds": text_embeds, "time_ids": time_ids} - for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate( - zip(controlnet_conds, controlnet_scales, self.controlnets) - ): - down_samples, mid_sample = controlnet( - sample, - timestep, - encoder_hidden_states=encoder_hidden_states, - controlnet_cond=controlnet_cond, - conditioning_scale=conditioning_scale, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - ) - - # merge samples - if i == 0: - down_block_res_samples, mid_block_res_sample = down_samples, mid_sample - else: - down_block_res_samples = [ - samples_prev + samples_curr - for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) - ] - mid_block_res_sample += mid_sample - - noise_pred = self.unet( - sample, - timestep, - encoder_hidden_states=encoder_hidden_states, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - added_cond_kwargs=added_cond_kwargs, - return_dict=False, - )[0] - return noise_pred - - -def onnx_export( - model, - model_args: tuple, - output_path: Path, - ordered_input_names, - output_names, - dynamic_axes, - opset, - use_external_data_format=False, -): - output_path.parent.mkdir(parents=True, exist_ok=True) - # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, - # so we check the torch version for backwards compatibility - with torch.inference_mode(), torch.autocast("cuda"): - if is_torch_less_than_1_11: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - use_external_data_format=use_external_data_format, - enable_onnx_checker=True, - opset_version=opset, - ) - else: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - opset_version=opset, - ) - - -@torch.no_grad() -def convert_models( - model_path: str, controlnet_path: list, output_path: str, opset: int, fp16: bool = False, sd_xl: bool = False -): - """ - Function to convert models in stable diffusion controlnet pipeline into ONNX format - - Example: - python convert_stable_diffusion_controlnet_to_onnx.py - --model_path danbrown/RevAnimated-v1-2-2 - --controlnet_path lllyasviel/control_v11f1e_sd15_tile ioclab/brightness-controlnet - --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2 - --fp16 - - Example for SD XL: - python convert_stable_diffusion_controlnet_to_onnx.py - --model_path stabilityai/stable-diffusion-xl-base-1.0 - --controlnet_path SargeZT/sdxl-controlnet-seg - --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0 - --fp16 - --sd_xl - - Returns: - create 4 onnx models in output path - text_encoder/model.onnx - unet/model.onnx + unet/weights.pb - vae_encoder/model.onnx - vae_decoder/model.onnx - - run test script in diffusers/examples/community - python test_onnx_controlnet.py - --sd_model danbrown/RevAnimated-v1-2-2 - --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2 - --qr_img_path path-to-qr-code-image - """ - dtype = torch.float16 if fp16 else torch.float32 - if fp16 and torch.cuda.is_available(): - device = "cuda" - elif fp16 and not torch.cuda.is_available(): - raise ValueError("`float16` model export is only supported on GPUs with CUDA") - else: - device = "cpu" - - # init controlnet - controlnets = [] - for path in controlnet_path: - controlnet = ControlNetModel.from_pretrained(path, torch_dtype=dtype).to(device) - if is_torch_2_0_1: - controlnet.set_attn_processor(AttnProcessor()) - controlnets.append(controlnet) - - if sd_xl: - if len(controlnets) == 1: - controlnet = controlnets[0] - else: - raise ValueError("MultiControlNet is not yet supported.") - pipeline = StableDiffusionXLControlNetPipeline.from_pretrained( - model_path, controlnet=controlnet, torch_dtype=dtype, variant="fp16", use_safetensors=True - ).to(device) - else: - pipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( - model_path, controlnet=controlnets, torch_dtype=dtype - ).to(device) - - output_path = Path(output_path) - if is_torch_2_0_1: - pipeline.unet.set_attn_processor(AttnProcessor()) - pipeline.vae.set_attn_processor(AttnProcessor()) - - # # TEXT ENCODER - num_tokens = pipeline.text_encoder.config.max_position_embeddings - text_hidden_size = pipeline.text_encoder.config.hidden_size - text_input = pipeline.tokenizer( - "A sample prompt", - padding="max_length", - max_length=pipeline.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - onnx_export( - pipeline.text_encoder, - # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files - model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)), - output_path=output_path / "text_encoder" / "model.onnx", - ordered_input_names=["input_ids"], - output_names=["last_hidden_state", "pooler_output"], - dynamic_axes={ - "input_ids": {0: "batch", 1: "sequence"}, - }, - opset=opset, - ) - del pipeline.text_encoder - - # # UNET - if sd_xl: - controlnets = torch.nn.ModuleList(controlnets) - unet_controlnet = UNet2DConditionXLControlNetModel(pipeline.unet, controlnets) - unet_in_channels = pipeline.unet.config.in_channels - unet_sample_size = pipeline.unet.config.sample_size - text_hidden_size = 2048 - img_size = 8 * unet_sample_size - unet_path = output_path / "unet" / "model.onnx" - - onnx_export( - unet_controlnet, - model_args=( - torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), - torch.tensor([1.0]).to(device=device, dtype=dtype), - torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), - torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype), - torch.randn(len(controlnets), 1).to(device=device, dtype=dtype), - torch.randn(2, 1280).to(device=device, dtype=dtype), - torch.rand(2, 6).to(device=device, dtype=dtype), - ), - output_path=unet_path, - ordered_input_names=[ - "sample", - "timestep", - "encoder_hidden_states", - "controlnet_conds", - "conditioning_scales", - "text_embeds", - "time_ids", - ], - output_names=["noise_pred"], # has to be different from "sample" for correct tracing - dynamic_axes={ - "sample": {0: "2B", 2: "H", 3: "W"}, - "encoder_hidden_states": {0: "2B"}, - "controlnet_conds": {1: "2B", 3: "8H", 4: "8W"}, - "text_embeds": {0: "2B"}, - "time_ids": {0: "2B"}, - }, - opset=opset, - use_external_data_format=True, # UNet is > 2GB, so the weights need to be split - ) - unet_model_path = str(unet_path.absolute().as_posix()) - unet_dir = os.path.dirname(unet_model_path) - # optimize onnx - shape_inference.infer_shapes_path(unet_model_path, unet_model_path) - unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True) - # clean up existing tensor files - shutil.rmtree(unet_dir) - os.mkdir(unet_dir) - # collate external tensor files into one - onnx.save_model( - unet_opt_graph, - unet_model_path, - save_as_external_data=True, - all_tensors_to_one_file=True, - location="weights.pb", - convert_attribute=False, - ) - del pipeline.unet - else: - controlnets = torch.nn.ModuleList(controlnets) - unet_controlnet = UNet2DConditionControlNetModel(pipeline.unet, controlnets) - unet_in_channels = pipeline.unet.config.in_channels - unet_sample_size = pipeline.unet.config.sample_size - img_size = 8 * unet_sample_size - unet_path = output_path / "unet" / "model.onnx" - - onnx_export( - unet_controlnet, - model_args=( - torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), - torch.tensor([1.0]).to(device=device, dtype=dtype), - torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), - torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype), - torch.randn(len(controlnets), 1).to(device=device, dtype=dtype), - ), - output_path=unet_path, - ordered_input_names=[ - "sample", - "timestep", - "encoder_hidden_states", - "controlnet_conds", - "conditioning_scales", - ], - output_names=["noise_pred"], # has to be different from "sample" for correct tracing - dynamic_axes={ - "sample": {0: "2B", 2: "H", 3: "W"}, - "encoder_hidden_states": {0: "2B"}, - "controlnet_conds": {1: "2B", 3: "8H", 4: "8W"}, - }, - opset=opset, - use_external_data_format=True, # UNet is > 2GB, so the weights need to be split - ) - unet_model_path = str(unet_path.absolute().as_posix()) - unet_dir = os.path.dirname(unet_model_path) - # optimize onnx - shape_inference.infer_shapes_path(unet_model_path, unet_model_path) - unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True) - # clean up existing tensor files - shutil.rmtree(unet_dir) - os.mkdir(unet_dir) - # collate external tensor files into one - onnx.save_model( - unet_opt_graph, - unet_model_path, - save_as_external_data=True, - all_tensors_to_one_file=True, - location="weights.pb", - convert_attribute=False, - ) - del pipeline.unet - - # VAE ENCODER - vae_encoder = pipeline.vae - vae_in_channels = vae_encoder.config.in_channels - vae_sample_size = vae_encoder.config.sample_size - # need to get the raw tensor output (sample) from the encoder - vae_encoder.forward = lambda sample: vae_encoder.encode(sample).latent_dist.sample() - onnx_export( - vae_encoder, - model_args=(torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),), - output_path=output_path / "vae_encoder" / "model.onnx", - ordered_input_names=["sample"], - output_names=["latent_sample"], - dynamic_axes={ - "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - }, - opset=opset, - ) - - # VAE DECODER - vae_decoder = pipeline.vae - vae_latent_channels = vae_decoder.config.latent_channels - # forward only through the decoder part - vae_decoder.forward = vae_encoder.decode - onnx_export( - vae_decoder, - model_args=( - torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), - ), - output_path=output_path / "vae_decoder" / "model.onnx", - ordered_input_names=["latent_sample"], - output_names=["sample"], - dynamic_axes={ - "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - }, - opset=opset, - ) - del pipeline.vae - - del pipeline - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--sd_xl", action="store_true", default=False, help="SD XL pipeline") - - parser.add_argument( - "--model_path", - type=str, - required=True, - help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", - ) - - parser.add_argument( - "--controlnet_path", - nargs="+", - required=True, - help="Path to the `controlnet` checkpoint to convert (either a local directory or on the Hub).", - ) - - parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--opset", - default=14, - type=int, - help="The version of the ONNX operator set to use.", - ) - parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") - - args = parser.parse_args() - - convert_models(args.model_path, args.controlnet_path, args.output_path, args.opset, args.fp16, args.sd_xl) diff --git a/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py b/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py deleted file mode 100644 index 52ab02c221e9..000000000000 --- a/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py +++ /dev/null @@ -1,121 +0,0 @@ -import argparse -import sys - -import tensorrt as trt - - -def convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False): - """ - Function to convert models in stable diffusion controlnet pipeline into TensorRT format - - Example: - python convert_stable_diffusion_controlnet_to_tensorrt.py - --onnx_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.onnx - --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.engine - --fp16 - --num_controlnet 2 - - Example for SD XL: - python convert_stable_diffusion_controlnet_to_tensorrt.py - --onnx_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.onnx - --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine - --fp16 - --num_controlnet 1 - --sd_xl - - Returns: - unet/model.engine - - run test script in diffusers/examples/community - python test_onnx_controlnet.py - --sd_model danbrown/RevAnimated-v1-2-2 - --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2 - --unet_engine_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine - --qr_img_path path-to-qr-code-image - """ - # UNET - if sd_xl: - batch_size = 1 - unet_in_channels = 4 - unet_sample_size = 64 - num_tokens = 77 - text_hidden_size = 2048 - img_size = 512 - - text_embeds_shape = (2 * batch_size, 1280) - time_ids_shape = (2 * batch_size, 6) - else: - batch_size = 1 - unet_in_channels = 4 - unet_sample_size = 64 - num_tokens = 77 - text_hidden_size = 768 - img_size = 512 - batch_size = 1 - - latents_shape = (2 * batch_size, unet_in_channels, unet_sample_size, unet_sample_size) - embed_shape = (2 * batch_size, num_tokens, text_hidden_size) - controlnet_conds_shape = (num_controlnet, 2 * batch_size, 3, img_size, img_size) - - TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) - TRT_BUILDER = trt.Builder(TRT_LOGGER) - TRT_RUNTIME = trt.Runtime(TRT_LOGGER) - - network = TRT_BUILDER.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) - onnx_parser = trt.OnnxParser(network, TRT_LOGGER) - - parse_success = onnx_parser.parse_from_file(onnx_path) - for idx in range(onnx_parser.num_errors): - print(onnx_parser.get_error(idx)) - if not parse_success: - sys.exit("ONNX model parsing failed") - print("Load Onnx model done") - - profile = TRT_BUILDER.create_optimization_profile() - - profile.set_shape("sample", latents_shape, latents_shape, latents_shape) - profile.set_shape("encoder_hidden_states", embed_shape, embed_shape, embed_shape) - profile.set_shape("controlnet_conds", controlnet_conds_shape, controlnet_conds_shape, controlnet_conds_shape) - if sd_xl: - profile.set_shape("text_embeds", text_embeds_shape, text_embeds_shape, text_embeds_shape) - profile.set_shape("time_ids", time_ids_shape, time_ids_shape, time_ids_shape) - - config = TRT_BUILDER.create_builder_config() - config.add_optimization_profile(profile) - config.set_preview_feature(trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805, True) - if fp16: - config.set_flag(trt.BuilderFlag.FP16) - - plan = TRT_BUILDER.build_serialized_network(network, config) - if plan is None: - sys.exit("Failed building engine") - print("Succeeded building engine") - - engine = TRT_RUNTIME.deserialize_cuda_engine(plan) - - ## save TRT engine - with open(output_path, "wb") as f: - f.write(engine.serialize()) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--sd_xl", action="store_true", default=False, help="SD XL pipeline") - - parser.add_argument( - "--onnx_path", - type=str, - required=True, - help="Path to the onnx checkpoint to convert", - ) - - parser.add_argument("--num_controlnet", type=int) - - parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") - - parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") - - args = parser.parse_args() - - convert_models(args.onnx_path, args.num_controlnet, args.output_path, args.fp16, args.sd_xl) diff --git a/scripts/convert_svd_to_diffusers.py b/scripts/convert_svd_to_diffusers.py deleted file mode 100644 index e46410ccb3bd..000000000000 --- a/scripts/convert_svd_to_diffusers.py +++ /dev/null @@ -1,730 +0,0 @@ -from diffusers.utils import is_accelerate_available, logging - - -if is_accelerate_available(): - pass - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): - """ - Creates a config for the diffusers based on the config of the LDM model. - """ - if controlnet: - unet_params = original_config.model.params.control_stage_config.params - else: - if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None: - unet_params = original_config.model.params.unet_config.params - else: - unet_params = original_config.model.params.network_config.params - - vae_params = original_config.model.params.first_stage_config.params.encoder_config.params - - block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = ( - "CrossAttnDownBlockSpatioTemporal" - if resolution in unet_params.attention_resolutions - else "DownBlockSpatioTemporal" - ) - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = ( - "CrossAttnUpBlockSpatioTemporal" - if resolution in unet_params.attention_resolutions - else "UpBlockSpatioTemporal" - ) - up_block_types.append(block_type) - resolution //= 2 - - if unet_params.transformer_depth is not None: - transformer_layers_per_block = ( - unet_params.transformer_depth - if isinstance(unet_params.transformer_depth, int) - else list(unet_params.transformer_depth) - ) - else: - transformer_layers_per_block = 1 - - vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) - - head_dim = unet_params.num_heads if "num_heads" in unet_params else None - use_linear_projection = ( - unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False - ) - if use_linear_projection: - # stable diffusion 2-base-512 and 2-768 - if head_dim is None: - head_dim_mult = unet_params.model_channels // unet_params.num_head_channels - head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)] - - class_embed_type = None - addition_embed_type = None - addition_time_embed_dim = None - projection_class_embeddings_input_dim = None - context_dim = None - - if unet_params.context_dim is not None: - context_dim = ( - unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0] - ) - - if "num_classes" in unet_params: - if unet_params.num_classes == "sequential": - addition_time_embed_dim = 256 - assert "adm_in_channels" in unet_params - projection_class_embeddings_input_dim = unet_params.adm_in_channels - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params.in_channels, - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params.num_res_blocks, - "cross_attention_dim": context_dim, - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "class_embed_type": class_embed_type, - "addition_embed_type": addition_embed_type, - "addition_time_embed_dim": addition_time_embed_dim, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "transformer_layers_per_block": transformer_layers_per_block, - } - - if "disable_self_attentions" in unet_params: - config["only_cross_attention"] = unet_params.disable_self_attentions - - if "num_classes" in unet_params and isinstance(unet_params.num_classes, int): - config["num_class_embeds"] = unet_params.num_classes - - if controlnet: - config["conditioning_channels"] = unet_params.hint_channels - else: - config["out_channels"] = unet_params.out_channels - config["up_block_types"] = tuple(up_block_types) - - return config - - -def assign_to_checkpoint( - paths, - checkpoint, - old_checkpoint, - attention_paths_to_split=None, - additional_replacements=None, - config=None, - mid_block_suffix="", -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - if mid_block_suffix is not None: - mid_block_suffix = f".{mid_block_suffix}" - else: - mid_block_suffix = "" - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", f"mid_block.resnets.0{mid_block_suffix}") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", f"mid_block.resnets.1{mid_block_suffix}") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - if new_path == "mid_block.resnets.0.spatial_res_block.norm1.weight": - print("yeyy") - - # proj_attn.weight has to be converted from conv 1D to linear - is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) - shape = old_checkpoint[path["old"]].shape - if is_attn_weight and len(shape) == 3: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - elif is_attn_weight and len(shape) == 4: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - new_item = new_item.replace("time_stack", "temporal_transformer_blocks") - - new_item = new_item.replace("time_pos_embed.0.bias", "time_pos_embed.linear_1.bias") - new_item = new_item.replace("time_pos_embed.0.weight", "time_pos_embed.linear_1.weight") - new_item = new_item.replace("time_pos_embed.2.bias", "time_pos_embed.linear_2.bias") - new_item = new_item.replace("time_pos_embed.2.weight", "time_pos_embed.linear_2.weight") - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = new_item.replace("time_stack.", "") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_unet_checkpoint( - checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False -): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - if skip_extract_state_dict: - unet_state_dict = checkpoint - else: - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") - logger.warning( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - logger.warning( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - # if config["addition_embed_type"] == "text_time": - new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - spatial_resnets = [ - key - for key in input_blocks[i] - if f"input_blocks.{i}.0" in key - and ( - f"input_blocks.{i}.0.op" not in key - and f"input_blocks.{i}.0.time_stack" not in key - and f"input_blocks.{i}.0.time_mixer" not in key - ) - ] - temporal_resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0.time_stack" in key] - # import ipdb; ipdb.set_trace() - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(spatial_resnets) - meta_path = { - "old": f"input_blocks.{i}.0", - "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - paths = renew_resnet_paths(temporal_resnets) - meta_path = { - "old": f"input_blocks.{i}.0", - "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - # TODO resnet time_mixer.mix_factor - if f"input_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = ( - unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"] - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - # import ipdb; ipdb.set_trace() - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_spatial = [key for key in resnet_0 if "time_stack" not in key and "time_mixer" not in key] - resnet_0_paths = renew_resnet_paths(resnet_0_spatial) - # import ipdb; ipdb.set_trace() - assign_to_checkpoint( - resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block" - ) - - resnet_0_temporal = [key for key in resnet_0 if "time_stack" in key and "time_mixer" not in key] - resnet_0_paths = renew_resnet_paths(resnet_0_temporal) - assign_to_checkpoint( - resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block" - ) - - resnet_1_spatial = [key for key in resnet_1 if "time_stack" not in key and "time_mixer" not in key] - resnet_1_paths = renew_resnet_paths(resnet_1_spatial) - assign_to_checkpoint( - resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block" - ) - - resnet_1_temporal = [key for key in resnet_1 if "time_stack" in key and "time_mixer" not in key] - resnet_1_paths = renew_resnet_paths(resnet_1_temporal) - assign_to_checkpoint( - resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block" - ) - - new_checkpoint["mid_block.resnets.0.time_mixer.mix_factor"] = unet_state_dict[ - "middle_block.0.time_mixer.mix_factor" - ] - new_checkpoint["mid_block.resnets.1.time_mixer.mix_factor"] = unet_state_dict[ - "middle_block.2.time_mixer.mix_factor" - ] - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - spatial_resnets = [ - key - for key in output_blocks[i] - if f"output_blocks.{i}.0" in key - and (f"output_blocks.{i}.0.time_stack" not in key and "time_mixer" not in key) - ] - # import ipdb; ipdb.set_trace() - - temporal_resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0.time_stack" in key] - - paths = renew_resnet_paths(spatial_resnets) - meta_path = { - "old": f"output_blocks.{i}.0", - "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - paths = renew_resnet_paths(temporal_resnets) - meta_path = { - "old": f"output_blocks.{i}.0", - "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if f"output_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict: - new_checkpoint[f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = ( - unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"] - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and "conv" not in key] - if len(attentions): - paths = renew_attention_paths(attentions) - # import ipdb; ipdb.set_trace() - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - spatial_layers = [ - layer for layer in output_block_layers if "time_stack" not in layer and "time_mixer" not in layer - ] - resnet_0_paths = renew_resnet_paths(spatial_layers, n_shave_prefix_segments=1) - # import ipdb; ipdb.set_trace() - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join( - ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "spatial_res_block", path["new"]] - ) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - temporal_layers = [ - layer for layer in output_block_layers if "time_stack" in layer and "time_mixer" not in key - ] - resnet_0_paths = renew_resnet_paths(temporal_layers, n_shave_prefix_segments=1) - # import ipdb; ipdb.set_trace() - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join( - ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "temporal_res_block", path["new"]] - ) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - new_checkpoint["up_blocks.0.resnets.0.time_mixer.mix_factor"] = unet_state_dict[ - f"output_blocks.{str(i)}.0.time_mixer.mix_factor" - ] - - return new_checkpoint - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0, is_temporal=False): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # Temporal resnet - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = new_item.replace("time_stack.", "temporal_res_block.") - - # Spatial resnet - new_item = new_item.replace("conv1", "spatial_res_block.conv1") - new_item = new_item.replace("norm1", "spatial_res_block.norm1") - - new_item = new_item.replace("conv2", "spatial_res_block.conv2") - new_item = new_item.replace("norm2", "spatial_res_block.norm2") - - new_item = new_item.replace("nin_shortcut", "spatial_res_block.conv_shortcut") - - new_item = new_item.replace("mix_factor", "spatial_res_block.time_mixer.mix_factor") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - keys = list(checkpoint.keys()) - vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else "" - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - new_checkpoint["decoder.time_conv_out.weight"] = vae_state_dict["decoder.time_mix_conv.weight"] - new_checkpoint["decoder.time_conv_out.bias"] = vae_state_dict["decoder.time_mix_conv.bias"] - - # new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - # new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - # new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - # new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint diff --git a/scripts/convert_tiny_autoencoder_to_diffusers.py b/scripts/convert_tiny_autoencoder_to_diffusers.py deleted file mode 100644 index 9bb2df98a77a..000000000000 --- a/scripts/convert_tiny_autoencoder_to_diffusers.py +++ /dev/null @@ -1,71 +0,0 @@ -import argparse - -import safetensors.torch - -from diffusers import AutoencoderTiny - - -""" -Example - From the diffusers root directory: - -Download the weights: -```sh -$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_encoder.safetensors -$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_decoder.safetensors -``` - -Convert the model: -```sh -$ python scripts/convert_tiny_autoencoder_to_diffusers.py \ - --encoder_ckpt_path taesd_encoder.safetensors \ - --decoder_ckpt_path taesd_decoder.safetensors \ - --dump_path taesd-diffusers -``` -""" - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument( - "--encoder_ckpt_path", - default=None, - type=str, - required=True, - help="Path to the encoder ckpt.", - ) - parser.add_argument( - "--decoder_ckpt_path", - default=None, - type=str, - required=True, - help="Path to the decoder ckpt.", - ) - parser.add_argument( - "--use_safetensors", action="store_true", help="Whether to serialize in the safetensors format." - ) - args = parser.parse_args() - - print("Loading the original state_dicts of the encoder and the decoder...") - encoder_state_dict = safetensors.torch.load_file(args.encoder_ckpt_path) - decoder_state_dict = safetensors.torch.load_file(args.decoder_ckpt_path) - - print("Populating the state_dicts in the diffusers format...") - tiny_autoencoder = AutoencoderTiny() - new_state_dict = {} - - # Modify the encoder state dict. - for k in encoder_state_dict: - new_state_dict.update({f"encoder.layers.{k}": encoder_state_dict[k]}) - - # Modify the decoder state dict. - for k in decoder_state_dict: - layer_id = int(k.split(".")[0]) - 1 - new_k = str(layer_id) + "." + ".".join(k.split(".")[1:]) - new_state_dict.update({f"decoder.layers.{new_k}": decoder_state_dict[k]}) - - # Assertion tests with the original implementation can be found here: - # https://gist.github.com/sayakpaul/337b0988f08bd2cf2b248206f760e28f - tiny_autoencoder.load_state_dict(new_state_dict) - print("Population successful, serializing...") - tiny_autoencoder.save_pretrained(args.dump_path, safe_serialization=args.use_safetensors) diff --git a/scripts/convert_unclip_txt2img_to_image_variation.py b/scripts/convert_unclip_txt2img_to_image_variation.py deleted file mode 100644 index 07f8ebf2a3d0..000000000000 --- a/scripts/convert_unclip_txt2img_to_image_variation.py +++ /dev/null @@ -1,41 +0,0 @@ -import argparse - -from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection - -from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--txt2img_unclip", - default="kakaobrain/karlo-v1-alpha", - type=str, - required=False, - help="The pretrained txt2img unclip.", - ) - - args = parser.parse_args() - - txt2img = UnCLIPPipeline.from_pretrained(args.txt2img_unclip) - - feature_extractor = CLIPImageProcessor() - image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - - img2img = UnCLIPImageVariationPipeline( - decoder=txt2img.decoder, - text_encoder=txt2img.text_encoder, - tokenizer=txt2img.tokenizer, - text_proj=txt2img.text_proj, - feature_extractor=feature_extractor, - image_encoder=image_encoder, - super_res_first=txt2img.super_res_first, - super_res_last=txt2img.super_res_last, - decoder_scheduler=txt2img.decoder_scheduler, - super_res_scheduler=txt2img.super_res_scheduler, - ) - - img2img.save_pretrained(args.dump_path) diff --git a/scripts/convert_unidiffuser_to_diffusers.py b/scripts/convert_unidiffuser_to_diffusers.py deleted file mode 100644 index 4c38172754f6..000000000000 --- a/scripts/convert_unidiffuser_to_diffusers.py +++ /dev/null @@ -1,786 +0,0 @@ -# Convert the original UniDiffuser checkpoints into diffusers equivalents. - -import argparse -from argparse import Namespace - -import torch -from transformers import ( - CLIPImageProcessor, - CLIPTextConfig, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionConfig, - CLIPVisionModelWithProjection, - GPT2Tokenizer, -) - -from diffusers import ( - AutoencoderKL, - DPMSolverMultistepScheduler, - UniDiffuserModel, - UniDiffuserPipeline, - UniDiffuserTextDecoder, -) - - -SCHEDULER_CONFIG = Namespace( - **{ - "beta_start": 0.00085, - "beta_end": 0.012, - "beta_schedule": "scaled_linear", - "solver_order": 3, - } -) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -# Modified from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint -# config.num_head_channels => num_head_channels -def assign_to_checkpoint( - paths, - checkpoint, - old_checkpoint, - attention_paths_to_split=None, - additional_replacements=None, - num_head_channels=1, -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // num_head_channels // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) - shape = old_checkpoint[path["old"]].shape - if is_attn_weight and len(shape) == 3: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - elif is_attn_weight and len(shape) == 4: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def create_vae_diffusers_config(config_type): - # Hardcoded for now - if args.config_type == "test": - vae_config = create_vae_diffusers_config_test() - elif args.config_type == "big": - vae_config = create_vae_diffusers_config_big() - else: - raise NotImplementedError( - f"Config type {config_type} is not implemented, currently only config types" - " 'test' and 'big' are available." - ) - return vae_config - - -def create_unidiffuser_unet_config(config_type, version): - # Hardcoded for now - if args.config_type == "test": - unet_config = create_unidiffuser_unet_config_test() - elif args.config_type == "big": - unet_config = create_unidiffuser_unet_config_big() - else: - raise NotImplementedError( - f"Config type {config_type} is not implemented, currently only config types" - " 'test' and 'big' are available." - ) - # Unidiffuser-v1 uses data type embeddings - if version == 1: - unet_config["use_data_type_embedding"] = True - return unet_config - - -def create_text_decoder_config(config_type): - # Hardcoded for now - if args.config_type == "test": - text_decoder_config = create_text_decoder_config_test() - elif args.config_type == "big": - text_decoder_config = create_text_decoder_config_big() - else: - raise NotImplementedError( - f"Config type {config_type} is not implemented, currently only config types" - " 'test' and 'big' are available." - ) - return text_decoder_config - - -# Hardcoded configs for test versions of the UniDiffuser models, corresponding to those in the fast default tests. -def create_vae_diffusers_config_test(): - vae_config = { - "sample_size": 32, - "in_channels": 3, - "out_channels": 3, - "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], - "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], - "block_out_channels": [32, 64], - "latent_channels": 4, - "layers_per_block": 1, - } - return vae_config - - -def create_unidiffuser_unet_config_test(): - unet_config = { - "text_dim": 32, - "clip_img_dim": 32, - "num_text_tokens": 77, - "num_attention_heads": 2, - "attention_head_dim": 8, - "in_channels": 4, - "out_channels": 4, - "num_layers": 2, - "dropout": 0.0, - "norm_num_groups": 32, - "attention_bias": False, - "sample_size": 16, - "patch_size": 2, - "activation_fn": "gelu", - "num_embeds_ada_norm": 1000, - "norm_type": "layer_norm", - "block_type": "unidiffuser", - "pre_layer_norm": False, - "use_timestep_embedding": False, - "norm_elementwise_affine": True, - "use_patch_pos_embed": False, - "ff_final_dropout": True, - "use_data_type_embedding": False, - } - return unet_config - - -def create_text_decoder_config_test(): - text_decoder_config = { - "prefix_length": 77, - "prefix_inner_dim": 32, - "prefix_hidden_dim": 32, - "vocab_size": 1025, # 1024 + 1 for new EOS token - "n_positions": 1024, - "n_embd": 32, - "n_layer": 5, - "n_head": 4, - "n_inner": 37, - "activation_function": "gelu", - "resid_pdrop": 0.1, - "embd_pdrop": 0.1, - "attn_pdrop": 0.1, - "layer_norm_epsilon": 1e-5, - "initializer_range": 0.02, - } - return text_decoder_config - - -# Hardcoded configs for the UniDiffuser V1 model at https://huggingface.co/thu-ml/unidiffuser-v1 -# See also https://github.com/thu-ml/unidiffuser/blob/main/configs/sample_unidiffuser_v1.py -def create_vae_diffusers_config_big(): - vae_config = { - "sample_size": 256, - "in_channels": 3, - "out_channels": 3, - "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"], - "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], - "block_out_channels": [128, 256, 512, 512], - "latent_channels": 4, - "layers_per_block": 2, - } - return vae_config - - -def create_unidiffuser_unet_config_big(): - unet_config = { - "text_dim": 64, - "clip_img_dim": 512, - "num_text_tokens": 77, - "num_attention_heads": 24, - "attention_head_dim": 64, - "in_channels": 4, - "out_channels": 4, - "num_layers": 30, - "dropout": 0.0, - "norm_num_groups": 32, - "attention_bias": False, - "sample_size": 64, - "patch_size": 2, - "activation_fn": "gelu", - "num_embeds_ada_norm": 1000, - "norm_type": "layer_norm", - "block_type": "unidiffuser", - "pre_layer_norm": False, - "use_timestep_embedding": False, - "norm_elementwise_affine": True, - "use_patch_pos_embed": False, - "ff_final_dropout": True, - "use_data_type_embedding": False, - } - return unet_config - - -# From https://huggingface.co/gpt2/blob/main/config.json, the GPT2 checkpoint used by UniDiffuser -def create_text_decoder_config_big(): - text_decoder_config = { - "prefix_length": 77, - "prefix_inner_dim": 768, - "prefix_hidden_dim": 64, - "vocab_size": 50258, # 50257 + 1 for new EOS token - "n_positions": 1024, - "n_embd": 768, - "n_layer": 12, - "n_head": 12, - "n_inner": 3072, - "activation_function": "gelu", - "resid_pdrop": 0.1, - "embd_pdrop": 0.1, - "attn_pdrop": 0.1, - "layer_norm_epsilon": 1e-5, - "initializer_range": 0.02, - } - return text_decoder_config - - -# Based on diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint -def convert_vae_to_diffusers(ckpt, diffusers_model, num_head_channels=1): - """ - Converts a UniDiffuser autoencoder_kl.pth checkpoint to a diffusers AutoencoderKL. - """ - # autoencoder_kl.pth ckpt is a torch state dict - vae_state_dict = torch.load(ckpt, map_location="cpu") - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - paths, - new_checkpoint, - vae_state_dict, - additional_replacements=[meta_path], - num_head_channels=num_head_channels, # not used in vae - ) - conv_attn_to_linear(new_checkpoint) - - missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_checkpoint) - for missing_key in missing_keys: - print(f"Missing key: {missing_key}") - for unexpected_key in unexpected_keys: - print(f"Unexpected key: {unexpected_key}") - - return diffusers_model - - -def convert_uvit_block_to_diffusers_block( - uvit_state_dict, - new_state_dict, - block_prefix, - new_prefix="transformer.transformer_", - skip_connection=False, -): - """ - Maps the keys in a UniDiffuser transformer block (`Block`) to the keys in a diffusers transformer block - (`UTransformerBlock`/`UniDiffuserBlock`). - """ - prefix = new_prefix + block_prefix - if skip_connection: - new_state_dict[prefix + ".skip.skip_linear.weight"] = uvit_state_dict[block_prefix + ".skip_linear.weight"] - new_state_dict[prefix + ".skip.skip_linear.bias"] = uvit_state_dict[block_prefix + ".skip_linear.bias"] - new_state_dict[prefix + ".skip.norm.weight"] = uvit_state_dict[block_prefix + ".norm1.weight"] - new_state_dict[prefix + ".skip.norm.bias"] = uvit_state_dict[block_prefix + ".norm1.bias"] - - # Create the prefix string for out_blocks. - prefix += ".block" - - # Split up attention qkv.weight into to_q.weight, to_k.weight, to_v.weight - qkv = uvit_state_dict[block_prefix + ".attn.qkv.weight"] - new_attn_keys = [".attn1.to_q.weight", ".attn1.to_k.weight", ".attn1.to_v.weight"] - new_attn_keys = [prefix + key for key in new_attn_keys] - shape = qkv.shape[0] // len(new_attn_keys) - for i, attn_key in enumerate(new_attn_keys): - new_state_dict[attn_key] = qkv[i * shape : (i + 1) * shape] - - new_state_dict[prefix + ".attn1.to_out.0.weight"] = uvit_state_dict[block_prefix + ".attn.proj.weight"] - new_state_dict[prefix + ".attn1.to_out.0.bias"] = uvit_state_dict[block_prefix + ".attn.proj.bias"] - new_state_dict[prefix + ".norm1.weight"] = uvit_state_dict[block_prefix + ".norm2.weight"] - new_state_dict[prefix + ".norm1.bias"] = uvit_state_dict[block_prefix + ".norm2.bias"] - new_state_dict[prefix + ".ff.net.0.proj.weight"] = uvit_state_dict[block_prefix + ".mlp.fc1.weight"] - new_state_dict[prefix + ".ff.net.0.proj.bias"] = uvit_state_dict[block_prefix + ".mlp.fc1.bias"] - new_state_dict[prefix + ".ff.net.2.weight"] = uvit_state_dict[block_prefix + ".mlp.fc2.weight"] - new_state_dict[prefix + ".ff.net.2.bias"] = uvit_state_dict[block_prefix + ".mlp.fc2.bias"] - new_state_dict[prefix + ".norm3.weight"] = uvit_state_dict[block_prefix + ".norm3.weight"] - new_state_dict[prefix + ".norm3.bias"] = uvit_state_dict[block_prefix + ".norm3.bias"] - - return uvit_state_dict, new_state_dict - - -def convert_uvit_to_diffusers(ckpt, diffusers_model): - """ - Converts a UniDiffuser uvit_v*.pth checkpoint to a diffusers UniDiffusersModel. - """ - # uvit_v*.pth ckpt is a torch state dict - uvit_state_dict = torch.load(ckpt, map_location="cpu") - - new_state_dict = {} - - # Input layers - new_state_dict["vae_img_in.proj.weight"] = uvit_state_dict["patch_embed.proj.weight"] - new_state_dict["vae_img_in.proj.bias"] = uvit_state_dict["patch_embed.proj.bias"] - new_state_dict["clip_img_in.weight"] = uvit_state_dict["clip_img_embed.weight"] - new_state_dict["clip_img_in.bias"] = uvit_state_dict["clip_img_embed.bias"] - new_state_dict["text_in.weight"] = uvit_state_dict["text_embed.weight"] - new_state_dict["text_in.bias"] = uvit_state_dict["text_embed.bias"] - - new_state_dict["pos_embed"] = uvit_state_dict["pos_embed"] - - # Handle data type token embeddings for UniDiffuser-v1 - if "token_embedding.weight" in uvit_state_dict and diffusers_model.use_data_type_embedding: - new_state_dict["data_type_pos_embed_token"] = uvit_state_dict["pos_embed_token"] - new_state_dict["data_type_token_embedding.weight"] = uvit_state_dict["token_embedding.weight"] - - # Also initialize the PatchEmbedding in UTransformer2DModel with the PatchEmbedding from the checkpoint. - # This isn't used in the current implementation, so might want to remove. - new_state_dict["transformer.pos_embed.proj.weight"] = uvit_state_dict["patch_embed.proj.weight"] - new_state_dict["transformer.pos_embed.proj.bias"] = uvit_state_dict["patch_embed.proj.bias"] - - # Output layers - new_state_dict["transformer.norm_out.weight"] = uvit_state_dict["norm.weight"] - new_state_dict["transformer.norm_out.bias"] = uvit_state_dict["norm.bias"] - - new_state_dict["vae_img_out.weight"] = uvit_state_dict["decoder_pred.weight"] - new_state_dict["vae_img_out.bias"] = uvit_state_dict["decoder_pred.bias"] - new_state_dict["clip_img_out.weight"] = uvit_state_dict["clip_img_out.weight"] - new_state_dict["clip_img_out.bias"] = uvit_state_dict["clip_img_out.bias"] - new_state_dict["text_out.weight"] = uvit_state_dict["text_out.weight"] - new_state_dict["text_out.bias"] = uvit_state_dict["text_out.bias"] - - # in_blocks - in_blocks_prefixes = {".".join(layer.split(".")[:2]) for layer in uvit_state_dict if "in_blocks" in layer} - for in_block_prefix in list(in_blocks_prefixes): - convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, in_block_prefix) - - # mid_block - # Assume there's only one mid block - convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, "mid_block") - - # out_blocks - out_blocks_prefixes = {".".join(layer.split(".")[:2]) for layer in uvit_state_dict if "out_blocks" in layer} - for out_block_prefix in list(out_blocks_prefixes): - convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, out_block_prefix, skip_connection=True) - - missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict) - for missing_key in missing_keys: - print(f"Missing key: {missing_key}") - for unexpected_key in unexpected_keys: - print(f"Unexpected key: {unexpected_key}") - - return diffusers_model - - -def convert_caption_decoder_to_diffusers(ckpt, diffusers_model): - """ - Converts a UniDiffuser caption_decoder.pth checkpoint to a diffusers UniDiffuserTextDecoder. - """ - # caption_decoder.pth ckpt is a torch state dict - checkpoint_state_dict = torch.load(ckpt, map_location="cpu") - decoder_state_dict = {} - # Remove the "module." prefix, if necessary - caption_decoder_key = "module." - for key in checkpoint_state_dict: - if key.startswith(caption_decoder_key): - decoder_state_dict[key.replace(caption_decoder_key, "")] = checkpoint_state_dict.get(key) - else: - decoder_state_dict[key] = checkpoint_state_dict.get(key) - - new_state_dict = {} - - # Encoder and Decoder - new_state_dict["encode_prefix.weight"] = decoder_state_dict["encode_prefix.weight"] - new_state_dict["encode_prefix.bias"] = decoder_state_dict["encode_prefix.bias"] - new_state_dict["decode_prefix.weight"] = decoder_state_dict["decode_prefix.weight"] - new_state_dict["decode_prefix.bias"] = decoder_state_dict["decode_prefix.bias"] - - # Internal GPT2LMHeadModel transformer model - for key, val in decoder_state_dict.items(): - if key.startswith("gpt"): - suffix = key[len("gpt") :] - new_state_dict["transformer" + suffix] = val - - missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict) - for missing_key in missing_keys: - print(f"Missing key: {missing_key}") - for unexpected_key in unexpected_keys: - print(f"Unexpected key: {unexpected_key}") - - return diffusers_model - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--caption_decoder_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to caption decoder checkpoint to convert.", - ) - parser.add_argument( - "--uvit_checkpoint_path", default=None, type=str, required=False, help="Path to U-ViT checkpoint to convert." - ) - parser.add_argument( - "--vae_checkpoint_path", - default=None, - type=str, - required=False, - help="Path to VAE checkpoint to convert.", - ) - parser.add_argument( - "--pipeline_output_path", - default=None, - type=str, - required=True, - help="Path to save the output pipeline to.", - ) - parser.add_argument( - "--config_type", - default="test", - type=str, - help=( - "Config type to use. Should be 'test' to create small models for testing or 'big' to convert a full" - " checkpoint." - ), - ) - parser.add_argument( - "--version", - default=0, - type=int, - help="The UniDiffuser model type to convert to. Should be 0 for UniDiffuser-v0 and 1 for UniDiffuser-v1.", - ) - parser.add_argument( - "--safe_serialization", - action="store_true", - help="Whether to use safetensors/safe seialization when saving the pipeline.", - ) - - args = parser.parse_args() - - # Convert the VAE model. - if args.vae_checkpoint_path is not None: - vae_config = create_vae_diffusers_config(args.config_type) - vae = AutoencoderKL(**vae_config) - vae = convert_vae_to_diffusers(args.vae_checkpoint_path, vae) - - # Convert the U-ViT ("unet") model. - if args.uvit_checkpoint_path is not None: - unet_config = create_unidiffuser_unet_config(args.config_type, args.version) - unet = UniDiffuserModel(**unet_config) - unet = convert_uvit_to_diffusers(args.uvit_checkpoint_path, unet) - - # Convert the caption decoder ("text_decoder") model. - if args.caption_decoder_checkpoint_path is not None: - text_decoder_config = create_text_decoder_config(args.config_type) - text_decoder = UniDiffuserTextDecoder(**text_decoder_config) - text_decoder = convert_caption_decoder_to_diffusers(args.caption_decoder_checkpoint_path, text_decoder) - - # Scheduler is the same for both the test and big models. - scheduler_config = SCHEDULER_CONFIG - scheduler = DPMSolverMultistepScheduler( - beta_start=scheduler_config.beta_start, - beta_end=scheduler_config.beta_end, - beta_schedule=scheduler_config.beta_schedule, - solver_order=scheduler_config.solver_order, - ) - - if args.config_type == "test": - # Make a small random CLIPTextModel - torch.manual_seed(0) - clip_text_encoder_config = CLIPTextConfig( - bos_token_id=0, - eos_token_id=2, - hidden_size=32, - intermediate_size=37, - layer_norm_eps=1e-05, - num_attention_heads=4, - num_hidden_layers=5, - pad_token_id=1, - vocab_size=1000, - ) - text_encoder = CLIPTextModel(clip_text_encoder_config) - clip_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") - - # Make a small random CLIPVisionModel and accompanying CLIPImageProcessor - torch.manual_seed(0) - clip_image_encoder_config = CLIPVisionConfig( - image_size=32, - patch_size=2, - num_channels=3, - hidden_size=32, - projection_dim=32, - num_hidden_layers=5, - num_attention_heads=4, - intermediate_size=37, - dropout=0.1, - attention_dropout=0.1, - initializer_range=0.02, - ) - image_encoder = CLIPVisionModelWithProjection(clip_image_encoder_config) - image_processor = CLIPImageProcessor(crop_size=32, size=32) - - # Note that the text_decoder should already have its token embeddings resized. - text_tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model") - eos = "<|EOS|>" - special_tokens_dict = {"eos_token": eos} - text_tokenizer.add_special_tokens(special_tokens_dict) - elif args.config_type == "big": - text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") - clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - - image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") - image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32") - - # Note that the text_decoder should already have its token embeddings resized. - text_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") - eos = "<|EOS|>" - special_tokens_dict = {"eos_token": eos} - text_tokenizer.add_special_tokens(special_tokens_dict) - else: - raise NotImplementedError( - f"Config type {args.config_type} is not implemented, currently only config types" - " 'test' and 'big' are available." - ) - - pipeline = UniDiffuserPipeline( - vae=vae, - text_encoder=text_encoder, - image_encoder=image_encoder, - clip_image_processor=image_processor, - clip_tokenizer=clip_tokenizer, - text_decoder=text_decoder, - text_tokenizer=text_tokenizer, - unet=unet, - scheduler=scheduler, - ) - pipeline.save_pretrained(args.pipeline_output_path, safe_serialization=args.safe_serialization) diff --git a/scripts/convert_vae_diff_to_onnx.py b/scripts/convert_vae_diff_to_onnx.py deleted file mode 100644 index e023e04b9497..000000000000 --- a/scripts/convert_vae_diff_to_onnx.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import argparse -from pathlib import Path - -import torch -from packaging import version -from torch.onnx import export - -from diffusers import AutoencoderKL - - -is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") - - -def onnx_export( - model, - model_args: tuple, - output_path: Path, - ordered_input_names, - output_names, - dynamic_axes, - opset, - use_external_data_format=False, -): - output_path.parent.mkdir(parents=True, exist_ok=True) - # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, - # so we check the torch version for backwards compatibility - if is_torch_less_than_1_11: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - use_external_data_format=use_external_data_format, - enable_onnx_checker=True, - opset_version=opset, - ) - else: - export( - model, - model_args, - f=output_path.as_posix(), - input_names=ordered_input_names, - output_names=output_names, - dynamic_axes=dynamic_axes, - do_constant_folding=True, - opset_version=opset, - ) - - -@torch.no_grad() -def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False): - dtype = torch.float16 if fp16 else torch.float32 - if fp16 and torch.cuda.is_available(): - device = "cuda" - elif fp16 and not torch.cuda.is_available(): - raise ValueError("`float16` model export is only supported on GPUs with CUDA") - else: - device = "cpu" - output_path = Path(output_path) - - # VAE DECODER - vae_decoder = AutoencoderKL.from_pretrained(model_path + "/vae") - vae_latent_channels = vae_decoder.config.latent_channels - # forward only through the decoder part - vae_decoder.forward = vae_decoder.decode - onnx_export( - vae_decoder, - model_args=( - torch.randn(1, vae_latent_channels, 25, 25).to(device=device, dtype=dtype), - False, - ), - output_path=output_path / "vae_decoder" / "model.onnx", - ordered_input_names=["latent_sample", "return_dict"], - output_names=["sample"], - dynamic_axes={ - "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, - }, - opset=opset, - ) - del vae_decoder - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--model_path", - type=str, - required=True, - help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", - ) - - parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") - parser.add_argument( - "--opset", - default=14, - type=int, - help="The version of the ONNX operator set to use.", - ) - parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") - - args = parser.parse_args() - print(args.output_path) - convert_models(args.model_path, args.output_path, args.opset, args.fp16) - print("SD: Done: ONNX") diff --git a/scripts/convert_vae_pt_to_diffusers.py b/scripts/convert_vae_pt_to_diffusers.py deleted file mode 100644 index 8c7dc71ddfd8..000000000000 --- a/scripts/convert_vae_pt_to_diffusers.py +++ /dev/null @@ -1,177 +0,0 @@ -import argparse -import io - -import requests -import torch -import yaml - -from diffusers import AutoencoderKL -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( - assign_to_checkpoint, - conv_attn_to_linear, - create_vae_diffusers_config, - renew_vae_attention_paths, - renew_vae_resnet_paths, -) -from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT - - -def custom_convert_ldm_vae_checkpoint(checkpoint, config): - vae_state_dict = checkpoint - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [ - key - for key in down_blocks[i] - if f"down.{i}" in key and f"down.{i}.downsample" not in key and "attn" not in key - ] - attentions = [key for key in down_blocks[i] if f"down.{i}.attn" in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - paths = renew_vae_attention_paths(attentions) - meta_path = {"old": f"down.{i}.attn", "new": f"down_blocks.{i}.attentions"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key - for key in up_blocks[block_id] - if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key and "attn" not in key - ] - attentions = [key for key in up_blocks[block_id] if f"up.{block_id}.attn" in key] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - paths = renew_vae_attention_paths(attentions) - meta_path = {"old": f"up.{block_id}.attn", "new": f"up_blocks.{i}.attentions"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -def vae_pt_to_vae_diffuser( - checkpoint_path: str, - output_path: str, -): - # Only support V1 - r = requests.get( - " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml", - timeout=DIFFUSERS_REQUEST_TIMEOUT, - ) - io_obj = io.BytesIO(r.content) - - original_config = yaml.safe_load(io_obj) - image_size = 512 - device = "cuda" if torch.cuda.is_available() else "cpu" - if checkpoint_path.endswith("safetensors"): - from safetensors import safe_open - - checkpoint = {} - with safe_open(checkpoint_path, framework="pt", device="cpu") as f: - for key in f.keys(): - checkpoint[key] = f.get_tensor(key) - else: - checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"] - - # Convert the VAE model. - vae_config = create_vae_diffusers_config(original_config, image_size=image_size) - converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - vae.save_pretrained(output_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") - - args = parser.parse_args() - - vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) diff --git a/scripts/convert_versatile_diffusion_to_diffusers.py b/scripts/convert_versatile_diffusion_to_diffusers.py deleted file mode 100644 index ce68bb4c2e8c..000000000000 --- a/scripts/convert_versatile_diffusion_to_diffusers.py +++ /dev/null @@ -1,791 +0,0 @@ -# coding=utf-8 -# Copyright 2025 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Conversion script for the Versatile Stable Diffusion checkpoints.""" - -import argparse -from argparse import Namespace - -import torch -from transformers import ( - CLIPImageProcessor, - CLIPTextModelWithProjection, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - DPMSolverMultistepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - LMSDiscreteScheduler, - PNDMScheduler, - UNet2DConditionModel, - VersatileDiffusionPipeline, -) -from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel - - -SCHEDULER_CONFIG = Namespace( - **{ - "beta_linear_start": 0.00085, - "beta_linear_end": 0.012, - "timesteps": 1000, - "scale_factor": 0.18215, - } -) - -IMAGE_UNET_CONFIG = Namespace( - **{ - "input_channels": 4, - "model_channels": 320, - "output_channels": 4, - "num_noattn_blocks": [2, 2, 2, 2], - "channel_mult": [1, 2, 4, 4], - "with_attn": [True, True, True, False], - "num_heads": 8, - "context_dim": 768, - "use_checkpoint": True, - } -) - -TEXT_UNET_CONFIG = Namespace( - **{ - "input_channels": 768, - "model_channels": 320, - "output_channels": 768, - "num_noattn_blocks": [2, 2, 2, 2], - "channel_mult": [1, 2, 4, 4], - "second_dim": [4, 4, 4, 4], - "with_attn": [True, True, True, False], - "num_heads": 8, - "context_dim": 768, - "use_checkpoint": True, - } -) - -AUTOENCODER_CONFIG = Namespace( - **{ - "double_z": True, - "z_channels": 4, - "resolution": 256, - "in_channels": 3, - "out_ch": 3, - "ch": 128, - "ch_mult": [1, 2, 4, 4], - "num_res_blocks": 2, - "attn_resolutions": [], - "dropout": 0.0, - } -) - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "query.weight") - new_item = new_item.replace("q.bias", "query.bias") - - new_item = new_item.replace("k.weight", "key.weight") - new_item = new_item.replace("k.bias", "key.bias") - - new_item = new_item.replace("v.weight", "value.weight") - new_item = new_item.replace("v.bias", "value.bias") - - new_item = new_item.replace("proj_out.weight", "proj_attn.weight") - new_item = new_item.replace("proj_out.bias", "proj_attn.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming - to them. It splits attention layers, and takes into account additional replacements - that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - if "proj_attn.weight" in new_path: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - elif path["old"] in old_checkpoint: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def create_image_unet_diffusers_config(unet_params): - """ - Creates a config for the diffusers based on the config of the VD model. - """ - - block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if unet_params.with_attn[i] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if unet_params.with_attn[-i - 1] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): - raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") - - config = { - "sample_size": None, - "in_channels": unet_params.input_channels, - "out_channels": unet_params.output_channels, - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params.num_noattn_blocks[0], - "cross_attention_dim": unet_params.context_dim, - "attention_head_dim": unet_params.num_heads, - } - - return config - - -def create_text_unet_diffusers_config(unet_params): - """ - Creates a config for the diffusers based on the config of the VD model. - """ - - block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlockFlat" if unet_params.with_attn[i] else "DownBlockFlat" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlockFlat" if unet_params.with_attn[-i - 1] else "UpBlockFlat" - up_block_types.append(block_type) - resolution //= 2 - - if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): - raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") - - config = { - "sample_size": None, - "in_channels": (unet_params.input_channels, 1, 1), - "out_channels": (unet_params.output_channels, 1, 1), - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params.num_noattn_blocks[0], - "cross_attention_dim": unet_params.context_dim, - "attention_head_dim": unet_params.num_heads, - } - - return config - - -def create_vae_diffusers_config(vae_params): - """ - Creates a config for the diffusers based on the config of the VD model. - """ - - block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - config = { - "sample_size": vae_params.resolution, - "in_channels": vae_params.in_channels, - "out_channels": vae_params.out_ch, - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params.z_channels, - "layers_per_block": vae_params.num_res_blocks, - } - return config - - -def create_diffusers_scheduler(original_config): - schedular = DDIMScheduler( - num_train_timesteps=original_config.model.params.timesteps, - beta_start=original_config.model.params.linear_start, - beta_end=original_config.model.params.linear_end, - beta_schedule="scaled_linear", - ) - return schedular - - -def convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=False): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100: - print("Checkpoint has both EMA and non-EMA weights.") - if extract_ema: - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["model.diffusion_model.time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["model.diffusion_model.time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["model.diffusion_model.time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["model.diffusion_model.time_embed.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - elif f"input_blocks.{i}.0.weight" in unet_state_dict: - # text_unet uses linear layers in place of downsamplers - shape = unet_state_dict[f"input_blocks.{i}.0.weight"].shape - if shape[0] != shape[1]: - continue - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if ["conv.weight", "conv.bias"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - elif f"output_blocks.{i}.1.weight" in unet_state_dict: - # text_unet uses linear layers in place of upsamplers - shape = unet_state_dict[f"output_blocks.{i}.1.weight"].shape - if shape[0] != shape[1]: - continue - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop( - f"output_blocks.{i}.1.weight" - ) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop( - f"output_blocks.{i}.1.bias" - ) - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - elif f"output_blocks.{i}.2.weight" in unet_state_dict: - # text_unet uses linear layers in place of upsamplers - shape = unet_state_dict[f"output_blocks.{i}.2.weight"].shape - if shape[0] != shape[1]: - continue - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop( - f"output_blocks.{i}.2.weight" - ) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop( - f"output_blocks.{i}.2.bias" - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - return new_checkpoint - - -def convert_vd_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - keys = list(checkpoint.keys()) - for key in keys: - vae_state_dict[key] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--unet_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--optimus_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--scheduler_type", - default="pndm", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - args = parser.parse_args() - - scheduler_config = SCHEDULER_CONFIG - - num_train_timesteps = scheduler_config.timesteps - beta_start = scheduler_config.beta_linear_start - beta_end = scheduler_config.beta_linear_end - if args.scheduler_type == "pndm": - scheduler = PNDMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - skip_prk_steps=True, - steps_offset=1, - ) - elif args.scheduler_type == "lms": - scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear") - elif args.scheduler_type == "euler": - scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear") - elif args.scheduler_type == "euler-ancestral": - scheduler = EulerAncestralDiscreteScheduler( - beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear" - ) - elif args.scheduler_type == "dpm": - scheduler = DPMSolverMultistepScheduler( - beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear" - ) - elif args.scheduler_type == "ddim": - scheduler = DDIMScheduler( - beta_start=beta_start, - beta_end=beta_end, - beta_schedule="scaled_linear", - clip_sample=False, - set_alpha_to_one=False, - steps_offset=1, - ) - else: - raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!") - - # Convert the UNet2DConditionModel models. - if args.unet_checkpoint_path is not None: - # image UNet - image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG) - checkpoint = torch.load(args.unet_checkpoint_path) - converted_image_unet_checkpoint = convert_vd_unet_checkpoint( - checkpoint, image_unet_config, unet_key="model.diffusion_model.unet_image.", extract_ema=args.extract_ema - ) - image_unet = UNet2DConditionModel(**image_unet_config) - image_unet.load_state_dict(converted_image_unet_checkpoint) - - # text UNet - text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG) - converted_text_unet_checkpoint = convert_vd_unet_checkpoint( - checkpoint, text_unet_config, unet_key="model.diffusion_model.unet_text.", extract_ema=args.extract_ema - ) - text_unet = UNetFlatConditionModel(**text_unet_config) - text_unet.load_state_dict(converted_text_unet_checkpoint) - - # Convert the VAE model. - if args.vae_checkpoint_path is not None: - vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG) - checkpoint = torch.load(args.vae_checkpoint_path) - converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - image_feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14") - text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - - pipe = VersatileDiffusionPipeline( - scheduler=scheduler, - tokenizer=tokenizer, - image_feature_extractor=image_feature_extractor, - text_encoder=text_encoder, - image_encoder=image_encoder, - image_unet=image_unet, - text_unet=text_unet, - vae=vae, - ) - pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_vq_diffusion_to_diffusers.py b/scripts/convert_vq_diffusion_to_diffusers.py deleted file mode 100644 index fe62d18faff0..000000000000 --- a/scripts/convert_vq_diffusion_to_diffusers.py +++ /dev/null @@ -1,916 +0,0 @@ -""" -This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers. - -It currently only supports porting the ITHQ dataset. - -ITHQ dataset: -```sh -# From the root directory of diffusers. - -# Download the VQVAE checkpoint -$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth - -# Download the VQVAE config -# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class -# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE` -# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml` -$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml - -# Download the main model checkpoint -$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth - -# Download the main model config -$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml - -# run the convert script -$ python ./scripts/convert_vq_diffusion_to_diffusers.py \ - --checkpoint_path ./ithq_learnable.pth \ - --original_config_file ./ithq.yaml \ - --vqvae_checkpoint_path ./ithq_vqvae.pth \ - --vqvae_original_config_file ./ithq_vqvae.yaml \ - --dump_path <path to save pre-trained `VQDiffusionPipeline`> -``` -""" - -import argparse -import tempfile - -import torch -import yaml -from accelerate import init_empty_weights, load_checkpoint_and_dispatch -from transformers import CLIPTextModel, CLIPTokenizer -from yaml.loader import FullLoader - -from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel -from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings - - -# vqvae model - -PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN"] - - -def vqvae_model_from_original_config(original_config): - assert original_config["target"] in PORTED_VQVAES, ( - f"{original_config['target']} has not yet been ported to diffusers." - ) - - original_config = original_config["params"] - - original_encoder_config = original_config["encoder_config"]["params"] - original_decoder_config = original_config["decoder_config"]["params"] - - in_channels = original_encoder_config["in_channels"] - out_channels = original_decoder_config["out_ch"] - - down_block_types = get_down_block_types(original_encoder_config) - up_block_types = get_up_block_types(original_decoder_config) - - assert original_encoder_config["ch"] == original_decoder_config["ch"] - assert original_encoder_config["ch_mult"] == original_decoder_config["ch_mult"] - block_out_channels = tuple( - [original_encoder_config["ch"] * a_ch_mult for a_ch_mult in original_encoder_config["ch_mult"]] - ) - - assert original_encoder_config["num_res_blocks"] == original_decoder_config["num_res_blocks"] - layers_per_block = original_encoder_config["num_res_blocks"] - - assert original_encoder_config["z_channels"] == original_decoder_config["z_channels"] - latent_channels = original_encoder_config["z_channels"] - - num_vq_embeddings = original_config["n_embed"] - - # Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion - norm_num_groups = 32 - - e_dim = original_config["embed_dim"] - - model = VQModel( - in_channels=in_channels, - out_channels=out_channels, - down_block_types=down_block_types, - up_block_types=up_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - latent_channels=latent_channels, - num_vq_embeddings=num_vq_embeddings, - norm_num_groups=norm_num_groups, - vq_embed_dim=e_dim, - ) - - return model - - -def get_down_block_types(original_encoder_config): - attn_resolutions = coerce_attn_resolutions(original_encoder_config["attn_resolutions"]) - num_resolutions = len(original_encoder_config["ch_mult"]) - resolution = coerce_resolution(original_encoder_config["resolution"]) - - curr_res = resolution - down_block_types = [] - - for _ in range(num_resolutions): - if curr_res in attn_resolutions: - down_block_type = "AttnDownEncoderBlock2D" - else: - down_block_type = "DownEncoderBlock2D" - - down_block_types.append(down_block_type) - - curr_res = [r // 2 for r in curr_res] - - return down_block_types - - -def get_up_block_types(original_decoder_config): - attn_resolutions = coerce_attn_resolutions(original_decoder_config["attn_resolutions"]) - num_resolutions = len(original_decoder_config["ch_mult"]) - resolution = coerce_resolution(original_decoder_config["resolution"]) - - curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution] - up_block_types = [] - - for _ in reversed(range(num_resolutions)): - if curr_res in attn_resolutions: - up_block_type = "AttnUpDecoderBlock2D" - else: - up_block_type = "UpDecoderBlock2D" - - up_block_types.append(up_block_type) - - curr_res = [r * 2 for r in curr_res] - - return up_block_types - - -def coerce_attn_resolutions(attn_resolutions): - attn_resolutions = list(attn_resolutions) - attn_resolutions_ = [] - for ar in attn_resolutions: - if isinstance(ar, (list, tuple)): - attn_resolutions_.append(list(ar)) - else: - attn_resolutions_.append([ar, ar]) - return attn_resolutions_ - - -def coerce_resolution(resolution): - if isinstance(resolution, int): - resolution = [resolution, resolution] # H, W - elif isinstance(resolution, (tuple, list)): - resolution = list(resolution) - else: - raise ValueError("Unknown type of resolution:", resolution) - return resolution - - -# done vqvae model - -# vqvae checkpoint - - -def vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint)) - - # quant_conv - - diffusers_checkpoint.update( - { - "quant_conv.weight": checkpoint["quant_conv.weight"], - "quant_conv.bias": checkpoint["quant_conv.bias"], - } - ) - - # quantize - diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding"]}) - - # post_quant_conv - diffusers_checkpoint.update( - { - "post_quant_conv.weight": checkpoint["post_quant_conv.weight"], - "post_quant_conv.bias": checkpoint["post_quant_conv.bias"], - } - ) - - # decoder - diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint)) - - return diffusers_checkpoint - - -def vqvae_encoder_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # conv_in - diffusers_checkpoint.update( - { - "encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"], - "encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"], - } - ) - - # down_blocks - for down_block_idx, down_block in enumerate(model.encoder.down_blocks): - diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}" - down_block_prefix = f"encoder.down.{down_block_idx}" - - # resnets - for resnet_idx, resnet in enumerate(down_block.resnets): - diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}" - resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}" - - diffusers_checkpoint.update( - vqvae_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - # downsample - - # do not include the downsample when on the last down block - # There is no downsample on the last down block - if down_block_idx != len(model.encoder.down_blocks) - 1: - # There's a single downsample in the original checkpoint but a list of downsamples - # in the diffusers model. - diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv" - downsample_prefix = f"{down_block_prefix}.downsample.conv" - diffusers_checkpoint.update( - { - f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], - f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], - } - ) - - # attentions - - if hasattr(down_block, "attentions"): - for attention_idx, _ in enumerate(down_block.attentions): - diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}" - attention_prefix = f"{down_block_prefix}.attn.{attention_idx}" - diffusers_checkpoint.update( - vqvae_attention_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - attention_prefix=attention_prefix, - ) - ) - - # mid block - - # mid block attentions - - # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder - diffusers_attention_prefix = "encoder.mid_block.attentions.0" - attention_prefix = "encoder.mid.attn_1" - diffusers_checkpoint.update( - vqvae_attention_to_diffusers_checkpoint( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # mid block resnets - - for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): - diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}" - - # the hardcoded prefixes to `block_` are 1 and 2 - orig_resnet_idx = diffusers_resnet_idx + 1 - # There are two hardcoded resnets in the middle of the VQ-diffusion encoder - resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}" - - diffusers_checkpoint.update( - vqvae_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - diffusers_checkpoint.update( - { - # conv_norm_out - "encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"], - "encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"], - # conv_out - "encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"], - "encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"], - } - ) - - return diffusers_checkpoint - - -def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - # conv in - diffusers_checkpoint.update( - { - "decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"], - "decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"], - } - ) - - # up_blocks - - for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks): - # up_blocks are stored in reverse order in the VQ-diffusion checkpoint - orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx - - diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}" - up_block_prefix = f"decoder.up.{orig_up_block_idx}" - - # resnets - for resnet_idx, resnet in enumerate(up_block.resnets): - diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" - resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}" - - diffusers_checkpoint.update( - vqvae_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - # upsample - - # there is no up sample on the last up block - if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1: - # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples - # in the diffusers model. - diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv" - downsample_prefix = f"{up_block_prefix}.upsample.conv" - diffusers_checkpoint.update( - { - f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], - f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], - } - ) - - # attentions - - if hasattr(up_block, "attentions"): - for attention_idx, _ in enumerate(up_block.attentions): - diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}" - attention_prefix = f"{up_block_prefix}.attn.{attention_idx}" - diffusers_checkpoint.update( - vqvae_attention_to_diffusers_checkpoint( - checkpoint, - diffusers_attention_prefix=diffusers_attention_prefix, - attention_prefix=attention_prefix, - ) - ) - - # mid block - - # mid block attentions - - # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder - diffusers_attention_prefix = "decoder.mid_block.attentions.0" - attention_prefix = "decoder.mid.attn_1" - diffusers_checkpoint.update( - vqvae_attention_to_diffusers_checkpoint( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # mid block resnets - - for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): - diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}" - - # the hardcoded prefixes to `block_` are 1 and 2 - orig_resnet_idx = diffusers_resnet_idx + 1 - # There are two hardcoded resnets in the middle of the VQ-diffusion decoder - resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}" - - diffusers_checkpoint.update( - vqvae_resnet_to_diffusers_checkpoint( - resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix - ) - ) - - diffusers_checkpoint.update( - { - # conv_norm_out - "decoder.conv_norm_out.weight": checkpoint["decoder.norm_out.weight"], - "decoder.conv_norm_out.bias": checkpoint["decoder.norm_out.bias"], - # conv_out - "decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"], - "decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"], - } - ) - - return diffusers_checkpoint - - -def vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): - rv = { - # norm1 - f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"], - f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"], - # conv1 - f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], - f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], - # norm2 - f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"], - f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"], - # conv2 - f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], - f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], - } - - if resnet.conv_shortcut is not None: - rv.update( - { - f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], - f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], - } - ) - - return rv - - -def vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): - return { - # group_norm - f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], - f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], - # query - f"{diffusers_attention_prefix}.query.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.query.bias": checkpoint[f"{attention_prefix}.q.bias"], - # key - f"{diffusers_attention_prefix}.key.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.key.bias": checkpoint[f"{attention_prefix}.k.bias"], - # value - f"{diffusers_attention_prefix}.value.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], - f"{diffusers_attention_prefix}.value.bias": checkpoint[f"{attention_prefix}.v.bias"], - # proj_attn - f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ - :, :, 0, 0 - ], - f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], - } - - -# done vqvae checkpoint - -# transformer model - -PORTED_DIFFUSIONS = ["image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer"] -PORTED_TRANSFORMERS = ["image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer"] -PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding"] - - -def transformer_model_from_original_config( - original_diffusion_config, original_transformer_config, original_content_embedding_config -): - assert original_diffusion_config["target"] in PORTED_DIFFUSIONS, ( - f"{original_diffusion_config['target']} has not yet been ported to diffusers." - ) - assert original_transformer_config["target"] in PORTED_TRANSFORMERS, ( - f"{original_transformer_config['target']} has not yet been ported to diffusers." - ) - assert original_content_embedding_config["target"] in PORTED_CONTENT_EMBEDDINGS, ( - f"{original_content_embedding_config['target']} has not yet been ported to diffusers." - ) - - original_diffusion_config = original_diffusion_config["params"] - original_transformer_config = original_transformer_config["params"] - original_content_embedding_config = original_content_embedding_config["params"] - - inner_dim = original_transformer_config["n_embd"] - - n_heads = original_transformer_config["n_head"] - - # VQ-Diffusion gives dimension of the multi-headed attention layers as the - # number of attention heads times the sequence length (the dimension) of a - # single head. We want to specify our attention blocks with those values - # specified separately - assert inner_dim % n_heads == 0 - d_head = inner_dim // n_heads - - depth = original_transformer_config["n_layer"] - context_dim = original_transformer_config["condition_dim"] - - num_embed = original_content_embedding_config["num_embed"] - # the number of embeddings in the transformer includes the mask embedding. - # the content embedding (the vqvae) does not include the mask embedding. - num_embed = num_embed + 1 - - height = original_transformer_config["content_spatial_size"][0] - width = original_transformer_config["content_spatial_size"][1] - - assert width == height, "width has to be equal to height" - dropout = original_transformer_config["resid_pdrop"] - num_embeds_ada_norm = original_diffusion_config["diffusion_step"] - - model_kwargs = { - "attention_bias": True, - "cross_attention_dim": context_dim, - "attention_head_dim": d_head, - "num_layers": depth, - "dropout": dropout, - "num_attention_heads": n_heads, - "num_vector_embeds": num_embed, - "num_embeds_ada_norm": num_embeds_ada_norm, - "norm_num_groups": 32, - "sample_size": width, - "activation_fn": "geglu-approximate", - } - - model = Transformer2DModel(**model_kwargs) - return model - - -# done transformer model - -# transformer checkpoint - - -def transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): - diffusers_checkpoint = {} - - transformer_prefix = "transformer.transformer" - - diffusers_latent_image_embedding_prefix = "latent_image_embedding" - latent_image_embedding_prefix = f"{transformer_prefix}.content_emb" - - # DalleMaskImageEmbedding - diffusers_checkpoint.update( - { - f"{diffusers_latent_image_embedding_prefix}.emb.weight": checkpoint[ - f"{latent_image_embedding_prefix}.emb.weight" - ], - f"{diffusers_latent_image_embedding_prefix}.height_emb.weight": checkpoint[ - f"{latent_image_embedding_prefix}.height_emb.weight" - ], - f"{diffusers_latent_image_embedding_prefix}.width_emb.weight": checkpoint[ - f"{latent_image_embedding_prefix}.width_emb.weight" - ], - } - ) - - # transformer blocks - for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks): - diffusers_transformer_block_prefix = f"transformer_blocks.{transformer_block_idx}" - transformer_block_prefix = f"{transformer_prefix}.blocks.{transformer_block_idx}" - - # ada norm block - diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm1" - ada_norm_prefix = f"{transformer_block_prefix}.ln1" - - diffusers_checkpoint.update( - transformer_ada_norm_to_diffusers_checkpoint( - checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix - ) - ) - - # attention block - diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn1" - attention_prefix = f"{transformer_block_prefix}.attn1" - - diffusers_checkpoint.update( - transformer_attention_to_diffusers_checkpoint( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # ada norm block - diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm2" - ada_norm_prefix = f"{transformer_block_prefix}.ln1_1" - - diffusers_checkpoint.update( - transformer_ada_norm_to_diffusers_checkpoint( - checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix - ) - ) - - # attention block - diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn2" - attention_prefix = f"{transformer_block_prefix}.attn2" - - diffusers_checkpoint.update( - transformer_attention_to_diffusers_checkpoint( - checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix - ) - ) - - # norm block - diffusers_norm_block_prefix = f"{diffusers_transformer_block_prefix}.norm3" - norm_block_prefix = f"{transformer_block_prefix}.ln2" - - diffusers_checkpoint.update( - { - f"{diffusers_norm_block_prefix}.weight": checkpoint[f"{norm_block_prefix}.weight"], - f"{diffusers_norm_block_prefix}.bias": checkpoint[f"{norm_block_prefix}.bias"], - } - ) - - # feedforward block - diffusers_feedforward_prefix = f"{diffusers_transformer_block_prefix}.ff" - feedforward_prefix = f"{transformer_block_prefix}.mlp" - - diffusers_checkpoint.update( - transformer_feedforward_to_diffusers_checkpoint( - checkpoint, - diffusers_feedforward_prefix=diffusers_feedforward_prefix, - feedforward_prefix=feedforward_prefix, - ) - ) - - # to logits - - diffusers_norm_out_prefix = "norm_out" - norm_out_prefix = f"{transformer_prefix}.to_logits.0" - - diffusers_checkpoint.update( - { - f"{diffusers_norm_out_prefix}.weight": checkpoint[f"{norm_out_prefix}.weight"], - f"{diffusers_norm_out_prefix}.bias": checkpoint[f"{norm_out_prefix}.bias"], - } - ) - - diffusers_out_prefix = "out" - out_prefix = f"{transformer_prefix}.to_logits.1" - - diffusers_checkpoint.update( - { - f"{diffusers_out_prefix}.weight": checkpoint[f"{out_prefix}.weight"], - f"{diffusers_out_prefix}.bias": checkpoint[f"{out_prefix}.bias"], - } - ) - - return diffusers_checkpoint - - -def transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix): - return { - f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[f"{ada_norm_prefix}.emb.weight"], - f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[f"{ada_norm_prefix}.linear.weight"], - f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[f"{ada_norm_prefix}.linear.bias"], - } - - -def transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): - return { - # key - f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.key.weight"], - f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.key.bias"], - # query - f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.query.weight"], - f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.query.bias"], - # value - f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.value.weight"], - f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.value.bias"], - # linear out - f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj.weight"], - f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj.bias"], - } - - -def transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix): - return { - f"{diffusers_feedforward_prefix}.net.0.proj.weight": checkpoint[f"{feedforward_prefix}.0.weight"], - f"{diffusers_feedforward_prefix}.net.0.proj.bias": checkpoint[f"{feedforward_prefix}.0.bias"], - f"{diffusers_feedforward_prefix}.net.2.weight": checkpoint[f"{feedforward_prefix}.2.weight"], - f"{diffusers_feedforward_prefix}.net.2.bias": checkpoint[f"{feedforward_prefix}.2.bias"], - } - - -# done transformer checkpoint - - -def read_config_file(filename): - # The yaml file contains annotations that certain values should - # loaded as tuples. - with open(filename) as f: - original_config = yaml.load(f, FullLoader) - - return original_config - - -# We take separate arguments for the vqvae because the ITHQ vqvae config file -# is separate from the config file for the rest of the model. -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--vqvae_checkpoint_path", - default=None, - type=str, - required=True, - help="Path to the vqvae checkpoint to convert.", - ) - - parser.add_argument( - "--vqvae_original_config_file", - default=None, - type=str, - required=True, - help="The YAML config file corresponding to the original architecture for the vqvae.", - ) - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - - parser.add_argument( - "--original_config_file", - default=None, - type=str, - required=True, - help="The YAML config file corresponding to the original architecture.", - ) - - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - - parser.add_argument( - "--checkpoint_load_device", - default="cpu", - type=str, - required=False, - help="The device passed to `map_location` when loading checkpoints.", - ) - - # See link for how ema weights are always selected - # https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65 - parser.add_argument( - "--no_use_ema", - action="store_true", - required=False, - help=( - "Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set" - " it as the original VQ-Diffusion always uses the ema weights when loading models." - ), - ) - - args = parser.parse_args() - - use_ema = not args.no_use_ema - - print(f"loading checkpoints to {args.checkpoint_load_device}") - - checkpoint_map_location = torch.device(args.checkpoint_load_device) - - # vqvae_model - - print(f"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}") - - vqvae_original_config = read_config_file(args.vqvae_original_config_file).model - vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)["model"] - - with init_empty_weights(): - vqvae_model = vqvae_model_from_original_config(vqvae_original_config) - - vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint) - - with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file: - torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name) - del vqvae_diffusers_checkpoint - del vqvae_checkpoint - load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map="auto") - - print("done loading vqvae") - - # done vqvae_model - - # transformer_model - - print( - f"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:" - f" {use_ema}" - ) - - original_config = read_config_file(args.original_config_file).model - - diffusion_config = original_config["params"]["diffusion_config"] - transformer_config = original_config["params"]["diffusion_config"]["params"]["transformer_config"] - content_embedding_config = original_config["params"]["diffusion_config"]["params"]["content_emb_config"] - - pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location) - - if use_ema: - if "ema" in pre_checkpoint: - checkpoint = {} - for k, v in pre_checkpoint["model"].items(): - checkpoint[k] = v - - for k, v in pre_checkpoint["ema"].items(): - # The ema weights are only used on the transformer. To mimic their key as if they came - # from the state_dict for the top level model, we prefix with an additional "transformer." - # See the source linked in the args.use_ema config for more information. - checkpoint[f"transformer.{k}"] = v - else: - print("attempted to load ema weights but no ema weights are specified in the loaded checkpoint.") - checkpoint = pre_checkpoint["model"] - else: - checkpoint = pre_checkpoint["model"] - - del pre_checkpoint - - with init_empty_weights(): - transformer_model = transformer_model_from_original_config( - diffusion_config, transformer_config, content_embedding_config - ) - - diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint( - transformer_model, checkpoint - ) - - # classifier free sampling embeddings interlude - - # The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate - # model, so we pull them off the checkpoint before the checkpoint is deleted. - - learnable_classifier_free_sampling_embeddings = diffusion_config["params"].learnable_cf - - if learnable_classifier_free_sampling_embeddings: - learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"] - else: - learned_classifier_free_sampling_embeddings_embeddings = None - - # done classifier free sampling embeddings interlude - - with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file: - torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name) - del diffusers_transformer_checkpoint - del checkpoint - load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map="auto") - - print("done loading transformer") - - # done transformer_model - - # text encoder - - print("loading CLIP text encoder") - - clip_name = "openai/clip-vit-base-patch32" - - # The original VQ-Diffusion specifies the pad value by the int used in the - # returned tokens. Each model uses `0` as the pad value. The transformers clip api - # specifies the pad value via the token before it has been tokenized. The `!` pad - # token is the same as padding with the `0` pad value. - pad_token = "!" - - tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto") - - assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0 - - text_encoder_model = CLIPTextModel.from_pretrained( - clip_name, - # `CLIPTextModel` does not support device_map="auto" - # device_map="auto" - ) - - print("done loading CLIP text encoder") - - # done text encoder - - # scheduler - - scheduler_model = VQDiffusionScheduler( - # the scheduler has the same number of embeddings as the transformer - num_vec_classes=transformer_model.num_vector_embeds - ) - - # done scheduler - - # learned classifier free sampling embeddings - - with init_empty_weights(): - learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings( - learnable_classifier_free_sampling_embeddings, - hidden_size=text_encoder_model.config.hidden_size, - length=tokenizer_model.model_max_length, - ) - - learned_classifier_free_sampling_checkpoint = { - "embeddings": learned_classifier_free_sampling_embeddings_embeddings.float() - } - - with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file: - torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name) - del learned_classifier_free_sampling_checkpoint - del learned_classifier_free_sampling_embeddings_embeddings - load_checkpoint_and_dispatch( - learned_classifier_free_sampling_embeddings_model, - learned_classifier_free_sampling_checkpoint_file.name, - device_map="auto", - ) - - # done learned classifier free sampling embeddings - - print(f"saving VQ diffusion model, path: {args.dump_path}") - - pipe = VQDiffusionPipeline( - vqvae=vqvae_model, - transformer=transformer_model, - tokenizer=tokenizer_model, - text_encoder=text_encoder_model, - learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model, - scheduler=scheduler_model, - ) - pipe.save_pretrained(args.dump_path) - - print("done writing VQ diffusion model") diff --git a/scripts/convert_wan_to_diffusers.py b/scripts/convert_wan_to_diffusers.py deleted file mode 100644 index eca1af61ae69..000000000000 --- a/scripts/convert_wan_to_diffusers.py +++ /dev/null @@ -1,1287 +0,0 @@ -import argparse -import pathlib -from typing import Any, Dict, Tuple - -import torch -from accelerate import init_empty_weights -from huggingface_hub import hf_hub_download, snapshot_download -from safetensors.torch import load_file -from transformers import ( - AutoProcessor, - AutoTokenizer, - CLIPImageProcessor, - CLIPVisionModel, - CLIPVisionModelWithProjection, - UMT5EncoderModel, -) - -from diffusers import ( - AutoencoderKLWan, - UniPCMultistepScheduler, - WanAnimatePipeline, - WanAnimateTransformer3DModel, - WanImageToVideoPipeline, - WanPipeline, - WanTransformer3DModel, - WanVACEPipeline, - WanVACETransformer3DModel, -) - - -TRANSFORMER_KEYS_RENAME_DICT = { - "time_embedding.0": "condition_embedder.time_embedder.linear_1", - "time_embedding.2": "condition_embedder.time_embedder.linear_2", - "text_embedding.0": "condition_embedder.text_embedder.linear_1", - "text_embedding.2": "condition_embedder.text_embedder.linear_2", - "time_projection.1": "condition_embedder.time_proj", - "head.modulation": "scale_shift_table", - "head.head": "proj_out", - "modulation": "scale_shift_table", - "ffn.0": "ffn.net.0.proj", - "ffn.2": "ffn.net.2", - # Hack to swap the layer names - # The original model calls the norms in following order: norm1, norm3, norm2 - # We convert it to: norm1, norm2, norm3 - "norm2": "norm__placeholder", - "norm3": "norm2", - "norm__placeholder": "norm3", - # For the I2V model - "img_emb.proj.0": "condition_embedder.image_embedder.norm1", - "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", - "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", - "img_emb.proj.4": "condition_embedder.image_embedder.norm2", - # for the FLF2V model - "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", - # Add attention component mappings - "self_attn.q": "attn1.to_q", - "self_attn.k": "attn1.to_k", - "self_attn.v": "attn1.to_v", - "self_attn.o": "attn1.to_out.0", - "self_attn.norm_q": "attn1.norm_q", - "self_attn.norm_k": "attn1.norm_k", - "cross_attn.q": "attn2.to_q", - "cross_attn.k": "attn2.to_k", - "cross_attn.v": "attn2.to_v", - "cross_attn.o": "attn2.to_out.0", - "cross_attn.norm_q": "attn2.norm_q", - "cross_attn.norm_k": "attn2.norm_k", - "attn2.to_k_img": "attn2.add_k_proj", - "attn2.to_v_img": "attn2.add_v_proj", - "attn2.norm_k_img": "attn2.norm_added_k", -} - -VACE_TRANSFORMER_KEYS_RENAME_DICT = { - "time_embedding.0": "condition_embedder.time_embedder.linear_1", - "time_embedding.2": "condition_embedder.time_embedder.linear_2", - "text_embedding.0": "condition_embedder.text_embedder.linear_1", - "text_embedding.2": "condition_embedder.text_embedder.linear_2", - "time_projection.1": "condition_embedder.time_proj", - "head.modulation": "scale_shift_table", - "head.head": "proj_out", - "modulation": "scale_shift_table", - "ffn.0": "ffn.net.0.proj", - "ffn.2": "ffn.net.2", - # Hack to swap the layer names - # The original model calls the norms in following order: norm1, norm3, norm2 - # We convert it to: norm1, norm2, norm3 - "norm2": "norm__placeholder", - "norm3": "norm2", - "norm__placeholder": "norm3", - # # For the I2V model - # "img_emb.proj.0": "condition_embedder.image_embedder.norm1", - # "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", - # "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", - # "img_emb.proj.4": "condition_embedder.image_embedder.norm2", - # # for the FLF2V model - # "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", - # Add attention component mappings - "self_attn.q": "attn1.to_q", - "self_attn.k": "attn1.to_k", - "self_attn.v": "attn1.to_v", - "self_attn.o": "attn1.to_out.0", - "self_attn.norm_q": "attn1.norm_q", - "self_attn.norm_k": "attn1.norm_k", - "cross_attn.q": "attn2.to_q", - "cross_attn.k": "attn2.to_k", - "cross_attn.v": "attn2.to_v", - "cross_attn.o": "attn2.to_out.0", - "cross_attn.norm_q": "attn2.norm_q", - "cross_attn.norm_k": "attn2.norm_k", - "attn2.to_k_img": "attn2.add_k_proj", - "attn2.to_v_img": "attn2.add_v_proj", - "attn2.norm_k_img": "attn2.norm_added_k", - "before_proj": "proj_in", - "after_proj": "proj_out", -} - -ANIMATE_TRANSFORMER_KEYS_RENAME_DICT = { - "time_embedding.0": "condition_embedder.time_embedder.linear_1", - "time_embedding.2": "condition_embedder.time_embedder.linear_2", - "text_embedding.0": "condition_embedder.text_embedder.linear_1", - "text_embedding.2": "condition_embedder.text_embedder.linear_2", - "time_projection.1": "condition_embedder.time_proj", - "head.modulation": "scale_shift_table", - "head.head": "proj_out", - "modulation": "scale_shift_table", - "ffn.0": "ffn.net.0.proj", - "ffn.2": "ffn.net.2", - # Hack to swap the layer names - # The original model calls the norms in following order: norm1, norm3, norm2 - # We convert it to: norm1, norm2, norm3 - "norm2": "norm__placeholder", - "norm3": "norm2", - "norm__placeholder": "norm3", - "img_emb.proj.0": "condition_embedder.image_embedder.norm1", - "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", - "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", - "img_emb.proj.4": "condition_embedder.image_embedder.norm2", - # Add attention component mappings - "self_attn.q": "attn1.to_q", - "self_attn.k": "attn1.to_k", - "self_attn.v": "attn1.to_v", - "self_attn.o": "attn1.to_out.0", - "self_attn.norm_q": "attn1.norm_q", - "self_attn.norm_k": "attn1.norm_k", - "cross_attn.q": "attn2.to_q", - "cross_attn.k": "attn2.to_k", - "cross_attn.v": "attn2.to_v", - "cross_attn.o": "attn2.to_out.0", - "cross_attn.norm_q": "attn2.norm_q", - "cross_attn.norm_k": "attn2.norm_k", - "cross_attn.k_img": "attn2.to_k_img", - "cross_attn.v_img": "attn2.to_v_img", - "cross_attn.norm_k_img": "attn2.norm_k_img", - # After cross_attn -> attn2 rename, we need to rename the img keys - "attn2.to_k_img": "attn2.add_k_proj", - "attn2.to_v_img": "attn2.add_v_proj", - "attn2.norm_k_img": "attn2.norm_added_k", - # Wan Animate-specific mappings (motion encoder, face encoder, face adapter) - # Motion encoder mappings - # The name mapping is complicated for the convolutional part so we handle that in its own function - "motion_encoder.enc.fc": "motion_encoder.motion_network", - "motion_encoder.dec.direction.weight": "motion_encoder.motion_synthesis_weight", - # Face encoder mappings - CausalConv1d has a .conv submodule that we need to flatten - "face_encoder.conv1_local.conv": "face_encoder.conv1_local", - "face_encoder.conv2.conv": "face_encoder.conv2", - "face_encoder.conv3.conv": "face_encoder.conv3", - # Face adapter mappings are handled in a separate function -} - - -# TODO: Verify this and simplify if possible. -def convert_animate_motion_encoder_weights(key: str, state_dict: Dict[str, Any], final_conv_idx: int = 8) -> None: - """ - Convert all motion encoder weights for Animate model. - - In the original model: - - All Linear layers in fc use EqualLinear - - All Conv2d layers in convs use EqualConv2d (except blur_conv which is initialized separately) - - Blur kernels are stored as buffers in Sequential modules - - ConvLayer is nn.Sequential with indices: [Blur (optional), EqualConv2d, FusedLeakyReLU (optional)] - - Conversion strategy: - 1. Drop .kernel buffers (blur kernels) - 2. Rename sequential indices to named components (e.g., 0 -> conv2d, 1 -> bias_leaky_relu) - """ - # Skip if not a weight, bias, or kernel - if ".weight" not in key and ".bias" not in key and ".kernel" not in key: - return - - # Handle Blur kernel buffers from original implementation. - # After renaming, these appear under: motion_encoder.res_blocks.*.conv{2,skip}.blur_kernel - # Diffusers constructs blur kernels as a non-persistent buffer so we must drop these keys - if ".kernel" in key and "motion_encoder" in key: - # Remove unexpected blur kernel buffers to avoid strict load errors - state_dict.pop(key, None) - return - - # Rename Sequential indices to named components in ConvLayer and ResBlock - if ".enc.net_app.convs." in key and (".weight" in key or ".bias" in key): - parts = key.split(".") - - # Find the sequential index (digit) after convs or after conv1/conv2/skip - # Examples: - # - enc.net_app.convs.0.0.weight -> conv_in.weight (initial conv layer weight) - # - enc.net_app.convs.0.1.bias -> conv_in.act_fn.bias (initial conv layer bias) - # - enc.net_app.convs.{n:1-7}.conv1.0.weight -> res_blocks.{(n-1):0-6}.conv1.weight (conv1 weight) - # - e.g. enc.net_app.convs.1.conv1.0.weight -> res_blocks.0.conv1.weight - # - enc.net_app.convs.{n:1-7}.conv1.1.bias -> res_blocks.{(n-1):0-6}.conv1.act_fn.bias (conv1 bias) - # - e.g. enc.net_app.convs.1.conv1.1.bias -> res_blocks.0.conv1.act_fn.bias - # - enc.net_app.convs.{n:1-7}.conv2.1.weight -> res_blocks.{(n-1):0-6}.conv2.weight (conv2 weight) - # - enc.net_app.convs.1.conv2.2.bias -> res_blocks.0.conv2.act_fn.bias (conv2 bias) - # - enc.net_app.convs.{n:1-7}.skip.1.weight -> res_blocks.{(n-1):0-6}.conv_skip.weight (skip conv weight) - # - enc.net_app.convs.8 -> conv_out (final conv layer) - - convs_idx = parts.index("convs") if "convs" in parts else -1 - if convs_idx >= 0 and len(parts) - convs_idx >= 2: - bias = False - # The nn.Sequential index will always follow convs - sequential_idx = int(parts[convs_idx + 1]) - if sequential_idx == 0: - if key.endswith(".weight"): - new_key = "motion_encoder.conv_in.weight" - elif key.endswith(".bias"): - new_key = "motion_encoder.conv_in.act_fn.bias" - bias = True - elif sequential_idx == final_conv_idx: - if key.endswith(".weight"): - new_key = "motion_encoder.conv_out.weight" - else: - # Intermediate .convs. layers, which get mapped to .res_blocks. - prefix = "motion_encoder.res_blocks." - - layer_name = parts[convs_idx + 2] - if layer_name == "skip": - layer_name = "conv_skip" - - if key.endswith(".weight"): - param_name = "weight" - elif key.endswith(".bias"): - param_name = "act_fn.bias" - bias = True - - suffix_parts = [str(sequential_idx - 1), layer_name, param_name] - suffix = ".".join(suffix_parts) - new_key = prefix + suffix - - param = state_dict.pop(key) - if bias: - param = param.squeeze() - state_dict[new_key] = param - return - return - return - - -def convert_animate_face_adapter_weights(key: str, state_dict: Dict[str, Any]) -> None: - """ - Convert face adapter weights for the Animate model. - - The original model uses a fused KV projection but the diffusers models uses separate K and V projections. - """ - # Skip if not a weight or bias - if ".weight" not in key and ".bias" not in key: - return - - prefix = "face_adapter." - if ".fuser_blocks." in key: - parts = key.split(".") - - module_list_idx = parts.index("fuser_blocks") if "fuser_blocks" in parts else -1 - if module_list_idx >= 0 and (len(parts) - 1) - module_list_idx == 3: - block_idx = parts[module_list_idx + 1] - layer_name = parts[module_list_idx + 2] - param_name = parts[module_list_idx + 3] - - if layer_name == "linear1_kv": - layer_name_k = "to_k" - layer_name_v = "to_v" - - suffix_k = ".".join([block_idx, layer_name_k, param_name]) - suffix_v = ".".join([block_idx, layer_name_v, param_name]) - new_key_k = prefix + suffix_k - new_key_v = prefix + suffix_v - - kv_proj = state_dict.pop(key) - k_proj, v_proj = torch.chunk(kv_proj, 2, dim=0) - state_dict[new_key_k] = k_proj - state_dict[new_key_v] = v_proj - return - else: - if layer_name == "q_norm": - new_layer_name = "norm_q" - elif layer_name == "k_norm": - new_layer_name = "norm_k" - elif layer_name == "linear1_q": - new_layer_name = "to_q" - elif layer_name == "linear2": - new_layer_name = "to_out" - - suffix_parts = [block_idx, new_layer_name, param_name] - suffix = ".".join(suffix_parts) - new_key = prefix + suffix - state_dict[new_key] = state_dict.pop(key) - return - return - - -TRANSFORMER_SPECIAL_KEYS_REMAP = {} -VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {} -ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP = { - "motion_encoder": convert_animate_motion_encoder_weights, - "face_adapter": convert_animate_face_adapter_weights, -} - - -def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: - state_dict[new_key] = state_dict.pop(old_key) - - -def load_sharded_safetensors(dir: pathlib.Path): - file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) - state_dict = {} - for path in file_paths: - state_dict.update(load_file(path)) - return state_dict - - -def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]: - if model_type == "Wan-T2V-1.3B": - config = { - "model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 8960, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 12, - "num_layers": 30, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-T2V-14B": - config = { - "model_id": "StevenZhang/Wan2.1-T2V-14B-Diff", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-I2V-14B-480p": - config = { - "model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff", - "diffusers_config": { - "image_dim": 1280, - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-I2V-14B-720p": - config = { - "model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff", - "diffusers_config": { - "image_dim": 1280, - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-FLF2V-14B-720P": - config = { - "model_id": "ypyp/Wan2.1-FLF2V-14B-720P", # This is just a placeholder - "diffusers_config": { - "image_dim": 1280, - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "rope_max_seq_len": 1024, - "pos_embed_seq_len": 257 * 2, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-VACE-1.3B": - config = { - "model_id": "Wan-AI/Wan2.1-VACE-1.3B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 8960, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 12, - "num_layers": 30, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28], - "vace_in_channels": 96, - }, - } - RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan-VACE-14B": - config = { - "model_id": "Wan-AI/Wan2.1-VACE-14B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35], - "vace_in_channels": 96, - }, - } - RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan2.2-VACE-Fun-14B": - config = { - "model_id": "alibaba-pai/Wan2.2-VACE-Fun-A14B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35], - "vace_in_channels": 96, - }, - } - RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan2.2-I2V-14B-720p": - config = { - "model_id": "Wan-AI/Wan2.2-I2V-A14B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan2.2-T2V-A14B": - config = { - "model_id": "Wan-AI/Wan2.2-T2V-A14B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 16, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan2.2-TI2V-5B": - config = { - "model_id": "Wan-AI/Wan2.2-TI2V-5B", - "diffusers_config": { - "added_kv_proj_dim": None, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 14336, - "freq_dim": 256, - "in_channels": 48, - "num_attention_heads": 24, - "num_layers": 30, - "out_channels": 48, - "patch_size": [1, 2, 2], - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - }, - } - RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP - elif model_type == "Wan2.2-Animate-14B": - config = { - "model_id": "Wan-AI/Wan2.2-Animate-14B", - "diffusers_config": { - "image_dim": 1280, - "added_kv_proj_dim": 5120, - "attention_head_dim": 128, - "cross_attn_norm": True, - "eps": 1e-06, - "ffn_dim": 13824, - "freq_dim": 256, - "in_channels": 36, - "num_attention_heads": 40, - "num_layers": 40, - "out_channels": 16, - "patch_size": (1, 2, 2), - "qk_norm": "rms_norm_across_heads", - "text_dim": 4096, - "rope_max_seq_len": 1024, - "pos_embed_seq_len": None, - "motion_encoder_size": 512, # Start of Wan Animate-specific configs - "motion_style_dim": 512, - "motion_dim": 20, - "motion_encoder_dim": 512, - "face_encoder_hidden_dim": 1024, - "face_encoder_num_heads": 4, - "inject_face_latents_blocks": 5, - }, - } - RENAME_DICT = ANIMATE_TRANSFORMER_KEYS_RENAME_DICT - SPECIAL_KEYS_REMAP = ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP - return config, RENAME_DICT, SPECIAL_KEYS_REMAP - - -def convert_transformer(model_type: str, stage: str = None): - config, RENAME_DICT, SPECIAL_KEYS_REMAP = get_transformer_config(model_type) - - diffusers_config = config["diffusers_config"] - model_id = config["model_id"] - model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model")) - - if stage is not None: - model_dir = model_dir / stage - - original_state_dict = load_sharded_safetensors(model_dir) - - with init_empty_weights(): - if "Animate" in model_type: - transformer = WanAnimateTransformer3DModel.from_config(diffusers_config) - elif "VACE" in model_type: - transformer = WanVACETransformer3DModel.from_config(diffusers_config) - else: - transformer = WanTransformer3DModel.from_config(diffusers_config) - - for key in list(original_state_dict.keys()): - new_key = key[:] - for replace_key, rename_key in RENAME_DICT.items(): - new_key = new_key.replace(replace_key, rename_key) - update_state_dict_(original_state_dict, key, new_key) - - for key in list(original_state_dict.keys()): - for special_key, handler_fn_inplace in SPECIAL_KEYS_REMAP.items(): - if special_key not in key: - continue - handler_fn_inplace(key, original_state_dict) - - # Load state dict into the meta model, which will materialize the tensors - transformer.load_state_dict(original_state_dict, strict=True, assign=True) - - # Move to CPU to ensure all tensors are materialized - transformer = transformer.to("cpu") - - return transformer - - -def convert_vae(): - vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth") - old_state_dict = torch.load(vae_ckpt_path, weights_only=True) - new_state_dict = {} - - # Create mappings for specific components - middle_key_mapping = { - # Encoder middle block - "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", - "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", - "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", - "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", - "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", - "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", - "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", - "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", - "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", - "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", - "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", - "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", - # Decoder middle block - "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", - "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", - "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", - "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", - "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", - "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", - "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", - "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", - "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", - "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", - "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", - "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", - } - - # Create a mapping for attention blocks - attention_mapping = { - # Encoder middle attention - "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", - "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", - "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", - "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", - "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", - # Decoder middle attention - "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", - "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", - "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", - "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", - "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", - } - - # Create a mapping for the head components - head_mapping = { - # Encoder head - "encoder.head.0.gamma": "encoder.norm_out.gamma", - "encoder.head.2.bias": "encoder.conv_out.bias", - "encoder.head.2.weight": "encoder.conv_out.weight", - # Decoder head - "decoder.head.0.gamma": "decoder.norm_out.gamma", - "decoder.head.2.bias": "decoder.conv_out.bias", - "decoder.head.2.weight": "decoder.conv_out.weight", - } - - # Create a mapping for the quant components - quant_mapping = { - "conv1.weight": "quant_conv.weight", - "conv1.bias": "quant_conv.bias", - "conv2.weight": "post_quant_conv.weight", - "conv2.bias": "post_quant_conv.bias", - } - - # Process each key in the state dict - for key, value in old_state_dict.items(): - # Handle middle block keys using the mapping - if key in middle_key_mapping: - new_key = middle_key_mapping[key] - new_state_dict[new_key] = value - # Handle attention blocks using the mapping - elif key in attention_mapping: - new_key = attention_mapping[key] - new_state_dict[new_key] = value - # Handle head keys using the mapping - elif key in head_mapping: - new_key = head_mapping[key] - new_state_dict[new_key] = value - # Handle quant keys using the mapping - elif key in quant_mapping: - new_key = quant_mapping[key] - new_state_dict[new_key] = value - # Handle encoder conv1 - elif key == "encoder.conv1.weight": - new_state_dict["encoder.conv_in.weight"] = value - elif key == "encoder.conv1.bias": - new_state_dict["encoder.conv_in.bias"] = value - # Handle decoder conv1 - elif key == "decoder.conv1.weight": - new_state_dict["decoder.conv_in.weight"] = value - elif key == "decoder.conv1.bias": - new_state_dict["decoder.conv_in.bias"] = value - # Handle encoder downsamples - elif key.startswith("encoder.downsamples."): - # Convert to down_blocks - new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") - - # Convert residual block naming but keep the original structure - if ".residual.0.gamma" in new_key: - new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") - elif ".residual.2.bias" in new_key: - new_key = new_key.replace(".residual.2.bias", ".conv1.bias") - elif ".residual.2.weight" in new_key: - new_key = new_key.replace(".residual.2.weight", ".conv1.weight") - elif ".residual.3.gamma" in new_key: - new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") - elif ".residual.6.bias" in new_key: - new_key = new_key.replace(".residual.6.bias", ".conv2.bias") - elif ".residual.6.weight" in new_key: - new_key = new_key.replace(".residual.6.weight", ".conv2.weight") - elif ".shortcut.bias" in new_key: - new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") - elif ".shortcut.weight" in new_key: - new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") - - new_state_dict[new_key] = value - - # Handle decoder upsamples - elif key.startswith("decoder.upsamples."): - # Convert to up_blocks - parts = key.split(".") - block_idx = int(parts[2]) - - # Group residual blocks - if "residual" in key: - if block_idx in [0, 1, 2]: - new_block_idx = 0 - resnet_idx = block_idx - elif block_idx in [4, 5, 6]: - new_block_idx = 1 - resnet_idx = block_idx - 4 - elif block_idx in [8, 9, 10]: - new_block_idx = 2 - resnet_idx = block_idx - 8 - elif block_idx in [12, 13, 14]: - new_block_idx = 3 - resnet_idx = block_idx - 12 - else: - # Keep as is for other blocks - new_state_dict[key] = value - continue - - # Convert residual block naming - if ".residual.0.gamma" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma" - elif ".residual.2.bias" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias" - elif ".residual.2.weight" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight" - elif ".residual.3.gamma" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma" - elif ".residual.6.bias" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias" - elif ".residual.6.weight" in key: - new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight" - else: - new_key = key - - new_state_dict[new_key] = value - - # Handle shortcut connections - elif ".shortcut." in key: - if block_idx == 4: - new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.") - new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1") - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - new_key = new_key.replace(".shortcut.", ".conv_shortcut.") - - new_state_dict[new_key] = value - - # Handle upsamplers - elif ".resample." in key or ".time_conv." in key: - if block_idx == 3: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0") - elif block_idx == 7: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0") - elif block_idx == 11: - new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0") - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - - new_state_dict[new_key] = value - else: - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - new_state_dict[new_key] = value - else: - # Keep other keys unchanged - new_state_dict[key] = value - - with init_empty_weights(): - vae = AutoencoderKLWan() - vae.load_state_dict(new_state_dict, strict=True, assign=True) - return vae - - -vae22_diffusers_config = { - "base_dim": 160, - "z_dim": 48, - "is_residual": True, - "in_channels": 12, - "out_channels": 12, - "decoder_base_dim": 256, - "scale_factor_temporal": 4, - "scale_factor_spatial": 16, - "patch_size": 2, - "latents_mean": [ - -0.2289, - -0.0052, - -0.1323, - -0.2339, - -0.2799, - 0.0174, - 0.1838, - 0.1557, - -0.1382, - 0.0542, - 0.2813, - 0.0891, - 0.1570, - -0.0098, - 0.0375, - -0.1825, - -0.2246, - -0.1207, - -0.0698, - 0.5109, - 0.2665, - -0.2108, - -0.2158, - 0.2502, - -0.2055, - -0.0322, - 0.1109, - 0.1567, - -0.0729, - 0.0899, - -0.2799, - -0.1230, - -0.0313, - -0.1649, - 0.0117, - 0.0723, - -0.2839, - -0.2083, - -0.0520, - 0.3748, - 0.0152, - 0.1957, - 0.1433, - -0.2944, - 0.3573, - -0.0548, - -0.1681, - -0.0667, - ], - "latents_std": [ - 0.4765, - 1.0364, - 0.4514, - 1.1677, - 0.5313, - 0.4990, - 0.4818, - 0.5013, - 0.8158, - 1.0344, - 0.5894, - 1.0901, - 0.6885, - 0.6165, - 0.8454, - 0.4978, - 0.5759, - 0.3523, - 0.7135, - 0.6804, - 0.5833, - 1.4146, - 0.8986, - 0.5659, - 0.7069, - 0.5338, - 0.4889, - 0.4917, - 0.4069, - 0.4999, - 0.6866, - 0.4093, - 0.5709, - 0.6065, - 0.6415, - 0.4944, - 0.5726, - 1.2042, - 0.5458, - 1.6887, - 0.3971, - 1.0600, - 0.3943, - 0.5537, - 0.5444, - 0.4089, - 0.7468, - 0.7744, - ], - "clip_output": False, -} - - -def convert_vae_22(): - vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.2-TI2V-5B", "Wan2.2_VAE.pth") - old_state_dict = torch.load(vae_ckpt_path, weights_only=True) - new_state_dict = {} - - # Create mappings for specific components - middle_key_mapping = { - # Encoder middle block - "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", - "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", - "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", - "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", - "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", - "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", - "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", - "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", - "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", - "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", - "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", - "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", - # Decoder middle block - "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", - "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", - "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", - "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", - "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", - "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", - "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", - "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", - "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", - "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", - "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", - "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", - } - - # Create a mapping for attention blocks - attention_mapping = { - # Encoder middle attention - "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", - "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", - "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", - "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", - "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", - # Decoder middle attention - "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", - "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", - "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", - "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", - "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", - } - - # Create a mapping for the head components - head_mapping = { - # Encoder head - "encoder.head.0.gamma": "encoder.norm_out.gamma", - "encoder.head.2.bias": "encoder.conv_out.bias", - "encoder.head.2.weight": "encoder.conv_out.weight", - # Decoder head - "decoder.head.0.gamma": "decoder.norm_out.gamma", - "decoder.head.2.bias": "decoder.conv_out.bias", - "decoder.head.2.weight": "decoder.conv_out.weight", - } - - # Create a mapping for the quant components - quant_mapping = { - "conv1.weight": "quant_conv.weight", - "conv1.bias": "quant_conv.bias", - "conv2.weight": "post_quant_conv.weight", - "conv2.bias": "post_quant_conv.bias", - } - - # Process each key in the state dict - for key, value in old_state_dict.items(): - # Handle middle block keys using the mapping - if key in middle_key_mapping: - new_key = middle_key_mapping[key] - new_state_dict[new_key] = value - # Handle attention blocks using the mapping - elif key in attention_mapping: - new_key = attention_mapping[key] - new_state_dict[new_key] = value - # Handle head keys using the mapping - elif key in head_mapping: - new_key = head_mapping[key] - new_state_dict[new_key] = value - # Handle quant keys using the mapping - elif key in quant_mapping: - new_key = quant_mapping[key] - new_state_dict[new_key] = value - # Handle encoder conv1 - elif key == "encoder.conv1.weight": - new_state_dict["encoder.conv_in.weight"] = value - elif key == "encoder.conv1.bias": - new_state_dict["encoder.conv_in.bias"] = value - # Handle decoder conv1 - elif key == "decoder.conv1.weight": - new_state_dict["decoder.conv_in.weight"] = value - elif key == "decoder.conv1.bias": - new_state_dict["decoder.conv_in.bias"] = value - # Handle encoder downsamples - elif key.startswith("encoder.downsamples."): - # Change encoder.downsamples to encoder.down_blocks - new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") - - # Handle residual blocks - change downsamples to resnets and rename components - if "residual" in new_key or "shortcut" in new_key: - # Change the second downsamples to resnets - new_key = new_key.replace(".downsamples.", ".resnets.") - - # Rename residual components - if ".residual.0.gamma" in new_key: - new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") - elif ".residual.2.weight" in new_key: - new_key = new_key.replace(".residual.2.weight", ".conv1.weight") - elif ".residual.2.bias" in new_key: - new_key = new_key.replace(".residual.2.bias", ".conv1.bias") - elif ".residual.3.gamma" in new_key: - new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") - elif ".residual.6.weight" in new_key: - new_key = new_key.replace(".residual.6.weight", ".conv2.weight") - elif ".residual.6.bias" in new_key: - new_key = new_key.replace(".residual.6.bias", ".conv2.bias") - elif ".shortcut.weight" in new_key: - new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") - elif ".shortcut.bias" in new_key: - new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") - - # Handle resample blocks - change downsamples to downsampler and remove index - elif "resample" in new_key or "time_conv" in new_key: - # Change the second downsamples to downsampler and remove the index - parts = new_key.split(".") - # Find the pattern: encoder.down_blocks.X.downsamples.Y.resample... - # We want to change it to: encoder.down_blocks.X.downsampler.resample... - if len(parts) >= 4 and parts[3] == "downsamples": - # Remove the index (parts[4]) and change downsamples to downsampler - new_parts = parts[:3] + ["downsampler"] + parts[5:] - new_key = ".".join(new_parts) - - new_state_dict[new_key] = value - - # Handle decoder upsamples - elif key.startswith("decoder.upsamples."): - # Change decoder.upsamples to decoder.up_blocks - new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") - - # Handle residual blocks - change upsamples to resnets and rename components - if "residual" in new_key or "shortcut" in new_key: - # Change the second upsamples to resnets - new_key = new_key.replace(".upsamples.", ".resnets.") - - # Rename residual components - if ".residual.0.gamma" in new_key: - new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") - elif ".residual.2.weight" in new_key: - new_key = new_key.replace(".residual.2.weight", ".conv1.weight") - elif ".residual.2.bias" in new_key: - new_key = new_key.replace(".residual.2.bias", ".conv1.bias") - elif ".residual.3.gamma" in new_key: - new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") - elif ".residual.6.weight" in new_key: - new_key = new_key.replace(".residual.6.weight", ".conv2.weight") - elif ".residual.6.bias" in new_key: - new_key = new_key.replace(".residual.6.bias", ".conv2.bias") - elif ".shortcut.weight" in new_key: - new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") - elif ".shortcut.bias" in new_key: - new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") - - # Handle resample blocks - change upsamples to upsampler and remove index - elif "resample" in new_key or "time_conv" in new_key: - # Change the second upsamples to upsampler and remove the index - parts = new_key.split(".") - # Find the pattern: encoder.down_blocks.X.downsamples.Y.resample... - # We want to change it to: encoder.down_blocks.X.downsampler.resample... - if len(parts) >= 4 and parts[3] == "upsamples": - # Remove the index (parts[4]) and change upsamples to upsampler - new_parts = parts[:3] + ["upsampler"] + parts[5:] - new_key = ".".join(new_parts) - - new_state_dict[new_key] = value - else: - # Keep other keys unchanged - new_state_dict[key] = value - - with init_empty_weights(): - vae = AutoencoderKLWan(**vae22_diffusers_config) - vae.load_state_dict(new_state_dict, strict=True, assign=True) - return vae - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--model_type", type=str, default=None) - parser.add_argument("--output_path", type=str, required=True) - parser.add_argument("--dtype", default="fp32", choices=["fp32", "fp16", "bf16", "none"]) - return parser.parse_args() - - -DTYPE_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, -} - - -if __name__ == "__main__": - args = get_args() - - if "Wan2.2" in args.model_type and "TI2V" not in args.model_type and "Animate" not in args.model_type: - transformer = convert_transformer(args.model_type, stage="high_noise_model") - transformer_2 = convert_transformer(args.model_type, stage="low_noise_model") - else: - transformer = convert_transformer(args.model_type) - transformer_2 = None - - if "Wan2.2" in args.model_type and "TI2V" in args.model_type: - vae = convert_vae_22() - else: - vae = convert_vae() - - text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16) - tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") - if "FLF2V" in args.model_type: - flow_shift = 16.0 - elif "TI2V" in args.model_type or "Animate" in args.model_type: - flow_shift = 5.0 - else: - flow_shift = 3.0 - scheduler = UniPCMultistepScheduler( - prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift - ) - - # If user has specified "none", we keep the original dtypes of the state dict without any conversion - if args.dtype != "none": - dtype = DTYPE_MAPPING[args.dtype] - transformer.to(dtype) - if transformer_2 is not None: - transformer_2.to(dtype) - - if "Wan2.2" and "I2V" in args.model_type and "TI2V" not in args.model_type: - pipe = WanImageToVideoPipeline( - transformer=transformer, - transformer_2=transformer_2, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - boundary_ratio=0.9, - ) - elif "Wan2.2" and "T2V" in args.model_type: - pipe = WanPipeline( - transformer=transformer, - transformer_2=transformer_2, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - boundary_ratio=0.875, - ) - elif "Wan2.2" and "TI2V" in args.model_type: - pipe = WanPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - expand_timesteps=True, - ) - elif "I2V" in args.model_type or "FLF2V" in args.model_type: - image_encoder = CLIPVisionModelWithProjection.from_pretrained( - "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16 - ) - image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") - pipe = WanImageToVideoPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - image_encoder=image_encoder, - image_processor=image_processor, - ) - elif "Wan2.2-VACE" in args.model_type: - pipe = WanVACEPipeline( - transformer=transformer, - transformer_2=transformer_2, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - boundary_ratio=0.875, - ) - elif "Wan-VACE" in args.model_type: - pipe = WanVACEPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - ) - elif "Animate" in args.model_type: - image_encoder = CLIPVisionModel.from_pretrained( - "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16 - ) - image_processor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") - - pipe = WanAnimatePipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - image_encoder=image_encoder, - image_processor=image_processor, - ) - else: - pipe = WanPipeline( - transformer=transformer, - text_encoder=text_encoder, - tokenizer=tokenizer, - vae=vae, - scheduler=scheduler, - ) - - pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") diff --git a/scripts/convert_wuerstchen.py b/scripts/convert_wuerstchen.py deleted file mode 100644 index 826b9b208181..000000000000 --- a/scripts/convert_wuerstchen.py +++ /dev/null @@ -1,115 +0,0 @@ -# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/ -import os - -import torch -from transformers import AutoTokenizer, CLIPTextModel -from vqgan import VQModel - -from diffusers import ( - DDPMWuerstchenScheduler, - WuerstchenCombinedPipeline, - WuerstchenDecoderPipeline, - WuerstchenPriorPipeline, -) -from diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior - - -model_path = "models/" -device = "cpu" - -paella_vqmodel = VQModel() -state_dict = torch.load(os.path.join(model_path, "vqgan_f4_v1_500k.pt"), map_location=device)["state_dict"] -paella_vqmodel.load_state_dict(state_dict) - -state_dict["vquantizer.embedding.weight"] = state_dict["vquantizer.codebook.weight"] -state_dict.pop("vquantizer.codebook.weight") -vqmodel = PaellaVQModel(num_vq_embeddings=paella_vqmodel.codebook_size, latent_channels=paella_vqmodel.c_latent) -vqmodel.load_state_dict(state_dict) - -# Clip Text encoder and tokenizer -text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") -tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") - -# Generator -gen_text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K").to("cpu") -gen_tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") - -orig_state_dict = torch.load(os.path.join(model_path, "model_v2_stage_b.pt"), map_location=device)["state_dict"] -state_dict = {} -for key in orig_state_dict.keys(): - if key.endswith("in_proj_weight"): - weights = orig_state_dict[key].chunk(3, 0) - state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0] - state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1] - state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2] - elif key.endswith("in_proj_bias"): - weights = orig_state_dict[key].chunk(3, 0) - state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0] - state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1] - state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2] - elif key.endswith("out_proj.weight"): - weights = orig_state_dict[key] - state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights - elif key.endswith("out_proj.bias"): - weights = orig_state_dict[key] - state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights - else: - state_dict[key] = orig_state_dict[key] -decoder = WuerstchenDiffNeXt() -decoder.load_state_dict(state_dict) - -# Prior -orig_state_dict = torch.load(os.path.join(model_path, "model_v3_stage_c.pt"), map_location=device)["ema_state_dict"] -state_dict = {} -for key in orig_state_dict.keys(): - if key.endswith("in_proj_weight"): - weights = orig_state_dict[key].chunk(3, 0) - state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0] - state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1] - state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2] - elif key.endswith("in_proj_bias"): - weights = orig_state_dict[key].chunk(3, 0) - state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0] - state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1] - state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2] - elif key.endswith("out_proj.weight"): - weights = orig_state_dict[key] - state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights - elif key.endswith("out_proj.bias"): - weights = orig_state_dict[key] - state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights - else: - state_dict[key] = orig_state_dict[key] -prior_model = WuerstchenPrior(c_in=16, c=1536, c_cond=1280, c_r=64, depth=32, nhead=24).to(device) -prior_model.load_state_dict(state_dict) - -# scheduler -scheduler = DDPMWuerstchenScheduler() - -# Prior pipeline -prior_pipeline = WuerstchenPriorPipeline( - prior=prior_model, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler -) - -prior_pipeline.save_pretrained("warp-ai/wuerstchen-prior") - -decoder_pipeline = WuerstchenDecoderPipeline( - text_encoder=gen_text_encoder, tokenizer=gen_tokenizer, vqgan=vqmodel, decoder=decoder, scheduler=scheduler -) -decoder_pipeline.save_pretrained("warp-ai/wuerstchen") - -# Wuerstchen pipeline -wuerstchen_pipeline = WuerstchenCombinedPipeline( - # Decoder - text_encoder=gen_text_encoder, - tokenizer=gen_tokenizer, - decoder=decoder, - scheduler=scheduler, - vqgan=vqmodel, - # Prior - prior_tokenizer=tokenizer, - prior_text_encoder=text_encoder, - prior=prior_model, - prior_scheduler=scheduler, -) -wuerstchen_pipeline.save_pretrained("warp-ai/WuerstchenCombinedPipeline") diff --git a/scripts/convert_zero123_to_diffusers.py b/scripts/convert_zero123_to_diffusers.py deleted file mode 100644 index b46633fae7ff..000000000000 --- a/scripts/convert_zero123_to_diffusers.py +++ /dev/null @@ -1,807 +0,0 @@ -""" -This script modified from -https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py - -Convert original Zero1to3 checkpoint to diffusers checkpoint. - -# run the convert script -$ python convert_zero123_to_diffusers.py \ - --checkpoint_path /path/zero123/105000.ckpt \ - --dump_path ./zero1to3 \ - --original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml -``` -""" - -import argparse - -import torch -import yaml -from accelerate import init_empty_weights -from accelerate.utils import set_module_tensor_to_device -from pipeline_zero1to3 import CCProjection, Zero1to3StableDiffusionPipeline -from transformers import ( - CLIPImageProcessor, - CLIPVisionModelWithProjection, -) - -from diffusers.models import ( - AutoencoderKL, - UNet2DConditionModel, -) -from diffusers.schedulers import DDIMScheduler -from diffusers.utils import logging - - -logger = logging.get_logger(__name__) - - -def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): - """ - Creates a config for the diffusers based on the config of the LDM model. - """ - if controlnet: - unet_params = original_config["model"]["params"]["control_stage_config"]["params"] - else: - if ( - "unet_config" in original_config["model"]["params"] - and original_config["model"]["params"]["unet_config"] is not None - ): - unet_params = original_config["model"]["params"]["unet_config"]["params"] - else: - unet_params = original_config["model"]["params"]["network_config"]["params"] - - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - - block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - if unet_params["transformer_depth"] is not None: - transformer_layers_per_block = ( - unet_params["transformer_depth"] - if isinstance(unet_params["transformer_depth"], int) - else list(unet_params["transformer_depth"]) - ) - else: - transformer_layers_per_block = 1 - - vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) - - head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None - use_linear_projection = ( - unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False - ) - if use_linear_projection: - # stable diffusion 2-base-512 and 2-768 - if head_dim is None: - head_dim_mult = unet_params["model_channels"] // unet_params["num_head_channels"] - head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])] - - class_embed_type = None - addition_embed_type = None - addition_time_embed_dim = None - projection_class_embeddings_input_dim = None - context_dim = None - - if unet_params["context_dim"] is not None: - context_dim = ( - unet_params["context_dim"] - if isinstance(unet_params["context_dim"], int) - else unet_params["context_dim"][0] - ) - - if "num_classes" in unet_params: - if unet_params["num_classes"] == "sequential": - if context_dim in [2048, 1280]: - # SDXL - addition_embed_type = "text_time" - addition_time_embed_dim = 256 - else: - class_embed_type = "projection" - assert "adm_in_channels" in unet_params - projection_class_embeddings_input_dim = unet_params["adm_in_channels"] - else: - raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params['num_classes']}") - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params["in_channels"], - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params["num_res_blocks"], - "cross_attention_dim": context_dim, - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "class_embed_type": class_embed_type, - "addition_embed_type": addition_embed_type, - "addition_time_embed_dim": addition_time_embed_dim, - "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, - "transformer_layers_per_block": transformer_layers_per_block, - } - - if controlnet: - config["conditioning_channels"] = unet_params["hint_channels"] - else: - config["out_channels"] = unet_params["out_channels"] - config["up_block_types"] = tuple(up_block_types) - - return config - - -def assign_to_checkpoint( - paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None -): - """ - This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits - attention layers, and takes into account additional replacements that may arise. - - Assigns the weights to the new checkpoint. - """ - assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." - - # Splits the attention layers into three variables. - if attention_paths_to_split is not None: - for path, path_map in attention_paths_to_split.items(): - old_tensor = old_checkpoint[path] - channels = old_tensor.shape[0] // 3 - - target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) - - num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 - - old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) - query, key, value = old_tensor.split(channels // num_heads, dim=1) - - checkpoint[path_map["query"]] = query.reshape(target_shape) - checkpoint[path_map["key"]] = key.reshape(target_shape) - checkpoint[path_map["value"]] = value.reshape(target_shape) - - for path in paths: - new_path = path["new"] - - # These have already been assigned - if attention_paths_to_split is not None and new_path in attention_paths_to_split: - continue - - # Global renaming happens here - new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") - new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") - new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") - - if additional_replacements is not None: - for replacement in additional_replacements: - new_path = new_path.replace(replacement["old"], replacement["new"]) - - # proj_attn.weight has to be converted from conv 1D to linear - is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) - shape = old_checkpoint[path["old"]].shape - if is_attn_weight and len(shape) == 3: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] - elif is_attn_weight and len(shape) == 4: - checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] - else: - checkpoint[new_path] = old_checkpoint[path["old"]] - - -def shave_segments(path, n_shave_prefix_segments=1): - """ - Removes segments. Positive values shave the first segments, negative shave the last segments. - """ - if n_shave_prefix_segments >= 0: - return ".".join(path.split(".")[n_shave_prefix_segments:]) - else: - return ".".join(path.split(".")[:n_shave_prefix_segments]) - - -def renew_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item.replace("in_layers.0", "norm1") - new_item = new_item.replace("in_layers.2", "conv1") - - new_item = new_item.replace("out_layers.0", "norm2") - new_item = new_item.replace("out_layers.3", "conv2") - - new_item = new_item.replace("emb_layers.1", "time_emb_proj") - new_item = new_item.replace("skip_connection", "conv_shortcut") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - # new_item = new_item.replace('norm.weight', 'group_norm.weight') - # new_item = new_item.replace('norm.bias', 'group_norm.bias') - - # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') - # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') - - # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def convert_ldm_unet_checkpoint( - checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False -): - """ - Takes a state dict and a config, and returns a converted checkpoint. - """ - - if skip_extract_state_dict: - unet_state_dict = checkpoint - else: - # extract state_dict for UNet - unet_state_dict = {} - keys = list(checkpoint.keys()) - - if controlnet: - unet_key = "control_model." - else: - unet_key = "model.diffusion_model." - - # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") - logger.warning( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint[flat_ema_key] - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - logger.warning( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - - for key in keys: - if key.startswith(unet_key): - unet_state_dict[key.replace(unet_key, "")] = checkpoint[key] - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - if config["class_embed_type"] is None: - # No parameters to port - ... - elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": - new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - else: - raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") - - if config["addition_embed_type"] == "text_time": - new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] - new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] - new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] - new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - if not controlnet: - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - if controlnet: - # conditioning embedding - - orig_index = 0 - - new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - - orig_index += 2 - - diffusers_index = 0 - - while diffusers_index < 6: - new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - diffusers_index += 1 - orig_index += 2 - - new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.weight" - ) - new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( - f"input_hint_block.{orig_index}.bias" - ) - - # down blocks - for i in range(num_input_blocks): - new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") - new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") - - # mid block - new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") - new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") - - return new_checkpoint - - -def create_vae_diffusers_config(original_config, image_size: int): - """ - Creates a config for the diffusers based on the config of the LDM model. - """ - vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] - _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] - - block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - config = { - "sample_size": image_size, - "in_channels": vae_params["in_channels"], - "out_channels": vae_params["out_ch"], - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params["z_channels"], - "layers_per_block": vae_params["num_res_blocks"], - } - return config - - -def convert_ldm_vae_checkpoint(checkpoint, config): - # extract state dict for VAE - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - if key.startswith(vae_key): - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - return new_checkpoint - - -def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside resnets to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("nin_shortcut", "conv_shortcut") - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): - """ - Updates paths inside attentions to the new naming scheme (local renaming) - """ - mapping = [] - for old_item in old_list: - new_item = old_item - - new_item = new_item.replace("norm.weight", "group_norm.weight") - new_item = new_item.replace("norm.bias", "group_norm.bias") - - new_item = new_item.replace("q.weight", "to_q.weight") - new_item = new_item.replace("q.bias", "to_q.bias") - - new_item = new_item.replace("k.weight", "to_k.weight") - new_item = new_item.replace("k.bias", "to_k.bias") - - new_item = new_item.replace("v.weight", "to_v.weight") - new_item = new_item.replace("v.bias", "to_v.bias") - - new_item = new_item.replace("proj_out.weight", "to_out.0.weight") - new_item = new_item.replace("proj_out.bias", "to_out.0.bias") - - new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) - - mapping.append({"old": old_item, "new": new_item}) - - return mapping - - -def conv_attn_to_linear(checkpoint): - keys = list(checkpoint.keys()) - attn_keys = ["query.weight", "key.weight", "value.weight"] - for key in keys: - if ".".join(key.split(".")[-2:]) in attn_keys: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0, 0] - elif "proj_attn.weight" in key: - if checkpoint[key].ndim > 2: - checkpoint[key] = checkpoint[key][:, :, 0] - - -def convert_from_original_zero123_ckpt(checkpoint_path, original_config_file, extract_ema, device): - ckpt = torch.load(checkpoint_path, map_location=device) - ckpt["global_step"] - checkpoint = ckpt["state_dict"] - del ckpt - torch.cuda.empty_cache() - - original_config = yaml.safe_load(original_config_file) - original_config["model"]["params"]["cond_stage_config"]["target"].split(".")[-1] - num_in_channels = 8 - original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels - prediction_type = "epsilon" - image_size = 256 - num_train_timesteps = getattr(original_config["model"]["params"], "timesteps", None) or 1000 - - beta_start = getattr(original_config["model"]["params"], "linear_start", None) or 0.02 - beta_end = getattr(original_config["model"]["params"], "linear_end", None) or 0.085 - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type=prediction_type, - ) - scheduler.register_to_config(clip_sample=False) - - # Convert the UNet2DConditionModel model. - upcast_attention = None - unet_config = create_unet_diffusers_config(original_config, image_size=image_size) - unet_config["upcast_attention"] = upcast_attention - with init_empty_weights(): - unet = UNet2DConditionModel(**unet_config) - converted_unet_checkpoint = convert_ldm_unet_checkpoint( - checkpoint, unet_config, path=None, extract_ema=extract_ema - ) - for param_name, param in converted_unet_checkpoint.items(): - set_module_tensor_to_device(unet, param_name, "cpu", value=param) - - # Convert the VAE model. - vae_config = create_vae_diffusers_config(original_config, image_size=image_size) - converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) - - if ( - "model" in original_config - and "params" in original_config["model"] - and "scale_factor" in original_config["model"]["params"] - ): - vae_scaling_factor = original_config["model"]["params"]["scale_factor"] - else: - vae_scaling_factor = 0.18215 # default SD scaling factor - - vae_config["scaling_factor"] = vae_scaling_factor - - with init_empty_weights(): - vae = AutoencoderKL(**vae_config) - - for param_name, param in converted_vae_checkpoint.items(): - set_module_tensor_to_device(vae, param_name, "cpu", value=param) - - feature_extractor = CLIPImageProcessor.from_pretrained( - "lambdalabs/sd-image-variations-diffusers", subfolder="feature_extractor" - ) - image_encoder = CLIPVisionModelWithProjection.from_pretrained( - "lambdalabs/sd-image-variations-diffusers", subfolder="image_encoder" - ) - - cc_projection = CCProjection() - cc_projection.load_state_dict( - { - "projection.weight": checkpoint["cc_projection.weight"].cpu(), - "projection.bias": checkpoint["cc_projection.bias"].cpu(), - } - ) - - pipe = Zero1to3StableDiffusionPipeline( - vae, image_encoder, unet, scheduler, None, feature_extractor, cc_projection, requires_safety_checker=False - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - args = parser.parse_args() - - pipe = convert_from_original_zero123_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - extract_ema=args.extract_ema, - device=args.device, - ) - - if args.half: - pipe.to(dtype=torch.float16) - - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/extract_lora_from_model.py b/scripts/extract_lora_from_model.py deleted file mode 100644 index 0e01ddea47f9..000000000000 --- a/scripts/extract_lora_from_model.py +++ /dev/null @@ -1,151 +0,0 @@ -""" -This script demonstrates how to extract a LoRA checkpoint from a fully finetuned model with the CogVideoX model. - -To make it work for other models: - -* Change the model class. Here we use `CogVideoXTransformer3DModel`. For Flux, it would be `FluxTransformer2DModel`, -for example. (TODO: more reason to add `AutoModel`). -* Spply path to the base checkpoint via `base_ckpt_path`. -* Supply path to the fully fine-tuned checkpoint via `--finetune_ckpt_path`. -* Change the `--rank` as needed. - -Example usage: - -```bash -python extract_lora_from_model.py \ - --base_ckpt_path=THUDM/CogVideoX-5b \ - --finetune_ckpt_path=finetrainers/cakeify-v0 \ - --lora_out_path=cakeify_lora.safetensors -``` - -Script is adapted from -https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py -""" - -import argparse - -import torch -from safetensors.torch import save_file -from tqdm.auto import tqdm - -from diffusers import CogVideoXTransformer3DModel - - -RANK = 64 -CLAMP_QUANTILE = 0.99 - - -# Comes from -# https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py#L9 -def extract_lora(diff, rank): - # Important to use CUDA otherwise, very slow! - if torch.cuda.is_available(): - diff = diff.to("cuda") - - is_conv2d = len(diff.shape) == 4 - kernel_size = None if not is_conv2d else diff.size()[2:4] - is_conv2d_3x3 = is_conv2d and kernel_size != (1, 1) - out_dim, in_dim = diff.size()[0:2] - rank = min(rank, in_dim, out_dim) - - if is_conv2d: - if is_conv2d_3x3: - diff = diff.flatten(start_dim=1) - else: - diff = diff.squeeze() - - U, S, Vh = torch.linalg.svd(diff.float()) - U = U[:, :rank] - S = S[:rank] - U = U @ torch.diag(S) - Vh = Vh[:rank, :] - - dist = torch.cat([U.flatten(), Vh.flatten()]) - hi_val = torch.quantile(dist, CLAMP_QUANTILE) - low_val = -hi_val - - U = U.clamp(low_val, hi_val) - Vh = Vh.clamp(low_val, hi_val) - if is_conv2d: - U = U.reshape(out_dim, rank, 1, 1) - Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) - return (U.cpu(), Vh.cpu()) - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--base_ckpt_path", - default=None, - type=str, - required=True, - help="Base checkpoint path from which the model was finetuned. Can be a model ID on the Hub.", - ) - parser.add_argument( - "--base_subfolder", - default="transformer", - type=str, - help="subfolder to load the base checkpoint from if any.", - ) - parser.add_argument( - "--finetune_ckpt_path", - default=None, - type=str, - required=True, - help="Fully fine-tuned checkpoint path. Can be a model ID on the Hub.", - ) - parser.add_argument( - "--finetune_subfolder", - default=None, - type=str, - help="subfolder to load the fulle finetuned checkpoint from if any.", - ) - parser.add_argument("--rank", default=64, type=int) - parser.add_argument("--lora_out_path", default=None, type=str, required=True) - args = parser.parse_args() - - if not args.lora_out_path.endswith(".safetensors"): - raise ValueError("`lora_out_path` must end with `.safetensors`.") - - return args - - -@torch.no_grad() -def main(args): - model_finetuned = CogVideoXTransformer3DModel.from_pretrained( - args.finetune_ckpt_path, subfolder=args.finetune_subfolder, torch_dtype=torch.bfloat16 - ) - state_dict_ft = model_finetuned.state_dict() - - # Change the `subfolder` as needed. - base_model = CogVideoXTransformer3DModel.from_pretrained( - args.base_ckpt_path, subfolder=args.base_subfolder, torch_dtype=torch.bfloat16 - ) - state_dict = base_model.state_dict() - output_dict = {} - - for k in tqdm(state_dict, desc="Extracting LoRA..."): - original_param = state_dict[k] - finetuned_param = state_dict_ft[k] - if len(original_param.shape) >= 2: - diff = finetuned_param.float() - original_param.float() - out = extract_lora(diff, RANK) - name = k - - if name.endswith(".weight"): - name = name[: -len(".weight")] - down_key = "{}.lora_A.weight".format(name) - up_key = "{}.lora_B.weight".format(name) - - output_dict[up_key] = out[0].contiguous().to(finetuned_param.dtype) - output_dict[down_key] = out[1].contiguous().to(finetuned_param.dtype) - - prefix = "transformer" if "transformer" in base_model.__class__.__name__.lower() else "unet" - output_dict = {f"{prefix}.{k}": v for k, v in output_dict.items()} - save_file(output_dict, args.lora_out_path) - print(f"LoRA saved and it contains {len(output_dict)} keys.") - - -if __name__ == "__main__": - args = parse_args() - main(args) diff --git a/scripts/generate_logits.py b/scripts/generate_logits.py deleted file mode 100644 index 99d46d6628a6..000000000000 --- a/scripts/generate_logits.py +++ /dev/null @@ -1,127 +0,0 @@ -import random - -import torch -from huggingface_hub import HfApi - -from diffusers import UNet2DModel - - -api = HfApi() - -results = {} -# fmt: off -results["google_ddpm_cifar10_32"] = torch.tensor([ - -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467, - 1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189, - -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839, - 0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557 -]) -results["google_ddpm_ema_bedroom_256"] = torch.tensor([ - -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436, - 1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208, - -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948, - 2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365 -]) -results["CompVis_ldm_celebahq_256"] = torch.tensor([ - -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869, - -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304, - -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925, - 0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943 -]) -results["google_ncsnpp_ffhq_1024"] = torch.tensor([ - 0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172, - -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309, - 0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805, - -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505 -]) -results["google_ncsnpp_bedroom_256"] = torch.tensor([ - 0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133, - -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395, - 0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559, - -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386 -]) -results["google_ncsnpp_celebahq_256"] = torch.tensor([ - 0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078, - -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330, - 0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683, - -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431 -]) -results["google_ncsnpp_church_256"] = torch.tensor([ - 0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042, - -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398, - 0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574, - -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390 -]) -results["google_ncsnpp_ffhq_256"] = torch.tensor([ - 0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042, - -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290, - 0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746, - -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473 -]) -results["google_ddpm_cat_256"] = torch.tensor([ - -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330, - 1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243, - -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810, - 1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251]) -results["google_ddpm_celebahq_256"] = torch.tensor([ - -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324, - 0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181, - -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259, - 1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266 -]) -results["google_ddpm_ema_celebahq_256"] = torch.tensor([ - -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212, - 0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027, - -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131, - 1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355 -]) -results["google_ddpm_church_256"] = torch.tensor([ - -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959, - 1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351, - -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341, - 3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066 -]) -results["google_ddpm_bedroom_256"] = torch.tensor([ - -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740, - 1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398, - -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395, - 2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243 -]) -results["google_ddpm_ema_church_256"] = torch.tensor([ - -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336, - 1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908, - -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560, - 3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343 -]) -results["google_ddpm_ema_cat_256"] = torch.tensor([ - -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344, - 1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391, - -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439, - 1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219 -]) -# fmt: on - -models = api.list_models(filter="diffusers") -for mod in models: - if "google" in mod.author or mod.id == "CompVis/ldm-celebahq-256": - local_checkpoint = "/home/patrick/google_checkpoints/" + mod.id.split("/")[-1] - - print(f"Started running {mod.id}!!!") - - if mod.id.startswith("CompVis"): - model = UNet2DModel.from_pretrained(local_checkpoint, subfolder="unet") - else: - model = UNet2DModel.from_pretrained(local_checkpoint) - - torch.manual_seed(0) - random.seed(0) - - noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) - time_step = torch.tensor([10] * noise.shape[0]) - with torch.no_grad(): - logits = model(noise, time_step).sample - - assert torch.allclose( - logits[0, 0, 0, :30], results["_".join("_".join(mod.id.split("/")).split("-"))], atol=1e-3 - ) - print(f"{mod.id} has passed successfully!!!") diff --git a/scripts/nsight_sdpa_mask_regression.py b/scripts/nsight_sdpa_mask_regression.py deleted file mode 100644 index 287e62890f12..000000000000 --- a/scripts/nsight_sdpa_mask_regression.py +++ /dev/null @@ -1,175 +0,0 @@ -import argparse -import os -import sys - -import pandas as pd -import torch -import torch.nn.functional as F - - -def _append_sys_path(path: str) -> None: - if path not in sys.path: - sys.path.insert(0, path) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Benchmark SDPA with and without masks and plot via nsight-python visualization." - ) - parser.add_argument( - "--seq-len", - type=int, - default=1024, - help="Sequence length for Q/K/V.", - ) - parser.add_argument( - "--num-heads", - type=int, - default=16, - help="Number of attention heads.", - ) - parser.add_argument( - "--head-dim", - type=int, - default=64, - help="Head dimension.", - ) - parser.add_argument( - "--batch-size", - type=int, - default=1, - help="Batch size.", - ) - parser.add_argument( - "--runs", - type=int, - default=3, - help="Number of timed runs per configuration.", - ) - parser.add_argument( - "--device", - default="cuda", - help="Device to run on.", - ) - parser.add_argument( - "--plot-path", - default="nsight_sdpa_mask_regression.png", - help="Output plot path.", - ) - return parser.parse_args() - - -def main() -> None: - args = parse_args() - - repo_root = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) - _append_sys_path(os.path.join(repo_root, "..", "nsight-python")) - import nsight.visualization - - device = torch.device(args.device) - torch.manual_seed(0) - - batch = args.batch_size - seq_len = args.seq_len - num_heads = args.num_heads - head_dim = args.head_dim - dtype = torch.bfloat16 - - q = torch.randn(batch, num_heads, seq_len, head_dim, device=device, dtype=dtype) - k = torch.randn(batch, num_heads, seq_len, head_dim, device=device, dtype=dtype) - v = torch.randn(batch, num_heads, seq_len, head_dim, device=device, dtype=dtype) - - full_mask = torch.ones((batch, num_heads, seq_len, seq_len), device=device, dtype=torch.bool) - full_mask[:, :, :, seq_len // 2 :] = 0 - - def _warmup(): - F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) - F.scaled_dot_product_attention(q, k, v, attn_mask=full_mask, dropout_p=0.0, is_causal=False) - torch.cuda.synchronize() - - _warmup() - - def _time_sdpa(use_mask: bool) -> float: - torch.cuda.synchronize() - start = torch.cuda.Event(enable_timing=True) - end = torch.cuda.Event(enable_timing=True) - start.record() - F.scaled_dot_product_attention( - q, - k, - v, - attn_mask=full_mask if use_mask else None, - dropout_p=0.0, - is_causal=False, - ) - end.record() - torch.cuda.synchronize() - return start.elapsed_time(end) * 1e6 - - timings = {"no_mask": [], "mask": []} - with torch.no_grad(): - for _ in range(args.runs): - timings["no_mask"].append(_time_sdpa(False)) - timings["mask"].append(_time_sdpa(True)) - - data = [] - for case, values in timings.items(): - avg = sum(values) / len(values) - min_v = min(values) - max_v = max(values) - std = (sum((v - avg) ** 2 for v in values) / len(values)) ** 0.5 - data.append( - { - "Annotation": "sdpa", - "case": case, - "AvgValue": avg, - "StdDev": std, - "MinValue": min_v, - "MaxValue": max_v, - "NumRuns": len(values), - "Metric": "gpu__time_duration.sum", - "GPU": torch.cuda.get_device_name(device), - "Host": os.uname().nodename, - "ComputeClock": 0, - "MemoryClock": 0, - } - ) - - df = pd.DataFrame(data)[ - [ - "Annotation", - "Metric", - "case", - "AvgValue", - "StdDev", - "MinValue", - "MaxValue", - "NumRuns", - "GPU", - "Host", - "ComputeClock", - "MemoryClock", - ] - ] - - nsight.visualization.visualize( - df, - metric="gpu__time_duration.sum", - row_panels=None, - col_panels=None, - x_keys=["case"], - title="SDPA runtime with vs without attention mask", - ylabel="Time (ns)", - filename=args.plot_path, - plot_type="bar", - show_geomean=False, - ) - - csv_path = os.path.splitext(args.plot_path)[0] + ".csv" - df.to_csv(csv_path, index=False) - print(f"Saved plot to {args.plot_path}") - print(f"Saved CSV to {csv_path}") - - -if __name__ == "__main__": - main() diff --git a/scripts/plot_qwenimage_branch_compare.py b/scripts/plot_qwenimage_branch_compare.py deleted file mode 100644 index 976cee89d56b..000000000000 --- a/scripts/plot_qwenimage_branch_compare.py +++ /dev/null @@ -1,49 +0,0 @@ -import argparse -import json -import os - -import matplotlib.pyplot as plt - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Plot QwenImage pipeline timing comparison.") - parser.add_argument("--main-json", required=True, help="JSON output from main branch.") - parser.add_argument("--branch-json", required=True, help="JSON output from current branch.") - parser.add_argument("--output", default="qwenimage_pipeline_branch_compare.png") - return parser.parse_args() - - -def _load(path: str) -> dict: - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def main() -> None: - args = parse_args() - - main_data = _load(args.main_json) - branch_data = _load(args.branch_json) - - labels = [ - f"main ({main_data.get('git_rev') or 'unknown'})", - f"branch ({branch_data.get('git_rev') or 'unknown'})", - ] - means = [main_data["mean_seconds"], branch_data["mean_seconds"]] - stds = [main_data["std_seconds"], branch_data["std_seconds"]] - - fig, ax = plt.subplots(figsize=(6, 4)) - bars = ax.bar(labels, means, yerr=stds, color=["#4E79A7", "#F28E2B"], edgecolor="black") - ax.set_ylabel("Time (seconds)") - ax.set_title("QwenImage pipeline runtime (lower is better)") - ax.grid(axis="y", alpha=0.3, linestyle="--") - - for bar, val in zip(bars, means): - ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f"{val:.2f}s", ha="center", va="bottom") - - plt.tight_layout() - plt.savefig(args.output, dpi=150) - print(f"Saved plot to {os.path.abspath(args.output)}") - - -if __name__ == "__main__": - main() diff --git a/scripts/plot_qwenimage_editplus_branch_compare.py b/scripts/plot_qwenimage_editplus_branch_compare.py deleted file mode 100644 index d4f645bdeb21..000000000000 --- a/scripts/plot_qwenimage_editplus_branch_compare.py +++ /dev/null @@ -1,51 +0,0 @@ -import argparse -import json -import os - -import matplotlib.pyplot as plt - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Plot QwenImageEditPlus timing comparison.") - parser.add_argument("--main-json", required=True, help="JSON output from main branch.") - parser.add_argument("--branch-json", required=True, help="JSON output from current branch.") - parser.add_argument("--output", default="qwenimage_editplus_branch_compare.png") - return parser.parse_args() - - -def _load(path: str) -> dict: - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def main() -> None: - args = parse_args() - - main_data = _load(args.main_json) - branch_data = _load(args.branch_json) - - labels = [ - f"main ({main_data.get('git_rev') or 'unknown'})", - f"branch ({branch_data.get('git_rev') or 'unknown'})", - ] - main_steps = main_data.get("steps") or 1 - branch_steps = branch_data.get("steps") or 1 - means = [main_data["mean_seconds"] / main_steps, branch_data["mean_seconds"] / branch_steps] - stds = [main_data["std_seconds"] / main_steps, branch_data["std_seconds"] / branch_steps] - - fig, ax = plt.subplots(figsize=(6, 4)) - bars = ax.bar(labels, means, yerr=stds, color=["#4E79A7", "#F28E2B"], edgecolor="black") - ax.set_ylabel("Time per step (seconds)") - ax.set_title("QwenImageEditPlus single-step runtime (lower is better)") - ax.grid(axis="y", alpha=0.3, linestyle="--") - - for bar, val in zip(bars, means): - ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f"{val:.3f}s", ha="center", va="bottom") - - plt.tight_layout() - plt.savefig(args.output, dpi=150) - print(f"Saved plot to {os.path.abspath(args.output)}") - - -if __name__ == "__main__": - main() diff --git a/scripts/push_rae_updated_weights.py b/scripts/push_rae_updated_weights.py deleted file mode 100644 index c6b81424cf37..000000000000 --- a/scripts/push_rae_updated_weights.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python -"""Re-save RAE checkpoints with updated weights (includes latent normalization buffers) -and push as PRs to the HuggingFace Hub. - -Usage: - python scripts/push_rae_updated_weights.py - python scripts/push_rae_updated_weights.py --dry-run # load and save locally only -""" - -import argparse -import tempfile - -import torch - -from diffusers import AutoencoderRAE - - -REPOS = [ - "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08", - "nyu-visionx/RAE-dinov2-wReg-base-ViTXL-n08-i512", - "nyu-visionx/RAE-dinov2-wReg-small-ViTXL-n08", - "nyu-visionx/RAE-dinov2-wReg-large-ViTXL-n08", - "nyu-visionx/RAE-siglip2-base-p16-i256-ViTXL-n08", - "nyu-visionx/RAE-mae-base-p16-ViTXL-n08", -] - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--dry-run", action="store_true", help="Only load and save locally, do not push.") - parser.add_argument("--repos", nargs="+", default=REPOS, help="Repos to update (default: all 6).") - args = parser.parse_args() - - for repo_id in args.repos: - print(f"\n{'=' * 60}") - print(f"Processing: {repo_id}") - print(f"{'=' * 60}") - - model = AutoencoderRAE.from_pretrained(repo_id) - model.eval() - - # Quick sanity check - enc_input_size = model.config.get("encoder_input_size", 224) or 224 - x = torch.rand(1, 3, enc_input_size, enc_input_size) - with torch.no_grad(): - out = model(x) - assert torch.isfinite(out.sample).all(), f"Non-finite output for {repo_id}" - print(f" Sanity check passed: output shape {out.sample.shape}") - - if args.dry_run: - with tempfile.TemporaryDirectory() as tmpdir: - model.save_pretrained(tmpdir) - print(f" Dry run: saved to {tmpdir}") - else: - model.push_to_hub( - repo_id, - commit_message="Update weights: include latent normalization buffers for diffusers compatibility", - create_pr=True, - ) - print(f" PR created for {repo_id}") - - -if __name__ == "__main__": - main() diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index e6d1841b80ee..83962ffc41f6 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -342,8 +342,6 @@ ) _import_structure["pipelines"].extend( [ - "BlockRefinementPipeline", - "BlockRefinementPipelineOutput", "HybridTokenDiffusionPipeline", "TokenDiffusionPipeline", "TokenDiffusionPipelineOutput", @@ -1129,8 +1127,6 @@ AutoPipelineForText2Image, BlipDiffusionControlNetPipeline, BlipDiffusionPipeline, - BlockRefinementPipeline, - BlockRefinementPipelineOutput, CLIPImageProjection, ConsistencyModelPipeline, DanceDiffusionPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 7db40e8b57c8..fcd61ecb0ebd 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -47,7 +47,6 @@ "AutoPipelineForInpainting", "AutoPipelineForText2Image", ] - _import_structure["block_refinement"] = ["BlockRefinementPipeline", "BlockRefinementPipelineOutput"] _import_structure["consistency_models"] = ["ConsistencyModelPipeline"] _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"] _import_structure["ddim"] = ["DDIMPipeline"] @@ -549,7 +548,6 @@ AutoPipelineForInpainting, AutoPipelineForText2Image, ) - from .block_refinement import BlockRefinementPipeline, BlockRefinementPipelineOutput from .consistency_models import ConsistencyModelPipeline from .dance_diffusion import DanceDiffusionPipeline from .ddim import DDIMPipeline diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 0354542166ce..4d6e5ba9c7d1 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2263,36 +2263,6 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BlockRefinementPipeline(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - -class BlockRefinementPipelineOutput(metaclass=DummyObject): - _backends = ["torch"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["torch"]) - - @classmethod - def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - @classmethod - def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch"]) - - class CLIPImageProjection(metaclass=DummyObject): _backends = ["torch"] From 3b1f34e6753b020d475e05a5a8b2877cf1af88c3 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 11:58:37 +0000 Subject: [PATCH 31/44] remove misc files from git tracking --- 2602.17270.md | 1117 ----------------------------------- 2602.17270.pdf | Bin 9044210 -> 0 bytes NSIGHT_REGRESSION_REPORT.md | 51 -- PLAN.md | 452 -------------- PR_RESPONSE.md | 82 --- RAE.md | 224 ------- RAE_PLAN.md | 108 ---- TORCH_COMPILE_STATUS.md | 156 ----- UL_PLAN.md | 247 -------- rae_recon_doc_cat.png | Bin 99064 -> 0 bytes stage1_recon_grid.png | Bin 882940 -> 0 bytes stage2_samples_grid.png | Bin 438779 -> 0 bytes 12 files changed, 2437 deletions(-) delete mode 100644 2602.17270.md delete mode 100644 2602.17270.pdf delete mode 100644 NSIGHT_REGRESSION_REPORT.md delete mode 100644 PLAN.md delete mode 100644 PR_RESPONSE.md delete mode 100644 RAE.md delete mode 100644 RAE_PLAN.md delete mode 100644 TORCH_COMPILE_STATUS.md delete mode 100644 UL_PLAN.md delete mode 100644 rae_recon_doc_cat.png delete mode 100644 stage1_recon_grid.png delete mode 100644 stage2_samples_grid.png diff --git a/2602.17270.md b/2602.17270.md deleted file mode 100644 index a91f37182e4d..000000000000 --- a/2602.17270.md +++ /dev/null @@ -1,1117 +0,0 @@ -6 -2 -0 -2 - -b -e -F -9 -1 - -] - -G -L -. -s -c -[ - -1 -v -0 -7 -2 -7 -1 -. -2 -0 -6 -2 -: -v -i -X -r -a - -Unified Latents (UL): How to train your latents - -Jonathan Heek1, Emiel Hoogeboom1, Thomas Mensink1 and Tim Salimans1 -1Google DeepMind Amsterdam - -We present Unified Latents (UL), a framework for learning latent representations that are jointly -regularized by a diffusion prior and decoded by a diffusion model. By linking the encoder’s output -noise to the prior’s minimum noise level, we obtain a simple training objective that provides a tight -upper bound on the latent bitrate. On ImageNet-512, our approach achieves competitive FID of 1.4, -with high reconstruction quality (PSNR) while requiring fewer training FLOPs than models trained on -Stable Diffusion latents. On Kinetics-600, we set a new state-of-the-art FVD of 1.3. - -1. Introduction - -Diffusion models have become remarkably successful for image, video, and audio generation. An -important factor in this success has been latent representations, compact encodings that allow -diffusion models to scale to higher resolutions more efficiently. - -However, it remains unclear how best to learn such latents. The original Latent Diffusion Model (Rom- -bach et al., 2022) uses a VAE-style KL penalty between the latent distribution and a standard Gaussian. -Since the decoder lacks a likelihood-based loss, the weight of the KL term must be set manually, -making it difficult to reason about the information content of the latents. - -Recently, works have focused on getting semantic representations from either pretrained networks -(e.g. from DINO) or heavily regularized autoencoders. These latents are usually easier to learn due to -their lower information density and obtain impressive FIDs. However, high frequency information -typically gets lost, which can be seen by worse PSNRs or heavy reconstruction artifacts. - -Simply put, there is a trade-off between the information content of the latent, and the reconstruction -quality of the output. If the structure of the latent is easier to learn, this generally leads to better -generation performance. The easier to learn a latent is while retaining its information density, the -better the resulting generation quality. - -In theory, even a single unregularized latent channel could encode an arbitrary amount of information. -In practice, the actual information is limited by machine precision and encoder smoothness. The -number of latent channels therefore determines the information capacity: fewer channels yield -easier-to-model latents at the cost of reconstruction quality, while more channels enable near-perfect -reconstruction but require greater modeling capacity. This paper shows how to navigate this trade-off -systematically. - -The key question we address is: How should latents be regularized when they will subsequently be -modeled by a diffusion model? Our answer: by co-training a diffusion prior on them. This approach, -which we call Unified Latents, rests on three key ideas: - -• Encode latents with a fixed amount of Gaussian noise. -• Align the prior diffusion model with the minimum noise level. As a consequence the KL term - -reduces to a simple weighted MSE over noise levels. - -• Use a reweighted elbo loss (sigmoid weighting) for the decoder. - -These components work together to train latents that are simultaneously encoded, regularized, and - -Corresponding author(s): {jheek, emielh, mensink, salimans}@google.com -© 2026 Google DeepMind. All rights reserved - - Unified Latents (UL): How to train your latents - -modeled using diffusion. This provides an interpretable bound on the bits in the latents, and simple -hyper-parameters to control the reconstruction-modelling tradeoff. - -2. Background - -Variational AutoEncoders Variational inference provides a principled approach to learning latent -representations. Given images 𝒙 that we wish to model, we can derive the Evidence Lower Bound -(ELBO) on the log-likelihood when using a latent variable 𝒛0: -+ KL(cid:104) - -− log 𝑝𝜃(𝒙) ≤ 𝔼𝒛0∼𝑝𝜃 (𝒛0 | 𝒙) - -= 𝐿(𝒙), - -(1) - -(cid:105) - -(cid:105) - -(cid:104) - -− log 𝑝𝜃(𝒙|𝒛0) -(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32) -(cid:125) -(cid:123)(cid:122) -decoder - -(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32)(cid:32) - -(cid:124) - -𝑝𝜃(𝒛0|𝒙) -(cid:32)(cid:32)(cid:32)(cid:32) -(cid:32)(cid:32)(cid:32)(cid:32) -(cid:125) -(cid:123)(cid:122) -(cid:124) -encoder - -(cid:12) -(cid:12) 𝑝𝜃(𝒛0) -(cid:32) -(cid:32) -(cid:125) -(cid:123)(cid:122) -(cid:124) -prior - -In this work both the decoder 𝑝𝜃(𝒙|𝒛0) and the prior 𝑝𝜃(𝒛0) will be learned with a diffusion model. - -Diffusion Models Diffusion models can be used to model arbitrary continuous distributions. A -diffusion model learns to revert a gradual destruction process which enables compression, likelihood -estimation, and sampling from the distribution of interest. - -Consider a data distribution 𝑞(𝑥) and a destruction process 𝒙𝑡 = 𝛼(𝑡)𝑥 + 𝜎(𝑡)𝜖 with 𝜖 ∼ N (0, 1). The -level of destruction is defined by the logsnr schedule 𝜆 (𝑡) = log(𝛼2 -𝑡 = 1 -for convenience. A learned model predicts the clean data ˆ𝒙(𝒛𝑡, 𝜃). One can show (Ho et al., 2020; -Kingma et al., 2021) that the information required to encode a sample 𝑝(𝒙0|𝒙) using the diffusion -model 𝑝(𝒙0) is - -𝑡 ). Additionally, we use 𝛼2 - -𝑡 +𝜎2 - -𝑡 /𝜎2 - -KL(cid:104) - -𝑝(𝒙0|𝒙)(cid:12) - -(cid:12)𝑝(𝒙0) - -(cid:105) - -≤ KL(cid:104) - -𝑝(𝒙0, . . . , 𝒙1|𝒙)(cid:12) - -= 𝔼𝑡∼U (0,1) - -(cid:104) - -− - -d𝜆 (𝑡) -d𝑡 - -(cid:105) - -(cid:12)𝑝𝜃(𝒙0, . . . , 𝒙1) -exp 𝜆 (𝑡) -2 - -𝑤(𝜆𝑡)||𝒙 − ˆ𝒙(𝒙𝑡, 𝜃)||2(cid:105) - -+ KL(cid:2) 𝑝(𝒙1|𝒙)(cid:12) - -(cid:12)𝑝(𝒙1)(cid:3), - -(2) - -where 𝑤(𝜆𝑡) = 1 is required for the bound to hold. However, in standard diffusion models a more -image-quality friendly weighting is chosen such as 𝑤(𝜆𝑡) = sigmoid(𝜆𝑡 − 𝑏). This re-weighted ELBO -formulation has the added benefit that the weighting is invariant to the choice of schedule 𝜆 (𝑡) -(Kingma & Gao, 2023). - -Note that although the destruction process is applied to a clean data point 𝒙, it only models up to -the minimal noise level 𝒙0. This will be important later, as our prior model will output slightly noisy -latents 𝒛0. If the VAE encoder does not predict a single encoding but a distribution 𝑝(𝑧|𝑥) the above -ELBO is insufficient. Prior work has generalized the KL for the more complex case of arbitrary encoder -distribution (Vahdat et al., 2021). - -Weighting diffusion ELBOs The above mentioned weighting means that diffusion models offer -a unique perspective on log-likelihood optimization. Their loss is decomposed over noise levels. -This can be used for example to down-weight the loss contributions of imperceptible high frequency -details. For a latent prior however, the encoder could abuse this by encoding information at the most -discounted noise levels. Therefore, a VAE with a diffusion prior should use unweighted ELBO loss -𝑤(𝜆 𝑧 (𝑡)) = 1. - -2 - - Unified Latents (UL): How to train your latents - -Figure 1 | Schematic overview of our model, include the Encoder (𝐸𝜃), the prior latent diffusion model -(𝑃𝜃), and the diffusion decoder model (𝐷𝜃). - -3. Unified Latent Diffusion - -This section describes how to train Unified Latents. The first section covers the latent encoding which -is regularized by a diffusion prior using Eq. 1. Secondly, we describe how to design a diffusion decoder -which models the reconstruction term log 𝑝𝜃(𝒙|𝒛0). Lastly, we describe the second stage of training -where the encoder and decoder are frozen and a new model is trained on the latents. An overview of -inputs and outputs during training is visualized in Figure 1. - -Algorithm 1 Training Unified Latents - -Sample 𝒙 ∼ 𝑝data -Encode the data 𝑧clean = 𝐸(𝒙, 𝜃) -Sample 𝑡 ∼ U (0, 1), 𝝐 ∼ N (0, I) -𝒛𝑡 = 𝛼𝑧 (𝑡)𝒛clean + 𝜎𝑧 (𝑡)𝝐 -Compute prior loss L𝑧 (𝜃) = − -Sample 𝑡 ∼ U (0, 1), 𝝐 ∼ N (0, I), 𝝐𝑧 ∼ N (0, I) -𝒛0 = 𝛼𝑧 (0)𝒛clean + 𝜎𝑧 (0)𝝐𝑧 -𝒙𝑡 = 𝛼𝑥 (𝑡)𝑥 + 𝜎𝑥 (𝑡)𝝐 -Compute decoder loss L𝑥 (𝜃) = -Optimize L (𝜃) = L𝑧 (𝜃) + L𝑥 (𝜃) - -exp 𝜆 𝑧 (𝑡) -2 - -d𝜆 𝑥 (𝑡) -d𝑡 - -d𝜆 𝑧 (𝑡) -d𝑡 - -exp 𝜆 𝑥 (𝑡) - -||𝒛clean − ˆ𝒛(𝒛𝑡, 𝜃)||2 + KL(cid:2) 𝑝(𝒛1|𝒙)(cid:12) - -(cid:12)𝑝(𝒛1)(cid:3) - -2 𝑤(𝜆 𝑥 (𝑡))||𝒙 − ˆ𝒙(𝒙𝑡, 𝒛0, 𝜃)||2 - -Algorithm 2 Sampling Unified Latents - -Sample 𝒛1 ∼ N (0, I) -Sample 𝒛0 ∼ 𝑝𝜃(𝒛0|𝒛1) from diffusion base model -Sample 𝒙1 ∼ N (0, I) -Sample 𝒙 ∼ 𝑝𝜃(𝒙|𝒛0, 𝒙1) from diffusion decoder model - -3.1. Encoding and Prior: Linking encoding noise and diffusion precision - -A key design decision is how much precision to use when encoding the latent. In principle, a continuous -variable can encode infinite bits, and floating-point representations typically support 16–32 bits— - -3 - -noisy latentclean latentencoderpredicted latentpredicted imagenoisy imagediffusion decoderdiffusion prior Unified Latents (UL): How to train your latents - -Figure 2 | Unified Latents overview. An image 𝒙 is encoded to 𝒛clean. A diffusion prior models the -path from pure noise 𝒛1 to a slightly noisy latent 𝒛0. This 𝒛0 is then used by a diffusion decoder to -reconstruct the image. The prior thus measures and regularizes the information content of 𝒛0. - -though encoder and decoder smoothness make it practically impossible to utilize this capacity in -full. In standard VAEs, encoder noise limits information content; for example, Stable Diffusion learns -this noise level. We take a different approach: we explicitly link the encoder noise to the maximum -precision of the prior diffusion model. - -Let 𝒛clean = 𝐸(𝒙, 𝜃) denote the deterministic latent encoding. The encoder should approximate the -posterior 𝑝(𝑧|𝑥), which is typically parameterized by a flexible distribution. However, following Vahdat -et al. (2021), we find that learning a flexible encoder distribution causes instability. - -We propose a simpler approach: the encoder predicts a single deterministic latent 𝑧clean, which is then -forward-noised to time 𝑡 = 0. We use a final log-SNR of 𝜆 (0) = 5, defining 𝑝(𝒛0|𝑧clean) = N (𝛼0𝑧clean, 𝜎0). -For a variance-preserving noise schedule, this corresponds to 𝛼0 = √︁sigmoid(+5) ≈ 1.0 and 𝜎0 = -√︁sigmoid(−5) ≈ 0.08. The KL term for the VAE loss is then: - -KL(cid:104) - -𝑝(𝒛0|𝒙)(cid:12) - -(cid:12)𝑝𝜃(𝒛0) - -(cid:105) - -≤ KL(cid:104) -(cid:104) - -= 𝔼𝑡 - -𝑝(𝒛0, . . . , 𝒛1|𝒙)(cid:12) -exp 𝜆 𝑧 (𝑡) -d𝜆 𝑧 (𝑡) -2 -d𝑡 - -− - -(cid:12)𝑝𝜃(𝒛0, . . . , 𝒛1) - -(cid:105) - -𝑤(𝜆 𝑧 (𝑡))||𝒛clean − ˆ𝒛(𝒛𝑡, 𝜃)||2(cid:105) - -+ KL(cid:2) 𝑝(𝒛1|𝒙)(cid:12) - -(cid:12)N (0, I)(cid:3) . - -(3) - -Thus, the latent 𝑧0 is sampled using a learned mean and fixed diagonal noise. - -3.2. Decoding: A diffusion decoder - -The decoder is also a diffusion model, but operating in the image space with 𝒙𝑡 = 𝛼𝑡 𝒙 + 𝜎𝑡𝝐. The -reconstruction loss can be written as: - -− log 𝑝𝜃(𝒙|𝒛0) ≤ KL(cid:104) - -(cid:12)𝑝𝜃(𝒙0, . . . , 𝒙1|𝒛0) - -(cid:105) - -𝑝(𝒙0, . . . , 𝒙1|𝒙)(cid:12) -(cid:104) d𝜆 𝑥 (𝑡) -d𝑡 - -exp 𝜆 𝑥 (𝑡) -2 - -= 𝔼𝑡∼U (0,1) - -𝑤𝑥 (𝜆 𝑥 (𝑡))||𝒙 − ˆ𝒙(𝒙𝑡, 𝒛0, 𝜃)||2(cid:105) - -(4) - -The key distinction is that the decoder network 𝐷𝜃 = ˆ𝒙(𝒙𝑡, 𝒛0, 𝜃) conditions on both the noisy data 𝒙𝑡 -and the latent 𝒛0. Since the decoder does not affect 𝒙, the prior term KL[ 𝑝(𝒙1|𝑥)∥N (0, I)] can be -ignored from the loss. - -In contrast with the prior, the decoder loss can be a re-weighted -Decoder weighting and loss factor -ELBO. By discounting low noise levels, high frequency features will always be modelled by the decoder -because the cost per bit of information is lower. For example, in many experiments we use the sigmoid -loss (Hoogeboom et al., 2024; Kingma & Gao, 2023), 𝑤(𝜆 (𝑡)) = sigmoid(𝜆 (𝑡) − 𝑏). - -4 - - Unified Latents (UL): How to train your latents - -Figure 3 | Decoder weighting on 𝝐-mse, 𝑤𝝐(𝜆𝑡) = 𝑐lf · sigmoid(𝑏 − 𝜆𝑡), showing which noise levels are -penalized (via a loss factor 𝑐lf = 1.6 in this case) and which noise levels are discounted. In theory, for -weightings above 1 the latent model is preferred and for weightings below 1 the decoder is preferred. -In practise, the decoder will model information even if the weighting is slightly above 1. - -Even with equal weighting, literature has shown that it is difficult to use the latent space in VAEs when -the decoder is powerful, a phenomenon referred to as posterior collapse (Razavi et al., 2019). For that -reason, we up-weigh the decoder loss with a loss factor (which is equivalent to down-weighting the -KL-term). See Figure 3 for a combined view of weighting and the loss factor. We find that we only -need a small loss factor (1.3 to 1.7 in most experiments). - -Thus, in our experiments we set the decoder ELBO weighting using the loss factor (𝑐lf) and sigmoid -bias 𝑏. These 2 hyper-parameters effectively control the amount of information in the latents. A -higher information latent naturally leads to better reconstruction quality. However, by using a more -informative latent we defer more of the modelling complexity to the base model. - -3.3. Base model: Stage 2 training - -In principle we could use the prior diffusion model as described above to generate 𝒛0 and subsequently -sample 𝑝(𝒙|𝒛0) using the diffusion decoder. However we find that a prior trained using an ELBO -loss does not produce good samples (see App. B). Because the prior can only be trained on an ELBO -weighting in stage 1, it places equal weight on low-frequency and high-frequency content in the -latent. Therefore, we find that performance can be improved considerably by retraining the prior -model as a base model with a sigmoid weighting. Because only a frozen encoder is required during -this stage, the base model size and batch size can be much larger than in stage 1. - -The training of the base model largely follows the same procedure as existing Latent Diffusion Models -(Rombach et al., 2022). The only difference is that Unified Latents have a fixed amount of noise so -there is a fixed logsnr max 𝜆 (0) for the base model which is the same as the one used in the prior. -This deviates from the standard approach where the final logsnr is a hyper-parameter and we use the -final prediction ˆ𝒛 instead of 𝒛0 as the sampled latent. - -There are alternative design choices that allow for single stage training. In this case the prior model -will already achieve better performance. This requires different weightings of decoder and prior -losses, and are discussed in Appendix B. - -5 - -decoder loss weight1.61.00.0-10.010.00.0latent preferreddecoder preferred Unified Latents (UL): How to train your latents - -4. Related Work - -Our work combines diffusion-based decoding with diffusion-based priors to learn latent representa- -tions optimized for generation. We review the most relevant prior work below. - -Diffusion Decoders Several works have explored using diffusion models as decoders in VAE-like -frameworks. DiffuseVAE (Pandey et al., 2022) trains a conventional MSE autoencoder first, then -finetunes a diffusion decoder using the original decoder’s output as conditioning. SWYCC (Birodkar -et al., 2024) and 𝜖-VAE (Zhao et al., 2025) train latents with a diffusion decoder, but still rely on a -channel bottleneck for regularization rather than a learned prior. DiVAE (Shi et al., 2022) combines a -diffusion decoder with discrete VQ-VAE tokens. In contrast, our approach uses continuous latents -regularized by a diffusion prior, providing interpretable control over the bitrate. - -Diffusion Priors LSGM (Vahdat et al., 2021) jointly trains a diffusion prior in a VAE framework, -but requires a separate encoder entropy term 𝔼𝑞(𝒛0 | 𝒙) log 𝑞(𝒛0|𝒙) that introduces training instability. -Our approach sidesteps this by using a deterministic encoder with fixed noise, absorbing the encoder -distribution into the diffusion forward process. This yields a simpler two-term objective (decoder loss -+ prior loss) while maintaining a tight bound on latent information. - -Diffusion Decoder and Prior DiffAE (Preechakul et al., 2022) uses diffusion for both encoding -and decoding, but its latent comes from a pre-trained “semantically meaningful” encoder rather than -being optimized for generation quality. Our work differs by jointly training the encoder, prior, and -decoder, with the explicit goal of maximizing generation efficiency. - -Latent Diffusion and Efficient Autoencoders The original Latent Diffusion Model (Rombach -et al., 2022) uses a GAN-trained autoencoder with channel-bottlenecked latents and a small KL -penalty, but provides no principled way to control latent information. Recent work on efficient -autoencoders (Chen et al., 2024) achieves high compression ratios but does not address the interplay -between autoencoder design and downstream diffusion modeling. Token-based approaches like -TiTok (Yu et al., 2024) compress images to discrete tokens, trading reconstruction quality for faster -sampling. Lastly, pretrained semi-supervised encoders like DINO (Caron et al., 2021) can be used to -focus on semantically meaningful representations (Shi et al., 2025; Zheng et al., 2025) and obtain -impressive generation quality metrics. A downside of these approaches is that PSNR scores are low -(≤ 20) causing reconstructions to appear different from their original in particular on high-frequency -details. - -Latents from Self-Supervised Representation A number of recent works have replaced the Au- -toEncoder all-together and model a semi-supervised representation like SigLip or Dino instead (Shi -et al., 2025; Zheng et al., 2025). - -5. Experiments - -We evaluate Unified Latents on their ability to improve pre-training efficiency—the relationship between -training compute and generation quality. We conduct experiments on ImageNet-512 and Kinetics-600 -for direct comparison with prior work, and include scaling studies on large-scale text-to-image and -text-to-video datasets. We focus on pre-training efficiency and avoid fine-tuning stages (such as - -6 - - Unified Latents (UL): How to train your latents - -aesthetics fine-tuning) or MS-COCO evaluations, as these introduce confounding factors unrelated to -the quality of the learned latents. - -5.1. Model Architecture - -Our encoder and decoder models use 2x2 patching to save compute. The encoder is a Resnet model -with [128, 256, 512, 512] channels and 2 residual blocks for downsampling stage and 3 blocks in the -final stage. - -The prior model is a single level ViT with 8 blocks and 1024 channels. In the base model we use a 2 -stage ViT with [512, 1024] channels [6, 16] blocks. The base model is regularized with a dropout -rate of 0.1 in both stages. - -The decoder is a UVit model (Hoogeboom et al., 2024) with channel counts [128, 256, 512] in the -convolutional down-sampling and up-sampling stages. The transformer in the middle has 8 blocks -and 1024 channels. We use a dropout rate of 0.1 for regularization. - -5.2. Evaluation Metrics - -To assess the quality of samples and autoencoder reconstructions we use FID and FVD for images and -videos, respectively. When sampling from a base model we denote the FID as gFID. For reconstruction -we use the term rFID and use the same samples from the dataset to compute reconstructions and -the FID references. This breaks the convention of standard FID where the reference statistics are -computed on the entire train (or sometimes eval) dataset. The same approach and the rFID and gFID -convention is used by the majority of existing literature. - -We also use PSNR (Peak Signal-to-Noise Ratio) to measure how closely reconstructions match their -originals. This complements FID, since a reconstruction can be in-distribution (low FID) while still -differing substantially from the original image. Additionally, because our models provide an upper -bound on latent information, we report the estimated bits per dimension (bpd) in the latent space. - -For computational cost, we count FLOPs for all linear projections and attention operations. For -training cost, we multiply by 3 to approximate the cost of computing gradients. - -5.3. Image Generation - -In this section we test Image Generation performance. The autoencoder operates on a resolution of -512 × 512 and downsamples 16 × 16 to produce 32 × 32 latents. For each experiment, the optimal -latent bitrate is chosen so that gFID is highest (for details see Sec. 5.4). - -ImageNet First we show scaling performance of Unified Latents in training flops vs generation FID -(Figure 4) on ImageNet5121. There are several important things to note. Firstly, UL outperforms -other approaches in literature in a training cost vs generation performance trade-off, which means it -is the most efficient pre-training approach on this dataset. Secondly, for a fair comparison we train -the exact architecture (a 2-level ViT) on Stable Diffusion latents (baselines small SD and medium -SD). Here we see that UL is outperforming the baselines to a greater extend. We find that patching is -detrimental to the performance of the base model. The UNet (SD) baseline is a small model that uses -an additional convolution stack instead of patching the SD latents. - -1The original time of writing was March 2025, in the meantime other (often complimentary) approaches may have - -discovered. - -7 - - Unified Latents (UL): How to train your latents - -small (SD) - -medium (SD) - -DiT-XL/2 (interval) - -Previous -Baselines (SD) -Unified latents (ours) - -5 - -3 - -2 - -UNet (SD) - -1.5 - -UL, small (tti AE) - -SiD2, small - -EDM2-S (interval) - -UL, small - -UL, medium (tti AE) -UL, medium - -RAE - -EDM2-XXL (interval) - -SiD2, flop - -1 - -0.0 - -0.5 - -1.0 - -1.5 - -2.0 - -2.5 - -3.0 - -3.5 - -Training cost (zettaflops per model) - -Figure 4 | FID vs. training cost on ImageNet-512. UL outperforms all other approaches on base -training compute versus generation equality We assume that one training iteration is three times as -expensive as evaluating the model (i.e., forward pass, backprop to inputs, backprop to weights). Note -that auto-encoder training cost is not included. - -AutoEncoder transfer Previous work like Stable Diffusion uses a auto-encoder that is trained on -another dataset than ImageNet. To test the effect of using an out-of-distribution autoencoder we also -train a base model on Unified Latents trained on an internal text-to-image dataset (tti AE). We did -not observe a significant difference in training efficiency. In-distribution autoencoders seem slightly -better when training small base models with a low information latent. - -Figure 5 | A selection of samples from a text-to-image trained with Unified Latents - -Text-To-Image In order to test our methods at scale we trained multiple AutoEncoders on internal -Text-To-Image datasets sweeping over loss factor (1.25-1.7). For each AutoEncoder we train base -models add various sizes (100, 300, and 970 GFlops). To evaluate these models we take 30k samples -without guidance and compute clip and FID scores against the training set. - -Figure 5 shows some hand-picked samples from one of the large models. See Figure 10 for additional -and non cherry picked samples. - -Figure. 6 shows how AutoEncoders with a low latent bitrate lead to better image quality as measure - -8 - - Unified Latents (UL): How to train your latents - -latents - -gFID@30K - -clip - -UL (LF=1.5) -Pixel (no latents) -StableDiffusion - -4.1 -5.0 -6.8 - -27.1 -27.0 -27.0 - -Table 1 | Generation quality and text alignment for text-to-image models trained with Unified Latents, -pixel diffusion (no latents), and StableDiffusion latents. - -Figure 6 | Image generation quality (left) and text alignment (right) against AutoEncoder Loss Factor -for various base model sizes. - -by gFID. This effect is more pronounced for smaller models. The text alignment (clip) on the other -hand suffers slightly from very low loss factors even for smaller models. Indicating that perhaps the -decoder would also benefit from text conditioning. However, we also note that text-alignment can be -easily improved by applying guidance. - -In Table 1 we compare the text-to-image models trained with Unified Latents to models trained with -pixel diffusion (Hoogeboom et al., 2024), and Stable Diffusion latents (Rombach et al., 2022). We -add additional convolution blocks to deal with the higher resolution but do not compensate the UL -model for the additional flops used by the other models. The UL el significantly outperform these -baselines on perceptual quality and has a slightly better text-alignment. - -5.4. Latent bitrate tuning - -Recall that there is a reconstruction FID vs generation FID trade-off. The goal of the combined -auto-encoder and base model stack is to achieve the highest gFID possible. On the other hand, it is -trivial to obtain a very good rFID by allowing more and more bits to flow through the latents. This is a -problem, because high bitrate latents will be more difficult to model. One way to control the amount -of bits in the latent is by changing the loss factor (see Table 2 and Fig. 7). Note that for smaller models, -typically lower bitrates are optimal: even though rFID (and thus decoding) is somewhat worse, the -smaller capacity models can only fit low bitrate latents properly. On the contrary, larger models are -less sensitive to latent bitrates, and can achieve even better performance on higher bitrates. - -An alternative method to tune latent bitrates is via the bias in the sigmoid loss, which is entangled -with the loss factor. For lower biases, one typically requires higher loss factors. In Figure 8 we show a -sweep over decoder bias and loss factors, which demonstrates that several settings give roughly equal -performance / latent bitrate curves. - -9 - -1.251.31.41.51.7Loss factor3.253.503.754.004.254.504.755.00gFID@30Ksmallmediumlarge1.251.31.41.51.7Loss factor0.26900.26950.27000.27050.27100.27150.27200.2725clip Unified Latents (UL): How to train your latents - -Table 2 | Increasing the loss factor leads to improved reconstruction metrics (rFID, PSNR) at the cost -of increased bitrate in the latent encoding. For small models, the loss factor (and bits in the latent) -matter a lot. For larger base models the loss factor is less sensitive. - -LF - -bits/pixel - -rFID@50k PSNR gFID (small) - -gFID (medium) - -1.3 -1.5 -1.7 -1.9 -2.1 - -0.035 -0.059 -0.083 -0.101 -0.116 - -0.79 -0.47 -0.36 -0.31 -0.27 - -25.7 -27.6 -28.9 -29.6 -30.1 - -1.42 -1.54 -1.77 -2.02 -2.38 - -1.37 -1.31 -1.38 -1.45 -1.58 - -Figure 7 | Reconstruction quality vs loss factor. Fine details like small text are lost for low bitrate -latents. - -5.5. Latent shape - -For Latent Diffusion Models the downsampling factor and latent channels are the main factors -determining the information bottleneck (Rombach et al., 2022). In this experiment we use a fixed -spatial downsampling to 32 × 32, and vary the number of latent channels (from 4 to 64). The results -are in Table 3. From the results we conclude that Unified Latents are mostly insensitive to the number -of latent channels. Only for a very low latent channel count the encoder is unable to pass enough -information to enable good reconstructions (4 and 8). - -In the next experiment, we vary the spatial downsampling (8𝑥 to 32𝑥), while using a fixed number -of latent channels (32). The results are in Table 4. First, we observe that 32 channels work well for -any of the spatial dimensions of the latents. Second, we see that the rFID results are similar for 16𝑥 -and 8𝑥 spatial downsampling (to 32 × 32 and to 64 × 64), while it seems that the former is easier to -model for the decoder, resulting in lower gFID numbers. - -5.6. L2 Regularization - -It can be cumbersome to train 2 diffusion models simultaneously. Here we find that for a slight -decrease in performance it is also possible to first train the encoder using a diffusion prior while -placing l2 regularization on the decoder and different loss weightings (see Table 5). - -This experiment may raise another question: How about a simpler version where the latents are -regularized by an l2 loss / normal prior, as is typical for VAEs? We find that training with a VAE with -a normal prior requires higher bitrate latents to reach good reconstruction quality. This then results -in more difficult to learn latents and worse gFID. - -10 - -Orig1.31.51.71.92.1 Unified Latents (UL): How to train your latents - -Figure 8 | Image quality for various latent bitrates (FID vs bits/pixel) for a small model variant. -Left: generation (gFID). Middle: reconstruction (rFID). Right: reconstruction PSNR. We sweep over -sigmoid shift and loss factor. For each shift, we sweep over loss factors [1.5, 1.75, 2., 3., 4.]. - -# chan rFID gFID@50K - -4 -8 -16 -32 -64 - -7.19 -1.53 -0.54 -0.42 -0.48 - -- -- -1.76 -1.60 -1.77 - -Table 3 | FID metrics on ImageNet512 are insensitive to latent channel count. The AE is unable to -obtain a good reconstruction quality (rFID) with to few channels (≤ 8). - -5.7. Video Generation - -Kinetics600 In this experiment we show that our method outperforms literature on k600 on a -training cost vs FVD tradeoff. Here we use 4 × 8 × 8 downsampling for 16 frames of 128 × 128 kinetics -videos. Following Video Diffusion, we condition on 5 frames and generate 11 frames. For MAGVIT and -W.A.L.T. due to tokenization choices the models operate on 17 frames, a temporal latent dimensions -of 5, and FVD is measured on 5-12 generations. To make comparison more fair, we discard the extra -token of processing in the FLOP computation of these baselines. - -Here again, UL outperforms other approaches on training cost vs FVD performance (see Figure 9). -Note that the small model already achieves 1.7 FVD, whereas the medium model achieves 1.3 FVD -which is currently SOTA. - -5.8. Ablations - -In this section we aim to ablate our approach by removing the key innovations. We also consider the -classic VAE setup where the encoder is allowed to predict a mean and variance. The results are listed -in Table 6. - -Firstly (A), we want to make sure the prior improves and regularizes the latents. To test this we added -a stop-gradient to the prior input so we still get a bitrate estimate but the encoder no longer receives -a gradient with respect to the prior. Instead, we regularize the latent with a strongly discounted KL to -N (0, 𝐼) like prior works (Rombach et al., 2022). To get a reasonable bitrate and gFID we must reduce -the latent channels. The best result reported here uses 8 latent channels vs. 32 in the baseline. - -Secondly (B), we ablate the noisy latents by using 𝜆 𝑧 (0) = 10 which corresponds to adding a very -small amount of noise (𝜎 ≈ 0.007). At this precision the prior fails to accurately model the bitrate - -11 - -0.000.050.100.150.20latent bits / pixel2.03.04.05.0gFID@50kshift 2.0shift 3.0shift 4.0shift 5.00.000.050.100.150.20latent bits / pixel0.80.91.01.251.5rFID@50kshift 2.0shift 3.0shift 4.0shift 5.00.000.050.100.150.20latent bits / pixel23.025.027.029.031.0PSNRshift 2.0shift 3.0shift 4.0shift 5.0 Unified Latents (UL): How to train your latents - -latent shape (ℎ × 𝑤 × 𝑐) - -rFID@50K, - -gFID@50K - -64 × 64 × 32 -32 × 32 × 32 -16 × 16 × 32 - -0.40 -0.41 -1.41 - -2.12 -1.63 -1.74 - -Table 4 | FID metrics on ImageNet512 for AutoEncoders with spatial downsampling factors between -8x and 32x. - -Prior - -Reconstruction loss - -Latent bpd Latent bpd -with prior with base model - -rFID@50K gFID@50K - -Diffusion Diffusion -Diffusion MSE -Normal - -Diffusion - -0.079 -0.072 -0.39 - -0.079 -0.072 -0.26 - -0.86 -1.1 -0.83 - -1.4 -2.4 -2.5 - -Table 5 | Ablations on the auto-encoder training. - -of the latent and the loss is reduced by simply modelling most information in the decoder. The -reconstructions (rFID) are too low quality to train a useful base model. - -Thirdly (C), we test what happens if we train on a text-to-image dataset rather than ImageNet. rFID -is strongly affected while generation still works well. Other work that trains autoencoders directly on -ImageNet data has also reported very low rFID scores (Chen et al., 2024). We hypothesize that this -is mostly caused by minor differences in high-frequency statistics that FID seems overly sensitive to -compared to human perception. - -Lastly (D), we consider a more traditional VAE setup with an encoder that predicts a mean and -variance. Prior work (Vahdat et al., 2021) shows that the KL term can be generalized to arbitrary -distribution. The generalization adds two entropy terms subtracting the encoder entropy from the -entropy. For an encoder distribution 𝑝(𝒛clean|𝑥) = N (𝜇𝑧, diag(𝜎2 - -𝑧 )) the extra terms reduce to - -L𝑒 = − - -1 -2 - -log (cid:2)𝜎2 - -𝑧 𝑒𝜆 𝑧 (0) + 1(cid:3) . - -(5) - -For the noisy latent setting 𝜆 𝑧 (0) = 5 we find that the learned noise quickly drops to 0 and the model -becomes unstable. For high precision latents 𝜆 𝑧 (0) = 10 the encoder does learn to inject additional -noise into the latent. The estimate of the KL term is high variance as reported before (Vahdat et al., -2021). The gFID is worse than the baseline. Thus, we conclude that the fixed encoder variance is a -useful simplification that increases both stability and performance. - -6. Discussion - -Larger base models benefit from more informative latents. A natural direction for future work is to -establish scaling laws for Unified Latents that predict the optimal bitrate given a training budget. -Such scaling laws would depend on implementation details including dataset, evaluation metrics, and -model architecture, and would be best studied in the context of production-scale foundation models. - -While this work focuses primarily on images with some extension to video, the Unified Latent -framework appears broadly applicable. With a discrete (diffusion) decoder discrete data like text -could in theory be compressed with latents as well. - -12 - - Unified Latents (UL): How to train your latents - -Table 6 | Ablations study on Unified Latents components. - -bits/pixel - -rFID@50k - -gFID@50k - -UL baseline (LF=1.5) -A. prior model -B. noisy latents -C. ImageNet data -D. learned variance - -0.059 -0.121 -0.008 -0.034 -0.060 - -0.47 -1.81 -28.27 -1.37 -0.69 - -1.54 -7.80 -- -1.63 -1.81 - -Previous -Unified Latents (ours) - -Video Diffusion - -RIN - -MAGVIT-v2 - -W.A.L.T. - -UL (small) - -UL (medium) - -FVD - -10 - -5 - -3 - -2 - -1 - -0.0 - -0.5 - -1.0 - -Training cost (zettaflops, base model only), Kinetics-600 - -Figure 9 | FVD vs. training cost on Kinetics-600. Plotted until convergence. - -6.1. Limitations - -Existing literature and this work show a trend towards less-informative latents (measured by bitrate -or reconstruction PSNR) being easier to model. To what extend are weaker latents moving part of the -modelling problem toward the decoder? This work uses U-Net diffusion models, while most prior -work uses a GAN based decoder with a discriminator loss but without a noise input (Rombach et al., -2022). A diffusion decoder is strictly more powerful than such GANs because it predicts a distribution -rather than a single image. However, the mode-collapsing nature of GAN training might help this -class of models producing better looking images with better rFID scores. - -Comparison between latent diffusion models is further complicated by differences in AutoEncoder -training data. The original Stable Diffusion autoencoder was trained on a large-scale web dataset (Rom- -bach et al., 2022), whereas most of our experiments use only ImageNet. Semi-supervised ap- -proaches (Caron et al., 2021; Shi et al., 2025; Zheng et al., 2025) introduce encoders trained -on large external datasets, making direct comparison even more challenging. - -Finally, diffusion decoders are an order of magnitude more expensive to sample from than GAN based -decoders. Without an additional distillation step for the decoder, the computational cost of using -Unified Latents is significantly higher than a standard LDM. - -13 - - Unified Latents (UL): How to train your latents - -6.2. Conclusion - -In summary, we have demonstrated that latent representations can be effectively learned by jointly -training an encoder, diffusion prior, and diffusion decoder. This approach outperforms existing -methods in both training efficiency and generation quality. Unified Latents provide stable, interpretable -control over latent information through simple hyper-parameters, making the reconstruction–modeling -trade-off explicit. We believe this principled approach to latent design will prove valuable as latent -diffusion models continue to scale. - -14 - - Unified Latents (UL): How to train your latents - -References - -Birodkar, V., Barcik, G., Lyon, J., Ioffe, S., Minnen, D., and Dillon, J. V. Sample what you cant compress. - -Technical report, arXiv, 2024. URL https://arxiv.org/abs/2409.02529. - -Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A. Emerging -properties in self-supervised vision transformers. 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(eds.), Advances in Neural Information -Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS, -pp. 11287–11302, 2021. - -15 - - Unified Latents (UL): How to train your latents - -Yu, Q., Weber, M., Deng, X., Shen, X., Cremers, D., and Chen, L. An image is worth 32 tokens for - -reconstruction and generation. CoRR, abs/2406.07550, 2024. - -Zhao, L., Woo, S., Wan, Z., Li, Y., Zhang, H., Gong, B., Adam, H., Jia, X., and Liu, T. Epsilon-vae: - -Denoising as visual decoding, 2025. URL https://arxiv.org/abs/2410.04081. - -Zheng, B., Ma, N., Tong, S., and Xie, S. Diffusion transformers with representation autoencoders. - -CoRR, abs/2510.11690, 2025. - -16 - - Unified Latents (UL): How to train your latents - -A. Additional samples - -Figure 10 | Generations from the text-to-image model. Guidance is set to 2. Images are not cherry- -picked. Used prompts: (1) A couple gets caught in the rain, oil on canvas, (2) A lone traveller walks in -a misty forest, (3) A walking figure made out of water, (4) In the swamp, a crocodile stealthily surfaces, -revealing only its eyes and the tip of its nose as it moves forward, (5) A fox dressed in suit dancing in park, -(6) Pouring chocolate sauce over vanilla ice cream in a cone, studio lighting, (7) An astronaut riding a -horse, (8) Aurora Borealis Green Loop Winter Mountain Ridges Northern Lights, (9) Sailboat sailing on -a sunny day in a mountain lake, (10) A dog driving a car on a suburban street wearing funny sunglasses. - -B. End-to-end latent training - -In addition to the 2-stage training approach described in this paper, we also tried training the encoder, -decoder, and base diffusion model end-to-end in a single stage. This can be done in two ways. In our -first attempt we shifted the loss of the decoder diffusion model towards more noisy data, following -Hoogeboom et al. (2024), combined with the standard ELBO loss on the base model. Both models -can then be trained jointly in a stable way, but we did not get FID below 2 using this approach. In a -second attempt we trained the base model with a weighted ELBO loss that is equivalent to training -this model with unweighted ELBO loss on data with additional added noise (Kingma & Gao, 2023). -This means it is possible to train the decoder and base model jointly, using differently weighted ELBO -losses on the base model and decoder, by randomizing the maximum log signal-to-noise ratio of the -base model according to a particular truncated logistic distribution. The decoder diffusion model is -then modified to condition on the log-SNR of the latents, similar to the conditioning augmentation of -Ho et al. (2022). Using the exact settings used in the 2-stage approach, but training in a single stage, -we achieved an FID of about 4 in 400k training steps. - -17 - - diff --git a/2602.17270.pdf b/2602.17270.pdf deleted file mode 100644 index be44dc5225042fc1840c222beb172db34d0c3703..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 9044210 zcmce;2Rzm7|37|Elte@ssgu3Wu=n004l;{_gOhP&D~XUuG7gcHvPV{BG(<|GJwyo= zEhU<?{IB=0s{1p(pU3}qKkmD@-q-teJzwK`jq7#2Z&7OlBUyr+GOOstuP1)760mqI z&0`y@h6WaANeLx;kweK?c`!)A;;hI4ln|@}^2??mN}#T1D3un7RYXU^Lj9?MV1^R% zyEWN|VoCF&U~%>#lufe_anNuGRvDRN7Zn7a>w`u7X+BsjEmlgP7qmPPJf4N28y4zI z3&u*A(E`b#zT`lx35DXPjx`LRQv9(dG@1{^gGLKb$J+VQ0>~j)OG;n}H4sd&qXuAY z$^O&;a$txgSR|N24u$BDgB_{iSYt{c;2i5J5lV?926=g_DJm-w@E+c-l3=dECR410 zZ)j*xh#C&(MWf2mf_-oVyqtoHq9Ps#6Obb+;fZntWuh`3ykJ4~1j|y8m%uyZ;7DpX z{FXoRBm@^o3#3GP`iF&3!zr>})R53%st1lBhsS}}t?djQ<&47o{XNKmffRtz&X*d3 z1^)tIb`(b$>}E=E2qbEGIf4Q~QI)8qC@)KtQv!-pz{?R;02^Q$)&h{m67fVuS4pg2 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zg3}yt4=~IR0vSr+*~4iC$1|39gUWSWE-bVc03-MrGT$)$1=ST0dlft=glvSE(vN)Q z?dh=8)!9=;S64-As+)_u3eW<5Pi5im?hRiKcl_IOsUP~Zih&ZwD)V)Di&zd$6lW3W zpe{gB6juYA+5!9xj-aiw9QAb_1&)yE0d7S{jtk9lV4T43@9YS4fscEDUD^5ze!C^W X!PC3r^BEXDNK=lKhK9*ZQ>p&~vg-gx diff --git a/NSIGHT_REGRESSION_REPORT.md b/NSIGHT_REGRESSION_REPORT.md deleted file mode 100644 index 69902a2b5f01..000000000000 --- a/NSIGHT_REGRESSION_REPORT.md +++ /dev/null @@ -1,51 +0,0 @@ -# SDPA Mask Regression Analysis (QwenImage) - -## What caused the regression - -The regression is caused by always passing an attention mask to SDPA, even when the mask is effectively all-ones. -In PyTorch, providing `attn_mask` disables the Flash SDPA fast path for most shapes, forcing the kernel selection -into the slower math/mem-efficient path. That path introduces extra elementwise work (masking + reductions) and -reduces effective throughput. - -In QwenImage, the joint attention mask is constructed unconditionally when `encoder_hidden_states_mask` is not -`None`. When the prompt lengths are uniform, the mask carries no information but still forces the slow path. -This is the source of the speed regression. - -## Nsight-python plot - -Plot: `nsight_sdpa_mask_regression.png` - -We benchmarked SDPA on H100 with batch=1, heads=16, seq_len=1024, head_dim=64. The mask zeros the -second half of tokens (representative of padding). The mask path is **~3.4x slower** than no-mask: - -- `no_mask` avg: **43,066 ns** -- `mask` avg: **145,888 ns** - -This data is saved in `nsight_sdpa_mask_regression.csv` and plotted via nsight-python visualization. - -Regeneration: -``` -/mnt/data/data/kashif/miniconda3/envs/py312/bin/python3.12 scripts/nsight_sdpa_mask_regression.py \ - --seq-len 1024 --num-heads 16 --head-dim 64 --batch-size 1 --runs 5 \ - --plot-path nsight_sdpa_mask_regression.png -``` - -## Output comparison (before vs after) - -Comparison image: `compare_default_vs_flash_varlen.png` - -The \"before\" (`compare_default.png`) and \"after\" (`compare_flash_varlen.png`) outputs are pixel-identical. -Diff stats: - -- Max diff: 0 -- Mean diff: 0.0 -- Non-zero pixels: 0 - -This confirms the PR only changes performance behavior, not output fidelity. - -Regeneration: -``` -/mnt/data/data/kashif/miniconda3/envs/py312/bin/python3.12 scripts/compare_output_images.py \ - --before compare_default.png --after compare_flash_varlen.png \ - --output compare_default_vs_flash_varlen.png -``` diff --git a/PLAN.md b/PLAN.md deleted file mode 100644 index f96fa42a8f4b..000000000000 --- a/PLAN.md +++ /dev/null @@ -1,452 +0,0 @@ -# Plan: Adding Discrete Token Diffusion (UNI-D2-style) to Diffusers - -This plan captures how to natively add support for **discrete diffusion models over tokens** (LLM-style) in `diffusers`, using UNI-D2’s modular structure as a guide while staying idiomatic to the `diffusers` ecosystem. - -## What UNI-D2 Provides (Useful Structure) - -UNI-D2 splits discrete diffusion into interchangeable components: - -- **Algorithm / Trainer**: loss definition + orchestration (LightningModule) -- **ForwardProcess**: forward corruption kernel `q(x_t | x_0)` -- **NoiseSchedule**: continuous-time schedule providing `α(t)` and `α'(t)` -- **Backbone model**: predicts denoising distribution from `(x_t, t/σ)` -- **Sampler**: reverse process (generation) from noise → data - -References: -- UNI-D2 architecture description: `3rd_party/UNI-D2/docs/architecture.md:9` -- UNI-D2 trainer base as LightningModule: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/base.py:39` - -## What Diffusers Already Has (Precedent for Discrete/Categorical Diffusion) - -Diffusers already contains discrete/categorical diffusion components that can be adapted for tokens: - -- `VQDiffusionScheduler` implements a categorical diffusion scheduler (masked class, categorical transitions, gumbel sampling). - - Location: `src/diffusers/schedulers/scheduling_vq_diffusion.py:106` -- A (deprecated) pipeline shows how a discrete scheduler integrates into a `DiffusionPipeline` sampling loop: - - Location: `src/diffusers/pipelines/deprecated/vq_diffusion/pipeline_vq_diffusion.py:52` - -These are strong templates for a **text-token discrete diffusion** pipeline + scheduler. - -## UNI-D2 → Diffusers Mapping (Native Design) - -UNI-D2 abstractions map cleanly onto diffusers concepts: - -- **NoiseSchedule** → scheduler configuration + schedule utilities (`α(t)`, optional `α'(t)`), or precomputed per-step arrays. -- **ForwardProcess** → scheduler `add_noise(...)` for token IDs (training-time forward corruption). -- **Sampler** → scheduler `step(...)` + pipeline denoising loop (inference-time reverse chain). -- **Algorithm/Trainer** → in diffusers, keep core library focused on inference primitives; provide trainers as `accelerate`-based examples + reusable loss helpers (diffusers doesn’t use Lightning internally). - -## Current Status (Implemented) - -- Scheduler: - - `src/diffusers/schedulers/scheduling_token_diffusion.py` (`TokenDiffusionScheduler`) - - `set_timesteps`, `add_noise`, `step`, `save_pretrained/from_pretrained` - - Forward process modes: `absorbing`, `uniform` (optional `exclude_mask_from_uniform`) - - Alpha schedules: `log_linear`, `linear`, `cosine`, `geometric` - - Schedule-aware MDLM weighting: `get_mdlm_loss_weights(...)` - - Final “noise removal” behavior on the last step (`alpha_prev=1`) -- Additional schedulers: - - `src/diffusers/schedulers/scheduling_block_token_diffusion.py` (`BlockTokenDiffusionScheduler`) - - `src/diffusers/schedulers/scheduling_hybrid_token_diffusion.py` (`HybridTokenDiffusionScheduler`) -- Pipeline: - - `src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py` (`TokenDiffusionPipeline`) - - Passes `timesteps` and `return_dict=True` to the model each step (like standard diffusers pipelines pass `t` to UNet) - - Supports both scheduler modes, optional decoding via tokenizer - - Conditioning: `prefix_ids` and `infill_mask` -- Additional pipelines: - - `src/diffusers/pipelines/block_token_diffusion/pipeline_block_token_diffusion.py` (`BlockTokenDiffusionPipeline`) - - `src/diffusers/pipelines/hybrid_token_diffusion/pipeline_hybrid_token_diffusion.py` (`HybridTokenDiffusionPipeline`) -- Examples: - - Training: `examples/discrete_diffusion/train_mdlm.py`, `examples/discrete_diffusion/train_udlm.py`, `examples/discrete_diffusion/train_hybrid_token_diffusion.py` - - Sampling: `examples/discrete_diffusion/sample_mdlm.py` (uses `TokenDiffusionPipeline`), `examples/discrete_diffusion/sample_block_token_diffusion.py`, `examples/discrete_diffusion/sample_hybrid_token_diffusion.py` - - All sampling scripts use their respective pipeline `__call__()` (no manual denoising loops) - - Docs: `examples/discrete_diffusion/README.md` -- Tests: - - Scheduler tests: `tests/schedulers/test_scheduler_token_diffusion.py` - - Pipeline tests: `tests/pipelines/test_pipeline_token_diffusion.py` - - Block/hybrid tests: `tests/schedulers/test_scheduler_block_token_diffusion.py`, `tests/schedulers/test_scheduler_hybrid_token_diffusion.py` - - Block/hybrid pipeline tests: `tests/pipelines/test_pipeline_block_token_diffusion.py`, `tests/pipelines/test_pipeline_hybrid_token_diffusion.py` -- Training utilities: - - Confidence-aware loss helper: `src/diffusers/training_utils.py` (`compute_confidence_aware_loss`) -- Block refinement (commit-by-confidence): - - Pipeline: `src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py` (`BlockRefinementPipeline`) - - Supports `attention_mask_mode` (`auto` tries 4D additive mask then falls back to 2D padding mask) - - Examples: `examples/discrete_diffusion/train_block_refinement_cap.py`, `examples/discrete_diffusion/sample_block_refinement.py` - - Tests: `tests/pipelines/test_pipeline_block_refinement.py` -- LLaDA2 (block-wise iterative refinement for LLMs): - - Pipeline: `src/diffusers/pipelines/llada2/pipeline_llada2.py` (`LLaDA2Pipeline`) - - Diffusers-native implementation (does NOT wrap model.generate()) - - Block-wise iterative refinement with confidence-based token selection - - Block-diagonal causal attention mask for parallel decoding within blocks - - Gumbel-max sampling for numerical stability (from UNI-D2 patterns) - - Flexible attention mask handling (`auto`/`4d`/`2d`/`none` modes) - - `editing_threshold` and `max_post_steps` support for post-mask token editing (LLaDA2.1 feature) - - Functionally equivalent to official LLaDA2.1 `generate()` for default settings - - Backward compatible: disabled by default (`editing_threshold=None`, `max_post_steps=0`) - - `callback_on_step_end` support for step-level callbacks - - Examples: `examples/discrete_diffusion/sample_llada2.py` - - Compatible with LLaDA2 models from HuggingFace (e.g., `inclusionAI/LLaDA2.0-mini`, `inclusionAI/LLaDA2.1-mini`) -- DFlash (block diffusion speculative decoding): - - Scheduler: `src/diffusers/schedulers/scheduling_dflash_token_diffusion.py` (`DFlashTokenDiffusionScheduler`) - - Pipeline: `src/diffusers/pipelines/dflash/pipeline_dflash.py` (`DFlashPipeline`) - - Diffusers-native denoising loop with target-hidden feature fusion - - `callback_on_step_end` support for step-level callbacks - - Example: `examples/discrete_diffusion/sample_dflash.py` -- SDAR (block diffusion with remasking): - - Scheduler: `src/diffusers/schedulers/scheduling_sdar_token_diffusion.py` (`SDARTokenDiffusionScheduler`) - - Pipeline: `src/diffusers/pipelines/sdar/pipeline_sdar.py` (`SDARPipeline`) - - `callback_on_step_end` support for step-level callbacks - - Example: `examples/discrete_diffusion/sample_sdar.py` -- BD3LM (block diffusion with first-hitting updates): - - Scheduler: `src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py` (`BD3LMTokenDiffusionScheduler`) - - Pipeline: `src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py` (`BD3LMPipeline`) - - `callback_on_step_end` support for step-level callbacks - - Example: `examples/discrete_diffusion/sample_bd3lm.py` - - **Note**: `BD3LMModel` is not yet exported from diffusers `__init__.py`; sample script imports fail - -## End-to-End Sampling Verification - -All sampling scripts use their respective pipeline's `__call__()` — no manual denoising loops. - -| Script | Model | Pipeline | Status | -|--------|-------|----------|--------| -| `sample_mdlm.py` | `kuleshov-group/mdlm-owt` | `TokenDiffusionPipeline` | Verified | -| `sample_llada2.py` | `inclusionAI/LLaDA2.1-mini` | `LLaDA2Pipeline` | Verified (with editing) | -| `sample_llada2.py` | `inclusionAI/LLaDA2.0-mini` | `LLaDA2Pipeline` | Verified (backward compat) | -| `sample_dflash.py` | `z-lab/Qwen3-4B-DFlash-b16` + `Qwen/Qwen3-4B` | `DFlashPipeline` | Verified | -| `sample_sdar.py` | `JetLM/SDAR-1.7B-Chat` (revision `refs/pr/1`) | `SDARPipeline` | Verified | -| `sample_bd3lm.py` | `kuleshov-group/bd3lm-owt-block_size4` | `BD3LMPipeline` | Blocked (`BD3LMModel` not exported) | -| `sample_block_refinement.py` | (local checkpoint only) | `BlockRefinementPipeline` | No public model | -| `sample_block_token_diffusion.py` | (local checkpoint only) | `BlockTokenDiffusionPipeline` | No public model | -| `sample_hybrid_token_diffusion.py` | (local checkpoint only) | `HybridTokenDiffusionPipeline` | No public model | - -### Official Code Parity Checks - -- **LLaDA2 vs official `generate()`**: Functionally identical for default settings (`steps=block_length=32`). Editing logic, post-steps counting, and confidence thresholds all match. Our design adds bounded loop + transfer_schedule (converges identically). -- **DFlash vs official `spec_generate()`**: Functionally identical. Prefill, draft generation, target verification, acceptance via cumprod, KV cache cropping, and stop token handling all match exactly. -- **MDLM**: `TokenDiffusionPipeline` passes `timesteps` per step (like standard diffusers UNet pipelines pass `t`), matching official MDLM sampling. - -### Design Note: Model Layer - -We rely entirely on `transformers` for the model layer (`AutoModelForCausalLM`, `AutoModelForMaskedLM`, `AutoModel`). All model-specific features (architectures, attention, RoPE, etc.) are handled by `transformers` via `trust_remote_code=True`. Our pipelines are model-agnostic orchestrators that call `model(input_ids, ...).logits`. - -## Design Insights from Reference Codebases (dllm-dev & UNI-D2) - -### Architecture Comparison - -| Concern | **dllm-dev** | **UNI-D2** | **diffusers (ours)** | -|---|---|---|---| -| Noise schedule | `Noise` → `(α_t, α'_t)` pair | `NoiseSchedule` module | `TokenDiffusionScheduler._alpha_t()` / `._alpha_prime_t()` | -| Forward process | Inline in `Denoiser._sample_q_xt()` | Separate `ForwardProcess` class | `TokenDiffusionScheduler.add_noise()` with `forward_process` config | -| Reverse step | Inline in denoiser generation | Separate `Sampler` class | `scheduler.step()` | -| Prior sampling | Inline in generation loop | `model.prior_sample()` | `scheduler.sample_prior()` | -| Training loss | `Denoiser._compute_loss()` | `Algorithm.nll_per_token()` | `scheduler.get_mdlm_loss_weights()` (weights only) | -| Pipeline | Monolithic `generate()` | Lightning `generate_samples()` | `DiffusionPipeline.__call__()` | -| Config system | Hydra YAML | Hydra YAML | `ConfigMixin` + `register_to_config` | - -References: -- dllm-dev noise schedules: `3rd_party/dllm-dev/src/noise_schedule/noise_schedules.py` -- dllm-dev denoiser hierarchy: `3rd_party/dllm-dev/src/denoiser/base.py` (abstract), `3rd_party/dllm-dev/src/denoiser/diffusion.py` (MDLM/BD3LM/E2D2) -- UNI-D2 noise schedules: `3rd_party/UNI-D2/src/discrete_diffusion/noise_schedules/` -- UNI-D2 forward processes: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/` -- UNI-D2 samplers: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/` -- UNI-D2 algorithms: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/` -- Our scheduler: `src/diffusers/schedulers/scheduling_token_diffusion.py` -- Our mixin: `src/diffusers/pipelines/pipeline_utils.py` (class `DiscreteDiffusionPipelineMixin`, line ~2340) - -### Noise Schedules — Fully Aligned - -All three codebases implement essentially the same alpha schedules. Our four (`log_linear`, `linear`, `cosine`, `geometric`) cover the standard set. dllm-dev adds `ExponentialNoise` (α = 1 - t^exp) which is a minor variant. - -Key design difference: dllm-dev and UNI-D2 return `(α_t, α'_t)` as a pair. Ours computes them separately. Both are fine. - -### Forward Processes — Sufficient But Less Modular - -UNI-D2 has separate `ForwardProcess` classes (`AbsorbingForwardProcess`, `UniformForwardProcess`, `BlockAbsorbingForwardProcess`), making it trivial to compose any schedule with any corruption kernel. We bundle this into `TokenDiffusionScheduler.add_noise()` with `forward_process` as a config string. - -**Trade-off**: Our approach is simpler for users. Separate classes would only matter if we added exotic forward processes (SEDD, FlexMDM, CANDI hybrid). For absorbing + uniform, our design is sufficient. - -References: -- UNI-D2 absorbing forward: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/absorbing.py` -- UNI-D2 uniform forward: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/uniform.py` -- Our add_noise: `src/diffusers/schedulers/scheduling_token_diffusion.py` (method `add_noise`, line ~336) - -### Reverse Sampling Strategies — Key Differences - -| Strategy | dllm-dev | UNI-D2 | diffusers | -|---|---|---|---| -| **Posterior** q(x_s\|x_t, x̂₀) | Default | `AbsorbingSampler` | `TokenDiffusionScheduler.step()` | -| **Predict-then-noise** | Yes (confidence + margin) | — | `BlockRefinementPipeline` (pipeline-level) | -| **Confidence-based remasking** | In predict-then-noise | — | `SDARTokenDiffusionScheduler.step()` (4 strategies) | -| **Speculative decoding** | — | — | `DFlashTokenDiffusionScheduler.step()` | -| **First-hitting time** | Yes | — | — | -| **Score-entropy (SEDD)** | — | `SEDDSampler` | — | -| **GIDD interpolation** | — | `GIDDSampler` | — | -| **Entropy-bounded** | — | `EBSampler` | `SDARTokenDiffusionScheduler` (entropy_bounded) | - -References: -- dllm-dev posterior sampling: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (method `_compute_posterior`) -- dllm-dev predict-then-noise: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (method `_generate_unconditional`, search `predict_then_noise`) -- dllm-dev first-hitting: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (method `_sample_generation_timesteps`) -- UNI-D2 absorbing sampler: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/absorbing.py` -- UNI-D2 uniform sampler: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/uniform.py` -- UNI-D2 SEDD sampler: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/sedd.py` -- UNI-D2 GIDD sampler: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/gidd.py` -- Our posterior step: `src/diffusers/schedulers/scheduling_token_diffusion.py` (method `step`, line ~398) -- Our SDAR remasking: `src/diffusers/schedulers/scheduling_sdar_token_diffusion.py` (method `step`, line ~141) -- Our block refinement: `src/diffusers/pipelines/block_refinement/pipeline_block_refinement.py` (confidence/threshold logic in `__call__`) -- Our DFlash speculative: `src/diffusers/schedulers/scheduling_dflash_token_diffusion.py` (method `step`, line ~77) - -### Model Output Processing Pattern - -Both reference codebases explicitly post-process model output before sampling: - -```python -# dllm-dev & UNI-D2 pattern: -model_output[:, :, mask_id] = -inf # Never predict mask token -model_output[xt != mask_id] = -inf # Unmasked positions → keep current token -model_output[xt != mask_id, xt] = 0.0 # Set identity logit to 0 -``` - -Our `TokenDiffusionScheduler.step()` achieves the same via: -```python -logits[..., self.mask_token_id] = -inf # Never predict mask token -is_masked = sample == self.mask_token_id -x_prev = torch.where(is_masked & should_denoise, sampled_x0, sample) # Keep unmasked -``` - -Both approaches are correct. The reference approach of zeroing non-masked logits is more numerically explicit but ours is equivalent because we only apply updates to masked positions. - -### Insights to Adopt - -#### 1. Training Timestep Sampling Utility (from both) - -Both codebases provide structured timestep sampling for training with optional antithetic (stratified) sampling for variance reduction. We could add: - -```python -# On TokenDiffusionScheduler: -def sample_training_timesteps(self, batch_size, generator=None, antithetic=False): - """Sample random training timesteps in [0, num_train_timesteps-1].""" -``` - -References: -- dllm-dev collator: `3rd_party/dllm-dev/src/datasets/collator.py` (class `DenoisingCollator`) -- UNI-D2 time sampling: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/base.py` (method `_sample_t`) - -#### 2. Confidence-Margin Remasking (from dllm-dev) - -dllm-dev's predict-then-noise mode uses top-2 probability margin for remasking decisions: tokens where `|top1_prob - top2_prob|` is small are re-masked. This complements our existing SDAR strategies (`low_confidence_static`, `low_confidence_dynamic`, `sequential`, `entropy_bounded`) and could be added as a `"confidence_margin"` strategy. - -Reference: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (search `confidence_margin_based_noising`) - -#### 3. First-Hitting Time Schedule (from dllm-dev) - -A special timestep schedule where each step targets changing exactly ~1 token (exponentially spaced). Could be an option in `set_timesteps()`: - -```python -scheduler.set_timesteps(num_inference_steps, schedule="first_hitting") -``` - -Reference: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (method `_sample_generation_timesteps`, search `first_hitting`) - -#### 4. σ(t) = -log(α(t)) Model Conditioning Convention (from both) - -Both codebases condition the model on `σ(t) = -log(α(t))` rather than raw timestep index. This is a model-side convention (not scheduler), but we should document it. Our sample scripts already handle this: `sample_mdlm.py` passes `timesteps=sigma` to the model. - -References: -- dllm-dev sigma: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (search `sigma` in `_prepare_inputs`) -- UNI-D2 sigma: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/base.py` (method `_sigma_from_alphat`) - -#### 5. Static Attention Mask Caching (from dllm-dev) - -dllm-dev registers block attention masks as model buffers and dynamically resizes them. Our `BlockRefinementPipeline` and `SDARPipeline` rebuild masks each call. For repeated generation at the same sequence length, caching would save compute. - -Reference: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (search `_create_static_mask`, `register_buffer`) - -#### 6. Separate Sampler Abstraction (from UNI-D2) — Architectural Consideration - -UNI-D2 separates `Sampler` from `NoiseSchedule`, making it easy to plug in `GIDDSampler`, `EBSampler`, etc. In diffusers, `scheduler.step()` is the single API. If we wanted to support multiple sampling strategies per scheduler, we could add a `sampling_strategy` config field rather than separate Sampler classes — this preserves the diffusers convention while enabling extensibility. - -### What We Already Do Well - -- **diffusers conventions**: `set_timesteps()`, `step()`, `sample_prior()`, `add_noise()`, `prepare_latents()`, `check_inputs()`, callbacks -- **DiscreteDiffusionPipelineMixin**: Shared SAR utilities (top-k, top-p, temperature sampling) — neither reference codebase has a clean shared mixin like this -- **Pre-computed alphas in `set_timesteps()`**: Matches dllm-dev's approach, avoids per-step recomputation -- **Block mask support via `block_mask` parameter**: Clean opt-in for block-wise diffusion without separate scheduler -- **Richest remasking strategy set**: Our `SDARTokenDiffusionScheduler` has 4 strategies; dllm-dev has 2, UNI-D2 has 1 -- **Speculative decoding**: `DFlashPipeline` — neither reference codebase supports this - -## Coverage Gaps vs Reference Codebases (Not Yet in Diffusers) - -Combined gaps from both dllm-dev and UNI-D2: - -### Sampling Strategies -- **First-hitting time schedule** (dllm-dev): Exponentially spaced timesteps targeting ~1 token change per step. Useful for token-by-token generation. - - Ref: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (search `first_hitting`) -- **SEDD (Score Entropy Discrete Diffusion)** (UNI-D2): Score-based parameterization, different from substitution-based MDLM. Requires different `step()` logic. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/sedd.py`, `3rd_party/UNI-D2/src/discrete_diffusion/sampling/sedd.py` -- **GIDD (Generalized Interpolating Discrete Diffusion)** (UNI-D2): Interpolating sampler between different discrete processes. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/gidd.py` -- **Confidence-margin remasking** (dllm-dev): Uses `|top1_prob - top2_prob|` for remasking decisions. - - Ref: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (search `confidence_margin_based_noising`) - -### Algorithms / Methods -- **FlexMDM** (UNI-D2): Any-order generation with flexible masking. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/flexmdm.py` -- **Partition MDLM** (UNI-D2): Partition-based generation. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/partition.py` -- **CANDI** (UNI-D2): Hybrid discrete-continuous noise process. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/candi.py` -- **E2D2** (dllm-dev): Encoder-decoder architecture for block diffusion (separate encoder/decoder LLMs with cross-attention). - - Ref: `3rd_party/dllm-dev/src/denoiser/diffusion.py` (class `E2D2`, line ~1090), `3rd_party/dllm-dev/src/backbone/encoder_decoder.py` - -### Training Utilities -- **Antithetic/stratified time sampling** (both): Variance reduction during training. - - Ref: `3rd_party/dllm-dev/src/datasets/collator.py`, `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/base.py` -- **Block size / noise level annealing** (dllm-dev): Curriculum learning via Composer algorithms. - - Ref: `3rd_party/dllm-dev/src/custom_composer/algorithms.py` - -### Forward Processes -- **Block absorbing** (UNI-D2): Per-block timestamps for block diffusion training. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/block_absorbing.py` -- **FlexMDM forward** (UNI-D2): Flexible masking patterns. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/flexmdm.py` -- **CANDI hybrid** (UNI-D2): Continuous+discrete noise. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/forward_process/candi.py` - -### Noise Schedules -- **Hybrid diffusion** (UNI-D2): Mixture of continuous/discrete schedule components. - - Ref: `3rd_party/UNI-D2/src/discrete_diffusion/noise_schedules/hybrid.py` -- **Exponential** (dllm-dev): `α = 1 - t^exp` with configurable exponent. - - Ref: `3rd_party/dllm-dev/src/noise_schedule/noise_schedules.py` (class `ExponentialNoise`) - -## Next Steps (Immediate) - -- Export `BD3LMModel` and `BD3LMPipeline` from `diffusers.__init__.py` so `sample_bd3lm.py` can run. -- Test `sample_bd3lm.py` end-to-end with `kuleshov-group/bd3lm-owt-block_size4`. -- SDAR model (`JetLM/SDAR-1.7B-Chat`): merge upstream PR #1 (remove `LossKwargs`) so `--revision refs/pr/1` is no longer needed. -- Run `make style` and `make quality` to ensure all changes pass linting. - -## Phased Implementation Plan (Updated) - -### Phase 1 — Define Discrete-Token Diffusion API Surface (Done) - -Goal: introduce a minimal, composable API consistent with diffusers schedulers/pipelines. - -- Add a first-class scheduler for categorical token diffusion: - - `TokenDiffusionScheduler` (or `DiscreteTokenDiffusionScheduler`) - - Required methods: `set_timesteps`, `step`, plus **`add_noise(input_ids, timesteps, ...)`** for training -- Define forward-process modes needed for initial coverage: - - `absorbing` (mask token replacement) - - `uniform` (random token replacement) - -Deliverables: -- Scheduler interface + configuration fields (vocab size, mask token id, schedule params) -- Clear contract for model outputs (logits/log-probs, shape `[B, L, V]`) - -### Phase 2 — Implement Noise Schedules + Forward Processes (Next) - -Goal: mirror UNI-D2’s separability while remaining diffusers-native. - -Options: -- **Option A (diffusers-native)**: implement schedules inside the scheduler (precomputed arrays or closed-form functions). -- **Option B (more UNI-D2-like)**: add lightweight helpers for `NoiseSchedule` and `ForwardProcess` under `src/diffusers/` (still driven by scheduler config). - -Next concrete work: -- Extend `TokenDiffusionScheduler` beyond log-linear by adding more `alpha(t)` families (cosine/linear/geometric). (Done) -- Expose schedule choice as a scheduler config option (e.g. `alpha_schedule=...`) in diffusers style. (Done) -- Keep training examples explicit about schedule-dependent weighting to avoid silent mismatches. (Done) - -### Phase 3 — Implement Additional Token Diffusion Scheduler(s) (Revised) - -Observation from reviewing more methods: -- Several methods have materially different reverse dynamics and/or state (block-wise sampling, hybrid transition models, variable length), which will likely be cleaner as separate scheduler classes (and sometimes separate pipelines), instead of one “mega-scheduler”. - -Next concrete work: -- Add a block-diffusion scheduler/pipeline pair for BD3LM-like behavior (block size, block updates, optional nucleus filtering). -- Add a hybrid scheduler class for GIDD-like behavior once the required transition helpers are formalized. -- Add separate families for score/rate-based methods (SEDD/FlexMDM) rather than forcing them into `TokenDiffusionScheduler`. -- Add CANDI and Partition-MDLM variants as dedicated schedulers/pipelines (reverse dynamics differ from MDLM/UDLM). - -### Phase 4 — Add Token Diffusion Pipeline + Wrappers (Done for Generic) - -Goal: a native pipeline that can generate token sequences via discrete diffusion. - -- New pipeline: `src/diffusers/pipelines/token_diffusion/pipeline_token_diffusion.py` -- Components: - - `tokenizer` - - `backbone` (transformer producing logits over vocab) - - `scheduler` (`TokenDiffusionScheduler`) -- Sampling loop: - - Mirrors UNI-D2 absorbing sampler + the VQDiffusion pipeline structure - - Supports BOS injection and optional caching strategies - -Current scope: -- Unconditional generation, optional start-token injection -- Prefix conditioning and infill/fixed-position masking - -### Phase 5 — Add Accelerate Trainer + Examples (In Progress) - -Goal: deliver practical training recipes without adding Lightning as a core dependency. - -Already added: -- Training loops in `examples/` for absorbing and uniform diffusion. - -Next concrete work: -- Add method-specific training scripts as new schedulers land (BD3LM/GIDD/SEDD/FlexMDM). -- Optional: factor common pieces into lightweight helpers if repetition becomes significant. -- Add a block-refinement training+sampling example that uses confidence-aware loss and the block refinement pipeline. - -### Phase 6 — Tests, Docs, and Serialization - -Goal: ship maintainable, testable, save/load-able primitives. - -- Unit tests for scheduler: - - mask token handling and invariants - - shape/dtype checks - - determinism via `torch.Generator` - - basic sampling loop sanity for absorbing -- Ensure `save_pretrained/from_pretrained` works for scheduler and pipeline components -- Add docs page: “Discrete token diffusion in diffusers” - - Describe mapping schedule/forward/step - - Provide minimal example usage + links to training scripts - -## Clarifying Questions (To Lock the Initial Scope) - -1) Should v1 target **MDLM absorbing-only** first, or include **UDLM/uniform** as well? -2) Do we require **time-conditioning** in the backbone from day one? -3) Should the first pipeline support **conditioning** (prefix/infill) or start **unconditional/BOS-only**? - -## Proposed Next Steps (Priority Order) - -1) ~~Add additional `alpha(t)` schedule types to `TokenDiffusionScheduler` (cosine/linear/geometric) with tests and example guidance.~~ (Done) -2) ~~Add LLaDA2-style block-wise iterative refinement pipeline.~~ (Done - `LLaDA2Pipeline`) -3) ~~Add BD3LM-style block diffusion as a separate scheduler/pipeline.~~ (Done - `BD3LMPipeline`) -4) ~~Add DFlash speculative decoding pipeline.~~ (Done - `DFlashPipeline`) -5) ~~Add SDAR block diffusion with remasking pipeline.~~ (Done - `SDARPipeline`) -6) ~~Create `DiscreteDiffusionPipelineMixin` with shared utilities.~~ (Done) -7) ~~Merge `BlockTokenDiffusionScheduler` into `TokenDiffusionScheduler`.~~ (Done - alias) -8) ~~Address all PR #12911 review comments from @dg845.~~ (Done) - -### Recently Completed -9) ~~Add `editing_threshold` and `max_post_steps` to `BlockRefinementPipeline` / `LLaDA2Pipeline`.~~ (Done — matches official LLaDA2.1 behavior) -10) ~~Fix `TokenDiffusionPipeline` to pass `timesteps` and `return_dict=True` to model.~~ (Done — now works with `kuleshov-group/mdlm-owt`) -11) ~~Rewrite `sample_mdlm.py` to use `TokenDiffusionPipeline` instead of manual loop.~~ (Done) -12) ~~Verify all sampling scripts end-to-end with public HF models.~~ (Done — MDLM, LLaDA2, DFlash, SDAR all verified) - -### Low-Hanging Fruit (from reference codebase analysis) -13) Export `BD3LMModel`/`BD3LMPipeline` from `__init__.py` and test `sample_bd3lm.py` with `kuleshov-group/bd3lm-owt-block_size4`. -14) Add `sample_training_timesteps(batch_size, generator, antithetic)` to `TokenDiffusionScheduler` — useful for training scripts, matches dllm-dev collator and UNI-D2 `_sample_t`. -15) Add `"confidence_margin"` remasking strategy to `SDARTokenDiffusionScheduler` — uses `|top1_prob - top2_prob|` from dllm-dev. - -### Medium Effort / High Value -16) Add first-hitting time schedule option to `set_timesteps()` — exponentially spaced steps targeting ~1 token/step, from dllm-dev. -17) Add SEDD parameterization support — score-based instead of substitution, requires `parameterization` config field and branching in `step()`. Ref: `3rd_party/UNI-D2/src/discrete_diffusion/algorithms/sedd.py`. -18) Add GIDD-style hybrid sampler — interpolating between discrete processes. Ref: `3rd_party/UNI-D2/src/discrete_diffusion/sampling/gidd.py`. - -### Longer Term -19) Add FlexMDM (any-order generation) as a separate scheduler/pipeline family. -20) Add CANDI hybrid discrete-continuous noise as a separate forward process. -21) Add E2D2 encoder-decoder architecture support (from dllm-dev) — separate encoder/decoder with cross-attention. -22) Add tests for `LLaDA2Pipeline`, `DFlashPipeline`, `SDARPipeline`, `BD3LMPipeline`. -23) Add training scripts for block refinement and SDAR models. diff --git a/PR_RESPONSE.md b/PR_RESPONSE.md deleted file mode 100644 index 6c032720373c..000000000000 --- a/PR_RESPONSE.md +++ /dev/null @@ -1,82 +0,0 @@ -# Response to PR #12702 Comments - -## Purpose of this PR - -The purpose of this PR is to **remove the `txt_seq_lens` parameter completely** and simplify the code by using only the encoder hidden states shape and mask. - -### Why was `txt_seq_lens` redundant? - -1. **Original usage was limited**: The original code only used `max(txt_seq_lens)` for RoPE computation - it didn't use per-sample sequence lengths at all -2. **Already available from tensor shape**: The max sequence length is already available as `encoder_hidden_states.shape[1]` -3. **Mask handles attention**: The `encoder_hidden_states_mask` already handles preventing attention to padding tokens - -### Changes Made - -#### 1. Removed `txt_seq_lens` parameter entirely -- ✅ Removed from `QwenImageTransformer2DModel.forward()` -- ✅ Removed from `QwenImageControlNetModel.forward()` -- ✅ Removed from `QwenImageMultiControlNetModel.forward()` -- ✅ Removed from all denoise modules - -#### 2. Simplified RoPE computation -**Before:** -```python -text_seq_len = max(text_seq_len, max(txt_seq_lens)) # or infer from mask.sum() -image_rotary_emb = self.pos_embed(img_shapes, text_seq_len, device=...) -``` - -**After:** -```python -batch_size, text_seq_len = encoder_hidden_states.shape[:2] -image_rotary_emb = self.pos_embed(img_shapes, text_seq_len, device=...) -``` - -#### 3. Mask handling clarification -- **RoPE**: Uses full `encoder_hidden_states.shape[1]` (works for any mask pattern) -- **Attention**: Uses `encoder_hidden_states_mask` applied element-wise in the attention processor - -### Addressing Specific Concerns - -#### "Why is it still a parameter of the transformer model?" -**It's NOT anymore!** We removed it completely from the transformer model signature. - -#### "Sequence lengths for each batch sample must be inferred from the mask and passed to transformer blocks" -This is **not necessary for the current implementation** because: - -1. **PyTorch's attention masking is element-wise**: The mask is applied per-token, not per-sequence-length. This means: - - A mask like `[1, 1, 0, 0, 0]` works perfectly without needing to know the length is 2 - - A non-contiguous mask like `[1, 0, 1, 0, 1]` also works correctly - -2. **RoPE frequencies need full sequence length**: RoPE operates on positions, so it needs frequencies for all positions in the tensor (including padding positions). Using `encoder_hidden_states.shape[1]` is correct. - -3. **Current attention dispatch doesn't support varlen**: As mentioned in issue #12344, to use varlen flash attention, the entire attention dispatch mechanism would need to be reworked. That's a much larger change beyond the scope of this PR. - -### Current Implementation is Correct - -The separation of concerns is: -- **RoPE**: Gets frequencies for full sequence length from tensor shape -- **Attention masking**: Prevents attending to padding via element-wise boolean mask - -This design: -- ✅ Simplifies the code (removes redundant parameter) -- ✅ Works with any mask pattern (contiguous or non-contiguous) -- ✅ Doesn't break any functionality -- ✅ Maintains correct behavior for all use cases - -### Future Work (Out of Scope) - -For true varlen flash attention support, you would need: -1. Rework `attention_dispatch.py` to support sequence length arrays -2. Pass per-sample sequence lengths through all transformer blocks -3. Convert attention masks to sequence lengths when the backend supports varlen - -This PR focuses on the simpler goal: removing the redundant `txt_seq_lens` parameter that wasn't providing any value in the current implementation. - -### Tests - -Added comprehensive tests to verify correctness: -- `test_infers_text_seq_len_from_mask` - Verifies basic mask handling -- `test_builds_attention_mask_from_encoder_mask` - Verifies contiguous padding masks -- `test_non_contiguous_attention_mask` - Verifies non-contiguous mask patterns work correctly - -All tests pass ✅ diff --git a/RAE.md b/RAE.md deleted file mode 100644 index e29aa2fdea9a..000000000000 --- a/RAE.md +++ /dev/null @@ -1,224 +0,0 @@ -# RAE (Representation Autoencoder) Integration Plan - -## Overview - -This document outlines the plan to integrate RAE (Representation Autoencoder) into diffusers. RAE is a novel autoencoder architecture that uses frozen pretrained encoders (DINOv2, SigLIP2, MAE) with trainable ViT decoders for high-fidelity image reconstruction. - -**Key Reference**: [RAE Paper](https://arxiv.org/abs/2510.11690) | [Official Repository](https://github.com/bytetriper/RAE) - -## Architecture Summary - -RAE consists of three main components: - -1. **Frozen Encoder** (pretrained, not trainable) - - DINOv2-with-Registers (facebook/dinov2-with-registers-base) - - SigLIP2 (google/siglip2-base-patch16) - - MAE (facebook/vit-mae-base) - -2. **Trainable Decoder** (ViT MAE-style) - - Based on ViTMAE architecture - - Takes encoder outputs and reconstructs images - - Configurable depth, hidden size, attention heads - -3. **Optional Latent Normalization** - - Statistics computed from encoder outputs - - Batchnorm-like normalization for stable latent space - -## Implementation Strategy - -### Phase 1: Core Autoencoder Model - -#### 1.1 Create `AutoencoderRAE` class - -**Location**: `src/diffusers/models/autoencoders/autoencoder_rae.py` - -**Key Design Decisions**: - -1. **Follow diffusers conventions**: - - Inherit from `ModelMixin`, `ConfigMixin`, `FromOriginalModelMixin` - - Implement `encode()` and `decode()` methods - - Return `DecoderOutput` for decode - -2. **Support `from_pretrained`**: - - Load full RAE model (encoder + decoder + stats) from HF Hub - - Use `nyu-visionx/RAE-collections` as the model hub - -3. **Encoder Types**: - - Map RAE encoder names to HF model IDs - - DINOv2, SigLIP2, MAE via transformers - -4. **Decoder Configuration**: - - Reuse/adapt ViTMAEDecoder from transformers or implement custom - - Support configurable decoder sizes (ViTB, ViTL, ViTXL) - -**Proposed API**: - -```python -from diffusers import AutoencoderRAE - -# Load from HF Hub -autoencoder = AutoencoderRAE.from_pretrained("nyu-visionx/RAE-collections") - -# Encode images to latents -latents = autoencoder.encode(images) # B, C, H, W - -# Decode latents to images -reconstructed = autodecoder.decode(latents) # B, 3, H, W - -# Full forward pass -output = autoencoder(images) -``` - -#### 1.2 Configuration Class - -**Location**: `src/diffusers/models/autoencoders/autoencoder_rae.py` (or separate config) - -**Parameters**: -- `encoder_type`: str = "dinov2" # or "siglip2", "mae" -- `encoder_name_or_path`: str = "facebook/dinov2-with-registers-base" -- `decoder_hidden_size`: int = 768 -- `decoder_num_hidden_layers`: int = 12 -- `decoder_num_attention_heads`: int = 12 -- `decoder_intermediate_size`: int = 3072 -- `decoder_patch_size`: int = 16 -- `patch_size`: int = 16 # encoder patch size -- `encoder_input_size`: int = 224 -- `image_size`: int = 256 -- `num_channels`: int = 3 -- `latent_mean`: Optional[torch.Tensor] = None -- `latent_var`: Optional[torch.Tensor] = None -- `noise_tau`: float = 0.0 -- `reshape_to_2d`: bool = True - -### Phase 2: Encoder Wrappers - -**Location**: `src/diffusers/models/encoders/` (new directory) - -Create lightweight wrappers for each encoder type: - -1. **`encoder_dinov2.py`**: Wraps `Dinov2WithRegistersModel` - - Strips CLS + register tokens (5 tokens) - - Applies LayerNorm without affine parameters - -2. **`encoder_siglip2.py`**: Wraps `SiglipModel` - - Uses vision transformer output - - Applies post-LayerNorm without affine - -3. **`encoder_mae.py`**: Wraps `ViTMAEForPreTraining.vit` - - No masking (mask_ratio=0) - - Returns patch tokens only - -### Phase 3: Decoder Implementation - -**Location**: `src/diffusers/models/autoencoders/vae.py` or new file - -Reuse existing patterns from `GeneralDecoder` in RAE repo: - -- `ViTMAEEmbeddings`: Patch embedding + positional encoding -- `ViTMAELayer`: Transformer block (pre-norm) -- `GeneralDecoder`: Full decoder with configurable depth - -Key features to implement: -- Sin-cos 2D positional embeddings -- Trainable CLS token -- Latent interpolation for variable sequence lengths -- Unpatchify for image reconstruction - -### Phase 4: Integration Points - -1. **`src/diffusers/models/autoencoders/__init__.py`**: - ```python - from .autoencoder_rae import AutoencoderRAE - ``` - -2. **`src/diffusers/__init__.py`**: - ```python - from .models import AutoencoderRAE - ``` - -3. **`src/diffusers/models/__init__.py`**: - - Export `AutoencoderRAE` - -### Phase 5: Optional Pipeline (Future) - -**Location**: `src/diffusers/pipelines/rae/` (new directory) - -Only if needed for complete integration: -- `RAEPipeline`: End-to-end encoding/decoding -- Integration with existing diffusion pipelines via latents - -## Weight Loading Strategy - -The pretrained weights in `nyu-visionx/RAE-collections` follow this structure: -- `models/decoders/<encoder>/<config>/model.pt` - decoder weights -- `models/stats/<encoder>/<dataset>/stat.pt` - normalization stats - -**Loading Approach**: -1. Download full collection or specific models -2. The model is saved as a full `RAE` module -3. Can be loaded directly with `torch.load()` for conversion - -## Code Reuse from RAE Repository - -| Component | RAE Location | Diffusers Approach | -|-----------|--------------|-------------------| -| RAE Model | `src/stage1/rae.py` | Create new `AutoencoderRAE` class | -| Encoders | `src/stage1/encoders/*.py` | Create lightweight wrappers | -| Decoder | `src/stage1/decoders/decoder.py` | Adapt `GeneralDecoder` | -| Config | Custom `ViTMAEConfig` | Use or adapt transformers ViTMAEConfig | - -## Testing Plan - -1. **Unit Tests**: - - `tests/models/test_autoencoder_rae.py` - - Test `from_pretrained` loading - - Test encode/decode roundtrip - - Test with different encoder types - -2. **Integration Tests**: - - Compare outputs with original RAE implementation - - Verify latent dimensions match expected shapes - -3. **Slow Tests** (optional): - - Full reconstruction quality test - - FID comparison with pretrained weights - -## Files to Create - -``` -src/diffusers/models/autoencoders/ -└── autoencoder_rae.py # Main AutoencoderRAE class - -src/diffusers/models/encoders/ # New directory -├── __init__.py -├── encoder_dinov2.py # DINOv2 wrapper -├── encoder_siglip2.py # SigLIP2 wrapper -└── encoder_mae.py # MAE wrapper - -src/diffusers/pipelines/rae/ # Optional, future -├── __init__.py -└── rae_pipeline.py -``` - -## Files to Modify - -1. `src/diffusers/models/autoencoders/__init__.py` -2. `src/diffusers/models/__init__.py` -3. `src/diffusers/__init__.py` -4. `tests/models/test_autoencoder_rae.py` (new file) - -## Notes - -- **Focus**: Primary goal is the autoencoder (encode/decode), not the full diffusion pipeline -- **Encoder Compatibility**: RAE already uses HF transformers for encoders, making integration straightforward -- **Latent Space**: The latent is 2D feature map (B, C, H, W), similar to other VAEs in diffusers -- **Scaling**: No scaling factor needed - latents are raw encoder outputs -- **Normalization Stats**: Optional latent normalization using pre-computed mean/var - -## References - -- [Original RAE Repository](https://github.com/bytetriper/RAE) -- [DINOv2 with Registers](https://huggingface.co/facebook/dinov2-with-registers-base) -- [SigLIP2](https://huggingface.co/google/siglip2-base-patch16-256) -- [ViTMAE](https://huggingface.co/facebook/vit-mae-base) -- [RAE Collections on HF](https://huggingface.co/nyu-visionx/RAE-collections) diff --git a/RAE_PLAN.md b/RAE_PLAN.md deleted file mode 100644 index c9ccc39d60b3..000000000000 --- a/RAE_PLAN.md +++ /dev/null @@ -1,108 +0,0 @@ -# RAE Plan (diffusers `rae` branch) - -This plan tracks concrete fixes/additions for `AutoencoderRAE`, grounded in: -- current implementation: `src/diffusers/models/autoencoders/autoencoder_rae.py` -- original RAE codebase: `../RAE/src/stage1/rae.py`, `../RAE/src/stage1/encoders/*`, `../RAE/src/stage1/decoders/decoder.py` -- older branch baseline: `rae_orig` - -## What was checked - -- Current branch implementation and tests: - - `src/diffusers/models/autoencoders/autoencoder_rae.py` - - `tests/models/autoencoders/test_models_autoencoder_rae.py` -- Original implementation references: - - `../RAE/src/stage1/rae.py` - - `../RAE/src/stage1/encoders/dinov2.py` - - `../RAE/src/stage1/encoders/siglip2.py` - - `../RAE/src/stage1/encoders/mae.py` - - `../RAE/src/stage1/decoders/decoder.py` -- `rae_orig` implementation and tests: - - `rae_orig:src/diffusers/models/autoencoders/autoencoder_rae.py` - - `rae_orig:tests/models/autoencoders/test_models_autoencoder_rae.py` - -## Status - -### Completed - -1. Fix encoder default-path mismatch by encoder type. -- Done in `src/diffusers/models/autoencoders/autoencoder_rae.py`: - - `encoder_name_or_path` now defaults to `None`. - - Added per-encoder default map (`dinov2`, `siglip2`, `mae`), matching original intent in `../RAE/src/stage1/encoders/*.py`. - -2. Freeze MAE encoder consistently (and align encoder freezing behavior). -- Done in `src/diffusers/models/autoencoders/autoencoder_rae.py`: - - Added `requires_grad_(False)` for MAE and SigLIP2 encoders. - - Added `@torch.no_grad()` on `MAEEncoder.forward`. -- Reference behavior: `../RAE/src/stage1/encoders/mae.py`, `../RAE/src/stage1/rae.py`. - -3. Make latent stats handling robust with config + runtime buffers. -- Done in `src/diffusers/models/autoencoders/autoencoder_rae.py`: - - Fixed config-vs-buffer name collision by using runtime buffers `_latents_mean` and `_latents_std`. - - Added conversion helper for tensor/list/tuple stats inputs. -- Follow-up done: - - Aligned public config names to `latents_mean` and `latents_std` (matching diffusers conventions). - - Removed deprecated alias args to keep the new API clean within this PR. -- This fixes the constructor failure caught by new fast tests. - -4. Restore fast unit coverage for RAE. -- Done in `tests/models/autoencoders/test_models_autoencoder_rae.py`: - - Added non-slow `AutoencoderRAETests` using a tiny local registered encoder. - - Coverage includes: - - encode/decode/forward shape contract - - scaling factor roundtrip - - latent normalization math - - slicing parity - - noise behavior in train vs eval -- Result: non-slow subset passes (`5 passed, 4 skipped`). - -5. Tighten processor fallback behavior and make it visible. -- Done in `src/diffusers/models/autoencoders/autoencoder_rae.py`: - - narrowed fallback exception handling to expected load errors - - added warning log when default mean/std fallback is used - -6. Implement `use_encoder_loss` training path following diffusers API conventions. -- Done: - - Kept `AutoencoderRAE.forward` aligned with other diffusers autoencoders (no model-level `return_loss` API). - - Moved reconstruction and optional encoder-feature loss computation into the training script: - `examples/research_projects/autoencoder_rae/train_autoencoder_rae.py`. - - Encoder forward supports optional gradient-to-input for feature consistency loss in training scripts. - -7. Add a diffusers-style RAE training example script. -- Done: - - `examples/research_projects/autoencoder_rae/train_autoencoder_rae.py` - - `examples/research_projects/autoencoder_rae/README.md` -- Script follows stage-1 convention: frozen encoder + train decoder + reconstruction loss + optional encoder feature loss. - -### Remaining - -1. Ensure hub collection compatibility in converter/loading utilities. -- Evidence: `nyu-visionx/RAE-collections` mixes decoder filenames (`model.pt` and `dinov2_decoder.pt`) and dataset casing (`imagenet1k` vs `ImageNet1k`). -- Action: - - Add robust filename and folder-case resolution in conversion/loading script. - - Add tests for path discovery logic. - -## Secondary TODOs (Open) - -1. Add dedicated unit tests for converter helper functions. -- Cover `resolve_decoder_file`, `resolve_stats_file`, and `extract_latent_stats`. - -2. Expand docs with end-to-end conversion/training snippets. -- Add a short “convert then finetune” flow using `scripts/convert_rae_to_diffusers.py`. - -3. Optional: align naming for consistency. -- Current uses `encoder_cls`; `rae_orig` used `encoder_type`; original `../RAE` used class-name registry. -- Action: - - Keep one public name and support alias for backward compatibility. - -## Suggested implementation order (remaining work) - -1. Hub collection compatibility helper/converter. -2. Docs and model API page for `AutoencoderRAE`. -3. Minor cleanup (`use_encoder_loss`, naming aliases). - -## Acceptance criteria - -- Non-slow RAE tests run in CI and cover core encode/decode behavior. -- All three encoders work with defaults and do not accidentally train encoder weights. -- `save_pretrained` / `from_pretrained` roundtrip works with latent stats. -- Loading/conversion from RAE collection is resilient to known filename/casing variants. diff --git a/TORCH_COMPILE_STATUS.md b/TORCH_COMPILE_STATUS.md deleted file mode 100644 index e3fa2b2d4881..000000000000 --- a/TORCH_COMPILE_STATUS.md +++ /dev/null @@ -1,156 +0,0 @@ -# torch.compile Status for QwenImage with Mask-Based Approach - -## Summary - -The mask-based approach (without `txt_seq_lens` parameter) is now **fully compatible with torch.compile** and provides **1.4-1.5x speedup** with proper warmup! - -## Changes Made to Support torch.compile - -### 1. Removed `.item()` Call (transformer_qwenimage.py:167-171) - -**Before:** -```python -per_sample_len = torch.where(has_active, active_positions.max(dim=1).values + 1, torch.as_tensor(text_seq_len)) -rope_text_seq_len = max(text_seq_len, int(per_sample_len.max().item())) # ← Graph break! -``` - -**After:** -```python -per_sample_len = torch.where(has_active, active_positions.max(dim=1).values + 1, torch.as_tensor(text_seq_len)) -# Keep as tensor to avoid graph breaks in torch.compile -text_seq_len_tensor = torch.tensor(text_seq_len, device=encoder_hidden_states.device, dtype=torch.long) -rope_text_seq_len = torch.maximum(text_seq_len_tensor, per_sample_len.max()) -``` - -**Impact:** Eliminates the primary graph break that prevented CUDA graphs from being used. - -### 2. Removed Conditional Device Transfers (transformer_qwenimage.py:244-247) - -**Before:** -```python -if self.pos_freqs.device != device: - self.pos_freqs = self.pos_freqs.to(device) - self.neg_freqs = self.neg_freqs.to(device) -``` - -**After:** -```python -# Move to device unconditionally to avoid graph breaks in torch.compile -# .to() is a no-op if already on the correct device -self.pos_freqs = self.pos_freqs.to(device) -self.neg_freqs = self.neg_freqs.to(device) -``` - -**Impact:** Prevents graph partitioning due to conditional control flow. - -## Performance Results (After Proper Warmup) - -### Eager Mode (No Compilation) -- Single image (20 steps): **1.63s** -- Batch (2 images, 15 steps): **1.90s** -- Performance: ~13 it/s - -### Compiled Mode (`torch.compile(mode="reduce-overhead")`) -- Single image (20 steps): **1.09s** (**1.50x faster**) -- Batch (2 images, 15 steps): **1.36s** (**1.40x faster**) -- Performance: ~20 it/s (single), ~11 it/s (batch) - -### Speedup Summary - -✅ **Single image generation:** +50% faster -✅ **Batch generation:** +40% faster -✅ **Consistent across iterations:** No recompilation after warmup - -## Why Proper Warmup Matters - -torch.compile caches compiled graphs based on input characteristics. To avoid recompilation: - -1. **Match step counts:** Warmup with same `num_inference_steps` as production -2. **Match prompt lengths:** Use prompts with similar token counts -3. **Match batch sizes:** Warmup with same batch size as production - -## Functional Status - -✅ **Works correctly** - Produces valid images -✅ **No crashes** - All graph breaks resolved -✅ **All backends compatible** - native, flash_varlen, xformers, flash_hub, flash_varlen_hub -✅ **Faster than eager** - 1.4-1.5x speedup with proper warmup - -## Recommendations - -### For Users -**Use torch.compile with QwenImage for production!** Follow the warmup pattern below for optimal performance. - -### Usage Pattern - -```python -import torch -from diffusers import QwenImagePipeline - -# Load pipeline -pipe = QwenImagePipeline.from_pretrained( - "Qwen/Qwen-Image", - torch_dtype=torch.bfloat16 -).to("cuda") - -# Compile transformer -pipe.transformer = torch.compile( - pipe.transformer, - mode="reduce-overhead", - fullgraph=False -) - -# CRITICAL: Warmup with matching parameters -# Match the exact num_inference_steps you'll use in production -pipe( - prompt="warmup", - height=512, - width=512, - num_inference_steps=20 # Same as production! -) - -# Now production runs are fast (1.5x speedup) -image = pipe( - prompt="Your actual prompt", - height=512, - width=512, - num_inference_steps=20 -).images[0] -``` - -### Advanced Tips - -1. **For batch generation**: Warmup with batch too - ```python - pipe(prompt=["warmup1", "warmup2"], num_inference_steps=15) - ``` - -2. **Multiple step counts**: Warmup for each step count you'll use - ```python - pipe(prompt="w", num_inference_steps=20) # For 20-step runs - pipe(prompt="w", num_inference_steps=50) # For 50-step runs - ``` - -3. **Variable prompts**: Different prompt lengths may trigger recompilation, but same token count should be fine - -## Verification - -Run these scripts to verify status: - -```bash -# Test eager mode works (fast, ~5s for 25 steps) -python test_real_qwen.py - -# Test compiled mode works (slow but functional, ~90s for 5 steps) -python test_compile_fix.py - -# Benchmark eager vs compiled (shows 24x slowdown) -python benchmark_compile_vs_eager.py -``` - -## Conclusion - -The mask-based approach successfully removes `txt_seq_lens` and is torch.compile-compatible. -However, **torch.compile does not improve performance** for QwenImage due to the nature of diffusion models. - -**Recommendation:** Use eager mode for optimal performance. diff --git a/UL_PLAN.md b/UL_PLAN.md deleted file mode 100644 index be127d032260..000000000000 --- a/UL_PLAN.md +++ /dev/null @@ -1,247 +0,0 @@ -# Unified Latents (UL) Implementation Plan for Diffusers - -This plan describes how to implement the method from `2602.17270.md` in a Diffusers-native way, with an incremental path from research script to reusable library components. - -## Brief Method Summary - -Unified Latents (UL) jointly trains three components: a deterministic encoder, a latent diffusion prior, and a diffusion decoder. The encoder produces `z_clean`, then a fixed amount of Gaussian noise is added to produce `z0`; this fixed encoder noise is explicitly tied to the prior's maximum precision (`lambda_z(0)=5` in the paper). Training uses two diffusion losses: - -- Prior loss `L_z` in latent space with unweighted ELBO-style denoising (`w=1`), so the latent bitrate is directly regularized by the diffusion prior. -- Decoder loss `L_x` in image space conditioned on `z0`, with sigmoid reweighting and a decoder loss factor `c_lf` to control reconstruction-vs-modeling tradeoff and avoid posterior collapse. - -Stage 1 trains encoder + prior + decoder jointly with `L = L_z + L_x`. Stage 2 freezes the encoder and retrains a latent base diffusion model (with sigmoid weighting) for improved generation quality from sampled latents. - -## Confirmed Author Clarifications - -The following items were confirmed by the paper authors and should be used as implementation ground truth: - -1. L2 reduction and bitrate units: -- Use true squared sums in denoising terms: `||.||^2 = sum_{c,h,w}(...)^2`. -- Compute bits-per-pixel by summing all terms and dividing full loss by `num_pixels * ln(2)`. -- With decoder terms removed, latent-only terms correspond to latent bitrate (latent bpp). - -2. Stage-1 decoder objective: -- Use sigmoid-weighted ELBO with ELBO prefactor: -- `0.5 * exp(lambda) * (-d lambda / dt) * w(lambda) * ||x - x_hat||^2` -- and `w(lambda) = sigmoid(lambda - b)`. - -3. Stage-2 base objective: -- Also use sigmoid-weighted ELBO (same principle as stage 1). -- Tuning note: smaller sigmoid bias is generally preferred for base models, especially at higher resolution. - -4. Stage-2 target and sampling: -- Train against clean latent (encoder mean, `z_clean`) to reduce variance. -- During sampling, stop when reaching `logsnr_0` because decoder conditioning expects noisy latent at that endpoint. -- Both v-prediction and direct `z0` prediction can work; direct `z0` tends to behave worse at initialization. -- Preferred choice: v-prediction (flow-matching style velocity prediction is expected to work similarly). - -5. Figure-3 epsilon/x-space equivalence: -- `0.5 * exp(lambda) * (-d lambda/dt) * w(lambda) * ||x - x_hat||^2` -- is equivalent to -- `0.5 * (-d lambda/dt) * w(lambda) * ||epsilon - epsilon_hat||^2`. -- The paper uses x-space in practice for convenience in math/numerics. - -## Paper Section 5.1 Architecture (Target) - -Per `2602.17270.md` Section 5.1, the reference architecture is: - -- Encoder: ResNet with channels `[128, 256, 512, 512]`, with 2 residual blocks in downsampling stages and 3 blocks in final stage. -- Prior model (stage 1): single-level ViT with 8 blocks and 1024 channels. -- Base model (stage 2): 2-stage ViT with channels `[512, 1024]` and blocks `[6, 16]`, dropout `0.1`. -- Decoder: UViT with conv down/up channels `[128, 256, 512]`, middle transformer with 8 blocks and 1024 channels, dropout `0.1`. - -Planned Diffusers mapping (as close as possible in this repo): - -- Encoder: VAE-style `Encoder` backbone configured to `[128, 256, 512, 512]` and deterministic latent head. -- Prior: `DiTTransformer2DModel` configured to 8 layers and width 1024-equivalent (`heads * head_dim = 1024`). -- Decoder: UViT-style approximation with conv down/up + attention blocks and `[128, 256, 512]`, dropout `0.1`. -- If exact UViT conditioning path is unavailable, use a concat-conditioned conv+attention UNet approximation while preserving the channel/dropout profile above. - -## Scope - -Implement UL with two stages: - -1. Stage 1 (joint latent learning): -- Encoder `E(x) -> z_clean` -- Prior diffusion model on latents (`z_t -> z_clean`) with **unweighted ELBO-style MSE** and true squared-sum reduction -- Diffusion decoder (`x_t, z0 -> x`) with **sigmoid-weighted ELBO MSE** (`w(lambda)=sigmoid(lambda-b)`) and true squared-sum reduction -- Fixed encoder noise linked to prior max precision (`lambda_z(0)=5`, i.e. `z0 = alpha0 * z_clean + sigma0 * eps`) - -2. Stage 2 (base model on frozen latents): -- Freeze encoder (and optionally decoder) -- Train base model with sigmoid-weighted ELBO using clean-latent target (`z_clean`) and preferred v-prediction parameterization - -## Paper-to-Diffusers Mapping - -Core equations in `2602.17270.md` map to: - -- Prior loss `L_z`: standard denoising MSE over latent noise levels with `w(lambda)=1` -- Decoder loss `L_x`: denoising MSE in image space with `w(lambda)=sigmoid(lambda - b)` (optionally scaled by `c_lf`), including ELBO prefactor -- Total stage-1 objective: `L = L_z + L_x` - -Implementation-level mapping: - -- Encoder: `ModelMixin` autoencoder encoder path (deterministic output `z_clean`) -- Prior denoiser: transformer/UNet-style model operating in latent space -- Decoder denoiser: conditional image diffusion model conditioned on `z0` -- Schedulers: custom logSNR-aware training utilities to compute `alpha(t), sigma(t), lambda(t), d lambda / dt` and per-sample weights - -## Proposed Deliverables - -1. Training utilities for UL losses/schedules. -2. Research training scripts for stage 1 and stage 2. -3. Optional reusable UL model wrappers (if we decide to productize in core API). -4. Tests (math/unit/smoke) and docs. - -## Phase 0: Design + API Decisions - -Decide minimal architecture choices before coding: - -- Latent shape/compression ratio (e.g. 16x downsample, channel count) -- Prior backbone (`Transformer2DModel` vs `UNet2DModel` in latent space) -- Decoder backbone (`UNet2DConditionModel`-style conditioning vs custom UViT-like module) -- Conditioning injection strategy for `z0` in decoder (concat, cross-attn, FiLM) -- Schedule parameterization (continuous `t in [0,1]` vs discretized timesteps with logSNR lookup) - -Output: -- frozen config schema for stage-1/stage-2 scripts (`configs/ul/*.yaml` or argparse equivalent) - -## Phase 1: Core UL Math Utilities - -Add shared training helpers (likely in `src/diffusers/training_utils.py` or a new UL helper module): - -- `sample_t(batch, device, antithetic=False)` -- `logsnr_schedule(t, schedule_type, lambda_min, lambda_max)` -- `alpha_sigma_from_logsnr(lambda_t)` -- `decoder_weight(lambda_t, bias_b, loss_factor, mode="sigmoid")` -- `prior_weight(lambda_t)` returning ones -- Optional exact/approx `d lambda / dt` utilities for ELBO-consistent scaling - -Acceptance checks: -- Unit tests for monotonic schedule behavior and alpha/sigma identities (`alpha^2 + sigma^2 ~= 1`) -- Weighting tests for expected limits at low/high noise - -## Phase 2: Stage-1 Training Script (MVP) - -Add `examples/research_projects/unified_latents/train_ul_stage1.py`: - -Per step: -1. Encode image: `z_clean = E(x)` -2. Prior branch: -- sample `t_z, eps_z` -- build `z_t = alpha_z(t_z) * z_clean + sigma_z(t_z) * eps_z` -- predict `z_clean_hat` -- compute `L_z` -3. Decoder branch: -- sample `eps0`, make `z0 = alpha_z(0) * z_clean + sigma_z(0) * eps0` -- sample `t_x, eps_x`, make `x_t` -- predict `x_hat = D(x_t, z0, t_x)` -- compute `L_x` with sigmoid weighting and `c_lf` -4. Optimize `L = L_z + L_x` - -Additional requirements: -- EMA for trainable modules -- mixed precision + accelerate -- checkpointing for encoder/prior/decoder (separable and joint) -- logging of proxy bitrate metrics from prior KL upper-bound terms - -Acceptance checks: -- script runs on a tiny dataset split -- losses decrease for both branches -- reconstruction sample grid is produced - -## Phase 3: Stage-2 Base Latent Model Training - -Add `examples/research_projects/unified_latents/train_ul_stage2_base.py`: - -- Load and freeze stage-1 encoder -- Build latent dataset on-the-fly from `z_clean` with fixed-noise forward process tied to `lambda_z(0)` -- Train base latent diffusion model with sigmoid-weighted ELBO, using clean-latent training target and preferred v-prediction parameterization -- Keep max logSNR tied to stage-1 prior (`lambda_z(0)=5`) - -Acceptance checks: -- stage-2 model samples latents stably -- decoder + stage-2 samples produce valid images - -## Phase 4: Inference / Sampling Pipeline - -Add `examples/research_projects/unified_latents/sample_ul.py` first, then optional pipeline: - -Sampling sequence: -1. sample `z1 ~ N(0, I)` -2. sample latents via stage-2 base model and stop at `logsnr_0` (decoder conditioning endpoint) -3. sample image with decoder conditioned on `z0` - -Optional productized pipeline: -- `src/diffusers/pipelines/unified_latents/pipeline_unified_latents.py` -- components: `base_model`, `decoder_model`, schedulers, optional tokenizer/text conditioner for T2I extension - -Acceptance checks: -- end-to-end generation script works from saved checkpoints -- deterministic outputs with fixed seed - -## Phase 5: Model/API Productization (Optional but Recommended) - -If we want reusable library APIs instead of only research scripts: - -- Add `AutoencoderUL` (encoder + diffusion decoder interface helpers) -- Add `ULPriorModel`/`ULBaseModel` wrappers or document approved generic backbones -- Add config + `save_pretrained/from_pretrained` coverage - -Keep this phase after MVP to reduce integration risk. - -## Phase 6: Tests - -Add targeted tests under `tests/`: - -- `tests/training/test_ul_weighting.py`: schedule + weights math -- `tests/models/autoencoders/test_autoencoder_ul.py` (if productized) -- `tests/pipelines/unified_latents/test_pipeline_unified_latents.py` (if productized) -- smoke tests for stage-1/2 scripts with tiny random tensors - -Minimum CI bar: -- UL math tests + at least one stage-1 smoke test - -## Phase 7: Documentation - -Add `examples/research_projects/unified_latents/README.md` covering: - -- method summary and equations used -- stage-1 and stage-2 command lines -- expected checkpoints and how to sample -- recommended hyperparameter ranges: -- `lambda_z(0)=5` -- `c_lf` roughly `1.3-1.7` -- sigmoid bias `b` as bitrate/reconstruction tradeoff knob - -## Suggested File Layout - -- `examples/research_projects/unified_latents/train_ul_stage1.py` -- `examples/research_projects/unified_latents/train_ul_stage2_base.py` -- `examples/research_projects/unified_latents/sample_ul.py` -- `examples/research_projects/unified_latents/README.md` -- `src/diffusers/training_utils.py` (or `src/diffusers/utils/unified_latents.py`) for UL math helpers -- optional: `src/diffusers/pipelines/unified_latents/pipeline_unified_latents.py` -- optional: UL model classes under `src/diffusers/models/...` - -## Risks and Mitigations - -- Objective mismatch (predicting `x0` vs `eps` vs `v` across branches): - - enforce one parameterization per branch and centralize conversion helpers. -- Posterior collapse in stage-1: - - monitor prior/decoder loss ratio; tune `c_lf` and sigmoid bias `b`. -- Training instability from schedule scaling: - - start with stable VP schedule and validated logSNR bounds. -- Over-committing to core API too early: - - ship research scripts first, then promote stable abstractions. - -## Milestones - -1. M1 (MVP math + stage-1 script): UL loss implemented and trains on small run. -2. M2 (stage-2 + sampling): end-to-end UL sample generation from checkpoints. -3. M3 (tests + docs): reproducible instructions and CI smoke coverage. -4. 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zHaf_KFt(WG52t7)l63SFRk55th9*-m;9TS*P;2Riw2qL|P(uD&UEqJWX7~^P;XnL` n|L`CF!+-b>|KUIU|Hl6T%e_@Rv{)Rj00000NkvXXu0mjfhs-;- From f6bd0d0ab9155e480425b06bba228ddf1ab0151b Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:00:17 +0000 Subject: [PATCH 32/44] remove unified_latents from git tracking --- .../unified_latents/README.md | 189 ------ .../unified_latents/eval_ul.py | 412 ------------ .../unified_latents/sample_ul_stage2.py | 131 ---- .../unified_latents/train_ul_stage1.py | 588 ------------------ .../unified_latents/train_ul_stage2_base.py | 462 -------------- .../unified_latents/ul_models.py | 61 -- 6 files changed, 1843 deletions(-) delete mode 100644 examples/research_projects/unified_latents/README.md delete mode 100644 examples/research_projects/unified_latents/eval_ul.py delete mode 100644 examples/research_projects/unified_latents/sample_ul_stage2.py delete mode 100644 examples/research_projects/unified_latents/train_ul_stage1.py delete mode 100644 examples/research_projects/unified_latents/train_ul_stage2_base.py delete mode 100644 examples/research_projects/unified_latents/ul_models.py diff --git a/examples/research_projects/unified_latents/README.md b/examples/research_projects/unified_latents/README.md deleted file mode 100644 index 97ef95ca033e..000000000000 --- a/examples/research_projects/unified_latents/README.md +++ /dev/null @@ -1,189 +0,0 @@ -# Unified Latents (UL) Training (Diffusers Research Scripts) - -This folder contains a Diffusers-based implementation of Unified Latents from `2602.17270`. - -Current scripts: -- `train_ul_stage1.py`: joint UL stage-1 training (`encoder + prior + decoder`) -- `train_ul_stage2_base.py`: UL stage-2 base model training on frozen stage-1 encoder latents - -## What Is Implemented - -- Stage 1 (UL latent learning): - - deterministic ResNet-like encoder (`AutoencoderULEncoder`) with Section 5.1-style stage depths `[2,2,2,3]` - - latent prior denoiser (DiT-style) - - decoder denoiser (UViT-style approximation) -- Stage 2 (UL base model): - - two-stage ViT-like latent denoiser approximation - - trained with stage-1 encoder frozen - -Notes on architecture fidelity: -- Section 5.1 defaults are used where practical. -- Decoder is an approximation (concat-conditioned conv+attention) rather than the paper's exact dedicated UViT implementation. - -## Requirements - -Run from repo root with Diffusers import path available: - -```bash -cd /path/to/diffusers -export PYTHONPATH=src -``` - -Use `accelerate launch ...` for training. - -## Dataset Format - -Both stages expect `torchvision.datasets.ImageFolder` layout: - -```text -data_root/ - class_a/ - img1.png - img2.png - class_b/ - img3.png -``` - -## Stage 1 Training - -Stage-1 objective implementation follows UL Algorithm 1: -- prior term uses `(-d lambda_z/dt) * exp(lambda_z) / 2 * ||z_clean - z_hat||^2` -- decoder term uses `(-d lambda_x/dt) * exp(lambda_x) / 2 * w(lambda_x) * ||x - x_hat||^2` with `w(lambda)=sigmoid(lambda-b)` -- prior terminal KL `KL[q(z1|x)||N(0,I)]` is always included (paper Algorithm 1) -- `||.||^2` uses true squared sums and losses are reported in bits-per-pixel via division by `num_pixels * ln(2)` - -Command: - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ - --train_data_dir /path/to/imagefolder \ - --output_dir ul-stage1 \ - --resolution 256 \ - --train_batch_size 8 \ - --max_train_steps 10000 -``` - -Recommended useful options: -- `--report_to tensorboard|wandb` -- `--tracker_project_name unified-latents-stage1` -- `--checkpoints_total_limit 3` -- `--resume_from_checkpoint latest` -- `--num_workers 0` (useful in restricted environments) - -Stage 1 outputs: -- `ul-stage1/checkpoint-*/`: - - accelerate state files - - `encoder/`, `prior/`, `decoder/` (Diffusers `save_pretrained` format) -- `ul-stage1/final/`: - - `encoder/`, `prior/`, `decoder/` (Diffusers format) - - `encoder.pt`, `prior.pt`, `decoder.pt` (raw state_dict) - -## Stage 2 Training - -Train the base model using the frozen stage-1 encoder: - -Stage-2 objective uses paper-style weighted ELBO on latents: -- diffusion target is the clean encoder mean latent `z_clean` -- training target is clean encoder latent (`z_clean`) for lower variance -- base model uses v-prediction parameterization and computes weighted ELBO in x-space -- loss uses `(-d lambda_z/dt) * exp(lambda_z) / 2 * w(lambda_z) * ||z_clean - z_hat||^2` with `w(lambda)=sigmoid(lambda-b)` -- sampling stops at `logsnr_0` and passes the resulting noisy latent `z0` to the decoder conditioning path - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ - --train_data_dir /path/to/imagefolder \ - --stage1_encoder_path /path/to/ul-stage1/final/encoder \ - --output_dir ul-stage2-base \ - --resolution 256 \ - --train_batch_size 8 \ - --max_train_steps 10000 -``` - -`--stage1_encoder_path` supports: -- Diffusers encoder directory (recommended), e.g. `.../final/encoder` -- raw checkpoint file, e.g. `.../final/encoder.pt` - -Recommended useful options: -- `--report_to tensorboard|wandb` -- `--tracker_project_name unified-latents-stage2` -- `--checkpoints_total_limit 3` -- `--resume_from_checkpoint latest` -- `--num_workers 0` (useful in restricted environments) - -Stage 2 outputs: -- `ul-stage2-base/checkpoint-*/`: - - accelerate state files - - `base_model/` (Diffusers `save_pretrained` format) -- `ul-stage2-base/final/`: - - `base_model/` (Diffusers format) - - `base_model.pt` (raw state_dict) - -## Resume Examples - -Stage 1 resume: - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ - --train_data_dir /path/to/imagefolder \ - --output_dir ul-stage1 \ - --resume_from_checkpoint latest -``` - -Stage 2 resume: - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ - --train_data_dir /path/to/imagefolder \ - --stage1_encoder_path /path/to/ul-stage1/final/encoder \ - --output_dir ul-stage2-base \ - --resume_from_checkpoint latest -``` - -## Quick Smoke-Test Settings - -For a fast sanity run on a tiny dataset: - -- `--resolution 64` -- `--train_batch_size 2` -- `--max_train_steps 1` -- `--save_steps 1` -- `--num_workers 0` -- `--mixed_precision no` - -These settings validate training loop, checkpointing, and serialization paths. - - -## ImageNet-512 + Weights & Biases (wandb) - -Use either a Hub dataset (`--dataset_name`) or local ImageNet-512 imagefolder (`--train_data_dir`). - -Stage 1 example (local ImageNet-512): - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ - --train_data_dir /path/to/imagenet512_imagefolder \ - --output_dir ul-stage1-imagenet512 \ - --resolution 512 \ - --train_batch_size 8 \ - --max_train_steps 100000 \ - --report_to wandb \ - --tracker_project_name ul-imagenet512-stage1 -``` - -Stage 2 example: - -```bash -accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ - --train_data_dir /path/to/imagenet512_imagefolder \ - --stage1_encoder_path /path/to/ul-stage1-imagenet512/final/encoder \ - --output_dir ul-stage2-imagenet512 \ - --resolution 512 \ - --train_batch_size 8 \ - --max_train_steps 100000 \ - --report_to wandb \ - --tracker_project_name ul-imagenet512-stage2 -``` - -Hub dataset variant: -- replace `--train_data_dir ...` with `--dataset_name <your_dataset>` -- optionally set `--dataset_config_name ...` and `--image_column ...` diff --git a/examples/research_projects/unified_latents/eval_ul.py b/examples/research_projects/unified_latents/eval_ul.py deleted file mode 100644 index 98003d9d0170..000000000000 --- a/examples/research_projects/unified_latents/eval_ul.py +++ /dev/null @@ -1,412 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import argparse -import json -from pathlib import Path - -import torch -import torch.nn.functional as F -from torchvision import transforms -from torchvision.datasets import ImageFolder -from torchvision.models import Inception_V3_Weights, inception_v3 -from torchvision.utils import make_grid, save_image -from ul_models import ULTwoStageBaseModel - -from diffusers import UNet2DModel -from diffusers.models.autoencoders import AutoencoderULEncoder -from diffusers.training_utils import ul_alpha_sigma_from_logsnr, ul_logsnr_schedule - - -def parse_args(): - parser = argparse.ArgumentParser( - description="Evaluate Unified Latents stage-1 reconstruction and stage-2 realism." - ) - parser.add_argument("--train_data_dir", type=str, required=True, help="ImageFolder root with real images.") - parser.add_argument("--stage1_encoder_path", type=str, required=True, help="Path to stage-1 encoder directory.") - parser.add_argument("--stage1_decoder_path", type=str, required=True, help="Path to stage-1 decoder directory.") - parser.add_argument("--stage2_base_path", type=str, required=True, help="Path to stage-2 base model directory.") - parser.add_argument("--output_dir", type=str, default="ul-eval", help="Directory to store evaluation outputs.") - parser.add_argument("--resolution", type=int, default=256) - parser.add_argument("--batch_size", type=int, default=8) - parser.add_argument("--num_workers", type=int, default=0) - parser.add_argument("--seed", type=int, default=42) - parser.add_argument("--device", type=str, default="cuda") - - parser.add_argument("--num_recon_samples", type=int, default=256) - parser.add_argument("--num_gen_samples", type=int, default=1024) - parser.add_argument("--num_sampling_steps", type=int, default=30) - parser.add_argument("--num_train_timesteps", type=int, default=1000) - parser.add_argument("--latent_channels", type=int, default=4) - parser.add_argument("--latent_downsample_factor", type=int, default=8) - - parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--lambda_z_min", type=float, default=-10.0) - parser.add_argument("--lambda_z_max", type=float, default=5.0) - parser.add_argument("--lambda_x_min", type=float, default=-10.0) - parser.add_argument("--lambda_x_max", type=float, default=10.0) - - parser.add_argument( - "--recon_use_noisy_z0", action="store_true", help="Use noisy z0 for recon instead of deterministic z0." - ) - parser.add_argument("--decoder_prediction_type", type=str, default="x0", choices=["epsilon", "x0"]) - parser.add_argument("--save_recon_grid", type=str, default="stage1_recon_grid.png") - parser.add_argument("--save_stage2_grid", type=str, default="stage2_samples_grid.png") - parser.add_argument("--grid_items", type=int, default=8) - - parser.add_argument("--kid_subset_size", type=int, default=256) - parser.add_argument("--kid_subsets", type=int, default=10) - - parser.add_argument("--pass_psnr", type=float, default=18.0) - parser.add_argument("--pass_mae", type=float, default=0.10) - parser.add_argument("--pass_kid", type=float, default=0.02) - - return parser.parse_args() - - -def _build_loader(data_dir: str, resolution: int, batch_size: int, num_workers: int): - image_transform = transforms.Compose( - [ - transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(resolution), - transforms.ToTensor(), - transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), - ] - ) - dataset = ImageFolder(root=data_dir, transform=image_transform) - loader = torch.utils.data.DataLoader( - dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_workers, - pin_memory=True, - drop_last=False, - ) - return dataset, loader - - -def _decode_image_diffusion( - decoder: UNet2DModel, - z0: torch.Tensor, - *, - resolution: int, - num_sampling_steps: int, - num_train_timesteps: int, - schedule: str, - lambda_min: float, - lambda_max: float, - decoder_prediction_type: str, -) -> torch.Tensor: - bsz = z0.shape[0] - z0_up = F.interpolate(z0, size=(resolution, resolution), mode="bilinear", align_corners=False) - - x_t = torch.randn(bsz, 3, resolution, resolution, device=z0.device, dtype=z0.dtype) - t_grid = torch.linspace(1.0, 0.0, num_sampling_steps + 1, device=z0.device, dtype=z0.dtype) - for i in range(num_sampling_steps): - t_cur = t_grid[i].repeat(bsz) - t_nxt = t_grid[i + 1].repeat(bsz) - - lambda_cur = ul_logsnr_schedule(t_cur, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) - lambda_nxt = ul_logsnr_schedule(t_nxt, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) - alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) - alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) - - timestep_idx = (t_cur * (num_train_timesteps - 1)).long().clamp(0, num_train_timesteps - 1) - decoder_pred = decoder(torch.cat([x_t, z0_up], dim=1), timestep_idx).sample - if decoder_prediction_type == "epsilon": - eps_hat = decoder_pred - x0_hat = (x_t - sigma_cur[:, None, None, None] * eps_hat) / alpha_cur[:, None, None, None].clamp_min(1e-5) - else: - x0_hat = decoder_pred - eps_hat = (x_t - alpha_cur[:, None, None, None] * x0_hat) / sigma_cur[:, None, None, None].clamp_min(1e-5) - x_t = alpha_nxt[:, None, None, None] * x0_hat + sigma_nxt[:, None, None, None] * eps_hat - - return x_t.clamp(-1, 1) - - -def _sample_stage2_latent( - base_model: ULTwoStageBaseModel, - *, - batch_size: int, - latent_channels: int, - latent_size: int, - num_sampling_steps: int, - num_train_timesteps: int, - schedule: str, - lambda_min: float, - lambda_max: float, - device: torch.device, - dtype: torch.dtype, -) -> torch.Tensor: - z_t = torch.randn(batch_size, latent_channels, latent_size, latent_size, device=device, dtype=dtype) - t_grid = torch.linspace(1.0, 0.0, num_sampling_steps + 1, device=device, dtype=dtype) - for i in range(num_sampling_steps): - t_cur = t_grid[i].repeat(batch_size) - t_nxt = t_grid[i + 1].repeat(batch_size) - - lambda_cur = ul_logsnr_schedule(t_cur, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) - lambda_nxt = ul_logsnr_schedule(t_nxt, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) - alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) - alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) - - timestep_idx = (t_cur * (num_train_timesteps - 1)).long().clamp(0, num_train_timesteps - 1) - dummy_labels = torch.zeros((batch_size,), device=device, dtype=torch.long) - v_pred = base_model(z_t, timestep_idx, dummy_labels) - - z0_hat = alpha_cur[:, None, None, None] * z_t - sigma_cur[:, None, None, None] * v_pred - eps_hat = sigma_cur[:, None, None, None] * z_t + alpha_cur[:, None, None, None] * v_pred - z_t = alpha_nxt[:, None, None, None] * z0_hat + sigma_nxt[:, None, None, None] * eps_hat - - # Paper-ground-truth: hand off noisy latent at logsnr_0. - return z_t - - -def _collect_inception_features( - images_m11: torch.Tensor, - inception: torch.nn.Module, - *, - device: torch.device, -) -> torch.Tensor: - x = (images_m11 + 1.0) / 2.0 - x = x.clamp(0.0, 1.0) - x = F.interpolate(x, size=(299, 299), mode="bilinear", align_corners=False) - mean = torch.tensor([0.485, 0.456, 0.406], device=device, dtype=x.dtype)[None, :, None, None] - std = torch.tensor([0.229, 0.224, 0.225], device=device, dtype=x.dtype)[None, :, None, None] - x = (x - mean) / std - with torch.no_grad(): - feats = inception(x) - return feats.float().cpu() - - -def _kid_mmd2_unbiased(x: torch.Tensor, y: torch.Tensor) -> float: - # Polynomial kernel used in KID. - # x: [n, d], y: [m, d] - n = x.shape[0] - m = y.shape[0] - d = x.shape[1] - k_xx = ((x @ x.T) / d + 1.0) ** 3 - k_yy = ((y @ y.T) / d + 1.0) ** 3 - k_xy = ((x @ y.T) / d + 1.0) ** 3 - - sum_xx = (k_xx.sum() - torch.diagonal(k_xx).sum()) / (n * (n - 1)) - sum_yy = (k_yy.sum() - torch.diagonal(k_yy).sum()) / (m * (m - 1)) - sum_xy = k_xy.mean() - return (sum_xx + sum_yy - 2.0 * sum_xy).item() - - -def _compute_kid( - real_feats: torch.Tensor, - fake_feats: torch.Tensor, - *, - subset_size: int, - subsets: int, - seed: int, -) -> tuple[float, float]: - g = torch.Generator(device="cpu") - g.manual_seed(seed) - - n = min(real_feats.shape[0], fake_feats.shape[0]) - subset_size = min(subset_size, n) - if subset_size < 2: - raise ValueError("Need at least 2 samples to compute KID.") - - values = [] - for _ in range(subsets): - idx_r = torch.randperm(real_feats.shape[0], generator=g)[:subset_size] - idx_f = torch.randperm(fake_feats.shape[0], generator=g)[:subset_size] - values.append(_kid_mmd2_unbiased(real_feats[idx_r], fake_feats[idx_f])) - - vals = torch.tensor(values, dtype=torch.float32) - return vals.mean().item(), vals.std(unbiased=False).item() - - -def main(): - args = parse_args() - out_dir = Path(args.output_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - torch.manual_seed(args.seed) - device = torch.device(args.device if torch.cuda.is_available() else "cpu") - - dataset, loader = _build_loader(args.train_data_dir, args.resolution, args.batch_size, args.num_workers) - - encoder = AutoencoderULEncoder.from_pretrained(args.stage1_encoder_path).to(device).eval() - decoder = UNet2DModel.from_pretrained(args.stage1_decoder_path).to(device).eval() - base_model = ULTwoStageBaseModel.from_pretrained(args.stage2_base_path).to(device).eval() - - latent_size = int(getattr(base_model.config, "latent_size", args.resolution // args.latent_downsample_factor)) - - # Stage-1 reconstruction metrics. - recon_count = 0 - recon_mae = [] - recon_mse = [] - vis_pairs = [] - - for x, _ in loader: - if recon_count >= args.num_recon_samples: - break - x = x.to(device) - if recon_count + x.shape[0] > args.num_recon_samples: - x = x[: args.num_recon_samples - recon_count] - - with torch.no_grad(): - z_clean = encoder.encode(x).latent - lambda0 = torch.full((x.shape[0],), args.lambda_z_max, device=device, dtype=x.dtype) - alpha0, sigma0 = ul_alpha_sigma_from_logsnr(lambda0) - if args.recon_use_noisy_z0: - eps0 = torch.randn_like(z_clean) - else: - eps0 = torch.zeros_like(z_clean) - z0 = alpha0[:, None, None, None] * z_clean + sigma0[:, None, None, None] * eps0 - - recon = _decode_image_diffusion( - decoder, - z0, - resolution=args.resolution, - num_sampling_steps=args.num_sampling_steps, - num_train_timesteps=args.num_train_timesteps, - schedule=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - decoder_prediction_type=args.decoder_prediction_type, - ) - - diff = recon - x - mse = diff.square().mean(dim=(1, 2, 3)) - mae = diff.abs().mean(dim=(1, 2, 3)) - recon_mse.append(mse.cpu()) - recon_mae.append(mae.cpu()) - - if len(vis_pairs) < args.grid_items: - take = min(args.grid_items - len(vis_pairs), x.shape[0]) - for i in range(take): - vis_pairs.append(torch.cat([x[i], recon[i]], dim=2).detach().cpu()) - - recon_count += x.shape[0] - - recon_mse = torch.cat(recon_mse) - recon_mae = torch.cat(recon_mae) - recon_psnr = -10.0 * torch.log10(recon_mse.clamp_min(1e-12)) - - if vis_pairs: - recon_grid = make_grid([(p + 1.0) / 2.0 for p in vis_pairs], nrow=2) - save_image(recon_grid, out_dir / args.save_recon_grid) - - # Stage-2 generation and realism metrics. - gen_images = [] - gen_count = 0 - while gen_count < args.num_gen_samples: - bsz = min(args.batch_size, args.num_gen_samples - gen_count) - with torch.no_grad(): - z0_sampled = _sample_stage2_latent( - base_model, - batch_size=bsz, - latent_channels=args.latent_channels, - latent_size=latent_size, - num_sampling_steps=args.num_sampling_steps, - num_train_timesteps=args.num_train_timesteps, - schedule=args.base_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - device=device, - dtype=torch.float32, - ) - x_gen = _decode_image_diffusion( - decoder, - z0_sampled, - resolution=args.resolution, - num_sampling_steps=args.num_sampling_steps, - num_train_timesteps=args.num_train_timesteps, - schedule=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - decoder_prediction_type=args.decoder_prediction_type, - ) - gen_images.append(x_gen.cpu()) - gen_count += bsz - - gen_images = torch.cat(gen_images, dim=0)[: args.num_gen_samples] - - stage2_vis = make_grid(((gen_images[: args.grid_items] + 1.0) / 2.0), nrow=2) - save_image(stage2_vis, out_dir / args.save_stage2_grid) - - # Real images for comparison features. - real_images = [] - real_count = 0 - for x, _ in loader: - if real_count >= args.num_gen_samples: - break - if real_count + x.shape[0] > args.num_gen_samples: - x = x[: args.num_gen_samples - real_count] - real_images.append(x) - real_count += x.shape[0] - real_images = torch.cat(real_images, dim=0) - - inception = inception_v3(weights=Inception_V3_Weights.IMAGENET1K_V1, aux_logits=True, transform_input=False) - inception.aux_logits = False - inception.AuxLogits = None - inception.fc = torch.nn.Identity() - inception = inception.to(device).eval() - - real_feats = [] - fake_feats = [] - with torch.no_grad(): - for i in range(0, real_images.shape[0], args.batch_size): - real_feats.append( - _collect_inception_features(real_images[i : i + args.batch_size].to(device), inception, device=device) - ) - fake_feats.append( - _collect_inception_features(gen_images[i : i + args.batch_size].to(device), inception, device=device) - ) - real_feats = torch.cat(real_feats, dim=0) - fake_feats = torch.cat(fake_feats, dim=0) - - kid_mean, kid_std = _compute_kid( - real_feats, - fake_feats, - subset_size=args.kid_subset_size, - subsets=args.kid_subsets, - seed=args.seed, - ) - - metrics = { - "dataset_size": len(dataset), - "num_recon_samples": int(recon_mse.numel()), - "num_gen_samples": int(gen_images.shape[0]), - "stage1": { - "mae_mean": float(recon_mae.mean().item()), - "mse_mean": float(recon_mse.mean().item()), - "psnr_mean_db": float(recon_psnr.mean().item()), - "psnr_median_db": float(recon_psnr.median().item()), - "recon_uses_noisy_z0": bool(args.recon_use_noisy_z0), - }, - "stage2": { - "kid_mean": float(kid_mean), - "kid_std": float(kid_std), - }, - "passes": { - "stage1_psnr": bool(recon_psnr.mean().item() >= args.pass_psnr), - "stage1_mae": bool(recon_mae.mean().item() <= args.pass_mae), - "stage2_kid": bool(kid_mean <= args.pass_kid), - }, - "thresholds": { - "pass_psnr": float(args.pass_psnr), - "pass_mae": float(args.pass_mae), - "pass_kid": float(args.pass_kid), - }, - "artifacts": { - "recon_grid": str((out_dir / args.save_recon_grid).resolve()), - "stage2_grid": str((out_dir / args.save_stage2_grid).resolve()), - }, - } - metrics["passes"]["all"] = all(metrics["passes"].values()) - - metrics_path = out_dir / "metrics.json" - metrics_path.write_text(json.dumps(metrics, indent=2)) - print(json.dumps(metrics, indent=2)) - print(f"Saved metrics to {metrics_path}") - - -if __name__ == "__main__": - main() diff --git a/examples/research_projects/unified_latents/sample_ul_stage2.py b/examples/research_projects/unified_latents/sample_ul_stage2.py deleted file mode 100644 index 34d4cb8e7613..000000000000 --- a/examples/research_projects/unified_latents/sample_ul_stage2.py +++ /dev/null @@ -1,131 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import argparse -from pathlib import Path - -import torch -import torch.nn.functional as F -from torchvision.utils import save_image -from ul_models import ULTwoStageBaseModel - -from diffusers import UNet2DModel -from diffusers.training_utils import ( - ul_alpha_sigma_from_logsnr, - ul_logsnr_schedule, -) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Sample UL Stage-2 latents and decode to images.") - parser.add_argument("--stage2_base_path", type=str, required=True, help="Path to stage2 base_model directory.") - parser.add_argument("--stage1_decoder_path", type=str, required=True, help="Path to stage1 decoder directory.") - parser.add_argument("--output_path", type=str, required=True, help="Output image path.") - parser.add_argument("--resolution", type=int, default=256) - parser.add_argument("--latent_channels", type=int, default=4) - parser.add_argument("--latent_downsample_factor", type=int, default=8) - parser.add_argument("--num_train_timesteps", type=int, default=1000) - parser.add_argument("--num_sampling_steps", type=int, default=50) - parser.add_argument("--batch_size", type=int, default=4) - parser.add_argument("--seed", type=int, default=42) - parser.add_argument("--device", type=str, default="cuda") - parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--lambda_z_min", type=float, default=-10.0) - parser.add_argument("--lambda_z_max", type=float, default=5.0) - parser.add_argument("--lambda_x_min", type=float, default=-10.0) - parser.add_argument("--lambda_x_max", type=float, default=10.0) - parser.add_argument("--decoder_prediction_type", type=str, default="x0", choices=["epsilon", "x0"]) - return parser.parse_args() - - -@torch.no_grad() -def main(): - args = parse_args() - device = torch.device(args.device if torch.cuda.is_available() else "cpu") - torch.manual_seed(args.seed) - latent_size = args.resolution // args.latent_downsample_factor - - base_model = ULTwoStageBaseModel.from_pretrained(args.stage2_base_path).to(device).eval() - decoder = UNet2DModel.from_pretrained(args.stage1_decoder_path).to(device).eval() - - # Stage-2 latent sampling (DDIM-like deterministic update with v-pred model). - z_t = torch.randn(args.batch_size, args.latent_channels, latent_size, latent_size, device=device) - t_grid = torch.linspace(1.0, 0.0, args.num_sampling_steps + 1, device=device) - - for i in range(args.num_sampling_steps): - t_cur = t_grid[i].repeat(args.batch_size) - t_nxt = t_grid[i + 1].repeat(args.batch_size) - - lambda_cur = ul_logsnr_schedule( - t_cur, - schedule_type=args.base_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - lambda_nxt = ul_logsnr_schedule( - t_nxt, - schedule_type=args.base_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) - alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) - - timestep_idx = (t_cur * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) - dummy_labels = torch.zeros((args.batch_size,), device=device, dtype=torch.long) - v_pred = base_model(z_t, timestep_idx, dummy_labels) - - z0_hat = alpha_cur[:, None, None, None] * z_t - sigma_cur[:, None, None, None] * v_pred - eps_hat = sigma_cur[:, None, None, None] * z_t + alpha_cur[:, None, None, None] * v_pred - - z_t = alpha_nxt[:, None, None, None] * z0_hat + sigma_nxt[:, None, None, None] * eps_hat - - # Paper Sec. 3.3: hand off the noisy latent at logsnr_0 to the decoder. - z_handoff = z_t - - # Decoder sampling conditioned on final stage-2 latent. - z0_up = F.interpolate(z_handoff, size=(args.resolution, args.resolution), mode="bilinear", align_corners=False) - - x_t = torch.randn(args.batch_size, 3, args.resolution, args.resolution, device=device) - x_grid = torch.linspace(1.0, 0.0, args.num_sampling_steps + 1, device=device) - - for i in range(args.num_sampling_steps): - t_cur = x_grid[i].repeat(args.batch_size) - t_nxt = x_grid[i + 1].repeat(args.batch_size) - - lambda_cur = ul_logsnr_schedule( - t_cur, - schedule_type=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - ) - lambda_nxt = ul_logsnr_schedule( - t_nxt, - schedule_type=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - ) - alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) - alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) - - timestep_idx = (t_cur * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) - decoder_input = torch.cat([x_t, z0_up], dim=1) - decoder_pred = decoder(decoder_input, timestep_idx).sample - if args.decoder_prediction_type == "epsilon": - eps_hat = decoder_pred - x0_hat = (x_t - sigma_cur[:, None, None, None] * eps_hat) / alpha_cur[:, None, None, None].clamp_min(1e-5) - else: - x0_hat = decoder_pred - eps_hat = (x_t - alpha_cur[:, None, None, None] * x0_hat) / sigma_cur[:, None, None, None].clamp_min(1e-5) - x_t = alpha_nxt[:, None, None, None] * x0_hat + sigma_nxt[:, None, None, None] * eps_hat - - x_final = x_t.clamp(-1, 1) - output_path = Path(args.output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - save_image((x_final + 1.0) / 2.0, output_path, nrow=2) - print(f"Saved samples to {output_path}") - - -if __name__ == "__main__": - main() diff --git a/examples/research_projects/unified_latents/train_ul_stage1.py b/examples/research_projects/unified_latents/train_ul_stage1.py deleted file mode 100644 index db6f445c032c..000000000000 --- a/examples/research_projects/unified_latents/train_ul_stage1.py +++ /dev/null @@ -1,588 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import argparse -import logging -import math -import os -import shutil -from pathlib import Path - -import torch -import torch.nn.functional as F -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from datasets import load_dataset -from torch import nn -from torch.utils.data import DataLoader, IterableDataset -from torchvision import transforms -from torchvision.utils import save_image -from tqdm.auto import tqdm - -from diffusers.models.autoencoders import AutoencoderULEncoder -from diffusers.models.transformers.dit_transformer_2d import DiTTransformer2DModel -from diffusers.models.unets.unet_2d import UNet2DModel -from diffusers.optimization import get_scheduler -from diffusers.training_utils import ( - ul_alpha_sigma_from_logsnr, - ul_decoder_loss_weight, - ul_dlogsnr_dt, - ul_elbo_prefactor, - ul_logsnr_schedule, - ul_sample_t, - ul_terminal_gaussian_kl, -) - - -logger = get_logger(__name__, log_level="INFO") - - -def parse_args(): - parser = argparse.ArgumentParser(description="Train Unified Latents Stage-1 (UL) with Diffusers.") - - parser.add_argument("--dataset_name", type=str, default=None, help="Dataset name on the Hub.") - parser.add_argument("--dataset_config_name", type=str, default=None, help="Dataset config name.") - parser.add_argument( - "--train_data_dir", type=str, default=None, help="Local dataset directory (imagefolder style)." - ) - parser.add_argument("--cache_dir", type=str, default=None, help="Hugging Face datasets cache directory.") - parser.add_argument("--image_column", type=str, default="image", help="Column containing images.") - parser.add_argument("--streaming", action="store_true", help="Stream dataset from the Hub/local files.") - parser.add_argument("--output_dir", type=str, default="ul-stage1", help="Where to save checkpoints and samples.") - parser.add_argument("--logging_dir", type=str, default="logs") - parser.add_argument("--report_to", type=str, default="tensorboard") - parser.add_argument("--tracker_project_name", type=str, default="unified-latents-stage1") - parser.add_argument("--resolution", type=int, default=256) - parser.add_argument("--center_crop", action="store_true") - parser.add_argument("--random_flip", action="store_true") - parser.add_argument("--num_workers", type=int, default=4) - - parser.add_argument("--train_batch_size", type=int, default=8) - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument("--max_train_steps", type=int, default=10000) - parser.add_argument("--gradient_accumulation_steps", type=int, default=1) - parser.add_argument("--learning_rate", type=float, default=1e-4) - parser.add_argument("--adam_beta1", type=float, default=0.9) - parser.add_argument("--adam_beta2", type=float, default=0.999) - parser.add_argument("--adam_weight_decay", type=float, default=1e-2) - parser.add_argument("--adam_epsilon", type=float, default=1e-8) - parser.add_argument("--max_grad_norm", type=float, default=1.0) - parser.add_argument("--lr_scheduler", type=str, default="cosine") - parser.add_argument("--lr_warmup_steps", type=int, default=500) - - parser.add_argument("--num_train_timesteps", type=int, default=1000) - parser.add_argument("--lambda_z_min", type=float, default=-10.0) - parser.add_argument("--lambda_z_max", type=float, default=5.0) - parser.add_argument("--lambda_x_min", type=float, default=-10.0) - parser.add_argument("--lambda_x_max", type=float, default=10.0) - parser.add_argument("--prior_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--decoder_loss_factor", type=float, default=1.6) - parser.add_argument("--decoder_sigmoid_bias", type=float, default=0.0) - parser.add_argument( - "--decoder_prediction_type", - type=str, - default="x0", - choices=["epsilon", "x0"], - help="Decoder target parameterization.", - ) - parser.add_argument("--latent_channels", type=int, default=4) - parser.add_argument( - "--latent_downsample_factor", - type=int, - default=8, - help="Downsample factor of encoder output. Current encoder config is 8x; paper target is 16x with 2x2 patching.", - ) - - # Prior (paper Sec. 5.1: single-level ViT, 8 blocks, 1024 channels) - parser.add_argument("--prior_num_layers", type=int, default=8) - parser.add_argument("--prior_num_heads", type=int, default=16) - parser.add_argument("--prior_head_dim", type=int, default=64) # 16 * 64 = 1024 - parser.add_argument("--prior_patch_size", type=int, default=1) - - # Decoder approximation (paper Sec. 5.1: UViT conv channels [128, 256, 512], dropout 0.1) - parser.add_argument("--decoder_block_out_channels", type=str, default="128,256,512") - parser.add_argument("--decoder_layers_per_block", type=int, default=2) - parser.add_argument("--decoder_dropout", type=float, default=0.1) - - parser.add_argument("--save_steps", type=int, default=500) - parser.add_argument("--checkpoints_total_limit", type=int, default=None) - parser.add_argument("--resume_from_checkpoint", type=str, default=None) - parser.add_argument("--sample_steps", type=int, default=250) - parser.add_argument("--seed", type=int, default=42) - parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"]) - - return parser.parse_args() - - -def build_transforms(args): - transform_list = [ - transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), - ] - if args.center_crop: - transform_list.append(transforms.CenterCrop(args.resolution)) - else: - transform_list.append(transforms.RandomCrop(args.resolution)) - - if args.random_flip: - transform_list.append(transforms.RandomHorizontalFlip()) - - transform_list.extend( - [ - transforms.ToTensor(), - transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), - ] - ) - return transforms.Compose(transform_list) - - -def parse_channels(channels_str: str) -> tuple[int, ...]: - values = [int(x.strip()) for x in channels_str.split(",") if x.strip()] - if not values: - raise ValueError("`decoder_block_out_channels` must contain at least one integer.") - return tuple(values) - - -class HFStreamingImageDataset(IterableDataset): - def __init__(self, hf_iterable, image_column: str, image_transform): - super().__init__() - self.hf_iterable = hf_iterable - self.image_column = image_column - self.image_transform = image_transform - - def __iter__(self): - for example in self.hf_iterable: - image = example[self.image_column].convert("RGB") - yield {"pixel_values": self.image_transform(image)} - - -def get_train_dataset_and_collate(args, image_transform): - if args.dataset_name is not None: - dataset = load_dataset( - args.dataset_name, - args.dataset_config_name, - cache_dir=args.cache_dir, - data_dir=args.train_data_dir, - streaming=args.streaming, - ) - else: - if args.train_data_dir is None: - raise ValueError("Provide either `--dataset_name` or `--train_data_dir`.") - data_files = {"train": os.path.join(args.train_data_dir, "**")} - dataset = load_dataset( - "imagefolder", data_files=data_files, cache_dir=args.cache_dir, streaming=args.streaming - ) - - train_dataset = dataset["train"] - - if args.streaming: - image_column = args.image_column - train_dataset = HFStreamingImageDataset( - train_dataset, image_column=image_column, image_transform=image_transform - ) - else: - column_names = train_dataset.column_names - image_column = args.image_column if args.image_column in column_names else column_names[0] - - def preprocess_train(examples): - images = [image.convert("RGB") for image in examples[image_column]] - examples["pixel_values"] = [image_transform(image) for image in images] - return examples - - train_dataset = train_dataset.with_transform(preprocess_train) - - def collate_fn(examples): - pixel_values = torch.stack([example["pixel_values"] for example in examples]) - return {"pixel_values": pixel_values.contiguous().float()} - - return train_dataset, collate_fn - - -def _save_pretrained_components( - accelerator: Accelerator, encoder: nn.Module, prior: nn.Module, decoder: nn.Module, output_dir -): - output_dir = Path(output_dir) - accelerator.unwrap_model(encoder).save_pretrained(output_dir / "encoder") - accelerator.unwrap_model(prior).save_pretrained(output_dir / "prior") - accelerator.unwrap_model(decoder).save_pretrained(output_dir / "decoder") - - -def main(): - args = parse_args() - if args.seed is not None: - set_seed(args.seed) - - output_dir = Path(args.output_dir) - logging_dir = output_dir / args.logging_dir - accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_dir)) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - project_config=accelerator_project_config, - ) - - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state) - - if accelerator.is_main_process: - output_dir.mkdir(parents=True, exist_ok=True) - (output_dir / "samples").mkdir(exist_ok=True) - logger.info("Starting UL Stage-1 training") - logger.info(f"Output dir: {output_dir}") - - image_transform = build_transforms(args) - train_dataset, collate_fn = get_train_dataset_and_collate(args, image_transform) - train_dataloader = DataLoader( - train_dataset, - batch_size=args.train_batch_size, - shuffle=not args.streaming, - collate_fn=collate_fn, - num_workers=args.num_workers, - pin_memory=True, - drop_last=True, - ) - - latent_size = args.resolution // args.latent_downsample_factor - if latent_size < 1: - raise ValueError("`resolution // latent_downsample_factor` must be >= 1.") - - encoder = AutoencoderULEncoder(in_channels=3, latent_channels=args.latent_channels) - - prior = DiTTransformer2DModel( - num_attention_heads=args.prior_num_heads, - attention_head_dim=args.prior_head_dim, - in_channels=args.latent_channels, - out_channels=args.latent_channels, - num_layers=args.prior_num_layers, - sample_size=latent_size, - patch_size=args.prior_patch_size, - num_embeds_ada_norm=args.num_train_timesteps, - ) - - decoder_channels = parse_channels(args.decoder_block_out_channels) - decoder = UNet2DModel( - sample_size=args.resolution, - in_channels=3 + args.latent_channels, - out_channels=3, - down_block_types=("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), - up_block_types=("AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), - block_out_channels=decoder_channels, - layers_per_block=args.decoder_layers_per_block, - dropout=args.decoder_dropout, - attention_head_dim=8, - add_attention=True, - num_train_timesteps=args.num_train_timesteps, - ) - - params = list(encoder.parameters()) + list(prior.parameters()) + list(decoder.parameters()) - optimizer = torch.optim.AdamW( - params, - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - has_dataloader_length = True - try: - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - except TypeError: - has_dataloader_length = False - num_update_steps_per_epoch = None - if args.max_train_steps is None or args.max_train_steps <= 0: - if not has_dataloader_length: - raise ValueError("For streaming datasets, set `--max_train_steps` to a positive value.") - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - - lr_scheduler = get_scheduler( - name=args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, - num_training_steps=args.max_train_steps * accelerator.num_processes, - ) - - encoder, prior, decoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - encoder, prior, decoder, optimizer, train_dataloader, lr_scheduler - ) - trainable_params = list(encoder.parameters()) + list(prior.parameters()) + list(decoder.parameters()) - - # Recompute schedule values after `accelerator.prepare`. - if has_dataloader_length: - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - else: - args.num_train_epochs = max(args.num_train_epochs, 1) - - if accelerator.is_main_process: - accelerator.init_trackers(args.tracker_project_name, config=vars(args)) - - logger.info("***** Running training *****") - if has_dataloader_length: - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num batches each epoch = {len(train_dataloader)}") - else: - logger.info(" Num examples = streaming (unknown)") - logger.info(" Num batches each epoch = streaming (unknown)") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info( - " Total train batch size (w. parallel, distributed & accumulation) = " - f"{args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps}" - ) - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - - global_step = 0 - first_epoch = 0 - - if args.resume_from_checkpoint: - if args.resume_from_checkpoint != "latest": - path = os.path.basename(args.resume_from_checkpoint) - else: - dirs = os.listdir(args.output_dir) if os.path.exists(args.output_dir) else [] - dirs = [d for d in dirs if d.startswith("checkpoint-")] - dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) - path = dirs[-1] if len(dirs) > 0 else None - - if path is None: - logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.") - else: - logger.info(f"Resuming from checkpoint {path}") - accelerator.load_state(os.path.join(args.output_dir, path)) - global_step = int(path.split("-")[1]) - first_epoch = global_step // num_update_steps_per_epoch if has_dataloader_length else 0 - - progress_bar = tqdm( - range(0, args.max_train_steps), - initial=global_step, - desc="Steps", - disable=not accelerator.is_local_main_process, - ) - - train_loss = 0.0 - fixed_eval_batch_cpu = None - for epoch in range(first_epoch, args.num_train_epochs): - encoder.train() - prior.train() - decoder.train() - - for step, batch in enumerate(train_dataloader): - with accelerator.accumulate(encoder, prior, decoder): - x = batch["pixel_values"].to(accelerator.device) - bsz = x.shape[0] - if fixed_eval_batch_cpu is None: - # Keep a fixed mini-batch for deterministic reconstruction snapshots. - fixed_eval_batch_cpu = x[: min(4, bsz)].detach().float().cpu() - - # Encode deterministic latent z_clean - z_clean = encoder.encode(x).latent - - # Prior branch: z_t -> z_clean with unweighted ELBO-style denoising loss - t_z = ul_sample_t(bsz, x.device) - lambda_z = ul_logsnr_schedule( - t_z, - schedule_type=args.prior_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - dlogsnr_dt_z = ul_dlogsnr_dt( - t_z, - schedule_type=args.prior_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - prefactor_z = ul_elbo_prefactor(lambda_z, dlogsnr_dt_z) - alpha_z, sigma_z = ul_alpha_sigma_from_logsnr(lambda_z) - eps_z = torch.randn_like(z_clean) - z_t = alpha_z[:, None, None, None] * z_clean + sigma_z[:, None, None, None] * eps_z - t_z_idx = (t_z * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) - dummy_labels = torch.zeros((bsz,), device=x.device, dtype=torch.long) - z_pred = prior(z_t, timestep=t_z_idx, class_labels=dummy_labels).sample - prior_per_sample = F.mse_loss(z_pred.float(), z_clean.float(), reduction="none").sum(dim=(1, 2, 3)) - prior_loss = (prefactor_z * prior_per_sample).mean() - prior_loss = prior_loss + ul_terminal_gaussian_kl(z_clean, logsnr_terminal=args.lambda_z_min).mean() - - # Decoder branch: x_t conditioned on fixed-noise z0 - lambda0 = torch.full((bsz,), args.lambda_z_max, device=x.device, dtype=x.dtype) - alpha0, sigma0 = ul_alpha_sigma_from_logsnr(lambda0) - eps_0 = torch.randn_like(z_clean) - z0 = alpha0[:, None, None, None] * z_clean + sigma0[:, None, None, None] * eps_0 - - t_x = ul_sample_t(bsz, x.device) - lambda_x = ul_logsnr_schedule( - t_x, - schedule_type=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - ) - dlogsnr_dt_x = ul_dlogsnr_dt( - t_x, - schedule_type=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - ) - prefactor_x = ul_elbo_prefactor(lambda_x, dlogsnr_dt_x) - alpha_x, sigma_x = ul_alpha_sigma_from_logsnr(lambda_x) - eps_x = torch.randn_like(x) - x_t = alpha_x[:, None, None, None] * x + sigma_x[:, None, None, None] * eps_x - t_x_idx = (t_x * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) - - z0_up = F.interpolate(z0, size=x.shape[-2:], mode="bilinear", align_corners=False) - decoder_input = torch.cat([x_t, z0_up], dim=1) - decoder_pred = decoder(decoder_input, t_x_idx).sample - if args.decoder_prediction_type == "epsilon": - decoder_target = eps_x - decoder_prefactor = 0.5 * (-dlogsnr_dt_x) - else: - decoder_target = x - decoder_prefactor = prefactor_x - - decoder_per_sample = F.mse_loss(decoder_pred.float(), decoder_target.float(), reduction="none").sum( - dim=(1, 2, 3) - ) - decoder_w = ul_decoder_loss_weight( - lambda_x, - bias=args.decoder_sigmoid_bias, - loss_factor=args.decoder_loss_factor, - invert=False, - ) - decoder_loss = (decoder_prefactor * decoder_w * decoder_per_sample).mean() - - num_pixels = x.shape[-2] * x.shape[-1] - bpp_denom = num_pixels * math.log(2.0) - prior_bpp = prior_loss / bpp_denom - decoder_bpp = decoder_loss / bpp_denom - loss = prior_bpp + decoder_bpp - accelerator.backward(loss) - - if accelerator.sync_gradients: - accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm) - - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad(set_to_none=True) - - avg_loss = accelerator.gather(loss.detach().repeat(args.train_batch_size)).mean() - train_loss += avg_loss.item() / args.gradient_accumulation_steps - - if accelerator.sync_gradients: - global_step += 1 - progress_bar.update(1) - accelerator.log({"train_loss": train_loss}, step=global_step) - train_loss = 0.0 - - if accelerator.is_main_process and global_step % 50 == 0: - logger.info( - f"step={global_step} loss_bpp={loss.item():.4f} prior_bpp={prior_bpp.item():.4f} " - f"decoder_bpp={decoder_bpp.item():.4f} lr={lr_scheduler.get_last_lr()[0]:.2e}" - ) - - if accelerator.is_main_process and global_step % args.sample_steps == 0: - with torch.no_grad(): - encoder.eval() - decoder.eval() - - eval_x = fixed_eval_batch_cpu.to(accelerator.device) - eval_z_clean = encoder.encode(eval_x).latent - eval_z_up = F.interpolate( - eval_z_clean, size=eval_x.shape[-2:], mode="bilinear", align_corners=False - ) - - # Deterministic decode from encoder latents at t=0 with zero x-noise input. - eval_x_t = torch.zeros_like(eval_x) - eval_t_idx = torch.zeros((eval_x.shape[0],), device=eval_x.device, dtype=torch.long) - eval_decoder_input = torch.cat([eval_x_t, eval_z_up], dim=1) - eval_decoder_out = decoder(eval_decoder_input, eval_t_idx).sample - if args.decoder_prediction_type == "epsilon": - eval_lambda0 = ul_logsnr_schedule( - torch.zeros((eval_x.shape[0],), device=eval_x.device), - schedule_type=args.decoder_schedule, - lambda_min=args.lambda_x_min, - lambda_max=args.lambda_x_max, - ) - eval_alpha0, eval_sigma0 = ul_alpha_sigma_from_logsnr(eval_lambda0) - eval_recon = ( - (eval_x_t - eval_sigma0[:, None, None, None] * eval_decoder_out) - / eval_alpha0[:, None, None, None].clamp_min(1e-5) - ).clamp(-1, 1) - else: - eval_recon = eval_decoder_out.clamp(-1, 1) - - # Save side-by-side: left=input, right=reconstruction. - side_by_side = torch.cat([eval_x.clamp(-1, 1), eval_recon], dim=3).detach().float().cpu() - save_image( - (side_by_side + 1.0) / 2.0, - output_dir / "samples" / f"step_{global_step:07d}.png", - nrow=2, - ) - - encoder.train() - decoder.train() - - if accelerator.is_main_process and global_step % args.save_steps == 0: - if args.checkpoints_total_limit is not None: - checkpoints = os.listdir(args.output_dir) - checkpoints = [d for d in checkpoints if d.startswith("checkpoint-")] - checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) - if len(checkpoints) >= args.checkpoints_total_limit: - num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 - removing_checkpoints = checkpoints[:num_to_remove] - logger.info( - f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)}" - ) - for removing_checkpoint in removing_checkpoints: - shutil.rmtree(os.path.join(args.output_dir, removing_checkpoint)) - - save_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}") - accelerator.save_state(save_dir) - _save_pretrained_components( - accelerator=accelerator, - encoder=encoder, - prior=prior, - decoder=decoder, - output_dir=save_dir, - ) - logger.info(f"Saved state to {save_dir}") - - logs = { - "step_loss_bpp": loss.detach().item(), - "prior_loss_bpp": prior_bpp.detach().item(), - "decoder_loss_bpp": decoder_bpp.detach().item(), - "lr": lr_scheduler.get_last_lr()[0], - } - progress_bar.set_postfix(**logs) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - if global_step >= args.max_train_steps: - break - - accelerator.wait_for_everyone() - if accelerator.is_main_process: - final_dir = output_dir / "final" - final_dir.mkdir(parents=True, exist_ok=True) - torch.save(accelerator.unwrap_model(encoder).state_dict(), final_dir / "encoder.pt") - torch.save(accelerator.unwrap_model(prior).state_dict(), final_dir / "prior.pt") - torch.save(accelerator.unwrap_model(decoder).state_dict(), final_dir / "decoder.pt") - _save_pretrained_components( - accelerator=accelerator, - encoder=encoder, - prior=prior, - decoder=decoder, - output_dir=final_dir, - ) - logger.info(f"Training finished. Saved final checkpoints to {final_dir}") - accelerator.end_training() - - -if __name__ == "__main__": - main() diff --git a/examples/research_projects/unified_latents/train_ul_stage2_base.py b/examples/research_projects/unified_latents/train_ul_stage2_base.py deleted file mode 100644 index 373a0bb5ffa7..000000000000 --- a/examples/research_projects/unified_latents/train_ul_stage2_base.py +++ /dev/null @@ -1,462 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import argparse -import logging -import math -import os -import shutil -from pathlib import Path - -import torch -import torch.nn.functional as F -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration, set_seed -from datasets import load_dataset -from torch import nn -from torch.utils.data import DataLoader, IterableDataset -from torchvision import transforms -from tqdm.auto import tqdm -from ul_models import ULTwoStageBaseModel - -from diffusers.models.autoencoders import AutoencoderULEncoder -from diffusers.optimization import get_scheduler -from diffusers.training_utils import ( - ul_alpha_sigma_from_logsnr, - ul_decoder_loss_weight, - ul_dlogsnr_dt, - ul_elbo_prefactor, - ul_logsnr_schedule, - ul_sample_t, -) - - -logger = get_logger(__name__, log_level="INFO") - - -def parse_args(): - parser = argparse.ArgumentParser(description="Train Unified Latents Stage-2 base model with Diffusers.") - - parser.add_argument("--dataset_name", type=str, default=None, help="Dataset name on the Hub.") - parser.add_argument("--dataset_config_name", type=str, default=None, help="Dataset config name.") - parser.add_argument( - "--train_data_dir", type=str, default=None, help="Local dataset directory (imagefolder style)." - ) - parser.add_argument("--cache_dir", type=str, default=None, help="Hugging Face datasets cache directory.") - parser.add_argument("--image_column", type=str, default="image", help="Column containing images.") - parser.add_argument("--streaming", action="store_true", help="Stream dataset from the Hub/local files.") - parser.add_argument( - "--stage1_encoder_path", - type=str, - required=True, - help="Path to stage-1 encoder checkpoint. Supports a `.pt` file or a `save_pretrained` directory.", - ) - - parser.add_argument("--output_dir", type=str, default="ul-stage2-base") - parser.add_argument("--logging_dir", type=str, default="logs") - parser.add_argument("--report_to", type=str, default="tensorboard") - parser.add_argument("--tracker_project_name", type=str, default="unified-latents-stage2") - - parser.add_argument("--resolution", type=int, default=256) - parser.add_argument("--center_crop", action="store_true") - parser.add_argument("--random_flip", action="store_true") - parser.add_argument("--num_workers", type=int, default=4) - - parser.add_argument("--train_batch_size", type=int, default=8) - parser.add_argument("--num_train_epochs", type=int, default=1) - parser.add_argument("--max_train_steps", type=int, default=10000) - parser.add_argument("--gradient_accumulation_steps", type=int, default=1) - - parser.add_argument("--learning_rate", type=float, default=1e-4) - parser.add_argument("--adam_beta1", type=float, default=0.9) - parser.add_argument("--adam_beta2", type=float, default=0.999) - parser.add_argument("--adam_weight_decay", type=float, default=1e-2) - parser.add_argument("--adam_epsilon", type=float, default=1e-8) - parser.add_argument("--max_grad_norm", type=float, default=1.0) - parser.add_argument("--lr_scheduler", type=str, default="cosine") - parser.add_argument("--lr_warmup_steps", type=int, default=500) - - parser.add_argument("--num_train_timesteps", type=int, default=1000) - parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) - parser.add_argument("--lambda_z_min", type=float, default=-10.0) - parser.add_argument("--lambda_z_max", type=float, default=5.0) - - parser.add_argument("--loss_factor", type=float, default=1.0) - parser.add_argument("--sigmoid_bias", type=float, default=0.0) - - parser.add_argument("--latent_channels", type=int, default=4) - parser.add_argument("--latent_downsample_factor", type=int, default=8) - parser.add_argument("--base_patch_size", type=int, default=1) - parser.add_argument("--base_stage_a_layers", type=int, default=6) - parser.add_argument("--base_stage_b_layers", type=int, default=16) - parser.add_argument("--base_stage_a_heads", type=int, default=8) - parser.add_argument("--base_stage_a_head_dim", type=int, default=64) - parser.add_argument("--base_stage_b_heads", type=int, default=16) - parser.add_argument("--base_stage_b_head_dim", type=int, default=64) - - parser.add_argument("--save_steps", type=int, default=500) - parser.add_argument("--checkpoints_total_limit", type=int, default=None) - parser.add_argument("--resume_from_checkpoint", type=str, default=None) - parser.add_argument("--seed", type=int, default=42) - parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"]) - - return parser.parse_args() - - -def build_transforms(args): - transform_list = [ - transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), - ] - if args.center_crop: - transform_list.append(transforms.CenterCrop(args.resolution)) - else: - transform_list.append(transforms.RandomCrop(args.resolution)) - - if args.random_flip: - transform_list.append(transforms.RandomHorizontalFlip()) - - transform_list.extend( - [ - transforms.ToTensor(), - transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), - ] - ) - return transforms.Compose(transform_list) - - -class HFStreamingImageDataset(IterableDataset): - def __init__(self, hf_iterable, image_column: str, image_transform): - super().__init__() - self.hf_iterable = hf_iterable - self.image_column = image_column - self.image_transform = image_transform - - def __iter__(self): - for example in self.hf_iterable: - image = example[self.image_column].convert("RGB") - yield {"pixel_values": self.image_transform(image)} - - -def get_train_dataset_and_collate(args, image_transform): - if args.dataset_name is not None: - dataset = load_dataset( - args.dataset_name, - args.dataset_config_name, - cache_dir=args.cache_dir, - data_dir=args.train_data_dir, - streaming=args.streaming, - ) - else: - if args.train_data_dir is None: - raise ValueError("Provide either `--dataset_name` or `--train_data_dir`.") - data_files = {"train": os.path.join(args.train_data_dir, "**")} - dataset = load_dataset( - "imagefolder", data_files=data_files, cache_dir=args.cache_dir, streaming=args.streaming - ) - - train_dataset = dataset["train"] - - if args.streaming: - image_column = args.image_column - train_dataset = HFStreamingImageDataset( - train_dataset, image_column=image_column, image_transform=image_transform - ) - else: - column_names = train_dataset.column_names - image_column = args.image_column if args.image_column in column_names else column_names[0] - - def preprocess_train(examples): - images = [image.convert("RGB") for image in examples[image_column]] - examples["pixel_values"] = [image_transform(image) for image in images] - return examples - - train_dataset = train_dataset.with_transform(preprocess_train) - - def collate_fn(examples): - pixel_values = torch.stack([example["pixel_values"] for example in examples]) - return {"pixel_values": pixel_values.contiguous().float()} - - return train_dataset, collate_fn - - -def _save_pretrained_base_model(accelerator: Accelerator, base_model: nn.Module, output_dir): - output_dir = Path(output_dir) - accelerator.unwrap_model(base_model).save_pretrained(output_dir / "base_model") - - -def main(): - args = parse_args() - if args.seed is not None: - set_seed(args.seed) - - output_dir = Path(args.output_dir) - logging_dir = output_dir / args.logging_dir - accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_dir)) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - project_config=accelerator_project_config, - ) - - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state) - - if accelerator.is_main_process: - output_dir.mkdir(parents=True, exist_ok=True) - logger.info("Starting UL Stage-2 base training") - logger.info(f"Output dir: {output_dir}") - - image_transform = build_transforms(args) - train_dataset, collate_fn = get_train_dataset_and_collate(args, image_transform) - train_dataloader = DataLoader( - train_dataset, - batch_size=args.train_batch_size, - shuffle=not args.streaming, - collate_fn=collate_fn, - num_workers=args.num_workers, - pin_memory=True, - drop_last=True, - ) - - latent_size = args.resolution // args.latent_downsample_factor - if latent_size < 1: - raise ValueError("`resolution // latent_downsample_factor` must be >= 1.") - - encoder_path = Path(args.stage1_encoder_path) - if encoder_path.is_dir(): - encoder = AutoencoderULEncoder.from_pretrained(str(encoder_path)) - else: - encoder = AutoencoderULEncoder(in_channels=3, latent_channels=args.latent_channels) - state_dict = torch.load(args.stage1_encoder_path, map_location="cpu") - missing, unexpected = encoder.load_state_dict(state_dict, strict=False) - if len(missing) > 0: - logger.warning(f"Missing keys when loading stage-1 encoder: {missing}") - if len(unexpected) > 0: - logger.warning(f"Unexpected keys when loading stage-1 encoder: {unexpected}") - encoder.requires_grad_(False) - encoder.eval() - - base_model = ULTwoStageBaseModel( - latent_channels=args.latent_channels, - latent_size=latent_size, - num_train_timesteps=args.num_train_timesteps, - stage_a_layers=args.base_stage_a_layers, - stage_b_layers=args.base_stage_b_layers, - stage_a_heads=args.base_stage_a_heads, - stage_a_head_dim=args.base_stage_a_head_dim, - stage_b_heads=args.base_stage_b_heads, - stage_b_head_dim=args.base_stage_b_head_dim, - patch_size=args.base_patch_size, - ) - - optimizer = torch.optim.AdamW( - base_model.parameters(), - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - has_dataloader_length = True - try: - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - except TypeError: - has_dataloader_length = False - num_update_steps_per_epoch = None - if args.max_train_steps is None or args.max_train_steps <= 0: - if not has_dataloader_length: - raise ValueError("For streaming datasets, set `--max_train_steps` to a positive value.") - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - - lr_scheduler = get_scheduler( - name=args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, - num_training_steps=args.max_train_steps * accelerator.num_processes, - ) - - encoder, base_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - encoder, base_model, optimizer, train_dataloader, lr_scheduler - ) - - if has_dataloader_length: - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - else: - args.num_train_epochs = max(args.num_train_epochs, 1) - - if accelerator.is_main_process: - accelerator.init_trackers(args.tracker_project_name, config=vars(args)) - - logger.info("***** Running stage-2 base training *****") - if has_dataloader_length: - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num batches each epoch = {len(train_dataloader)}") - else: - logger.info(" Num examples = streaming (unknown)") - logger.info(" Num batches each epoch = streaming (unknown)") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info( - " Total train batch size (w. parallel, distributed & accumulation) = " - f"{args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps}" - ) - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - - global_step = 0 - first_epoch = 0 - - if args.resume_from_checkpoint: - if args.resume_from_checkpoint != "latest": - path = os.path.basename(args.resume_from_checkpoint) - else: - dirs = os.listdir(args.output_dir) if os.path.exists(args.output_dir) else [] - dirs = [d for d in dirs if d.startswith("checkpoint-")] - dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) - path = dirs[-1] if len(dirs) > 0 else None - - if path is None: - logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.") - else: - logger.info(f"Resuming from checkpoint {path}") - accelerator.load_state(os.path.join(args.output_dir, path)) - global_step = int(path.split("-")[1]) - first_epoch = global_step // num_update_steps_per_epoch if has_dataloader_length else 0 - - progress_bar = tqdm( - range(0, args.max_train_steps), - initial=global_step, - desc="Steps", - disable=not accelerator.is_local_main_process, - ) - - train_loss = 0.0 - - for epoch in range(first_epoch, args.num_train_epochs): - base_model.train() - - for _, batch in enumerate(train_dataloader): - with accelerator.accumulate(base_model): - x = batch["pixel_values"].to(accelerator.device) - bsz = x.shape[0] - - with torch.no_grad(): - z_clean = encoder.encode(x).latent - - # Stage-2 uses clean encoder means as training targets. - z_target = z_clean - t = ul_sample_t(bsz, x.device) - lambda_t = ul_logsnr_schedule( - t, - schedule_type=args.base_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - dlogsnr_dt = ul_dlogsnr_dt( - t, - schedule_type=args.base_schedule, - lambda_min=args.lambda_z_min, - lambda_max=args.lambda_z_max, - ) - prefactor = ul_elbo_prefactor(lambda_t, dlogsnr_dt) - alpha_t, sigma_t = ul_alpha_sigma_from_logsnr(lambda_t) - eps = torch.randn_like(z_target) - z_t = alpha_t[:, None, None, None] * z_target + sigma_t[:, None, None, None] * eps - - timesteps = (t * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) - dummy_labels = torch.zeros((bsz,), device=x.device, dtype=torch.long) - - # Preferred stage-2 parameterization: predict velocity v. - v_pred = base_model(z_t, timesteps, dummy_labels) - z_target_hat = alpha_t[:, None, None, None] * z_t - sigma_t[:, None, None, None] * v_pred - - per_sample = F.mse_loss(z_target_hat.float(), z_target.float(), reduction="none").sum(dim=(1, 2, 3)) - weights = ul_decoder_loss_weight( - lambda_t, - bias=args.sigmoid_bias, - loss_factor=args.loss_factor, - invert=False, - ) - loss_raw = (prefactor * weights * per_sample).mean() - num_pixels = x.shape[-2] * x.shape[-1] - bpp_denom = num_pixels * math.log(2.0) - loss = loss_raw / bpp_denom - - accelerator.backward(loss) - if accelerator.sync_gradients: - accelerator.clip_grad_norm_(base_model.parameters(), args.max_grad_norm) - - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad(set_to_none=True) - - avg_loss = accelerator.gather(loss.detach().repeat(args.train_batch_size)).mean() - train_loss += avg_loss.item() / args.gradient_accumulation_steps - - if accelerator.sync_gradients: - global_step += 1 - progress_bar.update(1) - accelerator.log({"train_loss": train_loss}, step=global_step) - train_loss = 0.0 - - if accelerator.is_main_process and global_step % args.save_steps == 0: - if args.checkpoints_total_limit is not None: - checkpoints = os.listdir(args.output_dir) - checkpoints = [d for d in checkpoints if d.startswith("checkpoint-")] - checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) - if len(checkpoints) >= args.checkpoints_total_limit: - num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 - removing_checkpoints = checkpoints[:num_to_remove] - logger.info( - f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)}" - ) - for removing_checkpoint in removing_checkpoints: - shutil.rmtree(os.path.join(args.output_dir, removing_checkpoint)) - - save_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}") - accelerator.save_state(save_dir) - _save_pretrained_base_model( - accelerator=accelerator, - base_model=base_model, - output_dir=save_dir, - ) - logger.info(f"Saved state to {save_dir}") - - logs = { - "step_loss": loss.detach().item(), - "lr": lr_scheduler.get_last_lr()[0], - } - progress_bar.set_postfix(**logs) - accelerator.log(logs, step=global_step) - - if global_step >= args.max_train_steps: - break - - if global_step >= args.max_train_steps: - break - - accelerator.wait_for_everyone() - if accelerator.is_main_process: - final_dir = output_dir / "final" - final_dir.mkdir(parents=True, exist_ok=True) - torch.save(accelerator.unwrap_model(base_model).state_dict(), final_dir / "base_model.pt") - _save_pretrained_base_model( - accelerator=accelerator, - base_model=base_model, - output_dir=final_dir, - ) - logger.info(f"Training finished. Saved final checkpoint to {final_dir / 'base_model.pt'}") - - accelerator.end_training() - - -if __name__ == "__main__": - main() diff --git a/examples/research_projects/unified_latents/ul_models.py b/examples/research_projects/unified_latents/ul_models.py deleted file mode 100644 index 0c01d00ffa97..000000000000 --- a/examples/research_projects/unified_latents/ul_models.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import torch - -from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.models.modeling_utils import ModelMixin -from diffusers.models.transformers.dit_transformer_2d import DiTTransformer2DModel - - -class ULTwoStageBaseModel(ModelMixin, ConfigMixin): - """ - Approximation of UL Sec. 5.1 stage-2 base model: - - Stage A: ViT-like latent denoiser, width ~512, 6 layers - - Stage B: ViT-like latent denoiser, width ~1024, 16 layers - - Both stages are DiT blocks and predict residual denoised latents. - """ - - @register_to_config - def __init__( - self, - latent_channels: int, - latent_size: int, - num_train_timesteps: int, - stage_a_layers: int = 6, - stage_b_layers: int = 16, - stage_a_heads: int = 8, - stage_a_head_dim: int = 64, # 512 width - stage_b_heads: int = 16, - stage_b_head_dim: int = 64, # 1024 width - patch_size: int = 1, - ): - super().__init__() - self.stage_a = DiTTransformer2DModel( - num_attention_heads=stage_a_heads, - attention_head_dim=stage_a_head_dim, - in_channels=latent_channels, - out_channels=latent_channels, - num_layers=stage_a_layers, - sample_size=latent_size, - patch_size=patch_size, - num_embeds_ada_norm=num_train_timesteps, - dropout=0.1, - ) - self.stage_b = DiTTransformer2DModel( - num_attention_heads=stage_b_heads, - attention_head_dim=stage_b_head_dim, - in_channels=latent_channels, - out_channels=latent_channels, - num_layers=stage_b_layers, - sample_size=latent_size, - patch_size=patch_size, - num_embeds_ada_norm=num_train_timesteps, - dropout=0.1, - ) - - def forward(self, z_t: torch.Tensor, timesteps: torch.LongTensor, class_labels: torch.LongTensor) -> torch.Tensor: - h = self.stage_a(z_t, timestep=timesteps, class_labels=class_labels).sample - out = self.stage_b(h, timestep=timesteps, class_labels=class_labels).sample - return out From fbfbc5ef8cdac7252e1d1702dd640f6d00f4fd04 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:00:42 +0000 Subject: [PATCH 33/44] remove autoencoder_ul.py from git tracking --- .../models/autoencoders/autoencoder_ul.py | 162 ------------------ 1 file changed, 162 deletions(-) delete mode 100644 src/diffusers/models/autoencoders/autoencoder_ul.py diff --git a/src/diffusers/models/autoencoders/autoencoder_ul.py b/src/diffusers/models/autoencoders/autoencoder_ul.py deleted file mode 100644 index 2b1f701d9c5e..000000000000 --- a/src/diffusers/models/autoencoders/autoencoder_ul.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright 2026 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torch.nn as nn -from torch.utils.checkpoint import checkpoint - -from ...configuration_utils import ConfigMixin, register_to_config -from ...utils.accelerate_utils import apply_forward_hook -from ..modeling_utils import ModelMixin -from ..unets.unet_2d_blocks import UNetMidBlock2D, get_down_block -from .vae import EncoderOutput - - -class ULEncoder(nn.Module): - """ - ResNet-style encoder with per-stage residual depth. - - This is equivalent to the Diffusers VAE encoder structure, but supports `layers_per_block` as a tuple to match UL - paper Section 5.1 exactly. - """ - - def __init__( - self, - in_channels: int = 3, - out_channels: int = 4, - down_block_types: tuple[str, ...] = ( - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - ), - block_out_channels: tuple[int, ...] = (128, 256, 512, 512), - layers_per_block: tuple[int, ...] = (2, 2, 2, 3), - norm_num_groups: int = 32, - act_fn: str = "silu", - mid_block_add_attention: bool = True, - ): - super().__init__() - if len(down_block_types) != len(block_out_channels): - raise ValueError("`down_block_types` and `block_out_channels` must have the same length.") - if len(layers_per_block) != len(block_out_channels): - raise ValueError("`layers_per_block` must have the same length as `block_out_channels`.") - - self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) - self.down_blocks = nn.ModuleList([]) - - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - down_block = get_down_block( - down_block_type, - num_layers=layers_per_block[i], - in_channels=input_channel, - out_channels=output_channel, - add_downsample=not is_final_block, - resnet_eps=1e-6, - downsample_padding=0, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attention_head_dim=output_channel, - temb_channels=None, - ) - self.down_blocks.append(down_block) - - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default", - attention_head_dim=block_out_channels[-1], - resnet_groups=norm_num_groups, - temb_channels=None, - add_attention=mid_block_add_attention, - ) - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1) - self.gradient_checkpointing = False - - def forward(self, sample: torch.Tensor) -> torch.Tensor: - sample = self.conv_in(sample) - if torch.is_grad_enabled() and self.gradient_checkpointing: - for down_block in self.down_blocks: - sample = checkpoint(down_block, sample, use_reentrant=False) - sample = checkpoint(self.mid_block, sample, use_reentrant=False) - else: - for down_block in self.down_blocks: - sample = down_block(sample) - sample = self.mid_block(sample) - - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - return sample - - -class AutoencoderULEncoder(ModelMixin, ConfigMixin): - """ - Deterministic UL encoder model used in stage-1/stage-2 training. - """ - - _supports_gradient_checkpointing = True - _no_split_modules = ["ResnetBlock2D"] - - @register_to_config - def __init__( - self, - in_channels: int = 3, - latent_channels: int = 4, - down_block_types: tuple[str, ...] = ( - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - "DownEncoderBlock2D", - ), - block_out_channels: tuple[int, ...] = (128, 256, 512, 512), - layers_per_block: tuple[int, ...] = (2, 2, 2, 3), - norm_num_groups: int = 32, - act_fn: str = "silu", - mid_block_add_attention: bool = True, - ): - super().__init__() - self.encoder = ULEncoder( - in_channels=in_channels, - out_channels=latent_channels, - down_block_types=down_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - norm_num_groups=norm_num_groups, - act_fn=act_fn, - mid_block_add_attention=mid_block_add_attention, - ) - self.out_proj = nn.Conv2d(latent_channels, latent_channels, kernel_size=1) - - @apply_forward_hook - def encode(self, x: torch.Tensor, return_dict: bool = True) -> EncoderOutput | tuple[torch.Tensor]: - latents = self.out_proj(self.encoder(x)) - if not return_dict: - return (latents,) - return EncoderOutput(latent=latents) - - def forward(self, sample: torch.Tensor, return_dict: bool = True) -> EncoderOutput | tuple[torch.Tensor]: - return self.encode(sample, return_dict=return_dict) - - def _set_gradient_checkpointing(self, module, value=False): - if hasattr(module, "gradient_checkpointing"): - module.gradient_checkpointing = value From 976951773beb31219adf5b0f5793051eabd29499 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:01:05 +0000 Subject: [PATCH 34/44] remove test_models_autoencoder_ul and test_attention_dispatch from git tracking --- .../test_models_autoencoder_ul.py | 43 ------------------- tests/models/test_attention_dispatch.py | 38 ---------------- 2 files changed, 81 deletions(-) delete mode 100644 tests/models/autoencoders/test_models_autoencoder_ul.py delete mode 100644 tests/models/test_attention_dispatch.py diff --git a/tests/models/autoencoders/test_models_autoencoder_ul.py b/tests/models/autoencoders/test_models_autoencoder_ul.py deleted file mode 100644 index a301a14ab8f7..000000000000 --- a/tests/models/autoencoders/test_models_autoencoder_ul.py +++ /dev/null @@ -1,43 +0,0 @@ -# coding=utf-8 -# Copyright 2026 HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import tempfile -import unittest - -import torch - -from diffusers.models.autoencoders import AutoencoderULEncoder - - -class AutoencoderULEncoderTests(unittest.TestCase): - def test_ul_encoder_api(self): - model = AutoencoderULEncoder() - self.assertEqual(tuple(model.config.layers_per_block), (2, 2, 2, 3)) - - x = torch.randn(2, 3, 32, 32) - encoded = model.encode(x) - self.assertEqual(encoded.latent.shape, (2, 4, 4, 4)) - - def test_ul_encoder_save_load(self): - model = AutoencoderULEncoder() - x = torch.randn(1, 3, 32, 32) - y_ref = model.encode(x).latent - - with tempfile.TemporaryDirectory() as tmp_dir: - model.save_pretrained(tmp_dir) - loaded = AutoencoderULEncoder.from_pretrained(tmp_dir) - y_loaded = loaded.encode(x).latent - - self.assertTrue(torch.allclose(y_ref, y_loaded, atol=1e-6)) diff --git a/tests/models/test_attention_dispatch.py b/tests/models/test_attention_dispatch.py deleted file mode 100644 index 223a6f91c785..000000000000 --- a/tests/models/test_attention_dispatch.py +++ /dev/null @@ -1,38 +0,0 @@ -import pytest -import torch - -from diffusers.models.attention_dispatch import _AttentionBackendRegistry, dispatch_attention_fn - -from ..testing_utils import require_torch_gpu, torch_device - - -VARLEN_BACKENDS = [] -for name, fn in _AttentionBackendRegistry._backends.items(): - # Handle both enum and string keys - key = name.value if hasattr(name, "value") else name - if key in {"flash_varlen", "flash_varlen_3", "sage_varlen"} and callable(fn): - VARLEN_BACKENDS.append(name) - -if not VARLEN_BACKENDS: - pytest.skip("No varlen backends available", allow_module_level=True) - - -@require_torch_gpu -@pytest.mark.parametrize("backend", VARLEN_BACKENDS) -def test_varlen_backend_accepts_seq_lens(backend): - if torch_device != "cuda": - pytest.skip("CUDA required for varlen backends") - backend_fn = _AttentionBackendRegistry._backends.get(backend) - if not callable(backend_fn): - pytest.skip(f"{backend} backend not available") - - key = backend.value if hasattr(backend, "value") else backend - dtype = torch.float16 - seq_lens = torch.tensor([3, 5], dtype=torch.int32, device=torch_device) - - q = torch.randn(2, 5, 2, 8, device=torch_device, dtype=dtype) - k = torch.randn(2, 5, 2, 8, device=torch_device, dtype=dtype) - v = torch.randn(2, 5, 2, 8, device=torch_device, dtype=dtype) - - out = dispatch_attention_fn(q, k, v, backend=key, seq_lens=seq_lens) - assert out.shape == q.shape From 83d1e6aeb14a7d06ac8bce93bb5c716a07b94917 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:02:49 +0000 Subject: [PATCH 35/44] restore scripts/ from main --- scripts/__init__.py | 0 .../change_naming_configs_and_checkpoints.py | 113 ++ scripts/conversion_ldm_uncond.py | 56 + scripts/convert_amused.py | 523 ++++++ ...rt_animatediff_motion_lora_to_diffusers.py | 69 + ..._animatediff_motion_module_to_diffusers.py | 62 + ...ert_animatediff_sparsectrl_to_diffusers.py | 83 + .../convert_asymmetric_vqgan_to_diffusers.py | 184 +++ scripts/convert_aura_flow_to_diffusers.py | 131 ++ scripts/convert_blipdiffusion_to_diffusers.py | 344 ++++ scripts/convert_cogvideox_to_diffusers.py | 346 ++++ scripts/convert_cogview3_to_diffusers.py | 242 +++ scripts/convert_cogview4_to_diffusers.py | 254 +++ .../convert_cogview4_to_diffusers_megatron.py | 384 +++++ scripts/convert_consistency_decoder.py | 1131 +++++++++++++ scripts/convert_consistency_to_diffusers.py | 315 ++++ scripts/convert_cosmos_to_diffusers.py | 976 ++++++++++++ .../convert_dance_diffusion_to_diffusers.py | 346 ++++ scripts/convert_dcae_to_diffusers.py | 323 ++++ ...t_ddpm_original_checkpoint_to_diffusers.py | 431 +++++ .../convert_diffusers_sdxl_lora_to_webui.py | 56 + scripts/convert_diffusers_to_original_sdxl.py | 350 ++++ ..._diffusers_to_original_stable_diffusion.py | 353 +++++ scripts/convert_dit_to_diffusers.py | 162 ++ scripts/convert_flux2_to_diffusers.py | 536 +++++++ scripts/convert_flux_to_diffusers.py | 308 ++++ ...nvert_flux_xlabs_ipadapter_to_diffusers.py | 97 ++ scripts/convert_gligen_to_diffusers.py | 581 +++++++ scripts/convert_hunyuan_image_to_diffusers.py | 1044 ++++++++++++ .../convert_hunyuan_video1_5_to_diffusers.py | 875 ++++++++++ scripts/convert_hunyuan_video_to_diffusers.py | 353 +++++ ...vert_hunyuandit_controlnet_to_diffusers.py | 241 +++ scripts/convert_hunyuandit_to_diffusers.py | 266 ++++ scripts/convert_i2vgen_to_diffusers.py | 510 ++++++ scripts/convert_if.py | 1250 +++++++++++++++ scripts/convert_k_upscaler_to_diffusers.py | 297 ++++ ...convert_kakao_brain_unclip_to_diffusers.py | 1159 ++++++++++++++ scripts/convert_kandinsky3_unet.py | 98 ++ scripts/convert_kandinsky_to_diffusers.py | 1411 +++++++++++++++++ ...rt_ldm_original_checkpoint_to_diffusers.py | 359 +++++ .../convert_lora_safetensor_to_diffusers.py | 128 ++ scripts/convert_ltx2_to_diffusers.py | 1194 ++++++++++++++ scripts/convert_ltx_to_diffusers.py | 525 ++++++ scripts/convert_lumina_to_diffusers.py | 142 ++ scripts/convert_mochi_to_diffusers.py | 463 ++++++ .../convert_models_diffuser_to_diffusers.py | 100 ++ .../convert_ms_text_to_video_to_diffusers.py | 428 +++++ .../convert_music_spectrogram_to_diffusers.py | 203 +++ ...ncsnpp_original_checkpoint_to_diffusers.py | 185 +++ scripts/convert_omnigen_to_diffusers.py | 203 +++ ...convert_original_audioldm2_to_diffusers.py | 1135 +++++++++++++ .../convert_original_audioldm_to_diffusers.py | 1042 ++++++++++++ ...onvert_original_controlnet_to_diffusers.py | 109 ++ .../convert_original_musicldm_to_diffusers.py | 1056 ++++++++++++ ..._original_stable_diffusion_to_diffusers.py | 188 +++ scripts/convert_original_t2i_adapter.py | 250 +++ scripts/convert_ovis_image_to_diffusers.py | 263 +++ scripts/convert_pixart_alpha_to_diffusers.py | 198 +++ scripts/convert_pixart_sigma_to_diffusers.py | 223 +++ scripts/convert_prx_to_diffusers.py | 345 ++++ scripts/convert_rae_to_diffusers.py | 406 +++++ .../convert_sana_controlnet_to_diffusers.py | 216 +++ scripts/convert_sana_to_diffusers.py | 456 ++++++ scripts/convert_sana_video_to_diffusers.py | 360 +++++ .../convert_sd3_controlnet_to_diffusers.py | 185 +++ scripts/convert_sd3_to_diffusers.py | 351 ++++ scripts/convert_shap_e_to_diffusers.py | 1080 +++++++++++++ scripts/convert_skyreelsv2_to_diffusers.py | 637 ++++++++ scripts/convert_stable_audio.py | 279 ++++ scripts/convert_stable_cascade.py | 218 +++ scripts/convert_stable_cascade_lite.py | 226 +++ ...ert_stable_diffusion_checkpoint_to_onnx.py | 265 ++++ ...ert_stable_diffusion_controlnet_to_onnx.py | 505 ++++++ ...stable_diffusion_controlnet_to_tensorrt.py | 121 ++ scripts/convert_svd_to_diffusers.py | 730 +++++++++ .../convert_tiny_autoencoder_to_diffusers.py | 71 + ...nvert_unclip_txt2img_to_image_variation.py | 41 + scripts/convert_unidiffuser_to_diffusers.py | 786 +++++++++ scripts/convert_vae_diff_to_onnx.py | 122 ++ scripts/convert_vae_pt_to_diffusers.py | 177 +++ ...onvert_versatile_diffusion_to_diffusers.py | 791 +++++++++ scripts/convert_vq_diffusion_to_diffusers.py | 916 +++++++++++ scripts/convert_wan_to_diffusers.py | 1287 +++++++++++++++ scripts/convert_wuerstchen.py | 115 ++ scripts/convert_zero123_to_diffusers.py | 807 ++++++++++ scripts/extract_lora_from_model.py | 151 ++ scripts/generate_logits.py | 127 ++ 87 files changed, 36425 insertions(+) create mode 100644 scripts/__init__.py create mode 100644 scripts/change_naming_configs_and_checkpoints.py create mode 100644 scripts/conversion_ldm_uncond.py create mode 100644 scripts/convert_amused.py create mode 100644 scripts/convert_animatediff_motion_lora_to_diffusers.py create mode 100644 scripts/convert_animatediff_motion_module_to_diffusers.py create mode 100644 scripts/convert_animatediff_sparsectrl_to_diffusers.py create mode 100644 scripts/convert_asymmetric_vqgan_to_diffusers.py create mode 100644 scripts/convert_aura_flow_to_diffusers.py create mode 100644 scripts/convert_blipdiffusion_to_diffusers.py create mode 100644 scripts/convert_cogvideox_to_diffusers.py create mode 100644 scripts/convert_cogview3_to_diffusers.py create mode 100644 scripts/convert_cogview4_to_diffusers.py create mode 100644 scripts/convert_cogview4_to_diffusers_megatron.py create mode 100644 scripts/convert_consistency_decoder.py create mode 100644 scripts/convert_consistency_to_diffusers.py create mode 100644 scripts/convert_cosmos_to_diffusers.py create mode 100755 scripts/convert_dance_diffusion_to_diffusers.py create mode 100644 scripts/convert_dcae_to_diffusers.py create mode 100644 scripts/convert_ddpm_original_checkpoint_to_diffusers.py create mode 100644 scripts/convert_diffusers_sdxl_lora_to_webui.py create mode 100644 scripts/convert_diffusers_to_original_sdxl.py create mode 100644 scripts/convert_diffusers_to_original_stable_diffusion.py create mode 100644 scripts/convert_dit_to_diffusers.py create mode 100644 scripts/convert_flux2_to_diffusers.py create mode 100644 scripts/convert_flux_to_diffusers.py create mode 100644 scripts/convert_flux_xlabs_ipadapter_to_diffusers.py create mode 100644 scripts/convert_gligen_to_diffusers.py create mode 100644 scripts/convert_hunyuan_image_to_diffusers.py create mode 100644 scripts/convert_hunyuan_video1_5_to_diffusers.py create mode 100644 scripts/convert_hunyuan_video_to_diffusers.py create mode 100644 scripts/convert_hunyuandit_controlnet_to_diffusers.py create mode 100644 scripts/convert_hunyuandit_to_diffusers.py create mode 100644 scripts/convert_i2vgen_to_diffusers.py create mode 100644 scripts/convert_if.py create mode 100644 scripts/convert_k_upscaler_to_diffusers.py create mode 100644 scripts/convert_kakao_brain_unclip_to_diffusers.py create mode 100644 scripts/convert_kandinsky3_unet.py create mode 100644 scripts/convert_kandinsky_to_diffusers.py create mode 100644 scripts/convert_ldm_original_checkpoint_to_diffusers.py create mode 100644 scripts/convert_lora_safetensor_to_diffusers.py create mode 100644 scripts/convert_ltx2_to_diffusers.py create mode 100644 scripts/convert_ltx_to_diffusers.py create mode 100644 scripts/convert_lumina_to_diffusers.py create mode 100644 scripts/convert_mochi_to_diffusers.py create mode 100644 scripts/convert_models_diffuser_to_diffusers.py create mode 100644 scripts/convert_ms_text_to_video_to_diffusers.py create mode 100644 scripts/convert_music_spectrogram_to_diffusers.py create mode 100644 scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py create mode 100644 scripts/convert_omnigen_to_diffusers.py create mode 100644 scripts/convert_original_audioldm2_to_diffusers.py create mode 100644 scripts/convert_original_audioldm_to_diffusers.py create mode 100644 scripts/convert_original_controlnet_to_diffusers.py create mode 100644 scripts/convert_original_musicldm_to_diffusers.py create mode 100644 scripts/convert_original_stable_diffusion_to_diffusers.py create mode 100644 scripts/convert_original_t2i_adapter.py create mode 100644 scripts/convert_ovis_image_to_diffusers.py create mode 100644 scripts/convert_pixart_alpha_to_diffusers.py create mode 100644 scripts/convert_pixart_sigma_to_diffusers.py create mode 100644 scripts/convert_prx_to_diffusers.py create mode 100644 scripts/convert_rae_to_diffusers.py create mode 100644 scripts/convert_sana_controlnet_to_diffusers.py create mode 100644 scripts/convert_sana_to_diffusers.py create mode 100644 scripts/convert_sana_video_to_diffusers.py create mode 100644 scripts/convert_sd3_controlnet_to_diffusers.py create mode 100644 scripts/convert_sd3_to_diffusers.py create mode 100644 scripts/convert_shap_e_to_diffusers.py create mode 100644 scripts/convert_skyreelsv2_to_diffusers.py create mode 100644 scripts/convert_stable_audio.py create mode 100644 scripts/convert_stable_cascade.py create mode 100644 scripts/convert_stable_cascade_lite.py create mode 100644 scripts/convert_stable_diffusion_checkpoint_to_onnx.py create mode 100644 scripts/convert_stable_diffusion_controlnet_to_onnx.py create mode 100644 scripts/convert_stable_diffusion_controlnet_to_tensorrt.py create mode 100644 scripts/convert_svd_to_diffusers.py create mode 100644 scripts/convert_tiny_autoencoder_to_diffusers.py create mode 100644 scripts/convert_unclip_txt2img_to_image_variation.py create mode 100644 scripts/convert_unidiffuser_to_diffusers.py create mode 100644 scripts/convert_vae_diff_to_onnx.py create mode 100644 scripts/convert_vae_pt_to_diffusers.py create mode 100644 scripts/convert_versatile_diffusion_to_diffusers.py create mode 100644 scripts/convert_vq_diffusion_to_diffusers.py create mode 100644 scripts/convert_wan_to_diffusers.py create mode 100644 scripts/convert_wuerstchen.py create mode 100644 scripts/convert_zero123_to_diffusers.py create mode 100644 scripts/extract_lora_from_model.py create mode 100644 scripts/generate_logits.py diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/scripts/change_naming_configs_and_checkpoints.py b/scripts/change_naming_configs_and_checkpoints.py new file mode 100644 index 000000000000..4220901c13bf --- /dev/null +++ b/scripts/change_naming_configs_and_checkpoints.py @@ -0,0 +1,113 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the LDM checkpoints.""" + +import argparse +import json +import os + +import torch +from transformers.file_utils import has_file + +from diffusers import UNet2DConditionModel, UNet2DModel + + +do_only_config = False +do_only_weights = True +do_only_renaming = False + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--repo_path", + default=None, + type=str, + required=True, + help="The config json file corresponding to the architecture.", + ) + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + args = parser.parse_args() + + config_parameters_to_change = { + "image_size": "sample_size", + "num_res_blocks": "layers_per_block", + "block_channels": "block_out_channels", + "down_blocks": "down_block_types", + "up_blocks": "up_block_types", + "downscale_freq_shift": "freq_shift", + "resnet_num_groups": "norm_num_groups", + "resnet_act_fn": "act_fn", + "resnet_eps": "norm_eps", + "num_head_channels": "attention_head_dim", + } + + key_parameters_to_change = { + "time_steps": "time_proj", + "mid": "mid_block", + "downsample_blocks": "down_blocks", + "upsample_blocks": "up_blocks", + } + + subfolder = "" if has_file(args.repo_path, "config.json") else "unet" + + with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader: + text = reader.read() + config = json.loads(text) + + if do_only_config: + for key in config_parameters_to_change.keys(): + config.pop(key, None) + + if has_file(args.repo_path, "config.json"): + model = UNet2DModel(**config) + else: + class_name = UNet2DConditionModel if "ldm-text2im-large-256" in args.repo_path else UNet2DModel + model = class_name(**config) + + if do_only_config: + model.save_config(os.path.join(args.repo_path, subfolder)) + + config = dict(model.config) + + if do_only_renaming: + for key, value in config_parameters_to_change.items(): + if key in config: + config[value] = config[key] + del config[key] + + config["down_block_types"] = [k.replace("UNetRes", "") for k in config["down_block_types"]] + config["up_block_types"] = [k.replace("UNetRes", "") for k in config["up_block_types"]] + + if do_only_weights: + state_dict = torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin")) + + new_state_dict = {} + for param_key, param_value in state_dict.items(): + if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"): + continue + has_changed = False + for key, new_key in key_parameters_to_change.items(): + if not has_changed and param_key.split(".")[0] == key: + new_state_dict[".".join([new_key] + param_key.split(".")[1:])] = param_value + has_changed = True + if not has_changed: + new_state_dict[param_key] = param_value + + model.load_state_dict(new_state_dict) + model.save_pretrained(os.path.join(args.repo_path, subfolder)) diff --git a/scripts/conversion_ldm_uncond.py b/scripts/conversion_ldm_uncond.py new file mode 100644 index 000000000000..8c22cc1ce8f2 --- /dev/null +++ b/scripts/conversion_ldm_uncond.py @@ -0,0 +1,56 @@ +import argparse + +import torch +import yaml + +from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel + + +def convert_ldm_original(checkpoint_path, config_path, output_path): + config = yaml.safe_load(config_path) + state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] + keys = list(state_dict.keys()) + + # extract state_dict for VQVAE + first_stage_dict = {} + first_stage_key = "first_stage_model." + for key in keys: + if key.startswith(first_stage_key): + first_stage_dict[key.replace(first_stage_key, "")] = state_dict[key] + + # extract state_dict for UNetLDM + unet_state_dict = {} + unet_key = "model.diffusion_model." + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = state_dict[key] + + vqvae_init_args = config["model"]["params"]["first_stage_config"]["params"] + unet_init_args = config["model"]["params"]["unet_config"]["params"] + + vqvae = VQModel(**vqvae_init_args).eval() + vqvae.load_state_dict(first_stage_dict) + + unet = UNetLDMModel(**unet_init_args).eval() + unet.load_state_dict(unet_state_dict) + + noise_scheduler = DDIMScheduler( + timesteps=config["model"]["params"]["timesteps"], + beta_schedule="scaled_linear", + beta_start=config["model"]["params"]["linear_start"], + beta_end=config["model"]["params"]["linear_end"], + clip_sample=False, + ) + + pipeline = LDMPipeline(vqvae, unet, noise_scheduler) + pipeline.save_pretrained(output_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", type=str, required=True) + parser.add_argument("--config_path", type=str, required=True) + parser.add_argument("--output_path", type=str, required=True) + args = parser.parse_args() + + convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path) diff --git a/scripts/convert_amused.py b/scripts/convert_amused.py new file mode 100644 index 000000000000..ddd1bf508b6d --- /dev/null +++ b/scripts/convert_amused.py @@ -0,0 +1,523 @@ +import inspect +import os +from argparse import ArgumentParser + +import numpy as np +import torch +from muse import MaskGiTUViT, VQGANModel +from muse import PipelineMuse as OldPipelineMuse +from transformers import CLIPTextModelWithProjection, CLIPTokenizer + +from diffusers import VQModel +from diffusers.models.attention_processor import AttnProcessor +from diffusers.models.unets.uvit_2d import UVit2DModel +from diffusers.pipelines.amused.pipeline_amused import AmusedPipeline +from diffusers.schedulers import AmusedScheduler + + +torch.backends.cuda.enable_flash_sdp(False) +torch.backends.cuda.enable_mem_efficient_sdp(False) +torch.backends.cuda.enable_math_sdp(True) + +os.environ["CUDA_LAUNCH_BLOCKING"] = "1" +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8" +torch.use_deterministic_algorithms(True) + +# Enable CUDNN deterministic mode +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +torch.backends.cuda.matmul.allow_tf32 = False + +device = "cuda" + + +def main(): + args = ArgumentParser() + args.add_argument("--model_256", action="store_true") + args.add_argument("--write_to", type=str, required=False, default=None) + args.add_argument("--transformer_path", type=str, required=False, default=None) + args = args.parse_args() + + transformer_path = args.transformer_path + subfolder = "transformer" + + if transformer_path is None: + if args.model_256: + transformer_path = "openMUSE/muse-256" + else: + transformer_path = ( + "../research-run-512-checkpoints/research-run-512-with-downsample-checkpoint-554000/unwrapped_model/" + ) + subfolder = None + + old_transformer = MaskGiTUViT.from_pretrained(transformer_path, subfolder=subfolder) + + old_transformer.to(device) + + old_vae = VQGANModel.from_pretrained("openMUSE/muse-512", subfolder="vae") + old_vae.to(device) + + vqvae = make_vqvae(old_vae) + + tokenizer = CLIPTokenizer.from_pretrained("openMUSE/muse-512", subfolder="text_encoder") + + text_encoder = CLIPTextModelWithProjection.from_pretrained("openMUSE/muse-512", subfolder="text_encoder") + text_encoder.to(device) + + transformer = make_transformer(old_transformer, args.model_256) + + scheduler = AmusedScheduler(mask_token_id=old_transformer.config.mask_token_id) + + new_pipe = AmusedPipeline( + vqvae=vqvae, tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, scheduler=scheduler + ) + + old_pipe = OldPipelineMuse( + vae=old_vae, transformer=old_transformer, text_encoder=text_encoder, tokenizer=tokenizer + ) + old_pipe.to(device) + + if args.model_256: + transformer_seq_len = 256 + orig_size = (256, 256) + else: + transformer_seq_len = 1024 + orig_size = (512, 512) + + old_out = old_pipe( + "dog", + generator=torch.Generator(device).manual_seed(0), + transformer_seq_len=transformer_seq_len, + orig_size=orig_size, + timesteps=12, + )[0] + + new_out = new_pipe("dog", generator=torch.Generator(device).manual_seed(0)).images[0] + + old_out = np.array(old_out) + new_out = np.array(new_out) + + diff = np.abs(old_out.astype(np.float64) - new_out.astype(np.float64)) + + # assert diff diff.sum() == 0 + print("skipping pipeline full equivalence check") + + print(f"max diff: {diff.max()}, diff.sum() / diff.size {diff.sum() / diff.size}") + + if args.model_256: + assert diff.max() <= 3 + assert diff.sum() / diff.size < 0.7 + else: + assert diff.max() <= 1 + assert diff.sum() / diff.size < 0.4 + + if args.write_to is not None: + new_pipe.save_pretrained(args.write_to) + + +def make_transformer(old_transformer, model_256): + args = dict(old_transformer.config) + force_down_up_sample = args["force_down_up_sample"] + + signature = inspect.signature(UVit2DModel.__init__) + + args_ = { + "downsample": force_down_up_sample, + "upsample": force_down_up_sample, + "block_out_channels": args["block_out_channels"][0], + "sample_size": 16 if model_256 else 32, + } + + for s in list(signature.parameters.keys()): + if s in ["self", "downsample", "upsample", "sample_size", "block_out_channels"]: + continue + + args_[s] = args[s] + + new_transformer = UVit2DModel(**args_) + new_transformer.to(device) + + new_transformer.set_attn_processor(AttnProcessor()) + + state_dict = old_transformer.state_dict() + + state_dict["cond_embed.linear_1.weight"] = state_dict.pop("cond_embed.0.weight") + state_dict["cond_embed.linear_2.weight"] = state_dict.pop("cond_embed.2.weight") + + for i in range(22): + state_dict[f"transformer_layers.{i}.norm1.norm.weight"] = state_dict.pop( + f"transformer_layers.{i}.attn_layer_norm.weight" + ) + state_dict[f"transformer_layers.{i}.norm1.linear.weight"] = state_dict.pop( + f"transformer_layers.{i}.self_attn_adaLN_modulation.mapper.weight" + ) + + state_dict[f"transformer_layers.{i}.attn1.to_q.weight"] = state_dict.pop( + f"transformer_layers.{i}.attention.query.weight" + ) + state_dict[f"transformer_layers.{i}.attn1.to_k.weight"] = state_dict.pop( + f"transformer_layers.{i}.attention.key.weight" + ) + state_dict[f"transformer_layers.{i}.attn1.to_v.weight"] = state_dict.pop( + f"transformer_layers.{i}.attention.value.weight" + ) + state_dict[f"transformer_layers.{i}.attn1.to_out.0.weight"] = state_dict.pop( + f"transformer_layers.{i}.attention.out.weight" + ) + + state_dict[f"transformer_layers.{i}.norm2.norm.weight"] = state_dict.pop( + f"transformer_layers.{i}.crossattn_layer_norm.weight" + ) + state_dict[f"transformer_layers.{i}.norm2.linear.weight"] = state_dict.pop( + f"transformer_layers.{i}.cross_attn_adaLN_modulation.mapper.weight" + ) + + state_dict[f"transformer_layers.{i}.attn2.to_q.weight"] = state_dict.pop( + f"transformer_layers.{i}.crossattention.query.weight" + ) + state_dict[f"transformer_layers.{i}.attn2.to_k.weight"] = state_dict.pop( + f"transformer_layers.{i}.crossattention.key.weight" + ) + state_dict[f"transformer_layers.{i}.attn2.to_v.weight"] = state_dict.pop( + f"transformer_layers.{i}.crossattention.value.weight" + ) + state_dict[f"transformer_layers.{i}.attn2.to_out.0.weight"] = state_dict.pop( + f"transformer_layers.{i}.crossattention.out.weight" + ) + + state_dict[f"transformer_layers.{i}.norm3.norm.weight"] = state_dict.pop( + f"transformer_layers.{i}.ffn.pre_mlp_layer_norm.weight" + ) + state_dict[f"transformer_layers.{i}.norm3.linear.weight"] = state_dict.pop( + f"transformer_layers.{i}.ffn.adaLN_modulation.mapper.weight" + ) + + wi_0_weight = state_dict.pop(f"transformer_layers.{i}.ffn.wi_0.weight") + wi_1_weight = state_dict.pop(f"transformer_layers.{i}.ffn.wi_1.weight") + proj_weight = torch.concat([wi_1_weight, wi_0_weight], dim=0) + state_dict[f"transformer_layers.{i}.ff.net.0.proj.weight"] = proj_weight + + state_dict[f"transformer_layers.{i}.ff.net.2.weight"] = state_dict.pop(f"transformer_layers.{i}.ffn.wo.weight") + + if force_down_up_sample: + state_dict["down_block.downsample.norm.weight"] = state_dict.pop("down_blocks.0.downsample.0.norm.weight") + state_dict["down_block.downsample.conv.weight"] = state_dict.pop("down_blocks.0.downsample.1.weight") + + state_dict["up_block.upsample.norm.weight"] = state_dict.pop("up_blocks.0.upsample.0.norm.weight") + state_dict["up_block.upsample.conv.weight"] = state_dict.pop("up_blocks.0.upsample.1.weight") + + state_dict["mlm_layer.layer_norm.weight"] = state_dict.pop("mlm_layer.layer_norm.norm.weight") + + for i in range(3): + state_dict[f"down_block.res_blocks.{i}.norm.weight"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.norm.norm.weight" + ) + state_dict[f"down_block.res_blocks.{i}.channelwise_linear_1.weight"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.channelwise.0.weight" + ) + state_dict[f"down_block.res_blocks.{i}.channelwise_norm.gamma"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.channelwise.2.gamma" + ) + state_dict[f"down_block.res_blocks.{i}.channelwise_norm.beta"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.channelwise.2.beta" + ) + state_dict[f"down_block.res_blocks.{i}.channelwise_linear_2.weight"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.channelwise.4.weight" + ) + state_dict[f"down_block.res_blocks.{i}.cond_embeds_mapper.weight"] = state_dict.pop( + f"down_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight" + ) + + state_dict[f"down_block.attention_blocks.{i}.norm1.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.attn_layer_norm.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn1.to_q.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.attention.query.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn1.to_k.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.attention.key.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn1.to_v.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.attention.value.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn1.to_out.0.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.attention.out.weight" + ) + + state_dict[f"down_block.attention_blocks.{i}.norm2.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn2.to_q.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.crossattention.query.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn2.to_k.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.crossattention.key.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn2.to_v.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.crossattention.value.weight" + ) + state_dict[f"down_block.attention_blocks.{i}.attn2.to_out.0.weight"] = state_dict.pop( + f"down_blocks.0.attention_blocks.{i}.crossattention.out.weight" + ) + + state_dict[f"up_block.res_blocks.{i}.norm.weight"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.norm.norm.weight" + ) + state_dict[f"up_block.res_blocks.{i}.channelwise_linear_1.weight"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.channelwise.0.weight" + ) + state_dict[f"up_block.res_blocks.{i}.channelwise_norm.gamma"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.channelwise.2.gamma" + ) + state_dict[f"up_block.res_blocks.{i}.channelwise_norm.beta"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.channelwise.2.beta" + ) + state_dict[f"up_block.res_blocks.{i}.channelwise_linear_2.weight"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.channelwise.4.weight" + ) + state_dict[f"up_block.res_blocks.{i}.cond_embeds_mapper.weight"] = state_dict.pop( + f"up_blocks.0.res_blocks.{i}.adaLN_modulation.mapper.weight" + ) + + state_dict[f"up_block.attention_blocks.{i}.norm1.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.attn_layer_norm.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn1.to_q.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.attention.query.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn1.to_k.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.attention.key.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn1.to_v.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.attention.value.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn1.to_out.0.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.attention.out.weight" + ) + + state_dict[f"up_block.attention_blocks.{i}.norm2.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.crossattn_layer_norm.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn2.to_q.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.crossattention.query.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn2.to_k.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.crossattention.key.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn2.to_v.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.crossattention.value.weight" + ) + state_dict[f"up_block.attention_blocks.{i}.attn2.to_out.0.weight"] = state_dict.pop( + f"up_blocks.0.attention_blocks.{i}.crossattention.out.weight" + ) + + for key in list(state_dict.keys()): + if key.startswith("up_blocks.0"): + key_ = "up_block." + ".".join(key.split(".")[2:]) + state_dict[key_] = state_dict.pop(key) + + if key.startswith("down_blocks.0"): + key_ = "down_block." + ".".join(key.split(".")[2:]) + state_dict[key_] = state_dict.pop(key) + + new_transformer.load_state_dict(state_dict) + + input_ids = torch.randint(0, 10, (1, 32, 32), device=old_transformer.device) + encoder_hidden_states = torch.randn((1, 77, 768), device=old_transformer.device) + cond_embeds = torch.randn((1, 768), device=old_transformer.device) + micro_conds = torch.tensor([[512, 512, 0, 0, 6]], dtype=torch.float32, device=old_transformer.device) + + old_out = old_transformer(input_ids.reshape(1, -1), encoder_hidden_states, cond_embeds, micro_conds) + old_out = old_out.reshape(1, 32, 32, 8192).permute(0, 3, 1, 2) + + new_out = new_transformer(input_ids, encoder_hidden_states, cond_embeds, micro_conds) + + # NOTE: these differences are solely due to using the geglu block that has a single linear layer of + # double output dimension instead of two different linear layers + max_diff = (old_out - new_out).abs().max() + total_diff = (old_out - new_out).abs().sum() + print(f"Transformer max_diff: {max_diff} total_diff: {total_diff}") + assert max_diff < 0.01 + assert total_diff < 1500 + + return new_transformer + + +def make_vqvae(old_vae): + new_vae = VQModel( + act_fn="silu", + block_out_channels=[128, 256, 256, 512, 768], + down_block_types=[ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + ], + in_channels=3, + latent_channels=64, + layers_per_block=2, + norm_num_groups=32, + num_vq_embeddings=8192, + out_channels=3, + sample_size=32, + up_block_types=[ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + ], + mid_block_add_attention=False, + lookup_from_codebook=True, + ) + new_vae.to(device) + + # fmt: off + + new_state_dict = {} + + old_state_dict = old_vae.state_dict() + + new_state_dict["encoder.conv_in.weight"] = old_state_dict.pop("encoder.conv_in.weight") + new_state_dict["encoder.conv_in.bias"] = old_state_dict.pop("encoder.conv_in.bias") + + convert_vae_block_state_dict(old_state_dict, "encoder.down.0", new_state_dict, "encoder.down_blocks.0") + convert_vae_block_state_dict(old_state_dict, "encoder.down.1", new_state_dict, "encoder.down_blocks.1") + convert_vae_block_state_dict(old_state_dict, "encoder.down.2", new_state_dict, "encoder.down_blocks.2") + convert_vae_block_state_dict(old_state_dict, "encoder.down.3", new_state_dict, "encoder.down_blocks.3") + convert_vae_block_state_dict(old_state_dict, "encoder.down.4", new_state_dict, "encoder.down_blocks.4") + + new_state_dict["encoder.mid_block.resnets.0.norm1.weight"] = old_state_dict.pop("encoder.mid.block_1.norm1.weight") + new_state_dict["encoder.mid_block.resnets.0.norm1.bias"] = old_state_dict.pop("encoder.mid.block_1.norm1.bias") + new_state_dict["encoder.mid_block.resnets.0.conv1.weight"] = old_state_dict.pop("encoder.mid.block_1.conv1.weight") + new_state_dict["encoder.mid_block.resnets.0.conv1.bias"] = old_state_dict.pop("encoder.mid.block_1.conv1.bias") + new_state_dict["encoder.mid_block.resnets.0.norm2.weight"] = old_state_dict.pop("encoder.mid.block_1.norm2.weight") + new_state_dict["encoder.mid_block.resnets.0.norm2.bias"] = old_state_dict.pop("encoder.mid.block_1.norm2.bias") + new_state_dict["encoder.mid_block.resnets.0.conv2.weight"] = old_state_dict.pop("encoder.mid.block_1.conv2.weight") + new_state_dict["encoder.mid_block.resnets.0.conv2.bias"] = old_state_dict.pop("encoder.mid.block_1.conv2.bias") + new_state_dict["encoder.mid_block.resnets.1.norm1.weight"] = old_state_dict.pop("encoder.mid.block_2.norm1.weight") + new_state_dict["encoder.mid_block.resnets.1.norm1.bias"] = old_state_dict.pop("encoder.mid.block_2.norm1.bias") + new_state_dict["encoder.mid_block.resnets.1.conv1.weight"] = old_state_dict.pop("encoder.mid.block_2.conv1.weight") + new_state_dict["encoder.mid_block.resnets.1.conv1.bias"] = old_state_dict.pop("encoder.mid.block_2.conv1.bias") + new_state_dict["encoder.mid_block.resnets.1.norm2.weight"] = old_state_dict.pop("encoder.mid.block_2.norm2.weight") + new_state_dict["encoder.mid_block.resnets.1.norm2.bias"] = old_state_dict.pop("encoder.mid.block_2.norm2.bias") + new_state_dict["encoder.mid_block.resnets.1.conv2.weight"] = old_state_dict.pop("encoder.mid.block_2.conv2.weight") + new_state_dict["encoder.mid_block.resnets.1.conv2.bias"] = old_state_dict.pop("encoder.mid.block_2.conv2.bias") + new_state_dict["encoder.conv_norm_out.weight"] = old_state_dict.pop("encoder.norm_out.weight") + new_state_dict["encoder.conv_norm_out.bias"] = old_state_dict.pop("encoder.norm_out.bias") + new_state_dict["encoder.conv_out.weight"] = old_state_dict.pop("encoder.conv_out.weight") + new_state_dict["encoder.conv_out.bias"] = old_state_dict.pop("encoder.conv_out.bias") + new_state_dict["quant_conv.weight"] = old_state_dict.pop("quant_conv.weight") + new_state_dict["quant_conv.bias"] = old_state_dict.pop("quant_conv.bias") + new_state_dict["quantize.embedding.weight"] = old_state_dict.pop("quantize.embedding.weight") + new_state_dict["post_quant_conv.weight"] = old_state_dict.pop("post_quant_conv.weight") + new_state_dict["post_quant_conv.bias"] = old_state_dict.pop("post_quant_conv.bias") + new_state_dict["decoder.conv_in.weight"] = old_state_dict.pop("decoder.conv_in.weight") + new_state_dict["decoder.conv_in.bias"] = old_state_dict.pop("decoder.conv_in.bias") + new_state_dict["decoder.mid_block.resnets.0.norm1.weight"] = old_state_dict.pop("decoder.mid.block_1.norm1.weight") + new_state_dict["decoder.mid_block.resnets.0.norm1.bias"] = old_state_dict.pop("decoder.mid.block_1.norm1.bias") + new_state_dict["decoder.mid_block.resnets.0.conv1.weight"] = old_state_dict.pop("decoder.mid.block_1.conv1.weight") + new_state_dict["decoder.mid_block.resnets.0.conv1.bias"] = old_state_dict.pop("decoder.mid.block_1.conv1.bias") + new_state_dict["decoder.mid_block.resnets.0.norm2.weight"] = old_state_dict.pop("decoder.mid.block_1.norm2.weight") + new_state_dict["decoder.mid_block.resnets.0.norm2.bias"] = old_state_dict.pop("decoder.mid.block_1.norm2.bias") + new_state_dict["decoder.mid_block.resnets.0.conv2.weight"] = old_state_dict.pop("decoder.mid.block_1.conv2.weight") + new_state_dict["decoder.mid_block.resnets.0.conv2.bias"] = old_state_dict.pop("decoder.mid.block_1.conv2.bias") + new_state_dict["decoder.mid_block.resnets.1.norm1.weight"] = old_state_dict.pop("decoder.mid.block_2.norm1.weight") + new_state_dict["decoder.mid_block.resnets.1.norm1.bias"] = old_state_dict.pop("decoder.mid.block_2.norm1.bias") + new_state_dict["decoder.mid_block.resnets.1.conv1.weight"] = old_state_dict.pop("decoder.mid.block_2.conv1.weight") + new_state_dict["decoder.mid_block.resnets.1.conv1.bias"] = old_state_dict.pop("decoder.mid.block_2.conv1.bias") + new_state_dict["decoder.mid_block.resnets.1.norm2.weight"] = old_state_dict.pop("decoder.mid.block_2.norm2.weight") + new_state_dict["decoder.mid_block.resnets.1.norm2.bias"] = old_state_dict.pop("decoder.mid.block_2.norm2.bias") + new_state_dict["decoder.mid_block.resnets.1.conv2.weight"] = old_state_dict.pop("decoder.mid.block_2.conv2.weight") + new_state_dict["decoder.mid_block.resnets.1.conv2.bias"] = old_state_dict.pop("decoder.mid.block_2.conv2.bias") + + convert_vae_block_state_dict(old_state_dict, "decoder.up.0", new_state_dict, "decoder.up_blocks.4") + convert_vae_block_state_dict(old_state_dict, "decoder.up.1", new_state_dict, "decoder.up_blocks.3") + convert_vae_block_state_dict(old_state_dict, "decoder.up.2", new_state_dict, "decoder.up_blocks.2") + convert_vae_block_state_dict(old_state_dict, "decoder.up.3", new_state_dict, "decoder.up_blocks.1") + convert_vae_block_state_dict(old_state_dict, "decoder.up.4", new_state_dict, "decoder.up_blocks.0") + + new_state_dict["decoder.conv_norm_out.weight"] = old_state_dict.pop("decoder.norm_out.weight") + new_state_dict["decoder.conv_norm_out.bias"] = old_state_dict.pop("decoder.norm_out.bias") + new_state_dict["decoder.conv_out.weight"] = old_state_dict.pop("decoder.conv_out.weight") + new_state_dict["decoder.conv_out.bias"] = old_state_dict.pop("decoder.conv_out.bias") + + # fmt: on + + assert len(old_state_dict.keys()) == 0 + + new_vae.load_state_dict(new_state_dict) + + input = torch.randn((1, 3, 512, 512), device=device) + input = input.clamp(-1, 1) + + old_encoder_output = old_vae.quant_conv(old_vae.encoder(input)) + new_encoder_output = new_vae.quant_conv(new_vae.encoder(input)) + assert (old_encoder_output == new_encoder_output).all() + + old_decoder_output = old_vae.decoder(old_vae.post_quant_conv(old_encoder_output)) + new_decoder_output = new_vae.decoder(new_vae.post_quant_conv(new_encoder_output)) + + # assert (old_decoder_output == new_decoder_output).all() + print("kipping vae decoder equivalence check") + print(f"vae decoder diff {(old_decoder_output - new_decoder_output).float().abs().sum()}") + + old_output = old_vae(input)[0] + new_output = new_vae(input)[0] + + # assert (old_output == new_output).all() + print("skipping full vae equivalence check") + print(f"vae full diff {(old_output - new_output).float().abs().sum()}") + + return new_vae + + +def convert_vae_block_state_dict(old_state_dict, prefix_from, new_state_dict, prefix_to): + # fmt: off + + new_state_dict[f"{prefix_to}.resnets.0.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.norm1.weight") + new_state_dict[f"{prefix_to}.resnets.0.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.norm1.bias") + new_state_dict[f"{prefix_to}.resnets.0.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.conv1.weight") + new_state_dict[f"{prefix_to}.resnets.0.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.conv1.bias") + new_state_dict[f"{prefix_to}.resnets.0.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.norm2.weight") + new_state_dict[f"{prefix_to}.resnets.0.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.norm2.bias") + new_state_dict[f"{prefix_to}.resnets.0.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.conv2.weight") + new_state_dict[f"{prefix_to}.resnets.0.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.conv2.bias") + + if f"{prefix_from}.block.0.nin_shortcut.weight" in old_state_dict: + new_state_dict[f"{prefix_to}.resnets.0.conv_shortcut.weight"] = old_state_dict.pop(f"{prefix_from}.block.0.nin_shortcut.weight") + new_state_dict[f"{prefix_to}.resnets.0.conv_shortcut.bias"] = old_state_dict.pop(f"{prefix_from}.block.0.nin_shortcut.bias") + + new_state_dict[f"{prefix_to}.resnets.1.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.norm1.weight") + new_state_dict[f"{prefix_to}.resnets.1.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.norm1.bias") + new_state_dict[f"{prefix_to}.resnets.1.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.conv1.weight") + new_state_dict[f"{prefix_to}.resnets.1.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.conv1.bias") + new_state_dict[f"{prefix_to}.resnets.1.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.norm2.weight") + new_state_dict[f"{prefix_to}.resnets.1.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.norm2.bias") + new_state_dict[f"{prefix_to}.resnets.1.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.1.conv2.weight") + new_state_dict[f"{prefix_to}.resnets.1.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.1.conv2.bias") + + if f"{prefix_from}.downsample.conv.weight" in old_state_dict: + new_state_dict[f"{prefix_to}.downsamplers.0.conv.weight"] = old_state_dict.pop(f"{prefix_from}.downsample.conv.weight") + new_state_dict[f"{prefix_to}.downsamplers.0.conv.bias"] = old_state_dict.pop(f"{prefix_from}.downsample.conv.bias") + + if f"{prefix_from}.upsample.conv.weight" in old_state_dict: + new_state_dict[f"{prefix_to}.upsamplers.0.conv.weight"] = old_state_dict.pop(f"{prefix_from}.upsample.conv.weight") + new_state_dict[f"{prefix_to}.upsamplers.0.conv.bias"] = old_state_dict.pop(f"{prefix_from}.upsample.conv.bias") + + if f"{prefix_from}.block.2.norm1.weight" in old_state_dict: + new_state_dict[f"{prefix_to}.resnets.2.norm1.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.norm1.weight") + new_state_dict[f"{prefix_to}.resnets.2.norm1.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.norm1.bias") + new_state_dict[f"{prefix_to}.resnets.2.conv1.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.conv1.weight") + new_state_dict[f"{prefix_to}.resnets.2.conv1.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.conv1.bias") + new_state_dict[f"{prefix_to}.resnets.2.norm2.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.norm2.weight") + new_state_dict[f"{prefix_to}.resnets.2.norm2.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.norm2.bias") + new_state_dict[f"{prefix_to}.resnets.2.conv2.weight"] = old_state_dict.pop(f"{prefix_from}.block.2.conv2.weight") + new_state_dict[f"{prefix_to}.resnets.2.conv2.bias"] = old_state_dict.pop(f"{prefix_from}.block.2.conv2.bias") + + # fmt: on + + +if __name__ == "__main__": + main() diff --git a/scripts/convert_animatediff_motion_lora_to_diffusers.py b/scripts/convert_animatediff_motion_lora_to_diffusers.py new file mode 100644 index 000000000000..21567ffa9e7a --- /dev/null +++ b/scripts/convert_animatediff_motion_lora_to_diffusers.py @@ -0,0 +1,69 @@ +import argparse +import os + +import torch +from huggingface_hub import create_repo, upload_folder +from safetensors.torch import load_file, save_file + + +def convert_motion_module(original_state_dict): + converted_state_dict = {} + for k, v in original_state_dict.items(): + if "pos_encoder" in k: + continue + + else: + converted_state_dict[ + k.replace(".norms.0", ".norm1") + .replace(".norms.1", ".norm2") + .replace(".ff_norm", ".norm3") + .replace(".attention_blocks.0", ".attn1") + .replace(".attention_blocks.1", ".attn2") + .replace(".temporal_transformer", "") + ] = v + + return converted_state_dict + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--ckpt_path", type=str, required=True, help="Path to checkpoint") + parser.add_argument("--output_path", type=str, required=True, help="Path to output directory") + parser.add_argument( + "--push_to_hub", + action="store_true", + default=False, + help="Whether to push the converted model to the HF or not", + ) + + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + if args.ckpt_path.endswith(".safetensors"): + state_dict = load_file(args.ckpt_path) + else: + state_dict = torch.load(args.ckpt_path, map_location="cpu") + + if "state_dict" in state_dict.keys(): + state_dict = state_dict["state_dict"] + + conv_state_dict = convert_motion_module(state_dict) + + # convert to new format + output_dict = {} + for module_name, params in conv_state_dict.items(): + if type(params) is not torch.Tensor: + continue + output_dict.update({f"unet.{module_name}": params}) + + os.makedirs(args.output_path, exist_ok=True) + + filepath = os.path.join(args.output_path, "diffusion_pytorch_model.safetensors") + save_file(output_dict, filepath) + + if args.push_to_hub: + repo_id = create_repo(args.output_path, exist_ok=True).repo_id + upload_folder(repo_id=repo_id, folder_path=args.output_path, repo_type="model") diff --git a/scripts/convert_animatediff_motion_module_to_diffusers.py b/scripts/convert_animatediff_motion_module_to_diffusers.py new file mode 100644 index 000000000000..e188a6a533e8 --- /dev/null +++ b/scripts/convert_animatediff_motion_module_to_diffusers.py @@ -0,0 +1,62 @@ +import argparse + +import torch +from safetensors.torch import load_file + +from diffusers import MotionAdapter + + +def convert_motion_module(original_state_dict): + converted_state_dict = {} + for k, v in original_state_dict.items(): + if "pos_encoder" in k: + continue + + else: + converted_state_dict[ + k.replace(".norms.0", ".norm1") + .replace(".norms.1", ".norm2") + .replace(".ff_norm", ".norm3") + .replace(".attention_blocks.0", ".attn1") + .replace(".attention_blocks.1", ".attn2") + .replace(".temporal_transformer", "") + ] = v + + return converted_state_dict + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--ckpt_path", type=str, required=True) + parser.add_argument("--output_path", type=str, required=True) + parser.add_argument("--use_motion_mid_block", action="store_true") + parser.add_argument("--motion_max_seq_length", type=int, default=32) + parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int) + parser.add_argument("--save_fp16", action="store_true") + + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + if args.ckpt_path.endswith(".safetensors"): + state_dict = load_file(args.ckpt_path) + else: + state_dict = torch.load(args.ckpt_path, map_location="cpu") + + if "state_dict" in state_dict.keys(): + state_dict = state_dict["state_dict"] + + conv_state_dict = convert_motion_module(state_dict) + adapter = MotionAdapter( + block_out_channels=args.block_out_channels, + use_motion_mid_block=args.use_motion_mid_block, + motion_max_seq_length=args.motion_max_seq_length, + ) + # skip loading position embeddings + adapter.load_state_dict(conv_state_dict, strict=False) + adapter.save_pretrained(args.output_path) + + if args.save_fp16: + adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16") diff --git a/scripts/convert_animatediff_sparsectrl_to_diffusers.py b/scripts/convert_animatediff_sparsectrl_to_diffusers.py new file mode 100644 index 000000000000..b96b42d60f44 --- /dev/null +++ b/scripts/convert_animatediff_sparsectrl_to_diffusers.py @@ -0,0 +1,83 @@ +import argparse +from typing import Dict + +import torch +import torch.nn as nn + +from diffusers import SparseControlNetModel + + +KEYS_RENAME_MAPPING = { + ".attention_blocks.0": ".attn1", + ".attention_blocks.1": ".attn2", + ".attn1.pos_encoder": ".pos_embed", + ".ff_norm": ".norm3", + ".norms.0": ".norm1", + ".norms.1": ".norm2", + ".temporal_transformer": "", +} + + +def convert(original_state_dict: Dict[str, nn.Module]) -> dict[str, nn.Module]: + converted_state_dict = {} + + for key in list(original_state_dict.keys()): + renamed_key = key + for new_name, old_name in KEYS_RENAME_MAPPING.items(): + renamed_key = renamed_key.replace(new_name, old_name) + converted_state_dict[renamed_key] = original_state_dict.pop(key) + + return converted_state_dict + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--ckpt_path", type=str, required=True, help="Path to checkpoint") + parser.add_argument("--output_path", type=str, required=True, help="Path to output directory") + parser.add_argument( + "--max_motion_seq_length", + type=int, + default=32, + help="Max motion sequence length supported by the motion adapter", + ) + parser.add_argument( + "--conditioning_channels", type=int, default=4, help="Number of channels in conditioning input to controlnet" + ) + parser.add_argument( + "--use_simplified_condition_embedding", + action="store_true", + default=False, + help="Whether or not to use simplified condition embedding. When `conditioning_channels==4` i.e. latent inputs, set this to `True`. When `conditioning_channels==3` i.e. image inputs, set this to `False`", + ) + parser.add_argument( + "--save_fp16", + action="store_true", + default=False, + help="Whether or not to save model in fp16 precision along with fp32", + ) + parser.add_argument( + "--push_to_hub", action="store_true", default=False, help="Whether or not to push saved model to the HF hub" + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + state_dict = torch.load(args.ckpt_path, map_location="cpu") + if "state_dict" in state_dict.keys(): + state_dict: dict = state_dict["state_dict"] + + controlnet = SparseControlNetModel( + conditioning_channels=args.conditioning_channels, + motion_max_seq_length=args.max_motion_seq_length, + use_simplified_condition_embedding=args.use_simplified_condition_embedding, + ) + + state_dict = convert(state_dict) + controlnet.load_state_dict(state_dict, strict=True) + + controlnet.save_pretrained(args.output_path, push_to_hub=args.push_to_hub) + if args.save_fp16: + controlnet = controlnet.to(dtype=torch.float16) + controlnet.save_pretrained(args.output_path, variant="fp16", push_to_hub=args.push_to_hub) diff --git a/scripts/convert_asymmetric_vqgan_to_diffusers.py b/scripts/convert_asymmetric_vqgan_to_diffusers.py new file mode 100644 index 000000000000..a9f83a46c2d0 --- /dev/null +++ b/scripts/convert_asymmetric_vqgan_to_diffusers.py @@ -0,0 +1,184 @@ +import argparse +import time +from pathlib import Path +from typing import Any, Dict, Literal + +import torch + +from diffusers import AsymmetricAutoencoderKL + + +ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG = { + "in_channels": 3, + "out_channels": 3, + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + ], + "down_block_out_channels": [128, 256, 512, 512], + "layers_per_down_block": 2, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + ], + "up_block_out_channels": [192, 384, 768, 768], + "layers_per_up_block": 3, + "act_fn": "silu", + "latent_channels": 4, + "norm_num_groups": 32, + "sample_size": 256, + "scaling_factor": 0.18215, +} + +ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG = { + "in_channels": 3, + "out_channels": 3, + "down_block_types": [ + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + "DownEncoderBlock2D", + ], + "down_block_out_channels": [128, 256, 512, 512], + "layers_per_down_block": 2, + "up_block_types": [ + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + "UpDecoderBlock2D", + ], + "up_block_out_channels": [256, 512, 1024, 1024], + "layers_per_up_block": 5, + "act_fn": "silu", + "latent_channels": 4, + "norm_num_groups": 32, + "sample_size": 256, + "scaling_factor": 0.18215, +} + + +def convert_asymmetric_autoencoder_kl_state_dict(original_state_dict: Dict[str, Any]) -> dict[str, Any]: + converted_state_dict = {} + for k, v in original_state_dict.items(): + if k.startswith("encoder."): + converted_state_dict[ + k.replace("encoder.down.", "encoder.down_blocks.") + .replace("encoder.mid.", "encoder.mid_block.") + .replace("encoder.norm_out.", "encoder.conv_norm_out.") + .replace(".downsample.", ".downsamplers.0.") + .replace(".nin_shortcut.", ".conv_shortcut.") + .replace(".block.", ".resnets.") + .replace(".block_1.", ".resnets.0.") + .replace(".block_2.", ".resnets.1.") + .replace(".attn_1.k.", ".attentions.0.to_k.") + .replace(".attn_1.q.", ".attentions.0.to_q.") + .replace(".attn_1.v.", ".attentions.0.to_v.") + .replace(".attn_1.proj_out.", ".attentions.0.to_out.0.") + .replace(".attn_1.norm.", ".attentions.0.group_norm.") + ] = v + elif k.startswith("decoder.") and "up_layers" not in k: + converted_state_dict[ + k.replace("decoder.encoder.", "decoder.condition_encoder.") + .replace(".norm_out.", ".conv_norm_out.") + .replace(".up.0.", ".up_blocks.3.") + .replace(".up.1.", ".up_blocks.2.") + .replace(".up.2.", ".up_blocks.1.") + .replace(".up.3.", ".up_blocks.0.") + .replace(".block.", ".resnets.") + .replace("mid", "mid_block") + .replace(".0.upsample.", ".0.upsamplers.0.") + .replace(".1.upsample.", ".1.upsamplers.0.") + .replace(".2.upsample.", ".2.upsamplers.0.") + .replace(".nin_shortcut.", ".conv_shortcut.") + .replace(".block_1.", ".resnets.0.") + .replace(".block_2.", ".resnets.1.") + .replace(".attn_1.k.", ".attentions.0.to_k.") + .replace(".attn_1.q.", ".attentions.0.to_q.") + .replace(".attn_1.v.", ".attentions.0.to_v.") + .replace(".attn_1.proj_out.", ".attentions.0.to_out.0.") + .replace(".attn_1.norm.", ".attentions.0.group_norm.") + ] = v + elif k.startswith("quant_conv."): + converted_state_dict[k] = v + elif k.startswith("post_quant_conv."): + converted_state_dict[k] = v + else: + print(f" skipping key `{k}`") + # fix weights shape + for k, v in converted_state_dict.items(): + if ( + (k.startswith("encoder.mid_block.attentions.0") or k.startswith("decoder.mid_block.attentions.0")) + and k.endswith("weight") + and ("to_q" in k or "to_k" in k or "to_v" in k or "to_out" in k) + ): + converted_state_dict[k] = converted_state_dict[k][:, :, 0, 0] + + return converted_state_dict + + +def get_asymmetric_autoencoder_kl_from_original_checkpoint( + scale: Literal["1.5", "2"], original_checkpoint_path: str, map_location: torch.device +) -> AsymmetricAutoencoderKL: + print("Loading original state_dict") + original_state_dict = torch.load(original_checkpoint_path, map_location=map_location) + original_state_dict = original_state_dict["state_dict"] + print("Converting state_dict") + converted_state_dict = convert_asymmetric_autoencoder_kl_state_dict(original_state_dict) + kwargs = ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG if scale == "1.5" else ASYMMETRIC_AUTOENCODER_KL_x_2_CONFIG + print("Initializing AsymmetricAutoencoderKL model") + asymmetric_autoencoder_kl = AsymmetricAutoencoderKL(**kwargs) + print("Loading weight from converted state_dict") + asymmetric_autoencoder_kl.load_state_dict(converted_state_dict) + asymmetric_autoencoder_kl.eval() + print("AsymmetricAutoencoderKL successfully initialized") + return asymmetric_autoencoder_kl + + +if __name__ == "__main__": + start = time.time() + parser = argparse.ArgumentParser() + parser.add_argument( + "--scale", + default=None, + type=str, + required=True, + help="Asymmetric VQGAN scale: `1.5` or `2`", + ) + parser.add_argument( + "--original_checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the original Asymmetric VQGAN checkpoint", + ) + parser.add_argument( + "--output_path", + default=None, + type=str, + required=True, + help="Path to save pretrained AsymmetricAutoencoderKL model", + ) + parser.add_argument( + "--map_location", + default="cpu", + type=str, + required=False, + help="The device passed to `map_location` when loading the checkpoint", + ) + args = parser.parse_args() + + assert args.scale in ["1.5", "2"], f"{args.scale} should be `1.5` of `2`" + assert Path(args.original_checkpoint_path).is_file() + + asymmetric_autoencoder_kl = get_asymmetric_autoencoder_kl_from_original_checkpoint( + scale=args.scale, + original_checkpoint_path=args.original_checkpoint_path, + map_location=torch.device(args.map_location), + ) + print("Saving pretrained AsymmetricAutoencoderKL") + asymmetric_autoencoder_kl.save_pretrained(args.output_path) + print(f"Done in {time.time() - start:.2f} seconds") diff --git a/scripts/convert_aura_flow_to_diffusers.py b/scripts/convert_aura_flow_to_diffusers.py new file mode 100644 index 000000000000..74c34f4851ff --- /dev/null +++ b/scripts/convert_aura_flow_to_diffusers.py @@ -0,0 +1,131 @@ +import argparse + +import torch +from huggingface_hub import hf_hub_download + +from diffusers.models.transformers.auraflow_transformer_2d import AuraFlowTransformer2DModel + + +def load_original_state_dict(args): + model_pt = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename="aura_diffusion_pytorch_model.bin") + state_dict = torch.load(model_pt, map_location="cpu") + return state_dict + + +def calculate_layers(state_dict_keys, key_prefix): + dit_layers = set() + for k in state_dict_keys: + if key_prefix in k: + dit_layers.add(int(k.split(".")[2])) + print(f"{key_prefix}: {len(dit_layers)}") + return len(dit_layers) + + +# similar to SD3 but only for the last norm layer +def swap_scale_shift(weight, dim): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_transformer(state_dict): + converted_state_dict = {} + state_dict_keys = list(state_dict.keys()) + + converted_state_dict["register_tokens"] = state_dict.pop("model.register_tokens") + converted_state_dict["pos_embed.pos_embed"] = state_dict.pop("model.positional_encoding") + converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("model.init_x_linear.weight") + converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("model.init_x_linear.bias") + + converted_state_dict["time_step_proj.linear_1.weight"] = state_dict.pop("model.t_embedder.mlp.0.weight") + converted_state_dict["time_step_proj.linear_1.bias"] = state_dict.pop("model.t_embedder.mlp.0.bias") + converted_state_dict["time_step_proj.linear_2.weight"] = state_dict.pop("model.t_embedder.mlp.2.weight") + converted_state_dict["time_step_proj.linear_2.bias"] = state_dict.pop("model.t_embedder.mlp.2.bias") + + converted_state_dict["context_embedder.weight"] = state_dict.pop("model.cond_seq_linear.weight") + + mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers") + single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers") + + # MMDiT blocks 🎸. + for i in range(mmdit_layers): + # feed-forward + path_mapping = {"mlpX": "ff", "mlpC": "ff_context"} + weight_mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"} + for orig_k, diffuser_k in path_mapping.items(): + for k, v in weight_mapping.items(): + converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.{v}.weight"] = state_dict.pop( + f"model.double_layers.{i}.{orig_k}.{k}.weight" + ) + + # norms + path_mapping = {"modX": "norm1", "modC": "norm1_context"} + for orig_k, diffuser_k in path_mapping.items(): + converted_state_dict[f"joint_transformer_blocks.{i}.{diffuser_k}.linear.weight"] = state_dict.pop( + f"model.double_layers.{i}.{orig_k}.1.weight" + ) + + # attns + x_attn_mapping = {"w2q": "to_q", "w2k": "to_k", "w2v": "to_v", "w2o": "to_out.0"} + context_attn_mapping = {"w1q": "add_q_proj", "w1k": "add_k_proj", "w1v": "add_v_proj", "w1o": "to_add_out"} + for attn_mapping in [x_attn_mapping, context_attn_mapping]: + for k, v in attn_mapping.items(): + converted_state_dict[f"joint_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop( + f"model.double_layers.{i}.attn.{k}.weight" + ) + + # Single-DiT blocks. + for i in range(single_dit_layers): + # feed-forward + mapping = {"c_fc1": "linear_1", "c_fc2": "linear_2", "c_proj": "out_projection"} + for k, v in mapping.items(): + converted_state_dict[f"single_transformer_blocks.{i}.ff.{v}.weight"] = state_dict.pop( + f"model.single_layers.{i}.mlp.{k}.weight" + ) + + # norms + converted_state_dict[f"single_transformer_blocks.{i}.norm1.linear.weight"] = state_dict.pop( + f"model.single_layers.{i}.modCX.1.weight" + ) + + # attns + x_attn_mapping = {"w1q": "to_q", "w1k": "to_k", "w1v": "to_v", "w1o": "to_out.0"} + for k, v in x_attn_mapping.items(): + converted_state_dict[f"single_transformer_blocks.{i}.attn.{v}.weight"] = state_dict.pop( + f"model.single_layers.{i}.attn.{k}.weight" + ) + + # Final blocks. + converted_state_dict["proj_out.weight"] = state_dict.pop("model.final_linear.weight") + converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict.pop("model.modF.1.weight"), dim=None) + + return converted_state_dict + + +@torch.no_grad() +def populate_state_dict(args): + original_state_dict = load_original_state_dict(args) + state_dict_keys = list(original_state_dict.keys()) + mmdit_layers = calculate_layers(state_dict_keys, key_prefix="double_layers") + single_dit_layers = calculate_layers(state_dict_keys, key_prefix="single_layers") + + converted_state_dict = convert_transformer(original_state_dict) + model_diffusers = AuraFlowTransformer2DModel( + num_mmdit_layers=mmdit_layers, num_single_dit_layers=single_dit_layers + ) + model_diffusers.load_state_dict(converted_state_dict, strict=True) + + return model_diffusers + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--original_state_dict_repo_id", default="AuraDiffusion/auradiffusion-v0.1a0", type=str) + parser.add_argument("--dump_path", default="aura-flow", type=str) + parser.add_argument("--hub_id", default=None, type=str) + args = parser.parse_args() + + model_diffusers = populate_state_dict(args) + model_diffusers.save_pretrained(args.dump_path) + if args.hub_id is not None: + model_diffusers.push_to_hub(args.hub_id) diff --git a/scripts/convert_blipdiffusion_to_diffusers.py b/scripts/convert_blipdiffusion_to_diffusers.py new file mode 100644 index 000000000000..2c286ea0fdc7 --- /dev/null +++ b/scripts/convert_blipdiffusion_to_diffusers.py @@ -0,0 +1,344 @@ +""" +This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main. +""" + +import argparse +import os +import tempfile + +import torch +from lavis.models import load_model_and_preprocess +from transformers import CLIPTokenizer +from transformers.models.blip_2.configuration_blip_2 import Blip2Config + +from diffusers import ( + AutoencoderKL, + PNDMScheduler, + UNet2DConditionModel, +) +from diffusers.pipelines import BlipDiffusionPipeline +from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor +from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel +from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel + + +BLIP2_CONFIG = { + "vision_config": { + "hidden_size": 1024, + "num_hidden_layers": 23, + "num_attention_heads": 16, + "image_size": 224, + "patch_size": 14, + "intermediate_size": 4096, + "hidden_act": "quick_gelu", + }, + "qformer_config": { + "cross_attention_frequency": 1, + "encoder_hidden_size": 1024, + "vocab_size": 30523, + }, + "num_query_tokens": 16, +} +blip2config = Blip2Config(**BLIP2_CONFIG) + + +def qformer_model_from_original_config(): + qformer = Blip2QFormerModel(blip2config) + return qformer + + +def embeddings_from_original_checkpoint(model, diffuser_embeddings_prefix, original_embeddings_prefix): + embeddings = {} + embeddings.update( + { + f"{diffuser_embeddings_prefix}.word_embeddings.weight": model[ + f"{original_embeddings_prefix}.word_embeddings.weight" + ] + } + ) + embeddings.update( + { + f"{diffuser_embeddings_prefix}.position_embeddings.weight": model[ + f"{original_embeddings_prefix}.position_embeddings.weight" + ] + } + ) + embeddings.update( + {f"{diffuser_embeddings_prefix}.LayerNorm.weight": model[f"{original_embeddings_prefix}.LayerNorm.weight"]} + ) + embeddings.update( + {f"{diffuser_embeddings_prefix}.LayerNorm.bias": model[f"{original_embeddings_prefix}.LayerNorm.bias"]} + ) + return embeddings + + +def proj_layer_from_original_checkpoint(model, diffuser_proj_prefix, original_proj_prefix): + proj_layer = {} + proj_layer.update({f"{diffuser_proj_prefix}.dense1.weight": model[f"{original_proj_prefix}.dense1.weight"]}) + proj_layer.update({f"{diffuser_proj_prefix}.dense1.bias": model[f"{original_proj_prefix}.dense1.bias"]}) + proj_layer.update({f"{diffuser_proj_prefix}.dense2.weight": model[f"{original_proj_prefix}.dense2.weight"]}) + proj_layer.update({f"{diffuser_proj_prefix}.dense2.bias": model[f"{original_proj_prefix}.dense2.bias"]}) + proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.weight": model[f"{original_proj_prefix}.LayerNorm.weight"]}) + proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.bias": model[f"{original_proj_prefix}.LayerNorm.bias"]}) + return proj_layer + + +def attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix): + attention = {} + attention.update( + { + f"{diffuser_attention_prefix}.attention.query.weight": model[ + f"{original_attention_prefix}.self.query.weight" + ] + } + ) + attention.update( + {f"{diffuser_attention_prefix}.attention.query.bias": model[f"{original_attention_prefix}.self.query.bias"]} + ) + attention.update( + {f"{diffuser_attention_prefix}.attention.key.weight": model[f"{original_attention_prefix}.self.key.weight"]} + ) + attention.update( + {f"{diffuser_attention_prefix}.attention.key.bias": model[f"{original_attention_prefix}.self.key.bias"]} + ) + attention.update( + { + f"{diffuser_attention_prefix}.attention.value.weight": model[ + f"{original_attention_prefix}.self.value.weight" + ] + } + ) + attention.update( + {f"{diffuser_attention_prefix}.attention.value.bias": model[f"{original_attention_prefix}.self.value.bias"]} + ) + attention.update( + {f"{diffuser_attention_prefix}.output.dense.weight": model[f"{original_attention_prefix}.output.dense.weight"]} + ) + attention.update( + {f"{diffuser_attention_prefix}.output.dense.bias": model[f"{original_attention_prefix}.output.dense.bias"]} + ) + attention.update( + { + f"{diffuser_attention_prefix}.output.LayerNorm.weight": model[ + f"{original_attention_prefix}.output.LayerNorm.weight" + ] + } + ) + attention.update( + { + f"{diffuser_attention_prefix}.output.LayerNorm.bias": model[ + f"{original_attention_prefix}.output.LayerNorm.bias" + ] + } + ) + return attention + + +def output_layers_from_original_checkpoint(model, diffuser_output_prefix, original_output_prefix): + output_layers = {} + output_layers.update({f"{diffuser_output_prefix}.dense.weight": model[f"{original_output_prefix}.dense.weight"]}) + output_layers.update({f"{diffuser_output_prefix}.dense.bias": model[f"{original_output_prefix}.dense.bias"]}) + output_layers.update( + {f"{diffuser_output_prefix}.LayerNorm.weight": model[f"{original_output_prefix}.LayerNorm.weight"]} + ) + output_layers.update( + {f"{diffuser_output_prefix}.LayerNorm.bias": model[f"{original_output_prefix}.LayerNorm.bias"]} + ) + return output_layers + + +def encoder_from_original_checkpoint(model, diffuser_encoder_prefix, original_encoder_prefix): + encoder = {} + for i in range(blip2config.qformer_config.num_hidden_layers): + encoder.update( + attention_from_original_checkpoint( + model, f"{diffuser_encoder_prefix}.{i}.attention", f"{original_encoder_prefix}.{i}.attention" + ) + ) + encoder.update( + attention_from_original_checkpoint( + model, f"{diffuser_encoder_prefix}.{i}.crossattention", f"{original_encoder_prefix}.{i}.crossattention" + ) + ) + + encoder.update( + { + f"{diffuser_encoder_prefix}.{i}.intermediate.dense.weight": model[ + f"{original_encoder_prefix}.{i}.intermediate.dense.weight" + ] + } + ) + encoder.update( + { + f"{diffuser_encoder_prefix}.{i}.intermediate.dense.bias": model[ + f"{original_encoder_prefix}.{i}.intermediate.dense.bias" + ] + } + ) + encoder.update( + { + f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.weight": model[ + f"{original_encoder_prefix}.{i}.intermediate_query.dense.weight" + ] + } + ) + encoder.update( + { + f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.bias": model[ + f"{original_encoder_prefix}.{i}.intermediate_query.dense.bias" + ] + } + ) + + encoder.update( + output_layers_from_original_checkpoint( + model, f"{diffuser_encoder_prefix}.{i}.output", f"{original_encoder_prefix}.{i}.output" + ) + ) + encoder.update( + output_layers_from_original_checkpoint( + model, f"{diffuser_encoder_prefix}.{i}.output_query", f"{original_encoder_prefix}.{i}.output_query" + ) + ) + return encoder + + +def visual_encoder_layer_from_original_checkpoint(model, diffuser_prefix, original_prefix): + visual_encoder_layer = {} + + visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.weight": model[f"{original_prefix}.ln_1.weight"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.bias": model[f"{original_prefix}.ln_1.bias"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.weight": model[f"{original_prefix}.ln_2.weight"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.bias": model[f"{original_prefix}.ln_2.bias"]}) + visual_encoder_layer.update( + {f"{diffuser_prefix}.self_attn.qkv.weight": model[f"{original_prefix}.attn.in_proj_weight"]} + ) + visual_encoder_layer.update( + {f"{diffuser_prefix}.self_attn.qkv.bias": model[f"{original_prefix}.attn.in_proj_bias"]} + ) + visual_encoder_layer.update( + {f"{diffuser_prefix}.self_attn.projection.weight": model[f"{original_prefix}.attn.out_proj.weight"]} + ) + visual_encoder_layer.update( + {f"{diffuser_prefix}.self_attn.projection.bias": model[f"{original_prefix}.attn.out_proj.bias"]} + ) + visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.weight": model[f"{original_prefix}.mlp.c_fc.weight"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.bias": model[f"{original_prefix}.mlp.c_fc.bias"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.weight": model[f"{original_prefix}.mlp.c_proj.weight"]}) + visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.bias": model[f"{original_prefix}.mlp.c_proj.bias"]}) + + return visual_encoder_layer + + +def visual_encoder_from_original_checkpoint(model, diffuser_prefix, original_prefix): + visual_encoder = {} + + visual_encoder.update( + { + f"{diffuser_prefix}.embeddings.class_embedding": model[f"{original_prefix}.class_embedding"] + .unsqueeze(0) + .unsqueeze(0) + } + ) + visual_encoder.update( + { + f"{diffuser_prefix}.embeddings.position_embedding": model[ + f"{original_prefix}.positional_embedding" + ].unsqueeze(0) + } + ) + visual_encoder.update( + {f"{diffuser_prefix}.embeddings.patch_embedding.weight": model[f"{original_prefix}.conv1.weight"]} + ) + visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.weight": model[f"{original_prefix}.ln_pre.weight"]}) + visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.bias": model[f"{original_prefix}.ln_pre.bias"]}) + + for i in range(blip2config.vision_config.num_hidden_layers): + visual_encoder.update( + visual_encoder_layer_from_original_checkpoint( + model, f"{diffuser_prefix}.encoder.layers.{i}", f"{original_prefix}.transformer.resblocks.{i}" + ) + ) + + visual_encoder.update({f"{diffuser_prefix}.post_layernorm.weight": model["blip.ln_vision.weight"]}) + visual_encoder.update({f"{diffuser_prefix}.post_layernorm.bias": model["blip.ln_vision.bias"]}) + + return visual_encoder + + +def qformer_original_checkpoint_to_diffusers_checkpoint(model): + qformer_checkpoint = {} + qformer_checkpoint.update(embeddings_from_original_checkpoint(model, "embeddings", "blip.Qformer.bert.embeddings")) + qformer_checkpoint.update({"query_tokens": model["blip.query_tokens"]}) + qformer_checkpoint.update(proj_layer_from_original_checkpoint(model, "proj_layer", "proj_layer")) + qformer_checkpoint.update( + encoder_from_original_checkpoint(model, "encoder.layer", "blip.Qformer.bert.encoder.layer") + ) + qformer_checkpoint.update(visual_encoder_from_original_checkpoint(model, "visual_encoder", "blip.visual_encoder")) + return qformer_checkpoint + + +def get_qformer(model): + print("loading qformer") + + qformer = qformer_model_from_original_config() + qformer_diffusers_checkpoint = qformer_original_checkpoint_to_diffusers_checkpoint(model) + + load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer) + + print("done loading qformer") + return qformer + + +def load_checkpoint_to_model(checkpoint, model): + with tempfile.NamedTemporaryFile(delete=False) as file: + torch.save(checkpoint, file.name) + del checkpoint + model.load_state_dict(torch.load(file.name), strict=False) + + os.remove(file.name) + + +def save_blip_diffusion_model(model, args): + qformer = get_qformer(model) + qformer.eval() + + text_encoder = ContextCLIPTextModel.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="text_encoder" + ) + vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae") + unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet") + vae.eval() + text_encoder.eval() + scheduler = PNDMScheduler( + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + set_alpha_to_one=False, + skip_prk_steps=True, + ) + tokenizer = CLIPTokenizer.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="tokenizer") + image_processor = BlipImageProcessor() + blip_diffusion = BlipDiffusionPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + unet=unet, + scheduler=scheduler, + qformer=qformer, + image_processor=image_processor, + ) + blip_diffusion.save_pretrained(args.checkpoint_path) + + +def main(args): + model, _, _ = load_model_and_preprocess("blip_diffusion", "base", device="cpu", is_eval=True) + save_blip_diffusion_model(model.state_dict(), args) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") + args = parser.parse_args() + + main(args) diff --git a/scripts/convert_cogvideox_to_diffusers.py b/scripts/convert_cogvideox_to_diffusers.py new file mode 100644 index 000000000000..5220aa0bde9d --- /dev/null +++ b/scripts/convert_cogvideox_to_diffusers.py @@ -0,0 +1,346 @@ +import argparse +from typing import Any, Dict + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import ( + AutoencoderKLCogVideoX, + CogVideoXDDIMScheduler, + CogVideoXImageToVideoPipeline, + CogVideoXPipeline, + CogVideoXTransformer3DModel, +) + + +def reassign_query_key_value_inplace(key: str, state_dict: Dict[str, Any]): + to_q_key = key.replace("query_key_value", "to_q") + to_k_key = key.replace("query_key_value", "to_k") + to_v_key = key.replace("query_key_value", "to_v") + to_q, to_k, to_v = torch.chunk(state_dict[key], chunks=3, dim=0) + state_dict[to_q_key] = to_q + state_dict[to_k_key] = to_k + state_dict[to_v_key] = to_v + state_dict.pop(key) + + +def reassign_query_key_layernorm_inplace(key: str, state_dict: Dict[str, Any]): + layer_id, weight_or_bias = key.split(".")[-2:] + + if "query" in key: + new_key = f"transformer_blocks.{layer_id}.attn1.norm_q.{weight_or_bias}" + elif "key" in key: + new_key = f"transformer_blocks.{layer_id}.attn1.norm_k.{weight_or_bias}" + + state_dict[new_key] = state_dict.pop(key) + + +def reassign_adaln_norm_inplace(key: str, state_dict: Dict[str, Any]): + layer_id, _, weight_or_bias = key.split(".")[-3:] + + weights_or_biases = state_dict[key].chunk(12, dim=0) + norm1_weights_or_biases = torch.cat(weights_or_biases[0:3] + weights_or_biases[6:9]) + norm2_weights_or_biases = torch.cat(weights_or_biases[3:6] + weights_or_biases[9:12]) + + norm1_key = f"transformer_blocks.{layer_id}.norm1.linear.{weight_or_bias}" + state_dict[norm1_key] = norm1_weights_or_biases + + norm2_key = f"transformer_blocks.{layer_id}.norm2.linear.{weight_or_bias}" + state_dict[norm2_key] = norm2_weights_or_biases + + state_dict.pop(key) + + +def remove_keys_inplace(key: str, state_dict: Dict[str, Any]): + state_dict.pop(key) + + +def replace_up_keys_inplace(key: str, state_dict: Dict[str, Any]): + key_split = key.split(".") + layer_index = int(key_split[2]) + replace_layer_index = 4 - 1 - layer_index + + key_split[1] = "up_blocks" + key_split[2] = str(replace_layer_index) + new_key = ".".join(key_split) + + state_dict[new_key] = state_dict.pop(key) + + +TRANSFORMER_KEYS_RENAME_DICT = { + "transformer.final_layernorm": "norm_final", + "transformer": "transformer_blocks", + "attention": "attn1", + "mlp": "ff.net", + "dense_h_to_4h": "0.proj", + "dense_4h_to_h": "2", + ".layers": "", + "dense": "to_out.0", + "input_layernorm": "norm1.norm", + "post_attn1_layernorm": "norm2.norm", + "time_embed.0": "time_embedding.linear_1", + "time_embed.2": "time_embedding.linear_2", + "ofs_embed.0": "ofs_embedding.linear_1", + "ofs_embed.2": "ofs_embedding.linear_2", + "mixins.patch_embed": "patch_embed", + "mixins.final_layer.norm_final": "norm_out.norm", + "mixins.final_layer.linear": "proj_out", + "mixins.final_layer.adaLN_modulation.1": "norm_out.linear", + "mixins.pos_embed.pos_embedding": "patch_embed.pos_embedding", # Specific to CogVideoX-5b-I2V +} + +TRANSFORMER_SPECIAL_KEYS_REMAP = { + "query_key_value": reassign_query_key_value_inplace, + "query_layernorm_list": reassign_query_key_layernorm_inplace, + "key_layernorm_list": reassign_query_key_layernorm_inplace, + "adaln_layer.adaLN_modulations": reassign_adaln_norm_inplace, + "embed_tokens": remove_keys_inplace, + "freqs_sin": remove_keys_inplace, + "freqs_cos": remove_keys_inplace, + "position_embedding": remove_keys_inplace, +} + +VAE_KEYS_RENAME_DICT = { + "block.": "resnets.", + "down.": "down_blocks.", + "downsample": "downsamplers.0", + "upsample": "upsamplers.0", + "nin_shortcut": "conv_shortcut", + "encoder.mid.block_1": "encoder.mid_block.resnets.0", + "encoder.mid.block_2": "encoder.mid_block.resnets.1", + "decoder.mid.block_1": "decoder.mid_block.resnets.0", + "decoder.mid.block_2": "decoder.mid_block.resnets.1", +} + +VAE_SPECIAL_KEYS_REMAP = { + "loss": remove_keys_inplace, + "up.": replace_up_keys_inplace, +} + +TOKENIZER_MAX_LENGTH = 226 + + +def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def convert_transformer( + ckpt_path: str, + num_layers: int, + num_attention_heads: int, + use_rotary_positional_embeddings: bool, + i2v: bool, + dtype: torch.dtype, + init_kwargs: Dict[str, Any], +): + PREFIX_KEY = "model.diffusion_model." + + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) + transformer = CogVideoXTransformer3DModel( + in_channels=32 if i2v else 16, + num_layers=num_layers, + num_attention_heads=num_attention_heads, + use_rotary_positional_embeddings=use_rotary_positional_embeddings, + ofs_embed_dim=512 if (i2v and init_kwargs["patch_size_t"] is not None) else None, # CogVideoX1.5-5B-I2V + use_learned_positional_embeddings=i2v and init_kwargs["patch_size_t"] is None, # CogVideoX-5B-I2V + **init_kwargs, + ).to(dtype=dtype) + + for key in list(original_state_dict.keys()): + new_key = key[len(PREFIX_KEY) :] + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + transformer.load_state_dict(original_state_dict, strict=True) + return transformer + + +def convert_vae(ckpt_path: str, scaling_factor: float, version: str, dtype: torch.dtype): + init_kwargs = {"scaling_factor": scaling_factor} + if version == "1.5": + init_kwargs.update({"invert_scale_latents": True}) + + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) + vae = AutoencoderKLCogVideoX(**init_kwargs).to(dtype=dtype) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True) + return vae + + +def get_transformer_init_kwargs(version: str): + if version == "1.0": + vae_scale_factor_spatial = 8 + init_kwargs = { + "patch_size": 2, + "patch_size_t": None, + "patch_bias": True, + "sample_height": 480 // vae_scale_factor_spatial, + "sample_width": 720 // vae_scale_factor_spatial, + "sample_frames": 49, + } + + elif version == "1.5": + vae_scale_factor_spatial = 8 + init_kwargs = { + "patch_size": 2, + "patch_size_t": 2, + "patch_bias": False, + "sample_height": 300, + "sample_width": 300, + "sample_frames": 81, + } + else: + raise ValueError("Unsupported version of CogVideoX.") + + return init_kwargs + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" + ) + parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint") + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--fp16", action="store_true", default=False, help="Whether to save the model weights in fp16") + parser.add_argument("--bf16", action="store_true", default=False, help="Whether to save the model weights in bf16") + parser.add_argument( + "--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving" + ) + parser.add_argument( + "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory" + ) + parser.add_argument( + "--typecast_text_encoder", + action="store_true", + default=False, + help="Whether or not to apply fp16/bf16 precision to text_encoder", + ) + # For CogVideoX-2B, num_layers is 30. For 5B, it is 42 + parser.add_argument("--num_layers", type=int, default=30, help="Number of transformer blocks") + # For CogVideoX-2B, num_attention_heads is 30. For 5B, it is 48 + parser.add_argument("--num_attention_heads", type=int, default=30, help="Number of attention heads") + # For CogVideoX-2B, use_rotary_positional_embeddings is False. For 5B, it is True + parser.add_argument( + "--use_rotary_positional_embeddings", action="store_true", default=False, help="Whether to use RoPE or not" + ) + # For CogVideoX-2B, scaling_factor is 1.15258426. For 5B, it is 0.7 + parser.add_argument("--scaling_factor", type=float, default=1.15258426, help="Scaling factor in the VAE") + # For CogVideoX-2B, snr_shift_scale is 3.0. For 5B, it is 1.0 + parser.add_argument("--snr_shift_scale", type=float, default=3.0, help="Scaling factor in the VAE") + parser.add_argument( + "--i2v", + action="store_true", + default=False, + help="Whether the model to be converted is the Image-to-Video version of CogVideoX.", + ) + parser.add_argument( + "--version", + choices=["1.0", "1.5"], + default="1.0", + help="Which version of CogVideoX to use for initializing default modeling parameters.", + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + transformer = None + vae = None + + if args.fp16 and args.bf16: + raise ValueError("You cannot pass both --fp16 and --bf16 at the same time.") + + dtype = torch.float16 if args.fp16 else torch.bfloat16 if args.bf16 else torch.float32 + + if args.transformer_ckpt_path is not None: + init_kwargs = get_transformer_init_kwargs(args.version) + transformer = convert_transformer( + args.transformer_ckpt_path, + args.num_layers, + args.num_attention_heads, + args.use_rotary_positional_embeddings, + args.i2v, + dtype, + init_kwargs, + ) + if args.vae_ckpt_path is not None: + # Keep VAE in float32 for better quality + vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, args.version, torch.float32) + + text_encoder_id = "google/t5-v1_1-xxl" + tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) + text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) + + if args.typecast_text_encoder: + text_encoder = text_encoder.to(dtype=dtype) + + # Apparently, the conversion does not work anymore without this :shrug: + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + scheduler = CogVideoXDDIMScheduler.from_config( + { + "snr_shift_scale": args.snr_shift_scale, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": False, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "rescale_betas_zero_snr": True, + "set_alpha_to_one": True, + "timestep_spacing": "trailing", + } + ) + if args.i2v: + pipeline_cls = CogVideoXImageToVideoPipeline + else: + pipeline_cls = CogVideoXPipeline + + pipe = pipeline_cls( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + + # We don't use variant here because the model must be run in fp16 (2B) or bf16 (5B). It would be weird + # for users to specify variant when the default is not fp32 and they want to run with the correct default (which + # is either fp16/bf16 here). + + # This is necessary This is necessary for users with insufficient memory, + # such as those using Colab and notebooks, as it can save some memory used for model loading. + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) diff --git a/scripts/convert_cogview3_to_diffusers.py b/scripts/convert_cogview3_to_diffusers.py new file mode 100644 index 000000000000..605555ebdbef --- /dev/null +++ b/scripts/convert_cogview3_to_diffusers.py @@ -0,0 +1,242 @@ +""" +Convert a CogView3 checkpoint to the Diffusers format. + +This script converts a CogView3 checkpoint to the Diffusers format, which can then be used +with the Diffusers library. + +Example usage: + python scripts/convert_cogview3_to_diffusers.py \ + --transformer_checkpoint_path 'your path/cogview3plus_3b/1/mp_rank_00_model_states.pt' \ + --vae_checkpoint_path 'your path/3plus_ae/imagekl_ch16.pt' \ + --output_path "/raid/yiyi/cogview3_diffusers" \ + --dtype "bf16" + +Arguments: + --transformer_checkpoint_path: Path to Transformer state dict. + --vae_checkpoint_path: Path to VAE state dict. + --output_path: The path to save the converted model. + --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. + --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used + --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. + + Default is "bf16" because CogView3 uses bfloat16 for Training. + +Note: You must provide either --original_state_dict_repo_id or --checkpoint_path. +""" + +import argparse +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import AutoencoderKL, CogVideoXDDIMScheduler, CogView3PlusPipeline, CogView3PlusTransformer2DModel +from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +TOKENIZER_MAX_LENGTH = 224 + +parser = argparse.ArgumentParser() +parser.add_argument("--transformer_checkpoint_path", default=None, type=str) +parser.add_argument("--vae_checkpoint_path", default=None, type=str) +parser.add_argument("--output_path", required=True, type=str) +parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") +parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") +parser.add_argument("--dtype", type=str, default="bf16") + +args = parser.parse_args() + + +# this is specific to `AdaLayerNormContinuous`: +# diffusers implementation split the linear projection into the scale, shift while CogView3 split it tino shift, scale +def swap_scale_shift(weight, dim): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_cogview3_transformer_checkpoint_to_diffusers(ckpt_path): + original_state_dict = torch.load(ckpt_path, map_location="cpu") + original_state_dict = original_state_dict["module"] + original_state_dict = {k.replace("model.diffusion_model.", ""): v for k, v in original_state_dict.items()} + + new_state_dict = {} + + # Convert patch_embed + new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("mixins.patch_embed.proj.weight") + new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("mixins.patch_embed.proj.bias") + new_state_dict["patch_embed.text_proj.weight"] = original_state_dict.pop("mixins.patch_embed.text_proj.weight") + new_state_dict["patch_embed.text_proj.bias"] = original_state_dict.pop("mixins.patch_embed.text_proj.bias") + + # Convert time_condition_embed + new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "time_embed.0.weight" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "time_embed.0.bias" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "time_embed.2.weight" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "time_embed.2.bias" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = original_state_dict.pop( + "label_emb.0.0.weight" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = original_state_dict.pop( + "label_emb.0.0.bias" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = original_state_dict.pop( + "label_emb.0.2.weight" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = original_state_dict.pop( + "label_emb.0.2.bias" + ) + + # Convert transformer blocks + for i in range(30): + block_prefix = f"transformer_blocks.{i}." + old_prefix = f"transformer.layers.{i}." + adaln_prefix = f"mixins.adaln.adaln_modules.{i}." + + new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(adaln_prefix + "1.weight") + new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(adaln_prefix + "1.bias") + + qkv_weight = original_state_dict.pop(old_prefix + "attention.query_key_value.weight") + qkv_bias = original_state_dict.pop(old_prefix + "attention.query_key_value.bias") + q, k, v = qkv_weight.chunk(3, dim=0) + q_bias, k_bias, v_bias = qkv_bias.chunk(3, dim=0) + + new_state_dict[block_prefix + "attn1.to_q.weight"] = q + new_state_dict[block_prefix + "attn1.to_q.bias"] = q_bias + new_state_dict[block_prefix + "attn1.to_k.weight"] = k + new_state_dict[block_prefix + "attn1.to_k.bias"] = k_bias + new_state_dict[block_prefix + "attn1.to_v.weight"] = v + new_state_dict[block_prefix + "attn1.to_v.bias"] = v_bias + + new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( + old_prefix + "attention.dense.weight" + ) + new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop( + old_prefix + "attention.dense.bias" + ) + + new_state_dict[block_prefix + "ff.net.0.proj.weight"] = original_state_dict.pop( + old_prefix + "mlp.dense_h_to_4h.weight" + ) + new_state_dict[block_prefix + "ff.net.0.proj.bias"] = original_state_dict.pop( + old_prefix + "mlp.dense_h_to_4h.bias" + ) + new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop( + old_prefix + "mlp.dense_4h_to_h.weight" + ) + new_state_dict[block_prefix + "ff.net.2.bias"] = original_state_dict.pop(old_prefix + "mlp.dense_4h_to_h.bias") + + # Convert final norm and projection + new_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("mixins.final_layer.adaln.1.weight"), dim=0 + ) + new_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("mixins.final_layer.adaln.1.bias"), dim=0 + ) + new_state_dict["proj_out.weight"] = original_state_dict.pop("mixins.final_layer.linear.weight") + new_state_dict["proj_out.bias"] = original_state_dict.pop("mixins.final_layer.linear.bias") + + return new_state_dict + + +def convert_cogview3_vae_checkpoint_to_diffusers(ckpt_path, vae_config): + original_state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"] + return convert_ldm_vae_checkpoint(original_state_dict, vae_config) + + +def main(args): + if args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}") + + transformer = None + vae = None + + if args.transformer_checkpoint_path is not None: + converted_transformer_state_dict = convert_cogview3_transformer_checkpoint_to_diffusers( + args.transformer_checkpoint_path + ) + transformer = CogView3PlusTransformer2DModel() + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + if dtype is not None: + # Original checkpoint data type will be preserved + transformer = transformer.to(dtype=dtype) + + if args.vae_checkpoint_path is not None: + vae_config = { + "in_channels": 3, + "out_channels": 3, + "down_block_types": ("DownEncoderBlock2D",) * 4, + "up_block_types": ("UpDecoderBlock2D",) * 4, + "block_out_channels": (128, 512, 1024, 1024), + "layers_per_block": 3, + "act_fn": "silu", + "latent_channels": 16, + "norm_num_groups": 32, + "sample_size": 1024, + "scaling_factor": 1.0, + "force_upcast": True, + "use_quant_conv": False, + "use_post_quant_conv": False, + "mid_block_add_attention": False, + } + converted_vae_state_dict = convert_cogview3_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_state_dict, strict=True) + if dtype is not None: + vae = vae.to(dtype=dtype) + + text_encoder_id = "google/t5-v1_1-xxl" + tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) + text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) + + # Apparently, the conversion does not work anymore without this :shrug: + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + scheduler = CogVideoXDDIMScheduler.from_config( + { + "snr_shift_scale": 4.0, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": False, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "rescale_betas_zero_snr": True, + "set_alpha_to_one": True, + "timestep_spacing": "trailing", + } + ) + + pipe = CogView3PlusPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + + # This is necessary for users with insufficient memory, such as those using Colab and notebooks, as it can + # save some memory used for model loading. + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_cogview4_to_diffusers.py b/scripts/convert_cogview4_to_diffusers.py new file mode 100644 index 000000000000..b6d01c797aeb --- /dev/null +++ b/scripts/convert_cogview4_to_diffusers.py @@ -0,0 +1,254 @@ +""" +Convert a CogView4 checkpoint from SAT(https://github.com/THUDM/SwissArmyTransformer) to the Diffusers format. +(deprecated Since 2025-02-07 and will remove it in later CogView4 version) + +This script converts a CogView4 checkpoint to the Diffusers format, which can then be used +with the Diffusers library. + +Example usage: + python scripts/convert_cogview4_to_diffusers.py \ + --transformer_checkpoint_path 'your path/cogview4_6b/1/mp_rank_00_model_states.pt' \ + --vae_checkpoint_path 'your path/cogview4_6b/imagekl_ch16.pt' \ + --output_path "THUDM/CogView4-6B" \ + --dtype "bf16" + +Arguments: + --transformer_checkpoint_path: Path to Transformer state dict. + --vae_checkpoint_path: Path to VAE state dict. + --output_path: The path to save the converted model. + --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. + --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used + --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. + + Default is "bf16" because CogView4 uses bfloat16 for Training. + +Note: You must provide either --original_state_dict_repo_id or --checkpoint_path. +""" + +import argparse +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights +from transformers import GlmForCausalLM, PreTrainedTokenizerFast + +from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler +from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--transformer_checkpoint_path", default=None, type=str) +parser.add_argument("--vae_checkpoint_path", default=None, type=str) +parser.add_argument("--output_path", required=True, type=str) +parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") +parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") +parser.add_argument("--dtype", type=str, default="bf16") + +args = parser.parse_args() + + +# this is specific to `AdaLayerNormContinuous`: +# diffusers implementation split the linear projection into the scale, shift while CogView4 split it tino shift, scale +def swap_scale_shift(weight, dim): + """ + Swap the scale and shift components in the weight tensor. + + Args: + weight (torch.Tensor): The original weight tensor. + dim (int): The dimension along which to split. + + Returns: + torch.Tensor: The modified weight tensor with scale and shift swapped. + """ + shift, scale = weight.chunk(2, dim=dim) + new_weight = torch.cat([scale, shift], dim=dim) + return new_weight + + +def convert_cogview4_transformer_checkpoint_to_diffusers(ckpt_path): + original_state_dict = torch.load(ckpt_path, map_location="cpu") + original_state_dict = original_state_dict["module"] + original_state_dict = {k.replace("model.diffusion_model.", ""): v for k, v in original_state_dict.items()} + + new_state_dict = {} + + # Convert patch_embed + new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("mixins.patch_embed.proj.weight") + new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("mixins.patch_embed.proj.bias") + new_state_dict["patch_embed.text_proj.weight"] = original_state_dict.pop("mixins.patch_embed.text_proj.weight") + new_state_dict["patch_embed.text_proj.bias"] = original_state_dict.pop("mixins.patch_embed.text_proj.bias") + + # Convert time_condition_embed + new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "time_embed.0.weight" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "time_embed.0.bias" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "time_embed.2.weight" + ) + new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "time_embed.2.bias" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = original_state_dict.pop( + "label_emb.0.0.weight" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = original_state_dict.pop( + "label_emb.0.0.bias" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = original_state_dict.pop( + "label_emb.0.2.weight" + ) + new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = original_state_dict.pop( + "label_emb.0.2.bias" + ) + + # Convert transformer blocks, for cogview4 is 28 blocks + for i in range(28): + block_prefix = f"transformer_blocks.{i}." + old_prefix = f"transformer.layers.{i}." + adaln_prefix = f"mixins.adaln.adaln_modules.{i}." + new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(adaln_prefix + "1.weight") + new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(adaln_prefix + "1.bias") + + qkv_weight = original_state_dict.pop(old_prefix + "attention.query_key_value.weight") + qkv_bias = original_state_dict.pop(old_prefix + "attention.query_key_value.bias") + q, k, v = qkv_weight.chunk(3, dim=0) + q_bias, k_bias, v_bias = qkv_bias.chunk(3, dim=0) + + new_state_dict[block_prefix + "attn1.to_q.weight"] = q + new_state_dict[block_prefix + "attn1.to_q.bias"] = q_bias + new_state_dict[block_prefix + "attn1.to_k.weight"] = k + new_state_dict[block_prefix + "attn1.to_k.bias"] = k_bias + new_state_dict[block_prefix + "attn1.to_v.weight"] = v + new_state_dict[block_prefix + "attn1.to_v.bias"] = v_bias + + new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( + old_prefix + "attention.dense.weight" + ) + new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop( + old_prefix + "attention.dense.bias" + ) + + new_state_dict[block_prefix + "ff.net.0.proj.weight"] = original_state_dict.pop( + old_prefix + "mlp.dense_h_to_4h.weight" + ) + new_state_dict[block_prefix + "ff.net.0.proj.bias"] = original_state_dict.pop( + old_prefix + "mlp.dense_h_to_4h.bias" + ) + new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop( + old_prefix + "mlp.dense_4h_to_h.weight" + ) + new_state_dict[block_prefix + "ff.net.2.bias"] = original_state_dict.pop(old_prefix + "mlp.dense_4h_to_h.bias") + + # Convert final norm and projection + new_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("mixins.final_layer.adaln.1.weight"), dim=0 + ) + new_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("mixins.final_layer.adaln.1.bias"), dim=0 + ) + new_state_dict["proj_out.weight"] = original_state_dict.pop("mixins.final_layer.linear.weight") + new_state_dict["proj_out.bias"] = original_state_dict.pop("mixins.final_layer.linear.bias") + + return new_state_dict + + +def convert_cogview4_vae_checkpoint_to_diffusers(ckpt_path, vae_config): + original_state_dict = torch.load(ckpt_path, map_location="cpu")["state_dict"] + return convert_ldm_vae_checkpoint(original_state_dict, vae_config) + + +def main(args): + if args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}") + + transformer = None + vae = None + + if args.transformer_checkpoint_path is not None: + converted_transformer_state_dict = convert_cogview4_transformer_checkpoint_to_diffusers( + args.transformer_checkpoint_path + ) + transformer = CogView4Transformer2DModel( + patch_size=2, + in_channels=16, + num_layers=28, + attention_head_dim=128, + num_attention_heads=32, + out_channels=16, + text_embed_dim=4096, + time_embed_dim=512, + condition_dim=256, + pos_embed_max_size=128, + ) + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + if dtype is not None: + # Original checkpoint data type will be preserved + transformer = transformer.to(dtype=dtype) + + if args.vae_checkpoint_path is not None: + vae_config = { + "in_channels": 3, + "out_channels": 3, + "down_block_types": ("DownEncoderBlock2D",) * 4, + "up_block_types": ("UpDecoderBlock2D",) * 4, + "block_out_channels": (128, 512, 1024, 1024), + "layers_per_block": 3, + "act_fn": "silu", + "latent_channels": 16, + "norm_num_groups": 32, + "sample_size": 1024, + "scaling_factor": 1.0, + "shift_factor": 0.0, + "force_upcast": True, + "use_quant_conv": False, + "use_post_quant_conv": False, + "mid_block_add_attention": False, + } + converted_vae_state_dict = convert_cogview4_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_state_dict, strict=True) + if dtype is not None: + vae = vae.to(dtype=dtype) + + text_encoder_id = "THUDM/glm-4-9b-hf" + tokenizer = PreTrainedTokenizerFast.from_pretrained(text_encoder_id) + text_encoder = GlmForCausalLM.from_pretrained( + text_encoder_id, + cache_dir=args.text_encoder_cache_dir, + torch_dtype=torch.bfloat16 if args.dtype == "bf16" else torch.float32, + ) + + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + scheduler = FlowMatchEulerDiscreteScheduler( + base_shift=0.25, max_shift=0.75, base_image_seq_len=256, use_dynamic_shifting=True, time_shift_type="linear" + ) + + pipe = CogView4Pipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + + # This is necessary for users with insufficient memory, such as those using Colab and notebooks, as it can + # save some memory used for model loading. + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_cogview4_to_diffusers_megatron.py b/scripts/convert_cogview4_to_diffusers_megatron.py new file mode 100644 index 000000000000..8faeccb13888 --- /dev/null +++ b/scripts/convert_cogview4_to_diffusers_megatron.py @@ -0,0 +1,384 @@ +""" +Convert a CogView4 checkpoint from Megatron to the Diffusers format. + +Example usage: + python scripts/convert_cogview4_to_diffusers.py \ + --transformer_checkpoint_path 'your path/cogview4_6b/mp_rank_00/model_optim_rng.pt' \ + --vae_checkpoint_path 'your path/cogview4_6b/imagekl_ch16.pt' \ + --output_path "THUDM/CogView4-6B" \ + --dtype "bf16" + +Arguments: + --transformer_checkpoint_path: Path to Transformer state dict. + --vae_checkpoint_path: Path to VAE state dict. + --output_path: The path to save the converted model. + --push_to_hub: Whether to push the converted checkpoint to the HF Hub or not. Defaults to `False`. + --text_encoder_cache_dir: Cache directory where text encoder is located. Defaults to None, which means HF_HOME will be used. + --dtype: The dtype to save the model in (default: "bf16", options: "fp16", "bf16", "fp32"). If None, the dtype of the state dict is considered. + + Default is "bf16" because CogView4 uses bfloat16 for training. + +Note: You must provide either --transformer_checkpoint_path or --vae_checkpoint_path. +""" + +import argparse + +import torch +from tqdm import tqdm +from transformers import GlmModel, PreTrainedTokenizerFast + +from diffusers import ( + AutoencoderKL, + CogView4ControlPipeline, + CogView4Pipeline, + CogView4Transformer2DModel, + FlowMatchEulerDiscreteScheduler, +) +from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint + + +parser = argparse.ArgumentParser() +parser.add_argument( + "--transformer_checkpoint_path", + default=None, + type=str, + help="Path to Megatron (not SAT) Transformer checkpoint, e.g., 'model_optim_rng.pt'.", +) +parser.add_argument( + "--vae_checkpoint_path", + default=None, + type=str, + help="(Optional) Path to VAE checkpoint, e.g., 'imagekl_ch16.pt'.", +) +parser.add_argument( + "--output_path", + required=True, + type=str, + help="Directory to save the final Diffusers format pipeline.", +) +parser.add_argument( + "--push_to_hub", + action="store_true", + default=False, + help="Whether to push the converted model to the HuggingFace Hub.", +) +parser.add_argument( + "--text_encoder_cache_dir", + type=str, + default=None, + help="Specify the cache directory for the text encoder.", +) +parser.add_argument( + "--dtype", + type=str, + default="bf16", + choices=["fp16", "bf16", "fp32"], + help="Data type to save the model in.", +) + +parser.add_argument( + "--num_layers", + type=int, + default=28, + help="Number of Transformer layers (e.g., 28, 48...).", +) +parser.add_argument( + "--num_heads", + type=int, + default=32, + help="Number of attention heads.", +) +parser.add_argument( + "--hidden_size", + type=int, + default=4096, + help="Transformer hidden dimension size.", +) +parser.add_argument( + "--attention_head_dim", + type=int, + default=128, + help="Dimension of each attention head.", +) +parser.add_argument( + "--time_embed_dim", + type=int, + default=512, + help="Dimension of time embeddings.", +) +parser.add_argument( + "--condition_dim", + type=int, + default=256, + help="Dimension of condition embeddings.", +) +parser.add_argument( + "--pos_embed_max_size", + type=int, + default=128, + help="Maximum size for positional embeddings.", +) +parser.add_argument( + "--control", + action="store_true", + default=False, + help="Whether to use control model.", +) + +args = parser.parse_args() + + +def swap_scale_shift(weight, dim): + """ + Swap the scale and shift components in the weight tensor. + + Args: + weight (torch.Tensor): The original weight tensor. + dim (int): The dimension along which to split. + + Returns: + torch.Tensor: The modified weight tensor with scale and shift swapped. + """ + shift, scale = weight.chunk(2, dim=dim) + new_weight = torch.cat([scale, shift], dim=dim) + return new_weight + + +def convert_megatron_transformer_checkpoint_to_diffusers( + ckpt_path: str, + num_layers: int, + num_heads: int, + hidden_size: int, +): + """ + Convert a Megatron Transformer checkpoint to Diffusers format. + + Args: + ckpt_path (str): Path to the Megatron Transformer checkpoint. + num_layers (int): Number of Transformer layers. + num_heads (int): Number of attention heads. + hidden_size (int): Hidden size of the Transformer. + + Returns: + dict: The converted state dictionary compatible with Diffusers. + """ + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) + mega = ckpt["model"] + + new_state_dict = {} + + # Patch Embedding + new_state_dict["patch_embed.proj.weight"] = mega["encoder_expand_linear.weight"].reshape( + hidden_size, 128 if args.control else 64 + ) + new_state_dict["patch_embed.proj.bias"] = mega["encoder_expand_linear.bias"] + new_state_dict["patch_embed.text_proj.weight"] = mega["text_projector.weight"] + new_state_dict["patch_embed.text_proj.bias"] = mega["text_projector.bias"] + + # Time Condition Embedding + new_state_dict["time_condition_embed.timestep_embedder.linear_1.weight"] = mega[ + "time_embedding.time_embed.0.weight" + ] + new_state_dict["time_condition_embed.timestep_embedder.linear_1.bias"] = mega["time_embedding.time_embed.0.bias"] + new_state_dict["time_condition_embed.timestep_embedder.linear_2.weight"] = mega[ + "time_embedding.time_embed.2.weight" + ] + new_state_dict["time_condition_embed.timestep_embedder.linear_2.bias"] = mega["time_embedding.time_embed.2.bias"] + + new_state_dict["time_condition_embed.condition_embedder.linear_1.weight"] = mega[ + "label_embedding.label_embed.0.weight" + ] + new_state_dict["time_condition_embed.condition_embedder.linear_1.bias"] = mega[ + "label_embedding.label_embed.0.bias" + ] + new_state_dict["time_condition_embed.condition_embedder.linear_2.weight"] = mega[ + "label_embedding.label_embed.2.weight" + ] + new_state_dict["time_condition_embed.condition_embedder.linear_2.bias"] = mega[ + "label_embedding.label_embed.2.bias" + ] + + # Convert each Transformer layer + for i in tqdm(range(num_layers), desc="Converting layers (Megatron->Diffusers)"): + block_prefix = f"transformer_blocks.{i}." + + # AdaLayerNorm + new_state_dict[block_prefix + "norm1.linear.weight"] = mega[f"decoder.layers.{i}.adaln.weight"] + new_state_dict[block_prefix + "norm1.linear.bias"] = mega[f"decoder.layers.{i}.adaln.bias"] + qkv_weight = mega[f"decoder.layers.{i}.self_attention.linear_qkv.weight"] + qkv_bias = mega[f"decoder.layers.{i}.self_attention.linear_qkv.bias"] + + # Reshape to match SAT logic + qkv_weight = qkv_weight.view(num_heads, 3, hidden_size // num_heads, hidden_size) + qkv_weight = qkv_weight.permute(1, 0, 2, 3).reshape(3 * hidden_size, hidden_size) + + qkv_bias = qkv_bias.view(num_heads, 3, hidden_size // num_heads) + qkv_bias = qkv_bias.permute(1, 0, 2).reshape(3 * hidden_size) + + # Assign to Diffusers keys + q, k, v = torch.chunk(qkv_weight, 3, dim=0) + qb, kb, vb = torch.chunk(qkv_bias, 3, dim=0) + + new_state_dict[block_prefix + "attn1.to_q.weight"] = q + new_state_dict[block_prefix + "attn1.to_q.bias"] = qb + new_state_dict[block_prefix + "attn1.to_k.weight"] = k + new_state_dict[block_prefix + "attn1.to_k.bias"] = kb + new_state_dict[block_prefix + "attn1.to_v.weight"] = v + new_state_dict[block_prefix + "attn1.to_v.bias"] = vb + + # Attention Output + new_state_dict[block_prefix + "attn1.to_out.0.weight"] = mega[ + f"decoder.layers.{i}.self_attention.linear_proj.weight" + ] + new_state_dict[block_prefix + "attn1.to_out.0.bias"] = mega[ + f"decoder.layers.{i}.self_attention.linear_proj.bias" + ] + + # MLP + new_state_dict[block_prefix + "ff.net.0.proj.weight"] = mega[f"decoder.layers.{i}.mlp.linear_fc1.weight"] + new_state_dict[block_prefix + "ff.net.0.proj.bias"] = mega[f"decoder.layers.{i}.mlp.linear_fc1.bias"] + new_state_dict[block_prefix + "ff.net.2.weight"] = mega[f"decoder.layers.{i}.mlp.linear_fc2.weight"] + new_state_dict[block_prefix + "ff.net.2.bias"] = mega[f"decoder.layers.{i}.mlp.linear_fc2.bias"] + + # Final Layers + new_state_dict["norm_out.linear.weight"] = swap_scale_shift(mega["adaln_final.weight"], dim=0) + new_state_dict["norm_out.linear.bias"] = swap_scale_shift(mega["adaln_final.bias"], dim=0) + new_state_dict["proj_out.weight"] = mega["output_projector.weight"] + new_state_dict["proj_out.bias"] = mega["output_projector.bias"] + + return new_state_dict + + +def convert_cogview4_vae_checkpoint_to_diffusers(ckpt_path, vae_config): + """ + Convert a CogView4 VAE checkpoint to Diffusers format. + + Args: + ckpt_path (str): Path to the VAE checkpoint. + vae_config (dict): Configuration dictionary for the VAE. + + Returns: + dict: The converted VAE state dictionary compatible with Diffusers. + """ + original_state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=False)["state_dict"] + return convert_ldm_vae_checkpoint(original_state_dict, vae_config) + + +def main(args): + """ + Main function to convert CogView4 checkpoints to Diffusers format. + + Args: + args (argparse.Namespace): Parsed command-line arguments. + """ + # Determine the desired data type + if args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}") + + transformer = None + vae = None + + # Convert Transformer checkpoint if provided + if args.transformer_checkpoint_path is not None: + converted_transformer_state_dict = convert_megatron_transformer_checkpoint_to_diffusers( + ckpt_path=args.transformer_checkpoint_path, + num_layers=args.num_layers, + num_heads=args.num_heads, + hidden_size=args.hidden_size, + ) + transformer = CogView4Transformer2DModel( + patch_size=2, + in_channels=32 if args.control else 16, + num_layers=args.num_layers, + attention_head_dim=args.attention_head_dim, + num_attention_heads=args.num_heads, + out_channels=16, + text_embed_dim=args.hidden_size, + time_embed_dim=args.time_embed_dim, + condition_dim=args.condition_dim, + pos_embed_max_size=args.pos_embed_max_size, + ) + + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + # Convert to the specified dtype + if dtype is not None: + transformer = transformer.to(dtype=dtype) + + # Convert VAE checkpoint if provided + if args.vae_checkpoint_path is not None: + vae_config = { + "in_channels": 3, + "out_channels": 3, + "down_block_types": ("DownEncoderBlock2D",) * 4, + "up_block_types": ("UpDecoderBlock2D",) * 4, + "block_out_channels": (128, 512, 1024, 1024), + "layers_per_block": 3, + "act_fn": "silu", + "latent_channels": 16, + "norm_num_groups": 32, + "sample_size": 1024, + "scaling_factor": 1.0, + "shift_factor": 0.0, + "force_upcast": True, + "use_quant_conv": False, + "use_post_quant_conv": False, + "mid_block_add_attention": False, + } + converted_vae_state_dict = convert_cogview4_vae_checkpoint_to_diffusers(args.vae_checkpoint_path, vae_config) + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_state_dict, strict=True) + if dtype is not None: + vae = vae.to(dtype=dtype) + + # Load the text encoder and tokenizer + text_encoder_id = "THUDM/glm-4-9b-hf" + tokenizer = PreTrainedTokenizerFast.from_pretrained(text_encoder_id) + text_encoder = GlmModel.from_pretrained( + text_encoder_id, + cache_dir=args.text_encoder_cache_dir, + torch_dtype=torch.bfloat16 if args.dtype == "bf16" else torch.float32, + ) + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + # Initialize the scheduler + scheduler = FlowMatchEulerDiscreteScheduler( + base_shift=0.25, max_shift=0.75, base_image_seq_len=256, use_dynamic_shifting=True, time_shift_type="linear" + ) + + # Create the pipeline + if args.control: + pipe = CogView4ControlPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + else: + pipe = CogView4Pipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + + # Save the converted pipeline + pipe.save_pretrained( + args.output_path, + safe_serialization=True, + max_shard_size="5GB", + push_to_hub=args.push_to_hub, + ) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_consistency_decoder.py b/scripts/convert_consistency_decoder.py new file mode 100644 index 000000000000..9e289457752b --- /dev/null +++ b/scripts/convert_consistency_decoder.py @@ -0,0 +1,1131 @@ +import math +import os +import urllib +import warnings +from argparse import ArgumentParser + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.utils import insecure_hashlib +from safetensors.torch import load_file as stl +from tqdm import tqdm + +from diffusers import AutoencoderKL, ConsistencyDecoderVAE, DiffusionPipeline, StableDiffusionPipeline, UNet2DModel +from diffusers.models.autoencoders.vae import Encoder +from diffusers.models.embeddings import TimestepEmbedding +from diffusers.models.unets.unet_2d_blocks import ResnetDownsampleBlock2D, ResnetUpsampleBlock2D, UNetMidBlock2D + + +args = ArgumentParser() +args.add_argument("--save_pretrained", required=False, default=None, type=str) +args.add_argument("--test_image", required=True, type=str) +args = args.parse_args() + + +def _extract_into_tensor(arr, timesteps, broadcast_shape): + # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L895 + # """ + res = arr[timesteps].float() + dims_to_append = len(broadcast_shape) - len(res.shape) + return res[(...,) + (None,) * dims_to_append] + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + # from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/gaussian_diffusion.py#L45 + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return torch.tensor(betas) + + +def _download(url: str, root: str): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if insecure_hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError("Model has been downloaded but the SHA256 checksum does not match") + + return download_target + + +class ConsistencyDecoder: + def __init__(self, device="cuda:0", download_root=os.path.expanduser("~/.cache/clip")): + self.n_distilled_steps = 64 + download_target = _download( + "https://openaipublic.azureedge.net/diff-vae/c9cebd3132dd9c42936d803e33424145a748843c8f716c0814838bdc8a2fe7cb/decoder.pt", + download_root, + ) + self.ckpt = torch.jit.load(download_target).to(device) + self.device = device + sigma_data = 0.5 + betas = betas_for_alpha_bar(1024, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2).to(device) + alphas = 1.0 - betas + alphas_cumprod = torch.cumprod(alphas, dim=0) + self.sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod) + sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod) + sigmas = torch.sqrt(1.0 / alphas_cumprod - 1) + self.c_skip = sqrt_recip_alphas_cumprod * sigma_data**2 / (sigmas**2 + sigma_data**2) + self.c_out = sigmas * sigma_data / (sigmas**2 + sigma_data**2) ** 0.5 + self.c_in = sqrt_recip_alphas_cumprod / (sigmas**2 + sigma_data**2) ** 0.5 + + @staticmethod + def round_timesteps(timesteps, total_timesteps, n_distilled_steps, truncate_start=True): + with torch.no_grad(): + space = torch.div(total_timesteps, n_distilled_steps, rounding_mode="floor") + rounded_timesteps = (torch.div(timesteps, space, rounding_mode="floor") + 1) * space + if truncate_start: + rounded_timesteps[rounded_timesteps == total_timesteps] -= space + else: + rounded_timesteps[rounded_timesteps == total_timesteps] -= space + rounded_timesteps[rounded_timesteps == 0] += space + return rounded_timesteps + + @staticmethod + def ldm_transform_latent(z, extra_scale_factor=1): + channel_means = [0.38862467, 0.02253063, 0.07381133, -0.0171294] + channel_stds = [0.9654121, 1.0440036, 0.76147926, 0.77022034] + + if len(z.shape) != 4: + raise ValueError() + + z = z * 0.18215 + channels = [z[:, i] for i in range(z.shape[1])] + + channels = [extra_scale_factor * (c - channel_means[i]) / channel_stds[i] for i, c in enumerate(channels)] + return torch.stack(channels, dim=1) + + @torch.no_grad() + def __call__( + self, + features: torch.Tensor, + schedule=[1.0, 0.5], + generator=None, + ): + features = self.ldm_transform_latent(features) + ts = self.round_timesteps( + torch.arange(0, 1024), + 1024, + self.n_distilled_steps, + truncate_start=False, + ) + shape = ( + features.size(0), + 3, + 8 * features.size(2), + 8 * features.size(3), + ) + x_start = torch.zeros(shape, device=features.device, dtype=features.dtype) + schedule_timesteps = [int((1024 - 1) * s) for s in schedule] + for i in schedule_timesteps: + t = ts[i].item() + t_ = torch.tensor([t] * features.shape[0]).to(self.device) + # noise = torch.randn_like(x_start) + noise = torch.randn(x_start.shape, dtype=x_start.dtype, generator=generator).to(device=x_start.device) + x_start = ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t_, x_start.shape) * x_start + + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t_, x_start.shape) * noise + ) + c_in = _extract_into_tensor(self.c_in, t_, x_start.shape) + + import torch.nn.functional as F + + from diffusers import UNet2DModel + + if isinstance(self.ckpt, UNet2DModel): + input = torch.concat([c_in * x_start, F.upsample_nearest(features, scale_factor=8)], dim=1) + model_output = self.ckpt(input, t_).sample + else: + model_output = self.ckpt(c_in * x_start, t_, features=features) + + B, C = x_start.shape[:2] + model_output, _ = torch.split(model_output, C, dim=1) + pred_xstart = ( + _extract_into_tensor(self.c_out, t_, x_start.shape) * model_output + + _extract_into_tensor(self.c_skip, t_, x_start.shape) * x_start + ).clamp(-1, 1) + x_start = pred_xstart + return x_start + + +def save_image(image, name): + import numpy as np + from PIL import Image + + image = image[0].cpu().numpy() + image = (image + 1.0) * 127.5 + image = image.clip(0, 255).astype(np.uint8) + image = Image.fromarray(image.transpose(1, 2, 0)) + image.save(name) + + +def load_image(uri, size=None, center_crop=False): + import numpy as np + from PIL import Image + + image = Image.open(uri) + if center_crop: + image = image.crop( + ( + (image.width - min(image.width, image.height)) // 2, + (image.height - min(image.width, image.height)) // 2, + (image.width + min(image.width, image.height)) // 2, + (image.height + min(image.width, image.height)) // 2, + ) + ) + if size is not None: + image = image.resize(size) + image = torch.tensor(np.array(image).transpose(2, 0, 1)).unsqueeze(0).float() + image = image / 127.5 - 1.0 + return image + + +class TimestepEmbedding_(nn.Module): + def __init__(self, n_time=1024, n_emb=320, n_out=1280) -> None: + super().__init__() + self.emb = nn.Embedding(n_time, n_emb) + self.f_1 = nn.Linear(n_emb, n_out) + self.f_2 = nn.Linear(n_out, n_out) + + def forward(self, x) -> torch.Tensor: + x = self.emb(x) + x = self.f_1(x) + x = F.silu(x) + return self.f_2(x) + + +class ImageEmbedding(nn.Module): + def __init__(self, in_channels=7, out_channels=320) -> None: + super().__init__() + self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + + def forward(self, x) -> torch.Tensor: + return self.f(x) + + +class ImageUnembedding(nn.Module): + def __init__(self, in_channels=320, out_channels=6) -> None: + super().__init__() + self.gn = nn.GroupNorm(32, in_channels) + self.f = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1) + + def forward(self, x) -> torch.Tensor: + return self.f(F.silu(self.gn(x))) + + +class ConvResblock(nn.Module): + def __init__(self, in_features=320, out_features=320) -> None: + super().__init__() + self.f_t = nn.Linear(1280, out_features * 2) + + self.gn_1 = nn.GroupNorm(32, in_features) + self.f_1 = nn.Conv2d(in_features, out_features, kernel_size=3, padding=1) + + self.gn_2 = nn.GroupNorm(32, out_features) + self.f_2 = nn.Conv2d(out_features, out_features, kernel_size=3, padding=1) + + skip_conv = in_features != out_features + self.f_s = nn.Conv2d(in_features, out_features, kernel_size=1, padding=0) if skip_conv else nn.Identity() + + def forward(self, x, t): + x_skip = x + t = self.f_t(F.silu(t)) + t = t.chunk(2, dim=1) + t_1 = t[0].unsqueeze(dim=2).unsqueeze(dim=3) + 1 + t_2 = t[1].unsqueeze(dim=2).unsqueeze(dim=3) + + gn_1 = F.silu(self.gn_1(x)) + f_1 = self.f_1(gn_1) + + gn_2 = self.gn_2(f_1) + + return self.f_s(x_skip) + self.f_2(F.silu(gn_2 * t_1 + t_2)) + + +# Also ConvResblock +class Downsample(nn.Module): + def __init__(self, in_channels=320) -> None: + super().__init__() + self.f_t = nn.Linear(1280, in_channels * 2) + + self.gn_1 = nn.GroupNorm(32, in_channels) + self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) + self.gn_2 = nn.GroupNorm(32, in_channels) + + self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) + + def forward(self, x, t) -> torch.Tensor: + x_skip = x + + t = self.f_t(F.silu(t)) + t_1, t_2 = t.chunk(2, dim=1) + t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1 + t_2 = t_2.unsqueeze(2).unsqueeze(3) + + gn_1 = F.silu(self.gn_1(x)) + avg_pool2d = F.avg_pool2d(gn_1, kernel_size=(2, 2), stride=None) + + f_1 = self.f_1(avg_pool2d) + gn_2 = self.gn_2(f_1) + + f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2))) + + return f_2 + F.avg_pool2d(x_skip, kernel_size=(2, 2), stride=None) + + +# Also ConvResblock +class Upsample(nn.Module): + def __init__(self, in_channels=1024) -> None: + super().__init__() + self.f_t = nn.Linear(1280, in_channels * 2) + + self.gn_1 = nn.GroupNorm(32, in_channels) + self.f_1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) + self.gn_2 = nn.GroupNorm(32, in_channels) + + self.f_2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) + + def forward(self, x, t) -> torch.Tensor: + x_skip = x + + t = self.f_t(F.silu(t)) + t_1, t_2 = t.chunk(2, dim=1) + t_1 = t_1.unsqueeze(2).unsqueeze(3) + 1 + t_2 = t_2.unsqueeze(2).unsqueeze(3) + + gn_1 = F.silu(self.gn_1(x)) + upsample = F.upsample_nearest(gn_1, scale_factor=2) + f_1 = self.f_1(upsample) + gn_2 = self.gn_2(f_1) + + f_2 = self.f_2(F.silu(t_2 + (t_1 * gn_2))) + + return f_2 + F.upsample_nearest(x_skip, scale_factor=2) + + +class ConvUNetVAE(nn.Module): + def __init__(self) -> None: + super().__init__() + self.embed_image = ImageEmbedding() + self.embed_time = TimestepEmbedding_() + + down_0 = nn.ModuleList( + [ + ConvResblock(320, 320), + ConvResblock(320, 320), + ConvResblock(320, 320), + Downsample(320), + ] + ) + down_1 = nn.ModuleList( + [ + ConvResblock(320, 640), + ConvResblock(640, 640), + ConvResblock(640, 640), + Downsample(640), + ] + ) + down_2 = nn.ModuleList( + [ + ConvResblock(640, 1024), + ConvResblock(1024, 1024), + ConvResblock(1024, 1024), + Downsample(1024), + ] + ) + down_3 = nn.ModuleList( + [ + ConvResblock(1024, 1024), + ConvResblock(1024, 1024), + ConvResblock(1024, 1024), + ] + ) + self.down = nn.ModuleList( + [ + down_0, + down_1, + down_2, + down_3, + ] + ) + + self.mid = nn.ModuleList( + [ + ConvResblock(1024, 1024), + ConvResblock(1024, 1024), + ] + ) + + up_3 = nn.ModuleList( + [ + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 * 2, 1024), + Upsample(1024), + ] + ) + up_2 = nn.ModuleList( + [ + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 * 2, 1024), + ConvResblock(1024 + 640, 1024), + Upsample(1024), + ] + ) + up_1 = nn.ModuleList( + [ + ConvResblock(1024 + 640, 640), + ConvResblock(640 * 2, 640), + ConvResblock(640 * 2, 640), + ConvResblock(320 + 640, 640), + Upsample(640), + ] + ) + up_0 = nn.ModuleList( + [ + ConvResblock(320 + 640, 320), + ConvResblock(320 * 2, 320), + ConvResblock(320 * 2, 320), + ConvResblock(320 * 2, 320), + ] + ) + self.up = nn.ModuleList( + [ + up_0, + up_1, + up_2, + up_3, + ] + ) + + self.output = ImageUnembedding() + + def forward(self, x, t, features) -> torch.Tensor: + converted = hasattr(self, "converted") and self.converted + + x = torch.cat([x, F.upsample_nearest(features, scale_factor=8)], dim=1) + + if converted: + t = self.time_embedding(self.time_proj(t)) + else: + t = self.embed_time(t) + + x = self.embed_image(x) + + skips = [x] + for i, down in enumerate(self.down): + if converted and i in [0, 1, 2, 3]: + x, skips_ = down(x, t) + for skip in skips_: + skips.append(skip) + else: + for block in down: + x = block(x, t) + skips.append(x) + print(x.float().abs().sum()) + + if converted: + x = self.mid(x, t) + else: + for i in range(2): + x = self.mid[i](x, t) + print(x.float().abs().sum()) + + for i, up in enumerate(self.up[::-1]): + if converted and i in [0, 1, 2, 3]: + skip_4 = skips.pop() + skip_3 = skips.pop() + skip_2 = skips.pop() + skip_1 = skips.pop() + skips_ = (skip_1, skip_2, skip_3, skip_4) + x = up(x, skips_, t) + else: + for block in up: + if isinstance(block, ConvResblock): + x = torch.concat([x, skips.pop()], dim=1) + x = block(x, t) + + return self.output(x) + + +def rename_state_dict_key(k): + k = k.replace("blocks.", "") + for i in range(5): + k = k.replace(f"down_{i}_", f"down.{i}.") + k = k.replace(f"conv_{i}.", f"{i}.") + k = k.replace(f"up_{i}_", f"up.{i}.") + k = k.replace(f"mid_{i}", f"mid.{i}") + k = k.replace("upsamp.", "4.") + k = k.replace("downsamp.", "3.") + k = k.replace("f_t.w", "f_t.weight").replace("f_t.b", "f_t.bias") + k = k.replace("f_1.w", "f_1.weight").replace("f_1.b", "f_1.bias") + k = k.replace("f_2.w", "f_2.weight").replace("f_2.b", "f_2.bias") + k = k.replace("f_s.w", "f_s.weight").replace("f_s.b", "f_s.bias") + k = k.replace("f.w", "f.weight").replace("f.b", "f.bias") + k = k.replace("gn_1.g", "gn_1.weight").replace("gn_1.b", "gn_1.bias") + k = k.replace("gn_2.g", "gn_2.weight").replace("gn_2.b", "gn_2.bias") + k = k.replace("gn.g", "gn.weight").replace("gn.b", "gn.bias") + return k + + +def rename_state_dict(sd, embedding): + sd = {rename_state_dict_key(k): v for k, v in sd.items()} + sd["embed_time.emb.weight"] = embedding["weight"] + return sd + + +# encode with stable diffusion vae +pipe = StableDiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16 +) +pipe.vae.cuda() + +# construct original decoder with jitted model +decoder_consistency = ConsistencyDecoder(device="cuda:0") + +# construct UNet code, overwrite the decoder with conv_unet_vae +model = ConvUNetVAE() +model.load_state_dict( + rename_state_dict( + stl("consistency_decoder.safetensors"), + stl("embedding.safetensors"), + ) +) +model = model.cuda() + +decoder_consistency.ckpt = model + +image = load_image(args.test_image, size=(256, 256), center_crop=True) +latent = pipe.vae.encode(image.half().cuda()).latent_dist.sample() + +# decode with gan +sample_gan = pipe.vae.decode(latent).sample.detach() +save_image(sample_gan, "gan.png") + +# decode with conv_unet_vae +sample_consistency_orig = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) +save_image(sample_consistency_orig, "con_orig.png") + + +########### conversion + +print("CONVERSION") + +print("DOWN BLOCK ONE") + +block_one_sd_orig = model.down[0].state_dict() +block_one_sd_new = {} + +for i in range(3): + block_one_sd_new[f"resnets.{i}.norm1.weight"] = block_one_sd_orig.pop(f"{i}.gn_1.weight") + block_one_sd_new[f"resnets.{i}.norm1.bias"] = block_one_sd_orig.pop(f"{i}.gn_1.bias") + block_one_sd_new[f"resnets.{i}.conv1.weight"] = block_one_sd_orig.pop(f"{i}.f_1.weight") + block_one_sd_new[f"resnets.{i}.conv1.bias"] = block_one_sd_orig.pop(f"{i}.f_1.bias") + block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_one_sd_orig.pop(f"{i}.f_t.weight") + block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_one_sd_orig.pop(f"{i}.f_t.bias") + block_one_sd_new[f"resnets.{i}.norm2.weight"] = block_one_sd_orig.pop(f"{i}.gn_2.weight") + block_one_sd_new[f"resnets.{i}.norm2.bias"] = block_one_sd_orig.pop(f"{i}.gn_2.bias") + block_one_sd_new[f"resnets.{i}.conv2.weight"] = block_one_sd_orig.pop(f"{i}.f_2.weight") + block_one_sd_new[f"resnets.{i}.conv2.bias"] = block_one_sd_orig.pop(f"{i}.f_2.bias") + +block_one_sd_new["downsamplers.0.norm1.weight"] = block_one_sd_orig.pop("3.gn_1.weight") +block_one_sd_new["downsamplers.0.norm1.bias"] = block_one_sd_orig.pop("3.gn_1.bias") +block_one_sd_new["downsamplers.0.conv1.weight"] = block_one_sd_orig.pop("3.f_1.weight") +block_one_sd_new["downsamplers.0.conv1.bias"] = block_one_sd_orig.pop("3.f_1.bias") +block_one_sd_new["downsamplers.0.time_emb_proj.weight"] = block_one_sd_orig.pop("3.f_t.weight") +block_one_sd_new["downsamplers.0.time_emb_proj.bias"] = block_one_sd_orig.pop("3.f_t.bias") +block_one_sd_new["downsamplers.0.norm2.weight"] = block_one_sd_orig.pop("3.gn_2.weight") +block_one_sd_new["downsamplers.0.norm2.bias"] = block_one_sd_orig.pop("3.gn_2.bias") +block_one_sd_new["downsamplers.0.conv2.weight"] = block_one_sd_orig.pop("3.f_2.weight") +block_one_sd_new["downsamplers.0.conv2.bias"] = block_one_sd_orig.pop("3.f_2.bias") + +assert len(block_one_sd_orig) == 0 + +block_one = ResnetDownsampleBlock2D( + in_channels=320, + out_channels=320, + temb_channels=1280, + num_layers=3, + add_downsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +block_one.load_state_dict(block_one_sd_new) + +print("DOWN BLOCK TWO") + +block_two_sd_orig = model.down[1].state_dict() +block_two_sd_new = {} + +for i in range(3): + block_two_sd_new[f"resnets.{i}.norm1.weight"] = block_two_sd_orig.pop(f"{i}.gn_1.weight") + block_two_sd_new[f"resnets.{i}.norm1.bias"] = block_two_sd_orig.pop(f"{i}.gn_1.bias") + block_two_sd_new[f"resnets.{i}.conv1.weight"] = block_two_sd_orig.pop(f"{i}.f_1.weight") + block_two_sd_new[f"resnets.{i}.conv1.bias"] = block_two_sd_orig.pop(f"{i}.f_1.bias") + block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_two_sd_orig.pop(f"{i}.f_t.weight") + block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_two_sd_orig.pop(f"{i}.f_t.bias") + block_two_sd_new[f"resnets.{i}.norm2.weight"] = block_two_sd_orig.pop(f"{i}.gn_2.weight") + block_two_sd_new[f"resnets.{i}.norm2.bias"] = block_two_sd_orig.pop(f"{i}.gn_2.bias") + block_two_sd_new[f"resnets.{i}.conv2.weight"] = block_two_sd_orig.pop(f"{i}.f_2.weight") + block_two_sd_new[f"resnets.{i}.conv2.bias"] = block_two_sd_orig.pop(f"{i}.f_2.bias") + + if i == 0: + block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_two_sd_orig.pop(f"{i}.f_s.weight") + block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_two_sd_orig.pop(f"{i}.f_s.bias") + +block_two_sd_new["downsamplers.0.norm1.weight"] = block_two_sd_orig.pop("3.gn_1.weight") +block_two_sd_new["downsamplers.0.norm1.bias"] = block_two_sd_orig.pop("3.gn_1.bias") +block_two_sd_new["downsamplers.0.conv1.weight"] = block_two_sd_orig.pop("3.f_1.weight") +block_two_sd_new["downsamplers.0.conv1.bias"] = block_two_sd_orig.pop("3.f_1.bias") +block_two_sd_new["downsamplers.0.time_emb_proj.weight"] = block_two_sd_orig.pop("3.f_t.weight") +block_two_sd_new["downsamplers.0.time_emb_proj.bias"] = block_two_sd_orig.pop("3.f_t.bias") +block_two_sd_new["downsamplers.0.norm2.weight"] = block_two_sd_orig.pop("3.gn_2.weight") +block_two_sd_new["downsamplers.0.norm2.bias"] = block_two_sd_orig.pop("3.gn_2.bias") +block_two_sd_new["downsamplers.0.conv2.weight"] = block_two_sd_orig.pop("3.f_2.weight") +block_two_sd_new["downsamplers.0.conv2.bias"] = block_two_sd_orig.pop("3.f_2.bias") + +assert len(block_two_sd_orig) == 0 + +block_two = ResnetDownsampleBlock2D( + in_channels=320, + out_channels=640, + temb_channels=1280, + num_layers=3, + add_downsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +block_two.load_state_dict(block_two_sd_new) + +print("DOWN BLOCK THREE") + +block_three_sd_orig = model.down[2].state_dict() +block_three_sd_new = {} + +for i in range(3): + block_three_sd_new[f"resnets.{i}.norm1.weight"] = block_three_sd_orig.pop(f"{i}.gn_1.weight") + block_three_sd_new[f"resnets.{i}.norm1.bias"] = block_three_sd_orig.pop(f"{i}.gn_1.bias") + block_three_sd_new[f"resnets.{i}.conv1.weight"] = block_three_sd_orig.pop(f"{i}.f_1.weight") + block_three_sd_new[f"resnets.{i}.conv1.bias"] = block_three_sd_orig.pop(f"{i}.f_1.bias") + block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_three_sd_orig.pop(f"{i}.f_t.weight") + block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_three_sd_orig.pop(f"{i}.f_t.bias") + block_three_sd_new[f"resnets.{i}.norm2.weight"] = block_three_sd_orig.pop(f"{i}.gn_2.weight") + block_three_sd_new[f"resnets.{i}.norm2.bias"] = block_three_sd_orig.pop(f"{i}.gn_2.bias") + block_three_sd_new[f"resnets.{i}.conv2.weight"] = block_three_sd_orig.pop(f"{i}.f_2.weight") + block_three_sd_new[f"resnets.{i}.conv2.bias"] = block_three_sd_orig.pop(f"{i}.f_2.bias") + + if i == 0: + block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = block_three_sd_orig.pop(f"{i}.f_s.weight") + block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = block_three_sd_orig.pop(f"{i}.f_s.bias") + +block_three_sd_new["downsamplers.0.norm1.weight"] = block_three_sd_orig.pop("3.gn_1.weight") +block_three_sd_new["downsamplers.0.norm1.bias"] = block_three_sd_orig.pop("3.gn_1.bias") +block_three_sd_new["downsamplers.0.conv1.weight"] = block_three_sd_orig.pop("3.f_1.weight") +block_three_sd_new["downsamplers.0.conv1.bias"] = block_three_sd_orig.pop("3.f_1.bias") +block_three_sd_new["downsamplers.0.time_emb_proj.weight"] = block_three_sd_orig.pop("3.f_t.weight") +block_three_sd_new["downsamplers.0.time_emb_proj.bias"] = block_three_sd_orig.pop("3.f_t.bias") +block_three_sd_new["downsamplers.0.norm2.weight"] = block_three_sd_orig.pop("3.gn_2.weight") +block_three_sd_new["downsamplers.0.norm2.bias"] = block_three_sd_orig.pop("3.gn_2.bias") +block_three_sd_new["downsamplers.0.conv2.weight"] = block_three_sd_orig.pop("3.f_2.weight") +block_three_sd_new["downsamplers.0.conv2.bias"] = block_three_sd_orig.pop("3.f_2.bias") + +assert len(block_three_sd_orig) == 0 + +block_three = ResnetDownsampleBlock2D( + in_channels=640, + out_channels=1024, + temb_channels=1280, + num_layers=3, + add_downsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +block_three.load_state_dict(block_three_sd_new) + +print("DOWN BLOCK FOUR") + +block_four_sd_orig = model.down[3].state_dict() +block_four_sd_new = {} + +for i in range(3): + block_four_sd_new[f"resnets.{i}.norm1.weight"] = block_four_sd_orig.pop(f"{i}.gn_1.weight") + block_four_sd_new[f"resnets.{i}.norm1.bias"] = block_four_sd_orig.pop(f"{i}.gn_1.bias") + block_four_sd_new[f"resnets.{i}.conv1.weight"] = block_four_sd_orig.pop(f"{i}.f_1.weight") + block_four_sd_new[f"resnets.{i}.conv1.bias"] = block_four_sd_orig.pop(f"{i}.f_1.bias") + block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = block_four_sd_orig.pop(f"{i}.f_t.weight") + block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = block_four_sd_orig.pop(f"{i}.f_t.bias") + block_four_sd_new[f"resnets.{i}.norm2.weight"] = block_four_sd_orig.pop(f"{i}.gn_2.weight") + block_four_sd_new[f"resnets.{i}.norm2.bias"] = block_four_sd_orig.pop(f"{i}.gn_2.bias") + block_four_sd_new[f"resnets.{i}.conv2.weight"] = block_four_sd_orig.pop(f"{i}.f_2.weight") + block_four_sd_new[f"resnets.{i}.conv2.bias"] = block_four_sd_orig.pop(f"{i}.f_2.bias") + +assert len(block_four_sd_orig) == 0 + +block_four = ResnetDownsampleBlock2D( + in_channels=1024, + out_channels=1024, + temb_channels=1280, + num_layers=3, + add_downsample=False, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +block_four.load_state_dict(block_four_sd_new) + + +print("MID BLOCK 1") + +mid_block_one_sd_orig = model.mid.state_dict() +mid_block_one_sd_new = {} + +for i in range(2): + mid_block_one_sd_new[f"resnets.{i}.norm1.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.weight") + mid_block_one_sd_new[f"resnets.{i}.norm1.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_1.bias") + mid_block_one_sd_new[f"resnets.{i}.conv1.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_1.weight") + mid_block_one_sd_new[f"resnets.{i}.conv1.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_1.bias") + mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_t.weight") + mid_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_t.bias") + mid_block_one_sd_new[f"resnets.{i}.norm2.weight"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.weight") + mid_block_one_sd_new[f"resnets.{i}.norm2.bias"] = mid_block_one_sd_orig.pop(f"{i}.gn_2.bias") + mid_block_one_sd_new[f"resnets.{i}.conv2.weight"] = mid_block_one_sd_orig.pop(f"{i}.f_2.weight") + mid_block_one_sd_new[f"resnets.{i}.conv2.bias"] = mid_block_one_sd_orig.pop(f"{i}.f_2.bias") + +assert len(mid_block_one_sd_orig) == 0 + +mid_block_one = UNetMidBlock2D( + in_channels=1024, + temb_channels=1280, + num_layers=1, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, + add_attention=False, +) + +mid_block_one.load_state_dict(mid_block_one_sd_new) + +print("UP BLOCK ONE") + +up_block_one_sd_orig = model.up[-1].state_dict() +up_block_one_sd_new = {} + +for i in range(4): + up_block_one_sd_new[f"resnets.{i}.norm1.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_1.weight") + up_block_one_sd_new[f"resnets.{i}.norm1.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_1.bias") + up_block_one_sd_new[f"resnets.{i}.conv1.weight"] = up_block_one_sd_orig.pop(f"{i}.f_1.weight") + up_block_one_sd_new[f"resnets.{i}.conv1.bias"] = up_block_one_sd_orig.pop(f"{i}.f_1.bias") + up_block_one_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_one_sd_orig.pop(f"{i}.f_t.weight") + up_block_one_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_one_sd_orig.pop(f"{i}.f_t.bias") + up_block_one_sd_new[f"resnets.{i}.norm2.weight"] = up_block_one_sd_orig.pop(f"{i}.gn_2.weight") + up_block_one_sd_new[f"resnets.{i}.norm2.bias"] = up_block_one_sd_orig.pop(f"{i}.gn_2.bias") + up_block_one_sd_new[f"resnets.{i}.conv2.weight"] = up_block_one_sd_orig.pop(f"{i}.f_2.weight") + up_block_one_sd_new[f"resnets.{i}.conv2.bias"] = up_block_one_sd_orig.pop(f"{i}.f_2.bias") + up_block_one_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_one_sd_orig.pop(f"{i}.f_s.weight") + up_block_one_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_one_sd_orig.pop(f"{i}.f_s.bias") + +up_block_one_sd_new["upsamplers.0.norm1.weight"] = up_block_one_sd_orig.pop("4.gn_1.weight") +up_block_one_sd_new["upsamplers.0.norm1.bias"] = up_block_one_sd_orig.pop("4.gn_1.bias") +up_block_one_sd_new["upsamplers.0.conv1.weight"] = up_block_one_sd_orig.pop("4.f_1.weight") +up_block_one_sd_new["upsamplers.0.conv1.bias"] = up_block_one_sd_orig.pop("4.f_1.bias") +up_block_one_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_one_sd_orig.pop("4.f_t.weight") +up_block_one_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_one_sd_orig.pop("4.f_t.bias") +up_block_one_sd_new["upsamplers.0.norm2.weight"] = up_block_one_sd_orig.pop("4.gn_2.weight") +up_block_one_sd_new["upsamplers.0.norm2.bias"] = up_block_one_sd_orig.pop("4.gn_2.bias") +up_block_one_sd_new["upsamplers.0.conv2.weight"] = up_block_one_sd_orig.pop("4.f_2.weight") +up_block_one_sd_new["upsamplers.0.conv2.bias"] = up_block_one_sd_orig.pop("4.f_2.bias") + +assert len(up_block_one_sd_orig) == 0 + +up_block_one = ResnetUpsampleBlock2D( + in_channels=1024, + prev_output_channel=1024, + out_channels=1024, + temb_channels=1280, + num_layers=4, + add_upsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +up_block_one.load_state_dict(up_block_one_sd_new) + +print("UP BLOCK TWO") + +up_block_two_sd_orig = model.up[-2].state_dict() +up_block_two_sd_new = {} + +for i in range(4): + up_block_two_sd_new[f"resnets.{i}.norm1.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_1.weight") + up_block_two_sd_new[f"resnets.{i}.norm1.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_1.bias") + up_block_two_sd_new[f"resnets.{i}.conv1.weight"] = up_block_two_sd_orig.pop(f"{i}.f_1.weight") + up_block_two_sd_new[f"resnets.{i}.conv1.bias"] = up_block_two_sd_orig.pop(f"{i}.f_1.bias") + up_block_two_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_two_sd_orig.pop(f"{i}.f_t.weight") + up_block_two_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_two_sd_orig.pop(f"{i}.f_t.bias") + up_block_two_sd_new[f"resnets.{i}.norm2.weight"] = up_block_two_sd_orig.pop(f"{i}.gn_2.weight") + up_block_two_sd_new[f"resnets.{i}.norm2.bias"] = up_block_two_sd_orig.pop(f"{i}.gn_2.bias") + up_block_two_sd_new[f"resnets.{i}.conv2.weight"] = up_block_two_sd_orig.pop(f"{i}.f_2.weight") + up_block_two_sd_new[f"resnets.{i}.conv2.bias"] = up_block_two_sd_orig.pop(f"{i}.f_2.bias") + up_block_two_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_two_sd_orig.pop(f"{i}.f_s.weight") + up_block_two_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_two_sd_orig.pop(f"{i}.f_s.bias") + +up_block_two_sd_new["upsamplers.0.norm1.weight"] = up_block_two_sd_orig.pop("4.gn_1.weight") +up_block_two_sd_new["upsamplers.0.norm1.bias"] = up_block_two_sd_orig.pop("4.gn_1.bias") +up_block_two_sd_new["upsamplers.0.conv1.weight"] = up_block_two_sd_orig.pop("4.f_1.weight") +up_block_two_sd_new["upsamplers.0.conv1.bias"] = up_block_two_sd_orig.pop("4.f_1.bias") +up_block_two_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_two_sd_orig.pop("4.f_t.weight") +up_block_two_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_two_sd_orig.pop("4.f_t.bias") +up_block_two_sd_new["upsamplers.0.norm2.weight"] = up_block_two_sd_orig.pop("4.gn_2.weight") +up_block_two_sd_new["upsamplers.0.norm2.bias"] = up_block_two_sd_orig.pop("4.gn_2.bias") +up_block_two_sd_new["upsamplers.0.conv2.weight"] = up_block_two_sd_orig.pop("4.f_2.weight") +up_block_two_sd_new["upsamplers.0.conv2.bias"] = up_block_two_sd_orig.pop("4.f_2.bias") + +assert len(up_block_two_sd_orig) == 0 + +up_block_two = ResnetUpsampleBlock2D( + in_channels=640, + prev_output_channel=1024, + out_channels=1024, + temb_channels=1280, + num_layers=4, + add_upsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +up_block_two.load_state_dict(up_block_two_sd_new) + +print("UP BLOCK THREE") + +up_block_three_sd_orig = model.up[-3].state_dict() +up_block_three_sd_new = {} + +for i in range(4): + up_block_three_sd_new[f"resnets.{i}.norm1.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_1.weight") + up_block_three_sd_new[f"resnets.{i}.norm1.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_1.bias") + up_block_three_sd_new[f"resnets.{i}.conv1.weight"] = up_block_three_sd_orig.pop(f"{i}.f_1.weight") + up_block_three_sd_new[f"resnets.{i}.conv1.bias"] = up_block_three_sd_orig.pop(f"{i}.f_1.bias") + up_block_three_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_three_sd_orig.pop(f"{i}.f_t.weight") + up_block_three_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_three_sd_orig.pop(f"{i}.f_t.bias") + up_block_three_sd_new[f"resnets.{i}.norm2.weight"] = up_block_three_sd_orig.pop(f"{i}.gn_2.weight") + up_block_three_sd_new[f"resnets.{i}.norm2.bias"] = up_block_three_sd_orig.pop(f"{i}.gn_2.bias") + up_block_three_sd_new[f"resnets.{i}.conv2.weight"] = up_block_three_sd_orig.pop(f"{i}.f_2.weight") + up_block_three_sd_new[f"resnets.{i}.conv2.bias"] = up_block_three_sd_orig.pop(f"{i}.f_2.bias") + up_block_three_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_three_sd_orig.pop(f"{i}.f_s.weight") + up_block_three_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_three_sd_orig.pop(f"{i}.f_s.bias") + +up_block_three_sd_new["upsamplers.0.norm1.weight"] = up_block_three_sd_orig.pop("4.gn_1.weight") +up_block_three_sd_new["upsamplers.0.norm1.bias"] = up_block_three_sd_orig.pop("4.gn_1.bias") +up_block_three_sd_new["upsamplers.0.conv1.weight"] = up_block_three_sd_orig.pop("4.f_1.weight") +up_block_three_sd_new["upsamplers.0.conv1.bias"] = up_block_three_sd_orig.pop("4.f_1.bias") +up_block_three_sd_new["upsamplers.0.time_emb_proj.weight"] = up_block_three_sd_orig.pop("4.f_t.weight") +up_block_three_sd_new["upsamplers.0.time_emb_proj.bias"] = up_block_three_sd_orig.pop("4.f_t.bias") +up_block_three_sd_new["upsamplers.0.norm2.weight"] = up_block_three_sd_orig.pop("4.gn_2.weight") +up_block_three_sd_new["upsamplers.0.norm2.bias"] = up_block_three_sd_orig.pop("4.gn_2.bias") +up_block_three_sd_new["upsamplers.0.conv2.weight"] = up_block_three_sd_orig.pop("4.f_2.weight") +up_block_three_sd_new["upsamplers.0.conv2.bias"] = up_block_three_sd_orig.pop("4.f_2.bias") + +assert len(up_block_three_sd_orig) == 0 + +up_block_three = ResnetUpsampleBlock2D( + in_channels=320, + prev_output_channel=1024, + out_channels=640, + temb_channels=1280, + num_layers=4, + add_upsample=True, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +up_block_three.load_state_dict(up_block_three_sd_new) + +print("UP BLOCK FOUR") + +up_block_four_sd_orig = model.up[-4].state_dict() +up_block_four_sd_new = {} + +for i in range(4): + up_block_four_sd_new[f"resnets.{i}.norm1.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_1.weight") + up_block_four_sd_new[f"resnets.{i}.norm1.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_1.bias") + up_block_four_sd_new[f"resnets.{i}.conv1.weight"] = up_block_four_sd_orig.pop(f"{i}.f_1.weight") + up_block_four_sd_new[f"resnets.{i}.conv1.bias"] = up_block_four_sd_orig.pop(f"{i}.f_1.bias") + up_block_four_sd_new[f"resnets.{i}.time_emb_proj.weight"] = up_block_four_sd_orig.pop(f"{i}.f_t.weight") + up_block_four_sd_new[f"resnets.{i}.time_emb_proj.bias"] = up_block_four_sd_orig.pop(f"{i}.f_t.bias") + up_block_four_sd_new[f"resnets.{i}.norm2.weight"] = up_block_four_sd_orig.pop(f"{i}.gn_2.weight") + up_block_four_sd_new[f"resnets.{i}.norm2.bias"] = up_block_four_sd_orig.pop(f"{i}.gn_2.bias") + up_block_four_sd_new[f"resnets.{i}.conv2.weight"] = up_block_four_sd_orig.pop(f"{i}.f_2.weight") + up_block_four_sd_new[f"resnets.{i}.conv2.bias"] = up_block_four_sd_orig.pop(f"{i}.f_2.bias") + up_block_four_sd_new[f"resnets.{i}.conv_shortcut.weight"] = up_block_four_sd_orig.pop(f"{i}.f_s.weight") + up_block_four_sd_new[f"resnets.{i}.conv_shortcut.bias"] = up_block_four_sd_orig.pop(f"{i}.f_s.bias") + +assert len(up_block_four_sd_orig) == 0 + +up_block_four = ResnetUpsampleBlock2D( + in_channels=320, + prev_output_channel=640, + out_channels=320, + temb_channels=1280, + num_layers=4, + add_upsample=False, + resnet_time_scale_shift="scale_shift", + resnet_eps=1e-5, +) + +up_block_four.load_state_dict(up_block_four_sd_new) + +print("initial projection (conv_in)") + +conv_in_sd_orig = model.embed_image.state_dict() +conv_in_sd_new = {} + +conv_in_sd_new["weight"] = conv_in_sd_orig.pop("f.weight") +conv_in_sd_new["bias"] = conv_in_sd_orig.pop("f.bias") + +assert len(conv_in_sd_orig) == 0 + +block_out_channels = [320, 640, 1024, 1024] + +in_channels = 7 +conv_in_kernel = 3 +conv_in_padding = (conv_in_kernel - 1) // 2 +conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding) + +conv_in.load_state_dict(conv_in_sd_new) + +print("out projection (conv_out) (conv_norm_out)") +out_channels = 6 +norm_num_groups = 32 +norm_eps = 1e-5 +act_fn = "silu" +conv_out_kernel = 3 +conv_out_padding = (conv_out_kernel - 1) // 2 +conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) +# uses torch.functional in orig +# conv_act = get_activation(act_fn) +conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding) + +conv_norm_out.load_state_dict(model.output.gn.state_dict()) +conv_out.load_state_dict(model.output.f.state_dict()) + +print("timestep projection (time_proj) (time_embedding)") + +f1_sd = model.embed_time.f_1.state_dict() +f2_sd = model.embed_time.f_2.state_dict() + +time_embedding_sd = { + "linear_1.weight": f1_sd.pop("weight"), + "linear_1.bias": f1_sd.pop("bias"), + "linear_2.weight": f2_sd.pop("weight"), + "linear_2.bias": f2_sd.pop("bias"), +} + +assert len(f1_sd) == 0 +assert len(f2_sd) == 0 + +time_embedding_type = "learned" +num_train_timesteps = 1024 +time_embedding_dim = 1280 + +time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0]) +timestep_input_dim = block_out_channels[0] + +time_embedding = TimestepEmbedding(timestep_input_dim, time_embedding_dim) + +time_proj.load_state_dict(model.embed_time.emb.state_dict()) +time_embedding.load_state_dict(time_embedding_sd) + +print("CONVERT") + +time_embedding.to("cuda") +time_proj.to("cuda") +conv_in.to("cuda") + +block_one.to("cuda") +block_two.to("cuda") +block_three.to("cuda") +block_four.to("cuda") + +mid_block_one.to("cuda") + +up_block_one.to("cuda") +up_block_two.to("cuda") +up_block_three.to("cuda") +up_block_four.to("cuda") + +conv_norm_out.to("cuda") +conv_out.to("cuda") + +model.time_proj = time_proj +model.time_embedding = time_embedding +model.embed_image = conv_in + +model.down[0] = block_one +model.down[1] = block_two +model.down[2] = block_three +model.down[3] = block_four + +model.mid = mid_block_one + +model.up[-1] = up_block_one +model.up[-2] = up_block_two +model.up[-3] = up_block_three +model.up[-4] = up_block_four + +model.output.gn = conv_norm_out +model.output.f = conv_out + +model.converted = True + +sample_consistency_new = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) +save_image(sample_consistency_new, "con_new.png") + +assert (sample_consistency_orig == sample_consistency_new).all() + +print("making unet") + +unet = UNet2DModel( + in_channels=in_channels, + out_channels=out_channels, + down_block_types=( + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + ), + up_block_types=( + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + ), + block_out_channels=block_out_channels, + layers_per_block=3, + norm_num_groups=norm_num_groups, + norm_eps=norm_eps, + resnet_time_scale_shift="scale_shift", + time_embedding_type="learned", + num_train_timesteps=num_train_timesteps, + add_attention=False, +) + +unet_state_dict = {} + + +def add_state_dict(prefix, mod): + for k, v in mod.state_dict().items(): + unet_state_dict[f"{prefix}.{k}"] = v + + +add_state_dict("conv_in", conv_in) +add_state_dict("time_proj", time_proj) +add_state_dict("time_embedding", time_embedding) +add_state_dict("down_blocks.0", block_one) +add_state_dict("down_blocks.1", block_two) +add_state_dict("down_blocks.2", block_three) +add_state_dict("down_blocks.3", block_four) +add_state_dict("mid_block", mid_block_one) +add_state_dict("up_blocks.0", up_block_one) +add_state_dict("up_blocks.1", up_block_two) +add_state_dict("up_blocks.2", up_block_three) +add_state_dict("up_blocks.3", up_block_four) +add_state_dict("conv_norm_out", conv_norm_out) +add_state_dict("conv_out", conv_out) + +unet.load_state_dict(unet_state_dict) + +print("running with diffusers unet") + +unet.to("cuda") + +decoder_consistency.ckpt = unet + +sample_consistency_new_2 = decoder_consistency(latent, generator=torch.Generator("cpu").manual_seed(0)) +save_image(sample_consistency_new_2, "con_new_2.png") + +assert (sample_consistency_orig == sample_consistency_new_2).all() + +print("running with diffusers model") + +Encoder.old_constructor = Encoder.__init__ + + +def new_constructor(self, **kwargs): + self.old_constructor(**kwargs) + self.constructor_arguments = kwargs + + +Encoder.__init__ = new_constructor + + +vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae") +consistency_vae = ConsistencyDecoderVAE( + encoder_args=vae.encoder.constructor_arguments, + decoder_args=unet.config, + scaling_factor=vae.config.scaling_factor, + block_out_channels=vae.config.block_out_channels, + latent_channels=vae.config.latent_channels, +) +consistency_vae.encoder.load_state_dict(vae.encoder.state_dict()) +consistency_vae.quant_conv.load_state_dict(vae.quant_conv.state_dict()) +consistency_vae.decoder_unet.load_state_dict(unet.state_dict()) + +consistency_vae.to(dtype=torch.float16, device="cuda") + +sample_consistency_new_3 = consistency_vae.decode( + 0.18215 * latent, generator=torch.Generator("cpu").manual_seed(0) +).sample + +print("max difference") +print((sample_consistency_orig - sample_consistency_new_3).abs().max()) +print("total difference") +print((sample_consistency_orig - sample_consistency_new_3).abs().sum()) +# assert (sample_consistency_orig == sample_consistency_new_3).all() + +print("running with diffusers pipeline") + +pipe = DiffusionPipeline.from_pretrained( + "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=consistency_vae, torch_dtype=torch.float16 +) +pipe.to("cuda") + +pipe("horse", generator=torch.Generator("cpu").manual_seed(0)).images[0].save("horse.png") + + +if args.save_pretrained is not None: + consistency_vae.save_pretrained(args.save_pretrained) diff --git a/scripts/convert_consistency_to_diffusers.py b/scripts/convert_consistency_to_diffusers.py new file mode 100644 index 000000000000..2b918280ca05 --- /dev/null +++ b/scripts/convert_consistency_to_diffusers.py @@ -0,0 +1,315 @@ +import argparse +import os + +import torch + +from diffusers import ( + CMStochasticIterativeScheduler, + ConsistencyModelPipeline, + UNet2DModel, +) + + +TEST_UNET_CONFIG = { + "sample_size": 32, + "in_channels": 3, + "out_channels": 3, + "layers_per_block": 2, + "num_class_embeds": 1000, + "block_out_channels": [32, 64], + "attention_head_dim": 8, + "down_block_types": [ + "ResnetDownsampleBlock2D", + "AttnDownBlock2D", + ], + "up_block_types": [ + "AttnUpBlock2D", + "ResnetUpsampleBlock2D", + ], + "resnet_time_scale_shift": "scale_shift", + "attn_norm_num_groups": 32, + "upsample_type": "resnet", + "downsample_type": "resnet", +} + +IMAGENET_64_UNET_CONFIG = { + "sample_size": 64, + "in_channels": 3, + "out_channels": 3, + "layers_per_block": 3, + "num_class_embeds": 1000, + "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4], + "attention_head_dim": 64, + "down_block_types": [ + "ResnetDownsampleBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + ], + "up_block_types": [ + "AttnUpBlock2D", + "AttnUpBlock2D", + "AttnUpBlock2D", + "ResnetUpsampleBlock2D", + ], + "resnet_time_scale_shift": "scale_shift", + "attn_norm_num_groups": 32, + "upsample_type": "resnet", + "downsample_type": "resnet", +} + +LSUN_256_UNET_CONFIG = { + "sample_size": 256, + "in_channels": 3, + "out_channels": 3, + "layers_per_block": 2, + "num_class_embeds": None, + "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4], + "attention_head_dim": 64, + "down_block_types": [ + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + "AttnDownBlock2D", + ], + "up_block_types": [ + "AttnUpBlock2D", + "AttnUpBlock2D", + "AttnUpBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + ], + "resnet_time_scale_shift": "default", + "upsample_type": "resnet", + "downsample_type": "resnet", +} + +CD_SCHEDULER_CONFIG = { + "num_train_timesteps": 40, + "sigma_min": 0.002, + "sigma_max": 80.0, +} + +CT_IMAGENET_64_SCHEDULER_CONFIG = { + "num_train_timesteps": 201, + "sigma_min": 0.002, + "sigma_max": 80.0, +} + +CT_LSUN_256_SCHEDULER_CONFIG = { + "num_train_timesteps": 151, + "sigma_min": 0.002, + "sigma_max": 80.0, +} + + +def str2bool(v): + """ + https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse + """ + if isinstance(v, bool): + return v + if v.lower() in ("yes", "true", "t", "y", "1"): + return True + elif v.lower() in ("no", "false", "f", "n", "0"): + return False + else: + raise argparse.ArgumentTypeError("boolean value expected") + + +def convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=False): + new_checkpoint[f"{new_prefix}.norm1.weight"] = checkpoint[f"{old_prefix}.in_layers.0.weight"] + new_checkpoint[f"{new_prefix}.norm1.bias"] = checkpoint[f"{old_prefix}.in_layers.0.bias"] + new_checkpoint[f"{new_prefix}.conv1.weight"] = checkpoint[f"{old_prefix}.in_layers.2.weight"] + new_checkpoint[f"{new_prefix}.conv1.bias"] = checkpoint[f"{old_prefix}.in_layers.2.bias"] + new_checkpoint[f"{new_prefix}.time_emb_proj.weight"] = checkpoint[f"{old_prefix}.emb_layers.1.weight"] + new_checkpoint[f"{new_prefix}.time_emb_proj.bias"] = checkpoint[f"{old_prefix}.emb_layers.1.bias"] + new_checkpoint[f"{new_prefix}.norm2.weight"] = checkpoint[f"{old_prefix}.out_layers.0.weight"] + new_checkpoint[f"{new_prefix}.norm2.bias"] = checkpoint[f"{old_prefix}.out_layers.0.bias"] + new_checkpoint[f"{new_prefix}.conv2.weight"] = checkpoint[f"{old_prefix}.out_layers.3.weight"] + new_checkpoint[f"{new_prefix}.conv2.bias"] = checkpoint[f"{old_prefix}.out_layers.3.bias"] + + if has_skip: + new_checkpoint[f"{new_prefix}.conv_shortcut.weight"] = checkpoint[f"{old_prefix}.skip_connection.weight"] + new_checkpoint[f"{new_prefix}.conv_shortcut.bias"] = checkpoint[f"{old_prefix}.skip_connection.bias"] + + return new_checkpoint + + +def convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_dim=None): + weight_q, weight_k, weight_v = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3, dim=0) + bias_q, bias_k, bias_v = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3, dim=0) + + new_checkpoint[f"{new_prefix}.group_norm.weight"] = checkpoint[f"{old_prefix}.norm.weight"] + new_checkpoint[f"{new_prefix}.group_norm.bias"] = checkpoint[f"{old_prefix}.norm.bias"] + + new_checkpoint[f"{new_prefix}.to_q.weight"] = weight_q.squeeze(-1).squeeze(-1) + new_checkpoint[f"{new_prefix}.to_q.bias"] = bias_q.squeeze(-1).squeeze(-1) + new_checkpoint[f"{new_prefix}.to_k.weight"] = weight_k.squeeze(-1).squeeze(-1) + new_checkpoint[f"{new_prefix}.to_k.bias"] = bias_k.squeeze(-1).squeeze(-1) + new_checkpoint[f"{new_prefix}.to_v.weight"] = weight_v.squeeze(-1).squeeze(-1) + new_checkpoint[f"{new_prefix}.to_v.bias"] = bias_v.squeeze(-1).squeeze(-1) + + new_checkpoint[f"{new_prefix}.to_out.0.weight"] = ( + checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1).squeeze(-1) + ) + new_checkpoint[f"{new_prefix}.to_out.0.bias"] = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1).squeeze(-1) + + return new_checkpoint + + +def con_pt_to_diffuser(checkpoint_path: str, unet_config): + checkpoint = torch.load(checkpoint_path, map_location="cpu") + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] + + if unet_config["num_class_embeds"] is not None: + new_checkpoint["class_embedding.weight"] = checkpoint["label_emb.weight"] + + new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] + + down_block_types = unet_config["down_block_types"] + layers_per_block = unet_config["layers_per_block"] + attention_head_dim = unet_config["attention_head_dim"] + channels_list = unet_config["block_out_channels"] + current_layer = 1 + prev_channels = channels_list[0] + + for i, layer_type in enumerate(down_block_types): + current_channels = channels_list[i] + downsample_block_has_skip = current_channels != prev_channels + if layer_type == "ResnetDownsampleBlock2D": + for j in range(layers_per_block): + new_prefix = f"down_blocks.{i}.resnets.{j}" + old_prefix = f"input_blocks.{current_layer}.0" + has_skip = True if j == 0 and downsample_block_has_skip else False + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) + current_layer += 1 + + elif layer_type == "AttnDownBlock2D": + for j in range(layers_per_block): + new_prefix = f"down_blocks.{i}.resnets.{j}" + old_prefix = f"input_blocks.{current_layer}.0" + has_skip = True if j == 0 and downsample_block_has_skip else False + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=has_skip) + new_prefix = f"down_blocks.{i}.attentions.{j}" + old_prefix = f"input_blocks.{current_layer}.1" + new_checkpoint = convert_attention( + checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim + ) + current_layer += 1 + + if i != len(down_block_types) - 1: + new_prefix = f"down_blocks.{i}.downsamplers.0" + old_prefix = f"input_blocks.{current_layer}.0" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) + current_layer += 1 + + prev_channels = current_channels + + # hardcoded the mid-block for now + new_prefix = "mid_block.resnets.0" + old_prefix = "middle_block.0" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) + new_prefix = "mid_block.attentions.0" + old_prefix = "middle_block.1" + new_checkpoint = convert_attention(checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim) + new_prefix = "mid_block.resnets.1" + old_prefix = "middle_block.2" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) + + current_layer = 0 + up_block_types = unet_config["up_block_types"] + + for i, layer_type in enumerate(up_block_types): + if layer_type == "ResnetUpsampleBlock2D": + for j in range(layers_per_block + 1): + new_prefix = f"up_blocks.{i}.resnets.{j}" + old_prefix = f"output_blocks.{current_layer}.0" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) + current_layer += 1 + + if i != len(up_block_types) - 1: + new_prefix = f"up_blocks.{i}.upsamplers.0" + old_prefix = f"output_blocks.{current_layer - 1}.1" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) + elif layer_type == "AttnUpBlock2D": + for j in range(layers_per_block + 1): + new_prefix = f"up_blocks.{i}.resnets.{j}" + old_prefix = f"output_blocks.{current_layer}.0" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix, has_skip=True) + new_prefix = f"up_blocks.{i}.attentions.{j}" + old_prefix = f"output_blocks.{current_layer}.1" + new_checkpoint = convert_attention( + checkpoint, new_checkpoint, old_prefix, new_prefix, attention_head_dim + ) + current_layer += 1 + + if i != len(up_block_types) - 1: + new_prefix = f"up_blocks.{i}.upsamplers.0" + old_prefix = f"output_blocks.{current_layer - 1}.2" + new_checkpoint = convert_resnet(checkpoint, new_checkpoint, old_prefix, new_prefix) + + new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] + new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] + new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] + + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.") + parser.add_argument( + "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model." + ) + parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.") + + args = parser.parse_args() + args.class_cond = str2bool(args.class_cond) + + ckpt_name = os.path.basename(args.unet_path) + print(f"Checkpoint: {ckpt_name}") + + # Get U-Net config + if "imagenet64" in ckpt_name: + unet_config = IMAGENET_64_UNET_CONFIG + elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): + unet_config = LSUN_256_UNET_CONFIG + elif "test" in ckpt_name: + unet_config = TEST_UNET_CONFIG + else: + raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") + + if not args.class_cond: + unet_config["num_class_embeds"] = None + + converted_unet_ckpt = con_pt_to_diffuser(args.unet_path, unet_config) + + image_unet = UNet2DModel(**unet_config) + image_unet.load_state_dict(converted_unet_ckpt) + + # Get scheduler config + if "cd" in ckpt_name or "test" in ckpt_name: + scheduler_config = CD_SCHEDULER_CONFIG + elif "ct" in ckpt_name and "imagenet64" in ckpt_name: + scheduler_config = CT_IMAGENET_64_SCHEDULER_CONFIG + elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)): + scheduler_config = CT_LSUN_256_SCHEDULER_CONFIG + else: + raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.") + + cm_scheduler = CMStochasticIterativeScheduler(**scheduler_config) + + consistency_model = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler) + consistency_model.save_pretrained(args.dump_path) diff --git a/scripts/convert_cosmos_to_diffusers.py b/scripts/convert_cosmos_to_diffusers.py new file mode 100644 index 000000000000..b6f321b5b2fb --- /dev/null +++ b/scripts/convert_cosmos_to_diffusers.py @@ -0,0 +1,976 @@ +""" +# Cosmos 2 Predict + +Download checkpoint +```bash +hf download nvidia/Cosmos-Predict2-2B-Text2Image +``` + +convert checkpoint +```bash +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2-2B-Text2Image/snapshots/acdb5fde992a73ef0355f287977d002cbfd127e0/model.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_ckpt_path $transformer_ckpt_path \ + --transformer_type Cosmos-2.0-Diffusion-2B-Text2Image \ + --text_encoder_path google-t5/t5-11b \ + --tokenizer_path google-t5/t5-11b \ + --vae_type wan2.1 \ + --output_path converted/cosmos-p2-t2i-2b \ + --save_pipeline +``` + +# Cosmos 2.5 Predict + +Download checkpoint +```bash +hf download nvidia/Cosmos-Predict2.5-2B +``` + +Convert checkpoint +```bash +# pre-trained +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/pre-trained/d20b7120-df3e-4911-919d-db6e08bad31c_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Predict-Base-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/2b/d20b7120-df3e-4911-919d-db6e08bad31c \ + --save_pipeline + +# post-trained +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/post-trained/81edfebe-bd6a-4039-8c1d-737df1a790bf_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Predict-Base-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/2b/81edfebe-bd6a-4039-8c1d-737df1a790bf \ + --save_pipeline +``` + +## 14B + +```bash +hf download nvidia/Cosmos-Predict2.5-14B +``` + +```bash +# pre-trained +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/pre-trained/54937b8c-29de-4f04-862c-e67b04ec41e8_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Predict-Base-14B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/14b/54937b8c-29de-4f04-862c-e67b04ec41e8/ \ + --save_pipeline + +# post-trained +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/post-trained/e21d2a49-4747-44c8-ba44-9f6f9243715f_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Predict-Base-14B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/14b/e21d2a49-4747-44c8-ba44-9f6f9243715f/ \ + --save_pipeline +``` + +# Cosmos 2.5 Transfer + +Download checkpoint +```bash +hf download nvidia/Cosmos-Transfer2.5-2B +``` + +Convert checkpoint +```bash +# depth +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/depth/626e6618-bfcd-4d9a-a077-1409e2ce353f_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/depth/pipeline \ + --save_pipeline + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/depth/models + +# edge +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/edge/61f5694b-0ad5-4ecd-8ad7-c8545627d125_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/edge/pipeline \ + --save_pipeline + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/edge/models + +# blur +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/blur/ba2f44f2-c726-4fe7-949f-597069d9b91c_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/blur/pipeline \ + --save_pipeline + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/blur/models + +# seg +transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/seg/5136ef49-6d8d-42e8-8abf-7dac722a304a_ema_bf16.pt + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/seg/pipeline \ + --save_pipeline + +python scripts/convert_cosmos_to_diffusers.py \ + --transformer_type Cosmos-2.5-Transfer-General-2B \ + --transformer_ckpt_path $transformer_ckpt_path \ + --vae_type wan2.1 \ + --output_path converted/transfer/2b/general/seg/models +``` +""" + +import argparse +import pathlib +import sys +from typing import Any, Dict, Optional + +import torch +from accelerate import init_empty_weights +from huggingface_hub import snapshot_download +from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration, T5EncoderModel, T5TokenizerFast + +from diffusers import ( + AutoencoderKLCosmos, + AutoencoderKLWan, + Cosmos2TextToImagePipeline, + Cosmos2VideoToWorldPipeline, + CosmosControlNetModel, + CosmosTextToWorldPipeline, + CosmosTransformer3DModel, + CosmosVideoToWorldPipeline, + EDMEulerScheduler, + FlowMatchEulerDiscreteScheduler, + UniPCMultistepScheduler, +) +from diffusers.pipelines.cosmos.pipeline_cosmos2_5_predict import Cosmos2_5_PredictBasePipeline +from diffusers.pipelines.cosmos.pipeline_cosmos2_5_transfer import Cosmos2_5_TransferPipeline + + +def remove_keys_(key: str, state_dict: Dict[str, Any]): + state_dict.pop(key) + + +def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def rename_transformer_blocks_(key: str, state_dict: Dict[str, Any]): + block_index = int(key.split(".")[1].removeprefix("block")) + new_key = key + + old_prefix = f"blocks.block{block_index}" + new_prefix = f"transformer_blocks.{block_index}" + new_key = new_prefix + new_key.removeprefix(old_prefix) + + state_dict[new_key] = state_dict.pop(key) + + +TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 = { + "t_embedder.1": "time_embed.t_embedder", + "affline_norm": "time_embed.norm", + ".blocks.0.block.attn": ".attn1", + ".blocks.1.block.attn": ".attn2", + ".blocks.2.block": ".ff", + ".blocks.0.adaLN_modulation.1": ".norm1.linear_1", + ".blocks.0.adaLN_modulation.2": ".norm1.linear_2", + ".blocks.1.adaLN_modulation.1": ".norm2.linear_1", + ".blocks.1.adaLN_modulation.2": ".norm2.linear_2", + ".blocks.2.adaLN_modulation.1": ".norm3.linear_1", + ".blocks.2.adaLN_modulation.2": ".norm3.linear_2", + "to_q.0": "to_q", + "to_q.1": "norm_q", + "to_k.0": "to_k", + "to_k.1": "norm_k", + "to_v.0": "to_v", + "layer1": "net.0.proj", + "layer2": "net.2", + "proj.1": "proj", + "x_embedder": "patch_embed", + "extra_pos_embedder": "learnable_pos_embed", + "final_layer.adaLN_modulation.1": "norm_out.linear_1", + "final_layer.adaLN_modulation.2": "norm_out.linear_2", + "final_layer.linear": "proj_out", +} + +TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 = { + "blocks.block": rename_transformer_blocks_, + "logvar.0.freqs": remove_keys_, + "logvar.0.phases": remove_keys_, + "logvar.1.weight": remove_keys_, + "pos_embedder.seq": remove_keys_, +} + +TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 = { + "t_embedder.1": "time_embed.t_embedder", + "t_embedding_norm": "time_embed.norm", + "blocks": "transformer_blocks", + "adaln_modulation_self_attn.1": "norm1.linear_1", + "adaln_modulation_self_attn.2": "norm1.linear_2", + "adaln_modulation_cross_attn.1": "norm2.linear_1", + "adaln_modulation_cross_attn.2": "norm2.linear_2", + "adaln_modulation_mlp.1": "norm3.linear_1", + "adaln_modulation_mlp.2": "norm3.linear_2", + "self_attn": "attn1", + "cross_attn": "attn2", + "q_proj": "to_q", + "k_proj": "to_k", + "v_proj": "to_v", + "output_proj": "to_out.0", + "q_norm": "norm_q", + "k_norm": "norm_k", + "mlp.layer1": "ff.net.0.proj", + "mlp.layer2": "ff.net.2", + "x_embedder.proj.1": "patch_embed.proj", + "final_layer.adaln_modulation.1": "norm_out.linear_1", + "final_layer.adaln_modulation.2": "norm_out.linear_2", + "final_layer.linear": "proj_out", +} + +TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 = { + "accum_video_sample_counter": remove_keys_, + "accum_image_sample_counter": remove_keys_, + "accum_iteration": remove_keys_, + "accum_train_in_hours": remove_keys_, + "pos_embedder.seq": remove_keys_, + "pos_embedder.dim_spatial_range": remove_keys_, + "pos_embedder.dim_temporal_range": remove_keys_, + "_extra_state": remove_keys_, +} + + +TRANSFORMER_CONFIGS = { + "Cosmos-1.0-Diffusion-7B-Text2World": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 32, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (2.0, 1.0, 1.0), + "concat_padding_mask": True, + "extra_pos_embed_type": "learnable", + }, + "Cosmos-1.0-Diffusion-7B-Video2World": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 32, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (2.0, 1.0, 1.0), + "concat_padding_mask": True, + "extra_pos_embed_type": "learnable", + }, + "Cosmos-1.0-Diffusion-14B-Text2World": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 40, + "attention_head_dim": 128, + "num_layers": 36, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (2.0, 2.0, 2.0), + "concat_padding_mask": True, + "extra_pos_embed_type": "learnable", + }, + "Cosmos-1.0-Diffusion-14B-Video2World": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 40, + "attention_head_dim": 128, + "num_layers": 36, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (2.0, 2.0, 2.0), + "concat_padding_mask": True, + "extra_pos_embed_type": "learnable", + }, + "Cosmos-2.0-Diffusion-2B-Text2Image": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 16, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 4.0, 4.0), + "concat_padding_mask": True, + "extra_pos_embed_type": None, + }, + "Cosmos-2.0-Diffusion-14B-Text2Image": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 40, + "attention_head_dim": 128, + "num_layers": 36, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 4.0, 4.0), + "concat_padding_mask": True, + "extra_pos_embed_type": None, + }, + "Cosmos-2.0-Diffusion-2B-Video2World": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 16, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 3.0, 3.0), + "concat_padding_mask": True, + "extra_pos_embed_type": None, + }, + "Cosmos-2.0-Diffusion-14B-Video2World": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 40, + "attention_head_dim": 128, + "num_layers": 36, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (20 / 24, 2.0, 2.0), + "concat_padding_mask": True, + "extra_pos_embed_type": None, + }, + "Cosmos-2.5-Predict-Base-2B": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 16, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 3.0, 3.0), + "concat_padding_mask": True, + # NOTE: source config has pos_emb_learnable: 'True' - but params are missing + "extra_pos_embed_type": None, + "use_crossattn_projection": True, + "crossattn_proj_in_channels": 100352, + "encoder_hidden_states_channels": 1024, + }, + "Cosmos-2.5-Predict-Base-14B": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 40, + "attention_head_dim": 128, + "num_layers": 36, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 3.0, 3.0), + "concat_padding_mask": True, + # NOTE: source config has pos_emb_learnable: 'True' - but params are missing + "extra_pos_embed_type": None, + "use_crossattn_projection": True, + "crossattn_proj_in_channels": 100352, + "encoder_hidden_states_channels": 1024, + }, + "Cosmos-2.5-Transfer-General-2B": { + "in_channels": 16 + 1, + "out_channels": 16, + "num_attention_heads": 16, + "attention_head_dim": 128, + "num_layers": 28, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "max_size": (128, 240, 240), + "patch_size": (1, 2, 2), + "rope_scale": (1.0, 3.0, 3.0), + "concat_padding_mask": True, + "extra_pos_embed_type": None, + "use_crossattn_projection": True, + "crossattn_proj_in_channels": 100352, + "encoder_hidden_states_channels": 1024, + "controlnet_block_every_n": 7, + "img_context_dim_in": 1152, + "img_context_dim_out": 2048, + "img_context_num_tokens": 256, + }, +} + +CONTROLNET_CONFIGS = { + "Cosmos-2.5-Transfer-General-2B": { + "n_controlnet_blocks": 4, + "model_channels": 2048, + "in_channels": 130, + "latent_channels": 18, # (16 latent + 1 condition_mask) + 1 padding_mask = 18 + "num_attention_heads": 16, + "attention_head_dim": 128, + "mlp_ratio": 4.0, + "text_embed_dim": 1024, + "adaln_lora_dim": 256, + "patch_size": (1, 2, 2), + "max_size": (128, 240, 240), + "rope_scale": (1.0, 3.0, 3.0), + "extra_pos_embed_type": None, + "img_context_dim_in": 1152, + "img_context_dim_out": 2048, + "use_crossattn_projection": True, + "crossattn_proj_in_channels": 100352, + "encoder_hidden_states_channels": 1024, + }, +} + +CONTROLNET_KEYS_RENAME_DICT = { + **TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0, + "blocks": "blocks", + "control_embedder.proj.1": "patch_embed.proj", +} + + +CONTROLNET_SPECIAL_KEYS_REMAP = {**TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0} + +VAE_KEYS_RENAME_DICT = { + "down.0": "down_blocks.0", + "down.1": "down_blocks.1", + "down.2": "down_blocks.2", + "up.0": "up_blocks.2", + "up.1": "up_blocks.1", + "up.2": "up_blocks.0", + ".block.": ".resnets.", + "downsample": "downsamplers.0", + "upsample": "upsamplers.0", + "mid.block_1": "mid_block.resnets.0", + "mid.attn_1.0": "mid_block.attentions.0", + "mid.attn_1.1": "mid_block.temp_attentions.0", + "mid.block_2": "mid_block.resnets.1", + ".q.conv3d": ".to_q", + ".k.conv3d": ".to_k", + ".v.conv3d": ".to_v", + ".proj_out.conv3d": ".to_out.0", + ".0.conv3d": ".conv_s", + ".1.conv3d": ".conv_t", + "conv1.conv3d": "conv1", + "conv2.conv3d": "conv2", + "conv3.conv3d": "conv3", + "nin_shortcut.conv3d": "conv_shortcut", + "quant_conv.conv3d": "quant_conv", + "post_quant_conv.conv3d": "post_quant_conv", +} + +VAE_SPECIAL_KEYS_REMAP = { + "wavelets": remove_keys_, + "_arange": remove_keys_, + "patch_size_buffer": remove_keys_, +} + +VAE_CONFIGS = { + "CV8x8x8-0.1": { + "name": "nvidia/Cosmos-0.1-Tokenizer-CV8x8x8", + "diffusers_config": { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 16, + "encoder_block_out_channels": (128, 256, 512, 512), + "decode_block_out_channels": (256, 512, 512, 512), + "attention_resolutions": (32,), + "resolution": 1024, + "num_layers": 2, + "patch_size": 4, + "patch_type": "haar", + "scaling_factor": 1.0, + "spatial_compression_ratio": 8, + "temporal_compression_ratio": 8, + "latents_mean": None, + "latents_std": None, + }, + }, + "CV8x8x8-1.0": { + "name": "nvidia/Cosmos-1.0-Tokenizer-CV8x8x8", + "diffusers_config": { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 16, + "encoder_block_out_channels": (128, 256, 512, 512), + "decode_block_out_channels": (256, 512, 512, 512), + "attention_resolutions": (32,), + "resolution": 1024, + "num_layers": 2, + "patch_size": 4, + "patch_type": "haar", + "scaling_factor": 1.0, + "spatial_compression_ratio": 8, + "temporal_compression_ratio": 8, + "latents_mean": None, + "latents_std": None, + }, + }, +} + + +def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def convert_transformer( + transformer_type: str, + state_dict: Optional[Dict[str, Any]] = None, + weights_only: bool = True, +): + PREFIX_KEY = "net." + + if "Cosmos-1.0" in transformer_type: + TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 + TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 + elif "Cosmos-2.0" in transformer_type: + TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 + TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 + elif "Cosmos-2.5" in transformer_type: + TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 + TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 + else: + assert False + + with init_empty_weights(): + config = TRANSFORMER_CONFIGS[transformer_type] + transformer = CosmosTransformer3DModel(**config) + + old2new = {} + new2old = {} + for key in list(state_dict.keys()): + new_key = key[:] + if new_key.startswith(PREFIX_KEY): + new_key = new_key.removeprefix(PREFIX_KEY) + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + print(key, "->", new_key, flush=True) + assert new_key not in new2old, f"new key {new_key} already mapped" + assert key not in old2new, f"old key {key} already mapped" + old2new[key] = new_key + new2old[new_key] = key + update_state_dict_(state_dict, key, new_key) + + for key in list(state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, state_dict) + + expected_keys = set(transformer.state_dict().keys()) + mapped_keys = set(state_dict.keys()) + missing_keys = expected_keys - mapped_keys + unexpected_keys = mapped_keys - expected_keys + if missing_keys: + print(f"ERROR: missing keys ({len(missing_keys)} from state_dict:", flush=True, file=sys.stderr) + for k in missing_keys: + print(k) + sys.exit(1) + if unexpected_keys: + print(f"ERROR: unexpected keys ({len(unexpected_keys)}) from state_dict:", flush=True, file=sys.stderr) + for k in unexpected_keys: + print(k) + sys.exit(2) + + transformer.load_state_dict(state_dict, strict=True, assign=True) + return transformer + + +def convert_controlnet( + transformer_type: str, + control_state_dict: Dict[str, Any], + base_state_dict: Dict[str, Any], + weights_only: bool = True, +): + """ + Convert controlnet weights. + + Args: + transformer_type: The type of transformer/controlnet + control_state_dict: State dict containing controlnet-specific weights + base_state_dict: State dict containing base transformer weights (for shared modules) + weights_only: Whether to use weights_only loading + """ + if transformer_type not in CONTROLNET_CONFIGS: + raise AssertionError(f"{transformer_type} does not define a ControlNet config") + + PREFIX_KEY = "net." + + # Process control-specific keys + for key in list(control_state_dict.keys()): + new_key = key[:] + if new_key.startswith(PREFIX_KEY): + new_key = new_key.removeprefix(PREFIX_KEY) + for replace_key, rename_key in CONTROLNET_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(control_state_dict, key, new_key) + + for key in list(control_state_dict.keys()): + for special_key, handler_fn_inplace in CONTROLNET_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, control_state_dict) + + # Copy shared weights from base transformer to controlnet + # These are the duplicated modules: patch_embed_base, time_embed, learnable_pos_embed, img_context_proj, crossattn_proj + shared_module_mappings = { + # transformer key prefix -> controlnet key prefix + "patch_embed.": "patch_embed_base.", + "time_embed.": "time_embed.", + "learnable_pos_embed.": "learnable_pos_embed.", + "img_context_proj.": "img_context_proj.", + "crossattn_proj.": "crossattn_proj.", + } + + for key in list(base_state_dict.keys()): + for transformer_prefix, controlnet_prefix in shared_module_mappings.items(): + if key.startswith(transformer_prefix): + controlnet_key = controlnet_prefix + key[len(transformer_prefix) :] + control_state_dict[controlnet_key] = base_state_dict[key].clone() + print(f"Copied shared weight: {key} -> {controlnet_key}", flush=True) + break + + cfg = CONTROLNET_CONFIGS[transformer_type] + controlnet = CosmosControlNetModel(**cfg) + + expected_keys = set(controlnet.state_dict().keys()) + mapped_keys = set(control_state_dict.keys()) + missing_keys = expected_keys - mapped_keys + unexpected_keys = mapped_keys - expected_keys + if missing_keys: + print(f"WARNING: missing controlnet keys ({len(missing_keys)}):", file=sys.stderr, flush=True) + for k in sorted(missing_keys): + print(k, file=sys.stderr) + sys.exit(3) + if unexpected_keys: + print(f"WARNING: unexpected controlnet keys ({len(unexpected_keys)}):", file=sys.stderr, flush=True) + for k in sorted(unexpected_keys): + print(k, file=sys.stderr) + sys.exit(4) + + controlnet.load_state_dict(control_state_dict, strict=True, assign=True) + return controlnet + + +def convert_vae(vae_type: str): + model_name = VAE_CONFIGS[vae_type]["name"] + snapshot_directory = snapshot_download(model_name, repo_type="model") + directory = pathlib.Path(snapshot_directory) + + autoencoder_file = directory / "autoencoder.jit" + mean_std_file = directory / "mean_std.pt" + + original_state_dict = torch.jit.load(autoencoder_file.as_posix()).state_dict() + if mean_std_file.exists(): + mean_std = torch.load(mean_std_file, map_location="cpu", weights_only=True) + else: + mean_std = (None, None) + + config = VAE_CONFIGS[vae_type]["diffusers_config"] + config.update( + { + "latents_mean": mean_std[0].detach().cpu().numpy().tolist(), + "latents_std": mean_std[1].detach().cpu().numpy().tolist(), + } + ) + vae = AutoencoderKLCosmos(**config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True, assign=True) + return vae + + +def save_pipeline_cosmos_1_0(args, transformer, vae): + text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) + tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) + # The original code initializes EDM config with sigma_min=0.0002, but does not make use of it anywhere directly. + # So, the sigma_min values that is used is the default value of 0.002. + scheduler = EDMEulerScheduler( + sigma_min=0.002, + sigma_max=80, + sigma_data=0.5, + sigma_schedule="karras", + num_train_timesteps=1000, + prediction_type="epsilon", + rho=7.0, + final_sigmas_type="sigma_min", + ) + + pipe_cls = CosmosTextToWorldPipeline if "Text2World" in args.transformer_type else CosmosVideoToWorldPipeline + pipe = pipe_cls( + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + vae=vae, + scheduler=scheduler, + safety_checker=lambda *args, **kwargs: None, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + +def save_pipeline_cosmos_2_0(args, transformer, vae): + text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) + tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) + + scheduler = FlowMatchEulerDiscreteScheduler(use_karras_sigmas=True) + + pipe_cls = Cosmos2TextToImagePipeline if "Text2Image" in args.transformer_type else Cosmos2VideoToWorldPipeline + pipe = pipe_cls( + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + vae=vae, + scheduler=scheduler, + safety_checker=lambda *args, **kwargs: None, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + +def save_pipeline_cosmos2_5_predict(args, transformer, vae): + text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B" + tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct" + + text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained( + text_encoder_path, torch_dtype="auto", device_map="cpu" + ) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + + scheduler = UniPCMultistepScheduler( + use_karras_sigmas=True, + use_flow_sigmas=True, + prediction_type="flow_prediction", + sigma_max=200.0, + sigma_min=0.01, + ) + + pipe = Cosmos2_5_PredictBasePipeline( + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + vae=vae, + scheduler=scheduler, + safety_checker=lambda *args, **kwargs: None, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + +def save_pipeline_cosmos2_5_transfer(args, transformer, controlnet, vae): + text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B" + tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct" + + text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained( + text_encoder_path, torch_dtype="auto", device_map="cpu" + ) + tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + + scheduler = UniPCMultistepScheduler( + use_karras_sigmas=True, + use_flow_sigmas=True, + prediction_type="flow_prediction", + sigma_max=200.0, + sigma_min=0.01, + ) + + pipe = Cosmos2_5_TransferPipeline( + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + controlnet=controlnet, + vae=vae, + scheduler=scheduler, + safety_checker=lambda *args, **kwargs: None, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--transformer_type", type=str, default=None, choices=list(TRANSFORMER_CONFIGS.keys())) + parser.add_argument( + "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" + ) + parser.add_argument( + "--vae_type", type=str, default="wan2.1", choices=["wan2.1", *list(VAE_CONFIGS.keys())], help="Type of VAE" + ) + parser.add_argument("--text_encoder_path", type=str, default=None) + parser.add_argument("--tokenizer_path", type=str, default=None) + parser.add_argument("--save_pipeline", action="store_true") + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.") + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + args = get_args() + + transformer = None + controlnet = None + dtype = DTYPE_MAPPING[args.dtype] + + if args.save_pipeline: + assert args.transformer_ckpt_path is not None + assert args.vae_type is not None + + raw_state_dict = None + if args.transformer_ckpt_path is not None: + weights_only = "Cosmos-1.0" in args.transformer_type + raw_state_dict = get_state_dict( + torch.load(args.transformer_ckpt_path, map_location="cpu", weights_only=weights_only) + ) + + if raw_state_dict is not None: + if "Transfer" in args.transformer_type: + base_state_dict = {} + control_state_dict = {} + for k, v in raw_state_dict.items(): + plain_key = k.removeprefix("net.") if k.startswith("net.") else k + if "control" in plain_key.lower(): + control_state_dict[k] = v + else: + base_state_dict[k] = v + assert len(base_state_dict.keys() & control_state_dict.keys()) == 0 + + # Convert transformer first to get the processed base state dict + transformer = convert_transformer( + args.transformer_type, state_dict=base_state_dict, weights_only=weights_only + ) + transformer = transformer.to(dtype=dtype) + + # Get converted transformer state dict to copy shared weights to controlnet + converted_base_state_dict = transformer.state_dict() + + # Convert controlnet with both control-specific and shared weights from transformer + controlnet = convert_controlnet( + args.transformer_type, control_state_dict, converted_base_state_dict, weights_only=weights_only + ) + controlnet = controlnet.to(dtype=dtype) + + if not args.save_pipeline: + transformer.save_pretrained( + pathlib.Path(args.output_path) / "transformer", safe_serialization=True, max_shard_size="5GB" + ) + controlnet.save_pretrained( + pathlib.Path(args.output_path) / "controlnet", safe_serialization=True, max_shard_size="5GB" + ) + else: + transformer = convert_transformer( + args.transformer_type, state_dict=raw_state_dict, weights_only=weights_only + ) + transformer = transformer.to(dtype=dtype) + if not args.save_pipeline: + transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + if args.vae_type is not None: + if "Cosmos-1.0" in args.transformer_type: + vae = convert_vae(args.vae_type) + elif "Cosmos-2.0" in args.transformer_type or "Cosmos-2.5" in args.transformer_type: + vae = AutoencoderKLWan.from_pretrained( + "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32 + ) + else: + raise AssertionError(f"{args.transformer_type} not supported") + + if not args.save_pipeline: + vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + else: + vae = None + + if args.save_pipeline: + if "Cosmos-1.0" in args.transformer_type: + assert args.text_encoder_path is not None + assert args.tokenizer_path is not None + save_pipeline_cosmos_1_0(args, transformer, vae) + elif "Cosmos-2.0" in args.transformer_type: + assert args.text_encoder_path is not None + assert args.tokenizer_path is not None + save_pipeline_cosmos_2_0(args, transformer, vae) + elif "Cosmos-2.5" in args.transformer_type: + if "Predict" in args.transformer_type: + save_pipeline_cosmos2_5_predict(args, transformer, vae) + elif "Transfer" in args.transformer_type: + save_pipeline_cosmos2_5_transfer(args, transformer, None, vae) + else: + raise AssertionError(f"{args.transformer_type} not supported") + else: + raise AssertionError(f"{args.transformer_type} not supported") diff --git a/scripts/convert_dance_diffusion_to_diffusers.py b/scripts/convert_dance_diffusion_to_diffusers.py new file mode 100755 index 000000000000..e269a49070cc --- /dev/null +++ b/scripts/convert_dance_diffusion_to_diffusers.py @@ -0,0 +1,346 @@ +#!/usr/bin/env python3 +import argparse +import math +import os +from copy import deepcopy + +import requests +import torch +from audio_diffusion.models import DiffusionAttnUnet1D +from diffusion import sampling +from torch import nn + +from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel +from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT + + +MODELS_MAP = { + "gwf-440k": { + "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt", + "sample_rate": 48000, + "sample_size": 65536, + }, + "jmann-small-190k": { + "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt", + "sample_rate": 48000, + "sample_size": 65536, + }, + "jmann-large-580k": { + "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt", + "sample_rate": 48000, + "sample_size": 131072, + }, + "maestro-uncond-150k": { + "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt", + "sample_rate": 16000, + "sample_size": 65536, + }, + "unlocked-uncond-250k": { + "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt", + "sample_rate": 16000, + "sample_size": 65536, + }, + "honk-140k": { + "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt", + "sample_rate": 16000, + "sample_size": 65536, + }, +} + + +def alpha_sigma_to_t(alpha, sigma): + """Returns a timestep, given the scaling factors for the clean image and for + the noise.""" + return torch.atan2(sigma, alpha) / math.pi * 2 + + +def get_crash_schedule(t): + sigma = torch.sin(t * math.pi / 2) ** 2 + alpha = (1 - sigma**2) ** 0.5 + return alpha_sigma_to_t(alpha, sigma) + + +class Object(object): + pass + + +class DiffusionUncond(nn.Module): + def __init__(self, global_args): + super().__init__() + + self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4) + self.diffusion_ema = deepcopy(self.diffusion) + self.rng = torch.quasirandom.SobolEngine(1, scramble=True) + + +def download(model_name): + url = MODELS_MAP[model_name]["url"] + r = requests.get(url, stream=True, timeout=DIFFUSERS_REQUEST_TIMEOUT) + + local_filename = f"./{model_name}.ckpt" + with open(local_filename, "wb") as fp: + for chunk in r.iter_content(chunk_size=8192): + fp.write(chunk) + + return local_filename + + +DOWN_NUM_TO_LAYER = { + "1": "resnets.0", + "2": "attentions.0", + "3": "resnets.1", + "4": "attentions.1", + "5": "resnets.2", + "6": "attentions.2", +} +UP_NUM_TO_LAYER = { + "8": "resnets.0", + "9": "attentions.0", + "10": "resnets.1", + "11": "attentions.1", + "12": "resnets.2", + "13": "attentions.2", +} +MID_NUM_TO_LAYER = { + "1": "resnets.0", + "2": "attentions.0", + "3": "resnets.1", + "4": "attentions.1", + "5": "resnets.2", + "6": "attentions.2", + "8": "resnets.3", + "9": "attentions.3", + "10": "resnets.4", + "11": "attentions.4", + "12": "resnets.5", + "13": "attentions.5", +} +DEPTH_0_TO_LAYER = { + "0": "resnets.0", + "1": "resnets.1", + "2": "resnets.2", + "4": "resnets.0", + "5": "resnets.1", + "6": "resnets.2", +} + +RES_CONV_MAP = { + "skip": "conv_skip", + "main.0": "conv_1", + "main.1": "group_norm_1", + "main.3": "conv_2", + "main.4": "group_norm_2", +} + +ATTN_MAP = { + "norm": "group_norm", + "qkv_proj": ["query", "key", "value"], + "out_proj": ["proj_attn"], +} + + +def convert_resconv_naming(name): + if name.startswith("skip"): + return name.replace("skip", RES_CONV_MAP["skip"]) + + # name has to be of format main.{digit} + if not name.startswith("main."): + raise ValueError(f"ResConvBlock error with {name}") + + return name.replace(name[:6], RES_CONV_MAP[name[:6]]) + + +def convert_attn_naming(name): + for key, value in ATTN_MAP.items(): + if name.startswith(key) and not isinstance(value, list): + return name.replace(key, value) + elif name.startswith(key): + return [name.replace(key, v) for v in value] + raise ValueError(f"Attn error with {name}") + + +def rename(input_string, max_depth=13): + string = input_string + + if string.split(".")[0] == "timestep_embed": + return string.replace("timestep_embed", "time_proj") + + depth = 0 + if string.startswith("net.3."): + depth += 1 + string = string[6:] + elif string.startswith("net."): + string = string[4:] + + while string.startswith("main.7."): + depth += 1 + string = string[7:] + + if string.startswith("main."): + string = string[5:] + + # mid block + if string[:2].isdigit(): + layer_num = string[:2] + string_left = string[2:] + else: + layer_num = string[0] + string_left = string[1:] + + if depth == max_depth: + new_layer = MID_NUM_TO_LAYER[layer_num] + prefix = "mid_block" + elif depth > 0 and int(layer_num) < 7: + new_layer = DOWN_NUM_TO_LAYER[layer_num] + prefix = f"down_blocks.{depth}" + elif depth > 0 and int(layer_num) > 7: + new_layer = UP_NUM_TO_LAYER[layer_num] + prefix = f"up_blocks.{max_depth - depth - 1}" + elif depth == 0: + new_layer = DEPTH_0_TO_LAYER[layer_num] + prefix = f"up_blocks.{max_depth - 1}" if int(layer_num) > 3 else "down_blocks.0" + + if not string_left.startswith("."): + raise ValueError(f"Naming error with {input_string} and string_left: {string_left}.") + + string_left = string_left[1:] + + if "resnets" in new_layer: + string_left = convert_resconv_naming(string_left) + elif "attentions" in new_layer: + new_string_left = convert_attn_naming(string_left) + string_left = new_string_left + + if not isinstance(string_left, list): + new_string = prefix + "." + new_layer + "." + string_left + else: + new_string = [prefix + "." + new_layer + "." + s for s in string_left] + return new_string + + +def rename_orig_weights(state_dict): + new_state_dict = {} + for k, v in state_dict.items(): + if k.endswith("kernel"): + # up- and downsample layers, don't have trainable weights + continue + + new_k = rename(k) + + # check if we need to transform from Conv => Linear for attention + if isinstance(new_k, list): + new_state_dict = transform_conv_attns(new_state_dict, new_k, v) + else: + new_state_dict[new_k] = v + + return new_state_dict + + +def transform_conv_attns(new_state_dict, new_k, v): + if len(new_k) == 1: + if len(v.shape) == 3: + # weight + new_state_dict[new_k[0]] = v[:, :, 0] + else: + # bias + new_state_dict[new_k[0]] = v + else: + # qkv matrices + trippled_shape = v.shape[0] + single_shape = trippled_shape // 3 + for i in range(3): + if len(v.shape) == 3: + new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0] + else: + new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape] + return new_state_dict + + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + model_name = args.model_path.split("/")[-1].split(".")[0] + if not os.path.isfile(args.model_path): + assert model_name == args.model_path, ( + f"Make sure to provide one of the official model names {MODELS_MAP.keys()}" + ) + args.model_path = download(model_name) + + sample_rate = MODELS_MAP[model_name]["sample_rate"] + sample_size = MODELS_MAP[model_name]["sample_size"] + + config = Object() + config.sample_size = sample_size + config.sample_rate = sample_rate + config.latent_dim = 0 + + diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate) + diffusers_state_dict = diffusers_model.state_dict() + + orig_model = DiffusionUncond(config) + orig_model.load_state_dict(torch.load(args.model_path, map_location=device)["state_dict"]) + orig_model = orig_model.diffusion_ema.eval() + orig_model_state_dict = orig_model.state_dict() + renamed_state_dict = rename_orig_weights(orig_model_state_dict) + + renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys()) + diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys()) + + assert len(renamed_minus_diffusers) == 0, f"Problem with {renamed_minus_diffusers}" + assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}" + + for key, value in renamed_state_dict.items(): + assert diffusers_state_dict[key].squeeze().shape == value.squeeze().shape, ( + f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}" + ) + if key == "time_proj.weight": + value = value.squeeze() + + diffusers_state_dict[key] = value + + diffusers_model.load_state_dict(diffusers_state_dict) + + steps = 100 + seed = 33 + + diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps) + + generator = torch.manual_seed(seed) + noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device) + + t = torch.linspace(1, 0, steps + 1, device=device)[:-1] + step_list = get_crash_schedule(t) + + pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler) + + generator = torch.manual_seed(33) + audio = pipe(num_inference_steps=steps, generator=generator).audios + + generated = sampling.iplms_sample(orig_model, noise, step_list, {}) + generated = generated.clamp(-1, 1) + + diff_sum = (generated - audio).abs().sum() + diff_max = (generated - audio).abs().max() + + if args.save: + pipe.save_pretrained(args.checkpoint_path) + + print("Diff sum", diff_sum) + print("Diff max", diff_max) + + assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/" + + print(f"Conversion for {model_name} successful!") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") + parser.add_argument( + "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not." + ) + parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") + args = parser.parse_args() + + main(args) diff --git a/scripts/convert_dcae_to_diffusers.py b/scripts/convert_dcae_to_diffusers.py new file mode 100644 index 000000000000..1a94c4b06640 --- /dev/null +++ b/scripts/convert_dcae_to_diffusers.py @@ -0,0 +1,323 @@ +import argparse +from typing import Any, Dict + +import torch +from huggingface_hub import hf_hub_download +from safetensors.torch import load_file + +from diffusers import AutoencoderDC + + +def remap_qkv_(key: str, state_dict: Dict[str, Any]): + qkv = state_dict.pop(key) + q, k, v = torch.chunk(qkv, 3, dim=0) + parent_module, _, _ = key.rpartition(".qkv.conv.weight") + state_dict[f"{parent_module}.to_q.weight"] = q.squeeze() + state_dict[f"{parent_module}.to_k.weight"] = k.squeeze() + state_dict[f"{parent_module}.to_v.weight"] = v.squeeze() + + +def remap_proj_conv_(key: str, state_dict: Dict[str, Any]): + parent_module, _, _ = key.rpartition(".proj.conv.weight") + state_dict[f"{parent_module}.to_out.weight"] = state_dict.pop(key).squeeze() + + +AE_KEYS_RENAME_DICT = { + # common + "main.": "", + "op_list.": "", + "context_module": "attn", + "local_module": "conv_out", + # NOTE: The below two lines work because scales in the available configs only have a tuple length of 1 + # If there were more scales, there would be more layers, so a loop would be better to handle this + "aggreg.0.0": "to_qkv_multiscale.0.proj_in", + "aggreg.0.1": "to_qkv_multiscale.0.proj_out", + "depth_conv.conv": "conv_depth", + "inverted_conv.conv": "conv_inverted", + "point_conv.conv": "conv_point", + "point_conv.norm": "norm", + "conv.conv.": "conv.", + "conv1.conv": "conv1", + "conv2.conv": "conv2", + "conv2.norm": "norm", + "proj.norm": "norm_out", + # encoder + "encoder.project_in.conv": "encoder.conv_in", + "encoder.project_out.0.conv": "encoder.conv_out", + "encoder.stages": "encoder.down_blocks", + # decoder + "decoder.project_in.conv": "decoder.conv_in", + "decoder.project_out.0": "decoder.norm_out", + "decoder.project_out.2.conv": "decoder.conv_out", + "decoder.stages": "decoder.up_blocks", +} + +AE_F32C32_KEYS = { + # encoder + "encoder.project_in.conv": "encoder.conv_in.conv", + # decoder + "decoder.project_out.2.conv": "decoder.conv_out.conv", +} + +AE_F64C128_KEYS = { + # encoder + "encoder.project_in.conv": "encoder.conv_in.conv", + # decoder + "decoder.project_out.2.conv": "decoder.conv_out.conv", +} + +AE_F128C512_KEYS = { + # encoder + "encoder.project_in.conv": "encoder.conv_in.conv", + # decoder + "decoder.project_out.2.conv": "decoder.conv_out.conv", +} + +AE_SPECIAL_KEYS_REMAP = { + "qkv.conv.weight": remap_qkv_, + "proj.conv.weight": remap_proj_conv_, +} + + +def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def convert_ae(config_name: str, dtype: torch.dtype): + config = get_ae_config(config_name) + hub_id = f"mit-han-lab/{config_name}" + ckpt_path = hf_hub_download(hub_id, "model.safetensors") + original_state_dict = get_state_dict(load_file(ckpt_path)) + + ae = AutoencoderDC(**config).to(dtype=dtype) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in AE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in AE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + ae.load_state_dict(original_state_dict, strict=True) + return ae + + +def get_ae_config(name: str): + if name in ["dc-ae-f32c32-sana-1.0"]: + config = { + "latent_channels": 32, + "encoder_block_types": ( + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ), + "decoder_block_types": ( + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ), + "encoder_block_out_channels": (128, 256, 512, 512, 1024, 1024), + "decoder_block_out_channels": (128, 256, 512, 512, 1024, 1024), + "encoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)), + "decoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)), + "encoder_layers_per_block": (2, 2, 2, 3, 3, 3), + "decoder_layers_per_block": [3, 3, 3, 3, 3, 3], + "downsample_block_type": "conv", + "upsample_block_type": "interpolate", + "decoder_norm_types": "rms_norm", + "decoder_act_fns": "silu", + "scaling_factor": 0.41407, + } + elif name in ["dc-ae-f32c32-in-1.0", "dc-ae-f32c32-mix-1.0"]: + AE_KEYS_RENAME_DICT.update(AE_F32C32_KEYS) + config = { + "latent_channels": 32, + "encoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "decoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024], + "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024], + "encoder_layers_per_block": [0, 4, 8, 2, 2, 2], + "decoder_layers_per_block": [0, 5, 10, 2, 2, 2], + "encoder_qkv_multiscales": ((), (), (), (), (), ()), + "decoder_qkv_multiscales": ((), (), (), (), (), ()), + "decoder_norm_types": ["batch_norm", "batch_norm", "batch_norm", "rms_norm", "rms_norm", "rms_norm"], + "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu"], + } + if name == "dc-ae-f32c32-in-1.0": + config["scaling_factor"] = 0.3189 + elif name == "dc-ae-f32c32-mix-1.0": + config["scaling_factor"] = 0.4552 + elif name in ["dc-ae-f64c128-in-1.0", "dc-ae-f64c128-mix-1.0"]: + AE_KEYS_RENAME_DICT.update(AE_F64C128_KEYS) + config = { + "latent_channels": 128, + "encoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "decoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048], + "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048], + "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2], + "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2], + "encoder_qkv_multiscales": ((), (), (), (), (), (), ()), + "decoder_qkv_multiscales": ((), (), (), (), (), (), ()), + "decoder_norm_types": [ + "batch_norm", + "batch_norm", + "batch_norm", + "rms_norm", + "rms_norm", + "rms_norm", + "rms_norm", + ], + "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu"], + } + if name == "dc-ae-f64c128-in-1.0": + config["scaling_factor"] = 0.2889 + elif name == "dc-ae-f64c128-mix-1.0": + config["scaling_factor"] = 0.4538 + elif name in ["dc-ae-f128c512-in-1.0", "dc-ae-f128c512-mix-1.0"]: + AE_KEYS_RENAME_DICT.update(AE_F128C512_KEYS) + config = { + "latent_channels": 512, + "encoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "decoder_block_types": [ + "ResBlock", + "ResBlock", + "ResBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + "EfficientViTBlock", + ], + "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048], + "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048], + "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2, 2], + "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2, 2], + "encoder_qkv_multiscales": ((), (), (), (), (), (), (), ()), + "decoder_qkv_multiscales": ((), (), (), (), (), (), (), ()), + "decoder_norm_types": [ + "batch_norm", + "batch_norm", + "batch_norm", + "rms_norm", + "rms_norm", + "rms_norm", + "rms_norm", + "rms_norm", + ], + "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu", "silu"], + } + if name == "dc-ae-f128c512-in-1.0": + config["scaling_factor"] = 0.4883 + elif name == "dc-ae-f128c512-mix-1.0": + config["scaling_factor"] = 0.3620 + else: + raise ValueError("Invalid config name provided.") + + return config + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config_name", + type=str, + default="dc-ae-f32c32-sana-1.0", + choices=[ + "dc-ae-f32c32-sana-1.0", + "dc-ae-f32c32-in-1.0", + "dc-ae-f32c32-mix-1.0", + "dc-ae-f64c128-in-1.0", + "dc-ae-f64c128-mix-1.0", + "dc-ae-f128c512-in-1.0", + "dc-ae-f128c512-mix-1.0", + ], + help="The DCAE checkpoint to convert", + ) + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.") + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + +VARIANT_MAPPING = { + "fp32": None, + "fp16": "fp16", + "bf16": "bf16", +} + + +if __name__ == "__main__": + args = get_args() + + dtype = DTYPE_MAPPING[args.dtype] + variant = VARIANT_MAPPING[args.dtype] + + ae = convert_ae(args.config_name, dtype) + ae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", variant=variant) diff --git a/scripts/convert_ddpm_original_checkpoint_to_diffusers.py b/scripts/convert_ddpm_original_checkpoint_to_diffusers.py new file mode 100644 index 000000000000..46595784b0ba --- /dev/null +++ b/scripts/convert_ddpm_original_checkpoint_to_diffusers.py @@ -0,0 +1,431 @@ +import argparse +import json + +import torch + +from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + mapping = [] + for old_item in old_list: + new_item = old_item + new_item = new_item.replace("block.", "resnets.") + new_item = new_item.replace("conv_shorcut", "conv1") + new_item = new_item.replace("in_shortcut", "conv_shortcut") + new_item = new_item.replace("temb_proj", "time_emb_proj") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0, in_mid=False): + mapping = [] + for old_item in old_list: + new_item = old_item + + # In `model.mid`, the layer is called `attn`. + if not in_mid: + new_item = new_item.replace("attn", "attentions") + new_item = new_item.replace(".k.", ".key.") + new_item = new_item.replace(".v.", ".value.") + new_item = new_item.replace(".q.", ".query.") + + new_item = new_item.replace("proj_out", "proj_attn") + new_item = new_item.replace("norm", "group_norm") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + if attention_paths_to_split is not None: + if config is None: + raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'.") + + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config.get("num_head_channels", 1) // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape).squeeze() + checkpoint[path_map["key"]] = key.reshape(target_shape).squeeze() + checkpoint[path_map["value"]] = value.reshape(target_shape).squeeze() + + for path in paths: + new_path = path["new"] + + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + new_path = new_path.replace("down.", "down_blocks.") + new_path = new_path.replace("up.", "up_blocks.") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + if "attentions" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]].squeeze() + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def convert_ddpm_checkpoint(checkpoint, config): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["temb.dense.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["temb.dense.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["temb.dense.1.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["temb.dense.1.bias"] + + new_checkpoint["conv_norm_out.weight"] = checkpoint["norm_out.weight"] + new_checkpoint["conv_norm_out.bias"] = checkpoint["norm_out.bias"] + + new_checkpoint["conv_in.weight"] = checkpoint["conv_in.weight"] + new_checkpoint["conv_in.bias"] = checkpoint["conv_in.bias"] + new_checkpoint["conv_out.weight"] = checkpoint["conv_out.weight"] + new_checkpoint["conv_out.bias"] = checkpoint["conv_out.bias"] + + num_down_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "down" in layer}) + down_blocks = { + layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + num_up_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "up" in layer}) + up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} + + for i in range(num_down_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + + if any("downsample" in layer for layer in down_blocks[i]): + new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ + f"down.{i}.downsample.op.weight" + ] + new_checkpoint[f"down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[f"down.{i}.downsample.op.bias"] + # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight'] + # new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias'] + + if any("block" in layer for layer in down_blocks[i]): + num_blocks = len( + {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "block" in layer} + ) + blocks = { + layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] + for layer_id in range(num_blocks) + } + + if num_blocks > 0: + for j in range(config["layers_per_block"]): + paths = renew_resnet_paths(blocks[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint) + + if any("attn" in layer for layer in down_blocks[i]): + num_attn = len( + {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in down_blocks[i] if "attn" in layer} + ) + attns = { + layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] + for layer_id in range(num_blocks) + } + + if num_attn > 0: + for j in range(config["layers_per_block"]): + paths = renew_attention_paths(attns[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) + + mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] + mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] + mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] + + # Mid new 2 + paths = renew_resnet_paths(mid_block_1_layers) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], + ) + + paths = renew_resnet_paths(mid_block_2_layers) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], + ) + + paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], + ) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + + if any("upsample" in layer for layer in up_blocks[i]): + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ + f"up.{i}.upsample.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[f"up.{i}.upsample.conv.bias"] + + if any("block" in layer for layer in up_blocks[i]): + num_blocks = len( + {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "block" in layer} + ) + blocks = { + layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) + } + + if num_blocks > 0: + for j in range(config["layers_per_block"] + 1): + replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} + paths = renew_resnet_paths(blocks[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) + + if any("attn" in layer for layer in up_blocks[i]): + num_attn = len( + {".".join(shave_segments(layer, 2).split(".")[:2]) for layer in up_blocks[i] if "attn" in layer} + ) + attns = { + layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) + } + + if num_attn > 0: + for j in range(config["layers_per_block"] + 1): + replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} + paths = renew_attention_paths(attns[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) + + new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} + return new_checkpoint + + +def convert_vq_autoenc_checkpoint(checkpoint, config): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + new_checkpoint = {} + + new_checkpoint["encoder.conv_norm_out.weight"] = checkpoint["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = checkpoint["encoder.norm_out.bias"] + + new_checkpoint["encoder.conv_in.weight"] = checkpoint["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = checkpoint["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = checkpoint["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = checkpoint["encoder.conv_out.bias"] + + new_checkpoint["decoder.conv_norm_out.weight"] = checkpoint["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = checkpoint["decoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = checkpoint["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = checkpoint["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = checkpoint["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = checkpoint["decoder.conv_out.bias"] + + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "down" in layer}) + down_blocks = { + layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in checkpoint if "up" in layer}) + up_blocks = {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)} + + for i in range(num_down_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + + if any("downsample" in layer for layer in down_blocks[i]): + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = checkpoint[ + f"encoder.down.{i}.downsample.conv.weight" + ] + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = checkpoint[ + f"encoder.down.{i}.downsample.conv.bias" + ] + + if any("block" in layer for layer in down_blocks[i]): + num_blocks = len( + {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "block" in layer} + ) + blocks = { + layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key] + for layer_id in range(num_blocks) + } + + if num_blocks > 0: + for j in range(config["layers_per_block"]): + paths = renew_resnet_paths(blocks[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint) + + if any("attn" in layer for layer in down_blocks[i]): + num_attn = len( + {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in down_blocks[i] if "attn" in layer} + ) + attns = { + layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key] + for layer_id in range(num_blocks) + } + + if num_attn > 0: + for j in range(config["layers_per_block"]): + paths = renew_attention_paths(attns[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, config=config) + + mid_block_1_layers = [key for key in checkpoint if "mid.block_1" in key] + mid_block_2_layers = [key for key in checkpoint if "mid.block_2" in key] + mid_attn_1_layers = [key for key in checkpoint if "mid.attn_1" in key] + + # Mid new 2 + paths = renew_resnet_paths(mid_block_1_layers) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}], + ) + + paths = renew_resnet_paths(mid_block_2_layers) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}], + ) + + paths = renew_attention_paths(mid_attn_1_layers, in_mid=True) + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}], + ) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + + if any("upsample" in layer for layer in up_blocks[i]): + new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ + f"decoder.up.{i}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ + f"decoder.up.{i}.upsample.conv.bias" + ] + + if any("block" in layer for layer in up_blocks[i]): + num_blocks = len( + {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "block" in layer} + ) + blocks = { + layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(num_blocks) + } + + if num_blocks > 0: + for j in range(config["layers_per_block"] + 1): + replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} + paths = renew_resnet_paths(blocks[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) + + if any("attn" in layer for layer in up_blocks[i]): + num_attn = len( + {".".join(shave_segments(layer, 3).split(".")[:3]) for layer in up_blocks[i] if "attn" in layer} + ) + attns = { + layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(num_blocks) + } + + if num_attn > 0: + for j in range(config["layers_per_block"] + 1): + replace_indices = {"old": f"up_blocks.{i}", "new": f"up_blocks.{block_id}"} + paths = renew_attention_paths(attns[j]) + assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[replace_indices]) + + new_checkpoint = {k.replace("mid_new_2", "mid_block"): v for k, v in new_checkpoint.items()} + new_checkpoint["quant_conv.weight"] = checkpoint["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = checkpoint["quant_conv.bias"] + if "quantize.embedding.weight" in checkpoint: + new_checkpoint["quantize.embedding.weight"] = checkpoint["quantize.embedding.weight"] + new_checkpoint["post_quant_conv.weight"] = checkpoint["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = checkpoint["post_quant_conv.bias"] + + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + + parser.add_argument( + "--config_file", + default=None, + type=str, + required=True, + help="The config json file corresponding to the architecture.", + ) + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + args = parser.parse_args() + checkpoint = torch.load(args.checkpoint_path) + + with open(args.config_file) as f: + config = json.loads(f.read()) + + # unet case + key_prefix_set = {key.split(".")[0] for key in checkpoint.keys()} + if "encoder" in key_prefix_set and "decoder" in key_prefix_set: + converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config) + else: + converted_checkpoint = convert_ddpm_checkpoint(checkpoint, config) + + if "ddpm" in config: + del config["ddpm"] + + if config["_class_name"] == "VQModel": + model = VQModel(**config) + model.load_state_dict(converted_checkpoint) + model.save_pretrained(args.dump_path) + elif config["_class_name"] == "AutoencoderKL": + model = AutoencoderKL(**config) + model.load_state_dict(converted_checkpoint) + model.save_pretrained(args.dump_path) + else: + model = UNet2DModel(**config) + model.load_state_dict(converted_checkpoint) + + scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) + + pipe = DDPMPipeline(unet=model, scheduler=scheduler) + pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_diffusers_sdxl_lora_to_webui.py b/scripts/convert_diffusers_sdxl_lora_to_webui.py new file mode 100644 index 000000000000..dfb3871275cb --- /dev/null +++ b/scripts/convert_diffusers_sdxl_lora_to_webui.py @@ -0,0 +1,56 @@ +# Script for converting a Hugging Face Diffusers trained SDXL LoRAs to Kohya format +# This means that you can input your diffusers-trained LoRAs and +# Get the output to work with WebUIs such as AUTOMATIC1111, ComfyUI, SD.Next and others. + +# To get started you can find some cool `diffusers` trained LoRAs such as this cute Corgy +# https://huggingface.co/ignasbud/corgy_dog_LoRA/, download its `pytorch_lora_weights.safetensors` file +# and run the script: +# python convert_diffusers_sdxl_lora_to_webui.py --input_lora pytorch_lora_weights.safetensors --output_lora corgy.safetensors +# now you can use corgy.safetensors in your WebUI of choice! + +# To train your own, here are some diffusers training scripts and utils that you can use and then convert: +# LoRA Ease - no code SDXL Dreambooth LoRA trainer: https://huggingface.co/spaces/multimodalart/lora-ease +# Dreambooth Advanced Training Script - state of the art techniques such as pivotal tuning and prodigy optimizer: +# - Script: https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py +# - Colab (only on Pro): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_Dreambooth_LoRA_advanced_example.ipynb +# Canonical diffusers training scripts: +# - Script: https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py +# - Colab (runs on free tier): https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/SDXL_DreamBooth_LoRA_.ipynb + +import argparse +import os + +from safetensors.torch import load_file, save_file + +from diffusers.utils import convert_all_state_dict_to_peft, convert_state_dict_to_kohya + + +def convert_and_save(input_lora, output_lora=None): + if output_lora is None: + base_name = os.path.splitext(input_lora)[0] + output_lora = f"{base_name}_webui.safetensors" + + diffusers_state_dict = load_file(input_lora) + peft_state_dict = convert_all_state_dict_to_peft(diffusers_state_dict) + kohya_state_dict = convert_state_dict_to_kohya(peft_state_dict) + save_file(kohya_state_dict, output_lora) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert LoRA model to PEFT and then to Kohya format.") + parser.add_argument( + "--input_lora", + type=str, + required=True, + help="Path to the input LoRA model file in the diffusers format.", + ) + parser.add_argument( + "--output_lora", + type=str, + required=False, + help="Path for the converted LoRA (safetensors format for AUTOMATIC1111, ComfyUI, etc.). Optional, defaults to input name with a _webui suffix.", + ) + + args = parser.parse_args() + + convert_and_save(args.input_lora, args.output_lora) diff --git a/scripts/convert_diffusers_to_original_sdxl.py b/scripts/convert_diffusers_to_original_sdxl.py new file mode 100644 index 000000000000..1aa792b3f06a --- /dev/null +++ b/scripts/convert_diffusers_to_original_sdxl.py @@ -0,0 +1,350 @@ +# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. +# *Only* converts the UNet, VAE, and Text Encoder. +# Does not convert optimizer state or any other thing. + +import argparse +import os.path as osp +import re + +import torch +from safetensors.torch import load_file, save_file + + +# =================# +# UNet Conversion # +# =================# + +unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + # the following are for sdxl + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), +] + +unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), +] + +unet_conversion_map_layer = [] +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(3): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i > 0: + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(4): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i < 2: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) +unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv.")) + +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2 * j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +def convert_unet_state_dict(unet_state_dict): + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + # the following are for SDXL + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), +] + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + if not w.ndim == 1: + return w.reshape(*w.shape, 1, 1) + else: + return w + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + print(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v) + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("transformer.resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "text_model.final_layer_norm."), + ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_openclip_text_enc_state_dict(text_enc_dict): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_openai_text_enc_state_dict(text_enc_dict): + return text_enc_dict + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") + parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + parser.add_argument( + "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." + ) + + args = parser.parse_args() + + assert args.model_path is not None, "Must provide a model path!" + + assert args.checkpoint_path is not None, "Must provide a checkpoint path!" + + # Path for safetensors + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") + vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") + text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") + text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "model.safetensors") + + # Load models from safetensors if it exists, if it doesn't pytorch + if osp.exists(unet_path): + unet_state_dict = load_file(unet_path, device="cpu") + else: + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") + unet_state_dict = torch.load(unet_path, map_location="cpu") + + if osp.exists(vae_path): + vae_state_dict = load_file(vae_path, device="cpu") + else: + vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") + vae_state_dict = torch.load(vae_path, map_location="cpu") + + if osp.exists(text_enc_path): + text_enc_dict = load_file(text_enc_path, device="cpu") + else: + text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") + text_enc_dict = torch.load(text_enc_path, map_location="cpu") + + if osp.exists(text_enc_2_path): + text_enc_2_dict = load_file(text_enc_2_path, device="cpu") + else: + text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "pytorch_model.bin") + text_enc_2_dict = torch.load(text_enc_2_path, map_location="cpu") + + # Convert the UNet model + unet_state_dict = convert_unet_state_dict(unet_state_dict) + unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} + + # Convert the VAE model + vae_state_dict = convert_vae_state_dict(vae_state_dict) + vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} + + # Convert text encoder 1 + text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict) + text_enc_dict = {"conditioner.embedders.0.transformer." + k: v for k, v in text_enc_dict.items()} + + # Convert text encoder 2 + text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict) + text_enc_2_dict = {"conditioner.embedders.1.model." + k: v for k, v in text_enc_2_dict.items()} + # We call the `.T.contiguous()` to match what's done in + # https://github.com/huggingface/diffusers/blob/84905ca7287876b925b6bf8e9bb92fec21c78764/src/diffusers/loaders/single_file_utils.py#L1085 + text_enc_2_dict["conditioner.embedders.1.model.text_projection"] = text_enc_2_dict.pop( + "conditioner.embedders.1.model.text_projection.weight" + ).T.contiguous() + + # Put together new checkpoint + state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict} + + if args.half: + state_dict = {k: v.half() for k, v in state_dict.items()} + + if args.use_safetensors: + save_file(state_dict, args.checkpoint_path) + else: + state_dict = {"state_dict": state_dict} + torch.save(state_dict, args.checkpoint_path) diff --git a/scripts/convert_diffusers_to_original_stable_diffusion.py b/scripts/convert_diffusers_to_original_stable_diffusion.py new file mode 100644 index 000000000000..049dda7d42a7 --- /dev/null +++ b/scripts/convert_diffusers_to_original_stable_diffusion.py @@ -0,0 +1,353 @@ +# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. +# *Only* converts the UNet, VAE, and Text Encoder. +# Does not convert optimizer state or any other thing. + +import argparse +import os.path as osp +import re + +import torch +from safetensors.torch import load_file, save_file + + +# =================# +# UNet Conversion # +# =================# + +unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), +] + +unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), +] + +unet_conversion_map_layer = [] +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2 * j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +def convert_unet_state_dict(unet_state_dict): + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("proj_out.", "proj_attn."), +] + +# This is probably not the most ideal solution, but it does work. +vae_extra_conversion_map = [ + ("to_q", "q"), + ("to_k", "k"), + ("to_v", "v"), + ("to_out.0", "proj_out"), +] + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + if not w.ndim == 1: + return w.reshape(*w.shape, 1, 1) + else: + return w + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + keys_to_rename = {} + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + print(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v) + for weight_name, real_weight_name in vae_extra_conversion_map: + if f"mid.attn_1.{weight_name}.weight" in k or f"mid.attn_1.{weight_name}.bias" in k: + keys_to_rename[k] = k.replace(weight_name, real_weight_name) + for k, v in keys_to_rename.items(): + if k in new_state_dict: + print(f"Renaming {k} to {v}") + new_state_dict[v] = reshape_weight_for_sd(new_state_dict[k]) + del new_state_dict[k] + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_text_enc_state_dict_v20(text_enc_dict): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") + parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + parser.add_argument( + "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." + ) + + args = parser.parse_args() + + assert args.model_path is not None, "Must provide a model path!" + + assert args.checkpoint_path is not None, "Must provide a checkpoint path!" + + # Path for safetensors + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") + vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") + text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") + + # Load models from safetensors if it exists, if it doesn't pytorch + if osp.exists(unet_path): + unet_state_dict = load_file(unet_path, device="cpu") + else: + unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") + unet_state_dict = torch.load(unet_path, map_location="cpu") + + if osp.exists(vae_path): + vae_state_dict = load_file(vae_path, device="cpu") + else: + vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") + vae_state_dict = torch.load(vae_path, map_location="cpu") + + if osp.exists(text_enc_path): + text_enc_dict = load_file(text_enc_path, device="cpu") + else: + text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") + text_enc_dict = torch.load(text_enc_path, map_location="cpu") + + # Convert the UNet model + unet_state_dict = convert_unet_state_dict(unet_state_dict) + unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} + + # Convert the VAE model + vae_state_dict = convert_vae_state_dict(vae_state_dict) + vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} + + # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper + is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict + + if is_v20_model: + # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm + text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} + text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) + text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} + else: + text_enc_dict = convert_text_enc_state_dict(text_enc_dict) + text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} + + # Put together new checkpoint + state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} + if args.half: + state_dict = {k: v.half() for k, v in state_dict.items()} + + if args.use_safetensors: + save_file(state_dict, args.checkpoint_path) + else: + state_dict = {"state_dict": state_dict} + torch.save(state_dict, args.checkpoint_path) diff --git a/scripts/convert_dit_to_diffusers.py b/scripts/convert_dit_to_diffusers.py new file mode 100644 index 000000000000..dc127f69555c --- /dev/null +++ b/scripts/convert_dit_to_diffusers.py @@ -0,0 +1,162 @@ +import argparse +import os + +import torch +from torchvision.datasets.utils import download_url + +from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel + + +pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"} + + +def download_model(model_name): + """ + Downloads a pre-trained DiT model from the web. + """ + local_path = f"pretrained_models/{model_name}" + if not os.path.isfile(local_path): + os.makedirs("pretrained_models", exist_ok=True) + web_path = f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}" + download_url(web_path, "pretrained_models") + model = torch.load(local_path, map_location=lambda storage, loc: storage) + return model + + +def main(args): + state_dict = download_model(pretrained_models[args.image_size]) + + state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] + state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] + state_dict.pop("x_embedder.proj.weight") + state_dict.pop("x_embedder.proj.bias") + + for depth in range(28): + state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight"] = state_dict[ + "t_embedder.mlp.0.weight" + ] + state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias"] = state_dict[ + "t_embedder.mlp.0.bias" + ] + state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight"] = state_dict[ + "t_embedder.mlp.2.weight" + ] + state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias"] = state_dict[ + "t_embedder.mlp.2.bias" + ] + state_dict[f"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight"] = state_dict[ + "y_embedder.embedding_table.weight" + ] + + state_dict[f"transformer_blocks.{depth}.norm1.linear.weight"] = state_dict[ + f"blocks.{depth}.adaLN_modulation.1.weight" + ] + state_dict[f"transformer_blocks.{depth}.norm1.linear.bias"] = state_dict[ + f"blocks.{depth}.adaLN_modulation.1.bias" + ] + + q, k, v = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"], 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"], 3, dim=0) + + state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias + state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias + state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias + + state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict[ + f"blocks.{depth}.attn.proj.weight" + ] + state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict[f"blocks.{depth}.attn.proj.bias"] + + state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict[f"blocks.{depth}.mlp.fc1.weight"] + state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict[f"blocks.{depth}.mlp.fc1.bias"] + state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict[f"blocks.{depth}.mlp.fc2.weight"] + state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict[f"blocks.{depth}.mlp.fc2.bias"] + + state_dict.pop(f"blocks.{depth}.attn.qkv.weight") + state_dict.pop(f"blocks.{depth}.attn.qkv.bias") + state_dict.pop(f"blocks.{depth}.attn.proj.weight") + state_dict.pop(f"blocks.{depth}.attn.proj.bias") + state_dict.pop(f"blocks.{depth}.mlp.fc1.weight") + state_dict.pop(f"blocks.{depth}.mlp.fc1.bias") + state_dict.pop(f"blocks.{depth}.mlp.fc2.weight") + state_dict.pop(f"blocks.{depth}.mlp.fc2.bias") + state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.weight") + state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.bias") + + state_dict.pop("t_embedder.mlp.0.weight") + state_dict.pop("t_embedder.mlp.0.bias") + state_dict.pop("t_embedder.mlp.2.weight") + state_dict.pop("t_embedder.mlp.2.bias") + state_dict.pop("y_embedder.embedding_table.weight") + + state_dict["proj_out_1.weight"] = state_dict["final_layer.adaLN_modulation.1.weight"] + state_dict["proj_out_1.bias"] = state_dict["final_layer.adaLN_modulation.1.bias"] + state_dict["proj_out_2.weight"] = state_dict["final_layer.linear.weight"] + state_dict["proj_out_2.bias"] = state_dict["final_layer.linear.bias"] + + state_dict.pop("final_layer.linear.weight") + state_dict.pop("final_layer.linear.bias") + state_dict.pop("final_layer.adaLN_modulation.1.weight") + state_dict.pop("final_layer.adaLN_modulation.1.bias") + + # DiT XL/2 + transformer = Transformer2DModel( + sample_size=args.image_size // 8, + num_layers=28, + attention_head_dim=72, + in_channels=4, + out_channels=8, + patch_size=2, + attention_bias=True, + num_attention_heads=16, + activation_fn="gelu-approximate", + num_embeds_ada_norm=1000, + norm_type="ada_norm_zero", + norm_elementwise_affine=False, + ) + transformer.load_state_dict(state_dict, strict=True) + + scheduler = DDIMScheduler( + num_train_timesteps=1000, + beta_schedule="linear", + prediction_type="epsilon", + clip_sample=False, + ) + + vae = AutoencoderKL.from_pretrained(args.vae_model) + + pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler) + + if args.save: + pipeline.save_pretrained(args.checkpoint_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--image_size", + default=256, + type=int, + required=False, + help="Image size of pretrained model, either 256 or 512.", + ) + parser.add_argument( + "--vae_model", + default="stabilityai/sd-vae-ft-ema", + type=str, + required=False, + help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.", + ) + parser.add_argument( + "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." + ) + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline." + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_flux2_to_diffusers.py b/scripts/convert_flux2_to_diffusers.py new file mode 100644 index 000000000000..a8fa6f87eee1 --- /dev/null +++ b/scripts/convert_flux2_to_diffusers.py @@ -0,0 +1,536 @@ +import argparse +from contextlib import nullcontext +from typing import Any, Dict, Tuple + +import safetensors.torch +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download +from transformers import AutoProcessor, GenerationConfig, Mistral3ForConditionalGeneration + +from diffusers import AutoencoderKLFlux2, FlowMatchEulerDiscreteScheduler, Flux2Pipeline, Flux2Transformer2DModel +from diffusers.utils.import_utils import is_accelerate_available + + +""" +# VAE + +python scripts/convert_flux2_to_diffusers.py \ +--original_state_dict_repo_id "diffusers-internal-dev/new-model-image" \ +--vae_filename "flux2-vae.sft" \ +--output_path "/raid/yiyi/dummy-flux2-diffusers" \ +--vae + +# DiT + +python scripts/convert_flux2_to_diffusers.py \ + --original_state_dict_repo_id diffusers-internal-dev/new-model-image \ + --dit_filename flux-dev-dummy.sft \ + --dit \ + --output_path . + +# Full pipe + +python scripts/convert_flux2_to_diffusers.py \ + --original_state_dict_repo_id diffusers-internal-dev/new-model-image \ + --dit_filename flux-dev-dummy.sft \ + --vae_filename "flux2-vae.sft" \ + --dit --vae --full_pipe \ + --output_path . +""" + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--original_state_dict_repo_id", default=None, type=str) +parser.add_argument("--vae_filename", default="flux2-vae.sft", type=str) +parser.add_argument("--dit_filename", default="flux2-dev.safetensors", type=str) +parser.add_argument("--vae", action="store_true") +parser.add_argument("--dit", action="store_true") +parser.add_argument("--vae_dtype", type=str, default="fp32") +parser.add_argument("--dit_dtype", type=str, default="bf16") +parser.add_argument("--checkpoint_path", default=None, type=str) +parser.add_argument("--full_pipe", action="store_true") +parser.add_argument("--output_path", type=str) + +args = parser.parse_args() + + +def load_original_checkpoint(args, filename): + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=filename) + elif args.checkpoint_path is not None: + ckpt_path = args.checkpoint_path + else: + raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +DIFFUSERS_VAE_TO_FLUX2_MAPPING = { + "encoder.conv_in.weight": "encoder.conv_in.weight", + "encoder.conv_in.bias": "encoder.conv_in.bias", + "encoder.conv_out.weight": "encoder.conv_out.weight", + "encoder.conv_out.bias": "encoder.conv_out.bias", + "encoder.conv_norm_out.weight": "encoder.norm_out.weight", + "encoder.conv_norm_out.bias": "encoder.norm_out.bias", + "decoder.conv_in.weight": "decoder.conv_in.weight", + "decoder.conv_in.bias": "decoder.conv_in.bias", + "decoder.conv_out.weight": "decoder.conv_out.weight", + "decoder.conv_out.bias": "decoder.conv_out.bias", + "decoder.conv_norm_out.weight": "decoder.norm_out.weight", + "decoder.conv_norm_out.bias": "decoder.norm_out.bias", + "quant_conv.weight": "encoder.quant_conv.weight", + "quant_conv.bias": "encoder.quant_conv.bias", + "post_quant_conv.weight": "decoder.post_quant_conv.weight", + "post_quant_conv.bias": "decoder.post_quant_conv.bias", + "bn.running_mean": "bn.running_mean", + "bn.running_var": "bn.running_var", +} + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def update_vae_resnet_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping): + for ldm_key in keys: + diffusers_key = ldm_key.replace(mapping["old"], mapping["new"]).replace("nin_shortcut", "conv_shortcut") + new_checkpoint[diffusers_key] = checkpoint.get(ldm_key) + + +def update_vae_attentions_ldm_to_diffusers(keys, new_checkpoint, checkpoint, mapping): + for ldm_key in keys: + diffusers_key = ( + ldm_key.replace(mapping["old"], mapping["new"]) + .replace("norm.weight", "group_norm.weight") + .replace("norm.bias", "group_norm.bias") + .replace("q.weight", "to_q.weight") + .replace("q.bias", "to_q.bias") + .replace("k.weight", "to_k.weight") + .replace("k.bias", "to_k.bias") + .replace("v.weight", "to_v.weight") + .replace("v.bias", "to_v.bias") + .replace("proj_out.weight", "to_out.0.weight") + .replace("proj_out.bias", "to_out.0.bias") + ) + new_checkpoint[diffusers_key] = checkpoint.get(ldm_key) + + # proj_attn.weight has to be converted from conv 1D to linear + shape = new_checkpoint[diffusers_key].shape + + if len(shape) == 3: + new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0] + elif len(shape) == 4: + new_checkpoint[diffusers_key] = new_checkpoint[diffusers_key][:, :, 0, 0] + + +def convert_flux2_vae_checkpoint_to_diffusers(vae_state_dict, config): + new_checkpoint = {} + for diffusers_key, ldm_key in DIFFUSERS_VAE_TO_FLUX2_MAPPING.items(): + if ldm_key not in vae_state_dict: + continue + new_checkpoint[diffusers_key] = vae_state_dict[ldm_key] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len(config["down_block_types"]) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + update_vae_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + vae_state_dict, + mapping={"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}, + ) + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.get( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.get( + f"encoder.down.{i}.downsample.conv.bias" + ) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + update_vae_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + vae_state_dict, + mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}, + ) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + update_vae_attentions_ldm_to_diffusers( + mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"} + ) + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len(config["up_block_types"]) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + update_vae_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + vae_state_dict, + mapping={"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}, + ) + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + update_vae_resnet_ldm_to_diffusers( + resnets, + new_checkpoint, + vae_state_dict, + mapping={"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}, + ) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + update_vae_attentions_ldm_to_diffusers( + mid_attentions, new_checkpoint, vae_state_dict, mapping={"old": "mid.attn_1", "new": "mid_block.attentions.0"} + ) + conv_attn_to_linear(new_checkpoint) + + return new_checkpoint + + +FLUX2_TRANSFORMER_KEYS_RENAME_DICT = { + # Image and text input projections + "img_in": "x_embedder", + "txt_in": "context_embedder", + # Timestep and guidance embeddings + "time_in.in_layer": "time_guidance_embed.timestep_embedder.linear_1", + "time_in.out_layer": "time_guidance_embed.timestep_embedder.linear_2", + "guidance_in.in_layer": "time_guidance_embed.guidance_embedder.linear_1", + "guidance_in.out_layer": "time_guidance_embed.guidance_embedder.linear_2", + # Modulation parameters + "double_stream_modulation_img.lin": "double_stream_modulation_img.linear", + "double_stream_modulation_txt.lin": "double_stream_modulation_txt.linear", + "single_stream_modulation.lin": "single_stream_modulation.linear", + # Final output layer + # "final_layer.adaLN_modulation.1": "norm_out.linear", # Handle separately since we need to swap mod params + "final_layer.linear": "proj_out", +} + + +FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP = { + "final_layer.adaLN_modulation.1": "norm_out.linear", +} + + +FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP = { + # Handle fused QKV projections separately as we need to break into Q, K, V projections + "img_attn.norm.query_norm": "attn.norm_q", + "img_attn.norm.key_norm": "attn.norm_k", + "img_attn.proj": "attn.to_out.0", + "img_mlp.0": "ff.linear_in", + "img_mlp.2": "ff.linear_out", + "txt_attn.norm.query_norm": "attn.norm_added_q", + "txt_attn.norm.key_norm": "attn.norm_added_k", + "txt_attn.proj": "attn.to_add_out", + "txt_mlp.0": "ff_context.linear_in", + "txt_mlp.2": "ff_context.linear_out", +} + + +FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP = { + "linear1": "attn.to_qkv_mlp_proj", + "linear2": "attn.to_out", + "norm.query_norm": "attn.norm_q", + "norm.key_norm": "attn.norm_k", +} + + +# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; +# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use +# diffusers implementation +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_ada_layer_norm_weights(key: str, state_dict: Dict[str, Any]) -> None: + # Skip if not a weight + if ".weight" not in key: + return + + # If adaLN_modulation is in the key, swap scale and shift parameters + # Original implementation is (shift, scale); diffusers implementation is (scale, shift) + if "adaLN_modulation" in key: + key_without_param_type, param_type = key.rsplit(".", maxsplit=1) + # Assume all such keys are in the AdaLayerNorm key map + new_key_without_param_type = FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP[key_without_param_type] + new_key = ".".join([new_key_without_param_type, param_type]) + + swapped_weight = swap_scale_shift(state_dict.pop(key)) + state_dict[new_key] = swapped_weight + return + + +def convert_flux2_double_stream_blocks(key: str, state_dict: Dict[str, Any]) -> None: + # Skip if not a weight, bias, or scale + if ".weight" not in key and ".bias" not in key and ".scale" not in key: + return + + new_prefix = "transformer_blocks" + if "double_blocks." in key: + parts = key.split(".") + block_idx = parts[1] + modality_block_name = parts[2] # img_attn, img_mlp, txt_attn, txt_mlp + within_block_name = ".".join(parts[2:-1]) + param_type = parts[-1] + + if param_type == "scale": + param_type = "weight" + + if "qkv" in within_block_name: + fused_qkv_weight = state_dict.pop(key) + to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) + if "img" in modality_block_name: + # double_blocks.{N}.img_attn.qkv --> transformer_blocks.{N}.attn.{to_q|to_k|to_v} + to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) + new_q_name = "attn.to_q" + new_k_name = "attn.to_k" + new_v_name = "attn.to_v" + elif "txt" in modality_block_name: + # double_blocks.{N}.txt_attn.qkv --> transformer_blocks.{N}.attn.{add_q_proj|add_k_proj|add_v_proj} + to_q_weight, to_k_weight, to_v_weight = torch.chunk(fused_qkv_weight, 3, dim=0) + new_q_name = "attn.add_q_proj" + new_k_name = "attn.add_k_proj" + new_v_name = "attn.add_v_proj" + new_q_key = ".".join([new_prefix, block_idx, new_q_name, param_type]) + new_k_key = ".".join([new_prefix, block_idx, new_k_name, param_type]) + new_v_key = ".".join([new_prefix, block_idx, new_v_name, param_type]) + state_dict[new_q_key] = to_q_weight + state_dict[new_k_key] = to_k_weight + state_dict[new_v_key] = to_v_weight + else: + new_within_block_name = FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP[within_block_name] + new_key = ".".join([new_prefix, block_idx, new_within_block_name, param_type]) + + param = state_dict.pop(key) + state_dict[new_key] = param + return + + +def convert_flux2_single_stream_blocks(key: str, state_dict: Dict[str, Any]) -> None: + # Skip if not a weight, bias, or scale + if ".weight" not in key and ".bias" not in key and ".scale" not in key: + return + + # Mapping: + # - single_blocks.{N}.linear1 --> single_transformer_blocks.{N}.attn.to_qkv_mlp_proj + # - single_blocks.{N}.linear2 --> single_transformer_blocks.{N}.attn.to_out + # - single_blocks.{N}.norm.query_norm.scale --> single_transformer_blocks.{N}.attn.norm_q.weight + # - single_blocks.{N}.norm.key_norm.scale --> single_transformer_blocks.{N}.attn.norm_k.weight + new_prefix = "single_transformer_blocks" + if "single_blocks." in key: + parts = key.split(".") + block_idx = parts[1] + within_block_name = ".".join(parts[2:-1]) + param_type = parts[-1] + + if param_type == "scale": + param_type = "weight" + + new_within_block_name = FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP[within_block_name] + new_key = ".".join([new_prefix, block_idx, new_within_block_name, param_type]) + + param = state_dict.pop(key) + state_dict[new_key] = param + return + + +TRANSFORMER_SPECIAL_KEYS_REMAP = { + "adaLN_modulation": convert_ada_layer_norm_weights, + "double_blocks": convert_flux2_double_stream_blocks, + "single_blocks": convert_flux2_single_stream_blocks, +} + + +def update_state_dict(state_dict: Dict[str, Any], old_key: str, new_key: str) -> None: + state_dict[new_key] = state_dict.pop(old_key) + + +def get_flux2_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]: + if model_type == "flux2-dev": + config = { + "model_id": "black-forest-labs/FLUX.2-dev", + "diffusers_config": { + "patch_size": 1, + "in_channels": 128, + "num_layers": 8, + "num_single_layers": 48, + "attention_head_dim": 128, + "num_attention_heads": 48, + "joint_attention_dim": 15360, + "timestep_guidance_channels": 256, + "mlp_ratio": 3.0, + "axes_dims_rope": (32, 32, 32, 32), + "rope_theta": 2000, + "eps": 1e-6, + }, + } + rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "klein-4b": + config = { + "model_id": "diffusers-internal-dev/dummy0115", + "diffusers_config": { + "patch_size": 1, + "in_channels": 128, + "num_layers": 5, + "num_single_layers": 20, + "attention_head_dim": 128, + "num_attention_heads": 24, + "joint_attention_dim": 7680, + "timestep_guidance_channels": 256, + "mlp_ratio": 3.0, + "axes_dims_rope": (32, 32, 32, 32), + "rope_theta": 2000, + "eps": 1e-6, + "guidance_embeds": False, + }, + } + rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP + + elif model_type == "klein-9b": + config = { + "model_id": "diffusers-internal-dev/dummy0115", + "diffusers_config": { + "patch_size": 1, + "in_channels": 128, + "num_layers": 8, + "num_single_layers": 24, + "attention_head_dim": 128, + "num_attention_heads": 32, + "joint_attention_dim": 12288, + "timestep_guidance_channels": 256, + "mlp_ratio": 3.0, + "axes_dims_rope": (32, 32, 32, 32), + "rope_theta": 2000, + "eps": 1e-6, + "guidance_embeds": False, + }, + } + rename_dict = FLUX2_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = TRANSFORMER_SPECIAL_KEYS_REMAP + + else: + raise ValueError(f"Unknown model_type: {model_type}. Choose from: flux2-dev, klein-4b, klein-9b") + + return config, rename_dict, special_keys_remap + + +def convert_flux2_transformer_to_diffusers(original_state_dict: Dict[str, torch.Tensor], model_type: str): + config, rename_dict, special_keys_remap = get_flux2_transformer_config(model_type) + + diffusers_config = config["diffusers_config"] + + with init_empty_weights(): + transformer = Flux2Transformer2DModel.from_config(diffusers_config) + + # Handle official code --> diffusers key remapping via the remap dict + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict(original_state_dict, key, new_key) + + # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in + # special_keys_remap + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + transformer.load_state_dict(original_state_dict, strict=True, assign=True) + return transformer + + +def main(args): + if args.vae: + original_vae_ckpt = load_original_checkpoint(args, filename=args.vae_filename) + vae = AutoencoderKLFlux2() + converted_vae_state_dict = convert_flux2_vae_checkpoint_to_diffusers(original_vae_ckpt, vae.config) + vae.load_state_dict(converted_vae_state_dict, strict=True) + if not args.full_pipe: + vae_dtype = torch.bfloat16 if args.vae_dtype == "bf16" else torch.float32 + vae.to(vae_dtype).save_pretrained(f"{args.output_path}/vae") + + if args.dit: + original_dit_ckpt = load_original_checkpoint(args, filename=args.dit_filename) + + if "klein-4b" in args.dit_filename: + model_type = "klein-4b" + elif "klein-9b" in args.dit_filename: + model_type = "klein-9b" + else: + model_type = "flux2-dev" + transformer = convert_flux2_transformer_to_diffusers(original_dit_ckpt, model_type) + if not args.full_pipe: + dit_dtype = torch.bfloat16 if args.dit_dtype == "bf16" else torch.float32 + transformer.to(dit_dtype).save_pretrained(f"{args.output_path}/transformer") + + if args.full_pipe: + tokenizer_id = "mistralai/Mistral-Small-3.1-24B-Instruct-2503" + text_encoder_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506" + generate_config = GenerationConfig.from_pretrained(text_encoder_id) + generate_config.do_sample = True + text_encoder = Mistral3ForConditionalGeneration.from_pretrained( + text_encoder_id, generation_config=generate_config, torch_dtype=torch.bfloat16 + ) + tokenizer = AutoProcessor.from_pretrained(tokenizer_id) + scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained( + "black-forest-labs/FLUX.1-dev", subfolder="scheduler" + ) + + if_distilled = "base" not in args.dit_filename + + pipe = Flux2Pipeline( + vae=vae, + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + if_distilled=if_distilled, + ) + pipe.save_pretrained(args.output_path) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_flux_to_diffusers.py b/scripts/convert_flux_to_diffusers.py new file mode 100644 index 000000000000..ec31d842d4db --- /dev/null +++ b/scripts/convert_flux_to_diffusers.py @@ -0,0 +1,308 @@ +import argparse +from contextlib import nullcontext + +import safetensors.torch +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download + +from diffusers import AutoencoderKL, FluxTransformer2DModel +from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint +from diffusers.utils.import_utils import is_accelerate_available + + +""" +# Transformer + +python scripts/convert_flux_to_diffusers.py \ +--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \ +--filename "flux1-schnell.sft" +--output_path "flux-schnell" \ +--transformer +""" + +""" +# VAE + +python scripts/convert_flux_to_diffusers.py \ +--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \ +--filename "ae.sft" +--output_path "flux-schnell" \ +--vae +""" + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--original_state_dict_repo_id", default=None, type=str) +parser.add_argument("--filename", default="flux.safetensors", type=str) +parser.add_argument("--checkpoint_path", default=None, type=str) +parser.add_argument("--in_channels", type=int, default=64) +parser.add_argument("--out_channels", type=int, default=None) +parser.add_argument("--vae", action="store_true") +parser.add_argument("--transformer", action="store_true") +parser.add_argument("--output_path", type=str) +parser.add_argument("--dtype", type=str, default="bf16") + +args = parser.parse_args() +dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 + + +def load_original_checkpoint(args): + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) + elif args.checkpoint_path is not None: + ckpt_path = args.checkpoint_path + else: + raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; +# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_flux_transformer_checkpoint_to_diffusers( + original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0 +): + converted_state_dict = {} + + ## time_text_embed.timestep_embedder <- time_in + converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "time_in.in_layer.weight" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "time_in.in_layer.bias" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "time_in.out_layer.weight" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "time_in.out_layer.bias" + ) + + ## time_text_embed.text_embedder <- vector_in + converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( + "vector_in.in_layer.weight" + ) + converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( + "vector_in.in_layer.bias" + ) + converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( + "vector_in.out_layer.weight" + ) + converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( + "vector_in.out_layer.bias" + ) + + # guidance + has_guidance = any("guidance" in k for k in original_state_dict) + if has_guidance: + converted_state_dict["time_text_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop( + "guidance_in.in_layer.weight" + ) + converted_state_dict["time_text_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop( + "guidance_in.in_layer.bias" + ) + converted_state_dict["time_text_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop( + "guidance_in.out_layer.weight" + ) + converted_state_dict["time_text_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop( + "guidance_in.out_layer.bias" + ) + + # context_embedder + converted_state_dict["context_embedder.weight"] = original_state_dict.pop("txt_in.weight") + converted_state_dict["context_embedder.bias"] = original_state_dict.pop("txt_in.bias") + + # x_embedder + converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight") + converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias") + + # double transformer blocks + for i in range(num_layers): + block_prefix = f"transformer_blocks.{i}." + # norms. + ## norm1 + converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.lin.bias" + ) + ## norm1_context + converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.lin.bias" + ) + # Q, K, V + sample_q, sample_k, sample_v = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0 + ) + context_q, context_k, context_v = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0 + ) + sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0 + ) + context_q_bias, context_k_bias, context_v_bias = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0 + ) + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q]) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias]) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k]) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias]) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v]) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias]) + converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q]) + converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias]) + converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k]) + converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias]) + converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v]) + converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias]) + # qk_norm + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.norm.query_norm.scale" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.norm.key_norm.scale" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.norm.query_norm.scale" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.norm.key_norm.scale" + ) + # ff img_mlp + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.0.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.0.bias" + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.2.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.2.bias" + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.0.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.0.bias" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.2.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.2.bias" + ) + # output projections. + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.proj.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.proj.bias" + ) + + # single transformer blocks + for i in range(num_single_layers): + block_prefix = f"single_transformer_blocks.{i}." + # norm.linear <- single_blocks.0.modulation.lin + converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.lin.bias" + ) + # Q, K, V, mlp + mlp_hidden_dim = int(inner_dim * mlp_ratio) + split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim) + q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0) + q_bias, k_bias, v_bias, mlp_bias = torch.split( + original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0 + ) + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q]) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias]) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k]) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias]) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v]) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias]) + converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp]) + converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias]) + # qk norm + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"single_blocks.{i}.norm.query_norm.scale" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"single_blocks.{i}.norm.key_norm.scale" + ) + # output projections. + converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.weight" + ) + converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.bias" + ) + + converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.weight") + ) + converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.bias") + ) + + return converted_state_dict + + +def main(args): + original_ckpt = load_original_checkpoint(args) + has_guidance = any("guidance" in k for k in original_ckpt) + + if args.transformer: + num_layers = 19 + num_single_layers = 38 + inner_dim = 3072 + mlp_ratio = 4.0 + + converted_transformer_state_dict = convert_flux_transformer_checkpoint_to_diffusers( + original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio + ) + transformer = FluxTransformer2DModel( + in_channels=args.in_channels, out_channels=args.out_channels, guidance_embeds=has_guidance + ) + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + print( + f"Saving Flux Transformer in Diffusers format. Variant: {'guidance-distilled' if has_guidance else 'timestep-distilled'}" + ) + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + if args.vae: + config = AutoencoderKL.load_config("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="vae") + vae = AutoencoderKL.from_config(config, scaling_factor=0.3611, shift_factor=0.1159).to(torch.bfloat16) + + converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config) + vae.load_state_dict(converted_vae_state_dict, strict=True) + vae.to(dtype).save_pretrained(f"{args.output_path}/vae") + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py b/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py new file mode 100644 index 000000000000..b701b7fb40b1 --- /dev/null +++ b/scripts/convert_flux_xlabs_ipadapter_to_diffusers.py @@ -0,0 +1,97 @@ +import argparse +from contextlib import nullcontext + +import safetensors.torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download + +from diffusers.utils.import_utils import is_accelerate_available, is_transformers_available + + +if is_transformers_available(): + from transformers import CLIPVisionModelWithProjection + + vision = True +else: + vision = False + +""" +python scripts/convert_flux_xlabs_ipadapter_to_diffusers.py \ +--original_state_dict_repo_id "XLabs-AI/flux-ip-adapter" \ +--filename "flux-ip-adapter.safetensors" +--output_path "flux-ip-adapter-hf/" +""" + + +CTX = init_empty_weights if is_accelerate_available else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--original_state_dict_repo_id", default=None, type=str) +parser.add_argument("--filename", default="flux.safetensors", type=str) +parser.add_argument("--checkpoint_path", default=None, type=str) +parser.add_argument("--output_path", type=str) +parser.add_argument("--vision_pretrained_or_path", default="openai/clip-vit-large-patch14", type=str) + +args = parser.parse_args() + + +def load_original_checkpoint(args): + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) + elif args.checkpoint_path is not None: + ckpt_path = args.checkpoint_path + else: + raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +def convert_flux_ipadapter_checkpoint_to_diffusers(original_state_dict, num_layers): + converted_state_dict = {} + + # image_proj + ## norm + converted_state_dict["image_proj.norm.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight") + converted_state_dict["image_proj.norm.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias") + ## proj + converted_state_dict["image_proj.proj.weight"] = original_state_dict.pop("ip_adapter_proj_model.norm.weight") + converted_state_dict["image_proj.proj.bias"] = original_state_dict.pop("ip_adapter_proj_model.norm.bias") + + # double transformer blocks + for i in range(num_layers): + block_prefix = f"ip_adapter.{i}." + # to_k_ip + converted_state_dict[f"{block_prefix}to_k_ip.bias"] = original_state_dict.pop( + f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.bias" + ) + converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop( + f"double_blocks.{i}.processor.ip_adapter_double_stream_k_proj.weight" + ) + # to_v_ip + converted_state_dict[f"{block_prefix}to_v_ip.bias"] = original_state_dict.pop( + f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.bias" + ) + converted_state_dict[f"{block_prefix}to_k_ip.weight"] = original_state_dict.pop( + f"double_blocks.{i}.processor.ip_adapter_double_stream_v_proj.weight" + ) + + return converted_state_dict + + +def main(args): + original_ckpt = load_original_checkpoint(args) + + num_layers = 19 + converted_ip_adapter_state_dict = convert_flux_ipadapter_checkpoint_to_diffusers(original_ckpt, num_layers) + + print("Saving Flux IP-Adapter in Diffusers format.") + safetensors.torch.save_file(converted_ip_adapter_state_dict, f"{args.output_path}/model.safetensors") + + if vision: + model = CLIPVisionModelWithProjection.from_pretrained(args.vision_pretrained_or_path) + model.save_pretrained(f"{args.output_path}/image_encoder") + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_gligen_to_diffusers.py b/scripts/convert_gligen_to_diffusers.py new file mode 100644 index 000000000000..83c1f928e407 --- /dev/null +++ b/scripts/convert_gligen_to_diffusers.py @@ -0,0 +1,581 @@ +import argparse +import re + +import torch +import yaml +from transformers import ( + CLIPProcessor, + CLIPTextModel, + CLIPTokenizer, + CLIPVisionModelWithProjection, +) + +from diffusers import ( + AutoencoderKL, + DDIMScheduler, + StableDiffusionGLIGENPipeline, + StableDiffusionGLIGENTextImagePipeline, + UNet2DConditionModel, +) +from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( + assign_to_checkpoint, + conv_attn_to_linear, + protected, + renew_attention_paths, + renew_resnet_paths, + renew_vae_attention_paths, + renew_vae_resnet_paths, + shave_segments, + textenc_conversion_map, + textenc_pattern, +) + + +def convert_open_clip_checkpoint(checkpoint): + checkpoint = checkpoint["text_encoder"] + text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") + + keys = list(checkpoint.keys()) + + text_model_dict = {} + + if "cond_stage_model.model.text_projection" in checkpoint: + d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) + else: + d_model = 1024 + + for key in keys: + if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer + continue + if key in textenc_conversion_map: + text_model_dict[textenc_conversion_map[key]] = checkpoint[key] + # if key.startswith("cond_stage_model.model.transformer."): + new_key = key[len("transformer.") :] + if new_key.endswith(".in_proj_weight"): + new_key = new_key[: -len(".in_proj_weight")] + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] + text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] + text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] + elif new_key.endswith(".in_proj_bias"): + new_key = new_key[: -len(".in_proj_bias")] + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] + text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] + text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] + else: + if key != "transformer.text_model.embeddings.position_ids": + new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) + + text_model_dict[new_key] = checkpoint[key] + + if key == "transformer.text_model.embeddings.token_embedding.weight": + text_model_dict["text_model.embeddings.token_embedding.weight"] = checkpoint[key] + + text_model_dict.pop("text_model.embeddings.transformer.text_model.embeddings.token_embedding.weight") + + text_model.load_state_dict(text_model_dict) + + return text_model + + +def convert_gligen_vae_checkpoint(checkpoint, config): + checkpoint = checkpoint["autoencoder"] + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for key in new_checkpoint.keys(): + if "encoder.mid_block.attentions.0" in key or "decoder.mid_block.attentions.0" in key: + if "query" in key: + new_checkpoint[key.replace("query", "to_q")] = new_checkpoint.pop(key) + if "value" in key: + new_checkpoint[key.replace("value", "to_v")] = new_checkpoint.pop(key) + if "key" in key: + new_checkpoint[key.replace("key", "to_k")] = new_checkpoint.pop(key) + if "proj_attn" in key: + new_checkpoint[key.replace("proj_attn", "to_out.0")] = new_checkpoint.pop(key) + + return new_checkpoint + + +def convert_gligen_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + unet_state_dict = {} + checkpoint = checkpoint["model"] + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has bot EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + for key in keys: + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + for key in keys: + if "position_net" in key: + new_checkpoint[key] = unet_state_dict[key] + + return new_checkpoint + + +def create_vae_config(original_config, image_size: int): + vae_params = original_config["autoencoder"]["params"]["ddconfig"] + _ = original_config["autoencoder"]["params"]["embed_dim"] + + block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = { + "sample_size": image_size, + "in_channels": vae_params["in_channels"], + "out_channels": vae_params["out_ch"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params["z_channels"], + "layers_per_block": vae_params["num_res_blocks"], + } + + return config + + +def create_unet_config(original_config, image_size: int, attention_type): + unet_params = original_config["model"]["params"] + vae_params = original_config["autoencoder"]["params"]["ddconfig"] + + block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) + + head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None + use_linear_projection = ( + unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False + ) + if use_linear_projection: + if head_dim is None: + head_dim = [5, 10, 20, 20] + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params["in_channels"], + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params["num_res_blocks"], + "cross_attention_dim": unet_params["context_dim"], + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "attention_type": attention_type, + } + + return config + + +def convert_gligen_to_diffusers( + checkpoint_path: str, + original_config_file: str, + attention_type: str, + image_size: int = 512, + extract_ema: bool = False, + num_in_channels: int = None, + device: str = None, +): + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + if "global_step" in checkpoint: + checkpoint["global_step"] + else: + print("global_step key not found in model") + + original_config = yaml.safe_load(original_config_file) + + if num_in_channels is not None: + original_config["model"]["params"]["in_channels"] = num_in_channels + + num_train_timesteps = original_config["diffusion"]["params"]["timesteps"] + beta_start = original_config["diffusion"]["params"]["linear_start"] + beta_end = original_config["diffusion"]["params"]["linear_end"] + + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type="epsilon", + ) + + # Convert the UNet2DConditionalModel model + unet_config = create_unet_config(original_config, image_size, attention_type) + unet = UNet2DConditionModel(**unet_config) + + converted_unet_checkpoint = convert_gligen_unet_checkpoint( + checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema + ) + + unet.load_state_dict(converted_unet_checkpoint) + + # Convert the VAE model + vae_config = create_vae_config(original_config, image_size) + converted_vae_checkpoint = convert_gligen_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + + # Convert the text model + text_encoder = convert_open_clip_checkpoint(checkpoint) + tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") + + if attention_type == "gated-text-image": + image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") + + pipe = StableDiffusionGLIGENTextImagePipeline( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + image_encoder=image_encoder, + processor=processor, + unet=unet, + scheduler=scheduler, + safety_checker=None, + feature_extractor=None, + ) + elif attention_type == "gated": + pipe = StableDiffusionGLIGENPipeline( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=None, + feature_extractor=None, + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + required=True, + help="The YAML config file corresponding to the gligen architecture.", + ) + parser.add_argument( + "--num_in_channels", + default=None, + type=int, + help="The number of input channels. If `None` number of input channels will be automatically inferred.", + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--attention_type", + default=None, + type=str, + required=True, + help="Type of attention ex: gated or gated-text-image", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use.") + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + + args = parser.parse_args() + + pipe = convert_gligen_to_diffusers( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + attention_type=args.attention_type, + extract_ema=args.extract_ema, + num_in_channels=args.num_in_channels, + device=args.device, + ) + + if args.half: + pipe.to(dtype=torch.float16) + + pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_hunyuan_image_to_diffusers.py b/scripts/convert_hunyuan_image_to_diffusers.py new file mode 100644 index 000000000000..c41e934cc3d4 --- /dev/null +++ b/scripts/convert_hunyuan_image_to_diffusers.py @@ -0,0 +1,1044 @@ +import argparse +import logging + +import torch +from safetensors import safe_open + +from diffusers import AutoencoderKLHunyuanImage, AutoencoderKLHunyuanImageRefiner, HunyuanImageTransformer2DModel + + +logger = logging.getLogger(__name__) # pylint: disable=invalid-name + + +""" +Usage examples +============== + +python scripts/convert_hunyuan_image_to_diffusers.py \ + --model_type hunyuanimage2.1 \ + --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage2.1.safetensors" \ + --vae_checkpoint_path "HunyuanImage-2.1/ckpts/vae/vae_2_1/pytorch_model.ckpt" \ + --output_path "/raid/yiyi/test-hy21-diffusers" \ + --dtype fp32 + +python scripts/convert_hunyuan_image_to_diffusers.py \ + --model_type hunyuanimage2.1-distilled \ + --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage2.1-distilled.safetensors" \ + --vae_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/vae/vae_2_1/pytorch_model.ckpt" \ + --output_path "/raid/yiyi/test-hy21-distilled-diffusers" \ + --dtype fp32 + + +python scripts/convert_hunyuan_image_to_diffusers.py \ + --model_type hunyuanimage-refiner \ + --transformer_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/dit/hunyuanimage-refiner.safetensors" \ + --vae_checkpoint_path "/raid/yiyi/HunyuanImage-2.1/ckpts/vae/vae_refiner/pytorch_model.pt" \ + --output_path "/raid/yiyi/test-hy2-refiner-diffusers" \ + --dtype fp32 +""" + +parser = argparse.ArgumentParser() +parser.add_argument( + "--model_type", type=str, default=None +) # hunyuanimage2.1, hunyuanimage2.1-distilled, hunyuanimage-refiner +parser.add_argument("--transformer_checkpoint_path", default=None, type=str) # ckpts/dit/hunyuanimage2.1.safetensors +parser.add_argument("--vae_checkpoint_path", default=None, type=str) # ckpts/vae/vae_2_1/pytorch_model.ckpt +parser.add_argument("--output_path", type=str) +parser.add_argument("--dtype", type=str, default="fp32") + +args = parser.parse_args() +dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 + + +# copied from https://github.com/Tencent-Hunyuan/HunyuanImage-2.1/hyimage/models/hunyuan/modules/hunyuanimage_dit.py#L21 +def convert_hunyuan_dict_for_tensor_parallel(state_dict): + """ + Convert a Hunyuan model state dict to be compatible with tensor parallel architectures. + + Args: + state_dict: Original state dict + + Returns: + new_dict: Converted state dict + """ + new_dict = {} + for k, w in state_dict.items(): + if k.startswith("double_blocks") and "attn_qkv.weight" in k: + hidden_size = w.shape[1] + k1 = k.replace("attn_qkv.weight", "attn_q.weight") + w1 = w[:hidden_size, :] + new_dict[k1] = w1 + k2 = k.replace("attn_qkv.weight", "attn_k.weight") + w2 = w[hidden_size : 2 * hidden_size, :] + new_dict[k2] = w2 + k3 = k.replace("attn_qkv.weight", "attn_v.weight") + w3 = w[-hidden_size:, :] + new_dict[k3] = w3 + elif k.startswith("double_blocks") and "attn_qkv.bias" in k: + hidden_size = w.shape[0] // 3 + k1 = k.replace("attn_qkv.bias", "attn_q.bias") + w1 = w[:hidden_size] + new_dict[k1] = w1 + k2 = k.replace("attn_qkv.bias", "attn_k.bias") + w2 = w[hidden_size : 2 * hidden_size] + new_dict[k2] = w2 + k3 = k.replace("attn_qkv.bias", "attn_v.bias") + w3 = w[-hidden_size:] + new_dict[k3] = w3 + elif k.startswith("single_blocks") and "linear1" in k: + hidden_size = state_dict[k.replace("linear1", "linear2")].shape[0] + k1 = k.replace("linear1", "linear1_q") + w1 = w[:hidden_size] + new_dict[k1] = w1 + k2 = k.replace("linear1", "linear1_k") + w2 = w[hidden_size : 2 * hidden_size] + new_dict[k2] = w2 + k3 = k.replace("linear1", "linear1_v") + w3 = w[2 * hidden_size : 3 * hidden_size] + new_dict[k3] = w3 + k4 = k.replace("linear1", "linear1_mlp") + w4 = w[3 * hidden_size :] + new_dict[k4] = w4 + elif k.startswith("single_blocks") and "linear2" in k: + k1 = k.replace("linear2", "linear2.fc") + new_dict[k1] = w + else: + new_dict[k] = w + return new_dict + + +def load_original_vae_checkpoint(args): + # "ckpts/vae/vae_2_1/pytorch_model.ckpt" + state_dict = torch.load(args.vae_checkpoint_path) + + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + vae_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("vae."): + vae_state_dict[k.replace("vae.", "")] = v + + for k, v in vae_state_dict.items(): + if "weight" in k: + if len(v.shape) == 5 and v.shape[2] == 1: + vae_state_dict[k] = v.squeeze(2) + else: + vae_state_dict[k] = v + else: + vae_state_dict[k] = v + return vae_state_dict + + +def load_original_refiner_vae_checkpoint(args): + # "ckpts/vae/vae_refiner/pytorch_model.pt" + state_dict = torch.load(args.vae_checkpoint_path) + + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + vae_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("vae."): + vae_state_dict[k.replace("vae.", "")] = v + return vae_state_dict + + +def load_original_transformer_checkpoint(args): + # ckpts/dit/hunyuanimage-refiner.safetensors" + # ckpts/dit/hunyuanimage2.1.safetensors" + state_dict = {} + with safe_open(args.transformer_checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + state_dict[key] = f.get_tensor(key) + if args.model_type == "hunyuanimage-2.1": + state_dict = convert_hunyuan_dict_for_tensor_parallel(state_dict) + return state_dict + + +def convert_hunyuan_image_transformer_checkpoint_to_diffusers( + original_state_dict, use_byt5=True, guidance_distilled=False, use_meanflow=False +): + converted_state_dict = {} + + # 1. byt5_in -> context_embedder_2 + if use_byt5: + converted_state_dict["context_embedder_2.norm.weight"] = original_state_dict.pop("byt5_in.layernorm.weight") + converted_state_dict["context_embedder_2.norm.bias"] = original_state_dict.pop("byt5_in.layernorm.bias") + converted_state_dict["context_embedder_2.linear_1.weight"] = original_state_dict.pop("byt5_in.fc1.weight") + converted_state_dict["context_embedder_2.linear_1.bias"] = original_state_dict.pop("byt5_in.fc1.bias") + converted_state_dict["context_embedder_2.linear_2.weight"] = original_state_dict.pop("byt5_in.fc2.weight") + converted_state_dict["context_embedder_2.linear_2.bias"] = original_state_dict.pop("byt5_in.fc2.bias") + converted_state_dict["context_embedder_2.linear_3.weight"] = original_state_dict.pop("byt5_in.fc3.weight") + converted_state_dict["context_embedder_2.linear_3.bias"] = original_state_dict.pop("byt5_in.fc3.bias") + + # 2. img_in -> x_embedder + converted_state_dict["x_embedder.proj.weight"] = original_state_dict.pop("img_in.proj.weight") + converted_state_dict["x_embedder.proj.bias"] = original_state_dict.pop("img_in.proj.bias") + + # 3. txt_in -> context_embedder (complex mapping) + # txt_in.input_embedder -> context_embedder.proj_in + converted_state_dict["context_embedder.proj_in.weight"] = original_state_dict.pop("txt_in.input_embedder.weight") + converted_state_dict["context_embedder.proj_in.bias"] = original_state_dict.pop("txt_in.input_embedder.bias") + + # txt_in.t_embedder -> context_embedder.time_text_embed.timestep_embedder + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.weight"] = ( + original_state_dict.pop("txt_in.t_embedder.mlp.0.weight") + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "txt_in.t_embedder.mlp.0.bias" + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.weight"] = ( + original_state_dict.pop("txt_in.t_embedder.mlp.2.weight") + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "txt_in.t_embedder.mlp.2.bias" + ) + + # txt_in.c_embedder -> context_embedder.time_text_embed.text_embedder + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( + "txt_in.c_embedder.linear_1.weight" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( + "txt_in.c_embedder.linear_1.bias" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( + "txt_in.c_embedder.linear_2.weight" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( + "txt_in.c_embedder.linear_2.bias" + ) + + # txt_in.individual_token_refiner -> context_embedder.token_refiner + for i in range(2): # 2 refiner blocks + block_prefix = f"context_embedder.token_refiner.refiner_blocks.{i}." + # norm1 + converted_state_dict[f"{block_prefix}norm1.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.norm1.weight" + ) + converted_state_dict[f"{block_prefix}norm1.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.norm1.bias" + ) + # norm2 + converted_state_dict[f"{block_prefix}norm2.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.norm2.weight" + ) + converted_state_dict[f"{block_prefix}norm2.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.norm2.bias" + ) + + # Split QKV + qkv_weight = original_state_dict.pop(f"txt_in.individual_token_refiner.blocks.{i}.self_attn_qkv.weight") + qkv_bias = original_state_dict.pop(f"txt_in.individual_token_refiner.blocks.{i}.self_attn_qkv.bias") + q_weight, k_weight, v_weight = torch.chunk(qkv_weight, 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) + + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = q_weight + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = q_bias + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = k_weight + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = k_bias + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = v_weight + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = v_bias + + # attn projection + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.self_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.self_attn_proj.bias" + ) + + # MLP + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.mlp.fc2.bias" + ) + + # norm_out + converted_state_dict[f"{block_prefix}norm_out.linear.weight"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.adaLN_modulation.1.weight" + ) + converted_state_dict[f"{block_prefix}norm_out.linear.bias"] = original_state_dict.pop( + f"txt_in.individual_token_refiner.blocks.{i}.adaLN_modulation.1.bias" + ) + + # 4. time_in -> time_text_embed.timestep_embedder + converted_state_dict["time_guidance_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "time_in.mlp.0.weight" + ) + converted_state_dict["time_guidance_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "time_in.mlp.0.bias" + ) + converted_state_dict["time_guidance_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "time_in.mlp.2.weight" + ) + converted_state_dict["time_guidance_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "time_in.mlp.2.bias" + ) + + # time_r_in -> time_guidance_embed.timestep_r_embedder + if use_meanflow: + converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_1.weight"] = original_state_dict.pop( + "time_r_in.mlp.0.weight" + ) + converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_1.bias"] = original_state_dict.pop( + "time_r_in.mlp.0.bias" + ) + converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_2.weight"] = original_state_dict.pop( + "time_r_in.mlp.2.weight" + ) + converted_state_dict["time_guidance_embed.timestep_embedder_r.linear_2.bias"] = original_state_dict.pop( + "time_r_in.mlp.2.bias" + ) + + # guidance_in -> time_guidance_embed.guidance_embedder + if guidance_distilled: + converted_state_dict["time_guidance_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop( + "guidance_in.mlp.0.weight" + ) + converted_state_dict["time_guidance_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop( + "guidance_in.mlp.0.bias" + ) + converted_state_dict["time_guidance_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop( + "guidance_in.mlp.2.weight" + ) + converted_state_dict["time_guidance_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop( + "guidance_in.mlp.2.bias" + ) + + # 5. double_blocks -> transformer_blocks + for i in range(20): # 20 double blocks + block_prefix = f"transformer_blocks.{i}." + + # norm1 (img_mod) + converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.linear.weight" + ) + converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.linear.bias" + ) + + # norm1_context (txt_mod) + converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.linear.weight" + ) + converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.linear.bias" + ) + + # img attention + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_q.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_q.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_k.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_k.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_v.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_v.bias" + ) + + # img attention norms + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_q_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_k_norm.weight" + ) + + # img attention projection + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn_proj.bias" + ) + + # img MLP + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.fc2.bias" + ) + + # txt attention (additional projections) + converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_q.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_q.bias" + ) + converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_k.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_k.bias" + ) + converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_v.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_v.bias" + ) + + # txt attention norms + converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_q_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_k_norm.weight" + ) + + # txt attention projection + converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn_proj.bias" + ) + + # txt MLP (ff_context) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.fc2.bias" + ) + + # 6. single_blocks -> single_transformer_blocks + for i in range(40): # 40 single blocks + block_prefix = f"single_transformer_blocks.{i}." + + # norm + converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.linear.weight" + ) + converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.linear.bias" + ) + + # attention Q, K, V + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_q.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_q.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_k.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_k.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_v.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_v.bias" + ) + + # attention norms + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"single_blocks.{i}.q_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"single_blocks.{i}.k_norm.weight" + ) + + # MLP projection + converted_state_dict[f"{block_prefix}proj_mlp.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_mlp.weight" + ) + converted_state_dict[f"{block_prefix}proj_mlp.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear1_mlp.bias" + ) + + # output projection + converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.fc.weight" + ) + converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.fc.bias" + ) + + # 7. final_layer -> norm_out + proj_out + converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + shift_w, scale_w = original_state_dict.pop("final_layer.adaLN_modulation.1.weight").chunk(2, dim=0) + shift_b, scale_b = original_state_dict.pop("final_layer.adaLN_modulation.1.bias").chunk(2, dim=0) + converted_state_dict["norm_out.linear.weight"] = torch.cat([scale_w, shift_w], dim=0) + converted_state_dict["norm_out.linear.bias"] = torch.cat([scale_b, shift_b], dim=0) + + return converted_state_dict, original_state_dict + + +def convert_hunyuan_image_vae_checkpoint_to_diffusers( + original_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 +): + """Convert original VAE state dict to Diffusers format.""" + converted = {} + + # 1. Encoder + # 1.1 conv_in + converted["encoder.conv_in.weight"] = original_state_dict.pop("encoder.conv_in.weight") + converted["encoder.conv_in.bias"] = original_state_dict.pop("encoder.conv_in.bias") + + # 1.2 down blocks + diffusers_block_idx = 0 + + for block_index in range(len(block_out_channels)): + for resnet_block_index in range(layers_per_block): + orig_prefix = f"encoder.down.{block_index}.block.{resnet_block_index}" + diff_prefix = f"encoder.down_blocks.{diffusers_block_idx}" + + # resnet blocks + converted[f"{diff_prefix}.norm1.weight"] = original_state_dict.pop(f"{orig_prefix}.norm1.weight") + converted[f"{diff_prefix}.norm1.bias"] = original_state_dict.pop(f"{orig_prefix}.norm1.bias") + converted[f"{diff_prefix}.conv1.weight"] = original_state_dict.pop(f"{orig_prefix}.conv1.weight") + converted[f"{diff_prefix}.conv1.bias"] = original_state_dict.pop(f"{orig_prefix}.conv1.bias") + converted[f"{diff_prefix}.norm2.weight"] = original_state_dict.pop(f"{orig_prefix}.norm2.weight") + converted[f"{diff_prefix}.norm2.bias"] = original_state_dict.pop(f"{orig_prefix}.norm2.bias") + converted[f"{diff_prefix}.conv2.weight"] = original_state_dict.pop(f"{orig_prefix}.conv2.weight") + converted[f"{diff_prefix}.conv2.bias"] = original_state_dict.pop(f"{orig_prefix}.conv2.bias") + + diffusers_block_idx += 1 + + # downsample blocks + if f"encoder.down.{block_index}.downsample.conv.weight" in original_state_dict: + converted[f"encoder.down_blocks.{diffusers_block_idx}.conv.weight"] = original_state_dict.pop( + f"encoder.down.{block_index}.downsample.conv.weight" + ) + converted[f"encoder.down_blocks.{diffusers_block_idx}.conv.bias"] = original_state_dict.pop( + f"encoder.down.{block_index}.downsample.conv.bias" + ) + diffusers_block_idx += 1 + + # 1.3 mid block + converted["encoder.mid_block.resnets.0.norm1.weight"] = original_state_dict.pop("encoder.mid.block_1.norm1.weight") + converted["encoder.mid_block.resnets.0.norm1.bias"] = original_state_dict.pop("encoder.mid.block_1.norm1.bias") + converted["encoder.mid_block.resnets.0.conv1.weight"] = original_state_dict.pop("encoder.mid.block_1.conv1.weight") + converted["encoder.mid_block.resnets.0.conv1.bias"] = original_state_dict.pop("encoder.mid.block_1.conv1.bias") + converted["encoder.mid_block.resnets.0.norm2.weight"] = original_state_dict.pop("encoder.mid.block_1.norm2.weight") + converted["encoder.mid_block.resnets.0.norm2.bias"] = original_state_dict.pop("encoder.mid.block_1.norm2.bias") + converted["encoder.mid_block.resnets.0.conv2.weight"] = original_state_dict.pop("encoder.mid.block_1.conv2.weight") + converted["encoder.mid_block.resnets.0.conv2.bias"] = original_state_dict.pop("encoder.mid.block_1.conv2.bias") + + converted["encoder.mid_block.resnets.1.norm1.weight"] = original_state_dict.pop("encoder.mid.block_2.norm1.weight") + converted["encoder.mid_block.resnets.1.norm1.bias"] = original_state_dict.pop("encoder.mid.block_2.norm1.bias") + converted["encoder.mid_block.resnets.1.conv1.weight"] = original_state_dict.pop("encoder.mid.block_2.conv1.weight") + converted["encoder.mid_block.resnets.1.conv1.bias"] = original_state_dict.pop("encoder.mid.block_2.conv1.bias") + converted["encoder.mid_block.resnets.1.norm2.weight"] = original_state_dict.pop("encoder.mid.block_2.norm2.weight") + converted["encoder.mid_block.resnets.1.norm2.bias"] = original_state_dict.pop("encoder.mid.block_2.norm2.bias") + converted["encoder.mid_block.resnets.1.conv2.weight"] = original_state_dict.pop("encoder.mid.block_2.conv2.weight") + converted["encoder.mid_block.resnets.1.conv2.bias"] = original_state_dict.pop("encoder.mid.block_2.conv2.bias") + + converted["encoder.mid_block.attentions.0.norm.weight"] = original_state_dict.pop("encoder.mid.attn_1.norm.weight") + converted["encoder.mid_block.attentions.0.norm.bias"] = original_state_dict.pop("encoder.mid.attn_1.norm.bias") + converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") + converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") + converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") + converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") + converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") + converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") + converted["encoder.mid_block.attentions.0.proj.weight"] = original_state_dict.pop( + "encoder.mid.attn_1.proj_out.weight" + ) + converted["encoder.mid_block.attentions.0.proj.bias"] = original_state_dict.pop("encoder.mid.attn_1.proj_out.bias") + + # 1.4 encoder output + converted["encoder.norm_out.weight"] = original_state_dict.pop("encoder.norm_out.weight") + converted["encoder.norm_out.bias"] = original_state_dict.pop("encoder.norm_out.bias") + converted["encoder.conv_out.weight"] = original_state_dict.pop("encoder.conv_out.weight") + converted["encoder.conv_out.bias"] = original_state_dict.pop("encoder.conv_out.bias") + + # 2. Decoder + # 2.1 conv_in + converted["decoder.conv_in.weight"] = original_state_dict.pop("decoder.conv_in.weight") + converted["decoder.conv_in.bias"] = original_state_dict.pop("decoder.conv_in.bias") + + # 2.2 mid block + converted["decoder.mid_block.resnets.0.norm1.weight"] = original_state_dict.pop("decoder.mid.block_1.norm1.weight") + converted["decoder.mid_block.resnets.0.norm1.bias"] = original_state_dict.pop("decoder.mid.block_1.norm1.bias") + converted["decoder.mid_block.resnets.0.conv1.weight"] = original_state_dict.pop("decoder.mid.block_1.conv1.weight") + converted["decoder.mid_block.resnets.0.conv1.bias"] = original_state_dict.pop("decoder.mid.block_1.conv1.bias") + converted["decoder.mid_block.resnets.0.norm2.weight"] = original_state_dict.pop("decoder.mid.block_1.norm2.weight") + converted["decoder.mid_block.resnets.0.norm2.bias"] = original_state_dict.pop("decoder.mid.block_1.norm2.bias") + converted["decoder.mid_block.resnets.0.conv2.weight"] = original_state_dict.pop("decoder.mid.block_1.conv2.weight") + converted["decoder.mid_block.resnets.0.conv2.bias"] = original_state_dict.pop("decoder.mid.block_1.conv2.bias") + + converted["decoder.mid_block.resnets.1.norm1.weight"] = original_state_dict.pop("decoder.mid.block_2.norm1.weight") + converted["decoder.mid_block.resnets.1.norm1.bias"] = original_state_dict.pop("decoder.mid.block_2.norm1.bias") + converted["decoder.mid_block.resnets.1.conv1.weight"] = original_state_dict.pop("decoder.mid.block_2.conv1.weight") + converted["decoder.mid_block.resnets.1.conv1.bias"] = original_state_dict.pop("decoder.mid.block_2.conv1.bias") + converted["decoder.mid_block.resnets.1.norm2.weight"] = original_state_dict.pop("decoder.mid.block_2.norm2.weight") + converted["decoder.mid_block.resnets.1.norm2.bias"] = original_state_dict.pop("decoder.mid.block_2.norm2.bias") + converted["decoder.mid_block.resnets.1.conv2.weight"] = original_state_dict.pop("decoder.mid.block_2.conv2.weight") + converted["decoder.mid_block.resnets.1.conv2.bias"] = original_state_dict.pop("decoder.mid.block_2.conv2.bias") + + converted["decoder.mid_block.attentions.0.norm.weight"] = original_state_dict.pop("decoder.mid.attn_1.norm.weight") + converted["decoder.mid_block.attentions.0.norm.bias"] = original_state_dict.pop("decoder.mid.attn_1.norm.bias") + converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") + converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") + converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") + converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") + converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") + converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") + converted["decoder.mid_block.attentions.0.proj.weight"] = original_state_dict.pop( + "decoder.mid.attn_1.proj_out.weight" + ) + converted["decoder.mid_block.attentions.0.proj.bias"] = original_state_dict.pop("decoder.mid.attn_1.proj_out.bias") + + # 2.3 up blocks + diffusers_block_idx = 0 + for up_block_index in range(len(block_out_channels)): + # resnet blocks + for resnet_block_index in range(layers_per_block + 1): + orig_prefix = f"decoder.up.{up_block_index}.block.{resnet_block_index}" + diff_prefix = f"decoder.up_blocks.{diffusers_block_idx}" + + converted[f"{diff_prefix}.norm1.weight"] = original_state_dict.pop(f"{orig_prefix}.norm1.weight") + converted[f"{diff_prefix}.norm1.bias"] = original_state_dict.pop(f"{orig_prefix}.norm1.bias") + converted[f"{diff_prefix}.conv1.weight"] = original_state_dict.pop(f"{orig_prefix}.conv1.weight") + converted[f"{diff_prefix}.conv1.bias"] = original_state_dict.pop(f"{orig_prefix}.conv1.bias") + converted[f"{diff_prefix}.norm2.weight"] = original_state_dict.pop(f"{orig_prefix}.norm2.weight") + converted[f"{diff_prefix}.norm2.bias"] = original_state_dict.pop(f"{orig_prefix}.norm2.bias") + converted[f"{diff_prefix}.conv2.weight"] = original_state_dict.pop(f"{orig_prefix}.conv2.weight") + converted[f"{diff_prefix}.conv2.bias"] = original_state_dict.pop(f"{orig_prefix}.conv2.bias") + + diffusers_block_idx += 1 + + # upsample blocks + if f"decoder.up.{up_block_index}.upsample.conv.weight" in original_state_dict: + converted[f"decoder.up_blocks.{diffusers_block_idx}.conv.weight"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.weight" + ) + converted[f"decoder.up_blocks.{diffusers_block_idx}.conv.bias"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.bias" + ) + diffusers_block_idx += 1 + + # 2.4 decoder output + converted["decoder.norm_out.weight"] = original_state_dict.pop("decoder.norm_out.weight") + converted["decoder.norm_out.bias"] = original_state_dict.pop("decoder.norm_out.bias") + converted["decoder.conv_out.weight"] = original_state_dict.pop("decoder.conv_out.weight") + converted["decoder.conv_out.bias"] = original_state_dict.pop("decoder.conv_out.bias") + + return converted, original_state_dict + + +def convert_hunyuan_image_refiner_vae_checkpoint_to_diffusers( + original_state_dict, block_out_channels=[128, 256, 512, 1024, 1024], layers_per_block=2 +): + converted = {} + + # 1. Encoder + # 1.1 conv_in + converted["encoder.conv_in.conv.weight"] = original_state_dict.pop("encoder.conv_in.conv.weight") + converted["encoder.conv_in.conv.bias"] = original_state_dict.pop("encoder.conv_in.conv.bias") + + # 1.2 Down blocks + for down_block_index in range(len(block_out_channels)): # 0 to 4 + # ResNet blocks + for resnet_block_index in range(layers_per_block): # 0 to 1 + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm1.gamma") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( + original_state_dict.pop( + f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.weight" + ) + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.bias") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm2.gamma") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( + original_state_dict.pop( + f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.weight" + ) + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.bias") + ) + + # Downsample (if exists) + if f"encoder.down.{down_block_index}.downsample.conv.conv.weight" in original_state_dict: + converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.weight"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.weight") + ) + converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.bias") + ) + + # 1.3 Mid block + converted["encoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm1.gamma") + converted["encoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_1.conv1.conv.weight" + ) + converted["encoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_1.conv1.conv.bias" + ) + converted["encoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm2.gamma") + converted["encoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_1.conv2.conv.weight" + ) + converted["encoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_1.conv2.conv.bias" + ) + + converted["encoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm1.gamma") + converted["encoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_2.conv1.conv.weight" + ) + converted["encoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_2.conv1.conv.bias" + ) + converted["encoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm2.gamma") + converted["encoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_2.conv2.conv.weight" + ) + converted["encoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_2.conv2.conv.bias" + ) + + # Attention block + converted["encoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("encoder.mid.attn_1.norm.gamma") + converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") + converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") + converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") + converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") + converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") + converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") + converted["encoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( + "encoder.mid.attn_1.proj_out.weight" + ) + converted["encoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( + "encoder.mid.attn_1.proj_out.bias" + ) + + # 1.4 Encoder output + converted["encoder.norm_out.gamma"] = original_state_dict.pop("encoder.norm_out.gamma") + converted["encoder.conv_out.conv.weight"] = original_state_dict.pop("encoder.conv_out.conv.weight") + converted["encoder.conv_out.conv.bias"] = original_state_dict.pop("encoder.conv_out.conv.bias") + + # 2. Decoder + # 2.1 conv_in + converted["decoder.conv_in.conv.weight"] = original_state_dict.pop("decoder.conv_in.conv.weight") + converted["decoder.conv_in.conv.bias"] = original_state_dict.pop("decoder.conv_in.conv.bias") + + # 2.2 Mid block + converted["decoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm1.gamma") + converted["decoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_1.conv1.conv.weight" + ) + converted["decoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_1.conv1.conv.bias" + ) + converted["decoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm2.gamma") + converted["decoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_1.conv2.conv.weight" + ) + converted["decoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_1.conv2.conv.bias" + ) + + converted["decoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm1.gamma") + converted["decoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_2.conv1.conv.weight" + ) + converted["decoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_2.conv1.conv.bias" + ) + converted["decoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm2.gamma") + converted["decoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_2.conv2.conv.weight" + ) + converted["decoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_2.conv2.conv.bias" + ) + + # Decoder attention block + converted["decoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("decoder.mid.attn_1.norm.gamma") + converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") + converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") + converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") + converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") + converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") + converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") + converted["decoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( + "decoder.mid.attn_1.proj_out.weight" + ) + converted["decoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( + "decoder.mid.attn_1.proj_out.bias" + ) + + # 2.3 Up blocks + for up_block_index in range(len(block_out_channels)): # 0 to 5 + # ResNet blocks + for resnet_block_index in range(layers_per_block + 1): # 0 to 2 (decoder has 3 resnets per level) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm1.gamma") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.weight") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.bias") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm2.gamma") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.weight") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.bias") + ) + + # Upsample (if exists) + if f"decoder.up.{up_block_index}.upsample.conv.conv.weight" in original_state_dict: + converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.weight"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.conv.weight" + ) + converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.bias"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.conv.bias" + ) + + # 2.4 Decoder output + converted["decoder.norm_out.gamma"] = original_state_dict.pop("decoder.norm_out.gamma") + converted["decoder.conv_out.conv.weight"] = original_state_dict.pop("decoder.conv_out.conv.weight") + converted["decoder.conv_out.conv.bias"] = original_state_dict.pop("decoder.conv_out.conv.bias") + + return converted, original_state_dict + + +def main(args): + if args.model_type == "hunyuanimage2.1": + original_transformer_state_dict = load_original_transformer_checkpoint(args) + original_vae_state_dict = load_original_vae_checkpoint(args) + + transformer_config = { + "in_channels": 64, + "out_channels": 64, + "num_attention_heads": 28, + "attention_head_dim": 128, + "num_layers": 20, + "num_single_layers": 40, + "num_refiner_layers": 2, + "patch_size": (1, 1), + "qk_norm": "rms_norm", + "guidance_embeds": False, + "text_embed_dim": 3584, + "text_embed_2_dim": 1472, + "rope_theta": 256.0, + "rope_axes_dim": (64, 64), + } + + converted_transformer_state_dict, original_transformer_state_dict = ( + convert_hunyuan_image_transformer_checkpoint_to_diffusers( + original_transformer_state_dict, use_byt5=True, guidance_distilled=False + ) + ) + + if original_transformer_state_dict: + logger.warning( + f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" + ) + + transformer = HunyuanImageTransformer2DModel(**transformer_config) + missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + if missing_keys: + logger.warning(f"Missing keys for transformer: {missing_keys}") + if unexpected_key: + logger.warning(f"Unexpected keys for transformer: {unexpected_key}") + + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + vae_config_diffusers = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 64, + "block_out_channels": [128, 256, 512, 512, 1024, 1024], + "layers_per_block": 2, + "spatial_compression_ratio": 32, + "sample_size": 384, + "scaling_factor": 0.75289, + "downsample_match_channel": True, + "upsample_match_channel": True, + } + converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_vae_checkpoint_to_diffusers( + original_vae_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 + ) + if original_vae_state_dict: + logger.warning( + f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" + ) + + vae = AutoencoderKLHunyuanImage(**vae_config_diffusers) + missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) + + if missing_keys: + logger.warning(f"Missing keys for vae: {missing_keys}") + if unexpected_key: + logger.warning(f"Unexpected keys for vae: {unexpected_key}") + + vae.to(dtype).save_pretrained(f"{args.output_path}/vae") + + elif args.model_type == "hunyuanimage2.1-distilled": + original_transformer_state_dict = load_original_transformer_checkpoint(args) + original_vae_state_dict = load_original_vae_checkpoint(args) + + transformer_config = { + "in_channels": 64, + "out_channels": 64, + "num_attention_heads": 28, + "attention_head_dim": 128, + "num_layers": 20, + "num_single_layers": 40, + "num_refiner_layers": 2, + "patch_size": (1, 1), + "qk_norm": "rms_norm", + "guidance_embeds": True, + "text_embed_dim": 3584, + "text_embed_2_dim": 1472, + "rope_theta": 256.0, + "rope_axes_dim": (64, 64), + "use_meanflow": True, + } + + converted_transformer_state_dict, original_transformer_state_dict = ( + convert_hunyuan_image_transformer_checkpoint_to_diffusers( + original_transformer_state_dict, use_byt5=True, guidance_distilled=True, use_meanflow=True + ) + ) + + if original_transformer_state_dict: + logger.warning( + f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" + ) + + transformer = HunyuanImageTransformer2DModel(**transformer_config) + missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + if missing_keys: + logger.warning(f"Missing keys for transformer: {missing_keys}") + if unexpected_key: + logger.warning(f"Unexpected keys for transformer: {unexpected_key}") + + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + vae_config_diffusers = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 64, + "block_out_channels": [128, 256, 512, 512, 1024, 1024], + "layers_per_block": 2, + "spatial_compression_ratio": 32, + "sample_size": 384, + "scaling_factor": 0.75289, + "downsample_match_channel": True, + "upsample_match_channel": True, + } + converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_vae_checkpoint_to_diffusers( + original_vae_state_dict, block_out_channels=[128, 256, 512, 512, 1024, 1024], layers_per_block=2 + ) + if original_vae_state_dict: + logger.warning( + f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" + ) + + vae = AutoencoderKLHunyuanImage(**vae_config_diffusers) + missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) + + if missing_keys: + logger.warning(f"Missing keys for vae: {missing_keys}") + if unexpected_key: + logger.warning(f"Unexpected keys for vae: {unexpected_key}") + + vae.to(dtype).save_pretrained(f"{args.output_path}/vae") + + elif args.model_type == "hunyuanimage-refiner": + original_transformer_state_dict = load_original_transformer_checkpoint(args) + original_vae_state_dict = load_original_refiner_vae_checkpoint(args) + + transformer_config = { + "in_channels": 128, + "out_channels": 64, + "num_layers": 20, + "num_single_layers": 40, + "rope_axes_dim": [16, 56, 56], + "num_attention_heads": 26, + "attention_head_dim": 128, + "mlp_ratio": 4, + "patch_size": (1, 1, 1), + "text_embed_dim": 3584, + "guidance_embeds": True, + } + converted_transformer_state_dict, original_transformer_state_dict = ( + convert_hunyuan_image_transformer_checkpoint_to_diffusers( + original_transformer_state_dict, use_byt5=False, guidance_distilled=True + ) + ) + if original_transformer_state_dict: + logger.warning( + f"Unused {len(original_transformer_state_dict)} original keys for transformer: {list(original_transformer_state_dict.keys())}" + ) + + transformer = HunyuanImageTransformer2DModel(**transformer_config) + missing_keys, unexpected_key = transformer.load_state_dict(converted_transformer_state_dict, strict=True) + if missing_keys: + logger.warning(f"Missing keys for transformer: {missing_keys}") + if unexpected_key: + logger.warning(f"Unexpected keys for transformer: {unexpected_key}") + + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + vae = AutoencoderKLHunyuanImageRefiner() + + converted_vae_state_dict, original_vae_state_dict = convert_hunyuan_image_refiner_vae_checkpoint_to_diffusers( + original_vae_state_dict + ) + if original_vae_state_dict: + logger.warning( + f"Unused {len(original_vae_state_dict)} original keys for vae: {list(original_vae_state_dict.keys())}" + ) + + missing_keys, unexpected_key = vae.load_state_dict(converted_vae_state_dict, strict=True) + logger.warning(f"Missing keys for vae: {missing_keys}") + logger.warning(f"Unexpected keys for vae: {unexpected_key}") + + vae.to(dtype).save_pretrained(f"{args.output_path}/vae") + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_hunyuan_video1_5_to_diffusers.py b/scripts/convert_hunyuan_video1_5_to_diffusers.py new file mode 100644 index 000000000000..89e5cdb16956 --- /dev/null +++ b/scripts/convert_hunyuan_video1_5_to_diffusers.py @@ -0,0 +1,875 @@ +import argparse +import json +import os +import pathlib + +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download, snapshot_download +from safetensors.torch import load_file +from transformers import ( + AutoModel, + AutoTokenizer, + SiglipImageProcessor, + SiglipVisionModel, + T5EncoderModel, +) + +from diffusers import ( + AutoencoderKLHunyuanVideo15, + ClassifierFreeGuidance, + FlowMatchEulerDiscreteScheduler, + HunyuanVideo15ImageToVideoPipeline, + HunyuanVideo15Pipeline, + HunyuanVideo15Transformer3DModel, +) + + +# to convert only transformer +""" +python scripts/convert_hunyuan_video1_5_to_diffusers.py \ + --original_state_dict_repo_id tencent/HunyuanVideo-1.5\ + --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers/transformer\ + --transformer_type 480p_t2v +""" + +# to convert full pipeline +""" +python scripts/convert_hunyuan_video1_5_to_diffusers.py \ + --original_state_dict_repo_id tencent/HunyuanVideo-1.5\ + --output_path /fsx/yiyi/HunyuanVideo-1.5-Diffusers \ + --save_pipeline \ + --byt5_path /fsx/yiyi/hy15/text_encoder/Glyph-SDXL-v2\ + --transformer_type 480p_t2v +""" + + +TRANSFORMER_CONFIGS = { + "480p_t2v": { + "target_size": 640, + "task_type": "i2v", + }, + "720p_t2v": { + "target_size": 960, + "task_type": "t2v", + }, + "720p_i2v": { + "target_size": 960, + "task_type": "i2v", + }, + "480p_t2v_distilled": { + "target_size": 640, + "task_type": "t2v", + }, + "480p_i2v_distilled": { + "target_size": 640, + "task_type": "i2v", + }, + "720p_i2v_distilled": { + "target_size": 960, + "task_type": "i2v", + }, + "480p_i2v_step_distilled": { + "target_size": 640, + "task_type": "i2v", + "use_meanflow": True, + }, +} + +SCHEDULER_CONFIGS = { + "480p_t2v": { + "shift": 5.0, + }, + "480p_i2v": { + "shift": 5.0, + }, + "720p_t2v": { + "shift": 9.0, + }, + "720p_i2v": { + "shift": 7.0, + }, + "480p_t2v_distilled": { + "shift": 5.0, + }, + "480p_i2v_distilled": { + "shift": 5.0, + }, + "720p_i2v_distilled": { + "shift": 7.0, + }, + "480p_i2v_step_distilled": { + "shift": 7.0, + }, +} + +GUIDANCE_CONFIGS = { + "480p_t2v": { + "guidance_scale": 6.0, + }, + "480p_i2v": { + "guidance_scale": 6.0, + }, + "720p_t2v": { + "guidance_scale": 6.0, + }, + "720p_i2v": { + "guidance_scale": 6.0, + }, + "480p_t2v_distilled": { + "guidance_scale": 1.0, + }, + "480p_i2v_distilled": { + "guidance_scale": 1.0, + }, + "720p_i2v_distilled": { + "guidance_scale": 1.0, + }, + "480p_i2v_step_distilled": { + "guidance_scale": 1.0, + }, +} + + +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=None): + """ + Convert HunyuanVideo 1.5 original checkpoint to Diffusers format. + """ + converted_state_dict = {} + + # 1. time_embed.timestep_embedder <- time_in + converted_state_dict["time_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "time_in.mlp.0.weight" + ) + converted_state_dict["time_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop("time_in.mlp.0.bias") + converted_state_dict["time_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "time_in.mlp.2.weight" + ) + converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("time_in.mlp.2.bias") + + if config.use_meanflow: + converted_state_dict["time_embed.timestep_embedder_r.linear_1.weight"] = original_state_dict.pop( + "time_r_in.mlp.0.weight" + ) + converted_state_dict["time_embed.timestep_embedder_r.linear_1.bias"] = original_state_dict.pop( + "time_r_in.mlp.0.bias" + ) + converted_state_dict["time_embed.timestep_embedder_r.linear_2.weight"] = original_state_dict.pop( + "time_r_in.mlp.2.weight" + ) + converted_state_dict["time_embed.timestep_embedder_r.linear_2.bias"] = original_state_dict.pop( + "time_r_in.mlp.2.bias" + ) + + # 2. context_embedder.time_text_embed.timestep_embedder <- txt_in.t_embedder + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.weight"] = ( + original_state_dict.pop("txt_in.t_embedder.mlp.0.weight") + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "txt_in.t_embedder.mlp.0.bias" + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.weight"] = ( + original_state_dict.pop("txt_in.t_embedder.mlp.2.weight") + ) + converted_state_dict["context_embedder.time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "txt_in.t_embedder.mlp.2.bias" + ) + + # 3. context_embedder.time_text_embed.text_embedder <- txt_in.c_embedder + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( + "txt_in.c_embedder.linear_1.weight" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( + "txt_in.c_embedder.linear_1.bias" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( + "txt_in.c_embedder.linear_2.weight" + ) + converted_state_dict["context_embedder.time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( + "txt_in.c_embedder.linear_2.bias" + ) + + # 4. context_embedder.proj_in <- txt_in.input_embedder + converted_state_dict["context_embedder.proj_in.weight"] = original_state_dict.pop("txt_in.input_embedder.weight") + converted_state_dict["context_embedder.proj_in.bias"] = original_state_dict.pop("txt_in.input_embedder.bias") + + # 5. context_embedder.token_refiner <- txt_in.individual_token_refiner + num_refiner_blocks = 2 + for i in range(num_refiner_blocks): + block_prefix = f"context_embedder.token_refiner.refiner_blocks.{i}." + orig_prefix = f"txt_in.individual_token_refiner.blocks.{i}." + + # norm1 + converted_state_dict[f"{block_prefix}norm1.weight"] = original_state_dict.pop(f"{orig_prefix}norm1.weight") + converted_state_dict[f"{block_prefix}norm1.bias"] = original_state_dict.pop(f"{orig_prefix}norm1.bias") + + # Split self_attn_qkv into to_q, to_k, to_v + qkv_weight = original_state_dict.pop(f"{orig_prefix}self_attn_qkv.weight") + qkv_bias = original_state_dict.pop(f"{orig_prefix}self_attn_qkv.bias") + q, k, v = torch.chunk(qkv_weight, 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) + + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = q + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = q_bias + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = k + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = k_bias + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = v + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = v_bias + + # self_attn_proj -> attn.to_out.0 + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"{orig_prefix}self_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"{orig_prefix}self_attn_proj.bias" + ) + + # norm2 + converted_state_dict[f"{block_prefix}norm2.weight"] = original_state_dict.pop(f"{orig_prefix}norm2.weight") + converted_state_dict[f"{block_prefix}norm2.bias"] = original_state_dict.pop(f"{orig_prefix}norm2.bias") + + # mlp -> ff + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( + f"{orig_prefix}mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( + f"{orig_prefix}mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"{orig_prefix}mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop(f"{orig_prefix}mlp.fc2.bias") + + # adaLN_modulation -> norm_out + converted_state_dict[f"{block_prefix}norm_out.linear.weight"] = original_state_dict.pop( + f"{orig_prefix}adaLN_modulation.1.weight" + ) + converted_state_dict[f"{block_prefix}norm_out.linear.bias"] = original_state_dict.pop( + f"{orig_prefix}adaLN_modulation.1.bias" + ) + + # 6. context_embedder_2 <- byt5_in + converted_state_dict["context_embedder_2.norm.weight"] = original_state_dict.pop("byt5_in.layernorm.weight") + converted_state_dict["context_embedder_2.norm.bias"] = original_state_dict.pop("byt5_in.layernorm.bias") + converted_state_dict["context_embedder_2.linear_1.weight"] = original_state_dict.pop("byt5_in.fc1.weight") + converted_state_dict["context_embedder_2.linear_1.bias"] = original_state_dict.pop("byt5_in.fc1.bias") + converted_state_dict["context_embedder_2.linear_2.weight"] = original_state_dict.pop("byt5_in.fc2.weight") + converted_state_dict["context_embedder_2.linear_2.bias"] = original_state_dict.pop("byt5_in.fc2.bias") + converted_state_dict["context_embedder_2.linear_3.weight"] = original_state_dict.pop("byt5_in.fc3.weight") + converted_state_dict["context_embedder_2.linear_3.bias"] = original_state_dict.pop("byt5_in.fc3.bias") + + # 7. image_embedder <- vision_in + converted_state_dict["image_embedder.norm_in.weight"] = original_state_dict.pop("vision_in.proj.0.weight") + converted_state_dict["image_embedder.norm_in.bias"] = original_state_dict.pop("vision_in.proj.0.bias") + converted_state_dict["image_embedder.linear_1.weight"] = original_state_dict.pop("vision_in.proj.1.weight") + converted_state_dict["image_embedder.linear_1.bias"] = original_state_dict.pop("vision_in.proj.1.bias") + converted_state_dict["image_embedder.linear_2.weight"] = original_state_dict.pop("vision_in.proj.3.weight") + converted_state_dict["image_embedder.linear_2.bias"] = original_state_dict.pop("vision_in.proj.3.bias") + converted_state_dict["image_embedder.norm_out.weight"] = original_state_dict.pop("vision_in.proj.4.weight") + converted_state_dict["image_embedder.norm_out.bias"] = original_state_dict.pop("vision_in.proj.4.bias") + + # 8. x_embedder <- img_in + converted_state_dict["x_embedder.proj.weight"] = original_state_dict.pop("img_in.proj.weight") + converted_state_dict["x_embedder.proj.bias"] = original_state_dict.pop("img_in.proj.bias") + + # 9. cond_type_embed <- cond_type_embedding + converted_state_dict["cond_type_embed.weight"] = original_state_dict.pop("cond_type_embedding.weight") + + # 10. transformer_blocks <- double_blocks + num_layers = 54 + for i in range(num_layers): + block_prefix = f"transformer_blocks.{i}." + orig_prefix = f"double_blocks.{i}." + + # norm1 (img_mod) + converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( + f"{orig_prefix}img_mod.linear.weight" + ) + converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( + f"{orig_prefix}img_mod.linear.bias" + ) + + # norm1_context (txt_mod) + converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_mod.linear.weight" + ) + converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_mod.linear.bias" + ) + + # img attention (to_q, to_k, to_v) + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_q.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = original_state_dict.pop( + f"{orig_prefix}img_attn_q.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_k.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = original_state_dict.pop( + f"{orig_prefix}img_attn_k.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_v.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = original_state_dict.pop( + f"{orig_prefix}img_attn_v.bias" + ) + + # img attention qk norm + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_q_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_k_norm.weight" + ) + + # img attention output projection + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"{orig_prefix}img_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"{orig_prefix}img_attn_proj.bias" + ) + + # txt attention (add_q_proj, add_k_proj, add_v_proj) + converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_q.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_q.bias" + ) + converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_k.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_k.bias" + ) + converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_v.weight" + ) + converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_v.bias" + ) + + # txt attention qk norm + converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_q_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_k_norm.weight" + ) + + # txt attention output projection + converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_attn_proj.bias" + ) + + # norm2 and norm2_context (these don't have weights in the original, they're LayerNorm with elementwise_affine=False) + # So we skip them + + # img_mlp -> ff + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop( + f"{orig_prefix}img_mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop( + f"{orig_prefix}img_mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"{orig_prefix}img_mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( + f"{orig_prefix}img_mlp.fc2.bias" + ) + + # txt_mlp -> ff_context + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_mlp.fc1.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_mlp.fc1.bias" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( + f"{orig_prefix}txt_mlp.fc2.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( + f"{orig_prefix}txt_mlp.fc2.bias" + ) + + # 11. norm_out and proj_out <- final_layer + converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.weight") + ) + converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.bias") + ) + converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + + return converted_state_dict + + +def convert_hunyuan_video_15_vae_checkpoint_to_diffusers( + original_state_dict, block_out_channels=[128, 256, 512, 1024, 1024], layers_per_block=2 +): + converted = {} + + # 1. Encoder + # 1.1 conv_in + converted["encoder.conv_in.conv.weight"] = original_state_dict.pop("encoder.conv_in.conv.weight") + converted["encoder.conv_in.conv.bias"] = original_state_dict.pop("encoder.conv_in.conv.bias") + + # 1.2 Down blocks + for down_block_index in range(len(block_out_channels)): # 0 to 4 + # ResNet blocks + for resnet_block_index in range(layers_per_block): # 0 to 1 + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm1.gamma") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( + original_state_dict.pop( + f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.weight" + ) + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv1.conv.bias") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.norm2.gamma") + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( + original_state_dict.pop( + f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.weight" + ) + ) + converted[f"encoder.down_blocks.{down_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.block.{resnet_block_index}.conv2.conv.bias") + ) + + # Downsample (if exists) + if f"encoder.down.{down_block_index}.downsample.conv.conv.weight" in original_state_dict: + converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.weight"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.weight") + ) + converted[f"encoder.down_blocks.{down_block_index}.downsamplers.0.conv.conv.bias"] = ( + original_state_dict.pop(f"encoder.down.{down_block_index}.downsample.conv.conv.bias") + ) + + # 1.3 Mid block + converted["encoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm1.gamma") + converted["encoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_1.conv1.conv.weight" + ) + converted["encoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_1.conv1.conv.bias" + ) + converted["encoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_1.norm2.gamma") + converted["encoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_1.conv2.conv.weight" + ) + converted["encoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_1.conv2.conv.bias" + ) + + converted["encoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm1.gamma") + converted["encoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_2.conv1.conv.weight" + ) + converted["encoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_2.conv1.conv.bias" + ) + converted["encoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("encoder.mid.block_2.norm2.gamma") + converted["encoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( + "encoder.mid.block_2.conv2.conv.weight" + ) + converted["encoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( + "encoder.mid.block_2.conv2.conv.bias" + ) + + # Attention block + converted["encoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("encoder.mid.attn_1.norm.gamma") + converted["encoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("encoder.mid.attn_1.q.weight") + converted["encoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("encoder.mid.attn_1.q.bias") + converted["encoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("encoder.mid.attn_1.k.weight") + converted["encoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("encoder.mid.attn_1.k.bias") + converted["encoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("encoder.mid.attn_1.v.weight") + converted["encoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("encoder.mid.attn_1.v.bias") + converted["encoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( + "encoder.mid.attn_1.proj_out.weight" + ) + converted["encoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( + "encoder.mid.attn_1.proj_out.bias" + ) + + # 1.4 Encoder output + converted["encoder.norm_out.gamma"] = original_state_dict.pop("encoder.norm_out.gamma") + converted["encoder.conv_out.conv.weight"] = original_state_dict.pop("encoder.conv_out.conv.weight") + converted["encoder.conv_out.conv.bias"] = original_state_dict.pop("encoder.conv_out.conv.bias") + + # 2. Decoder + # 2.1 conv_in + converted["decoder.conv_in.conv.weight"] = original_state_dict.pop("decoder.conv_in.conv.weight") + converted["decoder.conv_in.conv.bias"] = original_state_dict.pop("decoder.conv_in.conv.bias") + + # 2.2 Mid block + converted["decoder.mid_block.resnets.0.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm1.gamma") + converted["decoder.mid_block.resnets.0.conv1.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_1.conv1.conv.weight" + ) + converted["decoder.mid_block.resnets.0.conv1.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_1.conv1.conv.bias" + ) + converted["decoder.mid_block.resnets.0.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_1.norm2.gamma") + converted["decoder.mid_block.resnets.0.conv2.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_1.conv2.conv.weight" + ) + converted["decoder.mid_block.resnets.0.conv2.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_1.conv2.conv.bias" + ) + + converted["decoder.mid_block.resnets.1.norm1.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm1.gamma") + converted["decoder.mid_block.resnets.1.conv1.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_2.conv1.conv.weight" + ) + converted["decoder.mid_block.resnets.1.conv1.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_2.conv1.conv.bias" + ) + converted["decoder.mid_block.resnets.1.norm2.gamma"] = original_state_dict.pop("decoder.mid.block_2.norm2.gamma") + converted["decoder.mid_block.resnets.1.conv2.conv.weight"] = original_state_dict.pop( + "decoder.mid.block_2.conv2.conv.weight" + ) + converted["decoder.mid_block.resnets.1.conv2.conv.bias"] = original_state_dict.pop( + "decoder.mid.block_2.conv2.conv.bias" + ) + + # Decoder attention block + converted["decoder.mid_block.attentions.0.norm.gamma"] = original_state_dict.pop("decoder.mid.attn_1.norm.gamma") + converted["decoder.mid_block.attentions.0.to_q.weight"] = original_state_dict.pop("decoder.mid.attn_1.q.weight") + converted["decoder.mid_block.attentions.0.to_q.bias"] = original_state_dict.pop("decoder.mid.attn_1.q.bias") + converted["decoder.mid_block.attentions.0.to_k.weight"] = original_state_dict.pop("decoder.mid.attn_1.k.weight") + converted["decoder.mid_block.attentions.0.to_k.bias"] = original_state_dict.pop("decoder.mid.attn_1.k.bias") + converted["decoder.mid_block.attentions.0.to_v.weight"] = original_state_dict.pop("decoder.mid.attn_1.v.weight") + converted["decoder.mid_block.attentions.0.to_v.bias"] = original_state_dict.pop("decoder.mid.attn_1.v.bias") + converted["decoder.mid_block.attentions.0.proj_out.weight"] = original_state_dict.pop( + "decoder.mid.attn_1.proj_out.weight" + ) + converted["decoder.mid_block.attentions.0.proj_out.bias"] = original_state_dict.pop( + "decoder.mid.attn_1.proj_out.bias" + ) + + # 2.3 Up blocks + for up_block_index in range(len(block_out_channels)): # 0 to 5 + # ResNet blocks + for resnet_block_index in range(layers_per_block + 1): # 0 to 2 (decoder has 3 resnets per level) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm1.gamma"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm1.gamma") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.weight"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.weight") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv1.conv.bias"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv1.conv.bias") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.norm2.gamma"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.norm2.gamma") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.weight"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.weight") + ) + converted[f"decoder.up_blocks.{up_block_index}.resnets.{resnet_block_index}.conv2.conv.bias"] = ( + original_state_dict.pop(f"decoder.up.{up_block_index}.block.{resnet_block_index}.conv2.conv.bias") + ) + + # Upsample (if exists) + if f"decoder.up.{up_block_index}.upsample.conv.conv.weight" in original_state_dict: + converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.weight"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.conv.weight" + ) + converted[f"decoder.up_blocks.{up_block_index}.upsamplers.0.conv.conv.bias"] = original_state_dict.pop( + f"decoder.up.{up_block_index}.upsample.conv.conv.bias" + ) + + # 2.4 Decoder output + converted["decoder.norm_out.gamma"] = original_state_dict.pop("decoder.norm_out.gamma") + converted["decoder.conv_out.conv.weight"] = original_state_dict.pop("decoder.conv_out.conv.weight") + converted["decoder.conv_out.conv.bias"] = original_state_dict.pop("decoder.conv_out.conv.bias") + + return converted + + +def load_sharded_safetensors(dir: pathlib.Path): + file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) + state_dict = {} + for path in file_paths: + state_dict.update(load_file(path)) + return state_dict + + +def load_original_transformer_state_dict(args): + if args.original_state_dict_repo_id is not None: + model_dir = snapshot_download( + args.original_state_dict_repo_id, + repo_type="model", + allow_patterns="transformer/" + args.transformer_type + "/*", + ) + elif args.original_state_dict_folder is not None: + model_dir = pathlib.Path(args.original_state_dict_folder) + else: + raise ValueError("Please provide either `original_state_dict_repo_id` or `original_state_dict_folder`") + model_dir = pathlib.Path(model_dir) + model_dir = model_dir / "transformer" / args.transformer_type + return load_sharded_safetensors(model_dir) + + +def load_original_vae_state_dict(args): + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download( + repo_id=args.original_state_dict_repo_id, filename="vae/diffusion_pytorch_model.safetensors" + ) + elif args.original_state_dict_folder is not None: + model_dir = pathlib.Path(args.original_state_dict_folder) + ckpt_path = model_dir / "vae/diffusion_pytorch_model.safetensors" + else: + raise ValueError("Please provide either `original_state_dict_repo_id` or `original_state_dict_folder`") + + original_state_dict = load_file(ckpt_path) + return original_state_dict + + +def convert_transformer(args): + original_state_dict = load_original_transformer_state_dict(args) + + config = TRANSFORMER_CONFIGS[args.transformer_type] + with init_empty_weights(): + transformer = HunyuanVideo15Transformer3DModel(**config) + state_dict = convert_hyvideo15_transformer_to_diffusers(original_state_dict, config=transformer.config) + transformer.load_state_dict(state_dict, strict=True, assign=True) + + return transformer + + +def convert_vae(args): + original_state_dict = load_original_vae_state_dict(args) + with init_empty_weights(): + vae = AutoencoderKLHunyuanVideo15() + state_dict = convert_hunyuan_video_15_vae_checkpoint_to_diffusers(original_state_dict) + vae.load_state_dict(state_dict, strict=True, assign=True) + return vae + + +def load_mllm(): + print(" loading from Qwen/Qwen2.5-VL-7B-Instruct") + text_encoder = AutoModel.from_pretrained( + "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True + ) + if hasattr(text_encoder, "language_model"): + text_encoder = text_encoder.language_model + tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", padding_side="right") + return text_encoder, tokenizer + + +# copied from https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/910da2a829c484ea28982e8cff3bbc2cacdf1681/hyvideo/models/text_encoders/byT5/__init__.py#L89 +def add_special_token( + tokenizer, + text_encoder, + add_color=True, + add_font=True, + multilingual=True, + color_ann_path="assets/color_idx.json", + font_ann_path="assets/multilingual_10-lang_idx.json", +): + """ + Add special tokens for color and font to tokenizer and text encoder. + + Args: + tokenizer: Huggingface tokenizer. + text_encoder: Huggingface T5 encoder. + add_color (bool): Whether to add color tokens. + add_font (bool): Whether to add font tokens. + color_ann_path (str): Path to color annotation JSON. + font_ann_path (str): Path to font annotation JSON. + multilingual (bool): Whether to use multilingual font tokens. + """ + with open(font_ann_path, "r") as f: + idx_font_dict = json.load(f) + with open(color_ann_path, "r") as f: + idx_color_dict = json.load(f) + + if multilingual: + font_token = [f"<{font_code[:2]}-font-{idx_font_dict[font_code]}>" for font_code in idx_font_dict] + else: + font_token = [f"<font-{i}>" for i in range(len(idx_font_dict))] + color_token = [f"<color-{i}>" for i in range(len(idx_color_dict))] + additional_special_tokens = [] + if add_color: + additional_special_tokens += color_token + if add_font: + additional_special_tokens += font_token + + tokenizer.add_tokens(additional_special_tokens, special_tokens=True) + # Set mean_resizing=False to avoid PyTorch LAPACK dependency + text_encoder.resize_token_embeddings(len(tokenizer), mean_resizing=False) + + +def load_byt5(args): + """ + Load ByT5 encoder with Glyph-SDXL-v2 weights and save in HuggingFace format. + """ + + # 1. Load base tokenizer and encoder + tokenizer = AutoTokenizer.from_pretrained("google/byt5-small") + + # Load as T5EncoderModel + encoder = T5EncoderModel.from_pretrained("google/byt5-small") + + byt5_checkpoint_path = os.path.join(args.byt5_path, "checkpoints/byt5_model.pt") + color_ann_path = os.path.join(args.byt5_path, "assets/color_idx.json") + font_ann_path = os.path.join(args.byt5_path, "assets/multilingual_10-lang_idx.json") + + # 2. Add special tokens + add_special_token( + tokenizer=tokenizer, + text_encoder=encoder, + add_color=True, + add_font=True, + color_ann_path=color_ann_path, + font_ann_path=font_ann_path, + multilingual=True, + ) + + # 3. Load Glyph-SDXL-v2 checkpoint + print(f"\n3. Loading Glyph-SDXL-v2 checkpoint: {byt5_checkpoint_path}") + checkpoint = torch.load(byt5_checkpoint_path, map_location="cpu") + + # Handle different checkpoint formats + if "state_dict" in checkpoint: + state_dict = checkpoint["state_dict"] + else: + state_dict = checkpoint + + # add 'encoder.' prefix to the keys + # Remove 'module.text_tower.encoder.' prefix if present + cleaned_state_dict = {} + for key, value in state_dict.items(): + if key.startswith("module.text_tower.encoder."): + new_key = "encoder." + key[len("module.text_tower.encoder.") :] + cleaned_state_dict[new_key] = value + else: + new_key = "encoder." + key + cleaned_state_dict[new_key] = value + + # 4. Load weights + missing_keys, unexpected_keys = encoder.load_state_dict(cleaned_state_dict, strict=False) + if unexpected_keys: + raise ValueError(f"Unexpected keys: {unexpected_keys}") + if "shared.weight" in missing_keys: + print(" Missing shared.weight as expected") + missing_keys.remove("shared.weight") + if missing_keys: + raise ValueError(f"Missing keys: {missing_keys}") + + return encoder, tokenizer + + +def load_siglip(): + image_encoder = SiglipVisionModel.from_pretrained( + "black-forest-labs/FLUX.1-Redux-dev", subfolder="image_encoder", torch_dtype=torch.bfloat16 + ) + feature_extractor = SiglipImageProcessor.from_pretrained( + "black-forest-labs/FLUX.1-Redux-dev", subfolder="feature_extractor" + ) + return image_encoder, feature_extractor + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--original_state_dict_repo_id", type=str, default=None, help="Path to original hub_id for the model" + ) + parser.add_argument( + "--original_state_dict_folder", type=str, default=None, help="Local folder name of the original state dict" + ) + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model(s) should be saved") + parser.add_argument("--transformer_type", type=str, default="480p_i2v", choices=list(TRANSFORMER_CONFIGS.keys())) + parser.add_argument( + "--byt5_path", + type=str, + default=None, + help=( + "path to the downloaded byt5 checkpoint & assets. " + "Note: They use Glyph-SDXL-v2 as byt5 encoder. You can download from modelscope like: " + "`modelscope download --model AI-ModelScope/Glyph-SDXL-v2 --local_dir ./ckpts/text_encoder/Glyph-SDXL-v2` " + "or manually download following the instructions on " + "https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/910da2a829c484ea28982e8cff3bbc2cacdf1681/checkpoints-download.md. " + "The path should point to the Glyph-SDXL-v2 folder which should contain an `assets` folder and a `checkpoints` folder, " + "like: Glyph-SDXL-v2/assets/... and Glyph-SDXL-v2/checkpoints/byt5_model.pt" + ), + ) + parser.add_argument("--save_pipeline", action="store_true") + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + if args.save_pipeline and args.byt5_path is None: + raise ValueError("Please provide --byt5_path when saving pipeline") + + transformer = None + + transformer = convert_transformer(args) + if not args.save_pipeline: + transformer.save_pretrained(args.output_path, safe_serialization=True) + else: + task_type = transformer.config.task_type + + vae = convert_vae(args) + + text_encoder, tokenizer = load_mllm() + text_encoder_2, tokenizer_2 = load_byt5(args) + + flow_shift = SCHEDULER_CONFIGS[args.transformer_type]["shift"] + scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) + + guidance_scale = GUIDANCE_CONFIGS[args.transformer_type]["guidance_scale"] + guider = ClassifierFreeGuidance(guidance_scale=guidance_scale) + + if task_type == "i2v": + image_encoder, feature_extractor = load_siglip() + pipeline = HunyuanVideo15ImageToVideoPipeline( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + transformer=transformer, + guider=guider, + scheduler=scheduler, + image_encoder=image_encoder, + feature_extractor=feature_extractor, + ) + elif task_type == "t2v": + pipeline = HunyuanVideo15Pipeline( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + transformer=transformer, + guider=guider, + scheduler=scheduler, + ) + else: + raise ValueError(f"Task type {task_type} is not supported") + + pipeline.save_pretrained(args.output_path, safe_serialization=True) diff --git a/scripts/convert_hunyuan_video_to_diffusers.py b/scripts/convert_hunyuan_video_to_diffusers.py new file mode 100644 index 000000000000..fb604c98b65b --- /dev/null +++ b/scripts/convert_hunyuan_video_to_diffusers.py @@ -0,0 +1,353 @@ +import argparse +from typing import Any, Dict + +import torch +from accelerate import init_empty_weights +from transformers import ( + AutoModel, + AutoTokenizer, + CLIPImageProcessor, + CLIPTextModel, + CLIPTokenizer, + LlavaForConditionalGeneration, +) + +from diffusers import ( + AutoencoderKLHunyuanVideo, + FlowMatchEulerDiscreteScheduler, + HunyuanVideoImageToVideoPipeline, + HunyuanVideoPipeline, + HunyuanVideoTransformer3DModel, +) + + +def remap_norm_scale_shift_(key, state_dict): + weight = state_dict.pop(key) + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + state_dict[key.replace("final_layer.adaLN_modulation.1", "norm_out.linear")] = new_weight + + +def remap_txt_in_(key, state_dict): + def rename_key(key): + new_key = key.replace("individual_token_refiner.blocks", "token_refiner.refiner_blocks") + new_key = new_key.replace("adaLN_modulation.1", "norm_out.linear") + new_key = new_key.replace("txt_in", "context_embedder") + new_key = new_key.replace("t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1") + new_key = new_key.replace("t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2") + new_key = new_key.replace("c_embedder", "time_text_embed.text_embedder") + new_key = new_key.replace("mlp", "ff") + return new_key + + if "self_attn_qkv" in key: + weight = state_dict.pop(key) + to_q, to_k, to_v = weight.chunk(3, dim=0) + state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_q"))] = to_q + state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_k"))] = to_k + state_dict[rename_key(key.replace("self_attn_qkv", "attn.to_v"))] = to_v + else: + state_dict[rename_key(key)] = state_dict.pop(key) + + +def remap_img_attn_qkv_(key, state_dict): + weight = state_dict.pop(key) + to_q, to_k, to_v = weight.chunk(3, dim=0) + state_dict[key.replace("img_attn_qkv", "attn.to_q")] = to_q + state_dict[key.replace("img_attn_qkv", "attn.to_k")] = to_k + state_dict[key.replace("img_attn_qkv", "attn.to_v")] = to_v + + +def remap_txt_attn_qkv_(key, state_dict): + weight = state_dict.pop(key) + to_q, to_k, to_v = weight.chunk(3, dim=0) + state_dict[key.replace("txt_attn_qkv", "attn.add_q_proj")] = to_q + state_dict[key.replace("txt_attn_qkv", "attn.add_k_proj")] = to_k + state_dict[key.replace("txt_attn_qkv", "attn.add_v_proj")] = to_v + + +def remap_single_transformer_blocks_(key, state_dict): + hidden_size = 3072 + + if "linear1.weight" in key: + linear1_weight = state_dict.pop(key) + split_size = (hidden_size, hidden_size, hidden_size, linear1_weight.size(0) - 3 * hidden_size) + q, k, v, mlp = torch.split(linear1_weight, split_size, dim=0) + new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.weight") + state_dict[f"{new_key}.attn.to_q.weight"] = q + state_dict[f"{new_key}.attn.to_k.weight"] = k + state_dict[f"{new_key}.attn.to_v.weight"] = v + state_dict[f"{new_key}.proj_mlp.weight"] = mlp + + elif "linear1.bias" in key: + linear1_bias = state_dict.pop(key) + split_size = (hidden_size, hidden_size, hidden_size, linear1_bias.size(0) - 3 * hidden_size) + q_bias, k_bias, v_bias, mlp_bias = torch.split(linear1_bias, split_size, dim=0) + new_key = key.replace("single_blocks", "single_transformer_blocks").removesuffix(".linear1.bias") + state_dict[f"{new_key}.attn.to_q.bias"] = q_bias + state_dict[f"{new_key}.attn.to_k.bias"] = k_bias + state_dict[f"{new_key}.attn.to_v.bias"] = v_bias + state_dict[f"{new_key}.proj_mlp.bias"] = mlp_bias + + else: + new_key = key.replace("single_blocks", "single_transformer_blocks") + new_key = new_key.replace("linear2", "proj_out") + new_key = new_key.replace("q_norm", "attn.norm_q") + new_key = new_key.replace("k_norm", "attn.norm_k") + state_dict[new_key] = state_dict.pop(key) + + +TRANSFORMER_KEYS_RENAME_DICT = { + "img_in": "x_embedder", + "time_in.mlp.0": "time_text_embed.timestep_embedder.linear_1", + "time_in.mlp.2": "time_text_embed.timestep_embedder.linear_2", + "guidance_in.mlp.0": "time_text_embed.guidance_embedder.linear_1", + "guidance_in.mlp.2": "time_text_embed.guidance_embedder.linear_2", + "vector_in.in_layer": "time_text_embed.text_embedder.linear_1", + "vector_in.out_layer": "time_text_embed.text_embedder.linear_2", + "double_blocks": "transformer_blocks", + "img_attn_q_norm": "attn.norm_q", + "img_attn_k_norm": "attn.norm_k", + "img_attn_proj": "attn.to_out.0", + "txt_attn_q_norm": "attn.norm_added_q", + "txt_attn_k_norm": "attn.norm_added_k", + "txt_attn_proj": "attn.to_add_out", + "img_mod.linear": "norm1.linear", + "img_norm1": "norm1.norm", + "img_norm2": "norm2", + "img_mlp": "ff", + "txt_mod.linear": "norm1_context.linear", + "txt_norm1": "norm1.norm", + "txt_norm2": "norm2_context", + "txt_mlp": "ff_context", + "self_attn_proj": "attn.to_out.0", + "modulation.linear": "norm.linear", + "pre_norm": "norm.norm", + "final_layer.norm_final": "norm_out.norm", + "final_layer.linear": "proj_out", + "fc1": "net.0.proj", + "fc2": "net.2", + "input_embedder": "proj_in", +} + +TRANSFORMER_SPECIAL_KEYS_REMAP = { + "txt_in": remap_txt_in_, + "img_attn_qkv": remap_img_attn_qkv_, + "txt_attn_qkv": remap_txt_attn_qkv_, + "single_blocks": remap_single_transformer_blocks_, + "final_layer.adaLN_modulation.1": remap_norm_scale_shift_, +} + +VAE_KEYS_RENAME_DICT = {} + +VAE_SPECIAL_KEYS_REMAP = {} + + +TRANSFORMER_CONFIGS = { + "HYVideo-T/2-cfgdistill": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 24, + "attention_head_dim": 128, + "num_layers": 20, + "num_single_layers": 40, + "num_refiner_layers": 2, + "mlp_ratio": 4.0, + "patch_size": 2, + "patch_size_t": 1, + "qk_norm": "rms_norm", + "guidance_embeds": True, + "text_embed_dim": 4096, + "pooled_projection_dim": 768, + "rope_theta": 256.0, + "rope_axes_dim": (16, 56, 56), + "image_condition_type": None, + }, + "HYVideo-T/2-I2V-33ch": { + "in_channels": 16 * 2 + 1, + "out_channels": 16, + "num_attention_heads": 24, + "attention_head_dim": 128, + "num_layers": 20, + "num_single_layers": 40, + "num_refiner_layers": 2, + "mlp_ratio": 4.0, + "patch_size": 2, + "patch_size_t": 1, + "qk_norm": "rms_norm", + "guidance_embeds": False, + "text_embed_dim": 4096, + "pooled_projection_dim": 768, + "rope_theta": 256.0, + "rope_axes_dim": (16, 56, 56), + "image_condition_type": "latent_concat", + }, + "HYVideo-T/2-I2V-16ch": { + "in_channels": 16, + "out_channels": 16, + "num_attention_heads": 24, + "attention_head_dim": 128, + "num_layers": 20, + "num_single_layers": 40, + "num_refiner_layers": 2, + "mlp_ratio": 4.0, + "patch_size": 2, + "patch_size_t": 1, + "qk_norm": "rms_norm", + "guidance_embeds": True, + "text_embed_dim": 4096, + "pooled_projection_dim": 768, + "rope_theta": 256.0, + "rope_axes_dim": (16, 56, 56), + "image_condition_type": "token_replace", + }, +} + + +def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def convert_transformer(ckpt_path: str, transformer_type: str): + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True)) + config = TRANSFORMER_CONFIGS[transformer_type] + + with init_empty_weights(): + transformer = HunyuanVideoTransformer3DModel(**config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + transformer.load_state_dict(original_state_dict, strict=True, assign=True) + return transformer + + +def convert_vae(ckpt_path: str): + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True)) + + with init_empty_weights(): + vae = AutoencoderKLHunyuanVideo() + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True, assign=True) + return vae + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" + ) + parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original VAE checkpoint") + parser.add_argument("--text_encoder_path", type=str, default=None, help="Path to original llama checkpoint") + parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to original llama tokenizer") + parser.add_argument("--text_encoder_2_path", type=str, default=None, help="Path to original clip checkpoint") + parser.add_argument("--save_pipeline", action="store_true") + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.") + parser.add_argument( + "--transformer_type", type=str, default="HYVideo-T/2-cfgdistill", choices=list(TRANSFORMER_CONFIGS.keys()) + ) + parser.add_argument("--flow_shift", type=float, default=7.0) + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + args = get_args() + + transformer = None + dtype = DTYPE_MAPPING[args.dtype] + + if args.save_pipeline: + assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None + assert args.text_encoder_path is not None + assert args.tokenizer_path is not None + assert args.text_encoder_2_path is not None + + if args.transformer_ckpt_path is not None: + transformer = convert_transformer(args.transformer_ckpt_path, args.transformer_type) + transformer = transformer.to(dtype=dtype) + if not args.save_pipeline: + transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + if args.vae_ckpt_path is not None: + vae = convert_vae(args.vae_ckpt_path) + if not args.save_pipeline: + vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + if args.save_pipeline: + if args.transformer_type == "HYVideo-T/2-cfgdistill": + text_encoder = AutoModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.float16) + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right") + text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16) + tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path) + scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift) + + pipe = HunyuanVideoPipeline( + transformer=transformer, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + text_encoder_2=text_encoder_2, + tokenizer_2=tokenizer_2, + scheduler=scheduler, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + else: + text_encoder = LlavaForConditionalGeneration.from_pretrained( + args.text_encoder_path, torch_dtype=torch.float16 + ) + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right") + text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16) + tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path) + scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift) + image_processor = CLIPImageProcessor.from_pretrained(args.text_encoder_path) + + pipe = HunyuanVideoImageToVideoPipeline( + transformer=transformer, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + text_encoder_2=text_encoder_2, + tokenizer_2=tokenizer_2, + scheduler=scheduler, + image_processor=image_processor, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") diff --git a/scripts/convert_hunyuandit_controlnet_to_diffusers.py b/scripts/convert_hunyuandit_controlnet_to_diffusers.py new file mode 100644 index 000000000000..5cef46c98983 --- /dev/null +++ b/scripts/convert_hunyuandit_controlnet_to_diffusers.py @@ -0,0 +1,241 @@ +import argparse + +import torch + +from diffusers import HunyuanDiT2DControlNetModel + + +def main(args): + state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu") + + if args.load_key != "none": + try: + state_dict = state_dict[args.load_key] + except KeyError: + raise KeyError( + f"{args.load_key} not found in the checkpoint." + "Please load from the following keys:{state_dict.keys()}" + ) + device = "cuda" + + model_config = HunyuanDiT2DControlNetModel.load_config( + "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", subfolder="transformer" + ) + model_config["use_style_cond_and_image_meta_size"] = ( + args.use_style_cond_and_image_meta_size + ) ### version <= v1.1: True; version >= v1.2: False + print(model_config) + + for key in state_dict: + print("local:", key) + + model = HunyuanDiT2DControlNetModel.from_config(model_config).to(device) + + for key in model.state_dict(): + print("diffusers:", key) + + num_layers = 19 + for i in range(num_layers): + # attn1 + # Wkqv -> to_q, to_k, to_v + q, k, v = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.weight"], 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.bias"], 3, dim=0) + state_dict[f"blocks.{i}.attn1.to_q.weight"] = q + state_dict[f"blocks.{i}.attn1.to_q.bias"] = q_bias + state_dict[f"blocks.{i}.attn1.to_k.weight"] = k + state_dict[f"blocks.{i}.attn1.to_k.bias"] = k_bias + state_dict[f"blocks.{i}.attn1.to_v.weight"] = v + state_dict[f"blocks.{i}.attn1.to_v.bias"] = v_bias + state_dict.pop(f"blocks.{i}.attn1.Wqkv.weight") + state_dict.pop(f"blocks.{i}.attn1.Wqkv.bias") + + # q_norm, k_norm -> norm_q, norm_k + state_dict[f"blocks.{i}.attn1.norm_q.weight"] = state_dict[f"blocks.{i}.attn1.q_norm.weight"] + state_dict[f"blocks.{i}.attn1.norm_q.bias"] = state_dict[f"blocks.{i}.attn1.q_norm.bias"] + state_dict[f"blocks.{i}.attn1.norm_k.weight"] = state_dict[f"blocks.{i}.attn1.k_norm.weight"] + state_dict[f"blocks.{i}.attn1.norm_k.bias"] = state_dict[f"blocks.{i}.attn1.k_norm.bias"] + + state_dict.pop(f"blocks.{i}.attn1.q_norm.weight") + state_dict.pop(f"blocks.{i}.attn1.q_norm.bias") + state_dict.pop(f"blocks.{i}.attn1.k_norm.weight") + state_dict.pop(f"blocks.{i}.attn1.k_norm.bias") + + # out_proj -> to_out + state_dict[f"blocks.{i}.attn1.to_out.0.weight"] = state_dict[f"blocks.{i}.attn1.out_proj.weight"] + state_dict[f"blocks.{i}.attn1.to_out.0.bias"] = state_dict[f"blocks.{i}.attn1.out_proj.bias"] + state_dict.pop(f"blocks.{i}.attn1.out_proj.weight") + state_dict.pop(f"blocks.{i}.attn1.out_proj.bias") + + # attn2 + # kq_proj -> to_k, to_v + k, v = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.weight"], 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.bias"], 2, dim=0) + state_dict[f"blocks.{i}.attn2.to_k.weight"] = k + state_dict[f"blocks.{i}.attn2.to_k.bias"] = k_bias + state_dict[f"blocks.{i}.attn2.to_v.weight"] = v + state_dict[f"blocks.{i}.attn2.to_v.bias"] = v_bias + state_dict.pop(f"blocks.{i}.attn2.kv_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.kv_proj.bias") + + # q_proj -> to_q + state_dict[f"blocks.{i}.attn2.to_q.weight"] = state_dict[f"blocks.{i}.attn2.q_proj.weight"] + state_dict[f"blocks.{i}.attn2.to_q.bias"] = state_dict[f"blocks.{i}.attn2.q_proj.bias"] + state_dict.pop(f"blocks.{i}.attn2.q_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.q_proj.bias") + + # q_norm, k_norm -> norm_q, norm_k + state_dict[f"blocks.{i}.attn2.norm_q.weight"] = state_dict[f"blocks.{i}.attn2.q_norm.weight"] + state_dict[f"blocks.{i}.attn2.norm_q.bias"] = state_dict[f"blocks.{i}.attn2.q_norm.bias"] + state_dict[f"blocks.{i}.attn2.norm_k.weight"] = state_dict[f"blocks.{i}.attn2.k_norm.weight"] + state_dict[f"blocks.{i}.attn2.norm_k.bias"] = state_dict[f"blocks.{i}.attn2.k_norm.bias"] + + state_dict.pop(f"blocks.{i}.attn2.q_norm.weight") + state_dict.pop(f"blocks.{i}.attn2.q_norm.bias") + state_dict.pop(f"blocks.{i}.attn2.k_norm.weight") + state_dict.pop(f"blocks.{i}.attn2.k_norm.bias") + + # out_proj -> to_out + state_dict[f"blocks.{i}.attn2.to_out.0.weight"] = state_dict[f"blocks.{i}.attn2.out_proj.weight"] + state_dict[f"blocks.{i}.attn2.to_out.0.bias"] = state_dict[f"blocks.{i}.attn2.out_proj.bias"] + state_dict.pop(f"blocks.{i}.attn2.out_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.out_proj.bias") + + # switch norm 2 and norm 3 + norm2_weight = state_dict[f"blocks.{i}.norm2.weight"] + norm2_bias = state_dict[f"blocks.{i}.norm2.bias"] + state_dict[f"blocks.{i}.norm2.weight"] = state_dict[f"blocks.{i}.norm3.weight"] + state_dict[f"blocks.{i}.norm2.bias"] = state_dict[f"blocks.{i}.norm3.bias"] + state_dict[f"blocks.{i}.norm3.weight"] = norm2_weight + state_dict[f"blocks.{i}.norm3.bias"] = norm2_bias + + # norm1 -> norm1.norm + # default_modulation.1 -> norm1.linear + state_dict[f"blocks.{i}.norm1.norm.weight"] = state_dict[f"blocks.{i}.norm1.weight"] + state_dict[f"blocks.{i}.norm1.norm.bias"] = state_dict[f"blocks.{i}.norm1.bias"] + state_dict[f"blocks.{i}.norm1.linear.weight"] = state_dict[f"blocks.{i}.default_modulation.1.weight"] + state_dict[f"blocks.{i}.norm1.linear.bias"] = state_dict[f"blocks.{i}.default_modulation.1.bias"] + state_dict.pop(f"blocks.{i}.norm1.weight") + state_dict.pop(f"blocks.{i}.norm1.bias") + state_dict.pop(f"blocks.{i}.default_modulation.1.weight") + state_dict.pop(f"blocks.{i}.default_modulation.1.bias") + + # mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2 + state_dict[f"blocks.{i}.ff.net.0.proj.weight"] = state_dict[f"blocks.{i}.mlp.fc1.weight"] + state_dict[f"blocks.{i}.ff.net.0.proj.bias"] = state_dict[f"blocks.{i}.mlp.fc1.bias"] + state_dict[f"blocks.{i}.ff.net.2.weight"] = state_dict[f"blocks.{i}.mlp.fc2.weight"] + state_dict[f"blocks.{i}.ff.net.2.bias"] = state_dict[f"blocks.{i}.mlp.fc2.bias"] + state_dict.pop(f"blocks.{i}.mlp.fc1.weight") + state_dict.pop(f"blocks.{i}.mlp.fc1.bias") + state_dict.pop(f"blocks.{i}.mlp.fc2.weight") + state_dict.pop(f"blocks.{i}.mlp.fc2.bias") + + # after_proj_list -> controlnet_blocks + state_dict[f"controlnet_blocks.{i}.weight"] = state_dict[f"after_proj_list.{i}.weight"] + state_dict[f"controlnet_blocks.{i}.bias"] = state_dict[f"after_proj_list.{i}.bias"] + state_dict.pop(f"after_proj_list.{i}.weight") + state_dict.pop(f"after_proj_list.{i}.bias") + + # before_proj -> input_block + state_dict["input_block.weight"] = state_dict["before_proj.weight"] + state_dict["input_block.bias"] = state_dict["before_proj.bias"] + state_dict.pop("before_proj.weight") + state_dict.pop("before_proj.bias") + + # pooler -> time_extra_emb + state_dict["time_extra_emb.pooler.positional_embedding"] = state_dict["pooler.positional_embedding"] + state_dict["time_extra_emb.pooler.k_proj.weight"] = state_dict["pooler.k_proj.weight"] + state_dict["time_extra_emb.pooler.k_proj.bias"] = state_dict["pooler.k_proj.bias"] + state_dict["time_extra_emb.pooler.q_proj.weight"] = state_dict["pooler.q_proj.weight"] + state_dict["time_extra_emb.pooler.q_proj.bias"] = state_dict["pooler.q_proj.bias"] + state_dict["time_extra_emb.pooler.v_proj.weight"] = state_dict["pooler.v_proj.weight"] + state_dict["time_extra_emb.pooler.v_proj.bias"] = state_dict["pooler.v_proj.bias"] + state_dict["time_extra_emb.pooler.c_proj.weight"] = state_dict["pooler.c_proj.weight"] + state_dict["time_extra_emb.pooler.c_proj.bias"] = state_dict["pooler.c_proj.bias"] + state_dict.pop("pooler.k_proj.weight") + state_dict.pop("pooler.k_proj.bias") + state_dict.pop("pooler.q_proj.weight") + state_dict.pop("pooler.q_proj.bias") + state_dict.pop("pooler.v_proj.weight") + state_dict.pop("pooler.v_proj.bias") + state_dict.pop("pooler.c_proj.weight") + state_dict.pop("pooler.c_proj.bias") + state_dict.pop("pooler.positional_embedding") + + # t_embedder -> time_embedding (`TimestepEmbedding`) + state_dict["time_extra_emb.timestep_embedder.linear_1.bias"] = state_dict["t_embedder.mlp.0.bias"] + state_dict["time_extra_emb.timestep_embedder.linear_1.weight"] = state_dict["t_embedder.mlp.0.weight"] + state_dict["time_extra_emb.timestep_embedder.linear_2.bias"] = state_dict["t_embedder.mlp.2.bias"] + state_dict["time_extra_emb.timestep_embedder.linear_2.weight"] = state_dict["t_embedder.mlp.2.weight"] + + state_dict.pop("t_embedder.mlp.0.bias") + state_dict.pop("t_embedder.mlp.0.weight") + state_dict.pop("t_embedder.mlp.2.bias") + state_dict.pop("t_embedder.mlp.2.weight") + + # x_embedder -> pos_embd (`PatchEmbed`) + state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] + state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] + state_dict.pop("x_embedder.proj.weight") + state_dict.pop("x_embedder.proj.bias") + + # mlp_t5 -> text_embedder + state_dict["text_embedder.linear_1.bias"] = state_dict["mlp_t5.0.bias"] + state_dict["text_embedder.linear_1.weight"] = state_dict["mlp_t5.0.weight"] + state_dict["text_embedder.linear_2.bias"] = state_dict["mlp_t5.2.bias"] + state_dict["text_embedder.linear_2.weight"] = state_dict["mlp_t5.2.weight"] + state_dict.pop("mlp_t5.0.bias") + state_dict.pop("mlp_t5.0.weight") + state_dict.pop("mlp_t5.2.bias") + state_dict.pop("mlp_t5.2.weight") + + # extra_embedder -> extra_embedder + state_dict["time_extra_emb.extra_embedder.linear_1.bias"] = state_dict["extra_embedder.0.bias"] + state_dict["time_extra_emb.extra_embedder.linear_1.weight"] = state_dict["extra_embedder.0.weight"] + state_dict["time_extra_emb.extra_embedder.linear_2.bias"] = state_dict["extra_embedder.2.bias"] + state_dict["time_extra_emb.extra_embedder.linear_2.weight"] = state_dict["extra_embedder.2.weight"] + state_dict.pop("extra_embedder.0.bias") + state_dict.pop("extra_embedder.0.weight") + state_dict.pop("extra_embedder.2.bias") + state_dict.pop("extra_embedder.2.weight") + + # style_embedder + if model_config["use_style_cond_and_image_meta_size"]: + print(state_dict["style_embedder.weight"]) + print(state_dict["style_embedder.weight"].shape) + state_dict["time_extra_emb.style_embedder.weight"] = state_dict["style_embedder.weight"][0:1] + state_dict.pop("style_embedder.weight") + + model.load_state_dict(state_dict) + + if args.save: + model.save_pretrained(args.output_checkpoint_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." + ) + parser.add_argument( + "--pt_checkpoint_path", default=None, type=str, required=True, help="Path to the .pt pretrained model." + ) + parser.add_argument( + "--output_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the output converted diffusers pipeline.", + ) + parser.add_argument( + "--load_key", default="none", type=str, required=False, help="The key to load from the pretrained .pt file" + ) + parser.add_argument( + "--use_style_cond_and_image_meta_size", + type=bool, + default=False, + help="version <= v1.1: True; version >= v1.2: False", + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_hunyuandit_to_diffusers.py b/scripts/convert_hunyuandit_to_diffusers.py new file mode 100644 index 000000000000..65fcccb22a1a --- /dev/null +++ b/scripts/convert_hunyuandit_to_diffusers.py @@ -0,0 +1,266 @@ +import argparse + +import torch + +from diffusers import HunyuanDiT2DModel + + +def main(args): + state_dict = torch.load(args.pt_checkpoint_path, map_location="cpu") + + if args.load_key != "none": + try: + state_dict = state_dict[args.load_key] + except KeyError: + raise KeyError( + f"{args.load_key} not found in the checkpoint.Please load from the following keys:{state_dict.keys()}" + ) + + device = "cuda" + model_config = HunyuanDiT2DModel.load_config("Tencent-Hunyuan/HunyuanDiT-Diffusers", subfolder="transformer") + model_config["use_style_cond_and_image_meta_size"] = ( + args.use_style_cond_and_image_meta_size + ) ### version <= v1.1: True; version >= v1.2: False + + # input_size -> sample_size, text_dim -> cross_attention_dim + for key in state_dict: + print("local:", key) + + model = HunyuanDiT2DModel.from_config(model_config).to(device) + + for key in model.state_dict(): + print("diffusers:", key) + + num_layers = 40 + for i in range(num_layers): + # attn1 + # Wkqv -> to_q, to_k, to_v + q, k, v = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.weight"], 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn1.Wqkv.bias"], 3, dim=0) + state_dict[f"blocks.{i}.attn1.to_q.weight"] = q + state_dict[f"blocks.{i}.attn1.to_q.bias"] = q_bias + state_dict[f"blocks.{i}.attn1.to_k.weight"] = k + state_dict[f"blocks.{i}.attn1.to_k.bias"] = k_bias + state_dict[f"blocks.{i}.attn1.to_v.weight"] = v + state_dict[f"blocks.{i}.attn1.to_v.bias"] = v_bias + state_dict.pop(f"blocks.{i}.attn1.Wqkv.weight") + state_dict.pop(f"blocks.{i}.attn1.Wqkv.bias") + + # q_norm, k_norm -> norm_q, norm_k + state_dict[f"blocks.{i}.attn1.norm_q.weight"] = state_dict[f"blocks.{i}.attn1.q_norm.weight"] + state_dict[f"blocks.{i}.attn1.norm_q.bias"] = state_dict[f"blocks.{i}.attn1.q_norm.bias"] + state_dict[f"blocks.{i}.attn1.norm_k.weight"] = state_dict[f"blocks.{i}.attn1.k_norm.weight"] + state_dict[f"blocks.{i}.attn1.norm_k.bias"] = state_dict[f"blocks.{i}.attn1.k_norm.bias"] + + state_dict.pop(f"blocks.{i}.attn1.q_norm.weight") + state_dict.pop(f"blocks.{i}.attn1.q_norm.bias") + state_dict.pop(f"blocks.{i}.attn1.k_norm.weight") + state_dict.pop(f"blocks.{i}.attn1.k_norm.bias") + + # out_proj -> to_out + state_dict[f"blocks.{i}.attn1.to_out.0.weight"] = state_dict[f"blocks.{i}.attn1.out_proj.weight"] + state_dict[f"blocks.{i}.attn1.to_out.0.bias"] = state_dict[f"blocks.{i}.attn1.out_proj.bias"] + state_dict.pop(f"blocks.{i}.attn1.out_proj.weight") + state_dict.pop(f"blocks.{i}.attn1.out_proj.bias") + + # attn2 + # kq_proj -> to_k, to_v + k, v = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.weight"], 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict[f"blocks.{i}.attn2.kv_proj.bias"], 2, dim=0) + state_dict[f"blocks.{i}.attn2.to_k.weight"] = k + state_dict[f"blocks.{i}.attn2.to_k.bias"] = k_bias + state_dict[f"blocks.{i}.attn2.to_v.weight"] = v + state_dict[f"blocks.{i}.attn2.to_v.bias"] = v_bias + state_dict.pop(f"blocks.{i}.attn2.kv_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.kv_proj.bias") + + # q_proj -> to_q + state_dict[f"blocks.{i}.attn2.to_q.weight"] = state_dict[f"blocks.{i}.attn2.q_proj.weight"] + state_dict[f"blocks.{i}.attn2.to_q.bias"] = state_dict[f"blocks.{i}.attn2.q_proj.bias"] + state_dict.pop(f"blocks.{i}.attn2.q_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.q_proj.bias") + + # q_norm, k_norm -> norm_q, norm_k + state_dict[f"blocks.{i}.attn2.norm_q.weight"] = state_dict[f"blocks.{i}.attn2.q_norm.weight"] + state_dict[f"blocks.{i}.attn2.norm_q.bias"] = state_dict[f"blocks.{i}.attn2.q_norm.bias"] + state_dict[f"blocks.{i}.attn2.norm_k.weight"] = state_dict[f"blocks.{i}.attn2.k_norm.weight"] + state_dict[f"blocks.{i}.attn2.norm_k.bias"] = state_dict[f"blocks.{i}.attn2.k_norm.bias"] + + state_dict.pop(f"blocks.{i}.attn2.q_norm.weight") + state_dict.pop(f"blocks.{i}.attn2.q_norm.bias") + state_dict.pop(f"blocks.{i}.attn2.k_norm.weight") + state_dict.pop(f"blocks.{i}.attn2.k_norm.bias") + + # out_proj -> to_out + state_dict[f"blocks.{i}.attn2.to_out.0.weight"] = state_dict[f"blocks.{i}.attn2.out_proj.weight"] + state_dict[f"blocks.{i}.attn2.to_out.0.bias"] = state_dict[f"blocks.{i}.attn2.out_proj.bias"] + state_dict.pop(f"blocks.{i}.attn2.out_proj.weight") + state_dict.pop(f"blocks.{i}.attn2.out_proj.bias") + + # switch norm 2 and norm 3 + norm2_weight = state_dict[f"blocks.{i}.norm2.weight"] + norm2_bias = state_dict[f"blocks.{i}.norm2.bias"] + state_dict[f"blocks.{i}.norm2.weight"] = state_dict[f"blocks.{i}.norm3.weight"] + state_dict[f"blocks.{i}.norm2.bias"] = state_dict[f"blocks.{i}.norm3.bias"] + state_dict[f"blocks.{i}.norm3.weight"] = norm2_weight + state_dict[f"blocks.{i}.norm3.bias"] = norm2_bias + + # norm1 -> norm1.norm + # default_modulation.1 -> norm1.linear + state_dict[f"blocks.{i}.norm1.norm.weight"] = state_dict[f"blocks.{i}.norm1.weight"] + state_dict[f"blocks.{i}.norm1.norm.bias"] = state_dict[f"blocks.{i}.norm1.bias"] + state_dict[f"blocks.{i}.norm1.linear.weight"] = state_dict[f"blocks.{i}.default_modulation.1.weight"] + state_dict[f"blocks.{i}.norm1.linear.bias"] = state_dict[f"blocks.{i}.default_modulation.1.bias"] + state_dict.pop(f"blocks.{i}.norm1.weight") + state_dict.pop(f"blocks.{i}.norm1.bias") + state_dict.pop(f"blocks.{i}.default_modulation.1.weight") + state_dict.pop(f"blocks.{i}.default_modulation.1.bias") + + # mlp.fc1 -> ff.net.0, mlp.fc2 -> ff.net.2 + state_dict[f"blocks.{i}.ff.net.0.proj.weight"] = state_dict[f"blocks.{i}.mlp.fc1.weight"] + state_dict[f"blocks.{i}.ff.net.0.proj.bias"] = state_dict[f"blocks.{i}.mlp.fc1.bias"] + state_dict[f"blocks.{i}.ff.net.2.weight"] = state_dict[f"blocks.{i}.mlp.fc2.weight"] + state_dict[f"blocks.{i}.ff.net.2.bias"] = state_dict[f"blocks.{i}.mlp.fc2.bias"] + state_dict.pop(f"blocks.{i}.mlp.fc1.weight") + state_dict.pop(f"blocks.{i}.mlp.fc1.bias") + state_dict.pop(f"blocks.{i}.mlp.fc2.weight") + state_dict.pop(f"blocks.{i}.mlp.fc2.bias") + + # pooler -> time_extra_emb + state_dict["time_extra_emb.pooler.positional_embedding"] = state_dict["pooler.positional_embedding"] + state_dict["time_extra_emb.pooler.k_proj.weight"] = state_dict["pooler.k_proj.weight"] + state_dict["time_extra_emb.pooler.k_proj.bias"] = state_dict["pooler.k_proj.bias"] + state_dict["time_extra_emb.pooler.q_proj.weight"] = state_dict["pooler.q_proj.weight"] + state_dict["time_extra_emb.pooler.q_proj.bias"] = state_dict["pooler.q_proj.bias"] + state_dict["time_extra_emb.pooler.v_proj.weight"] = state_dict["pooler.v_proj.weight"] + state_dict["time_extra_emb.pooler.v_proj.bias"] = state_dict["pooler.v_proj.bias"] + state_dict["time_extra_emb.pooler.c_proj.weight"] = state_dict["pooler.c_proj.weight"] + state_dict["time_extra_emb.pooler.c_proj.bias"] = state_dict["pooler.c_proj.bias"] + state_dict.pop("pooler.k_proj.weight") + state_dict.pop("pooler.k_proj.bias") + state_dict.pop("pooler.q_proj.weight") + state_dict.pop("pooler.q_proj.bias") + state_dict.pop("pooler.v_proj.weight") + state_dict.pop("pooler.v_proj.bias") + state_dict.pop("pooler.c_proj.weight") + state_dict.pop("pooler.c_proj.bias") + state_dict.pop("pooler.positional_embedding") + + # t_embedder -> time_embedding (`TimestepEmbedding`) + state_dict["time_extra_emb.timestep_embedder.linear_1.bias"] = state_dict["t_embedder.mlp.0.bias"] + state_dict["time_extra_emb.timestep_embedder.linear_1.weight"] = state_dict["t_embedder.mlp.0.weight"] + state_dict["time_extra_emb.timestep_embedder.linear_2.bias"] = state_dict["t_embedder.mlp.2.bias"] + state_dict["time_extra_emb.timestep_embedder.linear_2.weight"] = state_dict["t_embedder.mlp.2.weight"] + + state_dict.pop("t_embedder.mlp.0.bias") + state_dict.pop("t_embedder.mlp.0.weight") + state_dict.pop("t_embedder.mlp.2.bias") + state_dict.pop("t_embedder.mlp.2.weight") + + # x_embedder -> pos_embd (`PatchEmbed`) + state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"] + state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"] + state_dict.pop("x_embedder.proj.weight") + state_dict.pop("x_embedder.proj.bias") + + # mlp_t5 -> text_embedder + state_dict["text_embedder.linear_1.bias"] = state_dict["mlp_t5.0.bias"] + state_dict["text_embedder.linear_1.weight"] = state_dict["mlp_t5.0.weight"] + state_dict["text_embedder.linear_2.bias"] = state_dict["mlp_t5.2.bias"] + state_dict["text_embedder.linear_2.weight"] = state_dict["mlp_t5.2.weight"] + state_dict.pop("mlp_t5.0.bias") + state_dict.pop("mlp_t5.0.weight") + state_dict.pop("mlp_t5.2.bias") + state_dict.pop("mlp_t5.2.weight") + + # extra_embedder -> extra_embedder + state_dict["time_extra_emb.extra_embedder.linear_1.bias"] = state_dict["extra_embedder.0.bias"] + state_dict["time_extra_emb.extra_embedder.linear_1.weight"] = state_dict["extra_embedder.0.weight"] + state_dict["time_extra_emb.extra_embedder.linear_2.bias"] = state_dict["extra_embedder.2.bias"] + state_dict["time_extra_emb.extra_embedder.linear_2.weight"] = state_dict["extra_embedder.2.weight"] + state_dict.pop("extra_embedder.0.bias") + state_dict.pop("extra_embedder.0.weight") + state_dict.pop("extra_embedder.2.bias") + state_dict.pop("extra_embedder.2.weight") + + # model.final_adaLN_modulation.1 -> norm_out.linear + def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + state_dict["norm_out.linear.weight"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.weight"]) + state_dict["norm_out.linear.bias"] = swap_scale_shift(state_dict["final_layer.adaLN_modulation.1.bias"]) + state_dict.pop("final_layer.adaLN_modulation.1.weight") + state_dict.pop("final_layer.adaLN_modulation.1.bias") + + # final_linear -> proj_out + state_dict["proj_out.weight"] = state_dict["final_layer.linear.weight"] + state_dict["proj_out.bias"] = state_dict["final_layer.linear.bias"] + state_dict.pop("final_layer.linear.weight") + state_dict.pop("final_layer.linear.bias") + + # style_embedder + if model_config["use_style_cond_and_image_meta_size"]: + print(state_dict["style_embedder.weight"]) + print(state_dict["style_embedder.weight"].shape) + state_dict["time_extra_emb.style_embedder.weight"] = state_dict["style_embedder.weight"][0:1] + state_dict.pop("style_embedder.weight") + + model.load_state_dict(state_dict) + + from diffusers import HunyuanDiTPipeline + + if args.use_style_cond_and_image_meta_size: + pipe = HunyuanDiTPipeline.from_pretrained( + "Tencent-Hunyuan/HunyuanDiT-Diffusers", transformer=model, torch_dtype=torch.float32 + ) + else: + pipe = HunyuanDiTPipeline.from_pretrained( + "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers", transformer=model, torch_dtype=torch.float32 + ) + pipe.to("cuda") + pipe.to(dtype=torch.float32) + + if args.save: + pipe.save_pretrained(args.output_checkpoint_path) + + # ### NOTE: HunyuanDiT supports both Chinese and English inputs + prompt = "一个宇航员在骑马" + # prompt = "An astronaut riding a horse" + generator = torch.Generator(device="cuda").manual_seed(0) + image = pipe( + height=1024, width=1024, prompt=prompt, generator=generator, num_inference_steps=25, guidance_scale=5.0 + ).images[0] + + image.save("img.png") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not." + ) + parser.add_argument( + "--pt_checkpoint_path", default=None, type=str, required=True, help="Path to the .pt pretrained model." + ) + parser.add_argument( + "--output_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the output converted diffusers pipeline.", + ) + parser.add_argument( + "--load_key", default="none", type=str, required=False, help="The key to load from the pretrained .pt file" + ) + parser.add_argument( + "--use_style_cond_and_image_meta_size", + type=bool, + default=False, + help="version <= v1.1: True; version >= v1.2: False", + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_i2vgen_to_diffusers.py b/scripts/convert_i2vgen_to_diffusers.py new file mode 100644 index 000000000000..643780caac2d --- /dev/null +++ b/scripts/convert_i2vgen_to_diffusers.py @@ -0,0 +1,510 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the LDM checkpoints.""" + +import argparse + +import torch +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection + +from diffusers import DDIMScheduler, I2VGenXLPipeline, I2VGenXLUNet, StableDiffusionPipeline + + +CLIP_ID = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K" + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + weight = old_checkpoint[path["old"]] + names = ["proj_attn.weight"] + names_2 = ["proj_out.weight", "proj_in.weight"] + if any(k in new_path for k in names): + checkpoint[new_path] = weight[:, :, 0] + elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and ".attentions." not in new_path: + checkpoint[new_path] = weight[:, :, 0] + else: + checkpoint[new_path] = weight + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_temp_conv_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + mapping.append({"old": old_item, "new": old_item}) + + return mapping + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + if "temopral_conv" not in old_item: + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + additional_embedding_substrings = [ + "local_image_concat", + "context_embedding", + "local_image_embedding", + "fps_embedding", + ] + for k in unet_state_dict: + if any(substring in k for substring in additional_embedding_substrings): + diffusers_key = k.replace("local_image_concat", "image_latents_proj_in").replace( + "local_image_embedding", "image_latents_context_embedding" + ) + new_checkpoint[diffusers_key] = unet_state_dict[k] + + # temporal encoder. + new_checkpoint["image_latents_temporal_encoder.norm1.weight"] = unet_state_dict[ + "local_temporal_encoder.layers.0.0.norm.weight" + ] + new_checkpoint["image_latents_temporal_encoder.norm1.bias"] = unet_state_dict[ + "local_temporal_encoder.layers.0.0.norm.bias" + ] + + # attention + qkv = unet_state_dict["local_temporal_encoder.layers.0.0.fn.to_qkv.weight"] + q, k, v = torch.chunk(qkv, 3, dim=0) + new_checkpoint["image_latents_temporal_encoder.attn1.to_q.weight"] = q + new_checkpoint["image_latents_temporal_encoder.attn1.to_k.weight"] = k + new_checkpoint["image_latents_temporal_encoder.attn1.to_v.weight"] = v + new_checkpoint["image_latents_temporal_encoder.attn1.to_out.0.weight"] = unet_state_dict[ + "local_temporal_encoder.layers.0.0.fn.to_out.0.weight" + ] + new_checkpoint["image_latents_temporal_encoder.attn1.to_out.0.bias"] = unet_state_dict[ + "local_temporal_encoder.layers.0.0.fn.to_out.0.bias" + ] + + # feedforward + new_checkpoint["image_latents_temporal_encoder.ff.net.0.proj.weight"] = unet_state_dict[ + "local_temporal_encoder.layers.0.1.net.0.0.weight" + ] + new_checkpoint["image_latents_temporal_encoder.ff.net.0.proj.bias"] = unet_state_dict[ + "local_temporal_encoder.layers.0.1.net.0.0.bias" + ] + new_checkpoint["image_latents_temporal_encoder.ff.net.2.weight"] = unet_state_dict[ + "local_temporal_encoder.layers.0.1.net.2.weight" + ] + new_checkpoint["image_latents_temporal_encoder.ff.net.2.bias"] = unet_state_dict[ + "local_temporal_encoder.layers.0.1.net.2.bias" + ] + + if "class_embed_type" in config: + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + first_temp_attention = [v for v in unet_state_dict if v.startswith("input_blocks.0.1")] + paths = renew_attention_paths(first_temp_attention) + meta_path = {"old": "input_blocks.0.1", "new": "transformer_in"} + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + temp_attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key] + + if f"input_blocks.{i}.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temporal_convs = [key for key in resnets if "temopral_conv" in key] + paths = renew_temp_conv_paths(temporal_convs) + meta_path = { + "old": f"input_blocks.{i}.0.temopral_conv", + "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(temp_attentions): + paths = renew_attention_paths(temp_attentions) + meta_path = { + "old": f"input_blocks.{i}.2", + "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + temporal_convs_0 = [key for key in resnet_0 if "temopral_conv" in key] + attentions = middle_blocks[1] + temp_attentions = middle_blocks[2] + resnet_1 = middle_blocks[3] + temporal_convs_1 = [key for key in resnet_1 if "temopral_conv" in key] + + resnet_0_paths = renew_resnet_paths(resnet_0) + meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} + assign_to_checkpoint( + resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0) + meta_path = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"} + assign_to_checkpoint( + temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + resnet_1_paths = renew_resnet_paths(resnet_1) + meta_path = {"old": "middle_block.3", "new": "mid_block.resnets.1"} + assign_to_checkpoint( + resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1) + meta_path = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"} + assign_to_checkpoint( + temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temp_attentions_paths = renew_attention_paths(temp_attentions) + meta_path = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"} + assign_to_checkpoint( + temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + temp_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temporal_convs = [key for key in resnets if "temopral_conv" in key] + paths = renew_temp_conv_paths(temporal_convs) + meta_path = { + "old": f"output_blocks.{i}.0.temopral_conv", + "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(temp_attentions): + paths = renew_attention_paths(temp_attentions) + meta_path = { + "old": f"output_blocks.{i}.2", + "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + new_checkpoint[new_path] = unet_state_dict[old_path] + + temopral_conv_paths = [l for l in output_block_layers if "temopral_conv" in l] + for path in temopral_conv_paths: + pruned_path = path.split("temopral_conv.")[-1] + old_path = ".".join(["output_blocks", str(i), str(block_id), "temopral_conv", pruned_path]) + new_path = ".".join(["up_blocks", str(block_id), "temp_convs", str(layer_in_block_id), pruned_path]) + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--unet_checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--push_to_hub", action="store_true") + args = parser.parse_args() + + # UNet + unet_checkpoint = torch.load(args.unet_checkpoint_path, map_location="cpu") + unet_checkpoint = unet_checkpoint["state_dict"] + unet = I2VGenXLUNet(sample_size=32) + + converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config) + + diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys()) + diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys()) + + assert len(diff_0) == len(diff_1) == 0, "Converted weights don't match" + + unet.load_state_dict(converted_ckpt, strict=True) + + # vae + temp_pipe = StableDiffusionPipeline.from_single_file( + "https://huggingface.co/ali-vilab/i2vgen-xl/blob/main/models/v2-1_512-ema-pruned.ckpt" + ) + vae = temp_pipe.vae + del temp_pipe + + # text encoder and tokenizer + text_encoder = CLIPTextModel.from_pretrained(CLIP_ID) + tokenizer = CLIPTokenizer.from_pretrained(CLIP_ID) + + # image encoder and feature extractor + image_encoder = CLIPVisionModelWithProjection.from_pretrained(CLIP_ID) + feature_extractor = CLIPImageProcessor.from_pretrained(CLIP_ID) + + # scheduler + # https://github.com/ali-vilab/i2vgen-xl/blob/main/configs/i2vgen_xl_train.yaml + scheduler = DDIMScheduler( + beta_schedule="squaredcos_cap_v2", + rescale_betas_zero_snr=True, + set_alpha_to_one=True, + clip_sample=False, + steps_offset=1, + timestep_spacing="leading", + prediction_type="v_prediction", + ) + + # final + pipeline = I2VGenXLPipeline( + unet=unet, + vae=vae, + image_encoder=image_encoder, + feature_extractor=feature_extractor, + text_encoder=text_encoder, + tokenizer=tokenizer, + scheduler=scheduler, + ) + + pipeline.save_pretrained(args.dump_path, push_to_hub=args.push_to_hub) diff --git a/scripts/convert_if.py b/scripts/convert_if.py new file mode 100644 index 000000000000..85c739ca92f0 --- /dev/null +++ b/scripts/convert_if.py @@ -0,0 +1,1250 @@ +import argparse +import inspect +import os + +import numpy as np +import torch +import yaml +from torch.nn import functional as F +from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer + +from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel +from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", required=False, default=None, type=str) + + parser.add_argument("--dump_path_stage_2", required=False, default=None, type=str) + + parser.add_argument("--dump_path_stage_3", required=False, default=None, type=str) + + parser.add_argument("--unet_config", required=False, default=None, type=str, help="Path to unet config file") + + parser.add_argument( + "--unet_checkpoint_path", required=False, default=None, type=str, help="Path to unet checkpoint file" + ) + + parser.add_argument( + "--unet_checkpoint_path_stage_2", + required=False, + default=None, + type=str, + help="Path to stage 2 unet checkpoint file", + ) + + parser.add_argument( + "--unet_checkpoint_path_stage_3", + required=False, + default=None, + type=str, + help="Path to stage 3 unet checkpoint file", + ) + + parser.add_argument("--p_head_path", type=str, required=True) + + parser.add_argument("--w_head_path", type=str, required=True) + + args = parser.parse_args() + + return args + + +def main(args): + tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-xxl") + text_encoder = T5EncoderModel.from_pretrained("google/t5-v1_1-xxl") + + feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14") + safety_checker = convert_safety_checker(p_head_path=args.p_head_path, w_head_path=args.w_head_path) + + if args.unet_config is not None and args.unet_checkpoint_path is not None and args.dump_path is not None: + convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args) + + if args.unet_checkpoint_path_stage_2 is not None and args.dump_path_stage_2 is not None: + convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=2) + + if args.unet_checkpoint_path_stage_3 is not None and args.dump_path_stage_3 is not None: + convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=3) + + +def convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args): + unet = get_stage_1_unet(args.unet_config, args.unet_checkpoint_path) + + scheduler = DDPMScheduler( + variance_type="learned_range", + beta_schedule="squaredcos_cap_v2", + prediction_type="epsilon", + thresholding=True, + dynamic_thresholding_ratio=0.95, + sample_max_value=1.5, + ) + + pipe = IFPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + requires_safety_checker=True, + ) + + pipe.save_pretrained(args.dump_path) + + +def convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage): + if stage == 2: + unet_checkpoint_path = args.unet_checkpoint_path_stage_2 + sample_size = None + dump_path = args.dump_path_stage_2 + elif stage == 3: + unet_checkpoint_path = args.unet_checkpoint_path_stage_3 + sample_size = 1024 + dump_path = args.dump_path_stage_3 + else: + assert False + + unet = get_super_res_unet(unet_checkpoint_path, verify_param_count=False, sample_size=sample_size) + + image_noising_scheduler = DDPMScheduler( + beta_schedule="squaredcos_cap_v2", + ) + + scheduler = DDPMScheduler( + variance_type="learned_range", + beta_schedule="squaredcos_cap_v2", + prediction_type="epsilon", + thresholding=True, + dynamic_thresholding_ratio=0.95, + sample_max_value=1.0, + ) + + pipe = IFSuperResolutionPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + unet=unet, + scheduler=scheduler, + image_noising_scheduler=image_noising_scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + requires_safety_checker=True, + ) + + pipe.save_pretrained(dump_path) + + +def get_stage_1_unet(unet_config, unet_checkpoint_path): + original_unet_config = yaml.safe_load(unet_config) + original_unet_config = original_unet_config["params"] + + unet_diffusers_config = create_unet_diffusers_config(original_unet_config) + + unet = UNet2DConditionModel(**unet_diffusers_config) + + device = "cuda" if torch.cuda.is_available() else "cpu" + unet_checkpoint = torch.load(unet_checkpoint_path, map_location=device) + + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path + ) + + unet.load_state_dict(converted_unet_checkpoint) + + return unet + + +def convert_safety_checker(p_head_path, w_head_path): + state_dict = {} + + # p head + + p_head = np.load(p_head_path) + + p_head_weights = p_head["weights"] + p_head_weights = torch.from_numpy(p_head_weights) + p_head_weights = p_head_weights.unsqueeze(0) + + p_head_biases = p_head["biases"] + p_head_biases = torch.from_numpy(p_head_biases) + p_head_biases = p_head_biases.unsqueeze(0) + + state_dict["p_head.weight"] = p_head_weights + state_dict["p_head.bias"] = p_head_biases + + # w head + + w_head = np.load(w_head_path) + + w_head_weights = w_head["weights"] + w_head_weights = torch.from_numpy(w_head_weights) + w_head_weights = w_head_weights.unsqueeze(0) + + w_head_biases = w_head["biases"] + w_head_biases = torch.from_numpy(w_head_biases) + w_head_biases = w_head_biases.unsqueeze(0) + + state_dict["w_head.weight"] = w_head_weights + state_dict["w_head.bias"] = w_head_biases + + # vision model + + vision_model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + vision_model_state_dict = vision_model.state_dict() + + for key, value in vision_model_state_dict.items(): + key = f"vision_model.{key}" + state_dict[key] = value + + # full model + + config = CLIPConfig.from_pretrained("openai/clip-vit-large-patch14") + safety_checker = IFSafetyChecker(config) + + safety_checker.load_state_dict(state_dict) + + return safety_checker + + +def create_unet_diffusers_config(original_unet_config, class_embed_type=None): + attention_resolutions = parse_list(original_unet_config["attention_resolutions"]) + attention_resolutions = [original_unet_config["image_size"] // int(res) for res in attention_resolutions] + + channel_mult = parse_list(original_unet_config["channel_mult"]) + block_out_channels = [original_unet_config["model_channels"] * mult for mult in channel_mult] + + down_block_types = [] + resolution = 1 + + for i in range(len(block_out_channels)): + if resolution in attention_resolutions: + block_type = "SimpleCrossAttnDownBlock2D" + elif original_unet_config["resblock_updown"]: + block_type = "ResnetDownsampleBlock2D" + else: + block_type = "DownBlock2D" + + down_block_types.append(block_type) + + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + if resolution in attention_resolutions: + block_type = "SimpleCrossAttnUpBlock2D" + elif original_unet_config["resblock_updown"]: + block_type = "ResnetUpsampleBlock2D" + else: + block_type = "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + head_dim = original_unet_config["num_head_channels"] + + use_linear_projection = ( + original_unet_config["use_linear_in_transformer"] + if "use_linear_in_transformer" in original_unet_config + else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim = [5, 10, 20, 20] + + projection_class_embeddings_input_dim = None + + if class_embed_type is None: + if "num_classes" in original_unet_config: + if original_unet_config["num_classes"] == "sequential": + class_embed_type = "projection" + assert "adm_in_channels" in original_unet_config + projection_class_embeddings_input_dim = original_unet_config["adm_in_channels"] + else: + raise NotImplementedError( + f"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}" + ) + + config = { + "sample_size": original_unet_config["image_size"], + "in_channels": original_unet_config["in_channels"], + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": original_unet_config["num_res_blocks"], + "cross_attention_dim": original_unet_config["encoder_channels"], + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "out_channels": original_unet_config["out_channels"], + "up_block_types": tuple(up_block_types), + "upcast_attention": False, # TODO: guessing + "cross_attention_norm": "group_norm", + "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", + "addition_embed_type": "text", + "act_fn": "gelu", + } + + if original_unet_config["use_scale_shift_norm"]: + config["resnet_time_scale_shift"] = "scale_shift" + + if "encoder_dim" in original_unet_config: + config["encoder_hid_dim"] = original_unet_config["encoder_dim"] + + return config + + +def convert_ldm_unet_checkpoint(unet_state_dict, config, path=None): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] in [None, "identity"]: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + + # TODO need better check than i in [4, 8, 12, 16] + block_type = config["down_block_types"][block_id] + if (block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D") and i in [ + 4, + 8, + 12, + 16, + ]: + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"} + else: + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + old_path = f"input_blocks.{i}.1" + new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + paths = renew_attention_paths(attentions) + meta_path = {"old": old_path, "new": new_path} + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + old_path = "middle_block.1" + new_path = "mid_block.attentions.0" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + # len(output_block_list) == 1 -> resnet + # len(output_block_list) == 2 -> resnet, attention + # len(output_block_list) == 3 -> resnet, attention, upscale resnet + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + old_path = f"output_blocks.{i}.1" + new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + paths = renew_attention_paths(attentions) + meta_path = { + "old": old_path, + "new": new_path, + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(output_block_list) == 3: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"output_blocks.{i}.2", "new": f"up_blocks.{block_id}.upsamplers.0"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + if "encoder_proj.weight" in unet_state_dict: + new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict.pop("encoder_proj.weight") + new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict.pop("encoder_proj.bias") + + if "encoder_pooling.0.weight" in unet_state_dict: + new_checkpoint["add_embedding.norm1.weight"] = unet_state_dict.pop("encoder_pooling.0.weight") + new_checkpoint["add_embedding.norm1.bias"] = unet_state_dict.pop("encoder_pooling.0.bias") + + new_checkpoint["add_embedding.pool.positional_embedding"] = unet_state_dict.pop( + "encoder_pooling.1.positional_embedding" + ) + new_checkpoint["add_embedding.pool.k_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.k_proj.weight") + new_checkpoint["add_embedding.pool.k_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.k_proj.bias") + new_checkpoint["add_embedding.pool.q_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.q_proj.weight") + new_checkpoint["add_embedding.pool.q_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.q_proj.bias") + new_checkpoint["add_embedding.pool.v_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.v_proj.weight") + new_checkpoint["add_embedding.pool.v_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.v_proj.bias") + + new_checkpoint["add_embedding.proj.weight"] = unet_state_dict.pop("encoder_pooling.2.weight") + new_checkpoint["add_embedding.proj.bias"] = unet_state_dict.pop("encoder_pooling.2.bias") + + new_checkpoint["add_embedding.norm2.weight"] = unet_state_dict.pop("encoder_pooling.3.weight") + new_checkpoint["add_embedding.norm2.bias"] = unet_state_dict.pop("encoder_pooling.3.bias") + + return new_checkpoint + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + if "qkv" in new_item: + continue + + if "encoder_kv" in new_item: + continue + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = new_item.replace("norm_encoder.weight", "norm_cross.weight") + new_item = new_item.replace("norm_encoder.bias", "norm_cross.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_attention_to_checkpoint(new_checkpoint, unet_state_dict, old_path, new_path, config): + qkv_weight = unet_state_dict.pop(f"{old_path}.qkv.weight") + qkv_weight = qkv_weight[:, :, 0] + + qkv_bias = unet_state_dict.pop(f"{old_path}.qkv.bias") + + is_cross_attn_only = "only_cross_attention" in config and config["only_cross_attention"] + + split = 1 if is_cross_attn_only else 3 + + weights, bias = split_attentions( + weight=qkv_weight, + bias=qkv_bias, + split=split, + chunk_size=config["attention_head_dim"], + ) + + if is_cross_attn_only: + query_weight, q_bias = weights, bias + new_checkpoint[f"{new_path}.to_q.weight"] = query_weight[0] + new_checkpoint[f"{new_path}.to_q.bias"] = q_bias[0] + else: + [query_weight, key_weight, value_weight], [q_bias, k_bias, v_bias] = weights, bias + new_checkpoint[f"{new_path}.to_q.weight"] = query_weight + new_checkpoint[f"{new_path}.to_q.bias"] = q_bias + new_checkpoint[f"{new_path}.to_k.weight"] = key_weight + new_checkpoint[f"{new_path}.to_k.bias"] = k_bias + new_checkpoint[f"{new_path}.to_v.weight"] = value_weight + new_checkpoint[f"{new_path}.to_v.bias"] = v_bias + + encoder_kv_weight = unet_state_dict.pop(f"{old_path}.encoder_kv.weight") + encoder_kv_weight = encoder_kv_weight[:, :, 0] + + encoder_kv_bias = unet_state_dict.pop(f"{old_path}.encoder_kv.bias") + + [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( + weight=encoder_kv_weight, + bias=encoder_kv_bias, + split=2, + chunk_size=config["attention_head_dim"], + ) + + new_checkpoint[f"{new_path}.add_k_proj.weight"] = encoder_k_weight + new_checkpoint[f"{new_path}.add_k_proj.bias"] = encoder_k_bias + new_checkpoint[f"{new_path}.add_v_proj.weight"] = encoder_v_weight + new_checkpoint[f"{new_path}.add_v_proj.bias"] = encoder_v_bias + + +def assign_to_checkpoint(paths, checkpoint, old_checkpoint, additional_replacements=None, config=None): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + for path in paths: + new_path = path["new"] + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path or "to_out.0.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) +def split_attentions(*, weight, bias, split, chunk_size): + weights = [None] * split + biases = [None] * split + + weights_biases_idx = 0 + + for starting_row_index in range(0, weight.shape[0], chunk_size): + row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) + + weight_rows = weight[row_indices, :] + bias_rows = bias[row_indices] + + if weights[weights_biases_idx] is None: + weights[weights_biases_idx] = weight_rows + biases[weights_biases_idx] = bias_rows + else: + assert weights[weights_biases_idx] is not None + weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) + biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) + + weights_biases_idx = (weights_biases_idx + 1) % split + + return weights, biases + + +def parse_list(value): + if isinstance(value, str): + value = value.split(",") + value = [int(v) for v in value] + elif isinstance(value, list): + pass + else: + raise ValueError(f"Can't parse list for type: {type(value)}") + + return value + + +# below is copy and pasted from original convert_if_stage_2.py script + + +def get_super_res_unet(unet_checkpoint_path, verify_param_count=True, sample_size=None): + orig_path = unet_checkpoint_path + + original_unet_config = yaml.safe_load(os.path.join(orig_path, "config.yml")) + original_unet_config = original_unet_config["params"] + + unet_diffusers_config = superres_create_unet_diffusers_config(original_unet_config) + unet_diffusers_config["time_embedding_dim"] = original_unet_config["model_channels"] * int( + original_unet_config["channel_mult"].split(",")[-1] + ) + if original_unet_config["encoder_dim"] != original_unet_config["encoder_channels"]: + unet_diffusers_config["encoder_hid_dim"] = original_unet_config["encoder_dim"] + unet_diffusers_config["class_embed_type"] = "timestep" + unet_diffusers_config["addition_embed_type"] = "text" + + unet_diffusers_config["time_embedding_act_fn"] = "gelu" + unet_diffusers_config["resnet_skip_time_act"] = True + unet_diffusers_config["resnet_out_scale_factor"] = 1 / 0.7071 + unet_diffusers_config["mid_block_scale_factor"] = 1 / 0.7071 + unet_diffusers_config["only_cross_attention"] = ( + bool(original_unet_config["disable_self_attentions"]) + if ( + "disable_self_attentions" in original_unet_config + and isinstance(original_unet_config["disable_self_attentions"], int) + ) + else True + ) + + if sample_size is None: + unet_diffusers_config["sample_size"] = original_unet_config["image_size"] + else: + # The second upscaler unet's sample size is incorrectly specified + # in the config and is instead hardcoded in source + unet_diffusers_config["sample_size"] = sample_size + + unet_checkpoint = torch.load(os.path.join(unet_checkpoint_path, "pytorch_model.bin"), map_location="cpu") + + if verify_param_count: + # check that architecture matches - is a bit slow + verify_param_count(orig_path, unet_diffusers_config) + + converted_unet_checkpoint = superres_convert_ldm_unet_checkpoint( + unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path + ) + converted_keys = converted_unet_checkpoint.keys() + + model = UNet2DConditionModel(**unet_diffusers_config) + expected_weights = model.state_dict().keys() + + diff_c_e = set(converted_keys) - set(expected_weights) + diff_e_c = set(expected_weights) - set(converted_keys) + + assert len(diff_e_c) == 0, f"Expected, but not converted: {diff_e_c}" + assert len(diff_c_e) == 0, f"Converted, but not expected: {diff_c_e}" + + model.load_state_dict(converted_unet_checkpoint) + + return model + + +def superres_create_unet_diffusers_config(original_unet_config): + attention_resolutions = parse_list(original_unet_config["attention_resolutions"]) + attention_resolutions = [original_unet_config["image_size"] // int(res) for res in attention_resolutions] + + channel_mult = parse_list(original_unet_config["channel_mult"]) + block_out_channels = [original_unet_config["model_channels"] * mult for mult in channel_mult] + + down_block_types = [] + resolution = 1 + + for i in range(len(block_out_channels)): + if resolution in attention_resolutions: + block_type = "SimpleCrossAttnDownBlock2D" + elif original_unet_config["resblock_updown"]: + block_type = "ResnetDownsampleBlock2D" + else: + block_type = "DownBlock2D" + + down_block_types.append(block_type) + + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + if resolution in attention_resolutions: + block_type = "SimpleCrossAttnUpBlock2D" + elif original_unet_config["resblock_updown"]: + block_type = "ResnetUpsampleBlock2D" + else: + block_type = "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + head_dim = original_unet_config["num_head_channels"] + use_linear_projection = ( + original_unet_config["use_linear_in_transformer"] + if "use_linear_in_transformer" in original_unet_config + else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim = [5, 10, 20, 20] + + class_embed_type = None + projection_class_embeddings_input_dim = None + + if "num_classes" in original_unet_config: + if original_unet_config["num_classes"] == "sequential": + class_embed_type = "projection" + assert "adm_in_channels" in original_unet_config + projection_class_embeddings_input_dim = original_unet_config["adm_in_channels"] + else: + raise NotImplementedError( + f"Unknown conditional unet num_classes config: {original_unet_config['num_classes']}" + ) + + config = { + "in_channels": original_unet_config["in_channels"], + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": tuple(original_unet_config["num_res_blocks"]), + "cross_attention_dim": original_unet_config["encoder_channels"], + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "out_channels": original_unet_config["out_channels"], + "up_block_types": tuple(up_block_types), + "upcast_attention": False, # TODO: guessing + "cross_attention_norm": "group_norm", + "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", + "act_fn": "gelu", + } + + if original_unet_config["use_scale_shift_norm"]: + config["resnet_time_scale_shift"] = "scale_shift" + + return config + + +def superres_convert_ldm_unet_checkpoint(unet_state_dict, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["aug_proj.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["aug_proj.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["aug_proj.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["aug_proj.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + if "encoder_proj.weight" in unet_state_dict: + new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict["encoder_proj.weight"] + new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict["encoder_proj.bias"] + + if "encoder_pooling.0.weight" in unet_state_dict: + mapping = { + "encoder_pooling.0": "add_embedding.norm1", + "encoder_pooling.1": "add_embedding.pool", + "encoder_pooling.2": "add_embedding.proj", + "encoder_pooling.3": "add_embedding.norm2", + } + for key in unet_state_dict.keys(): + if key.startswith("encoder_pooling"): + prefix = key[: len("encoder_pooling.0")] + new_key = key.replace(prefix, mapping[prefix]) + new_checkpoint[new_key] = unet_state_dict[key] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] + for layer_id in range(num_output_blocks) + } + if not isinstance(config["layers_per_block"], int): + layers_per_block_list = [e + 1 for e in config["layers_per_block"]] + layers_per_block_cumsum = list(np.cumsum(layers_per_block_list)) + downsampler_ids = layers_per_block_cumsum + else: + # TODO need better check than i in [4, 8, 12, 16] + downsampler_ids = [4, 8, 12, 16] + + for i in range(1, num_input_blocks): + if isinstance(config["layers_per_block"], int): + layers_per_block = config["layers_per_block"] + block_id = (i - 1) // (layers_per_block + 1) + layer_in_block_id = (i - 1) % (layers_per_block + 1) + else: + block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if (i - 1) < n) + passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0 + layer_in_block_id = (i - 1) - passed_blocks + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + + block_type = config["down_block_types"][block_id] + if ( + block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D" + ) and i in downsampler_ids: + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"} + else: + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + old_path = f"input_blocks.{i}.1" + new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + paths = renew_attention_paths(attentions) + meta_path = {"old": old_path, "new": new_path} + assign_to_checkpoint( + paths, + new_checkpoint, + unet_state_dict, + additional_replacements=[meta_path], + config=config, + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + old_path = "middle_block.1" + new_path = "mid_block.attentions.0" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + if not isinstance(config["layers_per_block"], int): + layers_per_block_list = list(reversed([e + 1 for e in config["layers_per_block"]])) + layers_per_block_cumsum = list(np.cumsum(layers_per_block_list)) + + for i in range(num_output_blocks): + if isinstance(config["layers_per_block"], int): + layers_per_block = config["layers_per_block"] + block_id = i // (layers_per_block + 1) + layer_in_block_id = i % (layers_per_block + 1) + else: + block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if i < n) + passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0 + layer_in_block_id = i - passed_blocks + + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + # len(output_block_list) == 1 -> resnet + # len(output_block_list) == 2 -> resnet, attention or resnet, upscale resnet + # len(output_block_list) == 3 -> resnet, attention, upscale resnet + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + + has_attention = True + if len(output_block_list) == 2 and any("in_layers" in k for k in output_block_list["1"]): + has_attention = False + + maybe_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # this layer was no attention + has_attention = False + maybe_attentions = [] + + if has_attention: + old_path = f"output_blocks.{i}.1" + new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}" + + assign_attention_to_checkpoint( + new_checkpoint=new_checkpoint, + unet_state_dict=unet_state_dict, + old_path=old_path, + new_path=new_path, + config=config, + ) + + paths = renew_attention_paths(maybe_attentions) + meta_path = { + "old": old_path, + "new": new_path, + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(output_block_list) == 3 or (not has_attention and len(maybe_attentions) > 0): + layer_id = len(output_block_list) - 1 + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.{layer_id}" in key] + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"output_blocks.{i}.{layer_id}", "new": f"up_blocks.{block_id}.upsamplers.0"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +def verify_param_count(orig_path, unet_diffusers_config): + if "-II-" in orig_path: + from deepfloyd_if.modules import IFStageII + + if_II = IFStageII(device="cpu", dir_or_name=orig_path) + elif "-III-" in orig_path: + from deepfloyd_if.modules import IFStageIII + + if_II = IFStageIII(device="cpu", dir_or_name=orig_path) + else: + assert f"Weird name. Should have -II- or -III- in path: {orig_path}" + + unet = UNet2DConditionModel(**unet_diffusers_config) + + # in params + assert_param_count(unet.time_embedding, if_II.model.time_embed) + assert_param_count(unet.conv_in, if_II.model.input_blocks[:1]) + + # downblocks + assert_param_count(unet.down_blocks[0], if_II.model.input_blocks[1:4]) + assert_param_count(unet.down_blocks[1], if_II.model.input_blocks[4:7]) + assert_param_count(unet.down_blocks[2], if_II.model.input_blocks[7:11]) + + if "-II-" in orig_path: + assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:17]) + assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[17:]) + if "-III-" in orig_path: + assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:15]) + assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[15:20]) + assert_param_count(unet.down_blocks[5], if_II.model.input_blocks[20:]) + + # mid block + assert_param_count(unet.mid_block, if_II.model.middle_block) + + # up block + if "-II-" in orig_path: + assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:6]) + assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[6:12]) + assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[12:16]) + assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[16:19]) + assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[19:]) + if "-III-" in orig_path: + assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:5]) + assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[5:10]) + assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[10:14]) + assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[14:18]) + assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[18:21]) + assert_param_count(unet.up_blocks[5], if_II.model.output_blocks[21:24]) + + # out params + assert_param_count(unet.conv_norm_out, if_II.model.out[0]) + assert_param_count(unet.conv_out, if_II.model.out[2]) + + # make sure all model architecture has same param count + assert_param_count(unet, if_II.model) + + +def assert_param_count(model_1, model_2): + count_1 = sum(p.numel() for p in model_1.parameters()) + count_2 = sum(p.numel() for p in model_2.parameters()) + assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}" + + +def superres_check_against_original(dump_path, unet_checkpoint_path): + model_path = dump_path + model = UNet2DConditionModel.from_pretrained(model_path) + model.to("cuda") + orig_path = unet_checkpoint_path + + if "-II-" in orig_path: + from deepfloyd_if.modules import IFStageII + + if_II_model = IFStageII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model + elif "-III-" in orig_path: + from deepfloyd_if.modules import IFStageIII + + if_II_model = IFStageIII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model + + batch_size = 1 + channels = model.config.in_channels // 2 + height = model.config.sample_size + width = model.config.sample_size + height = 1024 + width = 1024 + + torch.manual_seed(0) + + latents = torch.randn((batch_size, channels, height, width), device=model.device) + image_small = torch.randn((batch_size, channels, height // 4, width // 4), device=model.device) + + interpolate_antialias = {} + if "antialias" in inspect.signature(F.interpolate).parameters: + interpolate_antialias["antialias"] = True + image_upscaled = F.interpolate( + image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias + ) + + latent_model_input = torch.cat([latents, image_upscaled], dim=1).to(model.dtype) + t = torch.tensor([5], device=model.device).to(model.dtype) + + seq_len = 64 + encoder_hidden_states = torch.randn((batch_size, seq_len, model.config.encoder_hid_dim), device=model.device).to( + model.dtype + ) + + fake_class_labels = torch.tensor([t], device=model.device).to(model.dtype) + + with torch.no_grad(): + out = if_II_model(latent_model_input, t, aug_steps=fake_class_labels, text_emb=encoder_hidden_states) + + if_II_model.to("cpu") + del if_II_model + import gc + + torch.cuda.empty_cache() + gc.collect() + print(50 * "=") + + with torch.no_grad(): + noise_pred = model( + sample=latent_model_input, + encoder_hidden_states=encoder_hidden_states, + class_labels=fake_class_labels, + timestep=t, + ).sample + + print("Out shape", noise_pred.shape) + print("Diff", (out - noise_pred).abs().sum()) + + +if __name__ == "__main__": + main(parse_args()) diff --git a/scripts/convert_k_upscaler_to_diffusers.py b/scripts/convert_k_upscaler_to_diffusers.py new file mode 100644 index 000000000000..cff845ef8099 --- /dev/null +++ b/scripts/convert_k_upscaler_to_diffusers.py @@ -0,0 +1,297 @@ +import argparse + +import huggingface_hub +import k_diffusion as K +import torch + +from diffusers import UNet2DConditionModel + + +UPSCALER_REPO = "pcuenq/k-upscaler" + + +def resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + rv = { + # norm1 + f"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[f"{resnet_prefix}.main.0.mapper.weight"], + f"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[f"{resnet_prefix}.main.0.mapper.bias"], + # conv1 + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.main.2.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.main.2.bias"], + # norm2 + f"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[f"{resnet_prefix}.main.4.mapper.weight"], + f"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[f"{resnet_prefix}.main.4.mapper.bias"], + # conv2 + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.main.6.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.main.6.bias"], + } + + if resnet.conv_shortcut is not None: + rv.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.skip.weight"], + } + ) + + return rv + + +def self_attn_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): + weight_q, weight_k, weight_v = checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3, dim=0) + bias_q, bias_k, bias_v = checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3, dim=0) + rv = { + # norm + f"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[f"{attention_prefix}.norm_in.mapper.weight"], + f"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[f"{attention_prefix}.norm_in.mapper.bias"], + # to_q + f"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1).squeeze(-1), + f"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q, + # to_k + f"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1).squeeze(-1), + f"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k, + # to_v + f"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1).squeeze(-1), + f"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v, + # to_out + f"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[f"{attention_prefix}.out_proj.weight"] + .squeeze(-1) + .squeeze(-1), + f"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[f"{attention_prefix}.out_proj.bias"], + } + + return rv + + +def cross_attn_to_diffusers_checkpoint( + checkpoint, *, diffusers_attention_prefix, diffusers_attention_index, attention_prefix +): + weight_k, weight_v = checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2, dim=0) + bias_k, bias_v = checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2, dim=0) + + rv = { + # norm2 (ada groupnorm) + f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[ + f"{attention_prefix}.norm_dec.mapper.weight" + ], + f"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[ + f"{attention_prefix}.norm_dec.mapper.bias" + ], + # layernorm on encoder_hidden_state + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[ + f"{attention_prefix}.norm_enc.weight" + ], + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[ + f"{attention_prefix}.norm_enc.bias" + ], + # to_q + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[ + f"{attention_prefix}.q_proj.weight" + ] + .squeeze(-1) + .squeeze(-1), + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[ + f"{attention_prefix}.q_proj.bias" + ], + # to_k + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1).squeeze(-1), + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k, + # to_v + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1).squeeze(-1), + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v, + # to_out + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[ + f"{attention_prefix}.out_proj.weight" + ] + .squeeze(-1) + .squeeze(-1), + f"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[ + f"{attention_prefix}.out_proj.bias" + ], + } + + return rv + + +def block_to_diffusers_checkpoint(block, checkpoint, block_idx, block_type): + block_prefix = "inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks" + block_prefix = f"{block_prefix}.{block_idx}" + + diffusers_checkpoint = {} + + if not hasattr(block, "attentions"): + n = 1 # resnet only + elif not block.attentions[0].add_self_attention: + n = 2 # resnet -> cross-attention + else: + n = 3 # resnet -> self-attention -> cross-attention) + + for resnet_idx, resnet in enumerate(block.resnets): + # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" + diffusers_resnet_prefix = f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}" + idx = n * resnet_idx if block_type == "up" else n * resnet_idx + 1 + resnet_prefix = f"{block_prefix}.{idx}" if block_type == "up" else f"{block_prefix}.{idx}" + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + if hasattr(block, "attentions"): + for attention_idx, attention in enumerate(block.attentions): + diffusers_attention_prefix = f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}" + idx = n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2 + self_attention_prefix = f"{block_prefix}.{idx}" + cross_attention_prefix = f"{block_prefix}.{idx}" + cross_attention_index = 1 if not attention.add_self_attention else 2 + idx = ( + n * attention_idx + cross_attention_index + if block_type == "up" + else n * attention_idx + cross_attention_index + 1 + ) + cross_attention_prefix = f"{block_prefix}.{idx}" + + diffusers_checkpoint.update( + cross_attn_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + diffusers_attention_index=2, + attention_prefix=cross_attention_prefix, + ) + ) + + if attention.add_self_attention is True: + diffusers_checkpoint.update( + self_attn_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + attention_prefix=self_attention_prefix, + ) + ) + + return diffusers_checkpoint + + +def unet_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # pre-processing + diffusers_checkpoint.update( + { + "conv_in.weight": checkpoint["inner_model.proj_in.weight"], + "conv_in.bias": checkpoint["inner_model.proj_in.bias"], + } + ) + + # timestep and class embedding + diffusers_checkpoint.update( + { + "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1), + "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"], + "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"], + "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"], + "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"], + "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"], + } + ) + + # down_blocks + for down_block_idx, down_block in enumerate(model.down_blocks): + diffusers_checkpoint.update(block_to_diffusers_checkpoint(down_block, checkpoint, down_block_idx, "down")) + + # up_blocks + for up_block_idx, up_block in enumerate(model.up_blocks): + diffusers_checkpoint.update(block_to_diffusers_checkpoint(up_block, checkpoint, up_block_idx, "up")) + + # post-processing + diffusers_checkpoint.update( + { + "conv_out.weight": checkpoint["inner_model.proj_out.weight"], + "conv_out.bias": checkpoint["inner_model.proj_out.bias"], + } + ) + + return diffusers_checkpoint + + +def unet_model_from_original_config(original_config): + in_channels = original_config["input_channels"] + original_config["unet_cond_dim"] + out_channels = original_config["input_channels"] + (1 if original_config["has_variance"] else 0) + + block_out_channels = original_config["channels"] + + assert len(set(original_config["depths"])) == 1, ( + "UNet2DConditionModel currently do not support blocks with different number of layers" + ) + layers_per_block = original_config["depths"][0] + + class_labels_dim = original_config["mapping_cond_dim"] + cross_attention_dim = original_config["cross_cond_dim"] + + attn1_types = [] + attn2_types = [] + for s, c in zip(original_config["self_attn_depths"], original_config["cross_attn_depths"]): + if s: + a1 = "self" + a2 = "cross" if c else None + elif c: + a1 = "cross" + a2 = None + else: + a1 = None + a2 = None + attn1_types.append(a1) + attn2_types.append(a2) + + unet = UNet2DConditionModel( + in_channels=in_channels, + out_channels=out_channels, + down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D"), + mid_block_type=None, + up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"), + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + act_fn="gelu", + norm_num_groups=None, + cross_attention_dim=cross_attention_dim, + attention_head_dim=64, + time_cond_proj_dim=class_labels_dim, + resnet_time_scale_shift="scale_shift", + time_embedding_type="fourier", + timestep_post_act="gelu", + conv_in_kernel=1, + conv_out_kernel=1, + ) + + return unet + + +def main(args): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + orig_config_path = huggingface_hub.hf_hub_download(UPSCALER_REPO, "config_laion_text_cond_latent_upscaler_2.json") + orig_weights_path = huggingface_hub.hf_hub_download( + UPSCALER_REPO, "laion_text_cond_latent_upscaler_2_1_00470000_slim.pth" + ) + print(f"loading original model configuration from {orig_config_path}") + print(f"loading original model checkpoint from {orig_weights_path}") + + print("converting to diffusers unet") + orig_config = K.config.load_config(open(orig_config_path))["model"] + model = unet_model_from_original_config(orig_config) + + orig_checkpoint = torch.load(orig_weights_path, map_location=device)["model_ema"] + converted_checkpoint = unet_to_diffusers_checkpoint(model, orig_checkpoint) + + model.load_state_dict(converted_checkpoint, strict=True) + model.save_pretrained(args.dump_path) + print(f"saving converted unet model in {args.dump_path}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + args = parser.parse_args() + + main(args) diff --git a/scripts/convert_kakao_brain_unclip_to_diffusers.py b/scripts/convert_kakao_brain_unclip_to_diffusers.py new file mode 100644 index 000000000000..5135eaed5b98 --- /dev/null +++ b/scripts/convert_kakao_brain_unclip_to_diffusers.py @@ -0,0 +1,1159 @@ +import argparse +import tempfile + +import torch +from accelerate import load_checkpoint_and_dispatch +from transformers import CLIPTextModelWithProjection, CLIPTokenizer + +from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel +from diffusers.models.transformers.prior_transformer import PriorTransformer +from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel +from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler + + +r""" +Example - From the diffusers root directory: + +Download weights: +```sh +$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/efdf6206d8ed593961593dc029a8affa/decoder-ckpt-step%3D01000000-of-01000000.ckpt +$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/4226b831ae0279020d134281f3c31590/improved-sr-ckpt-step%3D1.2M.ckpt +$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/85626483eaca9f581e2a78d31ff905ca/prior-ckpt-step%3D01000000-of-01000000.ckpt +$ wget https://arena.kakaocdn.net/brainrepo/models/karlo-public/v1.0.0.alpha/0b62380a75e56f073e2844ab5199153d/ViT-L-14_stats.th +``` + +Convert the model: +```sh +$ python scripts/convert_kakao_brain_unclip_to_diffusers.py \ + --decoder_checkpoint_path ./decoder-ckpt-step\=01000000-of-01000000.ckpt \ + --super_res_unet_checkpoint_path ./improved-sr-ckpt-step\=1.2M.ckpt \ + --prior_checkpoint_path ./prior-ckpt-step\=01000000-of-01000000.ckpt \ + --clip_stat_path ./ViT-L-14_stats.th \ + --dump_path <path where to save model> +``` +""" + + +# prior + +PRIOR_ORIGINAL_PREFIX = "model" + +# Uses default arguments +PRIOR_CONFIG = {} + + +def prior_model_from_original_config(): + model = PriorTransformer(**PRIOR_CONFIG) + + return model + + +def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint): + diffusers_checkpoint = {} + + # <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1 + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight"], + "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias"], + } + ) + + # <original>.clip_img_proj -> <diffusers>.proj_in + diffusers_checkpoint.update( + { + "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight"], + "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias"], + } + ) + + # <original>.text_emb_proj -> <diffusers>.embedding_proj + diffusers_checkpoint.update( + { + "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight"], + "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias"], + } + ) + + # <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj + diffusers_checkpoint.update( + { + "encoder_hidden_states_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight"], + "encoder_hidden_states_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias"], + } + ) + + # <original>.positional_embedding -> <diffusers>.positional_embedding + diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.positional_embedding"]}) + + # <original>.prd_emb -> <diffusers>.prd_embedding + diffusers_checkpoint.update({"prd_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.prd_emb"]}) + + # <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2 + diffusers_checkpoint.update( + { + "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight"], + "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias"], + } + ) + + # <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x> + for idx in range(len(model.transformer_blocks)): + diffusers_transformer_prefix = f"transformer_blocks.{idx}" + original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}" + + # <original>.attn -> <diffusers>.attn1 + diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" + original_attention_prefix = f"{original_transformer_prefix}.attn" + diffusers_checkpoint.update( + prior_attention_to_diffusers( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + original_attention_prefix=original_attention_prefix, + attention_head_dim=model.attention_head_dim, + ) + ) + + # <original>.mlp -> <diffusers>.ff + diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" + original_ff_prefix = f"{original_transformer_prefix}.mlp" + diffusers_checkpoint.update( + prior_ff_to_diffusers( + checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix + ) + ) + + # <original>.ln_1 -> <diffusers>.norm1 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ + f"{original_transformer_prefix}.ln_1.weight" + ], + f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], + } + ) + + # <original>.ln_2 -> <diffusers>.norm3 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ + f"{original_transformer_prefix}.ln_2.weight" + ], + f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], + } + ) + + # <original>.final_ln -> <diffusers>.norm_out + diffusers_checkpoint.update( + { + "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight"], + "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias"], + } + ) + + # <original>.out_proj -> <diffusers>.proj_to_clip_embeddings + diffusers_checkpoint.update( + { + "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight"], + "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias"], + } + ) + + # clip stats + clip_mean, clip_std = clip_stats_checkpoint + clip_mean = clip_mean[None, :] + clip_std = clip_std[None, :] + + diffusers_checkpoint.update({"clip_mean": clip_mean, "clip_std": clip_std}) + + return diffusers_checkpoint + + +def prior_attention_to_diffusers( + checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim +): + diffusers_checkpoint = {} + + # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} + [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( + weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], + bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], + split=3, + chunk_size=attention_head_dim, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_q.weight": q_weight, + f"{diffusers_attention_prefix}.to_q.bias": q_bias, + f"{diffusers_attention_prefix}.to_k.weight": k_weight, + f"{diffusers_attention_prefix}.to_k.bias": k_bias, + f"{diffusers_attention_prefix}.to_v.weight": v_weight, + f"{diffusers_attention_prefix}.to_v.bias": v_bias, + } + ) + + # <original>.c_proj -> <diffusers>.to_out.0 + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], + } + ) + + return diffusers_checkpoint + + +def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): + diffusers_checkpoint = { + # <original>.c_fc -> <diffusers>.net.0.proj + f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], + f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], + # <original>.c_proj -> <diffusers>.net.2 + f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], + f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], + } + + return diffusers_checkpoint + + +# done prior + + +# decoder + +DECODER_ORIGINAL_PREFIX = "model" + +# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can +# update then. +DECODER_CONFIG = { + "sample_size": 64, + "layers_per_block": 3, + "down_block_types": ( + "ResnetDownsampleBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + ), + "up_block_types": ( + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "ResnetUpsampleBlock2D", + ), + "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", + "block_out_channels": (320, 640, 960, 1280), + "in_channels": 3, + "out_channels": 6, + "cross_attention_dim": 1536, + "class_embed_type": "identity", + "attention_head_dim": 64, + "resnet_time_scale_shift": "scale_shift", +} + + +def decoder_model_from_original_config(): + model = UNet2DConditionModel(**DECODER_CONFIG) + + return model + + +def decoder_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + original_unet_prefix = DECODER_ORIGINAL_PREFIX + num_head_channels = DECODER_CONFIG["attention_head_dim"] + + diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) + + # <original>.input_blocks -> <diffusers>.down_blocks + + original_down_block_idx = 1 + + for diffusers_down_block_idx in range(len(model.down_blocks)): + checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_down_block_idx=diffusers_down_block_idx, + original_down_block_idx=original_down_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=num_head_channels, + ) + + original_down_block_idx += num_original_down_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.input_blocks -> <diffusers>.down_blocks + + diffusers_checkpoint.update( + unet_midblock_to_diffusers_checkpoint( + model, + checkpoint, + original_unet_prefix=original_unet_prefix, + num_head_channels=num_head_channels, + ) + ) + + # <original>.output_blocks -> <diffusers>.up_blocks + + original_up_block_idx = 0 + + for diffusers_up_block_idx in range(len(model.up_blocks)): + checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_up_block_idx=diffusers_up_block_idx, + original_up_block_idx=original_up_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=num_head_channels, + ) + + original_up_block_idx += num_original_up_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.output_blocks -> <diffusers>.up_blocks + + diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) + + return diffusers_checkpoint + + +# done decoder + +# text proj + + +def text_proj_from_original_config(): + # From the conditional unet constructor where the dimension of the projected time embeddings is + # constructed + time_embed_dim = DECODER_CONFIG["block_out_channels"][0] * 4 + + cross_attention_dim = DECODER_CONFIG["cross_attention_dim"] + + model = UnCLIPTextProjModel(time_embed_dim=time_embed_dim, cross_attention_dim=cross_attention_dim) + + return model + + +# Note that the input checkpoint is the original decoder checkpoint +def text_proj_original_checkpoint_to_diffusers_checkpoint(checkpoint): + diffusers_checkpoint = { + # <original>.text_seq_proj.0 -> <diffusers>.encoder_hidden_states_proj + "encoder_hidden_states_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.weight"], + "encoder_hidden_states_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.0.bias"], + # <original>.text_seq_proj.1 -> <diffusers>.text_encoder_hidden_states_norm + "text_encoder_hidden_states_norm.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.weight"], + "text_encoder_hidden_states_norm.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_seq_proj.1.bias"], + # <original>.clip_tok_proj -> <diffusers>.clip_extra_context_tokens_proj + "clip_extra_context_tokens_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.weight"], + "clip_extra_context_tokens_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.clip_tok_proj.bias"], + # <original>.text_feat_proj -> <diffusers>.embedding_proj + "embedding_proj.weight": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.weight"], + "embedding_proj.bias": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.text_feat_proj.bias"], + # <original>.cf_param -> <diffusers>.learned_classifier_free_guidance_embeddings + "learned_classifier_free_guidance_embeddings": checkpoint[f"{DECODER_ORIGINAL_PREFIX}.cf_param"], + # <original>.clip_emb -> <diffusers>.clip_image_embeddings_project_to_time_embeddings + "clip_image_embeddings_project_to_time_embeddings.weight": checkpoint[ + f"{DECODER_ORIGINAL_PREFIX}.clip_emb.weight" + ], + "clip_image_embeddings_project_to_time_embeddings.bias": checkpoint[ + f"{DECODER_ORIGINAL_PREFIX}.clip_emb.bias" + ], + } + + return diffusers_checkpoint + + +# done text proj + +# super res unet first steps + +SUPER_RES_UNET_FIRST_STEPS_PREFIX = "model_first_steps" + +SUPER_RES_UNET_FIRST_STEPS_CONFIG = { + "sample_size": 256, + "layers_per_block": 3, + "down_block_types": ( + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + ), + "up_block_types": ( + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + ), + "block_out_channels": (320, 640, 960, 1280), + "in_channels": 6, + "out_channels": 3, + "add_attention": False, +} + + +def super_res_unet_first_steps_model_from_original_config(): + model = UNet2DModel(**SUPER_RES_UNET_FIRST_STEPS_CONFIG) + + return model + + +def super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + original_unet_prefix = SUPER_RES_UNET_FIRST_STEPS_PREFIX + + diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) + + # <original>.input_blocks -> <diffusers>.down_blocks + + original_down_block_idx = 1 + + for diffusers_down_block_idx in range(len(model.down_blocks)): + checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_down_block_idx=diffusers_down_block_idx, + original_down_block_idx=original_down_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + + original_down_block_idx += num_original_down_blocks + + diffusers_checkpoint.update(checkpoint_update) + + diffusers_checkpoint.update( + unet_midblock_to_diffusers_checkpoint( + model, + checkpoint, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + ) + + # <original>.output_blocks -> <diffusers>.up_blocks + + original_up_block_idx = 0 + + for diffusers_up_block_idx in range(len(model.up_blocks)): + checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_up_block_idx=diffusers_up_block_idx, + original_up_block_idx=original_up_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + + original_up_block_idx += num_original_up_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.output_blocks -> <diffusers>.up_blocks + + diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) + + return diffusers_checkpoint + + +# done super res unet first steps + +# super res unet last step + +SUPER_RES_UNET_LAST_STEP_PREFIX = "model_last_step" + +SUPER_RES_UNET_LAST_STEP_CONFIG = { + "sample_size": 256, + "layers_per_block": 3, + "down_block_types": ( + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + "ResnetDownsampleBlock2D", + ), + "up_block_types": ( + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + "ResnetUpsampleBlock2D", + ), + "block_out_channels": (320, 640, 960, 1280), + "in_channels": 6, + "out_channels": 3, + "add_attention": False, +} + + +def super_res_unet_last_step_model_from_original_config(): + model = UNet2DModel(**SUPER_RES_UNET_LAST_STEP_CONFIG) + + return model + + +def super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + original_unet_prefix = SUPER_RES_UNET_LAST_STEP_PREFIX + + diffusers_checkpoint.update(unet_time_embeddings(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_in(checkpoint, original_unet_prefix)) + + # <original>.input_blocks -> <diffusers>.down_blocks + + original_down_block_idx = 1 + + for diffusers_down_block_idx in range(len(model.down_blocks)): + checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_down_block_idx=diffusers_down_block_idx, + original_down_block_idx=original_down_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + + original_down_block_idx += num_original_down_blocks + + diffusers_checkpoint.update(checkpoint_update) + + diffusers_checkpoint.update( + unet_midblock_to_diffusers_checkpoint( + model, + checkpoint, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + ) + + # <original>.output_blocks -> <diffusers>.up_blocks + + original_up_block_idx = 0 + + for diffusers_up_block_idx in range(len(model.up_blocks)): + checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_up_block_idx=diffusers_up_block_idx, + original_up_block_idx=original_up_block_idx, + original_unet_prefix=original_unet_prefix, + num_head_channels=None, + ) + + original_up_block_idx += num_original_up_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.output_blocks -> <diffusers>.up_blocks + + diffusers_checkpoint.update(unet_conv_norm_out(checkpoint, original_unet_prefix)) + diffusers_checkpoint.update(unet_conv_out(checkpoint, original_unet_prefix)) + + return diffusers_checkpoint + + +# done super res unet last step + + +# unet utils + + +# <original>.time_embed -> <diffusers>.time_embedding +def unet_time_embeddings(checkpoint, original_unet_prefix): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint[f"{original_unet_prefix}.time_embed.0.weight"], + "time_embedding.linear_1.bias": checkpoint[f"{original_unet_prefix}.time_embed.0.bias"], + "time_embedding.linear_2.weight": checkpoint[f"{original_unet_prefix}.time_embed.2.weight"], + "time_embedding.linear_2.bias": checkpoint[f"{original_unet_prefix}.time_embed.2.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.input_blocks.0 -> <diffusers>.conv_in +def unet_conv_in(checkpoint, original_unet_prefix): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_in.weight": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.weight"], + "conv_in.bias": checkpoint[f"{original_unet_prefix}.input_blocks.0.0.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.out.0 -> <diffusers>.conv_norm_out +def unet_conv_norm_out(checkpoint, original_unet_prefix): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_norm_out.weight": checkpoint[f"{original_unet_prefix}.out.0.weight"], + "conv_norm_out.bias": checkpoint[f"{original_unet_prefix}.out.0.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.out.2 -> <diffusers>.conv_out +def unet_conv_out(checkpoint, original_unet_prefix): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_out.weight": checkpoint[f"{original_unet_prefix}.out.2.weight"], + "conv_out.bias": checkpoint[f"{original_unet_prefix}.out.2.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.input_blocks -> <diffusers>.down_blocks +def unet_downblock_to_diffusers_checkpoint( + model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, original_unet_prefix, num_head_channels +): + diffusers_checkpoint = {} + + diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.resnets" + original_down_block_prefix = f"{original_unet_prefix}.input_blocks" + + down_block = model.down_blocks[diffusers_down_block_idx] + + num_resnets = len(down_block.resnets) + + if down_block.downsamplers is None: + downsampler = False + else: + assert len(down_block.downsamplers) == 1 + downsampler = True + # The downsample block is also a resnet + num_resnets += 1 + + for resnet_idx_inc in range(num_resnets): + full_resnet_prefix = f"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0" + + if downsampler and resnet_idx_inc == num_resnets - 1: + # this is a downsample block + full_diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.downsamplers.0" + else: + # this is a regular resnet block + full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix + ) + ) + + if hasattr(down_block, "attentions"): + num_attentions = len(down_block.attentions) + diffusers_attention_prefix = f"down_blocks.{diffusers_down_block_idx}.attentions" + + for attention_idx_inc in range(num_attentions): + full_attention_prefix = f"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1" + full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" + + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + attention_prefix=full_attention_prefix, + diffusers_attention_prefix=full_diffusers_attention_prefix, + num_head_channels=num_head_channels, + ) + ) + + num_original_down_blocks = num_resnets + + return diffusers_checkpoint, num_original_down_blocks + + +# <original>.middle_block -> <diffusers>.mid_block +def unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, original_unet_prefix, num_head_channels): + diffusers_checkpoint = {} + + # block 0 + + original_block_idx = 0 + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, + diffusers_resnet_prefix="mid_block.resnets.0", + resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", + ) + ) + + original_block_idx += 1 + + # optional block 1 + + if hasattr(model.mid_block, "attentions") and model.mid_block.attentions[0] is not None: + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix="mid_block.attentions.0", + attention_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", + num_head_channels=num_head_channels, + ) + ) + original_block_idx += 1 + + # block 1 or block 2 + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, + diffusers_resnet_prefix="mid_block.resnets.1", + resnet_prefix=f"{original_unet_prefix}.middle_block.{original_block_idx}", + ) + ) + + return diffusers_checkpoint + + +# <original>.output_blocks -> <diffusers>.up_blocks +def unet_upblock_to_diffusers_checkpoint( + model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, original_unet_prefix, num_head_channels +): + diffusers_checkpoint = {} + + diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.resnets" + original_up_block_prefix = f"{original_unet_prefix}.output_blocks" + + up_block = model.up_blocks[diffusers_up_block_idx] + + num_resnets = len(up_block.resnets) + + if up_block.upsamplers is None: + upsampler = False + else: + assert len(up_block.upsamplers) == 1 + upsampler = True + # The upsample block is also a resnet + num_resnets += 1 + + has_attentions = hasattr(up_block, "attentions") + + for resnet_idx_inc in range(num_resnets): + if upsampler and resnet_idx_inc == num_resnets - 1: + # this is an upsample block + if has_attentions: + # There is a middle attention block that we skip + original_resnet_block_idx = 2 + else: + original_resnet_block_idx = 1 + + # we add the `minus 1` because the last two resnets are stuck together in the same output block + full_resnet_prefix = ( + f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}" + ) + + full_diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.upsamplers.0" + else: + # this is a regular resnet block + full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0" + full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix + ) + ) + + if has_attentions: + num_attentions = len(up_block.attentions) + diffusers_attention_prefix = f"up_blocks.{diffusers_up_block_idx}.attentions" + + for attention_idx_inc in range(num_attentions): + full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1" + full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" + + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + attention_prefix=full_attention_prefix, + diffusers_attention_prefix=full_diffusers_attention_prefix, + num_head_channels=num_head_channels, + ) + ) + + num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets + + return diffusers_checkpoint, num_original_down_blocks + + +def resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + diffusers_checkpoint = { + f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.in_layers.0.weight"], + f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.in_layers.0.bias"], + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.in_layers.2.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.in_layers.2.bias"], + f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[f"{resnet_prefix}.emb_layers.1.weight"], + f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[f"{resnet_prefix}.emb_layers.1.bias"], + f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.out_layers.0.weight"], + f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.out_layers.0.bias"], + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.out_layers.3.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.out_layers.3.bias"], + } + + skip_connection_prefix = f"{resnet_prefix}.skip_connection" + + if f"{skip_connection_prefix}.weight" in checkpoint: + diffusers_checkpoint.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{skip_connection_prefix}.weight"], + f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{skip_connection_prefix}.bias"], + } + ) + + return diffusers_checkpoint + + +def attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels): + diffusers_checkpoint = {} + + # <original>.norm -> <diffusers>.group_norm + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], + f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], + } + ) + + # <original>.qkv -> <diffusers>.{query, key, value} + [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( + weight=checkpoint[f"{attention_prefix}.qkv.weight"][:, :, 0], + bias=checkpoint[f"{attention_prefix}.qkv.bias"], + split=3, + chunk_size=num_head_channels, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_q.weight": q_weight, + f"{diffusers_attention_prefix}.to_q.bias": q_bias, + f"{diffusers_attention_prefix}.to_k.weight": k_weight, + f"{diffusers_attention_prefix}.to_k.bias": k_bias, + f"{diffusers_attention_prefix}.to_v.weight": v_weight, + f"{diffusers_attention_prefix}.to_v.bias": v_bias, + } + ) + + # <original>.encoder_kv -> <diffusers>.{context_key, context_value} + [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( + weight=checkpoint[f"{attention_prefix}.encoder_kv.weight"][:, :, 0], + bias=checkpoint[f"{attention_prefix}.encoder_kv.bias"], + split=2, + chunk_size=num_head_channels, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.add_k_proj.weight": encoder_k_weight, + f"{diffusers_attention_prefix}.add_k_proj.bias": encoder_k_bias, + f"{diffusers_attention_prefix}.add_v_proj.weight": encoder_v_weight, + f"{diffusers_attention_prefix}.add_v_proj.bias": encoder_v_bias, + } + ) + + # <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear) + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ + :, :, 0 + ], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], + } + ) + + return diffusers_checkpoint + + +# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) +def split_attentions(*, weight, bias, split, chunk_size): + weights = [None] * split + biases = [None] * split + + weights_biases_idx = 0 + + for starting_row_index in range(0, weight.shape[0], chunk_size): + row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) + + weight_rows = weight[row_indices, :] + bias_rows = bias[row_indices] + + if weights[weights_biases_idx] is None: + assert weights[weights_biases_idx] is None + weights[weights_biases_idx] = weight_rows + biases[weights_biases_idx] = bias_rows + else: + assert weights[weights_biases_idx] is not None + weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) + biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) + + weights_biases_idx = (weights_biases_idx + 1) % split + + return weights, biases + + +# done unet utils + + +# Driver functions + + +def text_encoder(): + print("loading CLIP text encoder") + + clip_name = "openai/clip-vit-large-patch14" + + # sets pad_value to 0 + pad_token = "!" + + tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto") + + assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0 + + text_encoder_model = CLIPTextModelWithProjection.from_pretrained( + clip_name, + # `CLIPTextModel` does not support device_map="auto" + # device_map="auto" + ) + + print("done loading CLIP text encoder") + + return text_encoder_model, tokenizer_model + + +def prior(*, args, checkpoint_map_location): + print("loading prior") + + prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) + prior_checkpoint = prior_checkpoint["state_dict"] + + clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location) + + prior_model = prior_model_from_original_config() + + prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint( + prior_model, prior_checkpoint, clip_stats_checkpoint + ) + + del prior_checkpoint + del clip_stats_checkpoint + + load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True) + + print("done loading prior") + + return prior_model + + +def decoder(*, args, checkpoint_map_location): + print("loading decoder") + + decoder_checkpoint = torch.load(args.decoder_checkpoint_path, map_location=checkpoint_map_location) + decoder_checkpoint = decoder_checkpoint["state_dict"] + + decoder_model = decoder_model_from_original_config() + + decoder_diffusers_checkpoint = decoder_original_checkpoint_to_diffusers_checkpoint( + decoder_model, decoder_checkpoint + ) + + # text proj interlude + + # The original decoder implementation includes a set of parameters that are used + # for creating the `encoder_hidden_states` which are what the U-net is conditioned + # on. The diffusers conditional unet directly takes the encoder_hidden_states. We pull + # the parameters into the UnCLIPTextProjModel class + text_proj_model = text_proj_from_original_config() + + text_proj_checkpoint = text_proj_original_checkpoint_to_diffusers_checkpoint(decoder_checkpoint) + + load_checkpoint_to_model(text_proj_checkpoint, text_proj_model, strict=True) + + # done text proj interlude + + del decoder_checkpoint + + load_checkpoint_to_model(decoder_diffusers_checkpoint, decoder_model, strict=True) + + print("done loading decoder") + + return decoder_model, text_proj_model + + +def super_res_unet(*, args, checkpoint_map_location): + print("loading super resolution unet") + + super_res_checkpoint = torch.load(args.super_res_unet_checkpoint_path, map_location=checkpoint_map_location) + super_res_checkpoint = super_res_checkpoint["state_dict"] + + # model_first_steps + + super_res_first_model = super_res_unet_first_steps_model_from_original_config() + + super_res_first_steps_checkpoint = super_res_unet_first_steps_original_checkpoint_to_diffusers_checkpoint( + super_res_first_model, super_res_checkpoint + ) + + # model_last_step + super_res_last_model = super_res_unet_last_step_model_from_original_config() + + super_res_last_step_checkpoint = super_res_unet_last_step_original_checkpoint_to_diffusers_checkpoint( + super_res_last_model, super_res_checkpoint + ) + + del super_res_checkpoint + + load_checkpoint_to_model(super_res_first_steps_checkpoint, super_res_first_model, strict=True) + + load_checkpoint_to_model(super_res_last_step_checkpoint, super_res_last_model, strict=True) + + print("done loading super resolution unet") + + return super_res_first_model, super_res_last_model + + +def load_checkpoint_to_model(checkpoint, model, strict=False): + with tempfile.NamedTemporaryFile() as file: + torch.save(checkpoint, file.name) + del checkpoint + if strict: + model.load_state_dict(torch.load(file.name), strict=True) + else: + load_checkpoint_and_dispatch(model, file.name, device_map="auto") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--prior_checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the prior checkpoint to convert.", + ) + + parser.add_argument( + "--decoder_checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the decoder checkpoint to convert.", + ) + + parser.add_argument( + "--super_res_unet_checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the super resolution checkpoint to convert.", + ) + + parser.add_argument( + "--clip_stat_path", default=None, type=str, required=True, help="Path to the clip stats checkpoint to convert." + ) + + parser.add_argument( + "--checkpoint_load_device", + default="cpu", + type=str, + required=False, + help="The device passed to `map_location` when loading checkpoints.", + ) + + parser.add_argument( + "--debug", + default=None, + type=str, + required=False, + help="Only run a specific stage of the convert script. Used for debugging", + ) + + args = parser.parse_args() + + print(f"loading checkpoints to {args.checkpoint_load_device}") + + checkpoint_map_location = torch.device(args.checkpoint_load_device) + + if args.debug is not None: + print(f"debug: only executing {args.debug}") + + if args.debug is None: + text_encoder_model, tokenizer_model = text_encoder() + + prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) + + decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location) + + super_res_first_model, super_res_last_model = super_res_unet( + args=args, checkpoint_map_location=checkpoint_map_location + ) + + prior_scheduler = UnCLIPScheduler( + variance_type="fixed_small_log", + prediction_type="sample", + num_train_timesteps=1000, + clip_sample_range=5.0, + ) + + decoder_scheduler = UnCLIPScheduler( + variance_type="learned_range", + prediction_type="epsilon", + num_train_timesteps=1000, + ) + + super_res_scheduler = UnCLIPScheduler( + variance_type="fixed_small_log", + prediction_type="epsilon", + num_train_timesteps=1000, + ) + + print(f"saving Kakao Brain unCLIP to {args.dump_path}") + + pipe = UnCLIPPipeline( + prior=prior_model, + decoder=decoder_model, + text_proj=text_proj_model, + tokenizer=tokenizer_model, + text_encoder=text_encoder_model, + super_res_first=super_res_first_model, + super_res_last=super_res_last_model, + prior_scheduler=prior_scheduler, + decoder_scheduler=decoder_scheduler, + super_res_scheduler=super_res_scheduler, + ) + pipe.save_pretrained(args.dump_path) + + print("done writing Kakao Brain unCLIP") + elif args.debug == "text_encoder": + text_encoder_model, tokenizer_model = text_encoder() + elif args.debug == "prior": + prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) + elif args.debug == "decoder": + decoder_model, text_proj_model = decoder(args=args, checkpoint_map_location=checkpoint_map_location) + elif args.debug == "super_res_unet": + super_res_first_model, super_res_last_model = super_res_unet( + args=args, checkpoint_map_location=checkpoint_map_location + ) + else: + raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_kandinsky3_unet.py b/scripts/convert_kandinsky3_unet.py new file mode 100644 index 000000000000..4fe8c54eb7fc --- /dev/null +++ b/scripts/convert_kandinsky3_unet.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python3 +import argparse +import fnmatch + +from safetensors.torch import load_file + +from diffusers import Kandinsky3UNet + + +MAPPING = { + "to_time_embed.1": "time_embedding.linear_1", + "to_time_embed.3": "time_embedding.linear_2", + "in_layer": "conv_in", + "out_layer.0": "conv_norm_out", + "out_layer.2": "conv_out", + "down_samples": "down_blocks", + "up_samples": "up_blocks", + "projection_lin": "encoder_hid_proj.projection_linear", + "projection_ln": "encoder_hid_proj.projection_norm", + "feature_pooling": "add_time_condition", + "to_query": "to_q", + "to_key": "to_k", + "to_value": "to_v", + "output_layer": "to_out.0", + "self_attention_block": "attentions.0", +} + +DYNAMIC_MAP = { + "resnet_attn_blocks.*.0": "resnets_in.*", + "resnet_attn_blocks.*.1": ("attentions.*", 1), + "resnet_attn_blocks.*.2": "resnets_out.*", +} +# MAPPING = {} + + +def convert_state_dict(unet_state_dict): + """ + Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model. + Args: + unet_model (torch.nn.Module): The original U-Net model. + unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with. + + Returns: + OrderedDict: The converted state dictionary. + """ + # Example of renaming logic (this will vary based on your model's architecture) + converted_state_dict = {} + for key in unet_state_dict: + new_key = key + for pattern, new_pattern in MAPPING.items(): + new_key = new_key.replace(pattern, new_pattern) + + for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items(): + has_matched = False + if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched: + star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1]) + + if isinstance(dyn_new_pattern, tuple): + new_star = star + dyn_new_pattern[-1] + dyn_new_pattern = dyn_new_pattern[0] + else: + new_star = star + + pattern = dyn_pattern.replace("*", str(star)) + new_pattern = dyn_new_pattern.replace("*", str(new_star)) + + new_key = new_key.replace(pattern, new_pattern) + has_matched = True + + converted_state_dict[new_key] = unet_state_dict[key] + + return converted_state_dict + + +def main(model_path, output_path): + # Load your original U-Net model + unet_state_dict = load_file(model_path) + + # Initialize your Kandinsky3UNet model + config = {} + + # Convert the state dict + converted_state_dict = convert_state_dict(unet_state_dict) + + unet = Kandinsky3UNet(config) + unet.load_state_dict(converted_state_dict) + + unet.save_pretrained(output_path) + print(f"Converted model saved to {output_path}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format") + parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model") + parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model") + + args = parser.parse_args() + main(args.model_path, args.output_path) diff --git a/scripts/convert_kandinsky_to_diffusers.py b/scripts/convert_kandinsky_to_diffusers.py new file mode 100644 index 000000000000..8d3f7b63d0c1 --- /dev/null +++ b/scripts/convert_kandinsky_to_diffusers.py @@ -0,0 +1,1411 @@ +import argparse +import os +import tempfile + +import torch +from accelerate import load_checkpoint_and_dispatch + +from diffusers import UNet2DConditionModel +from diffusers.models.transformers.prior_transformer import PriorTransformer +from diffusers.models.vq_model import VQModel + + +""" +Example - From the diffusers root directory: + +Download weights: +```sh +$ wget https://huggingface.co/ai-forever/Kandinsky_2.1/blob/main/prior_fp16.ckpt +``` + +Convert the model: +```sh +python scripts/convert_kandinsky_to_diffusers.py \ + --prior_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/prior_fp16.ckpt \ + --clip_stat_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/ViT-L-14_stats.th \ + --text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/decoder_fp16.ckpt \ + --inpaint_text2img_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/inpainting_fp16.ckpt \ + --movq_checkpoint_path /home/yiyi_huggingface_co/Kandinsky-2/checkpoints_Kandinsky_2.1/movq_final.ckpt \ + --dump_path /home/yiyi_huggingface_co/dump \ + --debug decoder +``` +""" + + +# prior + +PRIOR_ORIGINAL_PREFIX = "model" + +# Uses default arguments +PRIOR_CONFIG = {} + + +def prior_model_from_original_config(): + model = PriorTransformer(**PRIOR_CONFIG) + + return model + + +def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint, clip_stats_checkpoint): + diffusers_checkpoint = {} + + # <original>.time_embed.0 -> <diffusers>.time_embedding.linear_1 + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.weight"], + "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.0.bias"], + } + ) + + # <original>.clip_img_proj -> <diffusers>.proj_in + diffusers_checkpoint.update( + { + "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.weight"], + "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_img_proj.bias"], + } + ) + + # <original>.text_emb_proj -> <diffusers>.embedding_proj + diffusers_checkpoint.update( + { + "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.weight"], + "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_emb_proj.bias"], + } + ) + + # <original>.text_enc_proj -> <diffusers>.encoder_hidden_states_proj + diffusers_checkpoint.update( + { + "encoder_hidden_states_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.weight"], + "encoder_hidden_states_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.text_enc_proj.bias"], + } + ) + + # <original>.positional_embedding -> <diffusers>.positional_embedding + diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.positional_embedding"]}) + + # <original>.prd_emb -> <diffusers>.prd_embedding + diffusers_checkpoint.update({"prd_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.prd_emb"]}) + + # <original>.time_embed.2 -> <diffusers>.time_embedding.linear_2 + diffusers_checkpoint.update( + { + "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.weight"], + "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.2.bias"], + } + ) + + # <original>.resblocks.<x> -> <diffusers>.transformer_blocks.<x> + for idx in range(len(model.transformer_blocks)): + diffusers_transformer_prefix = f"transformer_blocks.{idx}" + original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.transformer.resblocks.{idx}" + + # <original>.attn -> <diffusers>.attn1 + diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" + original_attention_prefix = f"{original_transformer_prefix}.attn" + diffusers_checkpoint.update( + prior_attention_to_diffusers( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + original_attention_prefix=original_attention_prefix, + attention_head_dim=model.attention_head_dim, + ) + ) + + # <original>.mlp -> <diffusers>.ff + diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" + original_ff_prefix = f"{original_transformer_prefix}.mlp" + diffusers_checkpoint.update( + prior_ff_to_diffusers( + checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix + ) + ) + + # <original>.ln_1 -> <diffusers>.norm1 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ + f"{original_transformer_prefix}.ln_1.weight" + ], + f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], + } + ) + + # <original>.ln_2 -> <diffusers>.norm3 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ + f"{original_transformer_prefix}.ln_2.weight" + ], + f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], + } + ) + + # <original>.final_ln -> <diffusers>.norm_out + diffusers_checkpoint.update( + { + "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.weight"], + "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.final_ln.bias"], + } + ) + + # <original>.out_proj -> <diffusers>.proj_to_clip_embeddings + diffusers_checkpoint.update( + { + "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.weight"], + "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.out_proj.bias"], + } + ) + + # clip stats + clip_mean, clip_std = clip_stats_checkpoint + clip_mean = clip_mean[None, :] + clip_std = clip_std[None, :] + + diffusers_checkpoint.update({"clip_mean": clip_mean, "clip_std": clip_std}) + + return diffusers_checkpoint + + +def prior_attention_to_diffusers( + checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim +): + diffusers_checkpoint = {} + + # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} + [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( + weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], + bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], + split=3, + chunk_size=attention_head_dim, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_q.weight": q_weight, + f"{diffusers_attention_prefix}.to_q.bias": q_bias, + f"{diffusers_attention_prefix}.to_k.weight": k_weight, + f"{diffusers_attention_prefix}.to_k.bias": k_bias, + f"{diffusers_attention_prefix}.to_v.weight": v_weight, + f"{diffusers_attention_prefix}.to_v.bias": v_bias, + } + ) + + # <original>.c_proj -> <diffusers>.to_out.0 + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], + } + ) + + return diffusers_checkpoint + + +def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): + diffusers_checkpoint = { + # <original>.c_fc -> <diffusers>.net.0.proj + f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], + f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], + # <original>.c_proj -> <diffusers>.net.2 + f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], + f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], + } + + return diffusers_checkpoint + + +# done prior + +# unet + +# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can +# update then. + +UNET_CONFIG = { + "act_fn": "silu", + "addition_embed_type": "text_image", + "addition_embed_type_num_heads": 64, + "attention_head_dim": 64, + "block_out_channels": [384, 768, 1152, 1536], + "center_input_sample": False, + "class_embed_type": None, + "class_embeddings_concat": False, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 768, + "cross_attention_norm": None, + "down_block_types": [ + "ResnetDownsampleBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + ], + "downsample_padding": 1, + "dual_cross_attention": False, + "encoder_hid_dim": 1024, + "encoder_hid_dim_type": "text_image_proj", + "flip_sin_to_cos": True, + "freq_shift": 0, + "in_channels": 4, + "layers_per_block": 3, + "mid_block_only_cross_attention": None, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_class_embeds": None, + "only_cross_attention": False, + "out_channels": 8, + "projection_class_embeddings_input_dim": None, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": False, + "resnet_time_scale_shift": "scale_shift", + "sample_size": 64, + "time_cond_proj_dim": None, + "time_embedding_act_fn": None, + "time_embedding_dim": None, + "time_embedding_type": "positional", + "timestep_post_act": None, + "up_block_types": [ + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "ResnetUpsampleBlock2D", + ], + "upcast_attention": False, + "use_linear_projection": False, +} + + +def unet_model_from_original_config(): + model = UNet2DConditionModel(**UNET_CONFIG) + + return model + + +def unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + num_head_channels = UNET_CONFIG["attention_head_dim"] + + diffusers_checkpoint.update(unet_time_embeddings(checkpoint)) + diffusers_checkpoint.update(unet_conv_in(checkpoint)) + diffusers_checkpoint.update(unet_add_embedding(checkpoint)) + diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint)) + + # <original>.input_blocks -> <diffusers>.down_blocks + + original_down_block_idx = 1 + + for diffusers_down_block_idx in range(len(model.down_blocks)): + checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_down_block_idx=diffusers_down_block_idx, + original_down_block_idx=original_down_block_idx, + num_head_channels=num_head_channels, + ) + + original_down_block_idx += num_original_down_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.input_blocks -> <diffusers>.down_blocks + + diffusers_checkpoint.update( + unet_midblock_to_diffusers_checkpoint( + model, + checkpoint, + num_head_channels=num_head_channels, + ) + ) + + # <original>.output_blocks -> <diffusers>.up_blocks + + original_up_block_idx = 0 + + for diffusers_up_block_idx in range(len(model.up_blocks)): + checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_up_block_idx=diffusers_up_block_idx, + original_up_block_idx=original_up_block_idx, + num_head_channels=num_head_channels, + ) + + original_up_block_idx += num_original_up_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.output_blocks -> <diffusers>.up_blocks + + diffusers_checkpoint.update(unet_conv_norm_out(checkpoint)) + diffusers_checkpoint.update(unet_conv_out(checkpoint)) + + return diffusers_checkpoint + + +# done unet + +# inpaint unet + +# We are hardcoding the model configuration for now. If we need to generalize to more model configurations, we can +# update then. + +INPAINT_UNET_CONFIG = { + "act_fn": "silu", + "addition_embed_type": "text_image", + "addition_embed_type_num_heads": 64, + "attention_head_dim": 64, + "block_out_channels": [384, 768, 1152, 1536], + "center_input_sample": False, + "class_embed_type": None, + "class_embeddings_concat": None, + "conv_in_kernel": 3, + "conv_out_kernel": 3, + "cross_attention_dim": 768, + "cross_attention_norm": None, + "down_block_types": [ + "ResnetDownsampleBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + "SimpleCrossAttnDownBlock2D", + ], + "downsample_padding": 1, + "dual_cross_attention": False, + "encoder_hid_dim": 1024, + "encoder_hid_dim_type": "text_image_proj", + "flip_sin_to_cos": True, + "freq_shift": 0, + "in_channels": 9, + "layers_per_block": 3, + "mid_block_only_cross_attention": None, + "mid_block_scale_factor": 1, + "mid_block_type": "UNetMidBlock2DSimpleCrossAttn", + "norm_eps": 1e-05, + "norm_num_groups": 32, + "num_class_embeds": None, + "only_cross_attention": False, + "out_channels": 8, + "projection_class_embeddings_input_dim": None, + "resnet_out_scale_factor": 1.0, + "resnet_skip_time_act": False, + "resnet_time_scale_shift": "scale_shift", + "sample_size": 64, + "time_cond_proj_dim": None, + "time_embedding_act_fn": None, + "time_embedding_dim": None, + "time_embedding_type": "positional", + "timestep_post_act": None, + "up_block_types": [ + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "SimpleCrossAttnUpBlock2D", + "ResnetUpsampleBlock2D", + ], + "upcast_attention": False, + "use_linear_projection": False, +} + + +def inpaint_unet_model_from_original_config(): + model = UNet2DConditionModel(**INPAINT_UNET_CONFIG) + + return model + + +def inpaint_unet_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + num_head_channels = INPAINT_UNET_CONFIG["attention_head_dim"] + + diffusers_checkpoint.update(unet_time_embeddings(checkpoint)) + diffusers_checkpoint.update(unet_conv_in(checkpoint)) + diffusers_checkpoint.update(unet_add_embedding(checkpoint)) + diffusers_checkpoint.update(unet_encoder_hid_proj(checkpoint)) + + # <original>.input_blocks -> <diffusers>.down_blocks + + original_down_block_idx = 1 + + for diffusers_down_block_idx in range(len(model.down_blocks)): + checkpoint_update, num_original_down_blocks = unet_downblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_down_block_idx=diffusers_down_block_idx, + original_down_block_idx=original_down_block_idx, + num_head_channels=num_head_channels, + ) + + original_down_block_idx += num_original_down_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.input_blocks -> <diffusers>.down_blocks + + diffusers_checkpoint.update( + unet_midblock_to_diffusers_checkpoint( + model, + checkpoint, + num_head_channels=num_head_channels, + ) + ) + + # <original>.output_blocks -> <diffusers>.up_blocks + + original_up_block_idx = 0 + + for diffusers_up_block_idx in range(len(model.up_blocks)): + checkpoint_update, num_original_up_blocks = unet_upblock_to_diffusers_checkpoint( + model, + checkpoint, + diffusers_up_block_idx=diffusers_up_block_idx, + original_up_block_idx=original_up_block_idx, + num_head_channels=num_head_channels, + ) + + original_up_block_idx += num_original_up_blocks + + diffusers_checkpoint.update(checkpoint_update) + + # done <original>.output_blocks -> <diffusers>.up_blocks + + diffusers_checkpoint.update(unet_conv_norm_out(checkpoint)) + diffusers_checkpoint.update(unet_conv_out(checkpoint)) + + return diffusers_checkpoint + + +# done inpaint unet + + +# unet utils + + +# <original>.time_embed -> <diffusers>.time_embedding +def unet_time_embeddings(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint["time_embed.0.weight"], + "time_embedding.linear_1.bias": checkpoint["time_embed.0.bias"], + "time_embedding.linear_2.weight": checkpoint["time_embed.2.weight"], + "time_embedding.linear_2.bias": checkpoint["time_embed.2.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.input_blocks.0 -> <diffusers>.conv_in +def unet_conv_in(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_in.weight": checkpoint["input_blocks.0.0.weight"], + "conv_in.bias": checkpoint["input_blocks.0.0.bias"], + } + ) + + return diffusers_checkpoint + + +def unet_add_embedding(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "add_embedding.text_norm.weight": checkpoint["ln_model_n.weight"], + "add_embedding.text_norm.bias": checkpoint["ln_model_n.bias"], + "add_embedding.text_proj.weight": checkpoint["proj_n.weight"], + "add_embedding.text_proj.bias": checkpoint["proj_n.bias"], + "add_embedding.image_proj.weight": checkpoint["img_layer.weight"], + "add_embedding.image_proj.bias": checkpoint["img_layer.bias"], + } + ) + + return diffusers_checkpoint + + +def unet_encoder_hid_proj(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "encoder_hid_proj.image_embeds.weight": checkpoint["clip_to_seq.weight"], + "encoder_hid_proj.image_embeds.bias": checkpoint["clip_to_seq.bias"], + "encoder_hid_proj.text_proj.weight": checkpoint["to_model_dim_n.weight"], + "encoder_hid_proj.text_proj.bias": checkpoint["to_model_dim_n.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.out.0 -> <diffusers>.conv_norm_out +def unet_conv_norm_out(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_norm_out.weight": checkpoint["out.0.weight"], + "conv_norm_out.bias": checkpoint["out.0.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.out.2 -> <diffusers>.conv_out +def unet_conv_out(checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update( + { + "conv_out.weight": checkpoint["out.2.weight"], + "conv_out.bias": checkpoint["out.2.bias"], + } + ) + + return diffusers_checkpoint + + +# <original>.input_blocks -> <diffusers>.down_blocks +def unet_downblock_to_diffusers_checkpoint( + model, checkpoint, *, diffusers_down_block_idx, original_down_block_idx, num_head_channels +): + diffusers_checkpoint = {} + + diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.resnets" + original_down_block_prefix = "input_blocks" + + down_block = model.down_blocks[diffusers_down_block_idx] + + num_resnets = len(down_block.resnets) + + if down_block.downsamplers is None: + downsampler = False + else: + assert len(down_block.downsamplers) == 1 + downsampler = True + # The downsample block is also a resnet + num_resnets += 1 + + for resnet_idx_inc in range(num_resnets): + full_resnet_prefix = f"{original_down_block_prefix}.{original_down_block_idx + resnet_idx_inc}.0" + + if downsampler and resnet_idx_inc == num_resnets - 1: + # this is a downsample block + full_diffusers_resnet_prefix = f"down_blocks.{diffusers_down_block_idx}.downsamplers.0" + else: + # this is a regular resnet block + full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix + ) + ) + + if hasattr(down_block, "attentions"): + num_attentions = len(down_block.attentions) + diffusers_attention_prefix = f"down_blocks.{diffusers_down_block_idx}.attentions" + + for attention_idx_inc in range(num_attentions): + full_attention_prefix = f"{original_down_block_prefix}.{original_down_block_idx + attention_idx_inc}.1" + full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" + + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + attention_prefix=full_attention_prefix, + diffusers_attention_prefix=full_diffusers_attention_prefix, + num_head_channels=num_head_channels, + ) + ) + + num_original_down_blocks = num_resnets + + return diffusers_checkpoint, num_original_down_blocks + + +# <original>.middle_block -> <diffusers>.mid_block +def unet_midblock_to_diffusers_checkpoint(model, checkpoint, *, num_head_channels): + diffusers_checkpoint = {} + + # block 0 + + original_block_idx = 0 + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, + diffusers_resnet_prefix="mid_block.resnets.0", + resnet_prefix=f"middle_block.{original_block_idx}", + ) + ) + + original_block_idx += 1 + + # optional block 1 + + if hasattr(model.mid_block, "attentions") and model.mid_block.attentions[0] is not None: + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix="mid_block.attentions.0", + attention_prefix=f"middle_block.{original_block_idx}", + num_head_channels=num_head_channels, + ) + ) + original_block_idx += 1 + + # block 1 or block 2 + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, + diffusers_resnet_prefix="mid_block.resnets.1", + resnet_prefix=f"middle_block.{original_block_idx}", + ) + ) + + return diffusers_checkpoint + + +# <original>.output_blocks -> <diffusers>.up_blocks +def unet_upblock_to_diffusers_checkpoint( + model, checkpoint, *, diffusers_up_block_idx, original_up_block_idx, num_head_channels +): + diffusers_checkpoint = {} + + diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.resnets" + original_up_block_prefix = "output_blocks" + + up_block = model.up_blocks[diffusers_up_block_idx] + + num_resnets = len(up_block.resnets) + + if up_block.upsamplers is None: + upsampler = False + else: + assert len(up_block.upsamplers) == 1 + upsampler = True + # The upsample block is also a resnet + num_resnets += 1 + + has_attentions = hasattr(up_block, "attentions") + + for resnet_idx_inc in range(num_resnets): + if upsampler and resnet_idx_inc == num_resnets - 1: + # this is an upsample block + if has_attentions: + # There is a middle attention block that we skip + original_resnet_block_idx = 2 + else: + original_resnet_block_idx = 1 + + # we add the `minus 1` because the last two resnets are stuck together in the same output block + full_resnet_prefix = ( + f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc - 1}.{original_resnet_block_idx}" + ) + + full_diffusers_resnet_prefix = f"up_blocks.{diffusers_up_block_idx}.upsamplers.0" + else: + # this is a regular resnet block + full_resnet_prefix = f"{original_up_block_prefix}.{original_up_block_idx + resnet_idx_inc}.0" + full_diffusers_resnet_prefix = f"{diffusers_resnet_prefix}.{resnet_idx_inc}" + + diffusers_checkpoint.update( + resnet_to_diffusers_checkpoint( + checkpoint, resnet_prefix=full_resnet_prefix, diffusers_resnet_prefix=full_diffusers_resnet_prefix + ) + ) + + if has_attentions: + num_attentions = len(up_block.attentions) + diffusers_attention_prefix = f"up_blocks.{diffusers_up_block_idx}.attentions" + + for attention_idx_inc in range(num_attentions): + full_attention_prefix = f"{original_up_block_prefix}.{original_up_block_idx + attention_idx_inc}.1" + full_diffusers_attention_prefix = f"{diffusers_attention_prefix}.{attention_idx_inc}" + + diffusers_checkpoint.update( + attention_to_diffusers_checkpoint( + checkpoint, + attention_prefix=full_attention_prefix, + diffusers_attention_prefix=full_diffusers_attention_prefix, + num_head_channels=num_head_channels, + ) + ) + + num_original_down_blocks = num_resnets - 1 if upsampler else num_resnets + + return diffusers_checkpoint, num_original_down_blocks + + +def resnet_to_diffusers_checkpoint(checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + diffusers_checkpoint = { + f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.in_layers.0.weight"], + f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.in_layers.0.bias"], + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.in_layers.2.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.in_layers.2.bias"], + f"{diffusers_resnet_prefix}.time_emb_proj.weight": checkpoint[f"{resnet_prefix}.emb_layers.1.weight"], + f"{diffusers_resnet_prefix}.time_emb_proj.bias": checkpoint[f"{resnet_prefix}.emb_layers.1.bias"], + f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.out_layers.0.weight"], + f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.out_layers.0.bias"], + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.out_layers.3.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.out_layers.3.bias"], + } + + skip_connection_prefix = f"{resnet_prefix}.skip_connection" + + if f"{skip_connection_prefix}.weight" in checkpoint: + diffusers_checkpoint.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{skip_connection_prefix}.weight"], + f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{skip_connection_prefix}.bias"], + } + ) + + return diffusers_checkpoint + + +def attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix, num_head_channels): + diffusers_checkpoint = {} + + # <original>.norm -> <diffusers>.group_norm + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], + f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], + } + ) + + # <original>.qkv -> <diffusers>.{query, key, value} + [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( + weight=checkpoint[f"{attention_prefix}.qkv.weight"][:, :, 0], + bias=checkpoint[f"{attention_prefix}.qkv.bias"], + split=3, + chunk_size=num_head_channels, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_q.weight": q_weight, + f"{diffusers_attention_prefix}.to_q.bias": q_bias, + f"{diffusers_attention_prefix}.to_k.weight": k_weight, + f"{diffusers_attention_prefix}.to_k.bias": k_bias, + f"{diffusers_attention_prefix}.to_v.weight": v_weight, + f"{diffusers_attention_prefix}.to_v.bias": v_bias, + } + ) + + # <original>.encoder_kv -> <diffusers>.{context_key, context_value} + [encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions( + weight=checkpoint[f"{attention_prefix}.encoder_kv.weight"][:, :, 0], + bias=checkpoint[f"{attention_prefix}.encoder_kv.bias"], + split=2, + chunk_size=num_head_channels, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.add_k_proj.weight": encoder_k_weight, + f"{diffusers_attention_prefix}.add_k_proj.bias": encoder_k_bias, + f"{diffusers_attention_prefix}.add_v_proj.weight": encoder_v_weight, + f"{diffusers_attention_prefix}.add_v_proj.bias": encoder_v_bias, + } + ) + + # <original>.proj_out (1d conv) -> <diffusers>.proj_attn (linear) + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ + :, :, 0 + ], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], + } + ) + + return diffusers_checkpoint + + +# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) +def split_attentions(*, weight, bias, split, chunk_size): + weights = [None] * split + biases = [None] * split + + weights_biases_idx = 0 + + for starting_row_index in range(0, weight.shape[0], chunk_size): + row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) + + weight_rows = weight[row_indices, :] + bias_rows = bias[row_indices] + + if weights[weights_biases_idx] is None: + assert weights[weights_biases_idx] is None + weights[weights_biases_idx] = weight_rows + biases[weights_biases_idx] = bias_rows + else: + assert weights[weights_biases_idx] is not None + weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) + biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) + + weights_biases_idx = (weights_biases_idx + 1) % split + + return weights, biases + + +# done unet utils + + +def prior(*, args, checkpoint_map_location): + print("loading prior") + + prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) + + clip_stats_checkpoint = torch.load(args.clip_stat_path, map_location=checkpoint_map_location) + + prior_model = prior_model_from_original_config() + + prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint( + prior_model, prior_checkpoint, clip_stats_checkpoint + ) + + del prior_checkpoint + del clip_stats_checkpoint + + load_checkpoint_to_model(prior_diffusers_checkpoint, prior_model, strict=True) + + print("done loading prior") + + return prior_model + + +def text2img(*, args, checkpoint_map_location): + print("loading text2img") + + text2img_checkpoint = torch.load(args.text2img_checkpoint_path, map_location=checkpoint_map_location) + + unet_model = unet_model_from_original_config() + + unet_diffusers_checkpoint = unet_original_checkpoint_to_diffusers_checkpoint(unet_model, text2img_checkpoint) + + del text2img_checkpoint + + load_checkpoint_to_model(unet_diffusers_checkpoint, unet_model, strict=True) + + print("done loading text2img") + + return unet_model + + +def inpaint_text2img(*, args, checkpoint_map_location): + print("loading inpaint text2img") + + inpaint_text2img_checkpoint = torch.load( + args.inpaint_text2img_checkpoint_path, map_location=checkpoint_map_location + ) + + inpaint_unet_model = inpaint_unet_model_from_original_config() + + inpaint_unet_diffusers_checkpoint = inpaint_unet_original_checkpoint_to_diffusers_checkpoint( + inpaint_unet_model, inpaint_text2img_checkpoint + ) + + del inpaint_text2img_checkpoint + + load_checkpoint_to_model(inpaint_unet_diffusers_checkpoint, inpaint_unet_model, strict=True) + + print("done loading inpaint text2img") + + return inpaint_unet_model + + +# movq + +MOVQ_CONFIG = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 4, + "down_block_types": ("DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "AttnDownEncoderBlock2D"), + "up_block_types": ("AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"), + "num_vq_embeddings": 16384, + "block_out_channels": (128, 256, 256, 512), + "vq_embed_dim": 4, + "layers_per_block": 2, + "norm_type": "spatial", +} + + +def movq_model_from_original_config(): + movq = VQModel(**MOVQ_CONFIG) + return movq + + +def movq_encoder_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # conv_in + diffusers_checkpoint.update( + { + "encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"], + "encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"], + } + ) + + # down_blocks + for down_block_idx, down_block in enumerate(model.encoder.down_blocks): + diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}" + down_block_prefix = f"encoder.down.{down_block_idx}" + + # resnets + for resnet_idx, resnet in enumerate(down_block.resnets): + diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}" + resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}" + + diffusers_checkpoint.update( + movq_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + # downsample + + # do not include the downsample when on the last down block + # There is no downsample on the last down block + if down_block_idx != len(model.encoder.down_blocks) - 1: + # There's a single downsample in the original checkpoint but a list of downsamples + # in the diffusers model. + diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv" + downsample_prefix = f"{down_block_prefix}.downsample.conv" + diffusers_checkpoint.update( + { + f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], + f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], + } + ) + + # attentions + + if hasattr(down_block, "attentions"): + for attention_idx, _ in enumerate(down_block.attentions): + diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}" + attention_prefix = f"{down_block_prefix}.attn.{attention_idx}" + diffusers_checkpoint.update( + movq_attention_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + attention_prefix=attention_prefix, + ) + ) + + # mid block + + # mid block attentions + + # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder + diffusers_attention_prefix = "encoder.mid_block.attentions.0" + attention_prefix = "encoder.mid.attn_1" + diffusers_checkpoint.update( + movq_attention_to_diffusers_checkpoint( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # mid block resnets + + for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): + diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}" + + # the hardcoded prefixes to `block_` are 1 and 2 + orig_resnet_idx = diffusers_resnet_idx + 1 + # There are two hardcoded resnets in the middle of the VQ-diffusion encoder + resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}" + + diffusers_checkpoint.update( + movq_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + diffusers_checkpoint.update( + { + # conv_norm_out + "encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"], + "encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"], + # conv_out + "encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"], + "encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"], + } + ) + + return diffusers_checkpoint + + +def movq_decoder_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # conv in + diffusers_checkpoint.update( + { + "decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"], + "decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"], + } + ) + + # up_blocks + + for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks): + # up_blocks are stored in reverse order in the VQ-diffusion checkpoint + orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx + + diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}" + up_block_prefix = f"decoder.up.{orig_up_block_idx}" + + # resnets + for resnet_idx, resnet in enumerate(up_block.resnets): + diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" + resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}" + + diffusers_checkpoint.update( + movq_resnet_to_diffusers_checkpoint_spatial_norm( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + # upsample + + # there is no up sample on the last up block + if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1: + # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples + # in the diffusers model. + diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv" + downsample_prefix = f"{up_block_prefix}.upsample.conv" + diffusers_checkpoint.update( + { + f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], + f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], + } + ) + + # attentions + + if hasattr(up_block, "attentions"): + for attention_idx, _ in enumerate(up_block.attentions): + diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}" + attention_prefix = f"{up_block_prefix}.attn.{attention_idx}" + diffusers_checkpoint.update( + movq_attention_to_diffusers_checkpoint_spatial_norm( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + attention_prefix=attention_prefix, + ) + ) + + # mid block + + # mid block attentions + + # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder + diffusers_attention_prefix = "decoder.mid_block.attentions.0" + attention_prefix = "decoder.mid.attn_1" + diffusers_checkpoint.update( + movq_attention_to_diffusers_checkpoint_spatial_norm( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # mid block resnets + + for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): + diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}" + + # the hardcoded prefixes to `block_` are 1 and 2 + orig_resnet_idx = diffusers_resnet_idx + 1 + # There are two hardcoded resnets in the middle of the VQ-diffusion decoder + resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}" + + diffusers_checkpoint.update( + movq_resnet_to_diffusers_checkpoint_spatial_norm( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + diffusers_checkpoint.update( + { + # conv_norm_out + "decoder.conv_norm_out.norm_layer.weight": checkpoint["decoder.norm_out.norm_layer.weight"], + "decoder.conv_norm_out.norm_layer.bias": checkpoint["decoder.norm_out.norm_layer.bias"], + "decoder.conv_norm_out.conv_y.weight": checkpoint["decoder.norm_out.conv_y.weight"], + "decoder.conv_norm_out.conv_y.bias": checkpoint["decoder.norm_out.conv_y.bias"], + "decoder.conv_norm_out.conv_b.weight": checkpoint["decoder.norm_out.conv_b.weight"], + "decoder.conv_norm_out.conv_b.bias": checkpoint["decoder.norm_out.conv_b.bias"], + # conv_out + "decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"], + "decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"], + } + ) + + return diffusers_checkpoint + + +def movq_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + rv = { + # norm1 + f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"], + f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"], + # conv1 + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], + # norm2 + f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"], + f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"], + # conv2 + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], + } + + if resnet.conv_shortcut is not None: + rv.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], + f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], + } + ) + + return rv + + +def movq_resnet_to_diffusers_checkpoint_spatial_norm(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + rv = { + # norm1 + f"{diffusers_resnet_prefix}.norm1.norm_layer.weight": checkpoint[f"{resnet_prefix}.norm1.norm_layer.weight"], + f"{diffusers_resnet_prefix}.norm1.norm_layer.bias": checkpoint[f"{resnet_prefix}.norm1.norm_layer.bias"], + f"{diffusers_resnet_prefix}.norm1.conv_y.weight": checkpoint[f"{resnet_prefix}.norm1.conv_y.weight"], + f"{diffusers_resnet_prefix}.norm1.conv_y.bias": checkpoint[f"{resnet_prefix}.norm1.conv_y.bias"], + f"{diffusers_resnet_prefix}.norm1.conv_b.weight": checkpoint[f"{resnet_prefix}.norm1.conv_b.weight"], + f"{diffusers_resnet_prefix}.norm1.conv_b.bias": checkpoint[f"{resnet_prefix}.norm1.conv_b.bias"], + # conv1 + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], + # norm2 + f"{diffusers_resnet_prefix}.norm2.norm_layer.weight": checkpoint[f"{resnet_prefix}.norm2.norm_layer.weight"], + f"{diffusers_resnet_prefix}.norm2.norm_layer.bias": checkpoint[f"{resnet_prefix}.norm2.norm_layer.bias"], + f"{diffusers_resnet_prefix}.norm2.conv_y.weight": checkpoint[f"{resnet_prefix}.norm2.conv_y.weight"], + f"{diffusers_resnet_prefix}.norm2.conv_y.bias": checkpoint[f"{resnet_prefix}.norm2.conv_y.bias"], + f"{diffusers_resnet_prefix}.norm2.conv_b.weight": checkpoint[f"{resnet_prefix}.norm2.conv_b.weight"], + f"{diffusers_resnet_prefix}.norm2.conv_b.bias": checkpoint[f"{resnet_prefix}.norm2.conv_b.bias"], + # conv2 + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], + } + + if resnet.conv_shortcut is not None: + rv.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], + f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], + } + ) + + return rv + + +def movq_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): + return { + # norm + f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], + f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], + # query + f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.q.bias"], + # key + f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.k.bias"], + # value + f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.v.bias"], + # proj_attn + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], + } + + +def movq_attention_to_diffusers_checkpoint_spatial_norm(checkpoint, *, diffusers_attention_prefix, attention_prefix): + return { + # norm + f"{diffusers_attention_prefix}.spatial_norm.norm_layer.weight": checkpoint[ + f"{attention_prefix}.norm.norm_layer.weight" + ], + f"{diffusers_attention_prefix}.spatial_norm.norm_layer.bias": checkpoint[ + f"{attention_prefix}.norm.norm_layer.bias" + ], + f"{diffusers_attention_prefix}.spatial_norm.conv_y.weight": checkpoint[ + f"{attention_prefix}.norm.conv_y.weight" + ], + f"{diffusers_attention_prefix}.spatial_norm.conv_y.bias": checkpoint[f"{attention_prefix}.norm.conv_y.bias"], + f"{diffusers_attention_prefix}.spatial_norm.conv_b.weight": checkpoint[ + f"{attention_prefix}.norm.conv_b.weight" + ], + f"{diffusers_attention_prefix}.spatial_norm.conv_b.bias": checkpoint[f"{attention_prefix}.norm.conv_b.bias"], + # query + f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.q.bias"], + # key + f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.k.bias"], + # value + f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.v.bias"], + # proj_attn + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], + } + + +def movq_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + diffusers_checkpoint.update(movq_encoder_to_diffusers_checkpoint(model, checkpoint)) + + # quant_conv + + diffusers_checkpoint.update( + { + "quant_conv.weight": checkpoint["quant_conv.weight"], + "quant_conv.bias": checkpoint["quant_conv.bias"], + } + ) + + # quantize + diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding.weight"]}) + + # post_quant_conv + diffusers_checkpoint.update( + { + "post_quant_conv.weight": checkpoint["post_quant_conv.weight"], + "post_quant_conv.bias": checkpoint["post_quant_conv.bias"], + } + ) + + # decoder + diffusers_checkpoint.update(movq_decoder_to_diffusers_checkpoint(model, checkpoint)) + + return diffusers_checkpoint + + +def movq(*, args, checkpoint_map_location): + print("loading movq") + + movq_checkpoint = torch.load(args.movq_checkpoint_path, map_location=checkpoint_map_location) + + movq_model = movq_model_from_original_config() + + movq_diffusers_checkpoint = movq_original_checkpoint_to_diffusers_checkpoint(movq_model, movq_checkpoint) + + del movq_checkpoint + + load_checkpoint_to_model(movq_diffusers_checkpoint, movq_model, strict=True) + + print("done loading movq") + + return movq_model + + +def load_checkpoint_to_model(checkpoint, model, strict=False): + with tempfile.NamedTemporaryFile(delete=False) as file: + torch.save(checkpoint, file.name) + del checkpoint + if strict: + model.load_state_dict(torch.load(file.name), strict=True) + else: + load_checkpoint_and_dispatch(model, file.name, device_map="auto") + os.remove(file.name) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--prior_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the prior checkpoint to convert.", + ) + parser.add_argument( + "--clip_stat_path", + default=None, + type=str, + required=False, + help="Path to the clip stats checkpoint to convert.", + ) + parser.add_argument( + "--text2img_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the text2img checkpoint to convert.", + ) + parser.add_argument( + "--movq_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the text2img checkpoint to convert.", + ) + parser.add_argument( + "--inpaint_text2img_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the inpaint text2img checkpoint to convert.", + ) + parser.add_argument( + "--checkpoint_load_device", + default="cpu", + type=str, + required=False, + help="The device passed to `map_location` when loading checkpoints.", + ) + + parser.add_argument( + "--debug", + default=None, + type=str, + required=False, + help="Only run a specific stage of the convert script. Used for debugging", + ) + + args = parser.parse_args() + + print(f"loading checkpoints to {args.checkpoint_load_device}") + + checkpoint_map_location = torch.device(args.checkpoint_load_device) + + if args.debug is not None: + print(f"debug: only executing {args.debug}") + + if args.debug is None: + print("to-do") + elif args.debug == "prior": + prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) + prior_model.save_pretrained(args.dump_path) + elif args.debug == "text2img": + unet_model = text2img(args=args, checkpoint_map_location=checkpoint_map_location) + unet_model.save_pretrained(f"{args.dump_path}/unet") + elif args.debug == "inpaint_text2img": + inpaint_unet_model = inpaint_text2img(args=args, checkpoint_map_location=checkpoint_map_location) + inpaint_unet_model.save_pretrained(f"{args.dump_path}/inpaint_unet") + elif args.debug == "decoder": + decoder = movq(args=args, checkpoint_map_location=checkpoint_map_location) + decoder.save_pretrained(f"{args.dump_path}/decoder") + else: + raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_ldm_original_checkpoint_to_diffusers.py b/scripts/convert_ldm_original_checkpoint_to_diffusers.py new file mode 100644 index 000000000000..cdaf317af752 --- /dev/null +++ b/scripts/convert_ldm_original_checkpoint_to_diffusers.py @@ -0,0 +1,359 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the LDM checkpoints.""" + +import argparse +import json + +import torch + +from diffusers import DDPMScheduler, LDMPipeline, UNet2DModel, VQModel + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("proj_out.weight", "proj_attn.weight") + new_item = new_item.replace("proj_out.bias", "proj_attn.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming + to them. It splits attention layers, and takes into account additional replacements + that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def convert_ldm_checkpoint(checkpoint, config): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["time_embed.2.bias"] + + new_checkpoint["conv_in.weight"] = checkpoint["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = checkpoint["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = checkpoint["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = checkpoint["out.0.bias"] + new_checkpoint["conv_out.weight"] = checkpoint["out.2.weight"] + new_checkpoint["conv_out.bias"] = checkpoint["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in checkpoint if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["num_res_blocks"] + 1) + layer_in_block_id = (i - 1) % (config["num_res_blocks"] + 1) + + resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in checkpoint: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = checkpoint[ + f"input_blocks.{i}.0.op.weight" + ] + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = checkpoint[ + f"input_blocks.{i}.0.op.bias" + ] + continue + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + resnet_op = {"old": "resnets.2.op", "new": "downsamplers.0.op"} + assign_to_checkpoint( + paths, new_checkpoint, checkpoint, additional_replacements=[meta_path, resnet_op], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"input_blocks.{i}.1", + "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}", + } + to_split = { + f"input_blocks.{i}.1.qkv.bias": { + "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", + "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", + "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", + }, + f"input_blocks.{i}.1.qkv.weight": { + "key": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", + "query": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", + "value": f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", + }, + } + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[meta_path], + attention_paths_to_split=to_split, + config=config, + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, checkpoint, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, checkpoint, config=config) + + attentions_paths = renew_attention_paths(attentions) + to_split = { + "middle_block.1.qkv.bias": { + "key": "mid_block.attentions.0.key.bias", + "query": "mid_block.attentions.0.query.bias", + "value": "mid_block.attentions.0.value.bias", + }, + "middle_block.1.qkv.weight": { + "key": "mid_block.attentions.0.key.weight", + "query": "mid_block.attentions.0.query.weight", + "value": "mid_block.attentions.0.value.weight", + }, + } + assign_to_checkpoint( + attentions_paths, new_checkpoint, checkpoint, attention_paths_to_split=to_split, config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["num_res_blocks"] + 1) + layer_in_block_id = i % (config["num_res_blocks"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint(paths, new_checkpoint, checkpoint, additional_replacements=[meta_path], config=config) + + if ["conv.weight", "conv.bias"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = checkpoint[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = checkpoint[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + to_split = { + f"output_blocks.{i}.1.qkv.bias": { + "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias", + "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias", + "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias", + }, + f"output_blocks.{i}.1.qkv.weight": { + "key": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight", + "query": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight", + "value": f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight", + }, + } + assign_to_checkpoint( + paths, + new_checkpoint, + checkpoint, + additional_replacements=[meta_path], + attention_paths_to_split=to_split if any("qkv" in key for key in attentions) else None, + config=config, + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = checkpoint[old_path] + + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + + parser.add_argument( + "--config_file", + default=None, + type=str, + required=True, + help="The config json file corresponding to the architecture.", + ) + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + args = parser.parse_args() + + checkpoint = torch.load(args.checkpoint_path) + + with open(args.config_file) as f: + config = json.loads(f.read()) + + converted_checkpoint = convert_ldm_checkpoint(checkpoint, config) + + if "ldm" in config: + del config["ldm"] + + model = UNet2DModel(**config) + model.load_state_dict(converted_checkpoint) + + try: + scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) + vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1])) + + pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae) + pipe.save_pretrained(args.dump_path) + except: # noqa: E722 + model.save_pretrained(args.dump_path) diff --git a/scripts/convert_lora_safetensor_to_diffusers.py b/scripts/convert_lora_safetensor_to_diffusers.py new file mode 100644 index 000000000000..4237452e2ed1 --- /dev/null +++ b/scripts/convert_lora_safetensor_to_diffusers.py @@ -0,0 +1,128 @@ +# coding=utf-8 +# Copyright 2024, Haofan Wang, Qixun Wang, All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Conversion script for the LoRA's safetensors checkpoints.""" + +import argparse + +import torch +from safetensors.torch import load_file + +from diffusers import StableDiffusionPipeline + + +def convert(base_model_path, checkpoint_path, LORA_PREFIX_UNET, LORA_PREFIX_TEXT_ENCODER, alpha): + # load base model + pipeline = StableDiffusionPipeline.from_pretrained(base_model_path, torch_dtype=torch.float32) + + # load LoRA weight from .safetensors + state_dict = load_file(checkpoint_path) + + visited = [] + + # directly update weight in diffusers model + for key in state_dict: + # it is suggested to print out the key, it usually will be something like below + # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" + + # as we have set the alpha beforehand, so just skip + if ".alpha" in key or key in visited: + continue + + if "text" in key: + layer_infos = key.split(".")[0].split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_") + curr_layer = pipeline.text_encoder + else: + layer_infos = key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_") + curr_layer = pipeline.unet + + # find the target layer + temp_name = layer_infos.pop(0) + while len(layer_infos) > -1: + try: + curr_layer = curr_layer.__getattr__(temp_name) + if len(layer_infos) > 0: + temp_name = layer_infos.pop(0) + elif len(layer_infos) == 0: + break + except Exception: + if len(temp_name) > 0: + temp_name += "_" + layer_infos.pop(0) + else: + temp_name = layer_infos.pop(0) + + pair_keys = [] + if "lora_down" in key: + pair_keys.append(key.replace("lora_down", "lora_up")) + pair_keys.append(key) + else: + pair_keys.append(key) + pair_keys.append(key.replace("lora_up", "lora_down")) + + # update weight + if len(state_dict[pair_keys[0]].shape) == 4: + weight_up = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32) + weight_down = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32) + curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + weight_up = state_dict[pair_keys[0]].to(torch.float32) + weight_down = state_dict[pair_keys[1]].to(torch.float32) + curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down) + + # update visited list + for item in pair_keys: + visited.append(item) + + return pipeline + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format." + ) + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument( + "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors" + ) + parser.add_argument( + "--lora_prefix_text_encoder", + default="lora_te", + type=str, + help="The prefix of text encoder weight in safetensors", + ) + parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW") + parser.add_argument( + "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not." + ) + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + + args = parser.parse_args() + + base_model_path = args.base_model_path + checkpoint_path = args.checkpoint_path + dump_path = args.dump_path + lora_prefix_unet = args.lora_prefix_unet + lora_prefix_text_encoder = args.lora_prefix_text_encoder + alpha = args.alpha + + pipe = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) + + pipe = pipe.to(args.device) + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_ltx2_to_diffusers.py b/scripts/convert_ltx2_to_diffusers.py new file mode 100644 index 000000000000..f1556557889f --- /dev/null +++ b/scripts/convert_ltx2_to_diffusers.py @@ -0,0 +1,1194 @@ +import argparse +import os +from contextlib import nullcontext +from typing import Any + +import safetensors.torch +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download +from transformers import AutoTokenizer, Gemma3ForConditionalGeneration, Gemma3Processor + +from diffusers import ( + AutoencoderKLLTX2Audio, + AutoencoderKLLTX2Video, + FlowMatchEulerDiscreteScheduler, + LTX2LatentUpsamplePipeline, + LTX2Pipeline, + LTX2VideoTransformer3DModel, +) +from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel, LTX2TextConnectors, LTX2Vocoder, LTX2VocoderWithBWE +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + + +LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT = { + # Input Patchify Projections + "patchify_proj": "proj_in", + "audio_patchify_proj": "audio_proj_in", + # Modulation Parameters + # Handle adaln_single --> time_embed, audioln_single --> audio_time_embed separately as the original keys are + # substrings of the other modulation parameters below + "av_ca_video_scale_shift_adaln_single": "av_cross_attn_video_scale_shift", + "av_ca_a2v_gate_adaln_single": "av_cross_attn_video_a2v_gate", + "av_ca_audio_scale_shift_adaln_single": "av_cross_attn_audio_scale_shift", + "av_ca_v2a_gate_adaln_single": "av_cross_attn_audio_v2a_gate", + # Transformer Blocks + # Per-Block Cross Attention Modulatin Parameters + "scale_shift_table_a2v_ca_video": "video_a2v_cross_attn_scale_shift_table", + "scale_shift_table_a2v_ca_audio": "audio_a2v_cross_attn_scale_shift_table", + # Attention QK Norms + "q_norm": "norm_q", + "k_norm": "norm_k", +} + +LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT = { + **LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT, + "audio_prompt_adaln_single": "audio_prompt_adaln", + "prompt_adaln_single": "prompt_adaln", +} + +LTX_2_0_VIDEO_VAE_RENAME_DICT = { + # Encoder + "down_blocks.0": "down_blocks.0", + "down_blocks.1": "down_blocks.0.downsamplers.0", + "down_blocks.2": "down_blocks.1", + "down_blocks.3": "down_blocks.1.downsamplers.0", + "down_blocks.4": "down_blocks.2", + "down_blocks.5": "down_blocks.2.downsamplers.0", + "down_blocks.6": "down_blocks.3", + "down_blocks.7": "down_blocks.3.downsamplers.0", + "down_blocks.8": "mid_block", + # Decoder + "up_blocks.0": "mid_block", + "up_blocks.1": "up_blocks.0.upsamplers.0", + "up_blocks.2": "up_blocks.0", + "up_blocks.3": "up_blocks.1.upsamplers.0", + "up_blocks.4": "up_blocks.1", + "up_blocks.5": "up_blocks.2.upsamplers.0", + "up_blocks.6": "up_blocks.2", + "last_time_embedder": "time_embedder", + "last_scale_shift_table": "scale_shift_table", + # Common + # For all 3D ResNets + "res_blocks": "resnets", + "per_channel_statistics.mean-of-means": "latents_mean", + "per_channel_statistics.std-of-means": "latents_std", +} + +LTX_2_3_VIDEO_VAE_RENAME_DICT = { + **LTX_2_0_VIDEO_VAE_RENAME_DICT, + # Decoder extra blocks + "up_blocks.7": "up_blocks.3.upsamplers.0", + "up_blocks.8": "up_blocks.3", +} + +LTX_2_0_AUDIO_VAE_RENAME_DICT = { + "per_channel_statistics.mean-of-means": "latents_mean", + "per_channel_statistics.std-of-means": "latents_std", +} + +LTX_2_0_VOCODER_RENAME_DICT = { + "ups": "upsamplers", + "resblocks": "resnets", + "conv_pre": "conv_in", + "conv_post": "conv_out", +} + +LTX_2_3_VOCODER_RENAME_DICT = { + # Handle upsamplers ("ups" --> "upsamplers") due to name clash + "resblocks": "resnets", + "conv_pre": "conv_in", + "conv_post": "conv_out", + "act_post": "act_out", + "downsample.lowpass": "downsample", +} + +LTX_2_0_CONNECTORS_KEYS_RENAME_DICT = { + "connectors.": "", + "video_embeddings_connector": "video_connector", + "audio_embeddings_connector": "audio_connector", + "transformer_1d_blocks": "transformer_blocks", + "text_embedding_projection.aggregate_embed": "text_proj_in", + # Attention QK Norms + "q_norm": "norm_q", + "k_norm": "norm_k", +} + +LTX_2_3_CONNECTORS_KEYS_RENAME_DICT = { + "connectors.": "", + "video_embeddings_connector": "video_connector", + "audio_embeddings_connector": "audio_connector", + "transformer_1d_blocks": "transformer_blocks", + # LTX-2.3 uses per-modality embedding projections + "text_embedding_projection.audio_aggregate_embed": "audio_text_proj_in", + "text_embedding_projection.video_aggregate_embed": "video_text_proj_in", + # Attention QK Norms + "q_norm": "norm_q", + "k_norm": "norm_k", +} + + +def update_state_dict_inplace(state_dict: dict[str, Any], old_key: str, new_key: str) -> None: + state_dict[new_key] = state_dict.pop(old_key) + + +def remove_keys_inplace(key: str, state_dict: dict[str, Any]) -> None: + state_dict.pop(key) + + +def convert_ltx2_transformer_adaln_single(key: str, state_dict: dict[str, Any]) -> None: + # Skip if not a weight, bias + if ".weight" not in key and ".bias" not in key: + return + + if key.startswith("adaln_single."): + new_key = key.replace("adaln_single.", "time_embed.") + param = state_dict.pop(key) + state_dict[new_key] = param + + if key.startswith("audio_adaln_single."): + new_key = key.replace("audio_adaln_single.", "audio_time_embed.") + param = state_dict.pop(key) + state_dict[new_key] = param + + return + + +def convert_ltx2_audio_vae_per_channel_statistics(key: str, state_dict: dict[str, Any]) -> None: + if key.startswith("per_channel_statistics"): + new_key = ".".join(["decoder", key]) + param = state_dict.pop(key) + state_dict[new_key] = param + + return + + +def convert_ltx2_3_vocoder_upsamplers(key: str, state_dict: dict[str, Any]) -> None: + # Skip if not a weight, bias + if ".weight" not in key and ".bias" not in key: + return + + if ".ups." in key: + new_key = key.replace(".ups.", ".upsamplers.") + param = state_dict.pop(key) + state_dict[new_key] = param + return + + +LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP = { + "video_embeddings_connector": remove_keys_inplace, + "audio_embeddings_connector": remove_keys_inplace, + "adaln_single": convert_ltx2_transformer_adaln_single, +} + +LTX_2_0_VAE_SPECIAL_KEYS_REMAP = { + "per_channel_statistics.channel": remove_keys_inplace, + "per_channel_statistics.mean-of-stds": remove_keys_inplace, +} + +LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP = {} + +LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP = {} + +LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP = { + ".ups.": convert_ltx2_3_vocoder_upsamplers, +} + +LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP = {} + + +def split_transformer_and_connector_state_dict(state_dict: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]: + connector_prefixes = ( + "video_embeddings_connector", + "audio_embeddings_connector", + "transformer_1d_blocks", + "text_embedding_projection", + "connectors.", + "video_connector", + "audio_connector", + "text_proj_in", + ) + + transformer_state_dict, connector_state_dict = {}, {} + for key, value in state_dict.items(): + if key.startswith(connector_prefixes): + connector_state_dict[key] = value + else: + transformer_state_dict[key] = value + + return transformer_state_dict, connector_state_dict + + +def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + if version == "test": + # Produces a transformer of the same size as used in test_models_transformer_ltx2.py + config = { + "model_id": "diffusers-internal-dev/dummy-ltx2", + "diffusers_config": { + "in_channels": 4, + "out_channels": 4, + "patch_size": 1, + "patch_size_t": 1, + "num_attention_heads": 2, + "attention_head_dim": 8, + "cross_attention_dim": 16, + "vae_scale_factors": (8, 32, 32), + "pos_embed_max_pos": 20, + "base_height": 2048, + "base_width": 2048, + "audio_in_channels": 4, + "audio_out_channels": 4, + "audio_patch_size": 1, + "audio_patch_size_t": 1, + "audio_num_attention_heads": 2, + "audio_attention_head_dim": 4, + "audio_cross_attention_dim": 8, + "audio_scale_factor": 4, + "audio_pos_embed_max_pos": 20, + "audio_sampling_rate": 16000, + "audio_hop_length": 160, + "num_layers": 2, + "activation_fn": "gelu-approximate", + "qk_norm": "rms_norm_across_heads", + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "caption_channels": 16, + "attention_bias": True, + "attention_out_bias": True, + "rope_theta": 10000.0, + "rope_double_precision": False, + "causal_offset": 1, + "timestep_scale_multiplier": 1000, + "cross_attn_timestep_scale_multiplier": 1, + }, + } + rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP + elif version == "2.0": + config = { + "model_id": "Lightricks/LTX-2", + "diffusers_config": { + "in_channels": 128, + "out_channels": 128, + "patch_size": 1, + "patch_size_t": 1, + "num_attention_heads": 32, + "attention_head_dim": 128, + "cross_attention_dim": 4096, + "vae_scale_factors": (8, 32, 32), + "pos_embed_max_pos": 20, + "base_height": 2048, + "base_width": 2048, + "gated_attn": False, + "cross_attn_mod": False, + "audio_in_channels": 128, + "audio_out_channels": 128, + "audio_patch_size": 1, + "audio_patch_size_t": 1, + "audio_num_attention_heads": 32, + "audio_attention_head_dim": 64, + "audio_cross_attention_dim": 2048, + "audio_scale_factor": 4, + "audio_pos_embed_max_pos": 20, + "audio_sampling_rate": 16000, + "audio_hop_length": 160, + "audio_gated_attn": False, + "audio_cross_attn_mod": False, + "num_layers": 48, + "activation_fn": "gelu-approximate", + "qk_norm": "rms_norm_across_heads", + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "caption_channels": 3840, + "attention_bias": True, + "attention_out_bias": True, + "rope_theta": 10000.0, + "rope_double_precision": True, + "causal_offset": 1, + "timestep_scale_multiplier": 1000, + "cross_attn_timestep_scale_multiplier": 1000, + "rope_type": "split", + "use_prompt_embeddings": True, + "perturbed_attn": False, + }, + } + rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP + elif version == "2.3": + config = { + "model_id": "Lightricks/LTX-2.3", + "diffusers_config": { + "in_channels": 128, + "out_channels": 128, + "patch_size": 1, + "patch_size_t": 1, + "num_attention_heads": 32, + "attention_head_dim": 128, + "cross_attention_dim": 4096, + "vae_scale_factors": (8, 32, 32), + "pos_embed_max_pos": 20, + "base_height": 2048, + "base_width": 2048, + "gated_attn": True, + "cross_attn_mod": True, + "audio_in_channels": 128, + "audio_out_channels": 128, + "audio_patch_size": 1, + "audio_patch_size_t": 1, + "audio_num_attention_heads": 32, + "audio_attention_head_dim": 64, + "audio_cross_attention_dim": 2048, + "audio_scale_factor": 4, + "audio_pos_embed_max_pos": 20, + "audio_sampling_rate": 16000, + "audio_hop_length": 160, + "audio_gated_attn": True, + "audio_cross_attn_mod": True, + "num_layers": 48, + "activation_fn": "gelu-approximate", + "qk_norm": "rms_norm_across_heads", + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "caption_channels": 3840, + "attention_bias": True, + "attention_out_bias": True, + "rope_theta": 10000.0, + "rope_double_precision": True, + "causal_offset": 1, + "timestep_scale_multiplier": 1000, + "cross_attn_timestep_scale_multiplier": 1000, + "rope_type": "split", + "use_prompt_embeddings": False, + "perturbed_attn": True, + }, + } + rename_dict = LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT + special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP + return config, rename_dict, special_keys_remap + + +def get_ltx2_connectors_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + if version == "test": + config = { + "model_id": "diffusers-internal-dev/dummy-ltx2", + "diffusers_config": { + "caption_channels": 16, + "text_proj_in_factor": 3, + "video_connector_num_attention_heads": 4, + "video_connector_attention_head_dim": 8, + "video_connector_num_layers": 1, + "video_connector_num_learnable_registers": None, + "audio_connector_num_attention_heads": 4, + "audio_connector_attention_head_dim": 8, + "audio_connector_num_layers": 1, + "audio_connector_num_learnable_registers": None, + "connector_rope_base_seq_len": 32, + "rope_theta": 10000.0, + "rope_double_precision": False, + "causal_temporal_positioning": False, + }, + } + elif version == "2.0": + config = { + "model_id": "Lightricks/LTX-2", + "diffusers_config": { + "caption_channels": 3840, + "text_proj_in_factor": 49, + "video_connector_num_attention_heads": 30, + "video_connector_attention_head_dim": 128, + "video_connector_num_layers": 2, + "video_connector_num_learnable_registers": 128, + "video_gated_attn": False, + "audio_connector_num_attention_heads": 30, + "audio_connector_attention_head_dim": 128, + "audio_connector_num_layers": 2, + "audio_connector_num_learnable_registers": 128, + "audio_gated_attn": False, + "connector_rope_base_seq_len": 4096, + "rope_theta": 10000.0, + "rope_double_precision": True, + "causal_temporal_positioning": False, + "rope_type": "split", + "per_modality_projections": False, + "proj_bias": False, + }, + } + rename_dict = LTX_2_0_CONNECTORS_KEYS_RENAME_DICT + special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP + elif version == "2.3": + config = { + "model_id": "Lightricks/LTX-2.3", + "diffusers_config": { + "caption_channels": 3840, + "text_proj_in_factor": 49, + "video_connector_num_attention_heads": 32, + "video_connector_attention_head_dim": 128, + "video_connector_num_layers": 8, + "video_connector_num_learnable_registers": 128, + "video_gated_attn": True, + "audio_connector_num_attention_heads": 32, + "audio_connector_attention_head_dim": 64, + "audio_connector_num_layers": 8, + "audio_connector_num_learnable_registers": 128, + "audio_gated_attn": True, + "connector_rope_base_seq_len": 4096, + "rope_theta": 10000.0, + "rope_double_precision": True, + "causal_temporal_positioning": False, + "rope_type": "split", + "per_modality_projections": True, + "video_hidden_dim": 4096, + "audio_hidden_dim": 2048, + "proj_bias": True, + }, + } + rename_dict = LTX_2_3_CONNECTORS_KEYS_RENAME_DICT + special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP + + return config, rename_dict, special_keys_remap + + +def convert_ltx2_transformer(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: + config, rename_dict, special_keys_remap = get_ltx2_transformer_config(version) + diffusers_config = config["diffusers_config"] + + transformer_state_dict, _ = split_transformer_and_connector_state_dict(original_state_dict) + + with init_empty_weights(): + transformer = LTX2VideoTransformer3DModel.from_config(diffusers_config) + + # Handle official code --> diffusers key remapping via the remap dict + for key in list(transformer_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(transformer_state_dict, key, new_key) + + # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in + # special_keys_remap + for key in list(transformer_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, transformer_state_dict) + + transformer.load_state_dict(transformer_state_dict, strict=True, assign=True) + return transformer + + +def convert_ltx2_connectors(original_state_dict: dict[str, Any], version: str) -> LTX2TextConnectors: + config, rename_dict, special_keys_remap = get_ltx2_connectors_config(version) + diffusers_config = config["diffusers_config"] + + _, connector_state_dict = split_transformer_and_connector_state_dict(original_state_dict) + if len(connector_state_dict) == 0: + raise ValueError("No connector weights found in the provided state dict.") + + with init_empty_weights(): + connectors = LTX2TextConnectors.from_config(diffusers_config) + + for key in list(connector_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(connector_state_dict, key, new_key) + + for key in list(connector_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, connector_state_dict) + + connectors.load_state_dict(connector_state_dict, strict=True, assign=True) + return connectors + + +def get_ltx2_video_vae_config( + version: str, timestep_conditioning: bool = False +) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + if version == "test": + config = { + "model_id": "diffusers-internal-dev/dummy-ltx2", + "diffusers_config": { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (256, 512, 1024, 2048), + "down_block_types": ( + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 1024), + "layers_per_block": (4, 6, 6, 2, 2), + "decoder_layers_per_block": (5, 5, 5, 5), + "spatio_temporal_scaling": (True, True, True, True), + "decoder_spatio_temporal_scaling": (True, True, True), + "decoder_inject_noise": (False, False, False, False), + "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), + "upsample_residual": (True, True, True), + "upsample_factor": (2, 2, 2), + "timestep_conditioning": timestep_conditioning, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "encoder_causal": True, + "decoder_causal": False, + "encoder_spatial_padding_mode": "zeros", + "decoder_spatial_padding_mode": "reflect", + "spatial_compression_ratio": 32, + "temporal_compression_ratio": 8, + }, + } + rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT + special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP + elif version == "2.0": + config = { + "model_id": "Lightricks/LTX-2", + "diffusers_config": { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (256, 512, 1024, 2048), + "down_block_types": ( + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 1024), + "layers_per_block": (4, 6, 6, 2, 2), + "decoder_layers_per_block": (5, 5, 5, 5), + "spatio_temporal_scaling": (True, True, True, True), + "decoder_spatio_temporal_scaling": (True, True, True), + "decoder_inject_noise": (False, False, False, False), + "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), + "upsample_type": ("spatiotemporal", "spatiotemporal", "spatiotemporal"), + "upsample_residual": (True, True, True), + "upsample_factor": (2, 2, 2), + "timestep_conditioning": timestep_conditioning, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "encoder_causal": True, + "decoder_causal": False, + "encoder_spatial_padding_mode": "zeros", + "decoder_spatial_padding_mode": "reflect", + "spatial_compression_ratio": 32, + "temporal_compression_ratio": 8, + }, + } + rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT + special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP + elif version == "2.3": + config = { + "model_id": "Lightricks/LTX-2.3", + "diffusers_config": { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (256, 512, 1024, 1024), + "down_block_types": ( + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + "LTX2VideoDownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 512, 1024), + "layers_per_block": (4, 6, 4, 2, 2), + "decoder_layers_per_block": (4, 6, 4, 2, 2), + "spatio_temporal_scaling": (True, True, True, True), + "decoder_spatio_temporal_scaling": (True, True, True, True), + "decoder_inject_noise": (False, False, False, False, False), + "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), + "upsample_type": ("spatiotemporal", "spatiotemporal", "temporal", "spatial"), + "upsample_residual": (False, False, False, False), + "upsample_factor": (2, 2, 1, 2), + "timestep_conditioning": timestep_conditioning, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "encoder_causal": True, + "decoder_causal": False, + "encoder_spatial_padding_mode": "zeros", + "decoder_spatial_padding_mode": "zeros", + "spatial_compression_ratio": 32, + "temporal_compression_ratio": 8, + }, + } + rename_dict = LTX_2_3_VIDEO_VAE_RENAME_DICT + special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP + return config, rename_dict, special_keys_remap + + +def convert_ltx2_video_vae( + original_state_dict: dict[str, Any], version: str, timestep_conditioning: bool +) -> dict[str, Any]: + config, rename_dict, special_keys_remap = get_ltx2_video_vae_config(version, timestep_conditioning) + diffusers_config = config["diffusers_config"] + + with init_empty_weights(): + vae = AutoencoderKLLTX2Video.from_config(diffusers_config) + + # Handle official code --> diffusers key remapping via the remap dict + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in + # special_keys_remap + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True, assign=True) + return vae + + +def get_ltx2_audio_vae_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + if version == "2.0": + config = { + "model_id": "Lightricks/LTX-2", + "diffusers_config": { + "base_channels": 128, + "output_channels": 2, + "ch_mult": (1, 2, 4), + "num_res_blocks": 2, + "attn_resolutions": None, + "in_channels": 2, + "resolution": 256, + "latent_channels": 8, + "norm_type": "pixel", + "causality_axis": "height", + "dropout": 0.0, + "mid_block_add_attention": False, + "sample_rate": 16000, + "mel_hop_length": 160, + "is_causal": True, + "mel_bins": 64, + "double_z": True, + }, + } + rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT + special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP + elif version == "2.3": + config = { + "model_id": "Lightricks/LTX-2.3", + "diffusers_config": { + "base_channels": 128, + "output_channels": 2, + "ch_mult": (1, 2, 4), + "num_res_blocks": 2, + "attn_resolutions": None, + "in_channels": 2, + "resolution": 256, + "latent_channels": 8, + "norm_type": "pixel", + "causality_axis": "height", + "dropout": 0.0, + "mid_block_add_attention": False, + "sample_rate": 16000, + "mel_hop_length": 160, + "is_causal": True, + "mel_bins": 64, + "double_z": True, + }, # Same config as LTX-2.0 + } + rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT + special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP + return config, rename_dict, special_keys_remap + + +def convert_ltx2_audio_vae(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: + config, rename_dict, special_keys_remap = get_ltx2_audio_vae_config(version) + diffusers_config = config["diffusers_config"] + + with init_empty_weights(): + vae = AutoencoderKLLTX2Audio.from_config(diffusers_config) + + # Handle official code --> diffusers key remapping via the remap dict + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in + # special_keys_remap + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True, assign=True) + return vae + + +def get_ltx2_vocoder_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]: + if version == "2.0": + config = { + "model_id": "Lightricks/LTX-2", + "diffusers_config": { + "in_channels": 128, + "hidden_channels": 1024, + "out_channels": 2, + "upsample_kernel_sizes": [16, 15, 8, 4, 4], + "upsample_factors": [6, 5, 2, 2, 2], + "resnet_kernel_sizes": [3, 7, 11], + "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "act_fn": "leaky_relu", + "leaky_relu_negative_slope": 0.1, + "antialias": False, + "final_act_fn": "tanh", + "final_bias": True, + "output_sampling_rate": 24000, + }, + } + rename_dict = LTX_2_0_VOCODER_RENAME_DICT + special_keys_remap = LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP + elif version == "2.3": + config = { + "model_id": "Lightricks/LTX-2.3", + "diffusers_config": { + "in_channels": 128, + "hidden_channels": 1536, + "out_channels": 2, + "upsample_kernel_sizes": [11, 4, 4, 4, 4, 4], + "upsample_factors": [5, 2, 2, 2, 2, 2], + "resnet_kernel_sizes": [3, 7, 11], + "resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "act_fn": "snakebeta", + "leaky_relu_negative_slope": 0.1, + "antialias": True, + "antialias_ratio": 2, + "antialias_kernel_size": 12, + "final_act_fn": None, + "final_bias": False, + "bwe_in_channels": 128, + "bwe_hidden_channels": 512, + "bwe_out_channels": 2, + "bwe_upsample_kernel_sizes": [12, 11, 4, 4, 4], + "bwe_upsample_factors": [6, 5, 2, 2, 2], + "bwe_resnet_kernel_sizes": [3, 7, 11], + "bwe_resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "bwe_act_fn": "snakebeta", + "bwe_leaky_relu_negative_slope": 0.1, + "bwe_antialias": True, + "bwe_antialias_ratio": 2, + "bwe_antialias_kernel_size": 12, + "bwe_final_act_fn": None, + "bwe_final_bias": False, + "filter_length": 512, + "hop_length": 80, + "window_length": 512, + "num_mel_channels": 64, + "input_sampling_rate": 16000, + "output_sampling_rate": 48000, + }, + } + rename_dict = LTX_2_3_VOCODER_RENAME_DICT + special_keys_remap = LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP + return config, rename_dict, special_keys_remap + + +def convert_ltx2_vocoder(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]: + config, rename_dict, special_keys_remap = get_ltx2_vocoder_config(version) + diffusers_config = config["diffusers_config"] + if version == "2.3": + vocoder_cls = LTX2VocoderWithBWE + else: + vocoder_cls = LTX2Vocoder + + with init_empty_weights(): + vocoder = vocoder_cls.from_config(diffusers_config) + + # Handle official code --> diffusers key remapping via the remap dict + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in rename_dict.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + # Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in + # special_keys_remap + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in special_keys_remap.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vocoder.load_state_dict(original_state_dict, strict=True, assign=True) + return vocoder + + +def get_ltx2_spatial_latent_upsampler_config(version: str): + if version == "2.0": + config = { + "in_channels": 128, + "mid_channels": 1024, + "num_blocks_per_stage": 4, + "dims": 3, + "spatial_upsample": True, + "temporal_upsample": False, + "rational_spatial_scale": 2.0, + "use_rational_resampler": True, + } + elif version == "2.3": + config = { + "in_channels": 128, + "mid_channels": 1024, + "num_blocks_per_stage": 4, + "dims": 3, + "spatial_upsample": True, + "temporal_upsample": False, + "rational_spatial_scale": 2.0, + "use_rational_resampler": False, + } + else: + raise ValueError(f"Unsupported version: {version}") + return config + + +def convert_ltx2_spatial_latent_upsampler( + original_state_dict: dict[str, Any], config: dict[str, Any], dtype: torch.dtype +): + with init_empty_weights(): + latent_upsampler = LTX2LatentUpsamplerModel(**config) + + latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True) + latent_upsampler.to(dtype) + return latent_upsampler + + +def load_original_checkpoint(args, filename: str | None) -> dict[str, Any]: + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=filename) + elif args.checkpoint_path is not None: + ckpt_path = args.checkpoint_path + else: + raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +def load_hub_or_local_checkpoint(repo_id: str | None = None, filename: str | None = None) -> dict[str, Any]: + if repo_id is None and filename is None: + raise ValueError("Please supply at least one of `repo_id` or `filename`") + + if repo_id is not None: + if filename is None: + raise ValueError("If repo_id is specified, filename must also be specified.") + ckpt_path = hf_hub_download(repo_id=repo_id, filename=filename) + else: + ckpt_path = filename + + _, ext = os.path.splitext(ckpt_path) + if ext in [".safetensors", ".sft"]: + state_dict = safetensors.torch.load_file(ckpt_path) + else: + state_dict = torch.load(ckpt_path, map_location="cpu") + + return state_dict + + +def get_model_state_dict_from_combined_ckpt(combined_ckpt: dict[str, Any], prefix: str) -> dict[str, Any]: + # Ensure that the key prefix ends with a dot (.) + if not prefix.endswith("."): + prefix = prefix + "." + + model_state_dict = {} + for param_name, param in combined_ckpt.items(): + if param_name.startswith(prefix): + model_state_dict[param_name.removeprefix(prefix)] = param + + if prefix == "model.diffusion_model.": + # Some checkpoints store the text connector projection outside the diffusion model prefix. + connector_prefixes = ["text_embedding_projection"] + for param_name, param in combined_ckpt.items(): + for prefix in connector_prefixes: + if param_name.startswith(prefix): + # Check to make sure we're not overwriting an existing key + if param_name not in model_state_dict: + model_state_dict[param_name] = combined_ckpt[param_name] + + return model_state_dict + + +def get_args(): + parser = argparse.ArgumentParser() + + def none_or_str(value: str): + if isinstance(value, str) and value.lower() == "none": + return None + return value + + parser.add_argument( + "--original_state_dict_repo_id", + default="Lightricks/LTX-2", + type=none_or_str, + help="HF Hub repo id with LTX 2.0 checkpoint", + ) + parser.add_argument( + "--checkpoint_path", + default=None, + type=str, + help="Local checkpoint path for LTX 2.0. Will be used if `original_state_dict_repo_id` is not specified.", + ) + parser.add_argument( + "--version", + type=str, + default="2.0", + choices=["test", "2.0", "2.3"], + help="Version of the LTX 2.0 model", + ) + + parser.add_argument( + "--combined_filename", + default="ltx-2-19b-dev.safetensors", + type=none_or_str, + help="Filename for combined checkpoint with all LTX 2.0 models (VAE, DiT, etc.)", + ) + parser.add_argument("--vae_prefix", default="vae.", type=str) + parser.add_argument("--audio_vae_prefix", default="audio_vae.", type=str) + parser.add_argument("--dit_prefix", default="model.diffusion_model.", type=str) + parser.add_argument("--vocoder_prefix", default="vocoder.", type=str) + + parser.add_argument("--vae_filename", default=None, type=str, help="VAE filename; overrides combined ckpt if set") + parser.add_argument( + "--audio_vae_filename", default=None, type=str, help="Audio VAE filename; overrides combined ckpt if set" + ) + parser.add_argument("--dit_filename", default=None, type=str, help="DiT filename; overrides combined ckpt if set") + parser.add_argument( + "--vocoder_filename", default=None, type=str, help="Vocoder filename; overrides combined ckpt if set" + ) + parser.add_argument( + "--text_encoder_model_id", + default="google/gemma-3-12b-it-qat-q4_0-unquantized", + type=none_or_str, + help="HF Hub id for the LTX 2.0 base text encoder model", + ) + parser.add_argument( + "--tokenizer_id", + default="google/gemma-3-12b-it-qat-q4_0-unquantized", + type=none_or_str, + help="HF Hub id for the LTX 2.0 text tokenizer", + ) + parser.add_argument( + "--latent_upsampler_filename", + default="ltx-2-spatial-upscaler-x2-1.0.safetensors", + type=none_or_str, + help="Latent upsampler filename", + ) + + parser.add_argument( + "--timestep_conditioning", action="store_true", help="Whether to add timestep condition to the video VAE model" + ) + parser.add_argument("--vae", action="store_true", help="Whether to convert the video VAE model") + parser.add_argument("--audio_vae", action="store_true", help="Whether to convert the audio VAE model") + parser.add_argument("--dit", action="store_true", help="Whether to convert the DiT model") + parser.add_argument("--connectors", action="store_true", help="Whether to convert the connector model") + parser.add_argument("--vocoder", action="store_true", help="Whether to convert the vocoder model") + parser.add_argument("--text_encoder", action="store_true", help="Whether to conver the text encoder") + parser.add_argument("--latent_upsampler", action="store_true", help="Whether to convert the latent upsampler") + parser.add_argument( + "--full_pipeline", + action="store_true", + help="Whether to save the pipeline. This will attempt to convert all models (e.g. vae, dit, etc.)", + ) + parser.add_argument( + "--upsample_pipeline", + action="store_true", + help="Whether to save a latent upsampling pipeline", + ) + parser.add_argument( + "--add_processor", + action="store_true", + help="Whether to add a Gemma3Processor to the pipeline for prompt enhancement.", + ) + + parser.add_argument("--vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--audio_vae_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--dit_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--vocoder_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + parser.add_argument("--text_encoder_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"]) + + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument( + "--upsample_output_path", + type=str, + default=None, + help="Path where converted upsampling pipeline should be saved", + ) + + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + +VARIANT_MAPPING = { + "fp32": None, + "fp16": "fp16", + "bf16": "bf16", +} + + +def main(args): + vae_dtype = DTYPE_MAPPING[args.vae_dtype] + audio_vae_dtype = DTYPE_MAPPING[args.audio_vae_dtype] + dit_dtype = DTYPE_MAPPING[args.dit_dtype] + vocoder_dtype = DTYPE_MAPPING[args.vocoder_dtype] + text_encoder_dtype = DTYPE_MAPPING[args.text_encoder_dtype] + + combined_ckpt = None + load_combined_models = any( + [ + args.vae, + args.audio_vae, + args.dit, + args.vocoder, + args.connectors, + args.full_pipeline, + args.upsample_pipeline, + ] + ) + if args.combined_filename is not None and load_combined_models: + combined_ckpt = load_original_checkpoint(args, filename=args.combined_filename) + + if args.vae or args.full_pipeline or args.upsample_pipeline: + if args.vae_filename is not None: + original_vae_ckpt = load_hub_or_local_checkpoint(filename=args.vae_filename) + elif combined_ckpt is not None: + original_vae_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.vae_prefix) + vae = convert_ltx2_video_vae( + original_vae_ckpt, version=args.version, timestep_conditioning=args.timestep_conditioning + ) + if not args.full_pipeline and not args.upsample_pipeline: + vae.to(vae_dtype).save_pretrained(os.path.join(args.output_path, "vae")) + + if args.audio_vae or args.full_pipeline: + if args.audio_vae_filename is not None: + original_audio_vae_ckpt = load_hub_or_local_checkpoint(filename=args.audio_vae_filename) + elif combined_ckpt is not None: + original_audio_vae_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.audio_vae_prefix) + audio_vae = convert_ltx2_audio_vae(original_audio_vae_ckpt, version=args.version) + if not args.full_pipeline: + audio_vae.to(audio_vae_dtype).save_pretrained(os.path.join(args.output_path, "audio_vae")) + + if args.dit or args.full_pipeline: + if args.dit_filename is not None: + original_dit_ckpt = load_hub_or_local_checkpoint(filename=args.dit_filename) + elif combined_ckpt is not None: + original_dit_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.dit_prefix) + transformer = convert_ltx2_transformer(original_dit_ckpt, version=args.version) + if not args.full_pipeline: + transformer.to(dit_dtype).save_pretrained(os.path.join(args.output_path, "transformer")) + + if args.connectors or args.full_pipeline: + if args.dit_filename is not None: + original_connectors_ckpt = load_hub_or_local_checkpoint(filename=args.dit_filename) + elif combined_ckpt is not None: + original_connectors_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.dit_prefix) + connectors = convert_ltx2_connectors(original_connectors_ckpt, version=args.version) + if not args.full_pipeline: + connectors.to(dit_dtype).save_pretrained(os.path.join(args.output_path, "connectors")) + + if args.vocoder or args.full_pipeline: + if args.vocoder_filename is not None: + original_vocoder_ckpt = load_hub_or_local_checkpoint(filename=args.vocoder_filename) + elif combined_ckpt is not None: + original_vocoder_ckpt = get_model_state_dict_from_combined_ckpt(combined_ckpt, args.vocoder_prefix) + vocoder = convert_ltx2_vocoder(original_vocoder_ckpt, version=args.version) + if not args.full_pipeline: + vocoder.to(vocoder_dtype).save_pretrained(os.path.join(args.output_path, "vocoder")) + + if args.text_encoder or args.full_pipeline: + # text_encoder = AutoModel.from_pretrained(args.text_encoder_model_id) + text_encoder = Gemma3ForConditionalGeneration.from_pretrained(args.text_encoder_model_id) + if not args.full_pipeline: + text_encoder.to(text_encoder_dtype).save_pretrained(os.path.join(args.output_path, "text_encoder")) + + tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_id) + if not args.full_pipeline: + tokenizer.save_pretrained(os.path.join(args.output_path, "tokenizer")) + + if args.add_processor: + processor = Gemma3Processor.from_pretrained(args.text_encoder_model_id) + if not args.full_pipeline: + processor.save_pretrained(os.path.join(args.output_path, "processor")) + + if args.latent_upsampler or args.upsample_pipeline: + original_latent_upsampler_ckpt = load_hub_or_local_checkpoint( + repo_id=args.original_state_dict_repo_id, filename=args.latent_upsampler_filename + ) + latent_upsampler_config = get_ltx2_spatial_latent_upsampler_config(args.version) + latent_upsampler = convert_ltx2_spatial_latent_upsampler( + original_latent_upsampler_ckpt, + latent_upsampler_config, + dtype=vae_dtype, + ) + if not args.full_pipeline and not args.upsample_pipeline: + latent_upsampler.save_pretrained(os.path.join(args.output_path, "latent_upsampler")) + + if args.full_pipeline: + is_distilled_ckpt = "distilled" in args.combined_filename + if is_distilled_ckpt: + # Disable dynamic shifting and terminal shift so that distilled sigmas are used as-is + scheduler = FlowMatchEulerDiscreteScheduler( + use_dynamic_shifting=False, + base_shift=0.95, + max_shift=2.05, + base_image_seq_len=1024, + max_image_seq_len=4096, + shift_terminal=None, + ) + else: + scheduler = FlowMatchEulerDiscreteScheduler( + use_dynamic_shifting=True, + base_shift=0.95, + max_shift=2.05, + base_image_seq_len=1024, + max_image_seq_len=4096, + shift_terminal=0.1, + ) + + pipe = LTX2Pipeline( + scheduler=scheduler, + vae=vae, + audio_vae=audio_vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + connectors=connectors, + transformer=transformer, + vocoder=vocoder, + ) + + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") + + if args.upsample_pipeline: + pipe = LTX2LatentUpsamplePipeline(vae=vae, latent_upsampler=latent_upsampler) + + # As two diffusers pipelines cannot be in the same directory, save the upsampling pipeline to its own directory + if args.upsample_output_path: + upsample_output_path = args.upsample_output_path + else: + upsample_output_path = args.output_path + pipe.save_pretrained(upsample_output_path, safe_serialization=True, max_shard_size="5GB") + + +if __name__ == "__main__": + args = get_args() + main(args) diff --git a/scripts/convert_ltx_to_diffusers.py b/scripts/convert_ltx_to_diffusers.py new file mode 100644 index 000000000000..b4ae57f9c8f0 --- /dev/null +++ b/scripts/convert_ltx_to_diffusers.py @@ -0,0 +1,525 @@ +import argparse +from pathlib import Path +from typing import Any, Dict + +import torch +from accelerate import init_empty_weights +from safetensors.torch import load_file +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import ( + AutoencoderKLLTXVideo, + FlowMatchEulerDiscreteScheduler, + LTXConditionPipeline, + LTXLatentUpsamplePipeline, + LTXPipeline, + LTXVideoTransformer3DModel, +) +from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel + + +def remove_keys_(key: str, state_dict: Dict[str, Any]): + state_dict.pop(key) + + +TOKENIZER_MAX_LENGTH = 128 + +TRANSFORMER_KEYS_RENAME_DICT = { + "patchify_proj": "proj_in", + "adaln_single": "time_embed", + "q_norm": "norm_q", + "k_norm": "norm_k", +} + +TRANSFORMER_SPECIAL_KEYS_REMAP = { + "vae": remove_keys_, +} + +VAE_KEYS_RENAME_DICT = { + # decoder + "up_blocks.0": "mid_block", + "up_blocks.1": "up_blocks.0", + "up_blocks.2": "up_blocks.1.upsamplers.0", + "up_blocks.3": "up_blocks.1", + "up_blocks.4": "up_blocks.2.conv_in", + "up_blocks.5": "up_blocks.2.upsamplers.0", + "up_blocks.6": "up_blocks.2", + "up_blocks.7": "up_blocks.3.conv_in", + "up_blocks.8": "up_blocks.3.upsamplers.0", + "up_blocks.9": "up_blocks.3", + # encoder + "down_blocks.0": "down_blocks.0", + "down_blocks.1": "down_blocks.0.downsamplers.0", + "down_blocks.2": "down_blocks.0.conv_out", + "down_blocks.3": "down_blocks.1", + "down_blocks.4": "down_blocks.1.downsamplers.0", + "down_blocks.5": "down_blocks.1.conv_out", + "down_blocks.6": "down_blocks.2", + "down_blocks.7": "down_blocks.2.downsamplers.0", + "down_blocks.8": "down_blocks.3", + "down_blocks.9": "mid_block", + # common + "conv_shortcut": "conv_shortcut.conv", + "res_blocks": "resnets", + "norm3.norm": "norm3", + "per_channel_statistics.mean-of-means": "latents_mean", + "per_channel_statistics.std-of-means": "latents_std", +} + +VAE_091_RENAME_DICT = { + # decoder + "up_blocks.0": "mid_block", + "up_blocks.1": "up_blocks.0.upsamplers.0", + "up_blocks.2": "up_blocks.0", + "up_blocks.3": "up_blocks.1.upsamplers.0", + "up_blocks.4": "up_blocks.1", + "up_blocks.5": "up_blocks.2.upsamplers.0", + "up_blocks.6": "up_blocks.2", + "up_blocks.7": "up_blocks.3.upsamplers.0", + "up_blocks.8": "up_blocks.3", + # common + "last_time_embedder": "time_embedder", + "last_scale_shift_table": "scale_shift_table", +} + +VAE_095_RENAME_DICT = { + # decoder + "up_blocks.0": "mid_block", + "up_blocks.1": "up_blocks.0.upsamplers.0", + "up_blocks.2": "up_blocks.0", + "up_blocks.3": "up_blocks.1.upsamplers.0", + "up_blocks.4": "up_blocks.1", + "up_blocks.5": "up_blocks.2.upsamplers.0", + "up_blocks.6": "up_blocks.2", + "up_blocks.7": "up_blocks.3.upsamplers.0", + "up_blocks.8": "up_blocks.3", + # encoder + "down_blocks.0": "down_blocks.0", + "down_blocks.1": "down_blocks.0.downsamplers.0", + "down_blocks.2": "down_blocks.1", + "down_blocks.3": "down_blocks.1.downsamplers.0", + "down_blocks.4": "down_blocks.2", + "down_blocks.5": "down_blocks.2.downsamplers.0", + "down_blocks.6": "down_blocks.3", + "down_blocks.7": "down_blocks.3.downsamplers.0", + "down_blocks.8": "mid_block", + # common + "last_time_embedder": "time_embedder", + "last_scale_shift_table": "scale_shift_table", +} + +VAE_SPECIAL_KEYS_REMAP = { + "per_channel_statistics.channel": remove_keys_, + "per_channel_statistics.mean-of-means": remove_keys_, + "per_channel_statistics.mean-of-stds": remove_keys_, + "model.diffusion_model": remove_keys_, +} + + +def get_state_dict(saved_dict: Dict[str, Any]) -> dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def convert_transformer(ckpt_path: str, config, dtype: torch.dtype): + PREFIX_KEY = "model.diffusion_model." + + original_state_dict = get_state_dict(load_file(ckpt_path)) + with init_empty_weights(): + transformer = LTXVideoTransformer3DModel(**config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + if new_key.startswith(PREFIX_KEY): + new_key = key[len(PREFIX_KEY) :] + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + transformer.load_state_dict(original_state_dict, strict=True, assign=True) + return transformer + + +def convert_vae(ckpt_path: str, config, dtype: torch.dtype): + PREFIX_KEY = "vae." + + original_state_dict = get_state_dict(load_file(ckpt_path)) + with init_empty_weights(): + vae = AutoencoderKLLTXVideo(**config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + if new_key.startswith(PREFIX_KEY): + new_key = key[len(PREFIX_KEY) :] + for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True, assign=True) + return vae + + +def convert_spatial_latent_upsampler(ckpt_path: str, config, dtype: torch.dtype): + original_state_dict = get_state_dict(load_file(ckpt_path)) + + with init_empty_weights(): + latent_upsampler = LTXLatentUpsamplerModel(**config) + + latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True) + latent_upsampler.to(dtype) + return latent_upsampler + + +def get_transformer_config(version: str) -> dict[str, Any]: + if version == "0.9.7": + config = { + "in_channels": 128, + "out_channels": 128, + "patch_size": 1, + "patch_size_t": 1, + "num_attention_heads": 32, + "attention_head_dim": 128, + "cross_attention_dim": 4096, + "num_layers": 48, + "activation_fn": "gelu-approximate", + "qk_norm": "rms_norm_across_heads", + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "caption_channels": 4096, + "attention_bias": True, + "attention_out_bias": True, + } + else: + config = { + "in_channels": 128, + "out_channels": 128, + "patch_size": 1, + "patch_size_t": 1, + "num_attention_heads": 32, + "attention_head_dim": 64, + "cross_attention_dim": 2048, + "num_layers": 28, + "activation_fn": "gelu-approximate", + "qk_norm": "rms_norm_across_heads", + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "caption_channels": 4096, + "attention_bias": True, + "attention_out_bias": True, + } + return config + + +def get_vae_config(version: str) -> dict[str, Any]: + if version in ["0.9.0"]: + config = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (128, 256, 512, 512), + "down_block_types": ( + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + ), + "decoder_block_out_channels": (128, 256, 512, 512), + "layers_per_block": (4, 3, 3, 3, 4), + "decoder_layers_per_block": (4, 3, 3, 3, 4), + "spatio_temporal_scaling": (True, True, True, False), + "decoder_spatio_temporal_scaling": (True, True, True, False), + "decoder_inject_noise": (False, False, False, False, False), + "downsample_type": ("conv", "conv", "conv", "conv"), + "upsample_residual": (False, False, False, False), + "upsample_factor": (1, 1, 1, 1), + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "scaling_factor": 1.0, + "encoder_causal": True, + "decoder_causal": False, + "timestep_conditioning": False, + } + elif version in ["0.9.1"]: + config = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (128, 256, 512, 512), + "down_block_types": ( + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + "LTXVideoDownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 1024), + "layers_per_block": (4, 3, 3, 3, 4), + "decoder_layers_per_block": (5, 6, 7, 8), + "spatio_temporal_scaling": (True, True, True, False), + "decoder_spatio_temporal_scaling": (True, True, True), + "decoder_inject_noise": (True, True, True, False), + "downsample_type": ("conv", "conv", "conv", "conv"), + "upsample_residual": (True, True, True), + "upsample_factor": (2, 2, 2), + "timestep_conditioning": True, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "scaling_factor": 1.0, + "encoder_causal": True, + "decoder_causal": False, + } + VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT) + elif version in ["0.9.5"]: + config = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (128, 256, 512, 1024, 2048), + "down_block_types": ( + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 1024), + "layers_per_block": (4, 6, 6, 2, 2), + "decoder_layers_per_block": (5, 5, 5, 5), + "spatio_temporal_scaling": (True, True, True, True), + "decoder_spatio_temporal_scaling": (True, True, True), + "decoder_inject_noise": (False, False, False, False), + "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), + "upsample_residual": (True, True, True), + "upsample_factor": (2, 2, 2), + "timestep_conditioning": True, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "scaling_factor": 1.0, + "encoder_causal": True, + "decoder_causal": False, + "spatial_compression_ratio": 32, + "temporal_compression_ratio": 8, + } + VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT) + elif version in ["0.9.7"]: + config = { + "in_channels": 3, + "out_channels": 3, + "latent_channels": 128, + "block_out_channels": (128, 256, 512, 1024, 2048), + "down_block_types": ( + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + "LTXVideo095DownBlock3D", + ), + "decoder_block_out_channels": (256, 512, 1024), + "layers_per_block": (4, 6, 6, 2, 2), + "decoder_layers_per_block": (5, 5, 5, 5), + "spatio_temporal_scaling": (True, True, True, True), + "decoder_spatio_temporal_scaling": (True, True, True), + "decoder_inject_noise": (False, False, False, False), + "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"), + "upsample_residual": (True, True, True), + "upsample_factor": (2, 2, 2), + "timestep_conditioning": True, + "patch_size": 4, + "patch_size_t": 1, + "resnet_norm_eps": 1e-6, + "scaling_factor": 1.0, + "encoder_causal": True, + "decoder_causal": False, + "spatial_compression_ratio": 32, + "temporal_compression_ratio": 8, + } + VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT) + return config + + +def get_spatial_latent_upsampler_config(version: str) -> dict[str, Any]: + if version == "0.9.7": + config = { + "in_channels": 128, + "mid_channels": 512, + "num_blocks_per_stage": 4, + "dims": 3, + "spatial_upsample": True, + "temporal_upsample": False, + } + elif version == "0.9.8": + config = { + "in_channels": 128, + "mid_channels": 512, + "num_blocks_per_stage": 4, + "dims": 3, + "spatial_upsample": True, + "temporal_upsample": False, + } + else: + raise ValueError(f"Unsupported version: {version}") + return config + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" + ) + parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint") + parser.add_argument( + "--spatial_latent_upsampler_path", + type=str, + default=None, + help="Path to original spatial latent upsampler checkpoint", + ) + parser.add_argument( + "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory" + ) + parser.add_argument( + "--typecast_text_encoder", + action="store_true", + default=False, + help="Whether or not to apply fp16/bf16 precision to text_encoder", + ) + parser.add_argument("--save_pipeline", action="store_true") + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.") + parser.add_argument( + "--version", + type=str, + default="0.9.0", + choices=["0.9.0", "0.9.1", "0.9.5", "0.9.7", "0.9.8"], + help="Version of the LTX model", + ) + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + +VARIANT_MAPPING = { + "fp32": None, + "fp16": "fp16", + "bf16": "bf16", +} + + +if __name__ == "__main__": + args = get_args() + + transformer = None + dtype = DTYPE_MAPPING[args.dtype] + variant = VARIANT_MAPPING[args.dtype] + output_path = Path(args.output_path) + + if args.transformer_ckpt_path is not None: + config = get_transformer_config(args.version) + transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, config, dtype) + if not args.save_pipeline: + transformer.save_pretrained( + output_path / "transformer", safe_serialization=True, max_shard_size="5GB", variant=variant + ) + + if args.vae_ckpt_path is not None: + config = get_vae_config(args.version) + vae: AutoencoderKLLTXVideo = convert_vae(args.vae_ckpt_path, config, dtype) + if not args.save_pipeline: + vae.save_pretrained(output_path / "vae", safe_serialization=True, max_shard_size="5GB", variant=variant) + + if args.spatial_latent_upsampler_path is not None: + config = get_spatial_latent_upsampler_config(args.version) + latent_upsampler: LTXLatentUpsamplerModel = convert_spatial_latent_upsampler( + args.spatial_latent_upsampler_path, config, dtype + ) + if not args.save_pipeline: + latent_upsampler.save_pretrained( + output_path / "latent_upsampler", safe_serialization=True, max_shard_size="5GB", variant=variant + ) + + if args.save_pipeline: + text_encoder_id = "google/t5-v1_1-xxl" + tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) + text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) + + if args.typecast_text_encoder: + text_encoder = text_encoder.to(dtype=dtype) + + # Apparently, the conversion does not work anymore without this :shrug: + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + if args.version in ["0.9.5", "0.9.7"]: + scheduler = FlowMatchEulerDiscreteScheduler(use_dynamic_shifting=False) + else: + scheduler = FlowMatchEulerDiscreteScheduler( + use_dynamic_shifting=True, + base_shift=0.95, + max_shift=2.05, + base_image_seq_len=1024, + max_image_seq_len=4096, + shift_terminal=0.1, + ) + + if args.version in ["0.9.0", "0.9.1", "0.9.5"]: + pipe = LTXPipeline( + scheduler=scheduler, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + ) + pipe.save_pretrained( + output_path.as_posix(), safe_serialization=True, variant=variant, max_shard_size="5GB" + ) + elif args.version in ["0.9.7"]: + pipe = LTXConditionPipeline( + scheduler=scheduler, + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + ) + pipe_upsample = LTXLatentUpsamplePipeline( + vae=vae, + latent_upsampler=latent_upsampler, + ) + pipe.save_pretrained( + (output_path / "ltx_pipeline").as_posix(), + safe_serialization=True, + variant=variant, + max_shard_size="5GB", + ) + pipe_upsample.save_pretrained( + (output_path / "ltx_upsample_pipeline").as_posix(), + safe_serialization=True, + variant=variant, + max_shard_size="5GB", + ) + else: + raise ValueError(f"Unsupported version: {args.version}") diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py new file mode 100644 index 000000000000..c14aad3c6bf2 --- /dev/null +++ b/scripts/convert_lumina_to_diffusers.py @@ -0,0 +1,142 @@ +import argparse +import os + +import torch +from safetensors.torch import load_file +from transformers import AutoModel, AutoTokenizer + +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaPipeline + + +def main(args): + # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I + all_sd = load_file(args.origin_ckpt_path, device="cpu") + converted_state_dict = {} + # pad token + converted_state_dict["pad_token"] = all_sd["pad_token"] + + # patch embed + converted_state_dict["patch_embedder.weight"] = all_sd["x_embedder.weight"] + converted_state_dict["patch_embedder.bias"] = all_sd["x_embedder.bias"] + + # time and caption embed + converted_state_dict["time_caption_embed.timestep_embedder.linear_1.weight"] = all_sd["t_embedder.mlp.0.weight"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_1.bias"] = all_sd["t_embedder.mlp.0.bias"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_2.weight"] = all_sd["t_embedder.mlp.2.weight"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_2.bias"] = all_sd["t_embedder.mlp.2.bias"] + converted_state_dict["time_caption_embed.caption_embedder.0.weight"] = all_sd["cap_embedder.0.weight"] + converted_state_dict["time_caption_embed.caption_embedder.0.bias"] = all_sd["cap_embedder.0.bias"] + converted_state_dict["time_caption_embed.caption_embedder.1.weight"] = all_sd["cap_embedder.1.weight"] + converted_state_dict["time_caption_embed.caption_embedder.1.bias"] = all_sd["cap_embedder.1.bias"] + + for i in range(24): + # adaln + converted_state_dict[f"layers.{i}.gate"] = all_sd[f"layers.{i}.attention.gate"] + converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] + converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] + + # qkv + converted_state_dict[f"layers.{i}.attn1.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attn1.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] + converted_state_dict[f"layers.{i}.attn1.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] + + # cap + converted_state_dict[f"layers.{i}.attn2.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attn2.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] + converted_state_dict[f"layers.{i}.attn2.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] + + # output + converted_state_dict[f"layers.{i}.attn2.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] + + # attention + # qk norm + converted_state_dict[f"layers.{i}.attn1.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attn1.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + + converted_state_dict[f"layers.{i}.attn1.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] + converted_state_dict[f"layers.{i}.attn1.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + + converted_state_dict[f"layers.{i}.attn2.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attn2.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + + converted_state_dict[f"layers.{i}.attn2.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.attn2.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] + + # attention norm + converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] + converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] + converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] + + # feed forward + converted_state_dict[f"layers.{i}.feed_forward.linear_1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] + converted_state_dict[f"layers.{i}.feed_forward.linear_2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] + converted_state_dict[f"layers.{i}.feed_forward.linear_3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] + + # feed forward norm + converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] + converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] + + # final layer + converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] + converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] + + converted_state_dict["final_layer.adaLN_modulation.1.weight"] = all_sd["final_layer.adaLN_modulation.1.weight"] + converted_state_dict["final_layer.adaLN_modulation.1.bias"] = all_sd["final_layer.adaLN_modulation.1.bias"] + + # Lumina-Next-SFT 2B + transformer = LuminaNextDiT2DModel( + sample_size=128, + patch_size=2, + in_channels=4, + hidden_size=2304, + num_layers=24, + num_attention_heads=32, + num_kv_heads=8, + multiple_of=256, + ffn_dim_multiplier=None, + norm_eps=1e-5, + learn_sigma=True, + qk_norm=True, + cross_attention_dim=2048, + scaling_factor=1.0, + ) + transformer.load_state_dict(converted_state_dict, strict=True) + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + if args.only_transformer: + transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) + else: + scheduler = FlowMatchEulerDiscreteScheduler() + + vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32) + + tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") + text_encoder = AutoModel.from_pretrained("google/gemma-2b") + + pipeline = LuminaPipeline( + tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler + ) + pipeline.save_pretrained(args.dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--origin_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[256, 512, 1024], + required=False, + help="Image size of pretrained model, either 512 or 1024.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--only_transformer", default=True, type=bool, required=True) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_mochi_to_diffusers.py b/scripts/convert_mochi_to_diffusers.py new file mode 100644 index 000000000000..64e4f69eac17 --- /dev/null +++ b/scripts/convert_mochi_to_diffusers.py @@ -0,0 +1,463 @@ +import argparse +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights +from safetensors.torch import load_file +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import AutoencoderKLMochi, FlowMatchEulerDiscreteScheduler, MochiPipeline, MochiTransformer3DModel +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +TOKENIZER_MAX_LENGTH = 256 + +parser = argparse.ArgumentParser() +parser.add_argument("--transformer_checkpoint_path", default=None, type=str) +parser.add_argument("--vae_encoder_checkpoint_path", default=None, type=str) +parser.add_argument("--vae_decoder_checkpoint_path", default=None, type=str) +parser.add_argument("--output_path", required=True, type=str) +parser.add_argument("--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving") +parser.add_argument("--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory") +parser.add_argument("--dtype", type=str, default=None) + +args = parser.parse_args() + + +# This is specific to `AdaLayerNormContinuous`: +# Diffusers implementation split the linear projection into the scale, shift while Mochi split it into shift, scale +def swap_scale_shift(weight, dim): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def swap_proj_gate(weight): + proj, gate = weight.chunk(2, dim=0) + new_weight = torch.cat([gate, proj], dim=0) + return new_weight + + +def convert_mochi_transformer_checkpoint_to_diffusers(ckpt_path): + original_state_dict = load_file(ckpt_path, device="cpu") + new_state_dict = {} + + # Convert patch_embed + new_state_dict["patch_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight") + new_state_dict["patch_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias") + + # Convert time_embed + new_state_dict["time_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop("t_embedder.mlp.0.weight") + new_state_dict["time_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop("t_embedder.mlp.0.bias") + new_state_dict["time_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop("t_embedder.mlp.2.weight") + new_state_dict["time_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop("t_embedder.mlp.2.bias") + new_state_dict["time_embed.pooler.to_kv.weight"] = original_state_dict.pop("t5_y_embedder.to_kv.weight") + new_state_dict["time_embed.pooler.to_kv.bias"] = original_state_dict.pop("t5_y_embedder.to_kv.bias") + new_state_dict["time_embed.pooler.to_q.weight"] = original_state_dict.pop("t5_y_embedder.to_q.weight") + new_state_dict["time_embed.pooler.to_q.bias"] = original_state_dict.pop("t5_y_embedder.to_q.bias") + new_state_dict["time_embed.pooler.to_out.weight"] = original_state_dict.pop("t5_y_embedder.to_out.weight") + new_state_dict["time_embed.pooler.to_out.bias"] = original_state_dict.pop("t5_y_embedder.to_out.bias") + new_state_dict["time_embed.caption_proj.weight"] = original_state_dict.pop("t5_yproj.weight") + new_state_dict["time_embed.caption_proj.bias"] = original_state_dict.pop("t5_yproj.bias") + + # Convert transformer blocks + num_layers = 48 + for i in range(num_layers): + block_prefix = f"transformer_blocks.{i}." + old_prefix = f"blocks.{i}." + + # norm1 + new_state_dict[block_prefix + "norm1.linear.weight"] = original_state_dict.pop(old_prefix + "mod_x.weight") + new_state_dict[block_prefix + "norm1.linear.bias"] = original_state_dict.pop(old_prefix + "mod_x.bias") + if i < num_layers - 1: + new_state_dict[block_prefix + "norm1_context.linear.weight"] = original_state_dict.pop( + old_prefix + "mod_y.weight" + ) + new_state_dict[block_prefix + "norm1_context.linear.bias"] = original_state_dict.pop( + old_prefix + "mod_y.bias" + ) + else: + new_state_dict[block_prefix + "norm1_context.linear_1.weight"] = original_state_dict.pop( + old_prefix + "mod_y.weight" + ) + new_state_dict[block_prefix + "norm1_context.linear_1.bias"] = original_state_dict.pop( + old_prefix + "mod_y.bias" + ) + + # Visual attention + qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_x.weight") + q, k, v = qkv_weight.chunk(3, dim=0) + + new_state_dict[block_prefix + "attn1.to_q.weight"] = q + new_state_dict[block_prefix + "attn1.to_k.weight"] = k + new_state_dict[block_prefix + "attn1.to_v.weight"] = v + new_state_dict[block_prefix + "attn1.norm_q.weight"] = original_state_dict.pop( + old_prefix + "attn.q_norm_x.weight" + ) + new_state_dict[block_prefix + "attn1.norm_k.weight"] = original_state_dict.pop( + old_prefix + "attn.k_norm_x.weight" + ) + new_state_dict[block_prefix + "attn1.to_out.0.weight"] = original_state_dict.pop( + old_prefix + "attn.proj_x.weight" + ) + new_state_dict[block_prefix + "attn1.to_out.0.bias"] = original_state_dict.pop(old_prefix + "attn.proj_x.bias") + + # Context attention + qkv_weight = original_state_dict.pop(old_prefix + "attn.qkv_y.weight") + q, k, v = qkv_weight.chunk(3, dim=0) + + new_state_dict[block_prefix + "attn1.add_q_proj.weight"] = q + new_state_dict[block_prefix + "attn1.add_k_proj.weight"] = k + new_state_dict[block_prefix + "attn1.add_v_proj.weight"] = v + new_state_dict[block_prefix + "attn1.norm_added_q.weight"] = original_state_dict.pop( + old_prefix + "attn.q_norm_y.weight" + ) + new_state_dict[block_prefix + "attn1.norm_added_k.weight"] = original_state_dict.pop( + old_prefix + "attn.k_norm_y.weight" + ) + if i < num_layers - 1: + new_state_dict[block_prefix + "attn1.to_add_out.weight"] = original_state_dict.pop( + old_prefix + "attn.proj_y.weight" + ) + new_state_dict[block_prefix + "attn1.to_add_out.bias"] = original_state_dict.pop( + old_prefix + "attn.proj_y.bias" + ) + + # MLP + new_state_dict[block_prefix + "ff.net.0.proj.weight"] = swap_proj_gate( + original_state_dict.pop(old_prefix + "mlp_x.w1.weight") + ) + new_state_dict[block_prefix + "ff.net.2.weight"] = original_state_dict.pop(old_prefix + "mlp_x.w2.weight") + if i < num_layers - 1: + new_state_dict[block_prefix + "ff_context.net.0.proj.weight"] = swap_proj_gate( + original_state_dict.pop(old_prefix + "mlp_y.w1.weight") + ) + new_state_dict[block_prefix + "ff_context.net.2.weight"] = original_state_dict.pop( + old_prefix + "mlp_y.w2.weight" + ) + + # Output layers + new_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("final_layer.mod.weight"), dim=0 + ) + new_state_dict["norm_out.linear.bias"] = swap_scale_shift(original_state_dict.pop("final_layer.mod.bias"), dim=0) + new_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + new_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + + new_state_dict["pos_frequencies"] = original_state_dict.pop("pos_frequencies") + + print("Remaining Keys:", original_state_dict.keys()) + + return new_state_dict + + +def convert_mochi_vae_state_dict_to_diffusers(encoder_ckpt_path, decoder_ckpt_path): + encoder_state_dict = load_file(encoder_ckpt_path, device="cpu") + decoder_state_dict = load_file(decoder_ckpt_path, device="cpu") + new_state_dict = {} + + # ==== Decoder ===== + prefix = "decoder." + + # Convert conv_in + new_state_dict[f"{prefix}conv_in.weight"] = decoder_state_dict.pop("blocks.0.0.weight") + new_state_dict[f"{prefix}conv_in.bias"] = decoder_state_dict.pop("blocks.0.0.bias") + + # Convert block_in (MochiMidBlock3D) + for i in range(3): # layers_per_block[-1] = 3 + new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.0.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.0.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.2.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.2.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.3.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.3.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.5.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( + f"blocks.0.{i + 1}.stack.5.bias" + ) + + # Convert up_blocks (MochiUpBlock3D) + down_block_layers = [6, 4, 3] # layers_per_block[-2], layers_per_block[-3], layers_per_block[-4] + for block in range(3): + for i in range(down_block_layers[block]): + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.0.weight" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.0.bias" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.2.weight" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.2.bias" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.3.weight" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.3.bias" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.5.weight" + ) + new_state_dict[f"{prefix}up_blocks.{block}.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( + f"blocks.{block + 1}.blocks.{i}.stack.5.bias" + ) + new_state_dict[f"{prefix}up_blocks.{block}.proj.weight"] = decoder_state_dict.pop( + f"blocks.{block + 1}.proj.weight" + ) + new_state_dict[f"{prefix}up_blocks.{block}.proj.bias"] = decoder_state_dict.pop( + f"blocks.{block + 1}.proj.bias" + ) + + # Convert block_out (MochiMidBlock3D) + for i in range(3): # layers_per_block[0] = 3 + new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.0.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.0.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.2.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.2.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.3.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.3.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.5.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = decoder_state_dict.pop( + f"blocks.4.{i}.stack.5.bias" + ) + + # Convert proj_out (Conv1x1 ~= nn.Linear) + new_state_dict[f"{prefix}proj_out.weight"] = decoder_state_dict.pop("output_proj.weight") + new_state_dict[f"{prefix}proj_out.bias"] = decoder_state_dict.pop("output_proj.bias") + + print("Remaining Decoder Keys:", decoder_state_dict.keys()) + + # ==== Encoder ===== + prefix = "encoder." + + new_state_dict[f"{prefix}proj_in.weight"] = encoder_state_dict.pop("layers.0.weight") + new_state_dict[f"{prefix}proj_in.bias"] = encoder_state_dict.pop("layers.0.bias") + + # Convert block_in (MochiMidBlock3D) + for i in range(3): # layers_per_block[0] = 3 + new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.0.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.0.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.2.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.2.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.3.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.3.bias" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.5.weight" + ) + new_state_dict[f"{prefix}block_in.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( + f"layers.{i + 1}.stack.5.bias" + ) + + # Convert down_blocks (MochiDownBlock3D) + down_block_layers = [3, 4, 6] # layers_per_block[1], layers_per_block[2], layers_per_block[3] + for block in range(3): + new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.weight"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.0.weight" + ) + new_state_dict[f"{prefix}down_blocks.{block}.conv_in.conv.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.0.bias" + ) + + for i in range(down_block_layers[block]): + # Convert resnets + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.weight"] = ( + encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.0.weight") + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.0.bias" + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.2.weight" + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.2.bias" + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.weight"] = ( + encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.stack.3.weight") + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.3.bias" + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.5.weight" + ) + new_state_dict[f"{prefix}down_blocks.{block}.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.stack.5.bias" + ) + + # Convert attentions + qkv_weight = encoder_state_dict.pop(f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.qkv.weight") + q, k, v = qkv_weight.chunk(3, dim=0) + + new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_q.weight"] = q + new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_k.weight"] = k + new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_v.weight"] = v + new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.weight" + ) + new_state_dict[f"{prefix}down_blocks.{block}.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.attn_block.attn.out.bias" + ) + new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.weight" + ) + new_state_dict[f"{prefix}down_blocks.{block}.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{block + 4}.layers.{i + 1}.attn_block.norm.bias" + ) + + # Convert block_out (MochiMidBlock3D) + for i in range(3): # layers_per_block[-1] = 3 + # Convert resnets + new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.0.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm1.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.0.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.2.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv1.conv.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.2.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.3.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.norm2.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.3.bias" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.5.weight" + ) + new_state_dict[f"{prefix}block_out.resnets.{i}.conv2.conv.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.stack.5.bias" + ) + + # Convert attentions + qkv_weight = encoder_state_dict.pop(f"layers.{i + 7}.attn_block.attn.qkv.weight") + q, k, v = qkv_weight.chunk(3, dim=0) + + new_state_dict[f"{prefix}block_out.attentions.{i}.to_q.weight"] = q + new_state_dict[f"{prefix}block_out.attentions.{i}.to_k.weight"] = k + new_state_dict[f"{prefix}block_out.attentions.{i}.to_v.weight"] = v + new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.attn_block.attn.out.weight" + ) + new_state_dict[f"{prefix}block_out.attentions.{i}.to_out.0.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.attn_block.attn.out.bias" + ) + new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.weight"] = encoder_state_dict.pop( + f"layers.{i + 7}.attn_block.norm.weight" + ) + new_state_dict[f"{prefix}block_out.norms.{i}.norm_layer.bias"] = encoder_state_dict.pop( + f"layers.{i + 7}.attn_block.norm.bias" + ) + + # Convert output layers + new_state_dict[f"{prefix}norm_out.norm_layer.weight"] = encoder_state_dict.pop("output_norm.weight") + new_state_dict[f"{prefix}norm_out.norm_layer.bias"] = encoder_state_dict.pop("output_norm.bias") + new_state_dict[f"{prefix}proj_out.weight"] = encoder_state_dict.pop("output_proj.weight") + + print("Remaining Encoder Keys:", encoder_state_dict.keys()) + + return new_state_dict + + +def main(args): + if args.dtype is None: + dtype = None + if args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}") + + transformer = None + vae = None + + if args.transformer_checkpoint_path is not None: + converted_transformer_state_dict = convert_mochi_transformer_checkpoint_to_diffusers( + args.transformer_checkpoint_path + ) + transformer = MochiTransformer3DModel() + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + if dtype is not None: + transformer = transformer.to(dtype=dtype) + + if args.vae_encoder_checkpoint_path is not None and args.vae_decoder_checkpoint_path is not None: + vae = AutoencoderKLMochi(latent_channels=12, out_channels=3) + converted_vae_state_dict = convert_mochi_vae_state_dict_to_diffusers( + args.vae_encoder_checkpoint_path, args.vae_decoder_checkpoint_path + ) + vae.load_state_dict(converted_vae_state_dict, strict=True) + if dtype is not None: + vae = vae.to(dtype=dtype) + + text_encoder_id = "google/t5-v1_1-xxl" + tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) + text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) + + # Apparently, the conversion does not work anymore without this :shrug: + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + pipe = MochiPipeline( + scheduler=FlowMatchEulerDiscreteScheduler(invert_sigmas=True), + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + transformer=transformer, + ) + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_models_diffuser_to_diffusers.py b/scripts/convert_models_diffuser_to_diffusers.py new file mode 100644 index 000000000000..cc5321e33fe0 --- /dev/null +++ b/scripts/convert_models_diffuser_to_diffusers.py @@ -0,0 +1,100 @@ +import json +import os + +import torch + +from diffusers import UNet1DModel + + +os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True) +os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True) + +os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True) + + +def unet(hor): + if hor == 128: + down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") + block_out_channels = (32, 128, 256) + up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D") + + elif hor == 32: + down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D") + block_out_channels = (32, 64, 128, 256) + up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D") + model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch") + state_dict = model.state_dict() + config = { + "down_block_types": down_block_types, + "block_out_channels": block_out_channels, + "up_block_types": up_block_types, + "layers_per_block": 1, + "use_timestep_embedding": True, + "out_block_type": "OutConv1DBlock", + "norm_num_groups": 8, + "downsample_each_block": False, + "in_channels": 14, + "out_channels": 14, + "extra_in_channels": 0, + "time_embedding_type": "positional", + "flip_sin_to_cos": False, + "freq_shift": 1, + "sample_size": 65536, + "mid_block_type": "MidResTemporalBlock1D", + "act_fn": "mish", + } + hf_value_function = UNet1DModel(**config) + print(f"length of state dict: {len(state_dict.keys())}") + print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") + mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys())) + for k, v in mapping.items(): + state_dict[v] = state_dict.pop(k) + hf_value_function.load_state_dict(state_dict) + + torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin") + with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f: + json.dump(config, f) + + +def value_function(): + config = { + "in_channels": 14, + "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"), + "up_block_types": (), + "out_block_type": "ValueFunction", + "mid_block_type": "ValueFunctionMidBlock1D", + "block_out_channels": (32, 64, 128, 256), + "layers_per_block": 1, + "downsample_each_block": True, + "sample_size": 65536, + "out_channels": 14, + "extra_in_channels": 0, + "time_embedding_type": "positional", + "use_timestep_embedding": True, + "flip_sin_to_cos": False, + "freq_shift": 1, + "norm_num_groups": 8, + "act_fn": "mish", + } + + model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch") + state_dict = model + hf_value_function = UNet1DModel(**config) + print(f"length of state dict: {len(state_dict.keys())}") + print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") + + mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys())) + for k, v in mapping.items(): + state_dict[v] = state_dict.pop(k) + + hf_value_function.load_state_dict(state_dict) + + torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin") + with open("hub/hopper-medium-v2/value_function/config.json", "w") as f: + json.dump(config, f) + + +if __name__ == "__main__": + unet(32) + # unet(128) + value_function() diff --git a/scripts/convert_ms_text_to_video_to_diffusers.py b/scripts/convert_ms_text_to_video_to_diffusers.py new file mode 100644 index 000000000000..e150a491a0b0 --- /dev/null +++ b/scripts/convert_ms_text_to_video_to_diffusers.py @@ -0,0 +1,428 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the LDM checkpoints.""" + +import argparse + +import torch + +from diffusers import UNet3DConditionModel + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + weight = old_checkpoint[path["old"]] + names = ["proj_attn.weight"] + names_2 = ["proj_out.weight", "proj_in.weight"] + if any(k in new_path for k in names): + checkpoint[new_path] = weight[:, :, 0] + elif any(k in new_path for k in names_2) and len(weight.shape) > 2 and ".attentions." not in new_path: + checkpoint[new_path] = weight[:, :, 0] + else: + checkpoint[new_path] = weight + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_temp_conv_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + mapping.append({"old": old_item, "new": old_item}) + + return mapping + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + if "temopral_conv" not in old_item: + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + first_temp_attention = [v for v in unet_state_dict if v.startswith("input_blocks.0.1")] + paths = renew_attention_paths(first_temp_attention) + meta_path = {"old": "input_blocks.0.1", "new": "transformer_in"} + assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config) + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + temp_attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key] + + if f"input_blocks.{i}.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temporal_convs = [key for key in resnets if "temopral_conv" in key] + paths = renew_temp_conv_paths(temporal_convs) + meta_path = { + "old": f"input_blocks.{i}.0.temopral_conv", + "new": f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(temp_attentions): + paths = renew_attention_paths(temp_attentions) + meta_path = { + "old": f"input_blocks.{i}.2", + "new": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + temporal_convs_0 = [key for key in resnet_0 if "temopral_conv" in key] + attentions = middle_blocks[1] + temp_attentions = middle_blocks[2] + resnet_1 = middle_blocks[3] + temporal_convs_1 = [key for key in resnet_1 if "temopral_conv" in key] + + resnet_0_paths = renew_resnet_paths(resnet_0) + meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} + assign_to_checkpoint( + resnet_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + temp_conv_0_paths = renew_temp_conv_paths(temporal_convs_0) + meta_path = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"} + assign_to_checkpoint( + temp_conv_0_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + resnet_1_paths = renew_resnet_paths(resnet_1) + meta_path = {"old": "middle_block.3", "new": "mid_block.resnets.1"} + assign_to_checkpoint( + resnet_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + temp_conv_1_paths = renew_temp_conv_paths(temporal_convs_1) + meta_path = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"} + assign_to_checkpoint( + temp_conv_1_paths, new_checkpoint, unet_state_dict, config=config, additional_replacements=[meta_path] + ) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temp_attentions_paths = renew_attention_paths(temp_attentions) + meta_path = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"} + assign_to_checkpoint( + temp_attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + temp_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + temporal_convs = [key for key in resnets if "temopral_conv" in key] + paths = renew_temp_conv_paths(temporal_convs) + meta_path = { + "old": f"output_blocks.{i}.0.temopral_conv", + "new": f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(temp_attentions): + paths = renew_attention_paths(temp_attentions) + meta_path = { + "old": f"output_blocks.{i}.2", + "new": f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + new_checkpoint[new_path] = unet_state_dict[old_path] + + temopral_conv_paths = [l for l in output_block_layers if "temopral_conv" in l] + for path in temopral_conv_paths: + pruned_path = path.split("temopral_conv.")[-1] + old_path = ".".join(["output_blocks", str(i), str(block_id), "temopral_conv", pruned_path]) + new_path = ".".join(["up_blocks", str(block_id), "temp_convs", str(layer_in_block_id), pruned_path]) + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + args = parser.parse_args() + + unet_checkpoint = torch.load(args.checkpoint_path, map_location="cpu") + unet = UNet3DConditionModel() + + converted_ckpt = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config) + + diff_0 = set(unet.state_dict().keys()) - set(converted_ckpt.keys()) + diff_1 = set(converted_ckpt.keys()) - set(unet.state_dict().keys()) + + assert len(diff_0) == len(diff_1) == 0, "Converted weights don't match" + + # load state_dict + unet.load_state_dict(converted_ckpt) + + unet.save_pretrained(args.dump_path) + + # -- finish converting the unet -- diff --git a/scripts/convert_music_spectrogram_to_diffusers.py b/scripts/convert_music_spectrogram_to_diffusers.py new file mode 100644 index 000000000000..1ac9ceddb57e --- /dev/null +++ b/scripts/convert_music_spectrogram_to_diffusers.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +import argparse +import os + +import jax as jnp +import numpy as onp +import torch +import torch.nn as nn +from music_spectrogram_diffusion import inference +from t5x import checkpoints + +from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline +from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder + + +MODEL = "base_with_context" + + +def load_notes_encoder(weights, model): + model.token_embedder.weight = nn.Parameter(torch.Tensor(weights["token_embedder"]["embedding"])) + model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) + for lyr_num, lyr in enumerate(model.encoders): + ly_weight = weights[f"layers_{lyr_num}"] + lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"])) + + attention_weights = ly_weight["attention"] + lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) + lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) + lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) + lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) + + lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) + + lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) + lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) + lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) + + model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"])) + return model + + +def load_continuous_encoder(weights, model): + model.input_proj.weight = nn.Parameter(torch.Tensor(weights["input_proj"]["kernel"].T)) + + model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) + + for lyr_num, lyr in enumerate(model.encoders): + ly_weight = weights[f"layers_{lyr_num}"] + attention_weights = ly_weight["attention"] + + lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) + lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) + lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) + lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) + lyr.layer[0].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_attention_layer_norm"]["scale"])) + + lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) + lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) + lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) + lyr.layer[1].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) + + model.layer_norm.weight = nn.Parameter(torch.Tensor(weights["encoder_norm"]["scale"])) + + return model + + +def load_decoder(weights, model): + model.conditioning_emb[0].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense0"]["kernel"].T)) + model.conditioning_emb[2].weight = nn.Parameter(torch.Tensor(weights["time_emb_dense1"]["kernel"].T)) + + model.position_encoding.weight = nn.Parameter(torch.Tensor(weights["Embed_0"]["embedding"]), requires_grad=False) + + model.continuous_inputs_projection.weight = nn.Parameter( + torch.Tensor(weights["continuous_inputs_projection"]["kernel"].T) + ) + + for lyr_num, lyr in enumerate(model.decoders): + ly_weight = weights[f"layers_{lyr_num}"] + lyr.layer[0].layer_norm.weight = nn.Parameter( + torch.Tensor(ly_weight["pre_self_attention_layer_norm"]["scale"]) + ) + + lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter( + torch.Tensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T) + ) + + attention_weights = ly_weight["self_attention"] + lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) + lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) + lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) + lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) + + attention_weights = ly_weight["MultiHeadDotProductAttention_0"] + lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.Tensor(attention_weights["query"]["kernel"].T)) + lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.Tensor(attention_weights["key"]["kernel"].T)) + lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.Tensor(attention_weights["value"]["kernel"].T)) + lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.Tensor(attention_weights["out"]["kernel"].T)) + lyr.layer[1].layer_norm.weight = nn.Parameter( + torch.Tensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]) + ) + + lyr.layer[2].layer_norm.weight = nn.Parameter(torch.Tensor(ly_weight["pre_mlp_layer_norm"]["scale"])) + lyr.layer[2].film.scale_bias.weight = nn.Parameter( + torch.Tensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T) + ) + lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_0"]["kernel"].T)) + lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wi_1"]["kernel"].T)) + lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.Tensor(ly_weight["mlp"]["wo"]["kernel"].T)) + + model.decoder_norm.weight = nn.Parameter(torch.Tensor(weights["decoder_norm"]["scale"])) + + model.spec_out.weight = nn.Parameter(torch.Tensor(weights["spec_out_dense"]["kernel"].T)) + + return model + + +def main(args): + t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path) + t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint) + + gin_overrides = [ + "from __gin__ import dynamic_registration", + "from music_spectrogram_diffusion.models.diffusion import diffusion_utils", + "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0", + "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()", + ] + + gin_file = os.path.join(args.checkpoint_path, "..", "config.gin") + gin_config = inference.parse_training_gin_file(gin_file, gin_overrides) + synth_model = inference.InferenceModel(args.checkpoint_path, gin_config) + + scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large") + + notes_encoder = SpectrogramNotesEncoder( + max_length=synth_model.sequence_length["inputs"], + vocab_size=synth_model.model.module.config.vocab_size, + d_model=synth_model.model.module.config.emb_dim, + dropout_rate=synth_model.model.module.config.dropout_rate, + num_layers=synth_model.model.module.config.num_encoder_layers, + num_heads=synth_model.model.module.config.num_heads, + d_kv=synth_model.model.module.config.head_dim, + d_ff=synth_model.model.module.config.mlp_dim, + feed_forward_proj="gated-gelu", + ) + + continuous_encoder = SpectrogramContEncoder( + input_dims=synth_model.audio_codec.n_dims, + targets_context_length=synth_model.sequence_length["targets_context"], + d_model=synth_model.model.module.config.emb_dim, + dropout_rate=synth_model.model.module.config.dropout_rate, + num_layers=synth_model.model.module.config.num_encoder_layers, + num_heads=synth_model.model.module.config.num_heads, + d_kv=synth_model.model.module.config.head_dim, + d_ff=synth_model.model.module.config.mlp_dim, + feed_forward_proj="gated-gelu", + ) + + decoder = T5FilmDecoder( + input_dims=synth_model.audio_codec.n_dims, + targets_length=synth_model.sequence_length["targets_context"], + max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time, + d_model=synth_model.model.module.config.emb_dim, + num_layers=synth_model.model.module.config.num_decoder_layers, + num_heads=synth_model.model.module.config.num_heads, + d_kv=synth_model.model.module.config.head_dim, + d_ff=synth_model.model.module.config.mlp_dim, + dropout_rate=synth_model.model.module.config.dropout_rate, + ) + + notes_encoder = load_notes_encoder(t5_checkpoint["target"]["token_encoder"], notes_encoder) + continuous_encoder = load_continuous_encoder(t5_checkpoint["target"]["continuous_encoder"], continuous_encoder) + decoder = load_decoder(t5_checkpoint["target"]["decoder"], decoder) + + melgan = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder") + + pipe = SpectrogramDiffusionPipeline( + notes_encoder=notes_encoder, + continuous_encoder=continuous_encoder, + decoder=decoder, + scheduler=scheduler, + melgan=melgan, + ) + if args.save: + pipe.save_pretrained(args.output_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.") + parser.add_argument( + "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not." + ) + parser.add_argument( + "--checkpoint_path", + default=f"{MODEL}/checkpoint_500000", + type=str, + required=False, + help="Path to the original jax model checkpoint.", + ) + args = parser.parse_args() + + main(args) diff --git a/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py b/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py new file mode 100644 index 000000000000..bcab90e2a3db --- /dev/null +++ b/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py @@ -0,0 +1,185 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the NCSNPP checkpoints.""" + +import argparse +import json + +import torch + +from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel + + +def convert_ncsnpp_checkpoint(checkpoint, config): + """ + Takes a state dict and the path to + """ + new_model_architecture = UNet2DModel(**config) + new_model_architecture.time_proj.W.data = checkpoint["all_modules.0.W"].data + new_model_architecture.time_proj.weight.data = checkpoint["all_modules.0.W"].data + new_model_architecture.time_embedding.linear_1.weight.data = checkpoint["all_modules.1.weight"].data + new_model_architecture.time_embedding.linear_1.bias.data = checkpoint["all_modules.1.bias"].data + + new_model_architecture.time_embedding.linear_2.weight.data = checkpoint["all_modules.2.weight"].data + new_model_architecture.time_embedding.linear_2.bias.data = checkpoint["all_modules.2.bias"].data + + new_model_architecture.conv_in.weight.data = checkpoint["all_modules.3.weight"].data + new_model_architecture.conv_in.bias.data = checkpoint["all_modules.3.bias"].data + + new_model_architecture.conv_norm_out.weight.data = checkpoint[list(checkpoint.keys())[-4]].data + new_model_architecture.conv_norm_out.bias.data = checkpoint[list(checkpoint.keys())[-3]].data + new_model_architecture.conv_out.weight.data = checkpoint[list(checkpoint.keys())[-2]].data + new_model_architecture.conv_out.bias.data = checkpoint[list(checkpoint.keys())[-1]].data + + module_index = 4 + + def set_attention_weights(new_layer, old_checkpoint, index): + new_layer.query.weight.data = old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T + new_layer.key.weight.data = old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T + new_layer.value.weight.data = old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T + + new_layer.query.bias.data = old_checkpoint[f"all_modules.{index}.NIN_0.b"].data + new_layer.key.bias.data = old_checkpoint[f"all_modules.{index}.NIN_1.b"].data + new_layer.value.bias.data = old_checkpoint[f"all_modules.{index}.NIN_2.b"].data + + new_layer.proj_attn.weight.data = old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T + new_layer.proj_attn.bias.data = old_checkpoint[f"all_modules.{index}.NIN_3.b"].data + + new_layer.group_norm.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data + new_layer.group_norm.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data + + def set_resnet_weights(new_layer, old_checkpoint, index): + new_layer.conv1.weight.data = old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data + new_layer.conv1.bias.data = old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data + new_layer.norm1.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data + new_layer.norm1.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data + + new_layer.conv2.weight.data = old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data + new_layer.conv2.bias.data = old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data + new_layer.norm2.weight.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data + new_layer.norm2.bias.data = old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data + + new_layer.time_emb_proj.weight.data = old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data + new_layer.time_emb_proj.bias.data = old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data + + if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down: + new_layer.conv_shortcut.weight.data = old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data + new_layer.conv_shortcut.bias.data = old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data + + for i, block in enumerate(new_model_architecture.downsample_blocks): + has_attentions = hasattr(block, "attentions") + for j in range(len(block.resnets)): + set_resnet_weights(block.resnets[j], checkpoint, module_index) + module_index += 1 + if has_attentions: + set_attention_weights(block.attentions[j], checkpoint, module_index) + module_index += 1 + + if hasattr(block, "downsamplers") and block.downsamplers is not None: + set_resnet_weights(block.resnet_down, checkpoint, module_index) + module_index += 1 + block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data + block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data + module_index += 1 + + set_resnet_weights(new_model_architecture.mid_block.resnets[0], checkpoint, module_index) + module_index += 1 + set_attention_weights(new_model_architecture.mid_block.attentions[0], checkpoint, module_index) + module_index += 1 + set_resnet_weights(new_model_architecture.mid_block.resnets[1], checkpoint, module_index) + module_index += 1 + + for i, block in enumerate(new_model_architecture.up_blocks): + has_attentions = hasattr(block, "attentions") + for j in range(len(block.resnets)): + set_resnet_weights(block.resnets[j], checkpoint, module_index) + module_index += 1 + if has_attentions: + set_attention_weights( + block.attentions[0], checkpoint, module_index + ) # why can there only be a single attention layer for up? + module_index += 1 + + if hasattr(block, "resnet_up") and block.resnet_up is not None: + block.skip_norm.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data + block.skip_norm.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data + module_index += 1 + block.skip_conv.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data + block.skip_conv.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data + module_index += 1 + set_resnet_weights(block.resnet_up, checkpoint, module_index) + module_index += 1 + + new_model_architecture.conv_norm_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data + new_model_architecture.conv_norm_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data + module_index += 1 + new_model_architecture.conv_out.weight.data = checkpoint[f"all_modules.{module_index}.weight"].data + new_model_architecture.conv_out.bias.data = checkpoint[f"all_modules.{module_index}.bias"].data + + return new_model_architecture.state_dict() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", + default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin", + type=str, + required=False, + help="Path to the checkpoint to convert.", + ) + + parser.add_argument( + "--config_file", + default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json", + type=str, + required=False, + help="The config json file corresponding to the architecture.", + ) + + parser.add_argument( + "--dump_path", + default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt", + type=str, + required=False, + help="Path to the output model.", + ) + + args = parser.parse_args() + + checkpoint = torch.load(args.checkpoint_path, map_location="cpu") + + with open(args.config_file) as f: + config = json.loads(f.read()) + + converted_checkpoint = convert_ncsnpp_checkpoint( + checkpoint, + config, + ) + + if "sde" in config: + del config["sde"] + + model = UNet2DModel(**config) + model.load_state_dict(converted_checkpoint) + + try: + scheduler = ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1])) + + pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler) + pipe.save_pretrained(args.dump_path) + except: # noqa: E722 + model.save_pretrained(args.dump_path) diff --git a/scripts/convert_omnigen_to_diffusers.py b/scripts/convert_omnigen_to_diffusers.py new file mode 100644 index 000000000000..96bc935633f0 --- /dev/null +++ b/scripts/convert_omnigen_to_diffusers.py @@ -0,0 +1,203 @@ +import argparse +import os + +import torch +from huggingface_hub import snapshot_download +from safetensors.torch import load_file +from transformers import AutoTokenizer + +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, OmniGenPipeline, OmniGenTransformer2DModel + + +def main(args): + # checkpoint from https://huggingface.co/Shitao/OmniGen-v1 + + if not os.path.exists(args.origin_ckpt_path): + print("Model not found, downloading...") + cache_folder = os.getenv("HF_HUB_CACHE") + args.origin_ckpt_path = snapshot_download( + repo_id=args.origin_ckpt_path, + cache_dir=cache_folder, + ignore_patterns=["flax_model.msgpack", "rust_model.ot", "tf_model.h5", "model.pt"], + ) + print(f"Downloaded model to {args.origin_ckpt_path}") + + ckpt = os.path.join(args.origin_ckpt_path, "model.safetensors") + ckpt = load_file(ckpt, device="cpu") + + mapping_dict = { + "pos_embed": "patch_embedding.pos_embed", + "x_embedder.proj.weight": "patch_embedding.output_image_proj.weight", + "x_embedder.proj.bias": "patch_embedding.output_image_proj.bias", + "input_x_embedder.proj.weight": "patch_embedding.input_image_proj.weight", + "input_x_embedder.proj.bias": "patch_embedding.input_image_proj.bias", + "final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", + "final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", + "final_layer.linear.weight": "proj_out.weight", + "final_layer.linear.bias": "proj_out.bias", + "time_token.mlp.0.weight": "time_token.linear_1.weight", + "time_token.mlp.0.bias": "time_token.linear_1.bias", + "time_token.mlp.2.weight": "time_token.linear_2.weight", + "time_token.mlp.2.bias": "time_token.linear_2.bias", + "t_embedder.mlp.0.weight": "t_embedder.linear_1.weight", + "t_embedder.mlp.0.bias": "t_embedder.linear_1.bias", + "t_embedder.mlp.2.weight": "t_embedder.linear_2.weight", + "t_embedder.mlp.2.bias": "t_embedder.linear_2.bias", + "llm.embed_tokens.weight": "embed_tokens.weight", + } + + converted_state_dict = {} + for k, v in ckpt.items(): + if k in mapping_dict: + converted_state_dict[mapping_dict[k]] = v + elif "qkv" in k: + to_q, to_k, to_v = v.chunk(3) + converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_q.weight"] = to_q + converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_k.weight"] = to_k + converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_v.weight"] = to_v + elif "o_proj" in k: + converted_state_dict[f"layers.{k.split('.')[2]}.self_attn.to_out.0.weight"] = v + else: + converted_state_dict[k[4:]] = v + + transformer = OmniGenTransformer2DModel( + rope_scaling={ + "long_factor": [ + 1.0299999713897705, + 1.0499999523162842, + 1.0499999523162842, + 1.0799999237060547, + 1.2299998998641968, + 1.2299998998641968, + 1.2999999523162842, + 1.4499999284744263, + 1.5999999046325684, + 1.6499998569488525, + 1.8999998569488525, + 2.859999895095825, + 3.68999981880188, + 5.419999599456787, + 5.489999771118164, + 5.489999771118164, + 9.09000015258789, + 11.579999923706055, + 15.65999984741211, + 15.769999504089355, + 15.789999961853027, + 18.360000610351562, + 21.989999771118164, + 23.079999923706055, + 30.009998321533203, + 32.35000228881836, + 32.590003967285156, + 35.56000518798828, + 39.95000457763672, + 53.840003967285156, + 56.20000457763672, + 57.95000457763672, + 59.29000473022461, + 59.77000427246094, + 59.920005798339844, + 61.190006256103516, + 61.96000671386719, + 62.50000762939453, + 63.3700065612793, + 63.48000717163086, + 63.48000717163086, + 63.66000747680664, + 63.850006103515625, + 64.08000946044922, + 64.760009765625, + 64.80001068115234, + 64.81001281738281, + 64.81001281738281, + ], + "short_factor": [ + 1.05, + 1.05, + 1.05, + 1.1, + 1.1, + 1.1, + 1.2500000000000002, + 1.2500000000000002, + 1.4000000000000004, + 1.4500000000000004, + 1.5500000000000005, + 1.8500000000000008, + 1.9000000000000008, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.000000000000001, + 2.1000000000000005, + 2.1000000000000005, + 2.2, + 2.3499999999999996, + 2.3499999999999996, + 2.3499999999999996, + 2.3499999999999996, + 2.3999999999999995, + 2.3999999999999995, + 2.6499999999999986, + 2.6999999999999984, + 2.8999999999999977, + 2.9499999999999975, + 3.049999999999997, + 3.049999999999997, + 3.049999999999997, + ], + "type": "su", + }, + patch_size=2, + in_channels=4, + pos_embed_max_size=192, + ) + transformer.load_state_dict(converted_state_dict, strict=True) + transformer.to(torch.bfloat16) + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + scheduler = FlowMatchEulerDiscreteScheduler(invert_sigmas=True, num_train_timesteps=1) + + vae = AutoencoderKL.from_pretrained(os.path.join(args.origin_ckpt_path, "vae"), torch_dtype=torch.float32) + + tokenizer = AutoTokenizer.from_pretrained(args.origin_ckpt_path) + + pipeline = OmniGenPipeline(tokenizer=tokenizer, transformer=transformer, vae=vae, scheduler=scheduler) + pipeline.save_pretrained(args.dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--origin_ckpt_path", + default="Shitao/OmniGen-v1", + type=str, + required=False, + help="Path to the checkpoint to convert.", + ) + + parser.add_argument( + "--dump_path", default="OmniGen-v1-diffusers", type=str, required=False, help="Path to the output pipeline." + ) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_original_audioldm2_to_diffusers.py b/scripts/convert_original_audioldm2_to_diffusers.py new file mode 100644 index 000000000000..2c0695ce5595 --- /dev/null +++ b/scripts/convert_original_audioldm2_to_diffusers.py @@ -0,0 +1,1135 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the AudioLDM2 checkpoints.""" + +import argparse +import re +from typing import List, Union + +import torch +import yaml +from transformers import ( + AutoFeatureExtractor, + AutoTokenizer, + ClapConfig, + ClapModel, + GPT2Config, + GPT2Model, + SpeechT5HifiGan, + SpeechT5HifiGanConfig, + T5Config, + T5EncoderModel, +) + +from diffusers import ( + AudioLDM2Pipeline, + AudioLDM2ProjectionModel, + AudioLDM2UNet2DConditionModel, + AutoencoderKL, + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import is_safetensors_available +from diffusers.utils.import_utils import BACKENDS_MAPPING + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths +def renew_attention_paths(old_list): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] + proj_key = "to_out.0.weight" + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys or ".".join(key.split(".")[-3:]) == proj_key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key].squeeze() + + +def create_unet_diffusers_config(original_config, image_size: int): + """ + Creates a UNet config for diffusers based on the config of the original AudioLDM2 model. + """ + unet_params = original_config["model"]["params"]["unet_config"]["params"] + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + + block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) + + cross_attention_dim = list(unet_params["context_dim"]) if "context_dim" in unet_params else block_out_channels + if len(cross_attention_dim) > 1: + # require two or more cross-attention layers per-block, each of different dimension + cross_attention_dim = [cross_attention_dim for _ in range(len(block_out_channels))] + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params["in_channels"], + "out_channels": unet_params["out_channels"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params["num_res_blocks"], + "transformer_layers_per_block": unet_params["transformer_depth"], + "cross_attention_dim": tuple(cross_attention_dim), + } + + return config + + +# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config +def create_vae_diffusers_config(original_config, checkpoint, image_size: int): + """ + Creates a VAE config for diffusers based on the config of the original AudioLDM2 model. Compared to the original + Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. + """ + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] + + block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 + + config = { + "sample_size": image_size, + "in_channels": vae_params["in_channels"], + "out_channels": vae_params["out_ch"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params["z_channels"], + "layers_per_block": vae_params["num_res_blocks"], + "scaling_factor": float(scaling_factor), + } + return config + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular +def create_diffusers_schedular(original_config): + schedular = DDIMScheduler( + num_train_timesteps=original_config["model"]["params"]["timesteps"], + beta_start=original_config["model"]["params"]["linear_start"], + beta_end=original_config["model"]["params"]["linear_end"], + beta_schedule="scaled_linear", + ) + return schedular + + +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted UNet checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + # strip the unet prefix from the weight names + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}." in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key] + for layer_id in range(num_output_blocks) + } + + # Check how many Transformer blocks we have per layer + if isinstance(config.get("cross_attention_dim"), (list, tuple)): + if isinstance(config["cross_attention_dim"][0], (list, tuple)): + # in this case we have multiple cross-attention layers per-block + num_attention_layers = len(config.get("cross_attention_dim")[0]) + else: + num_attention_layers = 1 + + if config.get("extra_self_attn_layer"): + num_attention_layers += 1 + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.0" not in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = [ + { + "old": f"input_blocks.{i}.{1 + layer_id}", + "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}", + } + for layer_id in range(num_attention_layers) + ] + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config + ) + + resnet_0 = middle_blocks[0] + resnet_1 = middle_blocks[num_middle_blocks - 1] + + resnet_0_paths = renew_resnet_paths(resnet_0) + meta_path = {"old": "middle_block.0", "new": "mid_block.resnets.0"} + assign_to_checkpoint( + resnet_0_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_1_paths = renew_resnet_paths(resnet_1) + meta_path = {"old": f"middle_block.{len(middle_blocks) - 1}", "new": "mid_block.resnets.1"} + assign_to_checkpoint( + resnet_1_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(1, num_middle_blocks - 1): + attentions = middle_blocks[i] + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": f"middle_block.{i}", "new": f"mid_block.attentions.{i - 1}"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.0" not in key] + + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + attentions.remove(f"output_blocks.{i}.{index}.conv.bias") + attentions.remove(f"output_blocks.{i}.{index}.conv.weight") + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = [ + { + "old": f"output_blocks.{i}.{1 + layer_id}", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id * num_attention_layers + layer_id}", + } + for layer_id in range(num_attention_layers) + ] + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=meta_path, config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +CLAP_KEYS_TO_MODIFY_MAPPING = { + "text_branch": "text_model", + "audio_branch": "audio_model.audio_encoder", + "attn": "attention.self", + "self.proj": "output.dense", + "attention.self_mask": "attn_mask", + "mlp.fc1": "intermediate.dense", + "mlp.fc2": "output.dense", + "norm1": "layernorm_before", + "norm2": "layernorm_after", + "bn0": "batch_norm", +} + +CLAP_KEYS_TO_IGNORE = [ + "text_transform", + "audio_transform", + "stft", + "logmel_extractor", + "tscam_conv", + "head", + "attn_mask", +] + +CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] + + +def convert_open_clap_checkpoint(checkpoint): + """ + Takes a state dict and returns a converted CLAP checkpoint. + """ + # extract state dict for CLAP text embedding model, discarding the audio component + model_state_dict = {} + model_key = "clap.model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(model_key): + model_state_dict[key.replace(model_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + sequential_layers_pattern = r".*sequential.(\d+).*" + text_projection_pattern = r".*_projection.(\d+).*" + + for key, value in model_state_dict.items(): + # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end + for key_to_ignore in CLAP_KEYS_TO_IGNORE: + if key_to_ignore in key: + key = "spectrogram" + + # check if any key needs to be modified + for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): + if key_to_modify in key: + key = key.replace(key_to_modify, new_key) + + if re.match(sequential_layers_pattern, key): + # replace sequential layers with list + sequential_layer = re.match(sequential_layers_pattern, key).group(1) + + key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") + elif re.match(text_projection_pattern, key): + projecton_layer = int(re.match(text_projection_pattern, key).group(1)) + + # Because in CLAP they use `nn.Sequential`... + transformers_projection_layer = 1 if projecton_layer == 0 else 2 + + key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") + + if "audio" and "qkv" in key: + # split qkv into query key and value + mixed_qkv = value + qkv_dim = mixed_qkv.size(0) // 3 + + query_layer = mixed_qkv[:qkv_dim] + key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] + value_layer = mixed_qkv[qkv_dim * 2 :] + + new_checkpoint[key.replace("qkv", "query")] = query_layer + new_checkpoint[key.replace("qkv", "key")] = key_layer + new_checkpoint[key.replace("qkv", "value")] = value_layer + elif key != "spectrogram": + new_checkpoint[key] = value + + return new_checkpoint + + +def create_transformers_vocoder_config(original_config): + """ + Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. + """ + vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] + + config = { + "model_in_dim": vocoder_params["num_mels"], + "sampling_rate": vocoder_params["sampling_rate"], + "upsample_initial_channel": vocoder_params["upsample_initial_channel"], + "upsample_rates": list(vocoder_params["upsample_rates"]), + "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), + "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), + "resblock_dilation_sizes": [ + list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] + ], + "normalize_before": False, + } + + return config + + +def extract_sub_model(checkpoint, key_prefix): + """ + Takes a state dict and returns the state dict for a particular sub-model. + """ + + sub_model_state_dict = {} + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(key_prefix): + sub_model_state_dict[key.replace(key_prefix, "")] = checkpoint.get(key) + + return sub_model_state_dict + + +def convert_hifigan_checkpoint(checkpoint, config): + """ + Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. + """ + # extract state dict for vocoder + vocoder_state_dict = extract_sub_model(checkpoint, key_prefix="first_stage_model.vocoder.") + + # fix upsampler keys, everything else is correct already + for i in range(len(config.upsample_rates)): + vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") + vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") + + if not config.normalize_before: + # if we don't set normalize_before then these variables are unused, so we set them to their initialised values + vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) + vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) + + return vocoder_state_dict + + +def convert_projection_checkpoint(checkpoint): + projection_state_dict = {} + conditioner_state_dict = extract_sub_model(checkpoint, key_prefix="cond_stage_models.0.") + + projection_state_dict["sos_embed"] = conditioner_state_dict["start_of_sequence_tokens.weight"][0] + projection_state_dict["sos_embed_1"] = conditioner_state_dict["start_of_sequence_tokens.weight"][1] + + projection_state_dict["eos_embed"] = conditioner_state_dict["end_of_sequence_tokens.weight"][0] + projection_state_dict["eos_embed_1"] = conditioner_state_dict["end_of_sequence_tokens.weight"][1] + + projection_state_dict["projection.weight"] = conditioner_state_dict["input_sequence_embed_linear.0.weight"] + projection_state_dict["projection.bias"] = conditioner_state_dict["input_sequence_embed_linear.0.bias"] + + projection_state_dict["projection_1.weight"] = conditioner_state_dict["input_sequence_embed_linear.1.weight"] + projection_state_dict["projection_1.bias"] = conditioner_state_dict["input_sequence_embed_linear.1.bias"] + + return projection_state_dict + + +# Adapted from https://github.com/haoheliu/AudioLDM2/blob/81ad2c6ce015c1310387695e2dae975a7d2ed6fd/audioldm2/utils.py#L143 +DEFAULT_CONFIG = { + "model": { + "params": { + "linear_start": 0.0015, + "linear_end": 0.0195, + "timesteps": 1000, + "channels": 8, + "scale_by_std": True, + "unet_config": { + "target": "audioldm2.latent_diffusion.openaimodel.UNetModel", + "params": { + "context_dim": [None, 768, 1024], + "in_channels": 8, + "out_channels": 8, + "model_channels": 128, + "attention_resolutions": [8, 4, 2], + "num_res_blocks": 2, + "channel_mult": [1, 2, 3, 5], + "num_head_channels": 32, + "transformer_depth": 1, + }, + }, + "first_stage_config": { + "target": "audioldm2.variational_autoencoder.autoencoder.AutoencoderKL", + "params": { + "embed_dim": 8, + "ddconfig": { + "z_channels": 8, + "resolution": 256, + "in_channels": 1, + "out_ch": 1, + "ch": 128, + "ch_mult": [1, 2, 4], + "num_res_blocks": 2, + }, + }, + }, + "cond_stage_config": { + "crossattn_audiomae_generated": { + "target": "audioldm2.latent_diffusion.modules.encoders.modules.SequenceGenAudioMAECond", + "params": { + "sequence_gen_length": 8, + "sequence_input_embed_dim": [512, 1024], + }, + } + }, + "vocoder_config": { + "target": "audioldm2.first_stage_model.vocoder", + "params": { + "upsample_rates": [5, 4, 2, 2, 2], + "upsample_kernel_sizes": [16, 16, 8, 4, 4], + "upsample_initial_channel": 1024, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "num_mels": 64, + "sampling_rate": 16000, + }, + }, + }, + }, +} + + +def load_pipeline_from_original_AudioLDM2_ckpt( + checkpoint_path: str, + original_config_file: str = None, + image_size: int = 1024, + prediction_type: str = None, + extract_ema: bool = False, + scheduler_type: str = "ddim", + cross_attention_dim: Union[List, List[List]] = None, + transformer_layers_per_block: int = None, + device: str = None, + from_safetensors: bool = False, +) -> AudioLDM2Pipeline: + """ + Load an AudioLDM2 pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. + + Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the + global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is + recommended that you override the default values and/or supply an `original_config_file` wherever possible. + + Args: + checkpoint_path (`str`): Path to `.ckpt` file. + original_config_file (`str`): + Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically + set to the AudioLDM2 base config. + image_size (`int`, *optional*, defaults to 1024): + The image size that the model was trained on. + prediction_type (`str`, *optional*): + The prediction type that the model was trained on. If `None`, will be automatically + inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. + scheduler_type (`str`, *optional*, defaults to 'ddim'): + Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", + "ddim"]`. + cross_attention_dim (`list`, *optional*, defaults to `None`): + The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be + automatically inferred. Set to `[768, 1024]` for the base model, or `[768, 1024, None]` for the large model. + transformer_layers_per_block (`int`, *optional*, defaults to `None`): + The number of transformer layers in each transformer block. If `None`, number of layers will be " + "automatically inferred. Set to `1` for the base model, or `2` for the large model. + extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for + checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to + `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for + inference. Non-EMA weights are usually better to continue fine-tuning. + device (`str`, *optional*, defaults to `None`): + The device to use. Pass `None` to determine automatically. + from_safetensors (`str`, *optional*, defaults to `False`): + If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. + return: An AudioLDM2Pipeline object representing the passed-in `.ckpt`/`.safetensors` file. + """ + + if from_safetensors: + if not is_safetensors_available(): + raise ValueError(BACKENDS_MAPPING["safetensors"][1]) + + from safetensors import safe_open + + checkpoint = {} + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + checkpoint[key] = f.get_tensor(key) + else: + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + if "state_dict" in checkpoint: + checkpoint = checkpoint["state_dict"] + + if original_config_file is None: + original_config = DEFAULT_CONFIG + else: + original_config = yaml.safe_load(original_config_file) + + if image_size is not None: + original_config["model"]["params"]["unet_config"]["params"]["image_size"] = image_size + + if cross_attention_dim is not None: + original_config["model"]["params"]["unet_config"]["params"]["context_dim"] = cross_attention_dim + + if transformer_layers_per_block is not None: + original_config["model"]["params"]["unet_config"]["params"]["transformer_depth"] = transformer_layers_per_block + + if ( + "parameterization" in original_config["model"]["params"] + and original_config["model"]["params"]["parameterization"] == "v" + ): + if prediction_type is None: + prediction_type = "v_prediction" + else: + if prediction_type is None: + prediction_type = "epsilon" + + num_train_timesteps = original_config["model"]["params"]["timesteps"] + beta_start = original_config["model"]["params"]["linear_start"] + beta_end = original_config["model"]["params"]["linear_end"] + + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + # make sure scheduler works correctly with DDIM + scheduler.register_to_config(clip_sample=False) + + if scheduler_type == "pndm": + config = dict(scheduler.config) + config["skip_prk_steps"] = True + scheduler = PNDMScheduler.from_config(config) + elif scheduler_type == "lms": + scheduler = LMSDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "heun": + scheduler = HeunDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler": + scheduler = EulerDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler-ancestral": + scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "dpm": + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + elif scheduler_type == "ddim": + scheduler = scheduler + else: + raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") + + # Convert the UNet2DModel + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet = AudioLDM2UNet2DConditionModel(**unet_config) + + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema + ) + + unet.load_state_dict(converted_unet_checkpoint) + + # Convert the VAE model + vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + + # Convert the joint audio-text encoding model + clap_config = ClapConfig.from_pretrained("laion/clap-htsat-unfused") + clap_config.audio_config.update( + { + "patch_embeds_hidden_size": 128, + "hidden_size": 1024, + "depths": [2, 2, 12, 2], + } + ) + # AudioLDM2 uses the same tokenizer and feature extractor as the original CLAP model + clap_tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") + clap_feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused") + + converted_clap_model = convert_open_clap_checkpoint(checkpoint) + clap_model = ClapModel(clap_config) + + missing_keys, unexpected_keys = clap_model.load_state_dict(converted_clap_model, strict=False) + # we expect not to have token_type_ids in our original state dict so let's ignore them + missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) + + if len(unexpected_keys) > 0: + raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") + + if len(missing_keys) > 0: + raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") + + # Convert the vocoder model + vocoder_config = create_transformers_vocoder_config(original_config) + vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) + converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) + + vocoder = SpeechT5HifiGan(vocoder_config) + vocoder.load_state_dict(converted_vocoder_checkpoint) + + # Convert the Flan-T5 encoder model: AudioLDM2 uses the same configuration and tokenizer as the original Flan-T5 large model + t5_config = T5Config.from_pretrained("google/flan-t5-large") + converted_t5_checkpoint = extract_sub_model(checkpoint, key_prefix="cond_stage_models.1.model.") + + t5_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large") + # hard-coded in the original implementation (i.e. not retrievable from the config) + t5_tokenizer.model_max_length = 128 + t5_model = T5EncoderModel(t5_config) + t5_model.load_state_dict(converted_t5_checkpoint) + + # Convert the GPT2 encoder model: AudioLDM2 uses the same configuration as the original GPT2 base model + gpt2_config = GPT2Config.from_pretrained("gpt2") + gpt2_model = GPT2Model(gpt2_config) + gpt2_model.config.max_new_tokens = original_config["model"]["params"]["cond_stage_config"][ + "crossattn_audiomae_generated" + ]["params"]["sequence_gen_length"] + + converted_gpt2_checkpoint = extract_sub_model(checkpoint, key_prefix="cond_stage_models.0.model.") + gpt2_model.load_state_dict(converted_gpt2_checkpoint) + + # Convert the extra embedding / projection layers + projection_model = AudioLDM2ProjectionModel(clap_config.projection_dim, t5_config.d_model, gpt2_config.n_embd) + + converted_projection_checkpoint = convert_projection_checkpoint(checkpoint) + projection_model.load_state_dict(converted_projection_checkpoint) + + # Instantiate the diffusers pipeline + pipe = AudioLDM2Pipeline( + vae=vae, + text_encoder=clap_model, + text_encoder_2=t5_model, + projection_model=projection_model, + language_model=gpt2_model, + tokenizer=clap_tokenizer, + tokenizer_2=t5_tokenizer, + feature_extractor=clap_feature_extractor, + unet=unet, + scheduler=scheduler, + vocoder=vocoder, + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--cross_attention_dim", + default=None, + type=int, + nargs="+", + help="The dimension of the cross-attention layers. If `None`, the cross-attention dimension will be " + "automatically inferred. Set to `768+1024` for the base model, or `768+1024+640` for the large model", + ) + parser.add_argument( + "--transformer_layers_per_block", + default=None, + type=int, + help="The number of transformer layers in each transformer block. If `None`, number of layers will be " + "automatically inferred. Set to `1` for the base model, or `2` for the large model.", + ) + parser.add_argument( + "--scheduler_type", + default="ddim", + type=str, + help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", + ) + parser.add_argument( + "--image_size", + default=1048, + type=int, + help="The image size that the model was trained on.", + ) + parser.add_argument( + "--prediction_type", + default=None, + type=str, + help=("The prediction type that the model was trained on."), + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--from_safetensors", + action="store_true", + help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + args = parser.parse_args() + + pipe = load_pipeline_from_original_AudioLDM2_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + image_size=args.image_size, + prediction_type=args.prediction_type, + extract_ema=args.extract_ema, + scheduler_type=args.scheduler_type, + cross_attention_dim=args.cross_attention_dim, + transformer_layers_per_block=args.transformer_layers_per_block, + from_safetensors=args.from_safetensors, + device=args.device, + ) + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_audioldm_to_diffusers.py b/scripts/convert_original_audioldm_to_diffusers.py new file mode 100644 index 000000000000..44183f1aea29 --- /dev/null +++ b/scripts/convert_original_audioldm_to_diffusers.py @@ -0,0 +1,1042 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the AudioLDM checkpoints.""" + +import argparse +import re + +import torch +import yaml +from transformers import ( + AutoTokenizer, + ClapTextConfig, + ClapTextModelWithProjection, + SpeechT5HifiGan, + SpeechT5HifiGanConfig, +) + +from diffusers import ( + AudioLDMPipeline, + AutoencoderKL, + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, + UNet2DConditionModel, +) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths +def renew_attention_paths(old_list): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "query.weight") + new_item = new_item.replace("q.bias", "query.bias") + + new_item = new_item.replace("k.weight", "key.weight") + new_item = new_item.replace("k.bias", "key.bias") + + new_item = new_item.replace("v.weight", "value.weight") + new_item = new_item.replace("v.bias", "value.bias") + + new_item = new_item.replace("proj_out.weight", "proj_attn.weight") + new_item = new_item.replace("proj_out.bias", "proj_attn.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def create_unet_diffusers_config(original_config, image_size: int): + """ + Creates a UNet config for diffusers based on the config of the original AudioLDM model. + """ + unet_params = original_config["model"]["params"]["unet_config"]["params"] + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + + block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) + + cross_attention_dim = ( + unet_params["cross_attention_dim"] if "cross_attention_dim" in unet_params else block_out_channels + ) + + class_embed_type = "simple_projection" if "extra_film_condition_dim" in unet_params else None + projection_class_embeddings_input_dim = ( + unet_params["extra_film_condition_dim"] if "extra_film_condition_dim" in unet_params else None + ) + class_embeddings_concat = unet_params["extra_film_use_concat"] if "extra_film_use_concat" in unet_params else None + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params["in_channels"], + "out_channels": unet_params["out_channels"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params["num_res_blocks"], + "cross_attention_dim": cross_attention_dim, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "class_embeddings_concat": class_embeddings_concat, + } + + return config + + +# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config +def create_vae_diffusers_config(original_config, checkpoint, image_size: int): + """ + Creates a VAE config for diffusers based on the config of the original AudioLDM model. Compared to the original + Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. + """ + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] + + block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 + + config = { + "sample_size": image_size, + "in_channels": vae_params["in_channels"], + "out_channels": vae_params["out_ch"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params["z_channels"], + "layers_per_block": vae_params["num_res_blocks"], + "scaling_factor": float(scaling_factor), + } + return config + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular +def create_diffusers_schedular(original_config): + schedular = DDIMScheduler( + num_train_timesteps=original_config["model"]["params"]["timesteps"], + beta_start=original_config["model"]["params"]["linear_start"], + beta_end=original_config["model"]["params"]["linear_end"], + beta_schedule="scaled_linear", + ) + return schedular + + +# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_unet_checkpoint +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion + conversion, this function additionally converts the learnt film embedding linear layer. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + new_checkpoint["class_embedding.weight"] = unet_state_dict["film_emb.weight"] + new_checkpoint["class_embedding.bias"] = unet_state_dict["film_emb.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +CLAP_KEYS_TO_MODIFY_MAPPING = { + "text_branch": "text_model", + "attn": "attention.self", + "self.proj": "output.dense", + "attention.self_mask": "attn_mask", + "mlp.fc1": "intermediate.dense", + "mlp.fc2": "output.dense", + "norm1": "layernorm_before", + "norm2": "layernorm_after", + "bn0": "batch_norm", +} + +CLAP_KEYS_TO_IGNORE = ["text_transform"] + +CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] + + +def convert_open_clap_checkpoint(checkpoint): + """ + Takes a state dict and returns a converted CLAP checkpoint. + """ + # extract state dict for CLAP text embedding model, discarding the audio component + model_state_dict = {} + model_key = "cond_stage_model.model.text_" + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(model_key): + model_state_dict[key.replace(model_key, "text_")] = checkpoint.get(key) + + new_checkpoint = {} + + sequential_layers_pattern = r".*sequential.(\d+).*" + text_projection_pattern = r".*_projection.(\d+).*" + + for key, value in model_state_dict.items(): + # check if key should be ignored in mapping + if key.split(".")[0] in CLAP_KEYS_TO_IGNORE: + continue + + # check if any key needs to be modified + for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): + if key_to_modify in key: + key = key.replace(key_to_modify, new_key) + + if re.match(sequential_layers_pattern, key): + # replace sequential layers with list + sequential_layer = re.match(sequential_layers_pattern, key).group(1) + + key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") + elif re.match(text_projection_pattern, key): + projecton_layer = int(re.match(text_projection_pattern, key).group(1)) + + # Because in CLAP they use `nn.Sequential`... + transformers_projection_layer = 1 if projecton_layer == 0 else 2 + + key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") + + if "audio" and "qkv" in key: + # split qkv into query key and value + mixed_qkv = value + qkv_dim = mixed_qkv.size(0) // 3 + + query_layer = mixed_qkv[:qkv_dim] + key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] + value_layer = mixed_qkv[qkv_dim * 2 :] + + new_checkpoint[key.replace("qkv", "query")] = query_layer + new_checkpoint[key.replace("qkv", "key")] = key_layer + new_checkpoint[key.replace("qkv", "value")] = value_layer + else: + new_checkpoint[key] = value + + return new_checkpoint + + +def create_transformers_vocoder_config(original_config): + """ + Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. + """ + vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] + + config = { + "model_in_dim": vocoder_params["num_mels"], + "sampling_rate": vocoder_params["sampling_rate"], + "upsample_initial_channel": vocoder_params["upsample_initial_channel"], + "upsample_rates": list(vocoder_params["upsample_rates"]), + "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), + "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), + "resblock_dilation_sizes": [ + list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] + ], + "normalize_before": False, + } + + return config + + +def convert_hifigan_checkpoint(checkpoint, config): + """ + Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. + """ + # extract state dict for vocoder + vocoder_state_dict = {} + vocoder_key = "first_stage_model.vocoder." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vocoder_key): + vocoder_state_dict[key.replace(vocoder_key, "")] = checkpoint.get(key) + + # fix upsampler keys, everything else is correct already + for i in range(len(config.upsample_rates)): + vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") + vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") + + if not config.normalize_before: + # if we don't set normalize_before then these variables are unused, so we set them to their initialised values + vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) + vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) + + return vocoder_state_dict + + +# Adapted from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/audioldm/utils.py#L72-L73 +DEFAULT_CONFIG = { + "model": { + "params": { + "linear_start": 0.0015, + "linear_end": 0.0195, + "timesteps": 1000, + "channels": 8, + "scale_by_std": True, + "unet_config": { + "target": "audioldm.latent_diffusion.openaimodel.UNetModel", + "params": { + "extra_film_condition_dim": 512, + "extra_film_use_concat": True, + "in_channels": 8, + "out_channels": 8, + "model_channels": 128, + "attention_resolutions": [8, 4, 2], + "num_res_blocks": 2, + "channel_mult": [1, 2, 3, 5], + "num_head_channels": 32, + }, + }, + "first_stage_config": { + "target": "audioldm.variational_autoencoder.autoencoder.AutoencoderKL", + "params": { + "embed_dim": 8, + "ddconfig": { + "z_channels": 8, + "resolution": 256, + "in_channels": 1, + "out_ch": 1, + "ch": 128, + "ch_mult": [1, 2, 4], + "num_res_blocks": 2, + }, + }, + }, + "vocoder_config": { + "target": "audioldm.first_stage_model.vocoder", + "params": { + "upsample_rates": [5, 4, 2, 2, 2], + "upsample_kernel_sizes": [16, 16, 8, 4, 4], + "upsample_initial_channel": 1024, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "num_mels": 64, + "sampling_rate": 16000, + }, + }, + }, + }, +} + + +def load_pipeline_from_original_audioldm_ckpt( + checkpoint_path: str, + original_config_file: str = None, + image_size: int = 512, + prediction_type: str = None, + extract_ema: bool = False, + scheduler_type: str = "ddim", + num_in_channels: int = None, + model_channels: int = None, + num_head_channels: int = None, + device: str = None, + from_safetensors: bool = False, +) -> AudioLDMPipeline: + """ + Load an AudioLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. + + Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the + global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is + recommended that you override the default values and/or supply an `original_config_file` wherever possible. + + Args: + checkpoint_path (`str`): Path to `.ckpt` file. + original_config_file (`str`): + Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically + set to the audioldm-s-full-v2 config. + image_size (`int`, *optional*, defaults to 512): + The image size that the model was trained on. + prediction_type (`str`, *optional*): + The prediction type that the model was trained on. If `None`, will be automatically + inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. + num_in_channels (`int`, *optional*, defaults to None): + The number of UNet input channels. If `None`, it will be automatically inferred from the config. + model_channels (`int`, *optional*, defaults to None): + The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override + to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large. + num_head_channels (`int`, *optional*, defaults to None): + The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override + to 32 for the small and medium checkpoints, and 64 for the large. + scheduler_type (`str`, *optional*, defaults to 'pndm'): + Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", + "ddim"]`. + extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for + checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to + `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for + inference. Non-EMA weights are usually better to continue fine-tuning. + device (`str`, *optional*, defaults to `None`): + The device to use. Pass `None` to determine automatically. + from_safetensors (`str`, *optional*, defaults to `False`): + If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. + return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file. + """ + + if from_safetensors: + from safetensors import safe_open + + checkpoint = {} + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + checkpoint[key] = f.get_tensor(key) + else: + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + if "state_dict" in checkpoint: + checkpoint = checkpoint["state_dict"] + + if original_config_file is None: + original_config = DEFAULT_CONFIG + else: + original_config = yaml.safe_load(original_config_file) + + if num_in_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels + + if model_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["model_channels"] = model_channels + + if num_head_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["num_head_channels"] = num_head_channels + + if ( + "parameterization" in original_config["model"]["params"] + and original_config["model"]["params"]["parameterization"] == "v" + ): + if prediction_type is None: + prediction_type = "v_prediction" + else: + if prediction_type is None: + prediction_type = "epsilon" + + if image_size is None: + image_size = 512 + + num_train_timesteps = original_config["model"]["params"]["timesteps"] + beta_start = original_config["model"]["params"]["linear_start"] + beta_end = original_config["model"]["params"]["linear_end"] + + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + # make sure scheduler works correctly with DDIM + scheduler.register_to_config(clip_sample=False) + + if scheduler_type == "pndm": + config = dict(scheduler.config) + config["skip_prk_steps"] = True + scheduler = PNDMScheduler.from_config(config) + elif scheduler_type == "lms": + scheduler = LMSDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "heun": + scheduler = HeunDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler": + scheduler = EulerDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler-ancestral": + scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "dpm": + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + elif scheduler_type == "ddim": + scheduler = scheduler + else: + raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") + + # Convert the UNet2DModel + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet = UNet2DConditionModel(**unet_config) + + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema + ) + + unet.load_state_dict(converted_unet_checkpoint) + + # Convert the VAE model + vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + + # Convert the text model + # AudioLDM uses the same configuration and tokenizer as the original CLAP model + config = ClapTextConfig.from_pretrained("laion/clap-htsat-unfused") + tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") + + converted_text_model = convert_open_clap_checkpoint(checkpoint) + text_model = ClapTextModelWithProjection(config) + + missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False) + # we expect not to have token_type_ids in our original state dict so let's ignore them + missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) + + if len(unexpected_keys) > 0: + raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") + + if len(missing_keys) > 0: + raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") + + # Convert the vocoder model + vocoder_config = create_transformers_vocoder_config(original_config) + vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) + converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) + + vocoder = SpeechT5HifiGan(vocoder_config) + vocoder.load_state_dict(converted_vocoder_checkpoint) + + # Instantiate the diffusers pipeline + pipe = AudioLDMPipeline( + vae=vae, + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + vocoder=vocoder, + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--num_in_channels", + default=None, + type=int, + help="The number of input channels. If `None` number of input channels will be automatically inferred.", + ) + parser.add_argument( + "--model_channels", + default=None, + type=int, + help="The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override" + " to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.", + ) + parser.add_argument( + "--num_head_channels", + default=None, + type=int, + help="The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override" + " to 32 for the small and medium checkpoints, and 64 for the large.", + ) + parser.add_argument( + "--scheduler_type", + default="ddim", + type=str, + help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", + ) + parser.add_argument( + "--image_size", + default=None, + type=int, + help=("The image size that the model was trained on."), + ) + parser.add_argument( + "--prediction_type", + default=None, + type=str, + help=("The prediction type that the model was trained on."), + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--from_safetensors", + action="store_true", + help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + args = parser.parse_args() + + pipe = load_pipeline_from_original_audioldm_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + image_size=args.image_size, + prediction_type=args.prediction_type, + extract_ema=args.extract_ema, + scheduler_type=args.scheduler_type, + num_in_channels=args.num_in_channels, + model_channels=args.model_channels, + num_head_channels=args.num_head_channels, + from_safetensors=args.from_safetensors, + device=args.device, + ) + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_controlnet_to_diffusers.py b/scripts/convert_original_controlnet_to_diffusers.py new file mode 100644 index 000000000000..4c6fe90cb09f --- /dev/null +++ b/scripts/convert_original_controlnet_to_diffusers.py @@ -0,0 +1,109 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for stable diffusion checkpoints which _only_ contain a controlnet.""" + +import argparse + +from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + type=str, + required=True, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--num_in_channels", + default=None, + type=int, + help="The number of input channels. If `None` number of input channels will be automatically inferred.", + ) + parser.add_argument( + "--image_size", + default=512, + type=int, + help=( + "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2" + " Base. Use 768 for Stable Diffusion v2." + ), + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--upcast_attention", + action="store_true", + help=( + "Whether the attention computation should always be upcasted. This is necessary when running stable" + " diffusion 2.1." + ), + ) + parser.add_argument( + "--from_safetensors", + action="store_true", + help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + + # small workaround to get argparser to parse a boolean input as either true _or_ false + def parse_bool(string): + if string == "True": + return True + elif string == "False": + return False + else: + raise ValueError(f"could not parse string as bool {string}") + + parser.add_argument( + "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool + ) + + parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int) + + args = parser.parse_args() + + controlnet = download_controlnet_from_original_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + image_size=args.image_size, + extract_ema=args.extract_ema, + num_in_channels=args.num_in_channels, + upcast_attention=args.upcast_attention, + from_safetensors=args.from_safetensors, + device=args.device, + use_linear_projection=args.use_linear_projection, + cross_attention_dim=args.cross_attention_dim, + ) + + controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_musicldm_to_diffusers.py b/scripts/convert_original_musicldm_to_diffusers.py new file mode 100644 index 000000000000..00836fde2592 --- /dev/null +++ b/scripts/convert_original_musicldm_to_diffusers.py @@ -0,0 +1,1056 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the MusicLDM checkpoints.""" + +import argparse +import re + +import torch +import yaml +from transformers import ( + AutoFeatureExtractor, + AutoTokenizer, + ClapConfig, + ClapModel, + SpeechT5HifiGan, + SpeechT5HifiGanConfig, +) + +from diffusers import ( + AutoencoderKL, + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + HeunDiscreteScheduler, + LMSDiscreteScheduler, + MusicLDMPipeline, + PNDMScheduler, + UNet2DConditionModel, +) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths +def renew_attention_paths(old_list): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] + proj_key = "to_out.0.weight" + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys or ".".join(key.split(".")[-3:]) == proj_key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key].squeeze() + + +def create_unet_diffusers_config(original_config, image_size: int): + """ + Creates a UNet config for diffusers based on the config of the original MusicLDM model. + """ + unet_params = original_config["model"]["params"]["unet_config"]["params"] + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + + block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) + + cross_attention_dim = ( + unet_params["cross_attention_dim"] if "cross_attention_dim" in unet_params else block_out_channels + ) + + class_embed_type = "simple_projection" if "extra_film_condition_dim" in unet_params else None + projection_class_embeddings_input_dim = ( + unet_params["extra_film_condition_dim"] if "extra_film_condition_dim" in unet_params else None + ) + class_embeddings_concat = unet_params["extra_film_use_concat"] if "extra_film_use_concat" in unet_params else None + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params["in_channels"], + "out_channels": unet_params["out_channels"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params["num_res_blocks"], + "cross_attention_dim": cross_attention_dim, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "class_embeddings_concat": class_embeddings_concat, + } + + return config + + +# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config +def create_vae_diffusers_config(original_config, checkpoint, image_size: int): + """ + Creates a VAE config for diffusers based on the config of the original MusicLDM model. Compared to the original + Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE. + """ + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] + + block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config["model"]["params"] else 0.18215 + + config = { + "sample_size": image_size, + "in_channels": vae_params["in_channels"], + "out_channels": vae_params["out_ch"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params["z_channels"], + "layers_per_block": vae_params["num_res_blocks"], + "scaling_factor": float(scaling_factor), + } + return config + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular +def create_diffusers_schedular(original_config): + schedular = DDIMScheduler( + num_train_timesteps=original_config["model"]["params"]["timesteps"], + beta_start=original_config["model"]["params"]["linear_start"], + beta_end=original_config["model"]["params"]["linear_end"], + beta_schedule="scaled_linear", + ) + return schedular + + +def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion + conversion, this function additionally converts the learnt film embedding linear layer. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + print(f"Checkpoint {path} has both EMA and non-EMA weights.") + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + new_checkpoint["class_embedding.weight"] = unet_state_dict["film_emb.weight"] + new_checkpoint["class_embedding.bias"] = unet_state_dict["film_emb.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +CLAP_KEYS_TO_MODIFY_MAPPING = { + "text_branch": "text_model", + "audio_branch": "audio_model.audio_encoder", + "attn": "attention.self", + "self.proj": "output.dense", + "attention.self_mask": "attn_mask", + "mlp.fc1": "intermediate.dense", + "mlp.fc2": "output.dense", + "norm1": "layernorm_before", + "norm2": "layernorm_after", + "bn0": "batch_norm", +} + +CLAP_KEYS_TO_IGNORE = [ + "text_transform", + "audio_transform", + "stft", + "logmel_extractor", + "tscam_conv", + "head", + "attn_mask", +] + +CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"] + + +def convert_open_clap_checkpoint(checkpoint): + """ + Takes a state dict and returns a converted CLAP checkpoint. + """ + # extract state dict for CLAP text embedding model, discarding the audio component + model_state_dict = {} + model_key = "cond_stage_model.model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(model_key): + model_state_dict[key.replace(model_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + sequential_layers_pattern = r".*sequential.(\d+).*" + text_projection_pattern = r".*_projection.(\d+).*" + + for key, value in model_state_dict.items(): + # check if key should be ignored in mapping - if so map it to a key name that we'll filter out at the end + for key_to_ignore in CLAP_KEYS_TO_IGNORE: + if key_to_ignore in key: + key = "spectrogram" + + # check if any key needs to be modified + for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items(): + if key_to_modify in key: + key = key.replace(key_to_modify, new_key) + + if re.match(sequential_layers_pattern, key): + # replace sequential layers with list + sequential_layer = re.match(sequential_layers_pattern, key).group(1) + + key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer) // 3}.linear.") + elif re.match(text_projection_pattern, key): + projecton_layer = int(re.match(text_projection_pattern, key).group(1)) + + # Because in CLAP they use `nn.Sequential`... + transformers_projection_layer = 1 if projecton_layer == 0 else 2 + + key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.") + + if "audio" and "qkv" in key: + # split qkv into query key and value + mixed_qkv = value + qkv_dim = mixed_qkv.size(0) // 3 + + query_layer = mixed_qkv[:qkv_dim] + key_layer = mixed_qkv[qkv_dim : qkv_dim * 2] + value_layer = mixed_qkv[qkv_dim * 2 :] + + new_checkpoint[key.replace("qkv", "query")] = query_layer + new_checkpoint[key.replace("qkv", "key")] = key_layer + new_checkpoint[key.replace("qkv", "value")] = value_layer + elif key != "spectrogram": + new_checkpoint[key] = value + + return new_checkpoint + + +def create_transformers_vocoder_config(original_config): + """ + Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model. + """ + vocoder_params = original_config["model"]["params"]["vocoder_config"]["params"] + + config = { + "model_in_dim": vocoder_params["num_mels"], + "sampling_rate": vocoder_params["sampling_rate"], + "upsample_initial_channel": vocoder_params["upsample_initial_channel"], + "upsample_rates": list(vocoder_params["upsample_rates"]), + "upsample_kernel_sizes": list(vocoder_params["upsample_kernel_sizes"]), + "resblock_kernel_sizes": list(vocoder_params["resblock_kernel_sizes"]), + "resblock_dilation_sizes": [ + list(resblock_dilation) for resblock_dilation in vocoder_params["resblock_dilation_sizes"] + ], + "normalize_before": False, + } + + return config + + +def convert_hifigan_checkpoint(checkpoint, config): + """ + Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint. + """ + # extract state dict for vocoder + vocoder_state_dict = {} + vocoder_key = "first_stage_model.vocoder." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vocoder_key): + vocoder_state_dict[key.replace(vocoder_key, "")] = checkpoint.get(key) + + # fix upsampler keys, everything else is correct already + for i in range(len(config.upsample_rates)): + vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight") + vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias") + + if not config.normalize_before: + # if we don't set normalize_before then these variables are unused, so we set them to their initialised values + vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim) + vocoder_state_dict["scale"] = torch.ones(config.model_in_dim) + + return vocoder_state_dict + + +# Adapted from https://huggingface.co/spaces/haoheliu/MusicLDM-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/MusicLDM/utils.py#L72-L73 +DEFAULT_CONFIG = { + "model": { + "params": { + "linear_start": 0.0015, + "linear_end": 0.0195, + "timesteps": 1000, + "channels": 8, + "scale_by_std": True, + "unet_config": { + "target": "MusicLDM.latent_diffusion.openaimodel.UNetModel", + "params": { + "extra_film_condition_dim": 512, + "extra_film_use_concat": True, + "in_channels": 8, + "out_channels": 8, + "model_channels": 128, + "attention_resolutions": [8, 4, 2], + "num_res_blocks": 2, + "channel_mult": [1, 2, 3, 5], + "num_head_channels": 32, + }, + }, + "first_stage_config": { + "target": "MusicLDM.variational_autoencoder.autoencoder.AutoencoderKL", + "params": { + "embed_dim": 8, + "ddconfig": { + "z_channels": 8, + "resolution": 256, + "in_channels": 1, + "out_ch": 1, + "ch": 128, + "ch_mult": [1, 2, 4], + "num_res_blocks": 2, + }, + }, + }, + "vocoder_config": { + "target": "MusicLDM.first_stage_model.vocoder", + "params": { + "upsample_rates": [5, 4, 2, 2, 2], + "upsample_kernel_sizes": [16, 16, 8, 4, 4], + "upsample_initial_channel": 1024, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "num_mels": 64, + "sampling_rate": 16000, + }, + }, + }, + }, +} + + +def load_pipeline_from_original_MusicLDM_ckpt( + checkpoint_path: str, + original_config_file: str = None, + image_size: int = 1024, + prediction_type: str = None, + extract_ema: bool = False, + scheduler_type: str = "ddim", + num_in_channels: int = None, + model_channels: int = None, + num_head_channels: int = None, + device: str = None, + from_safetensors: bool = False, +) -> MusicLDMPipeline: + """ + Load an MusicLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file. + + Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the + global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is + recommended that you override the default values and/or supply an `original_config_file` wherever possible. + + Args: + checkpoint_path (`str`): Path to `.ckpt` file. + original_config_file (`str`): + Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically + set to the MusicLDM-s-full-v2 config. + image_size (`int`, *optional*, defaults to 1024): + The image size that the model was trained on. + prediction_type (`str`, *optional*): + The prediction type that the model was trained on. If `None`, will be automatically + inferred by looking for a key in the config. For the default config, the prediction type is `'epsilon'`. + num_in_channels (`int`, *optional*, defaults to None): + The number of UNet input channels. If `None`, it will be automatically inferred from the config. + model_channels (`int`, *optional*, defaults to None): + The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override + to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large. + num_head_channels (`int`, *optional*, defaults to None): + The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override + to 32 for the small and medium checkpoints, and 64 for the large. + scheduler_type (`str`, *optional*, defaults to 'pndm'): + Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm", + "ddim"]`. + extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for + checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to + `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for + inference. Non-EMA weights are usually better to continue fine-tuning. + device (`str`, *optional*, defaults to `None`): + The device to use. Pass `None` to determine automatically. + from_safetensors (`str`, *optional*, defaults to `False`): + If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. + return: An MusicLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file. + """ + if from_safetensors: + from safetensors import safe_open + + checkpoint = {} + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + checkpoint[key] = f.get_tensor(key) + else: + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + checkpoint = torch.load(checkpoint_path, map_location=device) + else: + checkpoint = torch.load(checkpoint_path, map_location=device) + + if "state_dict" in checkpoint: + checkpoint = checkpoint["state_dict"] + + if original_config_file is None: + original_config = DEFAULT_CONFIG + else: + original_config = yaml.safe_load(original_config_file) + + if num_in_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels + + if model_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["model_channels"] = model_channels + + if num_head_channels is not None: + original_config["model"]["params"]["unet_config"]["params"]["num_head_channels"] = num_head_channels + + if ( + "parameterization" in original_config["model"]["params"] + and original_config["model"]["params"]["parameterization"] == "v" + ): + if prediction_type is None: + prediction_type = "v_prediction" + else: + if prediction_type is None: + prediction_type = "epsilon" + + if image_size is None: + image_size = 512 + + num_train_timesteps = original_config["model"]["params"]["timesteps"] + beta_start = original_config["model"]["params"]["linear_start"] + beta_end = original_config["model"]["params"]["linear_end"] + + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + # make sure scheduler works correctly with DDIM + scheduler.register_to_config(clip_sample=False) + + if scheduler_type == "pndm": + config = dict(scheduler.config) + config["skip_prk_steps"] = True + scheduler = PNDMScheduler.from_config(config) + elif scheduler_type == "lms": + scheduler = LMSDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "heun": + scheduler = HeunDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler": + scheduler = EulerDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "euler-ancestral": + scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config) + elif scheduler_type == "dpm": + scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config) + elif scheduler_type == "ddim": + scheduler = scheduler + else: + raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!") + + # Convert the UNet2DModel + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet = UNet2DConditionModel(**unet_config) + + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema + ) + + unet.load_state_dict(converted_unet_checkpoint) + + # Convert the VAE model + vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + + # Convert the text model + # MusicLDM uses the same tokenizer as the original CLAP model, but a slightly different configuration + config = ClapConfig.from_pretrained("laion/clap-htsat-unfused") + config.audio_config.update( + { + "patch_embeds_hidden_size": 128, + "hidden_size": 1024, + "depths": [2, 2, 12, 2], + } + ) + tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused") + feature_extractor = AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused") + + converted_text_model = convert_open_clap_checkpoint(checkpoint) + text_model = ClapModel(config) + + missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False) + # we expect not to have token_type_ids in our original state dict so let's ignore them + missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS)) + + if len(unexpected_keys) > 0: + raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}") + + if len(missing_keys) > 0: + raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}") + + # Convert the vocoder model + vocoder_config = create_transformers_vocoder_config(original_config) + vocoder_config = SpeechT5HifiGanConfig(**vocoder_config) + converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config) + + vocoder = SpeechT5HifiGan(vocoder_config) + vocoder.load_state_dict(converted_vocoder_checkpoint) + + # Instantiate the diffusers pipeline + pipe = MusicLDMPipeline( + vae=vae, + text_encoder=text_model, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + vocoder=vocoder, + feature_extractor=feature_extractor, + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--num_in_channels", + default=None, + type=int, + help="The number of input channels. If `None` number of input channels will be automatically inferred.", + ) + parser.add_argument( + "--model_channels", + default=None, + type=int, + help="The number of UNet model channels. If `None`, it will be automatically inferred from the config. Override" + " to 128 for the small checkpoints, 192 for the medium checkpoints and 256 for the large.", + ) + parser.add_argument( + "--num_head_channels", + default=None, + type=int, + help="The number of UNet head channels. If `None`, it will be automatically inferred from the config. Override" + " to 32 for the small and medium checkpoints, and 64 for the large.", + ) + parser.add_argument( + "--scheduler_type", + default="ddim", + type=str, + help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", + ) + parser.add_argument( + "--image_size", + default=None, + type=int, + help=("The image size that the model was trained on."), + ) + parser.add_argument( + "--prediction_type", + default=None, + type=str, + help=("The prediction type that the model was trained on."), + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--from_safetensors", + action="store_true", + help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + args = parser.parse_args() + + pipe = load_pipeline_from_original_MusicLDM_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + image_size=args.image_size, + prediction_type=args.prediction_type, + extract_ema=args.extract_ema, + scheduler_type=args.scheduler_type, + num_in_channels=args.num_in_channels, + model_channels=args.model_channels, + num_head_channels=args.num_head_channels, + from_safetensors=args.from_safetensors, + device=args.device, + ) + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_stable_diffusion_to_diffusers.py b/scripts/convert_original_stable_diffusion_to_diffusers.py new file mode 100644 index 000000000000..59eeeec24c79 --- /dev/null +++ b/scripts/convert_original_stable_diffusion_to_diffusers.py @@ -0,0 +1,188 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the LDM checkpoints.""" + +import argparse +import importlib + +import torch + +from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--config_files", + default=None, + type=str, + help="The YAML config file corresponding to the architecture.", + ) + parser.add_argument( + "--num_in_channels", + default=None, + type=int, + help="The number of input channels. If `None` number of input channels will be automatically inferred.", + ) + parser.add_argument( + "--scheduler_type", + default="pndm", + type=str, + help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", + ) + parser.add_argument( + "--pipeline_type", + default=None, + type=str, + help=( + "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" + ". If `None` pipeline will be automatically inferred." + ), + ) + parser.add_argument( + "--image_size", + default=None, + type=int, + help=( + "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2" + " Base. Use 768 for Stable Diffusion v2." + ), + ) + parser.add_argument( + "--prediction_type", + default=None, + type=str, + help=( + "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" + " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." + ), + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--upcast_attention", + action="store_true", + help=( + "Whether the attention computation should always be upcasted. This is necessary when running stable" + " diffusion 2.1." + ), + ) + parser.add_argument( + "--from_safetensors", + action="store_true", + help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + parser.add_argument( + "--stable_unclip", + type=str, + default=None, + required=False, + help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", + ) + parser.add_argument( + "--stable_unclip_prior", + type=str, + default=None, + required=False, + help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", + ) + parser.add_argument( + "--clip_stats_path", + type=str, + help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", + required=False, + ) + parser.add_argument( + "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint." + ) + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + parser.add_argument( + "--vae_path", + type=str, + default=None, + required=False, + help="Set to a path, hub id to an already converted vae to not convert it again.", + ) + parser.add_argument( + "--pipeline_class_name", + type=str, + default=None, + required=False, + help="Specify the pipeline class name", + ) + + args = parser.parse_args() + + if args.pipeline_class_name is not None: + library = importlib.import_module("diffusers") + class_obj = getattr(library, args.pipeline_class_name) + pipeline_class = class_obj + else: + pipeline_class = None + + pipe = download_from_original_stable_diffusion_ckpt( + checkpoint_path_or_dict=args.checkpoint_path, + original_config_file=args.original_config_file, + config_files=args.config_files, + image_size=args.image_size, + prediction_type=args.prediction_type, + model_type=args.pipeline_type, + extract_ema=args.extract_ema, + scheduler_type=args.scheduler_type, + num_in_channels=args.num_in_channels, + upcast_attention=args.upcast_attention, + from_safetensors=args.from_safetensors, + device=args.device, + stable_unclip=args.stable_unclip, + stable_unclip_prior=args.stable_unclip_prior, + clip_stats_path=args.clip_stats_path, + controlnet=args.controlnet, + vae_path=args.vae_path, + pipeline_class=pipeline_class, + ) + + if args.half: + pipe.to(dtype=torch.float16) + + if args.controlnet: + # only save the controlnet model + pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) + else: + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/convert_original_t2i_adapter.py b/scripts/convert_original_t2i_adapter.py new file mode 100644 index 000000000000..e23a2431ce9e --- /dev/null +++ b/scripts/convert_original_t2i_adapter.py @@ -0,0 +1,250 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Conversion script for the T2I-Adapter checkpoints. +""" + +import argparse + +import torch + +from diffusers import T2IAdapter + + +def convert_adapter(src_state, in_channels): + original_body_length = max([int(x.split(".")[1]) for x in src_state.keys() if "body." in x]) + 1 + + assert original_body_length == 8 + + # (0, 1) -> channels 1 + assert src_state["body.0.block1.weight"].shape == (320, 320, 3, 3) + + # (2, 3) -> channels 2 + assert src_state["body.2.in_conv.weight"].shape == (640, 320, 1, 1) + + # (4, 5) -> channels 3 + assert src_state["body.4.in_conv.weight"].shape == (1280, 640, 1, 1) + + # (6, 7) -> channels 4 + assert src_state["body.6.block1.weight"].shape == (1280, 1280, 3, 3) + + res_state = { + "adapter.conv_in.weight": src_state.pop("conv_in.weight"), + "adapter.conv_in.bias": src_state.pop("conv_in.bias"), + # 0.resnets.0 + "adapter.body.0.resnets.0.block1.weight": src_state.pop("body.0.block1.weight"), + "adapter.body.0.resnets.0.block1.bias": src_state.pop("body.0.block1.bias"), + "adapter.body.0.resnets.0.block2.weight": src_state.pop("body.0.block2.weight"), + "adapter.body.0.resnets.0.block2.bias": src_state.pop("body.0.block2.bias"), + # 0.resnets.1 + "adapter.body.0.resnets.1.block1.weight": src_state.pop("body.1.block1.weight"), + "adapter.body.0.resnets.1.block1.bias": src_state.pop("body.1.block1.bias"), + "adapter.body.0.resnets.1.block2.weight": src_state.pop("body.1.block2.weight"), + "adapter.body.0.resnets.1.block2.bias": src_state.pop("body.1.block2.bias"), + # 1 + "adapter.body.1.in_conv.weight": src_state.pop("body.2.in_conv.weight"), + "adapter.body.1.in_conv.bias": src_state.pop("body.2.in_conv.bias"), + # 1.resnets.0 + "adapter.body.1.resnets.0.block1.weight": src_state.pop("body.2.block1.weight"), + "adapter.body.1.resnets.0.block1.bias": src_state.pop("body.2.block1.bias"), + "adapter.body.1.resnets.0.block2.weight": src_state.pop("body.2.block2.weight"), + "adapter.body.1.resnets.0.block2.bias": src_state.pop("body.2.block2.bias"), + # 1.resnets.1 + "adapter.body.1.resnets.1.block1.weight": src_state.pop("body.3.block1.weight"), + "adapter.body.1.resnets.1.block1.bias": src_state.pop("body.3.block1.bias"), + "adapter.body.1.resnets.1.block2.weight": src_state.pop("body.3.block2.weight"), + "adapter.body.1.resnets.1.block2.bias": src_state.pop("body.3.block2.bias"), + # 2 + "adapter.body.2.in_conv.weight": src_state.pop("body.4.in_conv.weight"), + "adapter.body.2.in_conv.bias": src_state.pop("body.4.in_conv.bias"), + # 2.resnets.0 + "adapter.body.2.resnets.0.block1.weight": src_state.pop("body.4.block1.weight"), + "adapter.body.2.resnets.0.block1.bias": src_state.pop("body.4.block1.bias"), + "adapter.body.2.resnets.0.block2.weight": src_state.pop("body.4.block2.weight"), + "adapter.body.2.resnets.0.block2.bias": src_state.pop("body.4.block2.bias"), + # 2.resnets.1 + "adapter.body.2.resnets.1.block1.weight": src_state.pop("body.5.block1.weight"), + "adapter.body.2.resnets.1.block1.bias": src_state.pop("body.5.block1.bias"), + "adapter.body.2.resnets.1.block2.weight": src_state.pop("body.5.block2.weight"), + "adapter.body.2.resnets.1.block2.bias": src_state.pop("body.5.block2.bias"), + # 3.resnets.0 + "adapter.body.3.resnets.0.block1.weight": src_state.pop("body.6.block1.weight"), + "adapter.body.3.resnets.0.block1.bias": src_state.pop("body.6.block1.bias"), + "adapter.body.3.resnets.0.block2.weight": src_state.pop("body.6.block2.weight"), + "adapter.body.3.resnets.0.block2.bias": src_state.pop("body.6.block2.bias"), + # 3.resnets.1 + "adapter.body.3.resnets.1.block1.weight": src_state.pop("body.7.block1.weight"), + "adapter.body.3.resnets.1.block1.bias": src_state.pop("body.7.block1.bias"), + "adapter.body.3.resnets.1.block2.weight": src_state.pop("body.7.block2.weight"), + "adapter.body.3.resnets.1.block2.bias": src_state.pop("body.7.block2.bias"), + } + + assert len(src_state) == 0 + + adapter = T2IAdapter(in_channels=in_channels, adapter_type="full_adapter") + + adapter.load_state_dict(res_state) + + return adapter + + +def convert_light_adapter(src_state): + original_body_length = max([int(x.split(".")[1]) for x in src_state.keys() if "body." in x]) + 1 + + assert original_body_length == 4 + + res_state = { + # body.0.in_conv + "adapter.body.0.in_conv.weight": src_state.pop("body.0.in_conv.weight"), + "adapter.body.0.in_conv.bias": src_state.pop("body.0.in_conv.bias"), + # body.0.resnets.0 + "adapter.body.0.resnets.0.block1.weight": src_state.pop("body.0.body.0.block1.weight"), + "adapter.body.0.resnets.0.block1.bias": src_state.pop("body.0.body.0.block1.bias"), + "adapter.body.0.resnets.0.block2.weight": src_state.pop("body.0.body.0.block2.weight"), + "adapter.body.0.resnets.0.block2.bias": src_state.pop("body.0.body.0.block2.bias"), + # body.0.resnets.1 + "adapter.body.0.resnets.1.block1.weight": src_state.pop("body.0.body.1.block1.weight"), + "adapter.body.0.resnets.1.block1.bias": src_state.pop("body.0.body.1.block1.bias"), + "adapter.body.0.resnets.1.block2.weight": src_state.pop("body.0.body.1.block2.weight"), + "adapter.body.0.resnets.1.block2.bias": src_state.pop("body.0.body.1.block2.bias"), + # body.0.resnets.2 + "adapter.body.0.resnets.2.block1.weight": src_state.pop("body.0.body.2.block1.weight"), + "adapter.body.0.resnets.2.block1.bias": src_state.pop("body.0.body.2.block1.bias"), + "adapter.body.0.resnets.2.block2.weight": src_state.pop("body.0.body.2.block2.weight"), + "adapter.body.0.resnets.2.block2.bias": src_state.pop("body.0.body.2.block2.bias"), + # body.0.resnets.3 + "adapter.body.0.resnets.3.block1.weight": src_state.pop("body.0.body.3.block1.weight"), + "adapter.body.0.resnets.3.block1.bias": src_state.pop("body.0.body.3.block1.bias"), + "adapter.body.0.resnets.3.block2.weight": src_state.pop("body.0.body.3.block2.weight"), + "adapter.body.0.resnets.3.block2.bias": src_state.pop("body.0.body.3.block2.bias"), + # body.0.out_conv + "adapter.body.0.out_conv.weight": src_state.pop("body.0.out_conv.weight"), + "adapter.body.0.out_conv.bias": src_state.pop("body.0.out_conv.bias"), + # body.1.in_conv + "adapter.body.1.in_conv.weight": src_state.pop("body.1.in_conv.weight"), + "adapter.body.1.in_conv.bias": src_state.pop("body.1.in_conv.bias"), + # body.1.resnets.0 + "adapter.body.1.resnets.0.block1.weight": src_state.pop("body.1.body.0.block1.weight"), + "adapter.body.1.resnets.0.block1.bias": src_state.pop("body.1.body.0.block1.bias"), + "adapter.body.1.resnets.0.block2.weight": src_state.pop("body.1.body.0.block2.weight"), + "adapter.body.1.resnets.0.block2.bias": src_state.pop("body.1.body.0.block2.bias"), + # body.1.resnets.1 + "adapter.body.1.resnets.1.block1.weight": src_state.pop("body.1.body.1.block1.weight"), + "adapter.body.1.resnets.1.block1.bias": src_state.pop("body.1.body.1.block1.bias"), + "adapter.body.1.resnets.1.block2.weight": src_state.pop("body.1.body.1.block2.weight"), + "adapter.body.1.resnets.1.block2.bias": src_state.pop("body.1.body.1.block2.bias"), + # body.1.body.2 + "adapter.body.1.resnets.2.block1.weight": src_state.pop("body.1.body.2.block1.weight"), + "adapter.body.1.resnets.2.block1.bias": src_state.pop("body.1.body.2.block1.bias"), + "adapter.body.1.resnets.2.block2.weight": src_state.pop("body.1.body.2.block2.weight"), + "adapter.body.1.resnets.2.block2.bias": src_state.pop("body.1.body.2.block2.bias"), + # body.1.body.3 + "adapter.body.1.resnets.3.block1.weight": src_state.pop("body.1.body.3.block1.weight"), + "adapter.body.1.resnets.3.block1.bias": src_state.pop("body.1.body.3.block1.bias"), + "adapter.body.1.resnets.3.block2.weight": src_state.pop("body.1.body.3.block2.weight"), + "adapter.body.1.resnets.3.block2.bias": src_state.pop("body.1.body.3.block2.bias"), + # body.1.out_conv + "adapter.body.1.out_conv.weight": src_state.pop("body.1.out_conv.weight"), + "adapter.body.1.out_conv.bias": src_state.pop("body.1.out_conv.bias"), + # body.2.in_conv + "adapter.body.2.in_conv.weight": src_state.pop("body.2.in_conv.weight"), + "adapter.body.2.in_conv.bias": src_state.pop("body.2.in_conv.bias"), + # body.2.body.0 + "adapter.body.2.resnets.0.block1.weight": src_state.pop("body.2.body.0.block1.weight"), + "adapter.body.2.resnets.0.block1.bias": src_state.pop("body.2.body.0.block1.bias"), + "adapter.body.2.resnets.0.block2.weight": src_state.pop("body.2.body.0.block2.weight"), + "adapter.body.2.resnets.0.block2.bias": src_state.pop("body.2.body.0.block2.bias"), + # body.2.body.1 + "adapter.body.2.resnets.1.block1.weight": src_state.pop("body.2.body.1.block1.weight"), + "adapter.body.2.resnets.1.block1.bias": src_state.pop("body.2.body.1.block1.bias"), + "adapter.body.2.resnets.1.block2.weight": src_state.pop("body.2.body.1.block2.weight"), + "adapter.body.2.resnets.1.block2.bias": src_state.pop("body.2.body.1.block2.bias"), + # body.2.body.2 + "adapter.body.2.resnets.2.block1.weight": src_state.pop("body.2.body.2.block1.weight"), + "adapter.body.2.resnets.2.block1.bias": src_state.pop("body.2.body.2.block1.bias"), + "adapter.body.2.resnets.2.block2.weight": src_state.pop("body.2.body.2.block2.weight"), + "adapter.body.2.resnets.2.block2.bias": src_state.pop("body.2.body.2.block2.bias"), + # body.2.body.3 + "adapter.body.2.resnets.3.block1.weight": src_state.pop("body.2.body.3.block1.weight"), + "adapter.body.2.resnets.3.block1.bias": src_state.pop("body.2.body.3.block1.bias"), + "adapter.body.2.resnets.3.block2.weight": src_state.pop("body.2.body.3.block2.weight"), + "adapter.body.2.resnets.3.block2.bias": src_state.pop("body.2.body.3.block2.bias"), + # body.2.out_conv + "adapter.body.2.out_conv.weight": src_state.pop("body.2.out_conv.weight"), + "adapter.body.2.out_conv.bias": src_state.pop("body.2.out_conv.bias"), + # body.3.in_conv + "adapter.body.3.in_conv.weight": src_state.pop("body.3.in_conv.weight"), + "adapter.body.3.in_conv.bias": src_state.pop("body.3.in_conv.bias"), + # body.3.body.0 + "adapter.body.3.resnets.0.block1.weight": src_state.pop("body.3.body.0.block1.weight"), + "adapter.body.3.resnets.0.block1.bias": src_state.pop("body.3.body.0.block1.bias"), + "adapter.body.3.resnets.0.block2.weight": src_state.pop("body.3.body.0.block2.weight"), + "adapter.body.3.resnets.0.block2.bias": src_state.pop("body.3.body.0.block2.bias"), + # body.3.body.1 + "adapter.body.3.resnets.1.block1.weight": src_state.pop("body.3.body.1.block1.weight"), + "adapter.body.3.resnets.1.block1.bias": src_state.pop("body.3.body.1.block1.bias"), + "adapter.body.3.resnets.1.block2.weight": src_state.pop("body.3.body.1.block2.weight"), + "adapter.body.3.resnets.1.block2.bias": src_state.pop("body.3.body.1.block2.bias"), + # body.3.body.2 + "adapter.body.3.resnets.2.block1.weight": src_state.pop("body.3.body.2.block1.weight"), + "adapter.body.3.resnets.2.block1.bias": src_state.pop("body.3.body.2.block1.bias"), + "adapter.body.3.resnets.2.block2.weight": src_state.pop("body.3.body.2.block2.weight"), + "adapter.body.3.resnets.2.block2.bias": src_state.pop("body.3.body.2.block2.bias"), + # body.3.body.3 + "adapter.body.3.resnets.3.block1.weight": src_state.pop("body.3.body.3.block1.weight"), + "adapter.body.3.resnets.3.block1.bias": src_state.pop("body.3.body.3.block1.bias"), + "adapter.body.3.resnets.3.block2.weight": src_state.pop("body.3.body.3.block2.weight"), + "adapter.body.3.resnets.3.block2.bias": src_state.pop("body.3.body.3.block2.bias"), + # body.3.out_conv + "adapter.body.3.out_conv.weight": src_state.pop("body.3.out_conv.weight"), + "adapter.body.3.out_conv.bias": src_state.pop("body.3.out_conv.bias"), + } + + assert len(src_state) == 0 + + adapter = T2IAdapter(in_channels=3, channels=[320, 640, 1280], num_res_blocks=4, adapter_type="light_adapter") + + adapter.load_state_dict(res_state) + + return adapter + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--output_path", default=None, type=str, required=True, help="Path to the store the result checkpoint." + ) + parser.add_argument( + "--is_adapter_light", + action="store_true", + help="Is checkpoint come from Adapter-Light architecture. ex: color-adapter", + ) + parser.add_argument("--in_channels", required=False, type=int, help="Input channels for non-light adapter") + + args = parser.parse_args() + src_state = torch.load(args.checkpoint_path) + + if args.is_adapter_light: + adapter = convert_light_adapter(src_state) + else: + if args.in_channels is None: + raise ValueError("set `--in_channels=<n>`") + adapter = convert_adapter(src_state, args.in_channels) + + adapter.save_pretrained(args.output_path) diff --git a/scripts/convert_ovis_image_to_diffusers.py b/scripts/convert_ovis_image_to_diffusers.py new file mode 100644 index 000000000000..0d3d9cd44bf6 --- /dev/null +++ b/scripts/convert_ovis_image_to_diffusers.py @@ -0,0 +1,263 @@ +import argparse +from contextlib import nullcontext + +import safetensors.torch +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download + +from diffusers import OvisImageTransformer2DModel +from diffusers.utils.import_utils import is_accelerate_available + + +""" +# Transformer + +python scripts/convert_ovis_image_to_diffusers.py \ +--original_state_dict_repo_id "AIDC-AI/Ovis-Image-7B" \ +--filename "ovis_image.safetensors" +--output_path "ovis-image" \ +--transformer +""" + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--original_state_dict_repo_id", default=None, type=str) +parser.add_argument("--filename", default="ovis_image.safetensors", type=str) +parser.add_argument("--checkpoint_path", default=None, type=str) +parser.add_argument("--in_channels", type=int, default=64) +parser.add_argument("--out_channels", type=int, default=None) +parser.add_argument("--transformer", action="store_true") +parser.add_argument("--output_path", type=str) +parser.add_argument("--dtype", type=str, default="bf16") + +args = parser.parse_args() +dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32 + + +def load_original_checkpoint(args): + if args.original_state_dict_repo_id is not None: + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) + elif args.checkpoint_path is not None: + ckpt_path = args.checkpoint_path + else: + raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; +# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation +def swap_scale_shift(weight): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_ovis_image_transformer_checkpoint_to_diffusers( + original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0 +): + converted_state_dict = {} + + ## time_text_embed.timestep_embedder <- time_in + converted_state_dict["timestep_embedder.linear_1.weight"] = original_state_dict.pop("time_in.in_layer.weight") + converted_state_dict["timestep_embedder.linear_1.bias"] = original_state_dict.pop("time_in.in_layer.bias") + converted_state_dict["timestep_embedder.linear_2.weight"] = original_state_dict.pop("time_in.out_layer.weight") + converted_state_dict["timestep_embedder.linear_2.bias"] = original_state_dict.pop("time_in.out_layer.bias") + + # context_embedder + converted_state_dict["context_embedder_norm.weight"] = original_state_dict.pop("semantic_txt_norm.weight") + converted_state_dict["context_embedder.weight"] = original_state_dict.pop("semantic_txt_in.weight") + converted_state_dict["context_embedder.bias"] = original_state_dict.pop("semantic_txt_in.bias") + + # x_embedder + converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight") + converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias") + + # double transformer blocks + for i in range(num_layers): + block_prefix = f"transformer_blocks.{i}." + # norms. + ## norm1 + converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mod.lin.bias" + ) + ## norm1_context + converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mod.lin.bias" + ) + # Q, K, V + sample_q, sample_k, sample_v = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0 + ) + context_q, context_k, context_v = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0 + ) + sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0 + ) + context_q_bias, context_k_bias, context_v_bias = torch.chunk( + original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0 + ) + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q]) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias]) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k]) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias]) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v]) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias]) + converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q]) + converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias]) + converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k]) + converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias]) + converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v]) + converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias]) + # qk_norm + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.norm.query_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.norm.key_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.norm.query_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.norm.key_norm.weight" + ) + # ff img_mlp + converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = torch.cat( + [ + original_state_dict.pop(f"double_blocks.{i}.img_mlp.up_proj.weight"), + original_state_dict.pop(f"double_blocks.{i}.img_mlp.gate_proj.weight"), + ], + dim=0, + ) + converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = torch.cat( + [ + original_state_dict.pop(f"double_blocks.{i}.img_mlp.up_proj.bias"), + original_state_dict.pop(f"double_blocks.{i}.img_mlp.gate_proj.bias"), + ], + dim=0, + ) + converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.down_proj.weight" + ) + converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_mlp.down_proj.bias" + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = torch.cat( + [ + original_state_dict.pop(f"double_blocks.{i}.txt_mlp.up_proj.weight"), + original_state_dict.pop(f"double_blocks.{i}.txt_mlp.gate_proj.weight"), + ], + dim=0, + ) + converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = torch.cat( + [ + original_state_dict.pop(f"double_blocks.{i}.txt_mlp.up_proj.bias"), + original_state_dict.pop(f"double_blocks.{i}.txt_mlp.gate_proj.bias"), + ], + dim=0, + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.down_proj.weight" + ) + converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_mlp.down_proj.bias" + ) + # output projections. + converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop( + f"double_blocks.{i}.img_attn.proj.bias" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.proj.weight" + ) + converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop( + f"double_blocks.{i}.txt_attn.proj.bias" + ) + + # single transformer blocks + for i in range(num_single_layers): + block_prefix = f"single_transformer_blocks.{i}." + # norm.linear <- single_blocks.0.modulation.lin + converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.lin.weight" + ) + converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop( + f"single_blocks.{i}.modulation.lin.bias" + ) + # Q, K, V, mlp + mlp_hidden_dim = int(inner_dim * mlp_ratio) + split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim * 2) + q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0) + q_bias, k_bias, v_bias, mlp_bias = torch.split( + original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0 + ) + converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q]) + converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias]) + converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k]) + converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias]) + converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v]) + converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias]) + converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp]) + converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias]) + # qk norm + converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop( + f"single_blocks.{i}.norm.query_norm.weight" + ) + converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop( + f"single_blocks.{i}.norm.key_norm.weight" + ) + # output projections. + converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.weight" + ) + converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop( + f"single_blocks.{i}.linear2.bias" + ) + + converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.weight") + ) + converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.bias") + ) + + return converted_state_dict + + +def main(args): + original_ckpt = load_original_checkpoint(args) + + if args.transformer: + num_layers = 6 + num_single_layers = 27 + inner_dim = 3072 + mlp_ratio = 4.0 + + converted_transformer_state_dict = convert_ovis_image_transformer_checkpoint_to_diffusers( + original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio + ) + transformer = OvisImageTransformer2DModel(in_channels=args.in_channels, out_channels=args.out_channels) + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + print("Saving Ovis-Image Transformer in Diffusers format.") + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_pixart_alpha_to_diffusers.py b/scripts/convert_pixart_alpha_to_diffusers.py new file mode 100644 index 000000000000..228b479df090 --- /dev/null +++ b/scripts/convert_pixart_alpha_to_diffusers.py @@ -0,0 +1,198 @@ +import argparse +import os + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel + + +ckpt_id = "PixArt-alpha/PixArt-alpha" +# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/scripts/inference.py#L125 +interpolation_scale = {256: 0.5, 512: 1, 1024: 2} + + +def main(args): + all_state_dict = torch.load(args.orig_ckpt_path, map_location="cpu") + state_dict = all_state_dict.pop("state_dict") + converted_state_dict = {} + + # Patch embeddings. + converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") + converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") + + # Caption projection. + converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") + converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") + converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") + converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") + + # AdaLN-single LN + converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") + converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") + + if args.image_size == 1024: + # Resolution. + converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop( + "csize_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop( + "csize_embedder.mlp.0.bias" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop( + "csize_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop( + "csize_embedder.mlp.2.bias" + ) + # Aspect ratio. + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop( + "ar_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop( + "ar_embedder.mlp.0.bias" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop( + "ar_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop( + "ar_embedder.mlp.2.bias" + ) + # Shared norm. + converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight") + converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias") + + for depth in range(28): + # Transformer blocks. + converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( + f"blocks.{depth}.scale_shift_table" + ) + + # Attention is all you need 🤘 + + # Self attention. + q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias + # Projection. + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.attn.proj.bias" + ) + + # Feed-forward. + converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.fc1.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.fc1.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.fc2.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.fc2.bias" + ) + + # Cross-attention. + q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") + q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") + k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.bias" + ) + + # Final block. + converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") + converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") + + # DiT XL/2 + transformer = Transformer2DModel( + sample_size=args.image_size // 8, + num_layers=28, + attention_head_dim=72, + in_channels=4, + out_channels=8, + patch_size=2, + attention_bias=True, + num_attention_heads=16, + cross_attention_dim=1152, + activation_fn="gelu-approximate", + num_embeds_ada_norm=1000, + norm_type="ada_norm_single", + norm_elementwise_affine=False, + norm_eps=1e-6, + caption_channels=4096, + ) + transformer.load_state_dict(converted_state_dict, strict=True) + + assert transformer.pos_embed.pos_embed is not None + state_dict.pop("pos_embed") + state_dict.pop("y_embedder.y_embedding") + assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + if args.only_transformer: + transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) + else: + scheduler = DPMSolverMultistepScheduler() + + vae = AutoencoderKL.from_pretrained(ckpt_id, subfolder="sd-vae-ft-ema") + + tokenizer = T5Tokenizer.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl") + text_encoder = T5EncoderModel.from_pretrained(ckpt_id, subfolder="t5-v1_1-xxl") + + pipeline = PixArtAlphaPipeline( + tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler + ) + + pipeline.save_pretrained(args.dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[256, 512, 1024], + required=False, + help="Image size of pretrained model, either 512 or 1024.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--only_transformer", default=True, type=bool, required=True) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_pixart_sigma_to_diffusers.py b/scripts/convert_pixart_sigma_to_diffusers.py new file mode 100644 index 000000000000..9572a83c0644 --- /dev/null +++ b/scripts/convert_pixart_sigma_to_diffusers.py @@ -0,0 +1,223 @@ +import argparse +import os + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtSigmaPipeline, Transformer2DModel + + +ckpt_id = "PixArt-alpha" +# https://github.com/PixArt-alpha/PixArt-sigma/blob/dd087141864e30ec44f12cb7448dd654be065e88/scripts/inference.py#L158 +interpolation_scale = {256: 0.5, 512: 1, 1024: 2, 2048: 4} + + +def main(args): + all_state_dict = torch.load(args.orig_ckpt_path) + state_dict = all_state_dict.pop("state_dict") + converted_state_dict = {} + + # Patch embeddings. + converted_state_dict["pos_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") + converted_state_dict["pos_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") + + # Caption projection. + converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") + converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") + converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") + converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") + + # AdaLN-single LN + converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") + converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") + + if args.micro_condition: + # Resolution. + converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.weight"] = state_dict.pop( + "csize_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_1.bias"] = state_dict.pop( + "csize_embedder.mlp.0.bias" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.weight"] = state_dict.pop( + "csize_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.resolution_embedder.linear_2.bias"] = state_dict.pop( + "csize_embedder.mlp.2.bias" + ) + # Aspect ratio. + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.weight"] = state_dict.pop( + "ar_embedder.mlp.0.weight" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_1.bias"] = state_dict.pop( + "ar_embedder.mlp.0.bias" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.weight"] = state_dict.pop( + "ar_embedder.mlp.2.weight" + ) + converted_state_dict["adaln_single.emb.aspect_ratio_embedder.linear_2.bias"] = state_dict.pop( + "ar_embedder.mlp.2.bias" + ) + # Shared norm. + converted_state_dict["adaln_single.linear.weight"] = state_dict.pop("t_block.1.weight") + converted_state_dict["adaln_single.linear.bias"] = state_dict.pop("t_block.1.bias") + + for depth in range(28): + # Transformer blocks. + converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( + f"blocks.{depth}.scale_shift_table" + ) + # Attention is all you need 🤘 + + # Self attention. + q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.bias"), 3, dim=0) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias + # Projection. + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.attn.proj.bias" + ) + if args.qk_norm: + converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.weight"] = state_dict.pop( + f"blocks.{depth}.attn.q_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.q_norm.bias"] = state_dict.pop( + f"blocks.{depth}.attn.q_norm.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.weight"] = state_dict.pop( + f"blocks.{depth}.attn.k_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.k_norm.bias"] = state_dict.pop( + f"blocks.{depth}.attn.k_norm.bias" + ) + + # Feed-forward. + converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.fc1.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.fc1.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.fc2.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.fc2.bias" + ) + + # Cross-attention. + q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") + q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") + k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.bias" + ) + + # Final block. + converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") + converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") + + # PixArt XL/2 + transformer = Transformer2DModel( + sample_size=args.image_size // 8, + num_layers=28, + attention_head_dim=72, + in_channels=4, + out_channels=8, + patch_size=2, + attention_bias=True, + num_attention_heads=16, + cross_attention_dim=1152, + activation_fn="gelu-approximate", + num_embeds_ada_norm=1000, + norm_type="ada_norm_single", + norm_elementwise_affine=False, + norm_eps=1e-6, + caption_channels=4096, + interpolation_scale=interpolation_scale[args.image_size], + use_additional_conditions=args.micro_condition, + ) + transformer.load_state_dict(converted_state_dict, strict=True) + + assert transformer.pos_embed.pos_embed is not None + try: + state_dict.pop("y_embedder.y_embedding") + state_dict.pop("pos_embed") + except Exception as e: + print(f"Skipping {str(e)}") + pass + assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + if args.only_transformer: + transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) + else: + # pixart-Sigma vae link: https://huggingface.co/PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers/tree/main/vae + vae = AutoencoderKL.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="vae") + + scheduler = DPMSolverMultistepScheduler() + + tokenizer = T5Tokenizer.from_pretrained(f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="tokenizer") + text_encoder = T5EncoderModel.from_pretrained( + f"{ckpt_id}/pixart_sigma_sdxlvae_T5_diffusers", subfolder="text_encoder" + ) + + pipeline = PixArtSigmaPipeline( + tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler + ) + + pipeline.save_pretrained(args.dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--micro_condition", action="store_true", help="If use Micro-condition in PixArtMS structure during training." + ) + parser.add_argument("--qk_norm", action="store_true", help="If use qk norm during training.") + parser.add_argument( + "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[256, 512, 1024, 2048], + required=False, + help="Image size of pretrained model, 256, 512, 1024, or 2048.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--only_transformer", default=True, type=bool, required=True) + + args = parser.parse_args() + main(args) diff --git a/scripts/convert_prx_to_diffusers.py b/scripts/convert_prx_to_diffusers.py new file mode 100644 index 000000000000..00bb3f6fe99e --- /dev/null +++ b/scripts/convert_prx_to_diffusers.py @@ -0,0 +1,345 @@ +#!/usr/bin/env python3 +""" +Script to convert PRX checkpoint from original codebase to diffusers format. +""" + +import argparse +import json +import os +import sys +from dataclasses import asdict, dataclass +from typing import Dict, Tuple + +import torch +from safetensors.torch import save_file + +from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel +from diffusers.pipelines.prx import PRXPipeline + + +DEFAULT_RESOLUTION = 512 + + +@dataclass(frozen=True) +class PRXBase: + context_in_dim: int = 2304 + hidden_size: int = 1792 + mlp_ratio: float = 3.5 + num_heads: int = 28 + depth: int = 16 + axes_dim: Tuple[int, int] = (32, 32) + theta: int = 10_000 + time_factor: float = 1000.0 + time_max_period: int = 10_000 + + +@dataclass(frozen=True) +class PRXFlux(PRXBase): + in_channels: int = 16 + patch_size: int = 2 + + +@dataclass(frozen=True) +class PRXDCAE(PRXBase): + in_channels: int = 32 + patch_size: int = 1 + + +def build_config(vae_type: str) -> Tuple[dict, int]: + if vae_type == "flux": + cfg = PRXFlux() + elif vae_type == "dc-ae": + cfg = PRXDCAE() + else: + raise ValueError(f"Unsupported VAE type: {vae_type}. Use 'flux' or 'dc-ae'") + + config_dict = asdict(cfg) + config_dict["axes_dim"] = list(config_dict["axes_dim"]) # type: ignore[index] + return config_dict + + +def create_parameter_mapping(depth: int) -> dict: + """Create mapping from old parameter names to new diffusers names.""" + + # Key mappings for structural changes + mapping = {} + + # Map old structure (layers in PRXBlock) to new structure (layers in PRXAttention) + for i in range(depth): + # QKV projections moved to attention module + mapping[f"blocks.{i}.img_qkv_proj.weight"] = f"blocks.{i}.attention.img_qkv_proj.weight" + mapping[f"blocks.{i}.txt_kv_proj.weight"] = f"blocks.{i}.attention.txt_kv_proj.weight" + + # QK norm moved to attention module and renamed to match Attention's qk_norm structure + mapping[f"blocks.{i}.qk_norm.query_norm.scale"] = f"blocks.{i}.attention.norm_q.weight" + mapping[f"blocks.{i}.qk_norm.key_norm.scale"] = f"blocks.{i}.attention.norm_k.weight" + mapping[f"blocks.{i}.qk_norm.query_norm.weight"] = f"blocks.{i}.attention.norm_q.weight" + mapping[f"blocks.{i}.qk_norm.key_norm.weight"] = f"blocks.{i}.attention.norm_k.weight" + + # K norm for text tokens moved to attention module + mapping[f"blocks.{i}.k_norm.scale"] = f"blocks.{i}.attention.norm_added_k.weight" + mapping[f"blocks.{i}.k_norm.weight"] = f"blocks.{i}.attention.norm_added_k.weight" + + # Attention output projection + mapping[f"blocks.{i}.attn_out.weight"] = f"blocks.{i}.attention.to_out.0.weight" + + return mapping + + +def convert_checkpoint_parameters(old_state_dict: Dict[str, torch.Tensor], depth: int) -> dict[str, torch.Tensor]: + """Convert old checkpoint parameters to new diffusers format.""" + + print("Converting checkpoint parameters...") + + mapping = create_parameter_mapping(depth) + converted_state_dict = {} + + for key, value in old_state_dict.items(): + new_key = key + + # Apply specific mappings if needed + if key in mapping: + new_key = mapping[key] + print(f" Mapped: {key} -> {new_key}") + + converted_state_dict[new_key] = value + + print(f"✓ Converted {len(converted_state_dict)} parameters") + return converted_state_dict + + +def create_transformer_from_checkpoint(checkpoint_path: str, config: dict) -> PRXTransformer2DModel: + """Create and load PRXTransformer2DModel from old checkpoint.""" + + print(f"Loading checkpoint from: {checkpoint_path}") + + # Load old checkpoint + if not os.path.exists(checkpoint_path): + raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}") + + old_checkpoint = torch.load(checkpoint_path, map_location="cpu") + + # Handle different checkpoint formats + if isinstance(old_checkpoint, dict): + if "model" in old_checkpoint: + state_dict = old_checkpoint["model"] + elif "state_dict" in old_checkpoint: + state_dict = old_checkpoint["state_dict"] + else: + state_dict = old_checkpoint + else: + state_dict = old_checkpoint + + print(f"✓ Loaded checkpoint with {len(state_dict)} parameters") + + # Convert parameter names if needed + model_depth = int(config.get("depth", 16)) + converted_state_dict = convert_checkpoint_parameters(state_dict, depth=model_depth) + + # Create transformer with config + print("Creating PRXTransformer2DModel...") + transformer = PRXTransformer2DModel(**config) + + # Load state dict + print("Loading converted parameters...") + missing_keys, unexpected_keys = transformer.load_state_dict(converted_state_dict, strict=False) + + if missing_keys: + print(f"⚠ Missing keys: {missing_keys}") + if unexpected_keys: + print(f"⚠ Unexpected keys: {unexpected_keys}") + + if not missing_keys and not unexpected_keys: + print("✓ All parameters loaded successfully!") + + return transformer + + +def create_scheduler_config(output_path: str, shift: float): + """Create FlowMatchEulerDiscreteScheduler config.""" + + scheduler_config = {"_class_name": "FlowMatchEulerDiscreteScheduler", "num_train_timesteps": 1000, "shift": shift} + + scheduler_path = os.path.join(output_path, "scheduler") + os.makedirs(scheduler_path, exist_ok=True) + + with open(os.path.join(scheduler_path, "scheduler_config.json"), "w") as f: + json.dump(scheduler_config, f, indent=2) + + print("✓ Created scheduler config") + + +def download_and_save_vae(vae_type: str, output_path: str): + """Download and save VAE to local directory.""" + from diffusers import AutoencoderDC, AutoencoderKL + + vae_path = os.path.join(output_path, "vae") + os.makedirs(vae_path, exist_ok=True) + + if vae_type == "flux": + print("Downloading FLUX VAE from black-forest-labs/FLUX.1-dev...") + vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae") + else: # dc-ae + print("Downloading DC-AE VAE from mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers...") + vae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers") + + vae.save_pretrained(vae_path) + print(f"✓ Saved VAE to {vae_path}") + + +def download_and_save_text_encoder(output_path: str): + """Download and save T5Gemma text encoder and tokenizer.""" + from transformers import GemmaTokenizerFast + from transformers.models.t5gemma.modeling_t5gemma import T5GemmaModel + + text_encoder_path = os.path.join(output_path, "text_encoder") + tokenizer_path = os.path.join(output_path, "tokenizer") + os.makedirs(text_encoder_path, exist_ok=True) + os.makedirs(tokenizer_path, exist_ok=True) + + print("Downloading T5Gemma model from google/t5gemma-2b-2b-ul2...") + t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2") + + # Extract and save only the encoder + t5gemma_encoder = t5gemma_model.encoder + t5gemma_encoder.save_pretrained(text_encoder_path) + print(f"✓ Saved T5GemmaEncoder to {text_encoder_path}") + + print("Downloading tokenizer from google/t5gemma-2b-2b-ul2...") + tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2") + tokenizer.model_max_length = 256 + tokenizer.save_pretrained(tokenizer_path) + print(f"✓ Saved tokenizer to {tokenizer_path}") + + +def create_model_index(vae_type: str, default_image_size: int, output_path: str): + """Create model_index.json for the pipeline.""" + + if vae_type == "flux": + vae_class = "AutoencoderKL" + else: # dc-ae + vae_class = "AutoencoderDC" + + model_index = { + "_class_name": "PRXPipeline", + "_diffusers_version": "0.31.0.dev0", + "_name_or_path": os.path.basename(output_path), + "default_sample_size": default_image_size, + "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"], + "text_encoder": ["prx", "T5GemmaEncoder"], + "tokenizer": ["transformers", "GemmaTokenizerFast"], + "transformer": ["diffusers", "PRXTransformer2DModel"], + "vae": ["diffusers", vae_class], + } + + model_index_path = os.path.join(output_path, "model_index.json") + with open(model_index_path, "w") as f: + json.dump(model_index, f, indent=2) + + +def main(args): + # Validate inputs + if not os.path.exists(args.checkpoint_path): + raise FileNotFoundError(f"Checkpoint not found: {args.checkpoint_path}") + + config = build_config(args.vae_type) + + # Create output directory + os.makedirs(args.output_path, exist_ok=True) + print(f"✓ Output directory: {args.output_path}") + + # Create transformer from checkpoint + transformer = create_transformer_from_checkpoint(args.checkpoint_path, config) + + # Save transformer + transformer_path = os.path.join(args.output_path, "transformer") + os.makedirs(transformer_path, exist_ok=True) + + # Save config + with open(os.path.join(transformer_path, "config.json"), "w") as f: + json.dump(config, f, indent=2) + + # Save model weights as safetensors + state_dict = transformer.state_dict() + save_file(state_dict, os.path.join(transformer_path, "diffusion_pytorch_model.safetensors")) + print(f"✓ Saved transformer to {transformer_path}") + + # Create scheduler config + create_scheduler_config(args.output_path, args.shift) + + download_and_save_vae(args.vae_type, args.output_path) + download_and_save_text_encoder(args.output_path) + + # Create model_index.json + create_model_index(args.vae_type, args.resolution, args.output_path) + + # Verify the pipeline can be loaded + try: + pipeline = PRXPipeline.from_pretrained(args.output_path) + print("Pipeline loaded successfully!") + print(f"Transformer: {type(pipeline.transformer).__name__}") + print(f"VAE: {type(pipeline.vae).__name__}") + print(f"Text Encoder: {type(pipeline.text_encoder).__name__}") + print(f"Scheduler: {type(pipeline.scheduler).__name__}") + + # Display model info + num_params = sum(p.numel() for p in pipeline.transformer.parameters()) + print(f"✓ Transformer parameters: {num_params:,}") + + except Exception as e: + print(f"Pipeline verification failed: {e}") + return False + + print("Conversion completed successfully!") + print(f"Converted pipeline saved to: {args.output_path}") + print(f"VAE type: {args.vae_type}") + + return True + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Convert PRX checkpoint to diffusers format") + + parser.add_argument( + "--checkpoint_path", type=str, required=True, help="Path to the original PRX checkpoint (.pth file )" + ) + + parser.add_argument( + "--output_path", type=str, required=True, help="Output directory for the converted diffusers pipeline" + ) + + parser.add_argument( + "--vae_type", + type=str, + choices=["flux", "dc-ae"], + required=True, + help="VAE type to use: 'flux' for AutoencoderKL (16 channels) or 'dc-ae' for AutoencoderDC (32 channels)", + ) + + parser.add_argument( + "--resolution", + type=int, + choices=[256, 512, 1024], + default=DEFAULT_RESOLUTION, + help="Target resolution for the model (256, 512, or 1024). Affects the transformer's sample_size.", + ) + + parser.add_argument( + "--shift", + type=float, + default=3.0, + help="Shift for the scheduler", + ) + + args = parser.parse_args() + + try: + success = main(args) + if not success: + sys.exit(1) + except Exception as e: + print(f"Conversion failed: {e}") + import traceback + + traceback.print_exc() + sys.exit(1) diff --git a/scripts/convert_rae_to_diffusers.py b/scripts/convert_rae_to_diffusers.py new file mode 100644 index 000000000000..0502e49ef30c --- /dev/null +++ b/scripts/convert_rae_to_diffusers.py @@ -0,0 +1,406 @@ +import argparse +from pathlib import Path +from typing import Any + +import torch +from huggingface_hub import HfApi, hf_hub_download + +from diffusers import AutoencoderRAE + + +DECODER_CONFIGS = { + "ViTB": { + "decoder_hidden_size": 768, + "decoder_intermediate_size": 3072, + "decoder_num_attention_heads": 12, + "decoder_num_hidden_layers": 12, + }, + "ViTL": { + "decoder_hidden_size": 1024, + "decoder_intermediate_size": 4096, + "decoder_num_attention_heads": 16, + "decoder_num_hidden_layers": 24, + }, + "ViTXL": { + "decoder_hidden_size": 1152, + "decoder_intermediate_size": 4096, + "decoder_num_attention_heads": 16, + "decoder_num_hidden_layers": 28, + }, +} + +ENCODER_DEFAULT_NAME_OR_PATH = { + "dinov2": "facebook/dinov2-with-registers-base", + "siglip2": "google/siglip2-base-patch16-256", + "mae": "facebook/vit-mae-base", +} + +ENCODER_HIDDEN_SIZE = { + "dinov2": 768, + "siglip2": 768, + "mae": 768, +} + +ENCODER_PATCH_SIZE = { + "dinov2": 14, + "siglip2": 16, + "mae": 16, +} + +DEFAULT_DECODER_SUBDIR = { + "dinov2": "decoders/dinov2/wReg_base", + "mae": "decoders/mae/base_p16", + "siglip2": "decoders/siglip2/base_p16_i256", +} + +DEFAULT_STATS_SUBDIR = { + "dinov2": "stats/dinov2/wReg_base", + "mae": "stats/mae/base_p16", + "siglip2": "stats/siglip2/base_p16_i256", +} + +DECODER_FILE_CANDIDATES = ("dinov2_decoder.pt", "model.pt") +STATS_FILE_CANDIDATES = ("stat.pt",) + + +def dataset_case_candidates(name: str) -> tuple[str, ...]: + return (name, name.lower(), name.upper(), name.title(), "imagenet1k", "ImageNet1k") + + +class RepoAccessor: + def __init__(self, repo_or_path: str, cache_dir: str | None = None): + self.repo_or_path = repo_or_path + self.cache_dir = cache_dir + self.local_root: Path | None = None + self.repo_id: str | None = None + self.repo_files: set[str] | None = None + + root = Path(repo_or_path) + if root.exists() and root.is_dir(): + self.local_root = root + else: + self.repo_id = repo_or_path + self.repo_files = set(HfApi().list_repo_files(repo_or_path)) + + def exists(self, relative_path: str) -> bool: + relative_path = relative_path.replace("\\", "/") + if self.local_root is not None: + return (self.local_root / relative_path).is_file() + return relative_path in self.repo_files + + def fetch(self, relative_path: str) -> Path: + relative_path = relative_path.replace("\\", "/") + if self.local_root is not None: + return self.local_root / relative_path + downloaded = hf_hub_download(repo_id=self.repo_id, filename=relative_path, cache_dir=self.cache_dir) + return Path(downloaded) + + +def unwrap_state_dict(maybe_wrapped: dict[str, Any]) -> dict[str, Any]: + state_dict = maybe_wrapped + for k in ("model", "module", "state_dict"): + if isinstance(state_dict, dict) and k in state_dict and isinstance(state_dict[k], dict): + state_dict = state_dict[k] + + out = dict(state_dict) + if len(out) > 0 and all(key.startswith("module.") for key in out): + out = {key[len("module.") :]: value for key, value in out.items()} + if len(out) > 0 and all(key.startswith("decoder.") for key in out): + out = {key[len("decoder.") :]: value for key, value in out.items()} + return out + + +def remap_decoder_attention_keys_for_diffusers(state_dict: dict[str, Any]) -> dict[str, Any]: + """ + Map official RAE decoder attention key layout to diffusers Attention layout used by AutoencoderRAE decoder. + + Example mappings: + - `...attention.attention.query.*` -> `...attention.to_q.*` + - `...attention.attention.key.*` -> `...attention.to_k.*` + - `...attention.attention.value.*` -> `...attention.to_v.*` + - `...attention.output.dense.*` -> `...attention.to_out.0.*` + """ + remapped: dict[str, Any] = {} + for key, value in state_dict.items(): + new_key = key + new_key = new_key.replace(".attention.attention.query.", ".attention.to_q.") + new_key = new_key.replace(".attention.attention.key.", ".attention.to_k.") + new_key = new_key.replace(".attention.attention.value.", ".attention.to_v.") + new_key = new_key.replace(".attention.output.dense.", ".attention.to_out.0.") + remapped[new_key] = value + return remapped + + +def resolve_decoder_file( + accessor: RepoAccessor, encoder_type: str, variant: str, decoder_checkpoint: str | None +) -> str: + if decoder_checkpoint is not None: + if accessor.exists(decoder_checkpoint): + return decoder_checkpoint + raise FileNotFoundError(f"Decoder checkpoint not found: {decoder_checkpoint}") + + base = f"{DEFAULT_DECODER_SUBDIR[encoder_type]}/{variant}" + for name in DECODER_FILE_CANDIDATES: + candidate = f"{base}/{name}" + if accessor.exists(candidate): + return candidate + + raise FileNotFoundError( + f"Could not find decoder checkpoint under `{base}`. Tried: {list(DECODER_FILE_CANDIDATES)}" + ) + + +def resolve_stats_file( + accessor: RepoAccessor, + encoder_type: str, + dataset_name: str, + stats_checkpoint: str | None, +) -> str | None: + if stats_checkpoint is not None: + if accessor.exists(stats_checkpoint): + return stats_checkpoint + raise FileNotFoundError(f"Stats checkpoint not found: {stats_checkpoint}") + + base = DEFAULT_STATS_SUBDIR[encoder_type] + for dataset in dataset_case_candidates(dataset_name): + for name in STATS_FILE_CANDIDATES: + candidate = f"{base}/{dataset}/{name}" + if accessor.exists(candidate): + return candidate + + return None + + +def extract_latent_stats(stats_obj: Any) -> tuple[Any | None, Any | None]: + if not isinstance(stats_obj, dict): + return None, None + + if "latents_mean" in stats_obj or "latents_std" in stats_obj: + return stats_obj.get("latents_mean", None), stats_obj.get("latents_std", None) + + mean = stats_obj.get("mean", None) + var = stats_obj.get("var", None) + if mean is None and var is None: + return None, None + + latents_std = None + if var is not None: + if isinstance(var, torch.Tensor): + latents_std = torch.sqrt(var + 1e-5) + else: + latents_std = torch.sqrt(torch.tensor(var) + 1e-5) + return mean, latents_std + + +def _strip_final_layernorm_affine(state_dict: dict[str, Any], prefix: str = "") -> dict[str, Any]: + """Remove final layernorm weight/bias from encoder state dict. + + RAE uses non-affine layernorm (weight=1, bias=0 is the default identity). + Stripping these keys means the model keeps its default init values, which + is functionally equivalent to setting elementwise_affine=False. + """ + keys_to_strip = {f"{prefix}weight", f"{prefix}bias"} + return {k: v for k, v in state_dict.items() if k not in keys_to_strip} + + +def _load_hf_encoder_state_dict(encoder_type: str, encoder_name_or_path: str) -> dict[str, Any]: + """Download the HF encoder and extract the state dict for the inner model.""" + if encoder_type == "dinov2": + from transformers import Dinov2WithRegistersModel + + hf_model = Dinov2WithRegistersModel.from_pretrained(encoder_name_or_path) + sd = hf_model.state_dict() + return _strip_final_layernorm_affine(sd, prefix="layernorm.") + elif encoder_type == "siglip2": + from transformers import SiglipModel + + # SiglipModel.vision_model is a SiglipVisionTransformer. + # Our Siglip2Encoder wraps it inside SiglipVisionModel which nests it + # under .vision_model, so we add the prefix to match the diffusers key layout. + hf_model = SiglipModel.from_pretrained(encoder_name_or_path).vision_model + sd = {f"vision_model.{k}": v for k, v in hf_model.state_dict().items()} + return _strip_final_layernorm_affine(sd, prefix="vision_model.post_layernorm.") + elif encoder_type == "mae": + from transformers import ViTMAEForPreTraining + + hf_model = ViTMAEForPreTraining.from_pretrained(encoder_name_or_path).vit + sd = hf_model.state_dict() + return _strip_final_layernorm_affine(sd, prefix="layernorm.") + else: + raise ValueError(f"Unknown encoder_type: {encoder_type}") + + +def convert(args: argparse.Namespace) -> None: + accessor = RepoAccessor(args.repo_or_path, cache_dir=args.cache_dir) + encoder_name_or_path = args.encoder_name_or_path or ENCODER_DEFAULT_NAME_OR_PATH[args.encoder_type] + + decoder_relpath = resolve_decoder_file(accessor, args.encoder_type, args.variant, args.decoder_checkpoint) + stats_relpath = resolve_stats_file(accessor, args.encoder_type, args.dataset_name, args.stats_checkpoint) + + print(f"Using decoder checkpoint: {decoder_relpath}") + if stats_relpath is not None: + print(f"Using stats checkpoint: {stats_relpath}") + else: + print("No stats checkpoint found; conversion will proceed without latent stats.") + + if args.dry_run: + return + + decoder_path = accessor.fetch(decoder_relpath) + decoder_obj = torch.load(decoder_path, map_location="cpu") + decoder_state_dict = unwrap_state_dict(decoder_obj) + decoder_state_dict = remap_decoder_attention_keys_for_diffusers(decoder_state_dict) + + latents_mean, latents_std = None, None + if stats_relpath is not None: + stats_path = accessor.fetch(stats_relpath) + stats_obj = torch.load(stats_path, map_location="cpu") + latents_mean, latents_std = extract_latent_stats(stats_obj) + + decoder_cfg = DECODER_CONFIGS[args.decoder_config_name] + + # Read encoder normalization stats from the HF image processor (only place that downloads encoder info) + from transformers import AutoConfig, AutoImageProcessor + + proc = AutoImageProcessor.from_pretrained(encoder_name_or_path) + encoder_norm_mean = list(proc.image_mean) + encoder_norm_std = list(proc.image_std) + + # Read encoder hidden size and patch size from HF config + encoder_hidden_size = ENCODER_HIDDEN_SIZE[args.encoder_type] + encoder_patch_size = ENCODER_PATCH_SIZE[args.encoder_type] + try: + hf_config = AutoConfig.from_pretrained(encoder_name_or_path) + # For models like SigLIP that nest vision config + if hasattr(hf_config, "vision_config"): + hf_config = hf_config.vision_config + encoder_hidden_size = hf_config.hidden_size + encoder_patch_size = hf_config.patch_size + except Exception: + pass + + # Load the actual encoder weights from HF to include in the saved model + encoder_state_dict = _load_hf_encoder_state_dict(args.encoder_type, encoder_name_or_path) + + # Build model on meta device to avoid double init overhead + with torch.device("meta"): + model = AutoencoderRAE( + encoder_type=args.encoder_type, + encoder_hidden_size=encoder_hidden_size, + encoder_patch_size=encoder_patch_size, + encoder_input_size=args.encoder_input_size, + patch_size=args.patch_size, + image_size=args.image_size, + num_channels=args.num_channels, + encoder_norm_mean=encoder_norm_mean, + encoder_norm_std=encoder_norm_std, + decoder_hidden_size=decoder_cfg["decoder_hidden_size"], + decoder_num_hidden_layers=decoder_cfg["decoder_num_hidden_layers"], + decoder_num_attention_heads=decoder_cfg["decoder_num_attention_heads"], + decoder_intermediate_size=decoder_cfg["decoder_intermediate_size"], + latents_mean=latents_mean, + latents_std=latents_std, + scaling_factor=args.scaling_factor, + ) + + # Assemble full state dict and load with assign=True + full_state_dict = {} + + # Encoder weights (prefixed with "encoder.") + for k, v in encoder_state_dict.items(): + full_state_dict[f"encoder.{k}"] = v + + # Decoder weights (prefixed with "decoder.") + for k, v in decoder_state_dict.items(): + full_state_dict[f"decoder.{k}"] = v + + # Buffers from config + full_state_dict["encoder_mean"] = torch.tensor(encoder_norm_mean, dtype=torch.float32).view(1, 3, 1, 1) + full_state_dict["encoder_std"] = torch.tensor(encoder_norm_std, dtype=torch.float32).view(1, 3, 1, 1) + if latents_mean is not None: + latents_mean_t = latents_mean if isinstance(latents_mean, torch.Tensor) else torch.tensor(latents_mean) + full_state_dict["_latents_mean"] = latents_mean_t + else: + full_state_dict["_latents_mean"] = torch.zeros(1) + if latents_std is not None: + latents_std_t = latents_std if isinstance(latents_std, torch.Tensor) else torch.tensor(latents_std) + full_state_dict["_latents_std"] = latents_std_t + else: + full_state_dict["_latents_std"] = torch.ones(1) + + model.load_state_dict(full_state_dict, strict=False, assign=True) + + # Verify no critical keys are missing + model_keys = {name for name, _ in model.named_parameters()} + model_keys |= {name for name, _ in model.named_buffers()} + loaded_keys = set(full_state_dict.keys()) + missing = model_keys - loaded_keys + # decoder_pos_embed is initialized in-model. trainable_cls_token is only + # allowed to be missing if it was absent in the source decoder checkpoint. + allowed_missing = {"decoder.decoder_pos_embed"} + if "trainable_cls_token" not in decoder_state_dict: + allowed_missing.add("decoder.trainable_cls_token") + if missing - allowed_missing: + print(f"Warning: missing keys after conversion: {sorted(missing - allowed_missing)}") + + output_path = Path(args.output_path) + output_path.mkdir(parents=True, exist_ok=True) + model.save_pretrained(output_path) + + if args.verify_load: + print("Verifying converted checkpoint with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False)...") + loaded_model = AutoencoderRAE.from_pretrained(output_path, low_cpu_mem_usage=False) + if not isinstance(loaded_model, AutoencoderRAE): + raise RuntimeError("Verification failed: loaded object is not AutoencoderRAE.") + print("Verification passed.") + + print(f"Saved converted AutoencoderRAE to: {output_path}") + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert RAE decoder checkpoints to diffusers AutoencoderRAE format") + parser.add_argument( + "--repo_or_path", type=str, required=True, help="Hub repo id (e.g. nyu-visionx/RAE-collections) or local path" + ) + parser.add_argument("--output_path", type=str, required=True, help="Directory to save converted model") + + parser.add_argument("--encoder_type", type=str, choices=["dinov2", "mae", "siglip2"], required=True) + parser.add_argument( + "--encoder_name_or_path", type=str, default=None, help="Optional encoder HF model id or local path override" + ) + + parser.add_argument("--variant", type=str, default="ViTXL_n08", help="Decoder variant folder name") + parser.add_argument("--dataset_name", type=str, default="imagenet1k", help="Stats dataset folder name") + + parser.add_argument( + "--decoder_checkpoint", type=str, default=None, help="Relative path to decoder checkpoint inside repo/path" + ) + parser.add_argument( + "--stats_checkpoint", type=str, default=None, help="Relative path to stats checkpoint inside repo/path" + ) + + parser.add_argument("--decoder_config_name", type=str, choices=list(DECODER_CONFIGS.keys()), default="ViTXL") + parser.add_argument("--encoder_input_size", type=int, default=224) + parser.add_argument("--patch_size", type=int, default=16) + parser.add_argument("--image_size", type=int, default=None) + parser.add_argument("--num_channels", type=int, default=3) + parser.add_argument("--scaling_factor", type=float, default=1.0) + + parser.add_argument("--cache_dir", type=str, default=None) + parser.add_argument("--dry_run", action="store_true", help="Only resolve and print selected files") + parser.add_argument( + "--verify_load", + action="store_true", + help="After conversion, load back with AutoencoderRAE.from_pretrained(low_cpu_mem_usage=False).", + ) + + return parser.parse_args() + + +def main() -> None: + args = parse_args() + convert(args) + + +if __name__ == "__main__": + main() diff --git a/scripts/convert_sana_controlnet_to_diffusers.py b/scripts/convert_sana_controlnet_to_diffusers.py new file mode 100644 index 000000000000..f7fcd7252576 --- /dev/null +++ b/scripts/convert_sana_controlnet_to_diffusers.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights + +from diffusers import ( + SanaControlNetModel, +) +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available else nullcontext + + +def main(args): + file_path = args.orig_ckpt_path + + all_state_dict = torch.load(file_path, weights_only=True) + state_dict = all_state_dict.pop("state_dict") + converted_state_dict = {} + + # Patch embeddings. + converted_state_dict["patch_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") + converted_state_dict["patch_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") + + # Caption projection. + converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") + converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") + converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") + converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") + + # AdaLN-single LN + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") + + # Shared norm. + converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") + converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") + + # y norm + converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") + + # Positional embedding interpolation scale. + interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0} + + # ControlNet Input Projection. + converted_state_dict["input_block.weight"] = state_dict.pop("controlnet.0.before_proj.weight") + converted_state_dict["input_block.bias"] = state_dict.pop("controlnet.0.before_proj.bias") + + for depth in range(7): + # Transformer blocks. + converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( + f"controlnet.{depth}.copied_block.scale_shift_table" + ) + + # Linear Attention is all you need 🤘 + # Self attention. + q, k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.attn.qkv.weight"), 3, dim=0) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + # Projection. + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( + f"controlnet.{depth}.copied_block.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( + f"controlnet.{depth}.copied_block.attn.proj.bias" + ) + + # Feed-forward. + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( + f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( + f"controlnet.{depth}.copied_block.mlp.inverted_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( + f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( + f"controlnet.{depth}.copied_block.mlp.depth_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( + f"controlnet.{depth}.copied_block.mlp.point_conv.conv.weight" + ) + + # Cross-attention. + q = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.weight") + q_bias = state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.q_linear.bias") + k, v = torch.chunk(state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.weight"), 2, dim=0) + k_bias, v_bias = torch.chunk( + state_dict.pop(f"controlnet.{depth}.copied_block.cross_attn.kv_linear.bias"), 2, dim=0 + ) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( + f"controlnet.{depth}.copied_block.cross_attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( + f"controlnet.{depth}.copied_block.cross_attn.proj.bias" + ) + + # ControlNet After Projection + converted_state_dict[f"controlnet_blocks.{depth}.weight"] = state_dict.pop( + f"controlnet.{depth}.after_proj.weight" + ) + converted_state_dict[f"controlnet_blocks.{depth}.bias"] = state_dict.pop(f"controlnet.{depth}.after_proj.bias") + + # ControlNet + with CTX(): + controlnet = SanaControlNetModel( + num_attention_heads=model_kwargs[args.model_type]["num_attention_heads"], + attention_head_dim=model_kwargs[args.model_type]["attention_head_dim"], + num_layers=model_kwargs[args.model_type]["num_layers"], + num_cross_attention_heads=model_kwargs[args.model_type]["num_cross_attention_heads"], + cross_attention_head_dim=model_kwargs[args.model_type]["cross_attention_head_dim"], + cross_attention_dim=model_kwargs[args.model_type]["cross_attention_dim"], + caption_channels=2304, + sample_size=args.image_size // 32, + interpolation_scale=interpolation_scale[args.image_size], + ) + + if is_accelerate_available(): + load_model_dict_into_meta(controlnet, converted_state_dict) + else: + controlnet.load_state_dict(converted_state_dict, strict=True, assign=True) + + num_model_params = sum(p.numel() for p in controlnet.parameters()) + print(f"Total number of controlnet parameters: {num_model_params}") + + controlnet = controlnet.to(weight_dtype) + controlnet.load_state_dict(converted_state_dict, strict=True) + + print(f"Saving Sana ControlNet in Diffusers format in {args.dump_path}.") + controlnet.save_pretrained(args.dump_path) + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + +VARIANT_MAPPING = { + "fp32": None, + "fp16": "fp16", + "bf16": "bf16", +} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--orig_ckpt_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[512, 1024, 2048, 4096], + required=False, + help="Image size of pretrained model, 512, 1024, 2048 or 4096.", + ) + parser.add_argument( + "--model_type", + default="SanaMS_1600M_P1_ControlNet_D7", + type=str, + choices=["SanaMS_1600M_P1_ControlNet_D7", "SanaMS_600M_P1_ControlNet_D7"], + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--dtype", default="fp16", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") + + args = parser.parse_args() + + model_kwargs = { + "SanaMS_1600M_P1_ControlNet_D7": { + "num_attention_heads": 70, + "attention_head_dim": 32, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "num_layers": 7, + }, + "SanaMS_600M_P1_ControlNet_D7": { + "num_attention_heads": 36, + "attention_head_dim": 32, + "num_cross_attention_heads": 16, + "cross_attention_head_dim": 72, + "cross_attention_dim": 1152, + "num_layers": 7, + }, + } + + device = "cuda" if torch.cuda.is_available() else "cpu" + weight_dtype = DTYPE_MAPPING[args.dtype] + variant = VARIANT_MAPPING[args.dtype] + + main(args) diff --git a/scripts/convert_sana_to_diffusers.py b/scripts/convert_sana_to_diffusers.py new file mode 100644 index 000000000000..833e64ba75e7 --- /dev/null +++ b/scripts/convert_sana_to_diffusers.py @@ -0,0 +1,456 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +import os +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download, snapshot_download +from termcolor import colored +from transformers import AutoModelForCausalLM, AutoTokenizer + +from diffusers import ( + AutoencoderDC, + DPMSolverMultistepScheduler, + FlowMatchEulerDiscreteScheduler, + SanaPipeline, + SanaSprintPipeline, + SanaTransformer2DModel, + SCMScheduler, +) +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available else nullcontext + +ckpt_ids = [ + "Efficient-Large-Model/Sana_Sprint_0.6B_1024px/checkpoints/Sana_Sprint_0.6B_1024px.pth" + "Efficient-Large-Model/Sana_Sprint_1.6B_1024px/checkpoints/Sana_Sprint_1.6B_1024px.pth" + "Efficient-Large-Model/SANA1.5_4.8B_1024px/checkpoints/SANA1.5_4.8B_1024px.pth", + "Efficient-Large-Model/SANA1.5_1.6B_1024px/checkpoints/SANA1.5_1.6B_1024px.pth", + "Efficient-Large-Model/Sana_1600M_4Kpx_BF16/checkpoints/Sana_1600M_4Kpx_BF16.pth", + "Efficient-Large-Model/Sana_1600M_2Kpx_BF16/checkpoints/Sana_1600M_2Kpx_BF16.pth", + "Efficient-Large-Model/Sana_1600M_1024px_MultiLing/checkpoints/Sana_1600M_1024px_MultiLing.pth", + "Efficient-Large-Model/Sana_1600M_1024px_BF16/checkpoints/Sana_1600M_1024px_BF16.pth", + "Efficient-Large-Model/Sana_1600M_512px_MultiLing/checkpoints/Sana_1600M_512px_MultiLing.pth", + "Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth", + "Efficient-Large-Model/Sana_1600M_512px/checkpoints/Sana_1600M_512px.pth", + "Efficient-Large-Model/Sana_600M_1024px/checkpoints/Sana_600M_1024px_MultiLing.pth", + "Efficient-Large-Model/Sana_600M_512px/checkpoints/Sana_600M_512px_MultiLing.pth", +] +# https://github.com/NVlabs/Sana/blob/main/scripts/inference.py + + +def main(args): + cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub") + + if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids: + ckpt_id = args.orig_ckpt_path or ckpt_ids[0] + snapshot_download( + repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", + cache_dir=cache_dir_path, + repo_type="model", + ) + file_path = hf_hub_download( + repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", + filename=f"{'/'.join(ckpt_id.split('/')[2:])}", + cache_dir=cache_dir_path, + repo_type="model", + ) + else: + file_path = args.orig_ckpt_path + + print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"])) + all_state_dict = torch.load(file_path, weights_only=True) + state_dict = all_state_dict.pop("state_dict") + converted_state_dict = {} + + # Patch embeddings. + converted_state_dict["patch_embed.proj.weight"] = state_dict.pop("x_embedder.proj.weight") + converted_state_dict["patch_embed.proj.bias"] = state_dict.pop("x_embedder.proj.bias") + + # Caption projection. + converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") + converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") + converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") + converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") + + # Handle different time embedding structure based on model type + + if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: + # For Sana Sprint, the time embedding structure is different + converted_state_dict["time_embed.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["time_embed.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") + converted_state_dict["time_embed.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["time_embed.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") + + # Guidance embedder for Sana Sprint + converted_state_dict["time_embed.guidance_embedder.linear_1.weight"] = state_dict.pop( + "cfg_embedder.mlp.0.weight" + ) + converted_state_dict["time_embed.guidance_embedder.linear_1.bias"] = state_dict.pop("cfg_embedder.mlp.0.bias") + converted_state_dict["time_embed.guidance_embedder.linear_2.weight"] = state_dict.pop( + "cfg_embedder.mlp.2.weight" + ) + converted_state_dict["time_embed.guidance_embedder.linear_2.bias"] = state_dict.pop("cfg_embedder.mlp.2.bias") + else: + # Original Sana time embedding structure + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop( + "t_embedder.mlp.0.bias" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop( + "t_embedder.mlp.2.bias" + ) + + # Shared norm. + converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") + converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") + + # y norm + converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") + + # scheduler + if args.image_size == 4096: + flow_shift = 6.0 + else: + flow_shift = 3.0 + + # model config + if args.model_type in ["SanaMS_1600M_P1_D20", "SanaSprint_1600M_P1_D20", "SanaMS1.5_1600M_P1_D20"]: + layer_num = 20 + elif args.model_type in ["SanaMS_600M_P1_D28", "SanaSprint_600M_P1_D28"]: + layer_num = 28 + elif args.model_type == "SanaMS_4800M_P1_D60": + layer_num = 60 + else: + raise ValueError(f"{args.model_type} is not supported.") + # Positional embedding interpolation scale. + interpolation_scale = {512: None, 1024: None, 2048: 1.0, 4096: 2.0} + qk_norm = ( + "rms_norm_across_heads" + if args.model_type + in ["SanaMS1.5_1600M_P1_D20", "SanaMS1.5_4800M_P1_D60", "SanaSprint_600M_P1_D28", "SanaSprint_1600M_P1_D20"] + else None + ) + + for depth in range(layer_num): + # Transformer blocks. + converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( + f"blocks.{depth}.scale_shift_table" + ) + + # Linear Attention is all you need 🤘 + # Self attention. + q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + if qk_norm is not None: + # Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5 + converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop( + f"blocks.{depth}.attn.q_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop( + f"blocks.{depth}.attn.k_norm.weight" + ) + # Projection. + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.attn.proj.bias" + ) + + # Feed-forward. + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.inverted_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.inverted_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.depth_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.depth_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.point_conv.conv.weight" + ) + + # Cross-attention. + q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") + q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") + k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias + if qk_norm is not None: + # Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5 + converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.q_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.k_norm.weight" + ) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.bias" + ) + + # Final block. + converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") + converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") + + # Transformer + with CTX(): + transformer_kwargs = { + "in_channels": 32, + "out_channels": 32, + "num_attention_heads": model_kwargs[args.model_type]["num_attention_heads"], + "attention_head_dim": model_kwargs[args.model_type]["attention_head_dim"], + "num_layers": model_kwargs[args.model_type]["num_layers"], + "num_cross_attention_heads": model_kwargs[args.model_type]["num_cross_attention_heads"], + "cross_attention_head_dim": model_kwargs[args.model_type]["cross_attention_head_dim"], + "cross_attention_dim": model_kwargs[args.model_type]["cross_attention_dim"], + "caption_channels": 2304, + "mlp_ratio": 2.5, + "attention_bias": False, + "sample_size": args.image_size // 32, + "patch_size": 1, + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "interpolation_scale": interpolation_scale[args.image_size], + } + + # Add qk_norm parameter for Sana Sprint + if args.model_type in [ + "SanaMS1.5_1600M_P1_D20", + "SanaMS1.5_4800M_P1_D60", + "SanaSprint_600M_P1_D28", + "SanaSprint_1600M_P1_D20", + ]: + transformer_kwargs["qk_norm"] = "rms_norm_across_heads" + if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: + transformer_kwargs["guidance_embeds"] = True + + transformer = SanaTransformer2DModel(**transformer_kwargs) + + if is_accelerate_available(): + load_model_dict_into_meta(transformer, converted_state_dict) + else: + transformer.load_state_dict(converted_state_dict, strict=True, assign=True) + + try: + state_dict.pop("y_embedder.y_embedding") + state_dict.pop("pos_embed") + state_dict.pop("logvar_linear.weight") + state_dict.pop("logvar_linear.bias") + except KeyError: + print("y_embedder.y_embedding or pos_embed not found in the state_dict") + + assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + transformer = transformer.to(weight_dtype) + + if not args.save_full_pipeline: + print( + colored( + f"Only saving transformer model of {args.model_type}. " + f"Set --save_full_pipeline to save the whole Pipeline", + "green", + attrs=["bold"], + ) + ) + transformer.save_pretrained( + os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB" + ) + else: + print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"])) + # VAE + ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers", torch_dtype=torch.float32) + + # Text Encoder + text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it" + tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path) + tokenizer.padding_side = "right" + text_encoder = AutoModelForCausalLM.from_pretrained( + text_encoder_model_path, torch_dtype=torch.bfloat16 + ).get_decoder() + + # Choose the appropriate pipeline and scheduler based on model type + if args.model_type in ["SanaSprint_1600M_P1_D20", "SanaSprint_600M_P1_D28"]: + # Force SCM Scheduler for Sana Sprint regardless of scheduler_type + if args.scheduler_type != "scm": + print( + colored( + f"Warning: Overriding scheduler_type '{args.scheduler_type}' to 'scm' for SanaSprint model", + "yellow", + attrs=["bold"], + ) + ) + + # SCM Scheduler for Sana Sprint + scheduler_config = { + "prediction_type": "trigflow", + "sigma_data": 0.5, + } + scheduler = SCMScheduler(**scheduler_config) + pipe = SanaSprintPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + transformer=transformer, + vae=ae, + scheduler=scheduler, + ) + else: + # Original Sana scheduler + if args.scheduler_type == "flow-dpm_solver": + scheduler = DPMSolverMultistepScheduler( + flow_shift=flow_shift, + use_flow_sigmas=True, + prediction_type="flow_prediction", + ) + elif args.scheduler_type == "flow-euler": + scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) + else: + raise ValueError(f"Scheduler type {args.scheduler_type} is not supported") + + pipe = SanaPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + transformer=transformer, + vae=ae, + scheduler=scheduler, + ) + + pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB") + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[512, 1024, 2048, 4096], + required=False, + help="Image size of pretrained model, 512, 1024, 2048 or 4096.", + ) + parser.add_argument( + "--model_type", + default="SanaMS_1600M_P1_D20", + type=str, + choices=[ + "SanaMS_1600M_P1_D20", + "SanaMS_600M_P1_D28", + "SanaMS1.5_1600M_P1_D20", + "SanaMS1.5_4800M_P1_D60", + "SanaSprint_1600M_P1_D20", + "SanaSprint_600M_P1_D28", + ], + ) + parser.add_argument( + "--scheduler_type", + default="flow-dpm_solver", + type=str, + choices=["flow-dpm_solver", "flow-euler", "scm"], + help="Scheduler type to use. Use 'scm' for Sana Sprint models.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.") + parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") + + args = parser.parse_args() + + model_kwargs = { + "SanaMS_1600M_P1_D20": { + "num_attention_heads": 70, + "attention_head_dim": 32, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "num_layers": 20, + }, + "SanaMS_600M_P1_D28": { + "num_attention_heads": 36, + "attention_head_dim": 32, + "num_cross_attention_heads": 16, + "cross_attention_head_dim": 72, + "cross_attention_dim": 1152, + "num_layers": 28, + }, + "SanaMS1.5_1600M_P1_D20": { + "num_attention_heads": 70, + "attention_head_dim": 32, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "num_layers": 20, + }, + "SanaMS1.5_4800M_P1_D60": { + "num_attention_heads": 70, + "attention_head_dim": 32, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "num_layers": 60, + }, + "SanaSprint_600M_P1_D28": { + "num_attention_heads": 36, + "attention_head_dim": 32, + "num_cross_attention_heads": 16, + "cross_attention_head_dim": 72, + "cross_attention_dim": 1152, + "num_layers": 28, + }, + "SanaSprint_1600M_P1_D20": { + "num_attention_heads": 70, + "attention_head_dim": 32, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "num_layers": 20, + }, + } + + device = "cuda" if torch.cuda.is_available() else "cpu" + weight_dtype = DTYPE_MAPPING[args.dtype] + + main(args) diff --git a/scripts/convert_sana_video_to_diffusers.py b/scripts/convert_sana_video_to_diffusers.py new file mode 100644 index 000000000000..c6be52d455b8 --- /dev/null +++ b/scripts/convert_sana_video_to_diffusers.py @@ -0,0 +1,360 @@ +#!/usr/bin/env python +from __future__ import annotations + +import argparse +import os +from contextlib import nullcontext + +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download, snapshot_download +from termcolor import colored +from transformers import AutoModelForCausalLM, AutoTokenizer + +from diffusers import ( + AutoencoderKLLTX2Video, + AutoencoderKLWan, + DPMSolverMultistepScheduler, + FlowMatchEulerDiscreteScheduler, + SanaVideoPipeline, + SanaVideoTransformer3DModel, + UniPCMultistepScheduler, +) +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available else nullcontext + +ckpt_ids = [ + "Efficient-Large-Model/SANA-Video_2B_480p/checkpoints/SANA_Video_2B_480p.pth", + "Efficient-Large-Model/SANA-Video_2B_720p/checkpoints/SANA_Video_2B_720p_LTXVAE.pth", +] +# https://github.com/NVlabs/Sana/blob/main/inference_video_scripts/inference_sana_video.py + + +def main(args): + cache_dir_path = os.path.expanduser("~/.cache/huggingface/hub") + + if args.orig_ckpt_path is None or args.orig_ckpt_path in ckpt_ids: + ckpt_id = args.orig_ckpt_path or ckpt_ids[0] + snapshot_download( + repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", + cache_dir=cache_dir_path, + repo_type="model", + ) + file_path = hf_hub_download( + repo_id=f"{'/'.join(ckpt_id.split('/')[:2])}", + filename=f"{'/'.join(ckpt_id.split('/')[2:])}", + cache_dir=cache_dir_path, + repo_type="model", + ) + else: + file_path = args.orig_ckpt_path + + print(colored(f"Loading checkpoint from {file_path}", "green", attrs=["bold"])) + all_state_dict = torch.load(file_path, weights_only=True) + state_dict = all_state_dict.pop("state_dict") + converted_state_dict = {} + + # Patch embeddings. + converted_state_dict["patch_embedding.weight"] = state_dict.pop("x_embedder.proj.weight") + converted_state_dict["patch_embedding.bias"] = state_dict.pop("x_embedder.proj.bias") + + # Caption projection. + converted_state_dict["caption_projection.linear_1.weight"] = state_dict.pop("y_embedder.y_proj.fc1.weight") + converted_state_dict["caption_projection.linear_1.bias"] = state_dict.pop("y_embedder.y_proj.fc1.bias") + converted_state_dict["caption_projection.linear_2.weight"] = state_dict.pop("y_embedder.y_proj.fc2.weight") + converted_state_dict["caption_projection.linear_2.bias"] = state_dict.pop("y_embedder.y_proj.fc2.bias") + + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.weight"] = state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_1.bias"] = state_dict.pop("t_embedder.mlp.0.bias") + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.weight"] = state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["time_embed.emb.timestep_embedder.linear_2.bias"] = state_dict.pop("t_embedder.mlp.2.bias") + + # Shared norm. + converted_state_dict["time_embed.linear.weight"] = state_dict.pop("t_block.1.weight") + converted_state_dict["time_embed.linear.bias"] = state_dict.pop("t_block.1.bias") + + # y norm + converted_state_dict["caption_norm.weight"] = state_dict.pop("attention_y_norm.weight") + + # scheduler + flow_shift = 8.0 + if args.task == "i2v": + assert args.scheduler_type == "flow-euler", "Scheduler type must be flow-euler for i2v task." + + # model config + layer_num = 20 + # Positional embedding interpolation scale. + qk_norm = True + + # sample size + if args.video_size == 480: + sample_size = 30 # Wan-VAE: 8xp2 downsample factor + patch_size = (1, 2, 2) + in_channels = 16 + out_channels = 16 + elif args.video_size == 720: + sample_size = 22 # DC-AE-V: 32xp1 downsample factor + patch_size = (1, 1, 1) + in_channels = 32 + out_channels = 32 + else: + raise ValueError(f"Video size {args.video_size} is not supported.") + + if args.vae_type == "ltx2": + sample_size = 22 + patch_size = (1, 1, 1) + in_channels = 128 + out_channels = 128 + + for depth in range(layer_num): + # Transformer blocks. + converted_state_dict[f"transformer_blocks.{depth}.scale_shift_table"] = state_dict.pop( + f"blocks.{depth}.scale_shift_table" + ) + + # Linear Attention is all you need 🤘 + # Self attention. + q, k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.attn.qkv.weight"), 3, dim=0) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v + if qk_norm is not None: + # Add Q/K normalization for self-attention (attn1) - needed for Sana-Sprint and Sana-1.5 + converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_q.weight"] = state_dict.pop( + f"blocks.{depth}.attn.q_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.norm_k.weight"] = state_dict.pop( + f"blocks.{depth}.attn.k_norm.weight" + ) + # Projection. + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.attn.proj.bias" + ) + + # Feed-forward. + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.inverted_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_inverted.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.inverted_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.depth_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_depth.bias"] = state_dict.pop( + f"blocks.{depth}.mlp.depth_conv.conv.bias" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_point.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.point_conv.conv.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.ff.conv_temp.weight"] = state_dict.pop( + f"blocks.{depth}.mlp.t_conv.weight" + ) + + # Cross-attention. + q = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.weight") + q_bias = state_dict.pop(f"blocks.{depth}.cross_attn.q_linear.bias") + k, v = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.weight"), 2, dim=0) + k_bias, v_bias = torch.chunk(state_dict.pop(f"blocks.{depth}.cross_attn.kv_linear.bias"), 2, dim=0) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.weight"] = q + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_q.bias"] = q_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.weight"] = k + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_k.bias"] = k_bias + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.weight"] = v + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_v.bias"] = v_bias + if qk_norm is not None: + # Add Q/K normalization for cross-attention (attn2) - needed for Sana-Sprint and Sana-1.5 + converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_q.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.q_norm.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.norm_k.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.k_norm.weight" + ) + + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.weight"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{depth}.attn2.to_out.0.bias"] = state_dict.pop( + f"blocks.{depth}.cross_attn.proj.bias" + ) + + # Final block. + converted_state_dict["proj_out.weight"] = state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = state_dict.pop("final_layer.linear.bias") + converted_state_dict["scale_shift_table"] = state_dict.pop("final_layer.scale_shift_table") + + # Transformer + with CTX(): + transformer_kwargs = { + "in_channels": in_channels, + "out_channels": out_channels, + "num_attention_heads": 20, + "attention_head_dim": 112, + "num_layers": 20, + "num_cross_attention_heads": 20, + "cross_attention_head_dim": 112, + "cross_attention_dim": 2240, + "caption_channels": 2304, + "mlp_ratio": 3.0, + "attention_bias": False, + "sample_size": sample_size, + "patch_size": patch_size, + "norm_elementwise_affine": False, + "norm_eps": 1e-6, + "qk_norm": "rms_norm_across_heads", + "rope_max_seq_len": 1024, + } + + transformer = SanaVideoTransformer3DModel(**transformer_kwargs) + + transformer.load_state_dict(converted_state_dict, strict=True, assign=True) + + try: + state_dict.pop("y_embedder.y_embedding") + state_dict.pop("pos_embed") + state_dict.pop("logvar_linear.weight") + state_dict.pop("logvar_linear.bias") + except KeyError: + print("y_embedder.y_embedding or pos_embed not found in the state_dict") + + assert len(state_dict) == 0, f"State dict is not empty, {state_dict.keys()}" + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + transformer = transformer.to(weight_dtype) + + if not args.save_full_pipeline: + print( + colored( + f"Only saving transformer model of {args.model_type}. " + f"Set --save_full_pipeline to save the whole Pipeline", + "green", + attrs=["bold"], + ) + ) + transformer.save_pretrained( + os.path.join(args.dump_path, "transformer"), safe_serialization=True, max_shard_size="5GB" + ) + else: + print(colored(f"Saving the whole Pipeline containing {args.model_type}", "green", attrs=["bold"])) + # VAE + if args.vae_type == "ltx2": + vae_path = args.vae_path or "Lightricks/LTX-2" + vae = AutoencoderKLLTX2Video.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32) + else: + vae_path = args.vae_path or "Wan-AI/Wan2.1-T2V-1.3B-Diffusers" + vae = AutoencoderKLWan.from_pretrained(vae_path, subfolder="vae", torch_dtype=torch.float32) + + # Text Encoder + text_encoder_model_path = "Efficient-Large-Model/gemma-2-2b-it" + tokenizer = AutoTokenizer.from_pretrained(text_encoder_model_path) + tokenizer.padding_side = "right" + text_encoder = AutoModelForCausalLM.from_pretrained( + text_encoder_model_path, torch_dtype=torch.bfloat16 + ).get_decoder() + + # Choose the appropriate pipeline and scheduler based on model type + # Original Sana scheduler + if args.scheduler_type == "flow-dpm_solver": + scheduler = DPMSolverMultistepScheduler( + flow_shift=flow_shift, + use_flow_sigmas=True, + prediction_type="flow_prediction", + ) + elif args.scheduler_type == "flow-euler": + scheduler = FlowMatchEulerDiscreteScheduler(shift=flow_shift) + elif args.scheduler_type == "uni-pc": + scheduler = UniPCMultistepScheduler( + prediction_type="flow_prediction", + use_flow_sigmas=True, + num_train_timesteps=1000, + flow_shift=flow_shift, + ) + else: + raise ValueError(f"Scheduler type {args.scheduler_type} is not supported") + + pipe = SanaVideoPipeline( + tokenizer=tokenizer, + text_encoder=text_encoder, + transformer=transformer, + vae=vae, + scheduler=scheduler, + ) + + pipe.save_pretrained(args.dump_path, safe_serialization=True, max_shard_size="5GB") + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--orig_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--video_size", + default=480, + type=int, + choices=[480, 720], + required=False, + help="Video size of pretrained model, 480 or 720.", + ) + parser.add_argument( + "--model_type", + default="SanaVideo", + type=str, + choices=[ + "SanaVideo", + ], + ) + parser.add_argument( + "--scheduler_type", + default="flow-dpm_solver", + type=str, + choices=["flow-dpm_solver", "flow-euler", "uni-pc"], + help="Scheduler type to use.", + ) + parser.add_argument( + "--vae_type", + default="wan", + type=str, + choices=["wan", "ltx2"], + help="VAE type to use for saving full pipeline (ltx2 uses patchify 1x1x1).", + ) + parser.add_argument( + "--vae_path", + default=None, + type=str, + required=False, + help="Optional VAE path or repo id. If not set, a default is used per VAE type.", + ) + parser.add_argument( + "--task", default="t2v", type=str, required=True, choices=["t2v", "i2v"], help="Task to convert, t2v or i2v." + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--save_full_pipeline", action="store_true", help="save all the pipeline elements in one.") + parser.add_argument("--dtype", default="fp32", type=str, choices=["fp32", "fp16", "bf16"], help="Weight dtype.") + + args = parser.parse_args() + + device = "cuda" if torch.cuda.is_available() else "cpu" + weight_dtype = DTYPE_MAPPING[args.dtype] + + main(args) diff --git a/scripts/convert_sd3_controlnet_to_diffusers.py b/scripts/convert_sd3_controlnet_to_diffusers.py new file mode 100644 index 000000000000..171f40a7aa06 --- /dev/null +++ b/scripts/convert_sd3_controlnet_to_diffusers.py @@ -0,0 +1,185 @@ +""" +A script to convert Stable Diffusion 3.5 ControlNet checkpoints to the Diffusers format. + +Example: + Convert a SD3.5 ControlNet checkpoint to Diffusers format using local file: + ```bash + python scripts/convert_sd3_controlnet_to_diffusers.py \ + --checkpoint_path "path/to/local/sd3.5_large_controlnet_canny.safetensors" \ + --output_path "output/sd35-controlnet-canny" \ + --dtype "fp16" # optional, defaults to fp32 + ``` + + Or download and convert from HuggingFace repository: + ```bash + python scripts/convert_sd3_controlnet_to_diffusers.py \ + --original_state_dict_repo_id "stabilityai/stable-diffusion-3.5-controlnets" \ + --filename "sd3.5_large_controlnet_canny.safetensors" \ + --output_path "/raid/yiyi/sd35-controlnet-canny-diffusers" \ + --dtype "fp32" # optional, defaults to fp32 + ``` + +Note: + The script supports the following ControlNet types from SD3.5: + - Canny edge detection + - Depth estimation + - Blur detection + + The checkpoint files can be downloaded from: + https://huggingface.co/stabilityai/stable-diffusion-3.5-controlnets +""" + +import argparse + +import safetensors.torch +import torch +from huggingface_hub import hf_hub_download + +from diffusers import SD3ControlNetModel + + +parser = argparse.ArgumentParser() +parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to local checkpoint file") +parser.add_argument( + "--original_state_dict_repo_id", type=str, default=None, help="HuggingFace repo ID containing the checkpoint" +) +parser.add_argument("--filename", type=str, default=None, help="Filename of the checkpoint in the HF repo") +parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model") +parser.add_argument( + "--dtype", type=str, default="fp32", help="Data type for the converted model (fp16, bf16, or fp32)" +) + +args = parser.parse_args() + + +def load_original_checkpoint(args): + if args.original_state_dict_repo_id is not None: + if args.filename is None: + raise ValueError("When using `original_state_dict_repo_id`, `filename` must also be specified") + print(f"Downloading checkpoint from {args.original_state_dict_repo_id}/{args.filename}") + ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename) + elif args.checkpoint_path is not None: + print(f"Loading checkpoint from local path: {args.checkpoint_path}") + ckpt_path = args.checkpoint_path + else: + raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`") + + original_state_dict = safetensors.torch.load_file(ckpt_path) + return original_state_dict + + +def convert_sd3_controlnet_checkpoint_to_diffusers(original_state_dict): + converted_state_dict = {} + + # Direct mappings for controlnet blocks + for i in range(19): # 19 controlnet blocks + converted_state_dict[f"controlnet_blocks.{i}.weight"] = original_state_dict[f"controlnet_blocks.{i}.weight"] + converted_state_dict[f"controlnet_blocks.{i}.bias"] = original_state_dict[f"controlnet_blocks.{i}.bias"] + + # Positional embeddings + converted_state_dict["pos_embed_input.proj.weight"] = original_state_dict["pos_embed_input.proj.weight"] + converted_state_dict["pos_embed_input.proj.bias"] = original_state_dict["pos_embed_input.proj.bias"] + + # Time and text embeddings + time_text_mappings = { + "time_text_embed.timestep_embedder.linear_1.weight": "time_text_embed.timestep_embedder.linear_1.weight", + "time_text_embed.timestep_embedder.linear_1.bias": "time_text_embed.timestep_embedder.linear_1.bias", + "time_text_embed.timestep_embedder.linear_2.weight": "time_text_embed.timestep_embedder.linear_2.weight", + "time_text_embed.timestep_embedder.linear_2.bias": "time_text_embed.timestep_embedder.linear_2.bias", + "time_text_embed.text_embedder.linear_1.weight": "time_text_embed.text_embedder.linear_1.weight", + "time_text_embed.text_embedder.linear_1.bias": "time_text_embed.text_embedder.linear_1.bias", + "time_text_embed.text_embedder.linear_2.weight": "time_text_embed.text_embedder.linear_2.weight", + "time_text_embed.text_embedder.linear_2.bias": "time_text_embed.text_embedder.linear_2.bias", + } + + for new_key, old_key in time_text_mappings.items(): + if old_key in original_state_dict: + converted_state_dict[new_key] = original_state_dict[old_key] + + # Transformer blocks + for i in range(19): + # Split QKV into separate Q, K, V + qkv_weight = original_state_dict[f"transformer_blocks.{i}.attn.qkv.weight"] + qkv_bias = original_state_dict[f"transformer_blocks.{i}.attn.qkv.bias"] + q, k, v = torch.chunk(qkv_weight, 3, dim=0) + q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0) + + block_mappings = { + f"transformer_blocks.{i}.attn.to_q.weight": q, + f"transformer_blocks.{i}.attn.to_q.bias": q_bias, + f"transformer_blocks.{i}.attn.to_k.weight": k, + f"transformer_blocks.{i}.attn.to_k.bias": k_bias, + f"transformer_blocks.{i}.attn.to_v.weight": v, + f"transformer_blocks.{i}.attn.to_v.bias": v_bias, + # Output projections + f"transformer_blocks.{i}.attn.to_out.0.weight": original_state_dict[ + f"transformer_blocks.{i}.attn.proj.weight" + ], + f"transformer_blocks.{i}.attn.to_out.0.bias": original_state_dict[ + f"transformer_blocks.{i}.attn.proj.bias" + ], + # Feed forward + f"transformer_blocks.{i}.ff.net.0.proj.weight": original_state_dict[ + f"transformer_blocks.{i}.mlp.fc1.weight" + ], + f"transformer_blocks.{i}.ff.net.0.proj.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc1.bias"], + f"transformer_blocks.{i}.ff.net.2.weight": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.weight"], + f"transformer_blocks.{i}.ff.net.2.bias": original_state_dict[f"transformer_blocks.{i}.mlp.fc2.bias"], + # Norms + f"transformer_blocks.{i}.norm1.linear.weight": original_state_dict[ + f"transformer_blocks.{i}.adaLN_modulation.1.weight" + ], + f"transformer_blocks.{i}.norm1.linear.bias": original_state_dict[ + f"transformer_blocks.{i}.adaLN_modulation.1.bias" + ], + } + converted_state_dict.update(block_mappings) + + return converted_state_dict + + +def main(args): + original_ckpt = load_original_checkpoint(args) + original_dtype = next(iter(original_ckpt.values())).dtype + + # Initialize dtype with fp32 as default + if args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}. Must be one of: fp16, bf16, fp32") + + if dtype != original_dtype: + print( + f"Converting checkpoint from {original_dtype} to {dtype}. This can lead to unexpected results, proceed with caution." + ) + + converted_controlnet_state_dict = convert_sd3_controlnet_checkpoint_to_diffusers(original_ckpt) + + controlnet = SD3ControlNetModel( + patch_size=2, + in_channels=16, + num_layers=19, + attention_head_dim=64, + num_attention_heads=38, + joint_attention_dim=None, + caption_projection_dim=2048, + pooled_projection_dim=2048, + out_channels=16, + pos_embed_max_size=None, + pos_embed_type=None, + use_pos_embed=False, + force_zeros_for_pooled_projection=False, + ) + + controlnet.load_state_dict(converted_controlnet_state_dict, strict=True) + + print(f"Saving SD3 ControlNet in Diffusers format in {args.output_path}.") + controlnet.to(dtype).save_pretrained(args.output_path) + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_sd3_to_diffusers.py b/scripts/convert_sd3_to_diffusers.py new file mode 100644 index 000000000000..83cb436e6e32 --- /dev/null +++ b/scripts/convert_sd3_to_diffusers.py @@ -0,0 +1,351 @@ +import argparse +from contextlib import nullcontext + +import safetensors.torch +import torch +from accelerate import init_empty_weights + +from diffusers import AutoencoderKL, SD3Transformer2DModel +from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.utils.import_utils import is_accelerate_available + + +CTX = init_empty_weights if is_accelerate_available() else nullcontext + +parser = argparse.ArgumentParser() +parser.add_argument("--checkpoint_path", type=str) +parser.add_argument("--output_path", type=str) +parser.add_argument("--dtype", type=str) + +args = parser.parse_args() + + +def load_original_checkpoint(ckpt_path): + original_state_dict = safetensors.torch.load_file(ckpt_path) + keys = list(original_state_dict.keys()) + for k in keys: + if "model.diffusion_model." in k: + original_state_dict[k.replace("model.diffusion_model.", "")] = original_state_dict.pop(k) + + return original_state_dict + + +# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale; +# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation +def swap_scale_shift(weight, dim): + shift, scale = weight.chunk(2, dim=0) + new_weight = torch.cat([scale, shift], dim=0) + return new_weight + + +def convert_sd3_transformer_checkpoint_to_diffusers( + original_state_dict, num_layers, caption_projection_dim, dual_attention_layers, has_qk_norm +): + converted_state_dict = {} + + # Positional and patch embeddings. + converted_state_dict["pos_embed.pos_embed"] = original_state_dict.pop("pos_embed") + converted_state_dict["pos_embed.proj.weight"] = original_state_dict.pop("x_embedder.proj.weight") + converted_state_dict["pos_embed.proj.bias"] = original_state_dict.pop("x_embedder.proj.bias") + + # Timestep embeddings. + converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop( + "t_embedder.mlp.0.weight" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop( + "t_embedder.mlp.0.bias" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop( + "t_embedder.mlp.2.weight" + ) + converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop( + "t_embedder.mlp.2.bias" + ) + + # Context projections. + converted_state_dict["context_embedder.weight"] = original_state_dict.pop("context_embedder.weight") + converted_state_dict["context_embedder.bias"] = original_state_dict.pop("context_embedder.bias") + + # Pooled context projection. + converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop( + "y_embedder.mlp.0.weight" + ) + converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop( + "y_embedder.mlp.0.bias" + ) + converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop( + "y_embedder.mlp.2.weight" + ) + converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop( + "y_embedder.mlp.2.bias" + ) + + # Transformer blocks 🎸. + for i in range(num_layers): + # Q, K, V + sample_q, sample_k, sample_v = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.weight"), 3, dim=0 + ) + context_q, context_k, context_v = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.weight"), 3, dim=0 + ) + sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.x_block.attn.qkv.bias"), 3, dim=0 + ) + context_q_bias, context_k_bias, context_v_bias = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.context_block.attn.qkv.bias"), 3, dim=0 + ) + + converted_state_dict[f"transformer_blocks.{i}.attn.to_q.weight"] = torch.cat([sample_q]) + converted_state_dict[f"transformer_blocks.{i}.attn.to_q.bias"] = torch.cat([sample_q_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn.to_k.weight"] = torch.cat([sample_k]) + converted_state_dict[f"transformer_blocks.{i}.attn.to_k.bias"] = torch.cat([sample_k_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn.to_v.weight"] = torch.cat([sample_v]) + converted_state_dict[f"transformer_blocks.{i}.attn.to_v.bias"] = torch.cat([sample_v_bias]) + + converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.weight"] = torch.cat([context_q]) + converted_state_dict[f"transformer_blocks.{i}.attn.add_q_proj.bias"] = torch.cat([context_q_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.weight"] = torch.cat([context_k]) + converted_state_dict[f"transformer_blocks.{i}.attn.add_k_proj.bias"] = torch.cat([context_k_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.weight"] = torch.cat([context_v]) + converted_state_dict[f"transformer_blocks.{i}.attn.add_v_proj.bias"] = torch.cat([context_v_bias]) + + # qk norm + if has_qk_norm: + converted_state_dict[f"transformer_blocks.{i}.attn.norm_q.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn.ln_q.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn.norm_k.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn.ln_k.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_q.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.attn.ln_q.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn.norm_added_k.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.attn.ln_k.weight" + ) + + # output projections. + converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn.to_out.0.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn.proj.bias" + ) + if not (i == num_layers - 1): + converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.attn.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn.to_add_out.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.attn.proj.bias" + ) + + # attn2 + if i in dual_attention_layers: + # Q, K, V + sample_q2, sample_k2, sample_v2 = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.weight"), 3, dim=0 + ) + sample_q2_bias, sample_k2_bias, sample_v2_bias = torch.chunk( + original_state_dict.pop(f"joint_blocks.{i}.x_block.attn2.qkv.bias"), 3, dim=0 + ) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.weight"] = torch.cat([sample_q2]) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_q.bias"] = torch.cat([sample_q2_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.weight"] = torch.cat([sample_k2]) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_k.bias"] = torch.cat([sample_k2_bias]) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.weight"] = torch.cat([sample_v2]) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_v.bias"] = torch.cat([sample_v2_bias]) + + # qk norm + if has_qk_norm: + converted_state_dict[f"transformer_blocks.{i}.attn2.norm_q.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn2.ln_q.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn2.norm_k.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn2.ln_k.weight" + ) + + # output projections. + converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn2.proj.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.attn2.to_out.0.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.attn2.proj.bias" + ) + + # norms. + converted_state_dict[f"transformer_blocks.{i}.norm1.linear.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.adaLN_modulation.1.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.norm1.linear.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.adaLN_modulation.1.bias" + ) + if not (i == num_layers - 1): + converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias" + ) + else: + converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.weight"] = swap_scale_shift( + original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.weight"), + dim=caption_projection_dim, + ) + converted_state_dict[f"transformer_blocks.{i}.norm1_context.linear.bias"] = swap_scale_shift( + original_state_dict.pop(f"joint_blocks.{i}.context_block.adaLN_modulation.1.bias"), + dim=caption_projection_dim, + ) + + # ffs. + converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.mlp.fc1.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.ff.net.0.proj.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.mlp.fc1.bias" + ) + converted_state_dict[f"transformer_blocks.{i}.ff.net.2.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.mlp.fc2.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.ff.net.2.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.x_block.mlp.fc2.bias" + ) + if not (i == num_layers - 1): + converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.mlp.fc1.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.ff_context.net.0.proj.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.mlp.fc1.bias" + ) + converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.weight"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.mlp.fc2.weight" + ) + converted_state_dict[f"transformer_blocks.{i}.ff_context.net.2.bias"] = original_state_dict.pop( + f"joint_blocks.{i}.context_block.mlp.fc2.bias" + ) + + # Final blocks. + converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight") + converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias") + converted_state_dict["norm_out.linear.weight"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.weight"), dim=caption_projection_dim + ) + converted_state_dict["norm_out.linear.bias"] = swap_scale_shift( + original_state_dict.pop("final_layer.adaLN_modulation.1.bias"), dim=caption_projection_dim + ) + + return converted_state_dict + + +def is_vae_in_checkpoint(original_state_dict): + return ("first_stage_model.decoder.conv_in.weight" in original_state_dict) and ( + "first_stage_model.encoder.conv_in.weight" in original_state_dict + ) + + +def get_attn2_layers(state_dict): + attn2_layers = [] + for key in state_dict.keys(): + if "attn2." in key: + # Extract the layer number from the key + layer_num = int(key.split(".")[1]) + attn2_layers.append(layer_num) + return tuple(sorted(set(attn2_layers))) + + +def get_pos_embed_max_size(state_dict): + num_patches = state_dict["pos_embed"].shape[1] + pos_embed_max_size = int(num_patches**0.5) + return pos_embed_max_size + + +def get_caption_projection_dim(state_dict): + caption_projection_dim = state_dict["context_embedder.weight"].shape[0] + return caption_projection_dim + + +def main(args): + original_ckpt = load_original_checkpoint(args.checkpoint_path) + original_dtype = next(iter(original_ckpt.values())).dtype + + # Initialize dtype with a default value + dtype = None + + if args.dtype is None: + dtype = original_dtype + elif args.dtype == "fp16": + dtype = torch.float16 + elif args.dtype == "bf16": + dtype = torch.bfloat16 + elif args.dtype == "fp32": + dtype = torch.float32 + else: + raise ValueError(f"Unsupported dtype: {args.dtype}") + + if dtype != original_dtype: + print( + f"Checkpoint dtype {original_dtype} does not match requested dtype {dtype}. This can lead to unexpected results, proceed with caution." + ) + + num_layers = list(set(int(k.split(".", 2)[1]) for k in original_ckpt if "joint_blocks" in k))[-1] + 1 # noqa: C401 + + caption_projection_dim = get_caption_projection_dim(original_ckpt) + + # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5 + attn2_layers = get_attn2_layers(original_ckpt) + + # sd3.5 use qk norm("rms_norm") + has_qk_norm = any("ln_q" in key for key in original_ckpt.keys()) + + # sd3.5 2b use pox_embed_max_size=384 and sd3.0 and sd3.5 8b use 192 + pos_embed_max_size = get_pos_embed_max_size(original_ckpt) + + converted_transformer_state_dict = convert_sd3_transformer_checkpoint_to_diffusers( + original_ckpt, num_layers, caption_projection_dim, attn2_layers, has_qk_norm + ) + + with CTX(): + transformer = SD3Transformer2DModel( + sample_size=128, + patch_size=2, + in_channels=16, + joint_attention_dim=4096, + num_layers=num_layers, + caption_projection_dim=caption_projection_dim, + num_attention_heads=num_layers, + pos_embed_max_size=pos_embed_max_size, + qk_norm="rms_norm" if has_qk_norm else None, + dual_attention_layers=attn2_layers, + ) + if is_accelerate_available(): + load_model_dict_into_meta(transformer, converted_transformer_state_dict) + else: + transformer.load_state_dict(converted_transformer_state_dict, strict=True) + + print("Saving SD3 Transformer in Diffusers format.") + transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer") + + if is_vae_in_checkpoint(original_ckpt): + with CTX(): + vae = AutoencoderKL.from_config( + "stabilityai/stable-diffusion-xl-base-1.0", + subfolder="vae", + latent_channels=16, + use_post_quant_conv=False, + use_quant_conv=False, + scaling_factor=1.5305, + shift_factor=0.0609, + ) + converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config) + if is_accelerate_available(): + load_model_dict_into_meta(vae, converted_vae_state_dict) + else: + vae.load_state_dict(converted_vae_state_dict, strict=True) + + print("Saving SD3 Autoencoder in Diffusers format.") + vae.to(dtype).save_pretrained(f"{args.output_path}/vae") + + +if __name__ == "__main__": + main(args) diff --git a/scripts/convert_shap_e_to_diffusers.py b/scripts/convert_shap_e_to_diffusers.py new file mode 100644 index 000000000000..ac6543667af9 --- /dev/null +++ b/scripts/convert_shap_e_to_diffusers.py @@ -0,0 +1,1080 @@ +import argparse +import tempfile + +import torch +from accelerate import load_checkpoint_and_dispatch + +from diffusers.models.transformers.prior_transformer import PriorTransformer +from diffusers.pipelines.shap_e import ShapERenderer + + +""" +Example - From the diffusers root directory: + +Download weights: +```sh +$ wget "https://openaipublic.azureedge.net/main/shap-e/text_cond.pt" +``` + +Convert the model: +```sh +$ python scripts/convert_shap_e_to_diffusers.py \ + --prior_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/text_cond.pt \ + --prior_image_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/image_cond.pt \ + --transmitter_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/transmitter.pt\ + --dump_path /home/yiyi_huggingface_co/model_repo/shap-e-img2img/shap_e_renderer\ + --debug renderer +``` +""" + + +# prior + +PRIOR_ORIGINAL_PREFIX = "wrapped" + +PRIOR_CONFIG = { + "num_attention_heads": 16, + "attention_head_dim": 1024 // 16, + "num_layers": 24, + "embedding_dim": 1024, + "num_embeddings": 1024, + "additional_embeddings": 0, + "time_embed_act_fn": "gelu", + "norm_in_type": "layer", + "encoder_hid_proj_type": None, + "added_emb_type": None, + "time_embed_dim": 1024 * 4, + "embedding_proj_dim": 768, + "clip_embed_dim": 1024 * 2, +} + + +def prior_model_from_original_config(): + model = PriorTransformer(**PRIOR_CONFIG) + + return model + + +def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1 + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.weight"], + "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.bias"], + } + ) + + # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2 + diffusers_checkpoint.update( + { + "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.weight"], + "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.bias"], + } + ) + + # <original>.input_proj -> <diffusers>.proj_in + diffusers_checkpoint.update( + { + "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.weight"], + "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.bias"], + } + ) + + # <original>.clip_emb -> <diffusers>.embedding_proj + diffusers_checkpoint.update( + { + "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.weight"], + "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.bias"], + } + ) + + # <original>.pos_emb -> <diffusers>.positional_embedding + diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.pos_emb"][None, :]}) + + # <original>.ln_pre -> <diffusers>.norm_in + diffusers_checkpoint.update( + { + "norm_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.weight"], + "norm_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.bias"], + } + ) + + # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x> + for idx in range(len(model.transformer_blocks)): + diffusers_transformer_prefix = f"transformer_blocks.{idx}" + original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.backbone.resblocks.{idx}" + + # <original>.attn -> <diffusers>.attn1 + diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" + original_attention_prefix = f"{original_transformer_prefix}.attn" + diffusers_checkpoint.update( + prior_attention_to_diffusers( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + original_attention_prefix=original_attention_prefix, + attention_head_dim=model.attention_head_dim, + ) + ) + + # <original>.mlp -> <diffusers>.ff + diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" + original_ff_prefix = f"{original_transformer_prefix}.mlp" + diffusers_checkpoint.update( + prior_ff_to_diffusers( + checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix + ) + ) + + # <original>.ln_1 -> <diffusers>.norm1 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ + f"{original_transformer_prefix}.ln_1.weight" + ], + f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], + } + ) + + # <original>.ln_2 -> <diffusers>.norm3 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ + f"{original_transformer_prefix}.ln_2.weight" + ], + f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], + } + ) + + # <original>.ln_post -> <diffusers>.norm_out + diffusers_checkpoint.update( + { + "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.weight"], + "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.bias"], + } + ) + + # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings + diffusers_checkpoint.update( + { + "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.weight"], + "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.bias"], + } + ) + + return diffusers_checkpoint + + +def prior_attention_to_diffusers( + checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim +): + diffusers_checkpoint = {} + + # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v} + [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions( + weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"], + bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"], + split=3, + chunk_size=attention_head_dim, + ) + + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_q.weight": q_weight, + f"{diffusers_attention_prefix}.to_q.bias": q_bias, + f"{diffusers_attention_prefix}.to_k.weight": k_weight, + f"{diffusers_attention_prefix}.to_k.bias": k_bias, + f"{diffusers_attention_prefix}.to_v.weight": v_weight, + f"{diffusers_attention_prefix}.to_v.bias": v_bias, + } + ) + + # <original>.c_proj -> <diffusers>.to_out.0 + diffusers_checkpoint.update( + { + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"], + } + ) + + return diffusers_checkpoint + + +def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix): + diffusers_checkpoint = { + # <original>.c_fc -> <diffusers>.net.0.proj + f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"], + f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"], + # <original>.c_proj -> <diffusers>.net.2 + f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"], + f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"], + } + + return diffusers_checkpoint + + +# done prior + + +# prior_image (only slightly different from prior) + + +PRIOR_IMAGE_ORIGINAL_PREFIX = "wrapped" + +# Uses default arguments +PRIOR_IMAGE_CONFIG = { + "num_attention_heads": 8, + "attention_head_dim": 1024 // 8, + "num_layers": 24, + "embedding_dim": 1024, + "num_embeddings": 1024, + "additional_embeddings": 0, + "time_embed_act_fn": "gelu", + "norm_in_type": "layer", + "embedding_proj_norm_type": "layer", + "encoder_hid_proj_type": None, + "added_emb_type": None, + "time_embed_dim": 1024 * 4, + "embedding_proj_dim": 1024, + "clip_embed_dim": 1024 * 2, +} + + +def prior_image_model_from_original_config(): + model = PriorTransformer(**PRIOR_IMAGE_CONFIG) + + return model + + +def prior_image_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1 + diffusers_checkpoint.update( + { + "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.weight"], + "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.bias"], + } + ) + + # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2 + diffusers_checkpoint.update( + { + "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.weight"], + "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.bias"], + } + ) + + # <original>.input_proj -> <diffusers>.proj_in + diffusers_checkpoint.update( + { + "proj_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.weight"], + "proj_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.bias"], + } + ) + + # <original>.clip_embed.0 -> <diffusers>.embedding_proj_norm + diffusers_checkpoint.update( + { + "embedding_proj_norm.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.weight"], + "embedding_proj_norm.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.bias"], + } + ) + + # <original>..clip_embed.1 -> <diffusers>.embedding_proj + diffusers_checkpoint.update( + { + "embedding_proj.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.weight"], + "embedding_proj.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.bias"], + } + ) + + # <original>.pos_emb -> <diffusers>.positional_embedding + diffusers_checkpoint.update( + {"positional_embedding": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.pos_emb"][None, :]} + ) + + # <original>.ln_pre -> <diffusers>.norm_in + diffusers_checkpoint.update( + { + "norm_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.weight"], + "norm_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.bias"], + } + ) + + # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x> + for idx in range(len(model.transformer_blocks)): + diffusers_transformer_prefix = f"transformer_blocks.{idx}" + original_transformer_prefix = f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.backbone.resblocks.{idx}" + + # <original>.attn -> <diffusers>.attn1 + diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1" + original_attention_prefix = f"{original_transformer_prefix}.attn" + diffusers_checkpoint.update( + prior_attention_to_diffusers( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + original_attention_prefix=original_attention_prefix, + attention_head_dim=model.attention_head_dim, + ) + ) + + # <original>.mlp -> <diffusers>.ff + diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff" + original_ff_prefix = f"{original_transformer_prefix}.mlp" + diffusers_checkpoint.update( + prior_ff_to_diffusers( + checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix + ) + ) + + # <original>.ln_1 -> <diffusers>.norm1 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[ + f"{original_transformer_prefix}.ln_1.weight" + ], + f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"], + } + ) + + # <original>.ln_2 -> <diffusers>.norm3 + diffusers_checkpoint.update( + { + f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[ + f"{original_transformer_prefix}.ln_2.weight" + ], + f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"], + } + ) + + # <original>.ln_post -> <diffusers>.norm_out + diffusers_checkpoint.update( + { + "norm_out.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.weight"], + "norm_out.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.bias"], + } + ) + + # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings + diffusers_checkpoint.update( + { + "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.weight"], + "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.bias"], + } + ) + + return diffusers_checkpoint + + +# done prior_image + + +# renderer + +## create the lookup table for marching cubes method used in MeshDecoder + +MC_TABLE = [ + [], + [[0, 1, 0, 2, 0, 4]], + [[1, 0, 1, 5, 1, 3]], + [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2]], + [[2, 0, 2, 3, 2, 6]], + [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4]], + [[1, 0, 1, 5, 1, 3], [2, 6, 0, 2, 3, 2]], + [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4]], + [[3, 1, 3, 7, 3, 2]], + [[0, 2, 0, 4, 0, 1], [3, 7, 2, 3, 1, 3]], + [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0]], + [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5]], + [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6]], + [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6]], + [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7]], + [[0, 4, 1, 5, 3, 7], [0, 4, 3, 7, 2, 6]], + [[4, 0, 4, 6, 4, 5]], + [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1]], + [[1, 5, 1, 3, 1, 0], [4, 6, 5, 4, 0, 4]], + [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2]], + [[2, 0, 2, 3, 2, 6], [4, 5, 0, 4, 6, 4]], + [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1]], + [[2, 6, 2, 0, 3, 2], [1, 0, 1, 5, 3, 1], [6, 4, 5, 4, 0, 4]], + [[1, 3, 5, 4, 1, 5], [1, 3, 4, 6, 5, 4], [1, 3, 3, 2, 4, 6], [3, 2, 2, 6, 4, 6]], + [[3, 1, 3, 7, 3, 2], [6, 4, 5, 4, 0, 4]], + [[4, 5, 0, 1, 4, 6], [0, 1, 0, 2, 4, 6], [7, 3, 2, 3, 1, 3]], + [[3, 2, 1, 0, 3, 7], [1, 0, 1, 5, 3, 7], [6, 4, 5, 4, 0, 4]], + [[3, 7, 3, 2, 1, 5], [3, 2, 6, 4, 1, 5], [1, 5, 6, 4, 5, 4], [3, 2, 2, 0, 6, 4]], + [[3, 7, 2, 6, 3, 1], [2, 6, 2, 0, 3, 1], [5, 4, 0, 4, 6, 4]], + [[1, 0, 1, 3, 5, 4], [1, 3, 2, 6, 5, 4], [1, 3, 3, 7, 2, 6], [5, 4, 2, 6, 4, 6]], + [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7], [4, 5, 0, 4, 4, 6]], + [[6, 2, 4, 6, 4, 5], [4, 5, 5, 1, 6, 2], [6, 2, 5, 1, 7, 3]], + [[5, 1, 5, 4, 5, 7]], + [[0, 1, 0, 2, 0, 4], [5, 7, 1, 5, 4, 5]], + [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3]], + [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3]], + [[2, 0, 2, 3, 2, 6], [7, 5, 1, 5, 4, 5]], + [[2, 6, 0, 4, 2, 3], [0, 4, 0, 1, 2, 3], [7, 5, 1, 5, 4, 5]], + [[5, 7, 1, 3, 5, 4], [1, 3, 1, 0, 5, 4], [6, 2, 0, 2, 3, 2]], + [[3, 1, 3, 2, 7, 5], [3, 2, 0, 4, 7, 5], [3, 2, 2, 6, 0, 4], [7, 5, 0, 4, 5, 4]], + [[3, 7, 3, 2, 3, 1], [5, 4, 7, 5, 1, 5]], + [[0, 4, 0, 1, 2, 0], [3, 1, 3, 7, 2, 3], [4, 5, 7, 5, 1, 5]], + [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0]], + [[0, 4, 2, 3, 0, 2], [0, 4, 3, 7, 2, 3], [0, 4, 4, 5, 3, 7], [4, 5, 5, 7, 3, 7]], + [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6], [4, 5, 7, 5, 1, 5]], + [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6], [5, 7, 1, 5, 5, 4]], + [[2, 6, 2, 0, 3, 7], [2, 0, 4, 5, 3, 7], [3, 7, 4, 5, 7, 5], [2, 0, 0, 1, 4, 5]], + [[4, 0, 5, 4, 5, 7], [5, 7, 7, 3, 4, 0], [4, 0, 7, 3, 6, 2]], + [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0]], + [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6]], + [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7]], + [[0, 2, 4, 6, 5, 7], [0, 2, 5, 7, 1, 3]], + [[5, 1, 4, 0, 5, 7], [4, 0, 4, 6, 5, 7], [3, 2, 6, 2, 0, 2]], + [[2, 3, 2, 6, 0, 1], [2, 6, 7, 5, 0, 1], [0, 1, 7, 5, 1, 5], [2, 6, 6, 4, 7, 5]], + [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7], [2, 6, 0, 2, 2, 3]], + [[3, 1, 2, 3, 2, 6], [2, 6, 6, 4, 3, 1], [3, 1, 6, 4, 7, 5]], + [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0], [2, 3, 1, 3, 7, 3]], + [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6], [3, 2, 1, 3, 3, 7]], + [[0, 1, 0, 4, 2, 3], [0, 4, 5, 7, 2, 3], [0, 4, 4, 6, 5, 7], [2, 3, 5, 7, 3, 7]], + [[7, 5, 3, 7, 3, 2], [3, 2, 2, 0, 7, 5], [7, 5, 2, 0, 6, 4]], + [[0, 4, 4, 6, 5, 7], [0, 4, 5, 7, 1, 5], [0, 2, 1, 3, 3, 7], [3, 7, 2, 6, 0, 2]], + [ + [3, 1, 7, 3, 6, 2], + [6, 2, 0, 1, 3, 1], + [6, 4, 0, 1, 6, 2], + [6, 4, 5, 1, 0, 1], + [6, 4, 7, 5, 5, 1], + ], + [ + [4, 0, 6, 4, 7, 5], + [7, 5, 1, 0, 4, 0], + [7, 3, 1, 0, 7, 5], + [7, 3, 2, 0, 1, 0], + [7, 3, 6, 2, 2, 0], + ], + [[7, 3, 6, 2, 6, 4], [7, 5, 7, 3, 6, 4]], + [[6, 2, 6, 7, 6, 4]], + [[0, 4, 0, 1, 0, 2], [6, 7, 4, 6, 2, 6]], + [[1, 0, 1, 5, 1, 3], [7, 6, 4, 6, 2, 6]], + [[1, 3, 0, 2, 1, 5], [0, 2, 0, 4, 1, 5], [7, 6, 4, 6, 2, 6]], + [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0]], + [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3]], + [[6, 4, 2, 0, 6, 7], [2, 0, 2, 3, 6, 7], [5, 1, 3, 1, 0, 1]], + [[1, 5, 1, 3, 0, 4], [1, 3, 7, 6, 0, 4], [0, 4, 7, 6, 4, 6], [1, 3, 3, 2, 7, 6]], + [[3, 2, 3, 1, 3, 7], [6, 4, 2, 6, 7, 6]], + [[3, 7, 3, 2, 1, 3], [0, 2, 0, 4, 1, 0], [7, 6, 4, 6, 2, 6]], + [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0], [4, 6, 2, 6, 7, 6]], + [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5], [6, 4, 2, 6, 6, 7]], + [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0]], + [[0, 1, 4, 6, 0, 4], [0, 1, 6, 7, 4, 6], [0, 1, 1, 3, 6, 7], [1, 3, 3, 7, 6, 7]], + [[0, 2, 0, 1, 4, 6], [0, 1, 3, 7, 4, 6], [0, 1, 1, 5, 3, 7], [4, 6, 3, 7, 6, 7]], + [[7, 3, 6, 7, 6, 4], [6, 4, 4, 0, 7, 3], [7, 3, 4, 0, 5, 1]], + [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5]], + [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5]], + [[6, 7, 4, 5, 6, 2], [4, 5, 4, 0, 6, 2], [3, 1, 0, 1, 5, 1]], + [[2, 0, 2, 6, 3, 1], [2, 6, 4, 5, 3, 1], [2, 6, 6, 7, 4, 5], [3, 1, 4, 5, 1, 5]], + [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7]], + [[0, 1, 2, 3, 6, 7], [0, 1, 6, 7, 4, 5]], + [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7], [1, 3, 0, 1, 1, 5]], + [[5, 4, 1, 5, 1, 3], [1, 3, 3, 2, 5, 4], [5, 4, 3, 2, 7, 6]], + [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5], [1, 3, 7, 3, 2, 3]], + [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5], [3, 7, 2, 3, 3, 1]], + [[0, 1, 1, 5, 3, 7], [0, 1, 3, 7, 2, 3], [0, 4, 2, 6, 6, 7], [6, 7, 4, 5, 0, 4]], + [ + [6, 2, 7, 6, 5, 4], + [5, 4, 0, 2, 6, 2], + [5, 1, 0, 2, 5, 4], + [5, 1, 3, 2, 0, 2], + [5, 1, 7, 3, 3, 2], + ], + [[3, 1, 3, 7, 2, 0], [3, 7, 5, 4, 2, 0], [2, 0, 5, 4, 0, 4], [3, 7, 7, 6, 5, 4]], + [[1, 0, 3, 1, 3, 7], [3, 7, 7, 6, 1, 0], [1, 0, 7, 6, 5, 4]], + [ + [1, 0, 5, 1, 7, 3], + [7, 3, 2, 0, 1, 0], + [7, 6, 2, 0, 7, 3], + [7, 6, 4, 0, 2, 0], + [7, 6, 5, 4, 4, 0], + ], + [[7, 6, 5, 4, 5, 1], [7, 3, 7, 6, 5, 1]], + [[5, 7, 5, 1, 5, 4], [6, 2, 7, 6, 4, 6]], + [[0, 2, 0, 4, 1, 0], [5, 4, 5, 7, 1, 5], [2, 6, 7, 6, 4, 6]], + [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3], [2, 6, 7, 6, 4, 6]], + [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3], [6, 7, 4, 6, 6, 2]], + [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0], [1, 5, 4, 5, 7, 5]], + [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3], [5, 1, 4, 5, 5, 7]], + [[0, 2, 2, 3, 6, 7], [0, 2, 6, 7, 4, 6], [0, 1, 4, 5, 5, 7], [5, 7, 1, 3, 0, 1]], + [ + [5, 4, 7, 5, 3, 1], + [3, 1, 0, 4, 5, 4], + [3, 2, 0, 4, 3, 1], + [3, 2, 6, 4, 0, 4], + [3, 2, 7, 6, 6, 4], + ], + [[5, 4, 5, 7, 1, 5], [3, 7, 3, 2, 1, 3], [4, 6, 2, 6, 7, 6]], + [[1, 0, 0, 2, 0, 4], [1, 5, 5, 4, 5, 7], [3, 2, 1, 3, 3, 7], [2, 6, 7, 6, 4, 6]], + [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0], [6, 2, 7, 6, 6, 4]], + [ + [0, 4, 2, 3, 0, 2], + [0, 4, 3, 7, 2, 3], + [0, 4, 4, 5, 3, 7], + [4, 5, 5, 7, 3, 7], + [6, 7, 4, 6, 2, 6], + ], + [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0], [5, 4, 7, 5, 5, 1]], + [ + [0, 1, 4, 6, 0, 4], + [0, 1, 6, 7, 4, 6], + [0, 1, 1, 3, 6, 7], + [1, 3, 3, 7, 6, 7], + [5, 7, 1, 5, 4, 5], + ], + [ + [6, 7, 4, 6, 0, 2], + [0, 2, 3, 7, 6, 7], + [0, 1, 3, 7, 0, 2], + [0, 1, 5, 7, 3, 7], + [0, 1, 4, 5, 5, 7], + ], + [[4, 0, 6, 7, 4, 6], [4, 0, 7, 3, 6, 7], [4, 0, 5, 7, 7, 3], [4, 5, 5, 7, 4, 0]], + [[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0]], + [[0, 2, 1, 5, 0, 1], [0, 2, 5, 7, 1, 5], [0, 2, 2, 6, 5, 7], [2, 6, 6, 7, 5, 7]], + [[1, 3, 1, 0, 5, 7], [1, 0, 2, 6, 5, 7], [5, 7, 2, 6, 7, 6], [1, 0, 0, 4, 2, 6]], + [[2, 0, 6, 2, 6, 7], [6, 7, 7, 5, 2, 0], [2, 0, 7, 5, 3, 1]], + [[0, 4, 0, 2, 1, 5], [0, 2, 6, 7, 1, 5], [0, 2, 2, 3, 6, 7], [1, 5, 6, 7, 5, 7]], + [[7, 6, 5, 7, 5, 1], [5, 1, 1, 0, 7, 6], [7, 6, 1, 0, 3, 2]], + [ + [2, 0, 3, 2, 7, 6], + [7, 6, 4, 0, 2, 0], + [7, 5, 4, 0, 7, 6], + [7, 5, 1, 0, 4, 0], + [7, 5, 3, 1, 1, 0], + ], + [[7, 5, 3, 1, 3, 2], [7, 6, 7, 5, 3, 2]], + [[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0], [3, 1, 7, 3, 3, 2]], + [ + [0, 2, 1, 5, 0, 1], + [0, 2, 5, 7, 1, 5], + [0, 2, 2, 6, 5, 7], + [2, 6, 6, 7, 5, 7], + [3, 7, 2, 3, 1, 3], + ], + [ + [3, 7, 2, 3, 0, 1], + [0, 1, 5, 7, 3, 7], + [0, 4, 5, 7, 0, 1], + [0, 4, 6, 7, 5, 7], + [0, 4, 2, 6, 6, 7], + ], + [[2, 0, 3, 7, 2, 3], [2, 0, 7, 5, 3, 7], [2, 0, 6, 7, 7, 5], [2, 6, 6, 7, 2, 0]], + [ + [5, 7, 1, 5, 0, 4], + [0, 4, 6, 7, 5, 7], + [0, 2, 6, 7, 0, 4], + [0, 2, 3, 7, 6, 7], + [0, 2, 1, 3, 3, 7], + ], + [[1, 0, 5, 7, 1, 5], [1, 0, 7, 6, 5, 7], [1, 0, 3, 7, 7, 6], [1, 3, 3, 7, 1, 0]], + [[0, 2, 0, 1, 0, 4], [3, 7, 6, 7, 5, 7]], + [[7, 5, 7, 3, 7, 6]], + [[7, 3, 7, 5, 7, 6]], + [[0, 1, 0, 2, 0, 4], [6, 7, 3, 7, 5, 7]], + [[1, 3, 1, 0, 1, 5], [7, 6, 3, 7, 5, 7]], + [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2], [6, 7, 3, 7, 5, 7]], + [[2, 6, 2, 0, 2, 3], [7, 5, 6, 7, 3, 7]], + [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4], [5, 7, 6, 7, 3, 7]], + [[1, 5, 1, 3, 0, 1], [2, 3, 2, 6, 0, 2], [5, 7, 6, 7, 3, 7]], + [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4], [7, 6, 3, 7, 7, 5]], + [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2]], + [[7, 6, 3, 2, 7, 5], [3, 2, 3, 1, 7, 5], [4, 0, 1, 0, 2, 0]], + [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2]], + [[2, 3, 2, 0, 6, 7], [2, 0, 1, 5, 6, 7], [2, 0, 0, 4, 1, 5], [6, 7, 1, 5, 7, 5]], + [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1]], + [[0, 4, 0, 1, 2, 6], [0, 1, 5, 7, 2, 6], [2, 6, 5, 7, 6, 7], [0, 1, 1, 3, 5, 7]], + [[1, 5, 0, 2, 1, 0], [1, 5, 2, 6, 0, 2], [1, 5, 5, 7, 2, 6], [5, 7, 7, 6, 2, 6]], + [[5, 1, 7, 5, 7, 6], [7, 6, 6, 2, 5, 1], [5, 1, 6, 2, 4, 0]], + [[4, 5, 4, 0, 4, 6], [7, 3, 5, 7, 6, 7]], + [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1], [3, 7, 5, 7, 6, 7]], + [[4, 6, 4, 5, 0, 4], [1, 5, 1, 3, 0, 1], [6, 7, 3, 7, 5, 7]], + [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2], [7, 3, 5, 7, 7, 6]], + [[2, 3, 2, 6, 0, 2], [4, 6, 4, 5, 0, 4], [3, 7, 5, 7, 6, 7]], + [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [7, 5, 6, 7, 7, 3]], + [[0, 1, 1, 5, 1, 3], [0, 2, 2, 3, 2, 6], [4, 5, 0, 4, 4, 6], [5, 7, 6, 7, 3, 7]], + [ + [1, 3, 5, 4, 1, 5], + [1, 3, 4, 6, 5, 4], + [1, 3, 3, 2, 4, 6], + [3, 2, 2, 6, 4, 6], + [7, 6, 3, 7, 5, 7], + ], + [[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2], [0, 4, 6, 4, 5, 4]], + [[1, 0, 0, 2, 4, 6], [1, 0, 4, 6, 5, 4], [1, 3, 5, 7, 7, 6], [7, 6, 3, 2, 1, 3]], + [[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2], [4, 6, 5, 4, 4, 0]], + [ + [7, 5, 6, 7, 2, 3], + [2, 3, 1, 5, 7, 5], + [2, 0, 1, 5, 2, 3], + [2, 0, 4, 5, 1, 5], + [2, 0, 6, 4, 4, 5], + ], + [[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1], [4, 0, 6, 4, 4, 5]], + [ + [4, 6, 5, 4, 1, 0], + [1, 0, 2, 6, 4, 6], + [1, 3, 2, 6, 1, 0], + [1, 3, 7, 6, 2, 6], + [1, 3, 5, 7, 7, 6], + ], + [ + [1, 5, 0, 2, 1, 0], + [1, 5, 2, 6, 0, 2], + [1, 5, 5, 7, 2, 6], + [5, 7, 7, 6, 2, 6], + [4, 6, 5, 4, 0, 4], + ], + [[5, 1, 4, 6, 5, 4], [5, 1, 6, 2, 4, 6], [5, 1, 7, 6, 6, 2], [5, 7, 7, 6, 5, 1]], + [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1]], + [[7, 3, 5, 1, 7, 6], [5, 1, 5, 4, 7, 6], [2, 0, 4, 0, 1, 0]], + [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4]], + [[0, 2, 0, 4, 1, 3], [0, 4, 6, 7, 1, 3], [1, 3, 6, 7, 3, 7], [0, 4, 4, 5, 6, 7]], + [[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1], [0, 2, 3, 2, 6, 2]], + [[1, 5, 5, 4, 7, 6], [1, 5, 7, 6, 3, 7], [1, 0, 3, 2, 2, 6], [2, 6, 0, 4, 1, 0]], + [[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4], [2, 0, 3, 2, 2, 6]], + [ + [2, 3, 6, 2, 4, 0], + [4, 0, 1, 3, 2, 3], + [4, 5, 1, 3, 4, 0], + [4, 5, 7, 3, 1, 3], + [4, 5, 6, 7, 7, 3], + ], + [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6]], + [[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6], [0, 4, 1, 0, 0, 2]], + [[1, 0, 5, 4, 7, 6], [1, 0, 7, 6, 3, 2]], + [[2, 3, 0, 2, 0, 4], [0, 4, 4, 5, 2, 3], [2, 3, 4, 5, 6, 7]], + [[1, 3, 1, 5, 0, 2], [1, 5, 7, 6, 0, 2], [1, 5, 5, 4, 7, 6], [0, 2, 7, 6, 2, 6]], + [ + [5, 1, 4, 5, 6, 7], + [6, 7, 3, 1, 5, 1], + [6, 2, 3, 1, 6, 7], + [6, 2, 0, 1, 3, 1], + [6, 2, 4, 0, 0, 1], + ], + [[6, 7, 2, 6, 2, 0], [2, 0, 0, 1, 6, 7], [6, 7, 0, 1, 4, 5]], + [[6, 2, 4, 0, 4, 5], [6, 7, 6, 2, 4, 5]], + [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1]], + [[1, 5, 1, 0, 3, 7], [1, 0, 4, 6, 3, 7], [1, 0, 0, 2, 4, 6], [3, 7, 4, 6, 7, 6]], + [[1, 0, 3, 7, 1, 3], [1, 0, 7, 6, 3, 7], [1, 0, 0, 4, 7, 6], [0, 4, 4, 6, 7, 6]], + [[6, 4, 7, 6, 7, 3], [7, 3, 3, 1, 6, 4], [6, 4, 3, 1, 2, 0]], + [[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1], [2, 3, 6, 2, 2, 0]], + [ + [7, 6, 3, 7, 1, 5], + [1, 5, 4, 6, 7, 6], + [1, 0, 4, 6, 1, 5], + [1, 0, 2, 6, 4, 6], + [1, 0, 3, 2, 2, 6], + ], + [ + [1, 0, 3, 7, 1, 3], + [1, 0, 7, 6, 3, 7], + [1, 0, 0, 4, 7, 6], + [0, 4, 4, 6, 7, 6], + [2, 6, 0, 2, 3, 2], + ], + [[3, 1, 7, 6, 3, 7], [3, 1, 6, 4, 7, 6], [3, 1, 2, 6, 6, 4], [3, 2, 2, 6, 3, 1]], + [[3, 2, 3, 1, 7, 6], [3, 1, 0, 4, 7, 6], [7, 6, 0, 4, 6, 4], [3, 1, 1, 5, 0, 4]], + [ + [0, 1, 2, 0, 6, 4], + [6, 4, 5, 1, 0, 1], + [6, 7, 5, 1, 6, 4], + [6, 7, 3, 1, 5, 1], + [6, 7, 2, 3, 3, 1], + ], + [[0, 1, 4, 0, 4, 6], [4, 6, 6, 7, 0, 1], [0, 1, 6, 7, 2, 3]], + [[6, 7, 2, 3, 2, 0], [6, 4, 6, 7, 2, 0]], + [ + [2, 6, 0, 2, 1, 3], + [1, 3, 7, 6, 2, 6], + [1, 5, 7, 6, 1, 3], + [1, 5, 4, 6, 7, 6], + [1, 5, 0, 4, 4, 6], + ], + [[1, 5, 1, 0, 1, 3], [4, 6, 7, 6, 2, 6]], + [[0, 1, 2, 6, 0, 2], [0, 1, 6, 7, 2, 6], [0, 1, 4, 6, 6, 7], [0, 4, 4, 6, 0, 1]], + [[6, 7, 6, 2, 6, 4]], + [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4]], + [[7, 5, 6, 4, 7, 3], [6, 4, 6, 2, 7, 3], [1, 0, 2, 0, 4, 0]], + [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4], [0, 1, 5, 1, 3, 1]], + [[2, 0, 0, 4, 1, 5], [2, 0, 1, 5, 3, 1], [2, 6, 3, 7, 7, 5], [7, 5, 6, 4, 2, 6]], + [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4]], + [[3, 2, 3, 7, 1, 0], [3, 7, 6, 4, 1, 0], [3, 7, 7, 5, 6, 4], [1, 0, 6, 4, 0, 4]], + [[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4], [1, 5, 3, 1, 1, 0]], + [ + [7, 3, 5, 7, 4, 6], + [4, 6, 2, 3, 7, 3], + [4, 0, 2, 3, 4, 6], + [4, 0, 1, 3, 2, 3], + [4, 0, 5, 1, 1, 3], + ], + [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5]], + [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5], [0, 1, 2, 0, 0, 4]], + [[1, 0, 1, 5, 3, 2], [1, 5, 4, 6, 3, 2], [3, 2, 4, 6, 2, 6], [1, 5, 5, 7, 4, 6]], + [ + [0, 2, 4, 0, 5, 1], + [5, 1, 3, 2, 0, 2], + [5, 7, 3, 2, 5, 1], + [5, 7, 6, 2, 3, 2], + [5, 7, 4, 6, 6, 2], + ], + [[2, 0, 3, 1, 7, 5], [2, 0, 7, 5, 6, 4]], + [[4, 6, 0, 4, 0, 1], [0, 1, 1, 3, 4, 6], [4, 6, 1, 3, 5, 7]], + [[0, 2, 1, 0, 1, 5], [1, 5, 5, 7, 0, 2], [0, 2, 5, 7, 4, 6]], + [[5, 7, 4, 6, 4, 0], [5, 1, 5, 7, 4, 0]], + [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2]], + [[0, 1, 0, 2, 4, 5], [0, 2, 3, 7, 4, 5], [4, 5, 3, 7, 5, 7], [0, 2, 2, 6, 3, 7]], + [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2], [1, 0, 5, 1, 1, 3]], + [ + [1, 5, 3, 1, 2, 0], + [2, 0, 4, 5, 1, 5], + [2, 6, 4, 5, 2, 0], + [2, 6, 7, 5, 4, 5], + [2, 6, 3, 7, 7, 5], + ], + [[2, 3, 0, 4, 2, 0], [2, 3, 4, 5, 0, 4], [2, 3, 3, 7, 4, 5], [3, 7, 7, 5, 4, 5]], + [[3, 2, 7, 3, 7, 5], [7, 5, 5, 4, 3, 2], [3, 2, 5, 4, 1, 0]], + [ + [2, 3, 0, 4, 2, 0], + [2, 3, 4, 5, 0, 4], + [2, 3, 3, 7, 4, 5], + [3, 7, 7, 5, 4, 5], + [1, 5, 3, 1, 0, 1], + ], + [[3, 2, 1, 5, 3, 1], [3, 2, 5, 4, 1, 5], [3, 2, 7, 5, 5, 4], [3, 7, 7, 5, 3, 2]], + [[2, 6, 2, 3, 0, 4], [2, 3, 7, 5, 0, 4], [2, 3, 3, 1, 7, 5], [0, 4, 7, 5, 4, 5]], + [ + [3, 2, 1, 3, 5, 7], + [5, 7, 6, 2, 3, 2], + [5, 4, 6, 2, 5, 7], + [5, 4, 0, 2, 6, 2], + [5, 4, 1, 0, 0, 2], + ], + [ + [4, 5, 0, 4, 2, 6], + [2, 6, 7, 5, 4, 5], + [2, 3, 7, 5, 2, 6], + [2, 3, 1, 5, 7, 5], + [2, 3, 0, 1, 1, 5], + ], + [[2, 3, 2, 0, 2, 6], [1, 5, 7, 5, 4, 5]], + [[5, 7, 4, 5, 4, 0], [4, 0, 0, 2, 5, 7], [5, 7, 0, 2, 1, 3]], + [[5, 4, 1, 0, 1, 3], [5, 7, 5, 4, 1, 3]], + [[0, 2, 4, 5, 0, 4], [0, 2, 5, 7, 4, 5], [0, 2, 1, 5, 5, 7], [0, 1, 1, 5, 0, 2]], + [[5, 4, 5, 1, 5, 7]], + [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3]], + [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3], [0, 2, 4, 0, 0, 1]], + [[3, 7, 3, 1, 2, 6], [3, 1, 5, 4, 2, 6], [3, 1, 1, 0, 5, 4], [2, 6, 5, 4, 6, 4]], + [ + [6, 4, 2, 6, 3, 7], + [3, 7, 5, 4, 6, 4], + [3, 1, 5, 4, 3, 7], + [3, 1, 0, 4, 5, 4], + [3, 1, 2, 0, 0, 4], + ], + [[2, 0, 2, 3, 6, 4], [2, 3, 1, 5, 6, 4], [6, 4, 1, 5, 4, 5], [2, 3, 3, 7, 1, 5]], + [ + [0, 4, 1, 0, 3, 2], + [3, 2, 6, 4, 0, 4], + [3, 7, 6, 4, 3, 2], + [3, 7, 5, 4, 6, 4], + [3, 7, 1, 5, 5, 4], + ], + [ + [1, 3, 0, 1, 4, 5], + [4, 5, 7, 3, 1, 3], + [4, 6, 7, 3, 4, 5], + [4, 6, 2, 3, 7, 3], + [4, 6, 0, 2, 2, 3], + ], + [[3, 7, 3, 1, 3, 2], [5, 4, 6, 4, 0, 4]], + [[3, 1, 2, 6, 3, 2], [3, 1, 6, 4, 2, 6], [3, 1, 1, 5, 6, 4], [1, 5, 5, 4, 6, 4]], + [ + [3, 1, 2, 6, 3, 2], + [3, 1, 6, 4, 2, 6], + [3, 1, 1, 5, 6, 4], + [1, 5, 5, 4, 6, 4], + [0, 4, 1, 0, 2, 0], + ], + [[4, 5, 6, 4, 6, 2], [6, 2, 2, 3, 4, 5], [4, 5, 2, 3, 0, 1]], + [[2, 3, 6, 4, 2, 6], [2, 3, 4, 5, 6, 4], [2, 3, 0, 4, 4, 5], [2, 0, 0, 4, 2, 3]], + [[1, 3, 5, 1, 5, 4], [5, 4, 4, 6, 1, 3], [1, 3, 4, 6, 0, 2]], + [[1, 3, 0, 4, 1, 0], [1, 3, 4, 6, 0, 4], [1, 3, 5, 4, 4, 6], [1, 5, 5, 4, 1, 3]], + [[4, 6, 0, 2, 0, 1], [4, 5, 4, 6, 0, 1]], + [[4, 6, 4, 0, 4, 5]], + [[4, 0, 6, 2, 7, 3], [4, 0, 7, 3, 5, 1]], + [[1, 5, 0, 1, 0, 2], [0, 2, 2, 6, 1, 5], [1, 5, 2, 6, 3, 7]], + [[3, 7, 1, 3, 1, 0], [1, 0, 0, 4, 3, 7], [3, 7, 0, 4, 2, 6]], + [[3, 1, 2, 0, 2, 6], [3, 7, 3, 1, 2, 6]], + [[0, 4, 2, 0, 2, 3], [2, 3, 3, 7, 0, 4], [0, 4, 3, 7, 1, 5]], + [[3, 7, 1, 5, 1, 0], [3, 2, 3, 7, 1, 0]], + [[0, 4, 1, 3, 0, 1], [0, 4, 3, 7, 1, 3], [0, 4, 2, 3, 3, 7], [0, 2, 2, 3, 0, 4]], + [[3, 7, 3, 1, 3, 2]], + [[2, 6, 3, 2, 3, 1], [3, 1, 1, 5, 2, 6], [2, 6, 1, 5, 0, 4]], + [[1, 5, 3, 2, 1, 3], [1, 5, 2, 6, 3, 2], [1, 5, 0, 2, 2, 6], [1, 0, 0, 2, 1, 5]], + [[2, 3, 0, 1, 0, 4], [2, 6, 2, 3, 0, 4]], + [[2, 3, 2, 0, 2, 6]], + [[1, 5, 0, 4, 0, 2], [1, 3, 1, 5, 0, 2]], + [[1, 5, 1, 0, 1, 3]], + [[0, 2, 0, 1, 0, 4]], + [], +] + + +def create_mc_lookup_table(): + cases = torch.zeros(256, 5, 3, dtype=torch.long) + masks = torch.zeros(256, 5, dtype=torch.bool) + + edge_to_index = { + (0, 1): 0, + (2, 3): 1, + (4, 5): 2, + (6, 7): 3, + (0, 2): 4, + (1, 3): 5, + (4, 6): 6, + (5, 7): 7, + (0, 4): 8, + (1, 5): 9, + (2, 6): 10, + (3, 7): 11, + } + + for i, case in enumerate(MC_TABLE): + for j, tri in enumerate(case): + for k, (c1, c2) in enumerate(zip(tri[::2], tri[1::2])): + cases[i, j, k] = edge_to_index[(c1, c2) if c1 < c2 else (c2, c1)] + masks[i, j] = True + return cases, masks + + +RENDERER_CONFIG = {} + + +def renderer_model_from_original_config(): + model = ShapERenderer(**RENDERER_CONFIG) + + return model + + +RENDERER_MLP_ORIGINAL_PREFIX = "renderer.nerstf" + +RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX = "encoder.params_proj" + + +def renderer_model_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + diffusers_checkpoint.update( + {f"mlp.{k}": checkpoint[f"{RENDERER_MLP_ORIGINAL_PREFIX}.{k}"] for k in model.mlp.state_dict().keys()} + ) + + diffusers_checkpoint.update( + { + f"params_proj.{k}": checkpoint[f"{RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX}.{k}"] + for k in model.params_proj.state_dict().keys() + } + ) + + diffusers_checkpoint.update({"void.background": model.state_dict()["void.background"]}) + + cases, masks = create_mc_lookup_table() + + diffusers_checkpoint.update({"mesh_decoder.cases": cases}) + diffusers_checkpoint.update({"mesh_decoder.masks": masks}) + + return diffusers_checkpoint + + +# done renderer + + +# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?) +def split_attentions(*, weight, bias, split, chunk_size): + weights = [None] * split + biases = [None] * split + + weights_biases_idx = 0 + + for starting_row_index in range(0, weight.shape[0], chunk_size): + row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size) + + weight_rows = weight[row_indices, :] + bias_rows = bias[row_indices] + + if weights[weights_biases_idx] is None: + assert weights[weights_biases_idx] is None + weights[weights_biases_idx] = weight_rows + biases[weights_biases_idx] = bias_rows + else: + assert weights[weights_biases_idx] is not None + weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows]) + biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows]) + + weights_biases_idx = (weights_biases_idx + 1) % split + + return weights, biases + + +# done unet utils + + +# Driver functions + + +def prior(*, args, checkpoint_map_location): + print("loading prior") + + prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location) + + prior_model = prior_model_from_original_config() + + prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint) + + del prior_checkpoint + + load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model) + + print("done loading prior") + + return prior_model + + +def prior_image(*, args, checkpoint_map_location): + print("loading prior_image") + + print(f"load checkpoint from {args.prior_image_checkpoint_path}") + prior_checkpoint = torch.load(args.prior_image_checkpoint_path, map_location=checkpoint_map_location) + + prior_model = prior_image_model_from_original_config() + + prior_diffusers_checkpoint = prior_image_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint) + + del prior_checkpoint + + load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model) + + print("done loading prior_image") + + return prior_model + + +def renderer(*, args, checkpoint_map_location): + print(" loading renderer") + + renderer_checkpoint = torch.load(args.transmitter_checkpoint_path, map_location=checkpoint_map_location) + + renderer_model = renderer_model_from_original_config() + + renderer_diffusers_checkpoint = renderer_model_original_checkpoint_to_diffusers_checkpoint( + renderer_model, renderer_checkpoint + ) + + del renderer_checkpoint + + load_checkpoint_to_model(renderer_diffusers_checkpoint, renderer_model, strict=True) + + print("done loading renderer") + + return renderer_model + + +# prior model will expect clip_mean and clip_std, which are missing from the state_dict +PRIOR_EXPECTED_MISSING_KEYS = ["clip_mean", "clip_std"] + + +def load_prior_checkpoint_to_model(checkpoint, model): + with tempfile.NamedTemporaryFile() as file: + torch.save(checkpoint, file.name) + del checkpoint + missing_keys, unexpected_keys = model.load_state_dict(torch.load(file.name), strict=False) + missing_keys = list(set(missing_keys) - set(PRIOR_EXPECTED_MISSING_KEYS)) + + if len(unexpected_keys) > 0: + raise ValueError(f"Unexpected keys when loading prior model: {unexpected_keys}") + if len(missing_keys) > 0: + raise ValueError(f"Missing keys when loading prior model: {missing_keys}") + + +def load_checkpoint_to_model(checkpoint, model, strict=False): + with tempfile.NamedTemporaryFile() as file: + torch.save(checkpoint, file.name) + del checkpoint + if strict: + model.load_state_dict(torch.load(file.name), strict=True) + else: + load_checkpoint_and_dispatch(model, file.name, device_map="auto") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--prior_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the prior checkpoint to convert.", + ) + + parser.add_argument( + "--prior_image_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the prior_image checkpoint to convert.", + ) + + parser.add_argument( + "--transmitter_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to the transmitter checkpoint to convert.", + ) + + parser.add_argument( + "--checkpoint_load_device", + default="cpu", + type=str, + required=False, + help="The device passed to `map_location` when loading checkpoints.", + ) + + parser.add_argument( + "--debug", + default=None, + type=str, + required=False, + help="Only run a specific stage of the convert script. Used for debugging", + ) + + args = parser.parse_args() + + print(f"loading checkpoints to {args.checkpoint_load_device}") + + checkpoint_map_location = torch.device(args.checkpoint_load_device) + + if args.debug is not None: + print(f"debug: only executing {args.debug}") + + if args.debug is None: + print("YiYi TO-DO") + elif args.debug == "prior": + prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location) + prior_model.save_pretrained(args.dump_path) + elif args.debug == "prior_image": + prior_model = prior_image(args=args, checkpoint_map_location=checkpoint_map_location) + prior_model.save_pretrained(args.dump_path) + elif args.debug == "renderer": + renderer_model = renderer(args=args, checkpoint_map_location=checkpoint_map_location) + renderer_model.save_pretrained(args.dump_path) + else: + raise ValueError(f"unknown debug value : {args.debug}") diff --git a/scripts/convert_skyreelsv2_to_diffusers.py b/scripts/convert_skyreelsv2_to_diffusers.py new file mode 100644 index 000000000000..29d5a7666a6e --- /dev/null +++ b/scripts/convert_skyreelsv2_to_diffusers.py @@ -0,0 +1,637 @@ +import argparse +import os +import pathlib +from typing import Any, Dict + +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download +from safetensors.torch import load_file +from transformers import AutoProcessor, AutoTokenizer, CLIPVisionModelWithProjection, UMT5EncoderModel + +from diffusers import ( + AutoencoderKLWan, + SkyReelsV2DiffusionForcingPipeline, + SkyReelsV2ImageToVideoPipeline, + SkyReelsV2Pipeline, + SkyReelsV2Transformer3DModel, + UniPCMultistepScheduler, +) + + +TRANSFORMER_KEYS_RENAME_DICT = { + "time_embedding.0": "condition_embedder.time_embedder.linear_1", + "time_embedding.2": "condition_embedder.time_embedder.linear_2", + "text_embedding.0": "condition_embedder.text_embedder.linear_1", + "text_embedding.2": "condition_embedder.text_embedder.linear_2", + "time_projection.1": "condition_embedder.time_proj", + "head.modulation": "scale_shift_table", + "head.head": "proj_out", + "modulation": "scale_shift_table", + "ffn.0": "ffn.net.0.proj", + "ffn.2": "ffn.net.2", + "fps_projection.0": "fps_projection.net.0.proj", + "fps_projection.2": "fps_projection.net.2", + # Hack to swap the layer names + # The original model calls the norms in following order: norm1, norm3, norm2 + # We convert it to: norm1, norm2, norm3 + "norm2": "norm__placeholder", + "norm3": "norm2", + "norm__placeholder": "norm3", + # For the I2V model + "img_emb.proj.0": "condition_embedder.image_embedder.norm1", + "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", + "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", + "img_emb.proj.4": "condition_embedder.image_embedder.norm2", + # for the FLF2V model + "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", + # Add attention component mappings + "self_attn.q": "attn1.to_q", + "self_attn.k": "attn1.to_k", + "self_attn.v": "attn1.to_v", + "self_attn.o": "attn1.to_out.0", + "self_attn.norm_q": "attn1.norm_q", + "self_attn.norm_k": "attn1.norm_k", + "cross_attn.q": "attn2.to_q", + "cross_attn.k": "attn2.to_k", + "cross_attn.v": "attn2.to_v", + "cross_attn.o": "attn2.to_out.0", + "cross_attn.norm_q": "attn2.norm_q", + "cross_attn.norm_k": "attn2.norm_k", + "attn2.to_k_img": "attn2.add_k_proj", + "attn2.to_v_img": "attn2.add_v_proj", + "attn2.norm_k_img": "attn2.norm_added_k", +} + +TRANSFORMER_SPECIAL_KEYS_REMAP = {} + + +def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def load_sharded_safetensors(dir: pathlib.Path): + if "720P" in str(dir): + file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) + else: + file_paths = list(dir.glob("model*.safetensors")) + state_dict = {} + for path in file_paths: + state_dict.update(load_file(path)) + return state_dict + + +def get_transformer_config(model_type: str) -> dict[str, Any]: + if model_type == "SkyReels-V2-DF-1.3B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-DF-1.3B-540P", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 12, + "inject_sample_info": True, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + elif model_type == "SkyReels-V2-DF-14B-720P": + config = { + "model_id": "Skywork/SkyReels-V2-DF-14B-720P", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + elif model_type == "SkyReels-V2-DF-14B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-DF-14B-540P", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + elif model_type == "SkyReels-V2-T2V-14B-720P": + config = { + "model_id": "Skywork/SkyReels-V2-T2V-14B-720P", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + elif model_type == "SkyReels-V2-T2V-14B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-T2V-14B-540P", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + elif model_type == "SkyReels-V2-I2V-1.3B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P", + "diffusers_config": { + "added_kv_proj_dim": 1536, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 12, + "inject_sample_info": False, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + }, + } + elif model_type == "SkyReels-V2-I2V-14B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-14B-540P", + "diffusers_config": { + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + }, + } + elif model_type == "SkyReels-V2-I2V-14B-720P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-14B-720P", + "diffusers_config": { + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + }, + } + elif model_type == "SkyReels-V2-FLF2V-1.3B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-1.3B-540P", + "diffusers_config": { + "added_kv_proj_dim": 1536, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 12, + "inject_sample_info": False, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + "pos_embed_seq_len": 514, + }, + } + elif model_type == "SkyReels-V2-FLF2V-14B-540P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-14B-540P", + "diffusers_config": { + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + "pos_embed_seq_len": 514, + }, + } + elif model_type == "SkyReels-V2-FLF2V-14B-720P": + config = { + "model_id": "Skywork/SkyReels-V2-I2V-14B-720P", + "diffusers_config": { + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "inject_sample_info": False, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "image_dim": 1280, + "pos_embed_seq_len": 514, + }, + } + return config + + +def convert_transformer(model_type: str): + config = get_transformer_config(model_type) + diffusers_config = config["diffusers_config"] + model_id = config["model_id"] + + if "1.3B" in model_type: + original_state_dict = load_file(hf_hub_download(model_id, "model.safetensors")) + else: + os.makedirs(model_type, exist_ok=True) + model_dir = pathlib.Path(model_type) + if "720P" in model_type: + top_shard = 7 if "I2V" in model_type else 6 + zeros = "0" * (4 if "I2V" or "T2V" in model_type else 3) + model_name = "diffusion_pytorch_model" + elif "540P" in model_type: + top_shard = 14 if "I2V" in model_type else 12 + model_name = "model" + + for i in range(1, top_shard + 1): + shard_path = f"{model_name}-{i:05d}-of-{zeros}{top_shard}.safetensors" + hf_hub_download(model_id, shard_path, local_dir=model_dir) + original_state_dict = load_sharded_safetensors(model_dir) + + with init_empty_weights(): + transformer = SkyReelsV2Transformer3DModel.from_config(diffusers_config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + if "FLF2V" in model_type: + if ( + hasattr(transformer.condition_embedder, "image_embedder") + and hasattr(transformer.condition_embedder.image_embedder, "pos_embed") + and transformer.condition_embedder.image_embedder.pos_embed is not None + ): + pos_embed_shape = transformer.condition_embedder.image_embedder.pos_embed.shape + original_state_dict["condition_embedder.image_embedder.pos_embed"] = torch.zeros(pos_embed_shape) + + transformer.load_state_dict(original_state_dict, strict=True, assign=True) + return transformer + + +def convert_vae(): + vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth") + old_state_dict = torch.load(vae_ckpt_path, weights_only=True) + new_state_dict = {} + + # Create mappings for specific components + middle_key_mapping = { + # Encoder middle block + "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", + "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", + "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", + "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", + "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", + "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", + "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", + "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", + "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", + "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", + "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", + "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", + # Decoder middle block + "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", + "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", + "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", + "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", + "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", + "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", + "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", + "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", + "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", + "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", + "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", + "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", + } + + # Create a mapping for attention blocks + attention_mapping = { + # Encoder middle attention + "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", + "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", + "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", + "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", + "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", + # Decoder middle attention + "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", + "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", + "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", + "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", + "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", + } + + # Create a mapping for the head components + head_mapping = { + # Encoder head + "encoder.head.0.gamma": "encoder.norm_out.gamma", + "encoder.head.2.bias": "encoder.conv_out.bias", + "encoder.head.2.weight": "encoder.conv_out.weight", + # Decoder head + "decoder.head.0.gamma": "decoder.norm_out.gamma", + "decoder.head.2.bias": "decoder.conv_out.bias", + "decoder.head.2.weight": "decoder.conv_out.weight", + } + + # Create a mapping for the quant components + quant_mapping = { + "conv1.weight": "quant_conv.weight", + "conv1.bias": "quant_conv.bias", + "conv2.weight": "post_quant_conv.weight", + "conv2.bias": "post_quant_conv.bias", + } + + # Process each key in the state dict + for key, value in old_state_dict.items(): + # Handle middle block keys using the mapping + if key in middle_key_mapping: + new_key = middle_key_mapping[key] + new_state_dict[new_key] = value + # Handle attention blocks using the mapping + elif key in attention_mapping: + new_key = attention_mapping[key] + new_state_dict[new_key] = value + # Handle head keys using the mapping + elif key in head_mapping: + new_key = head_mapping[key] + new_state_dict[new_key] = value + # Handle quant keys using the mapping + elif key in quant_mapping: + new_key = quant_mapping[key] + new_state_dict[new_key] = value + # Handle encoder conv1 + elif key == "encoder.conv1.weight": + new_state_dict["encoder.conv_in.weight"] = value + elif key == "encoder.conv1.bias": + new_state_dict["encoder.conv_in.bias"] = value + # Handle decoder conv1 + elif key == "decoder.conv1.weight": + new_state_dict["decoder.conv_in.weight"] = value + elif key == "decoder.conv1.bias": + new_state_dict["decoder.conv_in.bias"] = value + # Handle encoder downsamples + elif key.startswith("encoder.downsamples."): + # Convert to down_blocks + new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") + + # Convert residual block naming but keep the original structure + if ".residual.0.gamma" in new_key: + new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") + elif ".residual.2.bias" in new_key: + new_key = new_key.replace(".residual.2.bias", ".conv1.bias") + elif ".residual.2.weight" in new_key: + new_key = new_key.replace(".residual.2.weight", ".conv1.weight") + elif ".residual.3.gamma" in new_key: + new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") + elif ".residual.6.bias" in new_key: + new_key = new_key.replace(".residual.6.bias", ".conv2.bias") + elif ".residual.6.weight" in new_key: + new_key = new_key.replace(".residual.6.weight", ".conv2.weight") + elif ".shortcut.bias" in new_key: + new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") + elif ".shortcut.weight" in new_key: + new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") + + new_state_dict[new_key] = value + + # Handle decoder upsamples + elif key.startswith("decoder.upsamples."): + # Convert to up_blocks + parts = key.split(".") + block_idx = int(parts[2]) + + # Group residual blocks + if "residual" in key: + if block_idx in [0, 1, 2]: + new_block_idx = 0 + resnet_idx = block_idx + elif block_idx in [4, 5, 6]: + new_block_idx = 1 + resnet_idx = block_idx - 4 + elif block_idx in [8, 9, 10]: + new_block_idx = 2 + resnet_idx = block_idx - 8 + elif block_idx in [12, 13, 14]: + new_block_idx = 3 + resnet_idx = block_idx - 12 + else: + # Keep as is for other blocks + new_state_dict[key] = value + continue + + # Convert residual block naming + if ".residual.0.gamma" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma" + elif ".residual.2.bias" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias" + elif ".residual.2.weight" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight" + elif ".residual.3.gamma" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma" + elif ".residual.6.bias" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias" + elif ".residual.6.weight" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight" + else: + new_key = key + + new_state_dict[new_key] = value + + # Handle shortcut connections + elif ".shortcut." in key: + if block_idx == 4: + new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.") + new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1") + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + new_key = new_key.replace(".shortcut.", ".conv_shortcut.") + + new_state_dict[new_key] = value + + # Handle upsamplers + elif ".resample." in key or ".time_conv." in key: + if block_idx == 3: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0") + elif block_idx == 7: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0") + elif block_idx == 11: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0") + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + + new_state_dict[new_key] = value + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + new_state_dict[new_key] = value + else: + # Keep other keys unchanged + new_state_dict[key] = value + + with init_empty_weights(): + vae = AutoencoderKLWan() + vae.load_state_dict(new_state_dict, strict=True, assign=True) + return vae + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--model_type", type=str, default=None) + parser.add_argument("--output_path", type=str, required=True) + parser.add_argument("--dtype", default="fp32") + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + args = get_args() + + transformer = None + dtype = DTYPE_MAPPING[args.dtype] + + transformer = convert_transformer(args.model_type).to(dtype=dtype) + vae = convert_vae() + text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl") + tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") + scheduler = UniPCMultistepScheduler( + prediction_type="flow_prediction", + num_train_timesteps=1000, + use_flow_sigmas=True, + ) + + if "I2V" in args.model_type or "FLF2V" in args.model_type: + image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + pipe = SkyReelsV2ImageToVideoPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + image_encoder=image_encoder, + image_processor=image_processor, + ) + elif "T2V" in args.model_type: + pipe = SkyReelsV2Pipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + ) + elif "DF" in args.model_type: + pipe = SkyReelsV2DiffusionForcingPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + ) + + pipe.save_pretrained( + args.output_path, + safe_serialization=True, + max_shard_size="5GB", + # push_to_hub=True, + # repo_id=f"<place_holder>/{args.model_type}-Diffusers", + ) diff --git a/scripts/convert_stable_audio.py b/scripts/convert_stable_audio.py new file mode 100644 index 000000000000..c3479fc6b2bb --- /dev/null +++ b/scripts/convert_stable_audio.py @@ -0,0 +1,279 @@ +# Run this script to convert the Stable Audio model weights to a diffusers pipeline. +import argparse +import json +import os +from contextlib import nullcontext + +import torch +from safetensors.torch import load_file +from transformers import ( + AutoTokenizer, + T5EncoderModel, +) + +from diffusers import ( + AutoencoderOobleck, + CosineDPMSolverMultistepScheduler, + StableAudioDiTModel, + StableAudioPipeline, + StableAudioProjectionModel, +) +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.utils import is_accelerate_available + + +if is_accelerate_available(): + from accelerate import init_empty_weights + + +def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_layers=5): + projection_model_state_dict = { + k.replace("conditioner.conditioners.", "").replace("embedder.embedding", "time_positional_embedding"): v + for (k, v) in state_dict.items() + if "conditioner.conditioners" in k + } + + # NOTE: we assume here that there's no projection layer from the text encoder to the latent space, script should be adapted a bit if there is. + for key, value in list(projection_model_state_dict.items()): + new_key = key.replace("seconds_start", "start_number_conditioner").replace( + "seconds_total", "end_number_conditioner" + ) + projection_model_state_dict[new_key] = projection_model_state_dict.pop(key) + + model_state_dict = {k.replace("model.model.", ""): v for (k, v) in state_dict.items() if "model.model." in k} + for key, value in list(model_state_dict.items()): + # attention layers + new_key = ( + key.replace("transformer.", "") + .replace("layers", "transformer_blocks") + .replace("self_attn", "attn1") + .replace("cross_attn", "attn2") + .replace("ff.ff", "ff.net") + ) + new_key = ( + new_key.replace("pre_norm", "norm1") + .replace("cross_attend_norm", "norm2") + .replace("ff_norm", "norm3") + .replace("to_out", "to_out.0") + ) + new_key = new_key.replace("gamma", "weight").replace("beta", "bias") # replace layernorm + + # other layers + new_key = ( + new_key.replace("project", "proj") + .replace("to_timestep_embed", "timestep_proj") + .replace("timestep_features", "time_proj") + .replace("to_global_embed", "global_proj") + .replace("to_cond_embed", "cross_attention_proj") + ) + + # we're using diffusers implementation of time_proj (GaussianFourierProjection) which creates a 1D tensor + if new_key == "time_proj.weight": + model_state_dict[key] = model_state_dict[key].squeeze(1) + + if "to_qkv" in new_key: + q, k, v = torch.chunk(model_state_dict.pop(key), 3, dim=0) + model_state_dict[new_key.replace("qkv", "q")] = q + model_state_dict[new_key.replace("qkv", "k")] = k + model_state_dict[new_key.replace("qkv", "v")] = v + elif "to_kv" in new_key: + k, v = torch.chunk(model_state_dict.pop(key), 2, dim=0) + model_state_dict[new_key.replace("kv", "k")] = k + model_state_dict[new_key.replace("kv", "v")] = v + else: + model_state_dict[new_key] = model_state_dict.pop(key) + + autoencoder_state_dict = { + k.replace("pretransform.model.", "").replace("coder.layers.0", "coder.conv1"): v + for (k, v) in state_dict.items() + if "pretransform.model." in k + } + + for key, _ in list(autoencoder_state_dict.items()): + new_key = key + if "coder.layers" in new_key: + # get idx of the layer + idx = int(new_key.split("coder.layers.")[1].split(".")[0]) + + new_key = new_key.replace(f"coder.layers.{idx}", f"coder.block.{idx - 1}") + + if "encoder" in new_key: + for i in range(3): + new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i + 1}") + new_key = new_key.replace(f"block.{idx - 1}.layers.3", f"block.{idx - 1}.snake1") + new_key = new_key.replace(f"block.{idx - 1}.layers.4", f"block.{idx - 1}.conv1") + else: + for i in range(2, 5): + new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i - 1}") + new_key = new_key.replace(f"block.{idx - 1}.layers.0", f"block.{idx - 1}.snake1") + new_key = new_key.replace(f"block.{idx - 1}.layers.1", f"block.{idx - 1}.conv_t1") + + new_key = new_key.replace("layers.0.beta", "snake1.beta") + new_key = new_key.replace("layers.0.alpha", "snake1.alpha") + new_key = new_key.replace("layers.2.beta", "snake2.beta") + new_key = new_key.replace("layers.2.alpha", "snake2.alpha") + new_key = new_key.replace("layers.1.bias", "conv1.bias") + new_key = new_key.replace("layers.1.weight_", "conv1.weight_") + new_key = new_key.replace("layers.3.bias", "conv2.bias") + new_key = new_key.replace("layers.3.weight_", "conv2.weight_") + + if idx == num_autoencoder_layers + 1: + new_key = new_key.replace(f"block.{idx - 1}", "snake1") + elif idx == num_autoencoder_layers + 2: + new_key = new_key.replace(f"block.{idx - 1}", "conv2") + + else: + new_key = new_key + + value = autoencoder_state_dict.pop(key) + if "snake" in new_key: + value = value.unsqueeze(0).unsqueeze(-1) + if new_key in autoencoder_state_dict: + raise ValueError(f"{new_key} already in state dict.") + autoencoder_state_dict[new_key] = value + + return model_state_dict, projection_model_state_dict, autoencoder_state_dict + + +parser = argparse.ArgumentParser(description="Convert Stable Audio 1.0 model weights to a diffusers pipeline") +parser.add_argument("--model_folder_path", type=str, help="Location of Stable Audio weights and config") +parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") +parser.add_argument( + "--save_directory", + type=str, + default="./tmp/stable-audio-1.0", + help="Directory to save a pipeline to. Will be created if it doesn't exist.", +) +parser.add_argument( + "--repo_id", + type=str, + default="stable-audio-1.0", + help="Hub organization to save the pipelines to", +) +parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") +parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") + +args = parser.parse_args() + +checkpoint_path = ( + os.path.join(args.model_folder_path, "model.safetensors") + if args.use_safetensors + else os.path.join(args.model_folder_path, "model.ckpt") +) +config_path = os.path.join(args.model_folder_path, "model_config.json") + +device = "cpu" +if args.variant == "bf16": + dtype = torch.bfloat16 +else: + dtype = torch.float32 + +with open(config_path) as f_in: + config_dict = json.load(f_in) + +conditioning_dict = { + conditioning["id"]: conditioning["config"] for conditioning in config_dict["model"]["conditioning"]["configs"] +} + +t5_model_config = conditioning_dict["prompt"] + +# T5 Text encoder +text_encoder = T5EncoderModel.from_pretrained(t5_model_config["t5_model_name"]) +tokenizer = AutoTokenizer.from_pretrained( + t5_model_config["t5_model_name"], truncation=True, model_max_length=t5_model_config["max_length"] +) + + +# scheduler +scheduler = CosineDPMSolverMultistepScheduler( + sigma_min=0.3, + sigma_max=500, + solver_order=2, + prediction_type="v_prediction", + sigma_data=1.0, + sigma_schedule="exponential", +) +ctx = init_empty_weights if is_accelerate_available() else nullcontext + + +if args.use_safetensors: + orig_state_dict = load_file(checkpoint_path, device=device) +else: + orig_state_dict = torch.load(checkpoint_path, map_location=device) + + +model_config = config_dict["model"]["diffusion"]["config"] + +model_state_dict, projection_model_state_dict, autoencoder_state_dict = convert_stable_audio_state_dict_to_diffusers( + orig_state_dict +) + + +with ctx(): + projection_model = StableAudioProjectionModel( + text_encoder_dim=text_encoder.config.d_model, + conditioning_dim=config_dict["model"]["conditioning"]["cond_dim"], + min_value=conditioning_dict["seconds_start"][ + "min_val" + ], # assume `seconds_start` and `seconds_total` have the same min / max values. + max_value=conditioning_dict["seconds_start"][ + "max_val" + ], # assume `seconds_start` and `seconds_total` have the same min / max values. + ) +if is_accelerate_available(): + load_model_dict_into_meta(projection_model, projection_model_state_dict) +else: + projection_model.load_state_dict(projection_model_state_dict) + +attention_head_dim = model_config["embed_dim"] // model_config["num_heads"] +with ctx(): + model = StableAudioDiTModel( + sample_size=int(config_dict["sample_size"]) + / int(config_dict["model"]["pretransform"]["config"]["downsampling_ratio"]), + in_channels=model_config["io_channels"], + num_layers=model_config["depth"], + attention_head_dim=attention_head_dim, + num_key_value_attention_heads=model_config["cond_token_dim"] // attention_head_dim, + num_attention_heads=model_config["num_heads"], + out_channels=model_config["io_channels"], + cross_attention_dim=model_config["cond_token_dim"], + time_proj_dim=256, + global_states_input_dim=model_config["global_cond_dim"], + cross_attention_input_dim=model_config["cond_token_dim"], + ) +if is_accelerate_available(): + load_model_dict_into_meta(model, model_state_dict) +else: + model.load_state_dict(model_state_dict) + + +autoencoder_config = config_dict["model"]["pretransform"]["config"] +with ctx(): + autoencoder = AutoencoderOobleck( + encoder_hidden_size=autoencoder_config["encoder"]["config"]["channels"], + downsampling_ratios=autoencoder_config["encoder"]["config"]["strides"], + decoder_channels=autoencoder_config["decoder"]["config"]["channels"], + decoder_input_channels=autoencoder_config["decoder"]["config"]["latent_dim"], + audio_channels=autoencoder_config["io_channels"], + channel_multiples=autoencoder_config["encoder"]["config"]["c_mults"], + sampling_rate=config_dict["sample_rate"], + ) + +if is_accelerate_available(): + load_model_dict_into_meta(autoencoder, autoencoder_state_dict) +else: + autoencoder.load_state_dict(autoencoder_state_dict) + + +# Prior pipeline +pipeline = StableAudioPipeline( + transformer=model, + tokenizer=tokenizer, + text_encoder=text_encoder, + scheduler=scheduler, + vae=autoencoder, + projection_model=projection_model, +) +pipeline.to(dtype).save_pretrained( + args.save_directory, repo_id=args.repo_id, push_to_hub=args.push_to_hub, variant=args.variant +) diff --git a/scripts/convert_stable_cascade.py b/scripts/convert_stable_cascade.py new file mode 100644 index 000000000000..97ed18d9b4d4 --- /dev/null +++ b/scripts/convert_stable_cascade.py @@ -0,0 +1,218 @@ +# Run this script to convert the Stable Cascade model weights to a diffusers pipeline. +import argparse +from contextlib import nullcontext + +import torch +from safetensors.torch import load_file +from transformers import ( + AutoTokenizer, + CLIPConfig, + CLIPImageProcessor, + CLIPTextModelWithProjection, + CLIPVisionModelWithProjection, +) + +from diffusers import ( + DDPMWuerstchenScheduler, + StableCascadeCombinedPipeline, + StableCascadeDecoderPipeline, + StableCascadePriorPipeline, +) +from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers +from diffusers.models import StableCascadeUNet +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.pipelines.wuerstchen import PaellaVQModel +from diffusers.utils import is_accelerate_available + + +if is_accelerate_available(): + from accelerate import init_empty_weights + +parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline") +parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights") +parser.add_argument("--stage_c_name", type=str, default="stage_c.safetensors", help="Name of stage c checkpoint file") +parser.add_argument("--stage_b_name", type=str, default="stage_b.safetensors", help="Name of stage b checkpoint file") +parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c") +parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b") +parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") +parser.add_argument( + "--prior_output_path", default="stable-cascade-prior", type=str, help="Hub organization to save the pipelines to" +) +parser.add_argument( + "--decoder_output_path", + type=str, + default="stable-cascade-decoder", + help="Hub organization to save the pipelines to", +) +parser.add_argument( + "--combined_output_path", + type=str, + default="stable-cascade-combined", + help="Hub organization to save the pipelines to", +) +parser.add_argument("--save_combined", action="store_true") +parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") +parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") + +args = parser.parse_args() + +if args.skip_stage_b and args.skip_stage_c: + raise ValueError("At least one stage should be converted") +if (args.skip_stage_b or args.skip_stage_c) and args.save_combined: + raise ValueError("Cannot skip stages when creating a combined pipeline") + +model_path = args.model_path + +device = "cpu" +if args.variant == "bf16": + dtype = torch.bfloat16 +else: + dtype = torch.float32 + +# set paths to model weights +prior_checkpoint_path = f"{model_path}/{args.stage_c_name}" +decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}" + +# Clip Text encoder and tokenizer +config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") +config.text_config.projection_dim = config.projection_dim +text_encoder = CLIPTextModelWithProjection.from_pretrained( + "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config +) +tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") + +# image processor +feature_extractor = CLIPImageProcessor() +image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + +# scheduler for prior and decoder +scheduler = DDPMWuerstchenScheduler() +ctx = init_empty_weights if is_accelerate_available() else nullcontext + +if not args.skip_stage_c: + # Prior + if args.use_safetensors: + prior_orig_state_dict = load_file(prior_checkpoint_path, device=device) + else: + prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device) + + prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict) + + with ctx(): + prior_model = StableCascadeUNet( + in_channels=16, + out_channels=16, + timestep_ratio_embedding_dim=64, + patch_size=1, + conditioning_dim=2048, + block_out_channels=[2048, 2048], + num_attention_heads=[32, 32], + down_num_layers_per_block=[8, 24], + up_num_layers_per_block=[24, 8], + down_blocks_repeat_mappers=[1, 1], + up_blocks_repeat_mappers=[1, 1], + block_types_per_layer=[ + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ], + clip_text_in_channels=1280, + clip_text_pooled_in_channels=1280, + clip_image_in_channels=768, + clip_seq=4, + kernel_size=3, + dropout=[0.1, 0.1], + self_attn=True, + timestep_conditioning_type=["sca", "crp"], + switch_level=[False], + ) + if is_accelerate_available(): + load_model_dict_into_meta(prior_model, prior_state_dict) + else: + prior_model.load_state_dict(prior_state_dict) + + # Prior pipeline + prior_pipeline = StableCascadePriorPipeline( + prior=prior_model, + tokenizer=tokenizer, + text_encoder=text_encoder, + image_encoder=image_encoder, + scheduler=scheduler, + feature_extractor=feature_extractor, + ) + prior_pipeline.to(dtype).save_pretrained( + args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) + +if not args.skip_stage_b: + # Decoder + if args.use_safetensors: + decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device) + else: + decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device) + + decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict) + with ctx(): + decoder = StableCascadeUNet( + in_channels=4, + out_channels=4, + timestep_ratio_embedding_dim=64, + patch_size=2, + conditioning_dim=1280, + block_out_channels=[320, 640, 1280, 1280], + down_num_layers_per_block=[2, 6, 28, 6], + up_num_layers_per_block=[6, 28, 6, 2], + down_blocks_repeat_mappers=[1, 1, 1, 1], + up_blocks_repeat_mappers=[3, 3, 2, 2], + num_attention_heads=[0, 0, 20, 20], + block_types_per_layer=[ + ["SDCascadeResBlock", "SDCascadeTimestepBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ], + clip_text_pooled_in_channels=1280, + clip_seq=4, + effnet_in_channels=16, + pixel_mapper_in_channels=3, + kernel_size=3, + dropout=[0, 0, 0.1, 0.1], + self_attn=True, + timestep_conditioning_type=["sca"], + ) + + if is_accelerate_available(): + load_model_dict_into_meta(decoder, decoder_state_dict) + else: + decoder.load_state_dict(decoder_state_dict) + + # VQGAN from Wuerstchen-V2 + vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan") + + # Decoder pipeline + decoder_pipeline = StableCascadeDecoderPipeline( + decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler + ) + decoder_pipeline.to(dtype).save_pretrained( + args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) + +if args.save_combined: + # Stable Cascade combined pipeline + stable_cascade_pipeline = StableCascadeCombinedPipeline( + # Decoder + text_encoder=text_encoder, + tokenizer=tokenizer, + decoder=decoder, + scheduler=scheduler, + vqgan=vqmodel, + # Prior + prior_text_encoder=text_encoder, + prior_tokenizer=tokenizer, + prior_prior=prior_model, + prior_scheduler=scheduler, + prior_image_encoder=image_encoder, + prior_feature_extractor=feature_extractor, + ) + stable_cascade_pipeline.to(dtype).save_pretrained( + args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) diff --git a/scripts/convert_stable_cascade_lite.py b/scripts/convert_stable_cascade_lite.py new file mode 100644 index 000000000000..5f4804e30f74 --- /dev/null +++ b/scripts/convert_stable_cascade_lite.py @@ -0,0 +1,226 @@ +# Run this script to convert the Stable Cascade model weights to a diffusers pipeline. +import argparse +from contextlib import nullcontext + +import torch +from safetensors.torch import load_file +from transformers import ( + AutoTokenizer, + CLIPConfig, + CLIPImageProcessor, + CLIPTextModelWithProjection, + CLIPVisionModelWithProjection, +) + +from diffusers import ( + DDPMWuerstchenScheduler, + StableCascadeCombinedPipeline, + StableCascadeDecoderPipeline, + StableCascadePriorPipeline, +) +from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers +from diffusers.models import StableCascadeUNet +from diffusers.models.model_loading_utils import load_model_dict_into_meta +from diffusers.pipelines.wuerstchen import PaellaVQModel +from diffusers.utils import is_accelerate_available + + +if is_accelerate_available(): + from accelerate import init_empty_weights + +parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline") +parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights") +parser.add_argument( + "--stage_c_name", type=str, default="stage_c_lite.safetensors", help="Name of stage c checkpoint file" +) +parser.add_argument( + "--stage_b_name", type=str, default="stage_b_lite.safetensors", help="Name of stage b checkpoint file" +) +parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c") +parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b") +parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion") +parser.add_argument( + "--prior_output_path", + default="stable-cascade-prior-lite", + type=str, + help="Hub organization to save the pipelines to", +) +parser.add_argument( + "--decoder_output_path", + type=str, + default="stable-cascade-decoder-lite", + help="Hub organization to save the pipelines to", +) +parser.add_argument( + "--combined_output_path", + type=str, + default="stable-cascade-combined-lite", + help="Hub organization to save the pipelines to", +) +parser.add_argument("--save_combined", action="store_true") +parser.add_argument("--push_to_hub", action="store_true", help="Push to hub") +parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights") + +args = parser.parse_args() + +if args.skip_stage_b and args.skip_stage_c: + raise ValueError("At least one stage should be converted") +if (args.skip_stage_b or args.skip_stage_c) and args.save_combined: + raise ValueError("Cannot skip stages when creating a combined pipeline") + +model_path = args.model_path + +device = "cpu" +if args.variant == "bf16": + dtype = torch.bfloat16 +else: + dtype = torch.float32 + +# set paths to model weights +prior_checkpoint_path = f"{model_path}/{args.stage_c_name}" +decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}" + +# Clip Text encoder and tokenizer +config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") +config.text_config.projection_dim = config.projection_dim +text_encoder = CLIPTextModelWithProjection.from_pretrained( + "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config +) +tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") + +# image processor +feature_extractor = CLIPImageProcessor() +image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") +# scheduler for prior and decoder +scheduler = DDPMWuerstchenScheduler() + +ctx = init_empty_weights if is_accelerate_available() else nullcontext + +if not args.skip_stage_c: + # Prior + if args.use_safetensors: + prior_orig_state_dict = load_file(prior_checkpoint_path, device=device) + else: + prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device) + + prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict) + with ctx(): + prior_model = StableCascadeUNet( + in_channels=16, + out_channels=16, + timestep_ratio_embedding_dim=64, + patch_size=1, + conditioning_dim=1536, + block_out_channels=[1536, 1536], + num_attention_heads=[24, 24], + down_num_layers_per_block=[4, 12], + up_num_layers_per_block=[12, 4], + down_blocks_repeat_mappers=[1, 1], + up_blocks_repeat_mappers=[1, 1], + block_types_per_layer=[ + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ], + clip_text_in_channels=1280, + clip_text_pooled_in_channels=1280, + clip_image_in_channels=768, + clip_seq=4, + kernel_size=3, + dropout=[0.1, 0.1], + self_attn=True, + timestep_conditioning_type=["sca", "crp"], + switch_level=[False], + ) + + if is_accelerate_available(): + load_model_dict_into_meta(prior_model, prior_state_dict) + else: + prior_model.load_state_dict(prior_state_dict) + + # Prior pipeline + prior_pipeline = StableCascadePriorPipeline( + prior=prior_model, + tokenizer=tokenizer, + text_encoder=text_encoder, + image_encoder=image_encoder, + scheduler=scheduler, + feature_extractor=feature_extractor, + ) + prior_pipeline.to(dtype).save_pretrained( + args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) + +if not args.skip_stage_b: + # Decoder + if args.use_safetensors: + decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device) + else: + decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device) + + decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict) + + with ctx(): + decoder = StableCascadeUNet( + in_channels=4, + out_channels=4, + timestep_ratio_embedding_dim=64, + patch_size=2, + conditioning_dim=1280, + block_out_channels=[320, 576, 1152, 1152], + down_num_layers_per_block=[2, 4, 14, 4], + up_num_layers_per_block=[4, 14, 4, 2], + down_blocks_repeat_mappers=[1, 1, 1, 1], + up_blocks_repeat_mappers=[2, 2, 2, 2], + num_attention_heads=[0, 9, 18, 18], + block_types_per_layer=[ + ["SDCascadeResBlock", "SDCascadeTimestepBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"], + ], + clip_text_pooled_in_channels=1280, + clip_seq=4, + effnet_in_channels=16, + pixel_mapper_in_channels=3, + kernel_size=3, + dropout=[0, 0, 0.1, 0.1], + self_attn=True, + timestep_conditioning_type=["sca"], + ) + + if is_accelerate_available(): + load_model_dict_into_meta(decoder, decoder_state_dict) + else: + decoder.load_state_dict(decoder_state_dict) + + # VQGAN from Wuerstchen-V2 + vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan") + + # Decoder pipeline + decoder_pipeline = StableCascadeDecoderPipeline( + decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler + ) + decoder_pipeline.to(dtype).save_pretrained( + args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) + +if args.save_combined: + # Stable Cascade combined pipeline + stable_cascade_pipeline = StableCascadeCombinedPipeline( + # Decoder + text_encoder=text_encoder, + tokenizer=tokenizer, + decoder=decoder, + scheduler=scheduler, + vqgan=vqmodel, + # Prior + prior_text_encoder=text_encoder, + prior_tokenizer=tokenizer, + prior_prior=prior_model, + prior_scheduler=scheduler, + prior_image_encoder=image_encoder, + prior_feature_extractor=feature_extractor, + ) + stable_cascade_pipeline.to(dtype).save_pretrained( + args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant + ) diff --git a/scripts/convert_stable_diffusion_checkpoint_to_onnx.py b/scripts/convert_stable_diffusion_checkpoint_to_onnx.py new file mode 100644 index 000000000000..1fb9f8d35bae --- /dev/null +++ b/scripts/convert_stable_diffusion_checkpoint_to_onnx.py @@ -0,0 +1,265 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import shutil +from pathlib import Path + +import onnx +import torch +from packaging import version +from torch.onnx import export + +from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline + + +is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") + + +def onnx_export( + model, + model_args: tuple, + output_path: Path, + ordered_input_names, + output_names, + dynamic_axes, + opset, + use_external_data_format=False, +): + output_path.parent.mkdir(parents=True, exist_ok=True) + # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, + # so we check the torch version for backwards compatibility + if is_torch_less_than_1_11: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + use_external_data_format=use_external_data_format, + enable_onnx_checker=True, + opset_version=opset, + ) + else: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + opset_version=opset, + ) + + +@torch.no_grad() +def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False): + dtype = torch.float16 if fp16 else torch.float32 + if fp16 and torch.cuda.is_available(): + device = "cuda" + elif fp16 and not torch.cuda.is_available(): + raise ValueError("`float16` model export is only supported on GPUs with CUDA") + else: + device = "cpu" + pipeline = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device) + output_path = Path(output_path) + + # TEXT ENCODER + num_tokens = pipeline.text_encoder.config.max_position_embeddings + text_hidden_size = pipeline.text_encoder.config.hidden_size + text_input = pipeline.tokenizer( + "A sample prompt", + padding="max_length", + max_length=pipeline.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + onnx_export( + pipeline.text_encoder, + # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files + model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)), + output_path=output_path / "text_encoder" / "model.onnx", + ordered_input_names=["input_ids"], + output_names=["last_hidden_state", "pooler_output"], + dynamic_axes={ + "input_ids": {0: "batch", 1: "sequence"}, + }, + opset=opset, + ) + del pipeline.text_encoder + + # UNET + unet_in_channels = pipeline.unet.config.in_channels + unet_sample_size = pipeline.unet.config.sample_size + unet_path = output_path / "unet" / "model.onnx" + onnx_export( + pipeline.unet, + model_args=( + torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), + torch.randn(2).to(device=device, dtype=dtype), + torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), + False, + ), + output_path=unet_path, + ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"], + output_names=["out_sample"], # has to be different from "sample" for correct tracing + dynamic_axes={ + "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + "timestep": {0: "batch"}, + "encoder_hidden_states": {0: "batch", 1: "sequence"}, + }, + opset=opset, + use_external_data_format=True, # UNet is > 2GB, so the weights need to be split + ) + unet_model_path = str(unet_path.absolute().as_posix()) + unet_dir = os.path.dirname(unet_model_path) + unet = onnx.load(unet_model_path) + # clean up existing tensor files + shutil.rmtree(unet_dir) + os.mkdir(unet_dir) + # collate external tensor files into one + onnx.save_model( + unet, + unet_model_path, + save_as_external_data=True, + all_tensors_to_one_file=True, + location="weights.pb", + convert_attribute=False, + ) + del pipeline.unet + + # VAE ENCODER + vae_encoder = pipeline.vae + vae_in_channels = vae_encoder.config.in_channels + vae_sample_size = vae_encoder.config.sample_size + # need to get the raw tensor output (sample) from the encoder + vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample() + onnx_export( + vae_encoder, + model_args=( + torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype), + False, + ), + output_path=output_path / "vae_encoder" / "model.onnx", + ordered_input_names=["sample", "return_dict"], + output_names=["latent_sample"], + dynamic_axes={ + "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + }, + opset=opset, + ) + + # VAE DECODER + vae_decoder = pipeline.vae + vae_latent_channels = vae_decoder.config.latent_channels + vae_out_channels = vae_decoder.config.out_channels + # forward only through the decoder part + vae_decoder.forward = vae_encoder.decode + onnx_export( + vae_decoder, + model_args=( + torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), + False, + ), + output_path=output_path / "vae_decoder" / "model.onnx", + ordered_input_names=["latent_sample", "return_dict"], + output_names=["sample"], + dynamic_axes={ + "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + }, + opset=opset, + ) + del pipeline.vae + + # SAFETY CHECKER + if pipeline.safety_checker is not None: + safety_checker = pipeline.safety_checker + clip_num_channels = safety_checker.config.vision_config.num_channels + clip_image_size = safety_checker.config.vision_config.image_size + safety_checker.forward = safety_checker.forward_onnx + onnx_export( + pipeline.safety_checker, + model_args=( + torch.randn( + 1, + clip_num_channels, + clip_image_size, + clip_image_size, + ).to(device=device, dtype=dtype), + torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype), + ), + output_path=output_path / "safety_checker" / "model.onnx", + ordered_input_names=["clip_input", "images"], + output_names=["out_images", "has_nsfw_concepts"], + dynamic_axes={ + "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + "images": {0: "batch", 1: "height", 2: "width", 3: "channels"}, + }, + opset=opset, + ) + del pipeline.safety_checker + safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker") + feature_extractor = pipeline.feature_extractor + else: + safety_checker = None + feature_extractor = None + + onnx_pipeline = OnnxStableDiffusionPipeline( + vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"), + vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"), + text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"), + tokenizer=pipeline.tokenizer, + unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"), + scheduler=pipeline.scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + requires_safety_checker=safety_checker is not None, + ) + + onnx_pipeline.save_pretrained(output_path) + print("ONNX pipeline saved to", output_path) + + del pipeline + del onnx_pipeline + _ = OnnxStableDiffusionPipeline.from_pretrained(output_path, provider="CPUExecutionProvider") + print("ONNX pipeline is loadable") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", + ) + + parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--opset", + default=14, + type=int, + help="The version of the ONNX operator set to use.", + ) + parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") + + args = parser.parse_args() + + convert_models(args.model_path, args.output_path, args.opset, args.fp16) diff --git a/scripts/convert_stable_diffusion_controlnet_to_onnx.py b/scripts/convert_stable_diffusion_controlnet_to_onnx.py new file mode 100644 index 000000000000..4af39b287836 --- /dev/null +++ b/scripts/convert_stable_diffusion_controlnet_to_onnx.py @@ -0,0 +1,505 @@ +import argparse +import os +import shutil +from pathlib import Path + +import onnx +import onnx_graphsurgeon as gs +import torch +from onnx import shape_inference +from packaging import version +from polygraphy.backend.onnx.loader import fold_constants +from torch.onnx import export + +from diffusers import ( + ControlNetModel, + StableDiffusionControlNetImg2ImgPipeline, +) +from diffusers.models.attention_processor import AttnProcessor +from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline + + +is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") +is_torch_2_0_1 = version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.1") + + +class Optimizer: + def __init__(self, onnx_graph, verbose=False): + self.graph = gs.import_onnx(onnx_graph) + self.verbose = verbose + + def info(self, prefix): + if self.verbose: + print( + f"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs" + ) + + def cleanup(self, return_onnx=False): + self.graph.cleanup().toposort() + if return_onnx: + return gs.export_onnx(self.graph) + + def select_outputs(self, keep, names=None): + self.graph.outputs = [self.graph.outputs[o] for o in keep] + if names: + for i, name in enumerate(names): + self.graph.outputs[i].name = name + + def fold_constants(self, return_onnx=False): + onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True) + self.graph = gs.import_onnx(onnx_graph) + if return_onnx: + return onnx_graph + + def infer_shapes(self, return_onnx=False): + onnx_graph = gs.export_onnx(self.graph) + if onnx_graph.ByteSize() > 2147483648: + raise TypeError("ERROR: model size exceeds supported 2GB limit") + else: + onnx_graph = shape_inference.infer_shapes(onnx_graph) + + self.graph = gs.import_onnx(onnx_graph) + if return_onnx: + return onnx_graph + + +def optimize(onnx_graph, name, verbose): + opt = Optimizer(onnx_graph, verbose=verbose) + opt.info(name + ": original") + opt.cleanup() + opt.info(name + ": cleanup") + opt.fold_constants() + opt.info(name + ": fold constants") + # opt.infer_shapes() + # opt.info(name + ': shape inference') + onnx_opt_graph = opt.cleanup(return_onnx=True) + opt.info(name + ": finished") + return onnx_opt_graph + + +class UNet2DConditionControlNetModel(torch.nn.Module): + def __init__( + self, + unet, + controlnets: ControlNetModel, + ): + super().__init__() + self.unet = unet + self.controlnets = controlnets + + def forward( + self, + sample, + timestep, + encoder_hidden_states, + controlnet_conds, + controlnet_scales, + ): + for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate( + zip(controlnet_conds, controlnet_scales, self.controlnets) + ): + down_samples, mid_sample = controlnet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + controlnet_cond=controlnet_cond, + conditioning_scale=conditioning_scale, + return_dict=False, + ) + + # merge samples + if i == 0: + down_block_res_samples, mid_block_res_sample = down_samples, mid_sample + else: + down_block_res_samples = [ + samples_prev + samples_curr + for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) + ] + mid_block_res_sample += mid_sample + + noise_pred = self.unet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + return_dict=False, + )[0] + return noise_pred + + +class UNet2DConditionXLControlNetModel(torch.nn.Module): + def __init__( + self, + unet, + controlnets: ControlNetModel, + ): + super().__init__() + self.unet = unet + self.controlnets = controlnets + + def forward( + self, + sample, + timestep, + encoder_hidden_states, + controlnet_conds, + controlnet_scales, + text_embeds, + time_ids, + ): + added_cond_kwargs = {"text_embeds": text_embeds, "time_ids": time_ids} + for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate( + zip(controlnet_conds, controlnet_scales, self.controlnets) + ): + down_samples, mid_sample = controlnet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + controlnet_cond=controlnet_cond, + conditioning_scale=conditioning_scale, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + ) + + # merge samples + if i == 0: + down_block_res_samples, mid_block_res_sample = down_samples, mid_sample + else: + down_block_res_samples = [ + samples_prev + samples_curr + for samples_prev, samples_curr in zip(down_block_res_samples, down_samples) + ] + mid_block_res_sample += mid_sample + + noise_pred = self.unet( + sample, + timestep, + encoder_hidden_states=encoder_hidden_states, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + return noise_pred + + +def onnx_export( + model, + model_args: tuple, + output_path: Path, + ordered_input_names, + output_names, + dynamic_axes, + opset, + use_external_data_format=False, +): + output_path.parent.mkdir(parents=True, exist_ok=True) + # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, + # so we check the torch version for backwards compatibility + with torch.inference_mode(), torch.autocast("cuda"): + if is_torch_less_than_1_11: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + use_external_data_format=use_external_data_format, + enable_onnx_checker=True, + opset_version=opset, + ) + else: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + opset_version=opset, + ) + + +@torch.no_grad() +def convert_models( + model_path: str, controlnet_path: list, output_path: str, opset: int, fp16: bool = False, sd_xl: bool = False +): + """ + Function to convert models in stable diffusion controlnet pipeline into ONNX format + + Example: + python convert_stable_diffusion_controlnet_to_onnx.py + --model_path danbrown/RevAnimated-v1-2-2 + --controlnet_path lllyasviel/control_v11f1e_sd15_tile ioclab/brightness-controlnet + --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2 + --fp16 + + Example for SD XL: + python convert_stable_diffusion_controlnet_to_onnx.py + --model_path stabilityai/stable-diffusion-xl-base-1.0 + --controlnet_path SargeZT/sdxl-controlnet-seg + --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0 + --fp16 + --sd_xl + + Returns: + create 4 onnx models in output path + text_encoder/model.onnx + unet/model.onnx + unet/weights.pb + vae_encoder/model.onnx + vae_decoder/model.onnx + + run test script in diffusers/examples/community + python test_onnx_controlnet.py + --sd_model danbrown/RevAnimated-v1-2-2 + --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2 + --qr_img_path path-to-qr-code-image + """ + dtype = torch.float16 if fp16 else torch.float32 + if fp16 and torch.cuda.is_available(): + device = "cuda" + elif fp16 and not torch.cuda.is_available(): + raise ValueError("`float16` model export is only supported on GPUs with CUDA") + else: + device = "cpu" + + # init controlnet + controlnets = [] + for path in controlnet_path: + controlnet = ControlNetModel.from_pretrained(path, torch_dtype=dtype).to(device) + if is_torch_2_0_1: + controlnet.set_attn_processor(AttnProcessor()) + controlnets.append(controlnet) + + if sd_xl: + if len(controlnets) == 1: + controlnet = controlnets[0] + else: + raise ValueError("MultiControlNet is not yet supported.") + pipeline = StableDiffusionXLControlNetPipeline.from_pretrained( + model_path, controlnet=controlnet, torch_dtype=dtype, variant="fp16", use_safetensors=True + ).to(device) + else: + pipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( + model_path, controlnet=controlnets, torch_dtype=dtype + ).to(device) + + output_path = Path(output_path) + if is_torch_2_0_1: + pipeline.unet.set_attn_processor(AttnProcessor()) + pipeline.vae.set_attn_processor(AttnProcessor()) + + # # TEXT ENCODER + num_tokens = pipeline.text_encoder.config.max_position_embeddings + text_hidden_size = pipeline.text_encoder.config.hidden_size + text_input = pipeline.tokenizer( + "A sample prompt", + padding="max_length", + max_length=pipeline.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + onnx_export( + pipeline.text_encoder, + # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files + model_args=(text_input.input_ids.to(device=device, dtype=torch.int32)), + output_path=output_path / "text_encoder" / "model.onnx", + ordered_input_names=["input_ids"], + output_names=["last_hidden_state", "pooler_output"], + dynamic_axes={ + "input_ids": {0: "batch", 1: "sequence"}, + }, + opset=opset, + ) + del pipeline.text_encoder + + # # UNET + if sd_xl: + controlnets = torch.nn.ModuleList(controlnets) + unet_controlnet = UNet2DConditionXLControlNetModel(pipeline.unet, controlnets) + unet_in_channels = pipeline.unet.config.in_channels + unet_sample_size = pipeline.unet.config.sample_size + text_hidden_size = 2048 + img_size = 8 * unet_sample_size + unet_path = output_path / "unet" / "model.onnx" + + onnx_export( + unet_controlnet, + model_args=( + torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), + torch.tensor([1.0]).to(device=device, dtype=dtype), + torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), + torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype), + torch.randn(len(controlnets), 1).to(device=device, dtype=dtype), + torch.randn(2, 1280).to(device=device, dtype=dtype), + torch.rand(2, 6).to(device=device, dtype=dtype), + ), + output_path=unet_path, + ordered_input_names=[ + "sample", + "timestep", + "encoder_hidden_states", + "controlnet_conds", + "conditioning_scales", + "text_embeds", + "time_ids", + ], + output_names=["noise_pred"], # has to be different from "sample" for correct tracing + dynamic_axes={ + "sample": {0: "2B", 2: "H", 3: "W"}, + "encoder_hidden_states": {0: "2B"}, + "controlnet_conds": {1: "2B", 3: "8H", 4: "8W"}, + "text_embeds": {0: "2B"}, + "time_ids": {0: "2B"}, + }, + opset=opset, + use_external_data_format=True, # UNet is > 2GB, so the weights need to be split + ) + unet_model_path = str(unet_path.absolute().as_posix()) + unet_dir = os.path.dirname(unet_model_path) + # optimize onnx + shape_inference.infer_shapes_path(unet_model_path, unet_model_path) + unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True) + # clean up existing tensor files + shutil.rmtree(unet_dir) + os.mkdir(unet_dir) + # collate external tensor files into one + onnx.save_model( + unet_opt_graph, + unet_model_path, + save_as_external_data=True, + all_tensors_to_one_file=True, + location="weights.pb", + convert_attribute=False, + ) + del pipeline.unet + else: + controlnets = torch.nn.ModuleList(controlnets) + unet_controlnet = UNet2DConditionControlNetModel(pipeline.unet, controlnets) + unet_in_channels = pipeline.unet.config.in_channels + unet_sample_size = pipeline.unet.config.sample_size + img_size = 8 * unet_sample_size + unet_path = output_path / "unet" / "model.onnx" + + onnx_export( + unet_controlnet, + model_args=( + torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), + torch.tensor([1.0]).to(device=device, dtype=dtype), + torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype), + torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype), + torch.randn(len(controlnets), 1).to(device=device, dtype=dtype), + ), + output_path=unet_path, + ordered_input_names=[ + "sample", + "timestep", + "encoder_hidden_states", + "controlnet_conds", + "conditioning_scales", + ], + output_names=["noise_pred"], # has to be different from "sample" for correct tracing + dynamic_axes={ + "sample": {0: "2B", 2: "H", 3: "W"}, + "encoder_hidden_states": {0: "2B"}, + "controlnet_conds": {1: "2B", 3: "8H", 4: "8W"}, + }, + opset=opset, + use_external_data_format=True, # UNet is > 2GB, so the weights need to be split + ) + unet_model_path = str(unet_path.absolute().as_posix()) + unet_dir = os.path.dirname(unet_model_path) + # optimize onnx + shape_inference.infer_shapes_path(unet_model_path, unet_model_path) + unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True) + # clean up existing tensor files + shutil.rmtree(unet_dir) + os.mkdir(unet_dir) + # collate external tensor files into one + onnx.save_model( + unet_opt_graph, + unet_model_path, + save_as_external_data=True, + all_tensors_to_one_file=True, + location="weights.pb", + convert_attribute=False, + ) + del pipeline.unet + + # VAE ENCODER + vae_encoder = pipeline.vae + vae_in_channels = vae_encoder.config.in_channels + vae_sample_size = vae_encoder.config.sample_size + # need to get the raw tensor output (sample) from the encoder + vae_encoder.forward = lambda sample: vae_encoder.encode(sample).latent_dist.sample() + onnx_export( + vae_encoder, + model_args=(torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),), + output_path=output_path / "vae_encoder" / "model.onnx", + ordered_input_names=["sample"], + output_names=["latent_sample"], + dynamic_axes={ + "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + }, + opset=opset, + ) + + # VAE DECODER + vae_decoder = pipeline.vae + vae_latent_channels = vae_decoder.config.latent_channels + # forward only through the decoder part + vae_decoder.forward = vae_encoder.decode + onnx_export( + vae_decoder, + model_args=( + torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype), + ), + output_path=output_path / "vae_decoder" / "model.onnx", + ordered_input_names=["latent_sample"], + output_names=["sample"], + dynamic_axes={ + "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + }, + opset=opset, + ) + del pipeline.vae + + del pipeline + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--sd_xl", action="store_true", default=False, help="SD XL pipeline") + + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", + ) + + parser.add_argument( + "--controlnet_path", + nargs="+", + required=True, + help="Path to the `controlnet` checkpoint to convert (either a local directory or on the Hub).", + ) + + parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--opset", + default=14, + type=int, + help="The version of the ONNX operator set to use.", + ) + parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") + + args = parser.parse_args() + + convert_models(args.model_path, args.controlnet_path, args.output_path, args.opset, args.fp16, args.sd_xl) diff --git a/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py b/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py new file mode 100644 index 000000000000..52ab02c221e9 --- /dev/null +++ b/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py @@ -0,0 +1,121 @@ +import argparse +import sys + +import tensorrt as trt + + +def convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False): + """ + Function to convert models in stable diffusion controlnet pipeline into TensorRT format + + Example: + python convert_stable_diffusion_controlnet_to_tensorrt.py + --onnx_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.onnx + --output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2/unet/model.engine + --fp16 + --num_controlnet 2 + + Example for SD XL: + python convert_stable_diffusion_controlnet_to_tensorrt.py + --onnx_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.onnx + --output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine + --fp16 + --num_controlnet 1 + --sd_xl + + Returns: + unet/model.engine + + run test script in diffusers/examples/community + python test_onnx_controlnet.py + --sd_model danbrown/RevAnimated-v1-2-2 + --onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2 + --unet_engine_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0/unet/model.engine + --qr_img_path path-to-qr-code-image + """ + # UNET + if sd_xl: + batch_size = 1 + unet_in_channels = 4 + unet_sample_size = 64 + num_tokens = 77 + text_hidden_size = 2048 + img_size = 512 + + text_embeds_shape = (2 * batch_size, 1280) + time_ids_shape = (2 * batch_size, 6) + else: + batch_size = 1 + unet_in_channels = 4 + unet_sample_size = 64 + num_tokens = 77 + text_hidden_size = 768 + img_size = 512 + batch_size = 1 + + latents_shape = (2 * batch_size, unet_in_channels, unet_sample_size, unet_sample_size) + embed_shape = (2 * batch_size, num_tokens, text_hidden_size) + controlnet_conds_shape = (num_controlnet, 2 * batch_size, 3, img_size, img_size) + + TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) + TRT_BUILDER = trt.Builder(TRT_LOGGER) + TRT_RUNTIME = trt.Runtime(TRT_LOGGER) + + network = TRT_BUILDER.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + onnx_parser = trt.OnnxParser(network, TRT_LOGGER) + + parse_success = onnx_parser.parse_from_file(onnx_path) + for idx in range(onnx_parser.num_errors): + print(onnx_parser.get_error(idx)) + if not parse_success: + sys.exit("ONNX model parsing failed") + print("Load Onnx model done") + + profile = TRT_BUILDER.create_optimization_profile() + + profile.set_shape("sample", latents_shape, latents_shape, latents_shape) + profile.set_shape("encoder_hidden_states", embed_shape, embed_shape, embed_shape) + profile.set_shape("controlnet_conds", controlnet_conds_shape, controlnet_conds_shape, controlnet_conds_shape) + if sd_xl: + profile.set_shape("text_embeds", text_embeds_shape, text_embeds_shape, text_embeds_shape) + profile.set_shape("time_ids", time_ids_shape, time_ids_shape, time_ids_shape) + + config = TRT_BUILDER.create_builder_config() + config.add_optimization_profile(profile) + config.set_preview_feature(trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805, True) + if fp16: + config.set_flag(trt.BuilderFlag.FP16) + + plan = TRT_BUILDER.build_serialized_network(network, config) + if plan is None: + sys.exit("Failed building engine") + print("Succeeded building engine") + + engine = TRT_RUNTIME.deserialize_cuda_engine(plan) + + ## save TRT engine + with open(output_path, "wb") as f: + f.write(engine.serialize()) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--sd_xl", action="store_true", default=False, help="SD XL pipeline") + + parser.add_argument( + "--onnx_path", + type=str, + required=True, + help="Path to the onnx checkpoint to convert", + ) + + parser.add_argument("--num_controlnet", type=int) + + parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") + + parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") + + args = parser.parse_args() + + convert_models(args.onnx_path, args.num_controlnet, args.output_path, args.fp16, args.sd_xl) diff --git a/scripts/convert_svd_to_diffusers.py b/scripts/convert_svd_to_diffusers.py new file mode 100644 index 000000000000..e46410ccb3bd --- /dev/null +++ b/scripts/convert_svd_to_diffusers.py @@ -0,0 +1,730 @@ +from diffusers.utils import is_accelerate_available, logging + + +if is_accelerate_available(): + pass + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + if controlnet: + unet_params = original_config.model.params.control_stage_config.params + else: + if "unet_config" in original_config.model.params and original_config.model.params.unet_config is not None: + unet_params = original_config.model.params.unet_config.params + else: + unet_params = original_config.model.params.network_config.params + + vae_params = original_config.model.params.first_stage_config.params.encoder_config.params + + block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = ( + "CrossAttnDownBlockSpatioTemporal" + if resolution in unet_params.attention_resolutions + else "DownBlockSpatioTemporal" + ) + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = ( + "CrossAttnUpBlockSpatioTemporal" + if resolution in unet_params.attention_resolutions + else "UpBlockSpatioTemporal" + ) + up_block_types.append(block_type) + resolution //= 2 + + if unet_params.transformer_depth is not None: + transformer_layers_per_block = ( + unet_params.transformer_depth + if isinstance(unet_params.transformer_depth, int) + else list(unet_params.transformer_depth) + ) + else: + transformer_layers_per_block = 1 + + vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) + + head_dim = unet_params.num_heads if "num_heads" in unet_params else None + use_linear_projection = ( + unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim_mult = unet_params.model_channels // unet_params.num_head_channels + head_dim = [head_dim_mult * c for c in list(unet_params.channel_mult)] + + class_embed_type = None + addition_embed_type = None + addition_time_embed_dim = None + projection_class_embeddings_input_dim = None + context_dim = None + + if unet_params.context_dim is not None: + context_dim = ( + unet_params.context_dim if isinstance(unet_params.context_dim, int) else unet_params.context_dim[0] + ) + + if "num_classes" in unet_params: + if unet_params.num_classes == "sequential": + addition_time_embed_dim = 256 + assert "adm_in_channels" in unet_params + projection_class_embeddings_input_dim = unet_params.adm_in_channels + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "addition_embed_type": addition_embed_type, + "addition_time_embed_dim": addition_time_embed_dim, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "transformer_layers_per_block": transformer_layers_per_block, + } + + if "disable_self_attentions" in unet_params: + config["only_cross_attention"] = unet_params.disable_self_attentions + + if "num_classes" in unet_params and isinstance(unet_params.num_classes, int): + config["num_class_embeds"] = unet_params.num_classes + + if controlnet: + config["conditioning_channels"] = unet_params.hint_channels + else: + config["out_channels"] = unet_params.out_channels + config["up_block_types"] = tuple(up_block_types) + + return config + + +def assign_to_checkpoint( + paths, + checkpoint, + old_checkpoint, + attention_paths_to_split=None, + additional_replacements=None, + config=None, + mid_block_suffix="", +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + if mid_block_suffix is not None: + mid_block_suffix = f".{mid_block_suffix}" + else: + mid_block_suffix = "" + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", f"mid_block.resnets.0{mid_block_suffix}") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", f"mid_block.resnets.1{mid_block_suffix}") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + if new_path == "mid_block.resnets.0.spatial_res_block.norm1.weight": + print("yeyy") + + # proj_attn.weight has to be converted from conv 1D to linear + is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) + shape = old_checkpoint[path["old"]].shape + if is_attn_weight and len(shape) == 3: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif is_attn_weight and len(shape) == 4: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + new_item = new_item.replace("time_stack", "temporal_transformer_blocks") + + new_item = new_item.replace("time_pos_embed.0.bias", "time_pos_embed.linear_1.bias") + new_item = new_item.replace("time_pos_embed.0.weight", "time_pos_embed.linear_1.weight") + new_item = new_item.replace("time_pos_embed.2.bias", "time_pos_embed.linear_2.bias") + new_item = new_item.replace("time_pos_embed.2.weight", "time_pos_embed.linear_2.weight") + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = new_item.replace("time_stack.", "") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_unet_checkpoint( + checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False +): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + if skip_extract_state_dict: + unet_state_dict = checkpoint + else: + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") + logger.warning( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + logger.warning( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + # if config["addition_embed_type"] == "text_time": + new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + spatial_resnets = [ + key + for key in input_blocks[i] + if f"input_blocks.{i}.0" in key + and ( + f"input_blocks.{i}.0.op" not in key + and f"input_blocks.{i}.0.time_stack" not in key + and f"input_blocks.{i}.0.time_mixer" not in key + ) + ] + temporal_resnets = [key for key in input_blocks[i] if f"input_blocks.{i}.0.time_stack" in key] + # import ipdb; ipdb.set_trace() + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(spatial_resnets) + meta_path = { + "old": f"input_blocks.{i}.0", + "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + paths = renew_resnet_paths(temporal_resnets) + meta_path = { + "old": f"input_blocks.{i}.0", + "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + # TODO resnet time_mixer.mix_factor + if f"input_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = ( + unet_state_dict[f"input_blocks.{i}.0.time_mixer.mix_factor"] + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + # import ipdb; ipdb.set_trace() + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_spatial = [key for key in resnet_0 if "time_stack" not in key and "time_mixer" not in key] + resnet_0_paths = renew_resnet_paths(resnet_0_spatial) + # import ipdb; ipdb.set_trace() + assign_to_checkpoint( + resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block" + ) + + resnet_0_temporal = [key for key in resnet_0 if "time_stack" in key and "time_mixer" not in key] + resnet_0_paths = renew_resnet_paths(resnet_0_temporal) + assign_to_checkpoint( + resnet_0_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block" + ) + + resnet_1_spatial = [key for key in resnet_1 if "time_stack" not in key and "time_mixer" not in key] + resnet_1_paths = renew_resnet_paths(resnet_1_spatial) + assign_to_checkpoint( + resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="spatial_res_block" + ) + + resnet_1_temporal = [key for key in resnet_1 if "time_stack" in key and "time_mixer" not in key] + resnet_1_paths = renew_resnet_paths(resnet_1_temporal) + assign_to_checkpoint( + resnet_1_paths, new_checkpoint, unet_state_dict, config=config, mid_block_suffix="temporal_res_block" + ) + + new_checkpoint["mid_block.resnets.0.time_mixer.mix_factor"] = unet_state_dict[ + "middle_block.0.time_mixer.mix_factor" + ] + new_checkpoint["mid_block.resnets.1.time_mixer.mix_factor"] = unet_state_dict[ + "middle_block.2.time_mixer.mix_factor" + ] + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + spatial_resnets = [ + key + for key in output_blocks[i] + if f"output_blocks.{i}.0" in key + and (f"output_blocks.{i}.0.time_stack" not in key and "time_mixer" not in key) + ] + # import ipdb; ipdb.set_trace() + + temporal_resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0.time_stack" in key] + + paths = renew_resnet_paths(spatial_resnets) + meta_path = { + "old": f"output_blocks.{i}.0", + "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.spatial_res_block", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + paths = renew_resnet_paths(temporal_resnets) + meta_path = { + "old": f"output_blocks.{i}.0", + "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}.temporal_res_block", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if f"output_blocks.{i}.0.time_mixer.mix_factor" in unet_state_dict: + new_checkpoint[f"up_blocks.{block_id}.resnets.{layer_in_block_id}.time_mixer.mix_factor"] = ( + unet_state_dict[f"output_blocks.{i}.0.time_mixer.mix_factor"] + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key and "conv" not in key] + if len(attentions): + paths = renew_attention_paths(attentions) + # import ipdb; ipdb.set_trace() + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + spatial_layers = [ + layer for layer in output_block_layers if "time_stack" not in layer and "time_mixer" not in layer + ] + resnet_0_paths = renew_resnet_paths(spatial_layers, n_shave_prefix_segments=1) + # import ipdb; ipdb.set_trace() + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join( + ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "spatial_res_block", path["new"]] + ) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + temporal_layers = [ + layer for layer in output_block_layers if "time_stack" in layer and "time_mixer" not in key + ] + resnet_0_paths = renew_resnet_paths(temporal_layers, n_shave_prefix_segments=1) + # import ipdb; ipdb.set_trace() + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join( + ["up_blocks", str(block_id), "resnets", str(layer_in_block_id), "temporal_res_block", path["new"]] + ) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + new_checkpoint["up_blocks.0.resnets.0.time_mixer.mix_factor"] = unet_state_dict[ + f"output_blocks.{str(i)}.0.time_mixer.mix_factor" + ] + + return new_checkpoint + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["to_q.weight", "to_k.weight", "to_v.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0, is_temporal=False): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # Temporal resnet + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = new_item.replace("time_stack.", "temporal_res_block.") + + # Spatial resnet + new_item = new_item.replace("conv1", "spatial_res_block.conv1") + new_item = new_item.replace("norm1", "spatial_res_block.norm1") + + new_item = new_item.replace("conv2", "spatial_res_block.conv2") + new_item = new_item.replace("norm2", "spatial_res_block.norm2") + + new_item = new_item.replace("nin_shortcut", "spatial_res_block.conv_shortcut") + + new_item = new_item.replace("mix_factor", "spatial_res_block.time_mixer.mix_factor") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + keys = list(checkpoint.keys()) + vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else "" + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + new_checkpoint["decoder.time_conv_out.weight"] = vae_state_dict["decoder.time_mix_conv.weight"] + new_checkpoint["decoder.time_conv_out.bias"] = vae_state_dict["decoder.time_mix_conv.bias"] + + # new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + # new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + # new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + # new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint diff --git a/scripts/convert_tiny_autoencoder_to_diffusers.py b/scripts/convert_tiny_autoencoder_to_diffusers.py new file mode 100644 index 000000000000..9bb2df98a77a --- /dev/null +++ b/scripts/convert_tiny_autoencoder_to_diffusers.py @@ -0,0 +1,71 @@ +import argparse + +import safetensors.torch + +from diffusers import AutoencoderTiny + + +""" +Example - From the diffusers root directory: + +Download the weights: +```sh +$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_encoder.safetensors +$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_decoder.safetensors +``` + +Convert the model: +```sh +$ python scripts/convert_tiny_autoencoder_to_diffusers.py \ + --encoder_ckpt_path taesd_encoder.safetensors \ + --decoder_ckpt_path taesd_decoder.safetensors \ + --dump_path taesd-diffusers +``` +""" + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument( + "--encoder_ckpt_path", + default=None, + type=str, + required=True, + help="Path to the encoder ckpt.", + ) + parser.add_argument( + "--decoder_ckpt_path", + default=None, + type=str, + required=True, + help="Path to the decoder ckpt.", + ) + parser.add_argument( + "--use_safetensors", action="store_true", help="Whether to serialize in the safetensors format." + ) + args = parser.parse_args() + + print("Loading the original state_dicts of the encoder and the decoder...") + encoder_state_dict = safetensors.torch.load_file(args.encoder_ckpt_path) + decoder_state_dict = safetensors.torch.load_file(args.decoder_ckpt_path) + + print("Populating the state_dicts in the diffusers format...") + tiny_autoencoder = AutoencoderTiny() + new_state_dict = {} + + # Modify the encoder state dict. + for k in encoder_state_dict: + new_state_dict.update({f"encoder.layers.{k}": encoder_state_dict[k]}) + + # Modify the decoder state dict. + for k in decoder_state_dict: + layer_id = int(k.split(".")[0]) - 1 + new_k = str(layer_id) + "." + ".".join(k.split(".")[1:]) + new_state_dict.update({f"decoder.layers.{new_k}": decoder_state_dict[k]}) + + # Assertion tests with the original implementation can be found here: + # https://gist.github.com/sayakpaul/337b0988f08bd2cf2b248206f760e28f + tiny_autoencoder.load_state_dict(new_state_dict) + print("Population successful, serializing...") + tiny_autoencoder.save_pretrained(args.dump_path, safe_serialization=args.use_safetensors) diff --git a/scripts/convert_unclip_txt2img_to_image_variation.py b/scripts/convert_unclip_txt2img_to_image_variation.py new file mode 100644 index 000000000000..07f8ebf2a3d0 --- /dev/null +++ b/scripts/convert_unclip_txt2img_to_image_variation.py @@ -0,0 +1,41 @@ +import argparse + +from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection + +from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--txt2img_unclip", + default="kakaobrain/karlo-v1-alpha", + type=str, + required=False, + help="The pretrained txt2img unclip.", + ) + + args = parser.parse_args() + + txt2img = UnCLIPPipeline.from_pretrained(args.txt2img_unclip) + + feature_extractor = CLIPImageProcessor() + image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + + img2img = UnCLIPImageVariationPipeline( + decoder=txt2img.decoder, + text_encoder=txt2img.text_encoder, + tokenizer=txt2img.tokenizer, + text_proj=txt2img.text_proj, + feature_extractor=feature_extractor, + image_encoder=image_encoder, + super_res_first=txt2img.super_res_first, + super_res_last=txt2img.super_res_last, + decoder_scheduler=txt2img.decoder_scheduler, + super_res_scheduler=txt2img.super_res_scheduler, + ) + + img2img.save_pretrained(args.dump_path) diff --git a/scripts/convert_unidiffuser_to_diffusers.py b/scripts/convert_unidiffuser_to_diffusers.py new file mode 100644 index 000000000000..4c38172754f6 --- /dev/null +++ b/scripts/convert_unidiffuser_to_diffusers.py @@ -0,0 +1,786 @@ +# Convert the original UniDiffuser checkpoints into diffusers equivalents. + +import argparse +from argparse import Namespace + +import torch +from transformers import ( + CLIPImageProcessor, + CLIPTextConfig, + CLIPTextModel, + CLIPTokenizer, + CLIPVisionConfig, + CLIPVisionModelWithProjection, + GPT2Tokenizer, +) + +from diffusers import ( + AutoencoderKL, + DPMSolverMultistepScheduler, + UniDiffuserModel, + UniDiffuserPipeline, + UniDiffuserTextDecoder, +) + + +SCHEDULER_CONFIG = Namespace( + **{ + "beta_start": 0.00085, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "solver_order": 3, + } +) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +# Modified from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint +# config.num_head_channels => num_head_channels +def assign_to_checkpoint( + paths, + checkpoint, + old_checkpoint, + attention_paths_to_split=None, + additional_replacements=None, + num_head_channels=1, +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // num_head_channels // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) + shape = old_checkpoint[path["old"]].shape + if is_attn_weight and len(shape) == 3: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif is_attn_weight and len(shape) == 4: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def create_vae_diffusers_config(config_type): + # Hardcoded for now + if args.config_type == "test": + vae_config = create_vae_diffusers_config_test() + elif args.config_type == "big": + vae_config = create_vae_diffusers_config_big() + else: + raise NotImplementedError( + f"Config type {config_type} is not implemented, currently only config types" + " 'test' and 'big' are available." + ) + return vae_config + + +def create_unidiffuser_unet_config(config_type, version): + # Hardcoded for now + if args.config_type == "test": + unet_config = create_unidiffuser_unet_config_test() + elif args.config_type == "big": + unet_config = create_unidiffuser_unet_config_big() + else: + raise NotImplementedError( + f"Config type {config_type} is not implemented, currently only config types" + " 'test' and 'big' are available." + ) + # Unidiffuser-v1 uses data type embeddings + if version == 1: + unet_config["use_data_type_embedding"] = True + return unet_config + + +def create_text_decoder_config(config_type): + # Hardcoded for now + if args.config_type == "test": + text_decoder_config = create_text_decoder_config_test() + elif args.config_type == "big": + text_decoder_config = create_text_decoder_config_big() + else: + raise NotImplementedError( + f"Config type {config_type} is not implemented, currently only config types" + " 'test' and 'big' are available." + ) + return text_decoder_config + + +# Hardcoded configs for test versions of the UniDiffuser models, corresponding to those in the fast default tests. +def create_vae_diffusers_config_test(): + vae_config = { + "sample_size": 32, + "in_channels": 3, + "out_channels": 3, + "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], + "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], + "block_out_channels": [32, 64], + "latent_channels": 4, + "layers_per_block": 1, + } + return vae_config + + +def create_unidiffuser_unet_config_test(): + unet_config = { + "text_dim": 32, + "clip_img_dim": 32, + "num_text_tokens": 77, + "num_attention_heads": 2, + "attention_head_dim": 8, + "in_channels": 4, + "out_channels": 4, + "num_layers": 2, + "dropout": 0.0, + "norm_num_groups": 32, + "attention_bias": False, + "sample_size": 16, + "patch_size": 2, + "activation_fn": "gelu", + "num_embeds_ada_norm": 1000, + "norm_type": "layer_norm", + "block_type": "unidiffuser", + "pre_layer_norm": False, + "use_timestep_embedding": False, + "norm_elementwise_affine": True, + "use_patch_pos_embed": False, + "ff_final_dropout": True, + "use_data_type_embedding": False, + } + return unet_config + + +def create_text_decoder_config_test(): + text_decoder_config = { + "prefix_length": 77, + "prefix_inner_dim": 32, + "prefix_hidden_dim": 32, + "vocab_size": 1025, # 1024 + 1 for new EOS token + "n_positions": 1024, + "n_embd": 32, + "n_layer": 5, + "n_head": 4, + "n_inner": 37, + "activation_function": "gelu", + "resid_pdrop": 0.1, + "embd_pdrop": 0.1, + "attn_pdrop": 0.1, + "layer_norm_epsilon": 1e-5, + "initializer_range": 0.02, + } + return text_decoder_config + + +# Hardcoded configs for the UniDiffuser V1 model at https://huggingface.co/thu-ml/unidiffuser-v1 +# See also https://github.com/thu-ml/unidiffuser/blob/main/configs/sample_unidiffuser_v1.py +def create_vae_diffusers_config_big(): + vae_config = { + "sample_size": 256, + "in_channels": 3, + "out_channels": 3, + "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"], + "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], + "block_out_channels": [128, 256, 512, 512], + "latent_channels": 4, + "layers_per_block": 2, + } + return vae_config + + +def create_unidiffuser_unet_config_big(): + unet_config = { + "text_dim": 64, + "clip_img_dim": 512, + "num_text_tokens": 77, + "num_attention_heads": 24, + "attention_head_dim": 64, + "in_channels": 4, + "out_channels": 4, + "num_layers": 30, + "dropout": 0.0, + "norm_num_groups": 32, + "attention_bias": False, + "sample_size": 64, + "patch_size": 2, + "activation_fn": "gelu", + "num_embeds_ada_norm": 1000, + "norm_type": "layer_norm", + "block_type": "unidiffuser", + "pre_layer_norm": False, + "use_timestep_embedding": False, + "norm_elementwise_affine": True, + "use_patch_pos_embed": False, + "ff_final_dropout": True, + "use_data_type_embedding": False, + } + return unet_config + + +# From https://huggingface.co/gpt2/blob/main/config.json, the GPT2 checkpoint used by UniDiffuser +def create_text_decoder_config_big(): + text_decoder_config = { + "prefix_length": 77, + "prefix_inner_dim": 768, + "prefix_hidden_dim": 64, + "vocab_size": 50258, # 50257 + 1 for new EOS token + "n_positions": 1024, + "n_embd": 768, + "n_layer": 12, + "n_head": 12, + "n_inner": 3072, + "activation_function": "gelu", + "resid_pdrop": 0.1, + "embd_pdrop": 0.1, + "attn_pdrop": 0.1, + "layer_norm_epsilon": 1e-5, + "initializer_range": 0.02, + } + return text_decoder_config + + +# Based on diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint +def convert_vae_to_diffusers(ckpt, diffusers_model, num_head_channels=1): + """ + Converts a UniDiffuser autoencoder_kl.pth checkpoint to a diffusers AutoencoderKL. + """ + # autoencoder_kl.pth ckpt is a torch state dict + vae_state_dict = torch.load(ckpt, map_location="cpu") + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + paths, + new_checkpoint, + vae_state_dict, + additional_replacements=[meta_path], + num_head_channels=num_head_channels, # not used in vae + ) + conv_attn_to_linear(new_checkpoint) + + missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_checkpoint) + for missing_key in missing_keys: + print(f"Missing key: {missing_key}") + for unexpected_key in unexpected_keys: + print(f"Unexpected key: {unexpected_key}") + + return diffusers_model + + +def convert_uvit_block_to_diffusers_block( + uvit_state_dict, + new_state_dict, + block_prefix, + new_prefix="transformer.transformer_", + skip_connection=False, +): + """ + Maps the keys in a UniDiffuser transformer block (`Block`) to the keys in a diffusers transformer block + (`UTransformerBlock`/`UniDiffuserBlock`). + """ + prefix = new_prefix + block_prefix + if skip_connection: + new_state_dict[prefix + ".skip.skip_linear.weight"] = uvit_state_dict[block_prefix + ".skip_linear.weight"] + new_state_dict[prefix + ".skip.skip_linear.bias"] = uvit_state_dict[block_prefix + ".skip_linear.bias"] + new_state_dict[prefix + ".skip.norm.weight"] = uvit_state_dict[block_prefix + ".norm1.weight"] + new_state_dict[prefix + ".skip.norm.bias"] = uvit_state_dict[block_prefix + ".norm1.bias"] + + # Create the prefix string for out_blocks. + prefix += ".block" + + # Split up attention qkv.weight into to_q.weight, to_k.weight, to_v.weight + qkv = uvit_state_dict[block_prefix + ".attn.qkv.weight"] + new_attn_keys = [".attn1.to_q.weight", ".attn1.to_k.weight", ".attn1.to_v.weight"] + new_attn_keys = [prefix + key for key in new_attn_keys] + shape = qkv.shape[0] // len(new_attn_keys) + for i, attn_key in enumerate(new_attn_keys): + new_state_dict[attn_key] = qkv[i * shape : (i + 1) * shape] + + new_state_dict[prefix + ".attn1.to_out.0.weight"] = uvit_state_dict[block_prefix + ".attn.proj.weight"] + new_state_dict[prefix + ".attn1.to_out.0.bias"] = uvit_state_dict[block_prefix + ".attn.proj.bias"] + new_state_dict[prefix + ".norm1.weight"] = uvit_state_dict[block_prefix + ".norm2.weight"] + new_state_dict[prefix + ".norm1.bias"] = uvit_state_dict[block_prefix + ".norm2.bias"] + new_state_dict[prefix + ".ff.net.0.proj.weight"] = uvit_state_dict[block_prefix + ".mlp.fc1.weight"] + new_state_dict[prefix + ".ff.net.0.proj.bias"] = uvit_state_dict[block_prefix + ".mlp.fc1.bias"] + new_state_dict[prefix + ".ff.net.2.weight"] = uvit_state_dict[block_prefix + ".mlp.fc2.weight"] + new_state_dict[prefix + ".ff.net.2.bias"] = uvit_state_dict[block_prefix + ".mlp.fc2.bias"] + new_state_dict[prefix + ".norm3.weight"] = uvit_state_dict[block_prefix + ".norm3.weight"] + new_state_dict[prefix + ".norm3.bias"] = uvit_state_dict[block_prefix + ".norm3.bias"] + + return uvit_state_dict, new_state_dict + + +def convert_uvit_to_diffusers(ckpt, diffusers_model): + """ + Converts a UniDiffuser uvit_v*.pth checkpoint to a diffusers UniDiffusersModel. + """ + # uvit_v*.pth ckpt is a torch state dict + uvit_state_dict = torch.load(ckpt, map_location="cpu") + + new_state_dict = {} + + # Input layers + new_state_dict["vae_img_in.proj.weight"] = uvit_state_dict["patch_embed.proj.weight"] + new_state_dict["vae_img_in.proj.bias"] = uvit_state_dict["patch_embed.proj.bias"] + new_state_dict["clip_img_in.weight"] = uvit_state_dict["clip_img_embed.weight"] + new_state_dict["clip_img_in.bias"] = uvit_state_dict["clip_img_embed.bias"] + new_state_dict["text_in.weight"] = uvit_state_dict["text_embed.weight"] + new_state_dict["text_in.bias"] = uvit_state_dict["text_embed.bias"] + + new_state_dict["pos_embed"] = uvit_state_dict["pos_embed"] + + # Handle data type token embeddings for UniDiffuser-v1 + if "token_embedding.weight" in uvit_state_dict and diffusers_model.use_data_type_embedding: + new_state_dict["data_type_pos_embed_token"] = uvit_state_dict["pos_embed_token"] + new_state_dict["data_type_token_embedding.weight"] = uvit_state_dict["token_embedding.weight"] + + # Also initialize the PatchEmbedding in UTransformer2DModel with the PatchEmbedding from the checkpoint. + # This isn't used in the current implementation, so might want to remove. + new_state_dict["transformer.pos_embed.proj.weight"] = uvit_state_dict["patch_embed.proj.weight"] + new_state_dict["transformer.pos_embed.proj.bias"] = uvit_state_dict["patch_embed.proj.bias"] + + # Output layers + new_state_dict["transformer.norm_out.weight"] = uvit_state_dict["norm.weight"] + new_state_dict["transformer.norm_out.bias"] = uvit_state_dict["norm.bias"] + + new_state_dict["vae_img_out.weight"] = uvit_state_dict["decoder_pred.weight"] + new_state_dict["vae_img_out.bias"] = uvit_state_dict["decoder_pred.bias"] + new_state_dict["clip_img_out.weight"] = uvit_state_dict["clip_img_out.weight"] + new_state_dict["clip_img_out.bias"] = uvit_state_dict["clip_img_out.bias"] + new_state_dict["text_out.weight"] = uvit_state_dict["text_out.weight"] + new_state_dict["text_out.bias"] = uvit_state_dict["text_out.bias"] + + # in_blocks + in_blocks_prefixes = {".".join(layer.split(".")[:2]) for layer in uvit_state_dict if "in_blocks" in layer} + for in_block_prefix in list(in_blocks_prefixes): + convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, in_block_prefix) + + # mid_block + # Assume there's only one mid block + convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, "mid_block") + + # out_blocks + out_blocks_prefixes = {".".join(layer.split(".")[:2]) for layer in uvit_state_dict if "out_blocks" in layer} + for out_block_prefix in list(out_blocks_prefixes): + convert_uvit_block_to_diffusers_block(uvit_state_dict, new_state_dict, out_block_prefix, skip_connection=True) + + missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict) + for missing_key in missing_keys: + print(f"Missing key: {missing_key}") + for unexpected_key in unexpected_keys: + print(f"Unexpected key: {unexpected_key}") + + return diffusers_model + + +def convert_caption_decoder_to_diffusers(ckpt, diffusers_model): + """ + Converts a UniDiffuser caption_decoder.pth checkpoint to a diffusers UniDiffuserTextDecoder. + """ + # caption_decoder.pth ckpt is a torch state dict + checkpoint_state_dict = torch.load(ckpt, map_location="cpu") + decoder_state_dict = {} + # Remove the "module." prefix, if necessary + caption_decoder_key = "module." + for key in checkpoint_state_dict: + if key.startswith(caption_decoder_key): + decoder_state_dict[key.replace(caption_decoder_key, "")] = checkpoint_state_dict.get(key) + else: + decoder_state_dict[key] = checkpoint_state_dict.get(key) + + new_state_dict = {} + + # Encoder and Decoder + new_state_dict["encode_prefix.weight"] = decoder_state_dict["encode_prefix.weight"] + new_state_dict["encode_prefix.bias"] = decoder_state_dict["encode_prefix.bias"] + new_state_dict["decode_prefix.weight"] = decoder_state_dict["decode_prefix.weight"] + new_state_dict["decode_prefix.bias"] = decoder_state_dict["decode_prefix.bias"] + + # Internal GPT2LMHeadModel transformer model + for key, val in decoder_state_dict.items(): + if key.startswith("gpt"): + suffix = key[len("gpt") :] + new_state_dict["transformer" + suffix] = val + + missing_keys, unexpected_keys = diffusers_model.load_state_dict(new_state_dict) + for missing_key in missing_keys: + print(f"Missing key: {missing_key}") + for unexpected_key in unexpected_keys: + print(f"Unexpected key: {unexpected_key}") + + return diffusers_model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--caption_decoder_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to caption decoder checkpoint to convert.", + ) + parser.add_argument( + "--uvit_checkpoint_path", default=None, type=str, required=False, help="Path to U-ViT checkpoint to convert." + ) + parser.add_argument( + "--vae_checkpoint_path", + default=None, + type=str, + required=False, + help="Path to VAE checkpoint to convert.", + ) + parser.add_argument( + "--pipeline_output_path", + default=None, + type=str, + required=True, + help="Path to save the output pipeline to.", + ) + parser.add_argument( + "--config_type", + default="test", + type=str, + help=( + "Config type to use. Should be 'test' to create small models for testing or 'big' to convert a full" + " checkpoint." + ), + ) + parser.add_argument( + "--version", + default=0, + type=int, + help="The UniDiffuser model type to convert to. Should be 0 for UniDiffuser-v0 and 1 for UniDiffuser-v1.", + ) + parser.add_argument( + "--safe_serialization", + action="store_true", + help="Whether to use safetensors/safe seialization when saving the pipeline.", + ) + + args = parser.parse_args() + + # Convert the VAE model. + if args.vae_checkpoint_path is not None: + vae_config = create_vae_diffusers_config(args.config_type) + vae = AutoencoderKL(**vae_config) + vae = convert_vae_to_diffusers(args.vae_checkpoint_path, vae) + + # Convert the U-ViT ("unet") model. + if args.uvit_checkpoint_path is not None: + unet_config = create_unidiffuser_unet_config(args.config_type, args.version) + unet = UniDiffuserModel(**unet_config) + unet = convert_uvit_to_diffusers(args.uvit_checkpoint_path, unet) + + # Convert the caption decoder ("text_decoder") model. + if args.caption_decoder_checkpoint_path is not None: + text_decoder_config = create_text_decoder_config(args.config_type) + text_decoder = UniDiffuserTextDecoder(**text_decoder_config) + text_decoder = convert_caption_decoder_to_diffusers(args.caption_decoder_checkpoint_path, text_decoder) + + # Scheduler is the same for both the test and big models. + scheduler_config = SCHEDULER_CONFIG + scheduler = DPMSolverMultistepScheduler( + beta_start=scheduler_config.beta_start, + beta_end=scheduler_config.beta_end, + beta_schedule=scheduler_config.beta_schedule, + solver_order=scheduler_config.solver_order, + ) + + if args.config_type == "test": + # Make a small random CLIPTextModel + torch.manual_seed(0) + clip_text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(clip_text_encoder_config) + clip_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + # Make a small random CLIPVisionModel and accompanying CLIPImageProcessor + torch.manual_seed(0) + clip_image_encoder_config = CLIPVisionConfig( + image_size=32, + patch_size=2, + num_channels=3, + hidden_size=32, + projection_dim=32, + num_hidden_layers=5, + num_attention_heads=4, + intermediate_size=37, + dropout=0.1, + attention_dropout=0.1, + initializer_range=0.02, + ) + image_encoder = CLIPVisionModelWithProjection(clip_image_encoder_config) + image_processor = CLIPImageProcessor(crop_size=32, size=32) + + # Note that the text_decoder should already have its token embeddings resized. + text_tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model") + eos = "<|EOS|>" + special_tokens_dict = {"eos_token": eos} + text_tokenizer.add_special_tokens(special_tokens_dict) + elif args.config_type == "big": + text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") + clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") + + image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") + image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32") + + # Note that the text_decoder should already have its token embeddings resized. + text_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") + eos = "<|EOS|>" + special_tokens_dict = {"eos_token": eos} + text_tokenizer.add_special_tokens(special_tokens_dict) + else: + raise NotImplementedError( + f"Config type {args.config_type} is not implemented, currently only config types" + " 'test' and 'big' are available." + ) + + pipeline = UniDiffuserPipeline( + vae=vae, + text_encoder=text_encoder, + image_encoder=image_encoder, + clip_image_processor=image_processor, + clip_tokenizer=clip_tokenizer, + text_decoder=text_decoder, + text_tokenizer=text_tokenizer, + unet=unet, + scheduler=scheduler, + ) + pipeline.save_pretrained(args.pipeline_output_path, safe_serialization=args.safe_serialization) diff --git a/scripts/convert_vae_diff_to_onnx.py b/scripts/convert_vae_diff_to_onnx.py new file mode 100644 index 000000000000..e023e04b9497 --- /dev/null +++ b/scripts/convert_vae_diff_to_onnx.py @@ -0,0 +1,122 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from pathlib import Path + +import torch +from packaging import version +from torch.onnx import export + +from diffusers import AutoencoderKL + + +is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11") + + +def onnx_export( + model, + model_args: tuple, + output_path: Path, + ordered_input_names, + output_names, + dynamic_axes, + opset, + use_external_data_format=False, +): + output_path.parent.mkdir(parents=True, exist_ok=True) + # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11, + # so we check the torch version for backwards compatibility + if is_torch_less_than_1_11: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + use_external_data_format=use_external_data_format, + enable_onnx_checker=True, + opset_version=opset, + ) + else: + export( + model, + model_args, + f=output_path.as_posix(), + input_names=ordered_input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + do_constant_folding=True, + opset_version=opset, + ) + + +@torch.no_grad() +def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False): + dtype = torch.float16 if fp16 else torch.float32 + if fp16 and torch.cuda.is_available(): + device = "cuda" + elif fp16 and not torch.cuda.is_available(): + raise ValueError("`float16` model export is only supported on GPUs with CUDA") + else: + device = "cpu" + output_path = Path(output_path) + + # VAE DECODER + vae_decoder = AutoencoderKL.from_pretrained(model_path + "/vae") + vae_latent_channels = vae_decoder.config.latent_channels + # forward only through the decoder part + vae_decoder.forward = vae_decoder.decode + onnx_export( + vae_decoder, + model_args=( + torch.randn(1, vae_latent_channels, 25, 25).to(device=device, dtype=dtype), + False, + ), + output_path=output_path / "vae_decoder" / "model.onnx", + ordered_input_names=["latent_sample", "return_dict"], + output_names=["sample"], + dynamic_axes={ + "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"}, + }, + opset=opset, + ) + del vae_decoder + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--model_path", + type=str, + required=True, + help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).", + ) + + parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.") + parser.add_argument( + "--opset", + default=14, + type=int, + help="The version of the ONNX operator set to use.", + ) + parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode") + + args = parser.parse_args() + print(args.output_path) + convert_models(args.model_path, args.output_path, args.opset, args.fp16) + print("SD: Done: ONNX") diff --git a/scripts/convert_vae_pt_to_diffusers.py b/scripts/convert_vae_pt_to_diffusers.py new file mode 100644 index 000000000000..8c7dc71ddfd8 --- /dev/null +++ b/scripts/convert_vae_pt_to_diffusers.py @@ -0,0 +1,177 @@ +import argparse +import io + +import requests +import torch +import yaml + +from diffusers import AutoencoderKL +from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( + assign_to_checkpoint, + conv_attn_to_linear, + create_vae_diffusers_config, + renew_vae_attention_paths, + renew_vae_resnet_paths, +) +from diffusers.utils.constants import DIFFUSERS_REQUEST_TIMEOUT + + +def custom_convert_ldm_vae_checkpoint(checkpoint, config): + vae_state_dict = checkpoint + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [ + key + for key in down_blocks[i] + if f"down.{i}" in key and f"down.{i}.downsample" not in key and "attn" not in key + ] + attentions = [key for key in down_blocks[i] if f"down.{i}.attn" in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + paths = renew_vae_attention_paths(attentions) + meta_path = {"old": f"down.{i}.attn", "new": f"down_blocks.{i}.attentions"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key + for key in up_blocks[block_id] + if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key and "attn" not in key + ] + attentions = [key for key in up_blocks[block_id] if f"up.{block_id}.attn" in key] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + paths = renew_vae_attention_paths(attentions) + meta_path = {"old": f"up.{block_id}.attn", "new": f"up_blocks.{i}.attentions"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +def vae_pt_to_vae_diffuser( + checkpoint_path: str, + output_path: str, +): + # Only support V1 + r = requests.get( + " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml", + timeout=DIFFUSERS_REQUEST_TIMEOUT, + ) + io_obj = io.BytesIO(r.content) + + original_config = yaml.safe_load(io_obj) + image_size = 512 + device = "cuda" if torch.cuda.is_available() else "cpu" + if checkpoint_path.endswith("safetensors"): + from safetensors import safe_open + + checkpoint = {} + with safe_open(checkpoint_path, framework="pt", device="cpu") as f: + for key in f.keys(): + checkpoint[key] = f.get_tensor(key) + else: + checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"] + + # Convert the VAE model. + vae_config = create_vae_diffusers_config(original_config, image_size=image_size) + converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + vae.save_pretrained(output_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.") + + args = parser.parse_args() + + vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path) diff --git a/scripts/convert_versatile_diffusion_to_diffusers.py b/scripts/convert_versatile_diffusion_to_diffusers.py new file mode 100644 index 000000000000..ce68bb4c2e8c --- /dev/null +++ b/scripts/convert_versatile_diffusion_to_diffusers.py @@ -0,0 +1,791 @@ +# coding=utf-8 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Conversion script for the Versatile Stable Diffusion checkpoints.""" + +import argparse +from argparse import Namespace + +import torch +from transformers import ( + CLIPImageProcessor, + CLIPTextModelWithProjection, + CLIPTokenizer, + CLIPVisionModelWithProjection, +) + +from diffusers import ( + AutoencoderKL, + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, + UNet2DConditionModel, + VersatileDiffusionPipeline, +) +from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel + + +SCHEDULER_CONFIG = Namespace( + **{ + "beta_linear_start": 0.00085, + "beta_linear_end": 0.012, + "timesteps": 1000, + "scale_factor": 0.18215, + } +) + +IMAGE_UNET_CONFIG = Namespace( + **{ + "input_channels": 4, + "model_channels": 320, + "output_channels": 4, + "num_noattn_blocks": [2, 2, 2, 2], + "channel_mult": [1, 2, 4, 4], + "with_attn": [True, True, True, False], + "num_heads": 8, + "context_dim": 768, + "use_checkpoint": True, + } +) + +TEXT_UNET_CONFIG = Namespace( + **{ + "input_channels": 768, + "model_channels": 320, + "output_channels": 768, + "num_noattn_blocks": [2, 2, 2, 2], + "channel_mult": [1, 2, 4, 4], + "second_dim": [4, 4, 4, 4], + "with_attn": [True, True, True, False], + "num_heads": 8, + "context_dim": 768, + "use_checkpoint": True, + } +) + +AUTOENCODER_CONFIG = Namespace( + **{ + "double_z": True, + "z_channels": 4, + "resolution": 256, + "in_channels": 3, + "out_ch": 3, + "ch": 128, + "ch_mult": [1, 2, 4, 4], + "num_res_blocks": 2, + "attn_resolutions": [], + "dropout": 0.0, + } +) + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "query.weight") + new_item = new_item.replace("q.bias", "query.bias") + + new_item = new_item.replace("k.weight", "key.weight") + new_item = new_item.replace("k.bias", "key.bias") + + new_item = new_item.replace("v.weight", "value.weight") + new_item = new_item.replace("v.bias", "value.bias") + + new_item = new_item.replace("proj_out.weight", "proj_attn.weight") + new_item = new_item.replace("proj_out.bias", "proj_attn.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming + to them. It splits attention layers, and takes into account additional replacements + that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + if "proj_attn.weight" in new_path: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif path["old"] in old_checkpoint: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def create_image_unet_diffusers_config(unet_params): + """ + Creates a config for the diffusers based on the config of the VD model. + """ + + block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if unet_params.with_attn[i] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if unet_params.with_attn[-i - 1] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): + raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") + + config = { + "sample_size": None, + "in_channels": unet_params.input_channels, + "out_channels": unet_params.output_channels, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_noattn_blocks[0], + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": unet_params.num_heads, + } + + return config + + +def create_text_unet_diffusers_config(unet_params): + """ + Creates a config for the diffusers based on the config of the VD model. + """ + + block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlockFlat" if unet_params.with_attn[i] else "DownBlockFlat" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlockFlat" if unet_params.with_attn[-i - 1] else "UpBlockFlat" + up_block_types.append(block_type) + resolution //= 2 + + if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): + raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") + + config = { + "sample_size": None, + "in_channels": (unet_params.input_channels, 1, 1), + "out_channels": (unet_params.output_channels, 1, 1), + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_noattn_blocks[0], + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": unet_params.num_heads, + } + + return config + + +def create_vae_diffusers_config(vae_params): + """ + Creates a config for the diffusers based on the config of the VD model. + """ + + block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = { + "sample_size": vae_params.resolution, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } + return config + + +def create_diffusers_scheduler(original_config): + schedular = DDIMScheduler( + num_train_timesteps=original_config.model.params.timesteps, + beta_start=original_config.model.params.linear_start, + beta_end=original_config.model.params.linear_end, + beta_schedule="scaled_linear", + ) + return schedular + + +def convert_vd_unet_checkpoint(checkpoint, config, unet_key, extract_ema=False): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100: + print("Checkpoint has both EMA and non-EMA weights.") + if extract_ema: + print( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) + else: + print( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = checkpoint["model.diffusion_model.time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = checkpoint["model.diffusion_model.time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = checkpoint["model.diffusion_model.time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = checkpoint["model.diffusion_model.time_embed.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + elif f"input_blocks.{i}.0.weight" in unet_state_dict: + # text_unet uses linear layers in place of downsamplers + shape = unet_state_dict[f"input_blocks.{i}.0.weight"].shape + if shape[0] != shape[1]: + continue + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if ["conv.weight", "conv.bias"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.weight", "conv.bias"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + elif f"output_blocks.{i}.1.weight" in unet_state_dict: + # text_unet uses linear layers in place of upsamplers + shape = unet_state_dict[f"output_blocks.{i}.1.weight"].shape + if shape[0] != shape[1]: + continue + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop( + f"output_blocks.{i}.1.weight" + ) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop( + f"output_blocks.{i}.1.bias" + ) + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + elif f"output_blocks.{i}.2.weight" in unet_state_dict: + # text_unet uses linear layers in place of upsamplers + shape = unet_state_dict[f"output_blocks.{i}.2.weight"].shape + if shape[0] != shape[1]: + continue + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.weight"] = unet_state_dict.pop( + f"output_blocks.{i}.2.weight" + ) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.bias"] = unet_state_dict.pop( + f"output_blocks.{i}.2.bias" + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + return new_checkpoint + + +def convert_vd_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + keys = list(checkpoint.keys()) + for key in keys: + vae_state_dict[key] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--unet_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--vae_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--optimus_checkpoint_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--scheduler_type", + default="pndm", + type=str, + help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + args = parser.parse_args() + + scheduler_config = SCHEDULER_CONFIG + + num_train_timesteps = scheduler_config.timesteps + beta_start = scheduler_config.beta_linear_start + beta_end = scheduler_config.beta_linear_end + if args.scheduler_type == "pndm": + scheduler = PNDMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + skip_prk_steps=True, + steps_offset=1, + ) + elif args.scheduler_type == "lms": + scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear") + elif args.scheduler_type == "euler": + scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear") + elif args.scheduler_type == "euler-ancestral": + scheduler = EulerAncestralDiscreteScheduler( + beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear" + ) + elif args.scheduler_type == "dpm": + scheduler = DPMSolverMultistepScheduler( + beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear" + ) + elif args.scheduler_type == "ddim": + scheduler = DDIMScheduler( + beta_start=beta_start, + beta_end=beta_end, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + steps_offset=1, + ) + else: + raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!") + + # Convert the UNet2DConditionModel models. + if args.unet_checkpoint_path is not None: + # image UNet + image_unet_config = create_image_unet_diffusers_config(IMAGE_UNET_CONFIG) + checkpoint = torch.load(args.unet_checkpoint_path) + converted_image_unet_checkpoint = convert_vd_unet_checkpoint( + checkpoint, image_unet_config, unet_key="model.diffusion_model.unet_image.", extract_ema=args.extract_ema + ) + image_unet = UNet2DConditionModel(**image_unet_config) + image_unet.load_state_dict(converted_image_unet_checkpoint) + + # text UNet + text_unet_config = create_text_unet_diffusers_config(TEXT_UNET_CONFIG) + converted_text_unet_checkpoint = convert_vd_unet_checkpoint( + checkpoint, text_unet_config, unet_key="model.diffusion_model.unet_text.", extract_ema=args.extract_ema + ) + text_unet = UNetFlatConditionModel(**text_unet_config) + text_unet.load_state_dict(converted_text_unet_checkpoint) + + # Convert the VAE model. + if args.vae_checkpoint_path is not None: + vae_config = create_vae_diffusers_config(AUTOENCODER_CONFIG) + checkpoint = torch.load(args.vae_checkpoint_path) + converted_vae_checkpoint = convert_vd_vae_checkpoint(checkpoint, vae_config) + + vae = AutoencoderKL(**vae_config) + vae.load_state_dict(converted_vae_checkpoint) + + tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") + image_feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14") + text_encoder = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") + + pipe = VersatileDiffusionPipeline( + scheduler=scheduler, + tokenizer=tokenizer, + image_feature_extractor=image_feature_extractor, + text_encoder=text_encoder, + image_encoder=image_encoder, + image_unet=image_unet, + text_unet=text_unet, + vae=vae, + ) + pipe.save_pretrained(args.dump_path) diff --git a/scripts/convert_vq_diffusion_to_diffusers.py b/scripts/convert_vq_diffusion_to_diffusers.py new file mode 100644 index 000000000000..fe62d18faff0 --- /dev/null +++ b/scripts/convert_vq_diffusion_to_diffusers.py @@ -0,0 +1,916 @@ +""" +This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers. + +It currently only supports porting the ITHQ dataset. + +ITHQ dataset: +```sh +# From the root directory of diffusers. + +# Download the VQVAE checkpoint +$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_vqvae.pth?sv=2020-10-02&st=2022-05-30T15%3A17%3A18Z&se=2030-05-31T15%3A17%3A00Z&sr=b&sp=r&sig=1jVavHFPpUjDs%2FTO1V3PTezaNbPp2Nx8MxiWI7y6fEY%3D -O ithq_vqvae.pth + +# Download the VQVAE config +# NOTE that in VQ-diffusion the documented file is `configs/ithq.yaml` but the target class +# `image_synthesis.modeling.codecs.image_codec.ema_vqvae.PatchVQVAE` +# loads `OUTPUT/pretrained_model/taming_dvae/config.yaml` +$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/OUTPUT/pretrained_model/taming_dvae/config.yaml -O ithq_vqvae.yaml + +# Download the main model checkpoint +$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Improved-VQ-Diffusion_model_release/ithq_learnable.pth?sv=2020-10-02&st=2022-05-30T10%3A22%3A06Z&se=2030-05-31T10%3A22%3A00Z&sr=b&sp=r&sig=GOE%2Bza02%2FPnGxYVOOPtwrTR4RA3%2F5NVgMxdW4kjaEZ8%3D -O ithq_learnable.pth + +# Download the main model config +$ wget https://raw.githubusercontent.com/microsoft/VQ-Diffusion/main/configs/ithq.yaml -O ithq.yaml + +# run the convert script +$ python ./scripts/convert_vq_diffusion_to_diffusers.py \ + --checkpoint_path ./ithq_learnable.pth \ + --original_config_file ./ithq.yaml \ + --vqvae_checkpoint_path ./ithq_vqvae.pth \ + --vqvae_original_config_file ./ithq_vqvae.yaml \ + --dump_path <path to save pre-trained `VQDiffusionPipeline`> +``` +""" + +import argparse +import tempfile + +import torch +import yaml +from accelerate import init_empty_weights, load_checkpoint_and_dispatch +from transformers import CLIPTextModel, CLIPTokenizer +from yaml.loader import FullLoader + +from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel +from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings + + +# vqvae model + +PORTED_VQVAES = ["image_synthesis.modeling.codecs.image_codec.patch_vqgan.PatchVQGAN"] + + +def vqvae_model_from_original_config(original_config): + assert original_config["target"] in PORTED_VQVAES, ( + f"{original_config['target']} has not yet been ported to diffusers." + ) + + original_config = original_config["params"] + + original_encoder_config = original_config["encoder_config"]["params"] + original_decoder_config = original_config["decoder_config"]["params"] + + in_channels = original_encoder_config["in_channels"] + out_channels = original_decoder_config["out_ch"] + + down_block_types = get_down_block_types(original_encoder_config) + up_block_types = get_up_block_types(original_decoder_config) + + assert original_encoder_config["ch"] == original_decoder_config["ch"] + assert original_encoder_config["ch_mult"] == original_decoder_config["ch_mult"] + block_out_channels = tuple( + [original_encoder_config["ch"] * a_ch_mult for a_ch_mult in original_encoder_config["ch_mult"]] + ) + + assert original_encoder_config["num_res_blocks"] == original_decoder_config["num_res_blocks"] + layers_per_block = original_encoder_config["num_res_blocks"] + + assert original_encoder_config["z_channels"] == original_decoder_config["z_channels"] + latent_channels = original_encoder_config["z_channels"] + + num_vq_embeddings = original_config["n_embed"] + + # Hard coded value for ResnetBlock.GoupNorm(num_groups) in VQ-diffusion + norm_num_groups = 32 + + e_dim = original_config["embed_dim"] + + model = VQModel( + in_channels=in_channels, + out_channels=out_channels, + down_block_types=down_block_types, + up_block_types=up_block_types, + block_out_channels=block_out_channels, + layers_per_block=layers_per_block, + latent_channels=latent_channels, + num_vq_embeddings=num_vq_embeddings, + norm_num_groups=norm_num_groups, + vq_embed_dim=e_dim, + ) + + return model + + +def get_down_block_types(original_encoder_config): + attn_resolutions = coerce_attn_resolutions(original_encoder_config["attn_resolutions"]) + num_resolutions = len(original_encoder_config["ch_mult"]) + resolution = coerce_resolution(original_encoder_config["resolution"]) + + curr_res = resolution + down_block_types = [] + + for _ in range(num_resolutions): + if curr_res in attn_resolutions: + down_block_type = "AttnDownEncoderBlock2D" + else: + down_block_type = "DownEncoderBlock2D" + + down_block_types.append(down_block_type) + + curr_res = [r // 2 for r in curr_res] + + return down_block_types + + +def get_up_block_types(original_decoder_config): + attn_resolutions = coerce_attn_resolutions(original_decoder_config["attn_resolutions"]) + num_resolutions = len(original_decoder_config["ch_mult"]) + resolution = coerce_resolution(original_decoder_config["resolution"]) + + curr_res = [r // 2 ** (num_resolutions - 1) for r in resolution] + up_block_types = [] + + for _ in reversed(range(num_resolutions)): + if curr_res in attn_resolutions: + up_block_type = "AttnUpDecoderBlock2D" + else: + up_block_type = "UpDecoderBlock2D" + + up_block_types.append(up_block_type) + + curr_res = [r * 2 for r in curr_res] + + return up_block_types + + +def coerce_attn_resolutions(attn_resolutions): + attn_resolutions = list(attn_resolutions) + attn_resolutions_ = [] + for ar in attn_resolutions: + if isinstance(ar, (list, tuple)): + attn_resolutions_.append(list(ar)) + else: + attn_resolutions_.append([ar, ar]) + return attn_resolutions_ + + +def coerce_resolution(resolution): + if isinstance(resolution, int): + resolution = [resolution, resolution] # H, W + elif isinstance(resolution, (tuple, list)): + resolution = list(resolution) + else: + raise ValueError("Unknown type of resolution:", resolution) + return resolution + + +# done vqvae model + +# vqvae checkpoint + + +def vqvae_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + diffusers_checkpoint.update(vqvae_encoder_to_diffusers_checkpoint(model, checkpoint)) + + # quant_conv + + diffusers_checkpoint.update( + { + "quant_conv.weight": checkpoint["quant_conv.weight"], + "quant_conv.bias": checkpoint["quant_conv.bias"], + } + ) + + # quantize + diffusers_checkpoint.update({"quantize.embedding.weight": checkpoint["quantize.embedding"]}) + + # post_quant_conv + diffusers_checkpoint.update( + { + "post_quant_conv.weight": checkpoint["post_quant_conv.weight"], + "post_quant_conv.bias": checkpoint["post_quant_conv.bias"], + } + ) + + # decoder + diffusers_checkpoint.update(vqvae_decoder_to_diffusers_checkpoint(model, checkpoint)) + + return diffusers_checkpoint + + +def vqvae_encoder_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # conv_in + diffusers_checkpoint.update( + { + "encoder.conv_in.weight": checkpoint["encoder.conv_in.weight"], + "encoder.conv_in.bias": checkpoint["encoder.conv_in.bias"], + } + ) + + # down_blocks + for down_block_idx, down_block in enumerate(model.encoder.down_blocks): + diffusers_down_block_prefix = f"encoder.down_blocks.{down_block_idx}" + down_block_prefix = f"encoder.down.{down_block_idx}" + + # resnets + for resnet_idx, resnet in enumerate(down_block.resnets): + diffusers_resnet_prefix = f"{diffusers_down_block_prefix}.resnets.{resnet_idx}" + resnet_prefix = f"{down_block_prefix}.block.{resnet_idx}" + + diffusers_checkpoint.update( + vqvae_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + # downsample + + # do not include the downsample when on the last down block + # There is no downsample on the last down block + if down_block_idx != len(model.encoder.down_blocks) - 1: + # There's a single downsample in the original checkpoint but a list of downsamples + # in the diffusers model. + diffusers_downsample_prefix = f"{diffusers_down_block_prefix}.downsamplers.0.conv" + downsample_prefix = f"{down_block_prefix}.downsample.conv" + diffusers_checkpoint.update( + { + f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], + f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], + } + ) + + # attentions + + if hasattr(down_block, "attentions"): + for attention_idx, _ in enumerate(down_block.attentions): + diffusers_attention_prefix = f"{diffusers_down_block_prefix}.attentions.{attention_idx}" + attention_prefix = f"{down_block_prefix}.attn.{attention_idx}" + diffusers_checkpoint.update( + vqvae_attention_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + attention_prefix=attention_prefix, + ) + ) + + # mid block + + # mid block attentions + + # There is a single hardcoded attention block in the middle of the VQ-diffusion encoder + diffusers_attention_prefix = "encoder.mid_block.attentions.0" + attention_prefix = "encoder.mid.attn_1" + diffusers_checkpoint.update( + vqvae_attention_to_diffusers_checkpoint( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # mid block resnets + + for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): + diffusers_resnet_prefix = f"encoder.mid_block.resnets.{diffusers_resnet_idx}" + + # the hardcoded prefixes to `block_` are 1 and 2 + orig_resnet_idx = diffusers_resnet_idx + 1 + # There are two hardcoded resnets in the middle of the VQ-diffusion encoder + resnet_prefix = f"encoder.mid.block_{orig_resnet_idx}" + + diffusers_checkpoint.update( + vqvae_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + diffusers_checkpoint.update( + { + # conv_norm_out + "encoder.conv_norm_out.weight": checkpoint["encoder.norm_out.weight"], + "encoder.conv_norm_out.bias": checkpoint["encoder.norm_out.bias"], + # conv_out + "encoder.conv_out.weight": checkpoint["encoder.conv_out.weight"], + "encoder.conv_out.bias": checkpoint["encoder.conv_out.bias"], + } + ) + + return diffusers_checkpoint + + +def vqvae_decoder_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + # conv in + diffusers_checkpoint.update( + { + "decoder.conv_in.weight": checkpoint["decoder.conv_in.weight"], + "decoder.conv_in.bias": checkpoint["decoder.conv_in.bias"], + } + ) + + # up_blocks + + for diffusers_up_block_idx, up_block in enumerate(model.decoder.up_blocks): + # up_blocks are stored in reverse order in the VQ-diffusion checkpoint + orig_up_block_idx = len(model.decoder.up_blocks) - 1 - diffusers_up_block_idx + + diffusers_up_block_prefix = f"decoder.up_blocks.{diffusers_up_block_idx}" + up_block_prefix = f"decoder.up.{orig_up_block_idx}" + + # resnets + for resnet_idx, resnet in enumerate(up_block.resnets): + diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}" + resnet_prefix = f"{up_block_prefix}.block.{resnet_idx}" + + diffusers_checkpoint.update( + vqvae_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + # upsample + + # there is no up sample on the last up block + if diffusers_up_block_idx != len(model.decoder.up_blocks) - 1: + # There's a single upsample in the VQ-diffusion checkpoint but a list of downsamples + # in the diffusers model. + diffusers_downsample_prefix = f"{diffusers_up_block_prefix}.upsamplers.0.conv" + downsample_prefix = f"{up_block_prefix}.upsample.conv" + diffusers_checkpoint.update( + { + f"{diffusers_downsample_prefix}.weight": checkpoint[f"{downsample_prefix}.weight"], + f"{diffusers_downsample_prefix}.bias": checkpoint[f"{downsample_prefix}.bias"], + } + ) + + # attentions + + if hasattr(up_block, "attentions"): + for attention_idx, _ in enumerate(up_block.attentions): + diffusers_attention_prefix = f"{diffusers_up_block_prefix}.attentions.{attention_idx}" + attention_prefix = f"{up_block_prefix}.attn.{attention_idx}" + diffusers_checkpoint.update( + vqvae_attention_to_diffusers_checkpoint( + checkpoint, + diffusers_attention_prefix=diffusers_attention_prefix, + attention_prefix=attention_prefix, + ) + ) + + # mid block + + # mid block attentions + + # There is a single hardcoded attention block in the middle of the VQ-diffusion decoder + diffusers_attention_prefix = "decoder.mid_block.attentions.0" + attention_prefix = "decoder.mid.attn_1" + diffusers_checkpoint.update( + vqvae_attention_to_diffusers_checkpoint( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # mid block resnets + + for diffusers_resnet_idx, resnet in enumerate(model.encoder.mid_block.resnets): + diffusers_resnet_prefix = f"decoder.mid_block.resnets.{diffusers_resnet_idx}" + + # the hardcoded prefixes to `block_` are 1 and 2 + orig_resnet_idx = diffusers_resnet_idx + 1 + # There are two hardcoded resnets in the middle of the VQ-diffusion decoder + resnet_prefix = f"decoder.mid.block_{orig_resnet_idx}" + + diffusers_checkpoint.update( + vqvae_resnet_to_diffusers_checkpoint( + resnet, checkpoint, diffusers_resnet_prefix=diffusers_resnet_prefix, resnet_prefix=resnet_prefix + ) + ) + + diffusers_checkpoint.update( + { + # conv_norm_out + "decoder.conv_norm_out.weight": checkpoint["decoder.norm_out.weight"], + "decoder.conv_norm_out.bias": checkpoint["decoder.norm_out.bias"], + # conv_out + "decoder.conv_out.weight": checkpoint["decoder.conv_out.weight"], + "decoder.conv_out.bias": checkpoint["decoder.conv_out.bias"], + } + ) + + return diffusers_checkpoint + + +def vqvae_resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix): + rv = { + # norm1 + f"{diffusers_resnet_prefix}.norm1.weight": checkpoint[f"{resnet_prefix}.norm1.weight"], + f"{diffusers_resnet_prefix}.norm1.bias": checkpoint[f"{resnet_prefix}.norm1.bias"], + # conv1 + f"{diffusers_resnet_prefix}.conv1.weight": checkpoint[f"{resnet_prefix}.conv1.weight"], + f"{diffusers_resnet_prefix}.conv1.bias": checkpoint[f"{resnet_prefix}.conv1.bias"], + # norm2 + f"{diffusers_resnet_prefix}.norm2.weight": checkpoint[f"{resnet_prefix}.norm2.weight"], + f"{diffusers_resnet_prefix}.norm2.bias": checkpoint[f"{resnet_prefix}.norm2.bias"], + # conv2 + f"{diffusers_resnet_prefix}.conv2.weight": checkpoint[f"{resnet_prefix}.conv2.weight"], + f"{diffusers_resnet_prefix}.conv2.bias": checkpoint[f"{resnet_prefix}.conv2.bias"], + } + + if resnet.conv_shortcut is not None: + rv.update( + { + f"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[f"{resnet_prefix}.nin_shortcut.weight"], + f"{diffusers_resnet_prefix}.conv_shortcut.bias": checkpoint[f"{resnet_prefix}.nin_shortcut.bias"], + } + ) + + return rv + + +def vqvae_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): + return { + # group_norm + f"{diffusers_attention_prefix}.group_norm.weight": checkpoint[f"{attention_prefix}.norm.weight"], + f"{diffusers_attention_prefix}.group_norm.bias": checkpoint[f"{attention_prefix}.norm.bias"], + # query + f"{diffusers_attention_prefix}.query.weight": checkpoint[f"{attention_prefix}.q.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.query.bias": checkpoint[f"{attention_prefix}.q.bias"], + # key + f"{diffusers_attention_prefix}.key.weight": checkpoint[f"{attention_prefix}.k.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.key.bias": checkpoint[f"{attention_prefix}.k.bias"], + # value + f"{diffusers_attention_prefix}.value.weight": checkpoint[f"{attention_prefix}.v.weight"][:, :, 0, 0], + f"{diffusers_attention_prefix}.value.bias": checkpoint[f"{attention_prefix}.v.bias"], + # proj_attn + f"{diffusers_attention_prefix}.proj_attn.weight": checkpoint[f"{attention_prefix}.proj_out.weight"][ + :, :, 0, 0 + ], + f"{diffusers_attention_prefix}.proj_attn.bias": checkpoint[f"{attention_prefix}.proj_out.bias"], + } + + +# done vqvae checkpoint + +# transformer model + +PORTED_DIFFUSIONS = ["image_synthesis.modeling.transformers.diffusion_transformer.DiffusionTransformer"] +PORTED_TRANSFORMERS = ["image_synthesis.modeling.transformers.transformer_utils.Text2ImageTransformer"] +PORTED_CONTENT_EMBEDDINGS = ["image_synthesis.modeling.embeddings.dalle_mask_image_embedding.DalleMaskImageEmbedding"] + + +def transformer_model_from_original_config( + original_diffusion_config, original_transformer_config, original_content_embedding_config +): + assert original_diffusion_config["target"] in PORTED_DIFFUSIONS, ( + f"{original_diffusion_config['target']} has not yet been ported to diffusers." + ) + assert original_transformer_config["target"] in PORTED_TRANSFORMERS, ( + f"{original_transformer_config['target']} has not yet been ported to diffusers." + ) + assert original_content_embedding_config["target"] in PORTED_CONTENT_EMBEDDINGS, ( + f"{original_content_embedding_config['target']} has not yet been ported to diffusers." + ) + + original_diffusion_config = original_diffusion_config["params"] + original_transformer_config = original_transformer_config["params"] + original_content_embedding_config = original_content_embedding_config["params"] + + inner_dim = original_transformer_config["n_embd"] + + n_heads = original_transformer_config["n_head"] + + # VQ-Diffusion gives dimension of the multi-headed attention layers as the + # number of attention heads times the sequence length (the dimension) of a + # single head. We want to specify our attention blocks with those values + # specified separately + assert inner_dim % n_heads == 0 + d_head = inner_dim // n_heads + + depth = original_transformer_config["n_layer"] + context_dim = original_transformer_config["condition_dim"] + + num_embed = original_content_embedding_config["num_embed"] + # the number of embeddings in the transformer includes the mask embedding. + # the content embedding (the vqvae) does not include the mask embedding. + num_embed = num_embed + 1 + + height = original_transformer_config["content_spatial_size"][0] + width = original_transformer_config["content_spatial_size"][1] + + assert width == height, "width has to be equal to height" + dropout = original_transformer_config["resid_pdrop"] + num_embeds_ada_norm = original_diffusion_config["diffusion_step"] + + model_kwargs = { + "attention_bias": True, + "cross_attention_dim": context_dim, + "attention_head_dim": d_head, + "num_layers": depth, + "dropout": dropout, + "num_attention_heads": n_heads, + "num_vector_embeds": num_embed, + "num_embeds_ada_norm": num_embeds_ada_norm, + "norm_num_groups": 32, + "sample_size": width, + "activation_fn": "geglu-approximate", + } + + model = Transformer2DModel(**model_kwargs) + return model + + +# done transformer model + +# transformer checkpoint + + +def transformer_original_checkpoint_to_diffusers_checkpoint(model, checkpoint): + diffusers_checkpoint = {} + + transformer_prefix = "transformer.transformer" + + diffusers_latent_image_embedding_prefix = "latent_image_embedding" + latent_image_embedding_prefix = f"{transformer_prefix}.content_emb" + + # DalleMaskImageEmbedding + diffusers_checkpoint.update( + { + f"{diffusers_latent_image_embedding_prefix}.emb.weight": checkpoint[ + f"{latent_image_embedding_prefix}.emb.weight" + ], + f"{diffusers_latent_image_embedding_prefix}.height_emb.weight": checkpoint[ + f"{latent_image_embedding_prefix}.height_emb.weight" + ], + f"{diffusers_latent_image_embedding_prefix}.width_emb.weight": checkpoint[ + f"{latent_image_embedding_prefix}.width_emb.weight" + ], + } + ) + + # transformer blocks + for transformer_block_idx, transformer_block in enumerate(model.transformer_blocks): + diffusers_transformer_block_prefix = f"transformer_blocks.{transformer_block_idx}" + transformer_block_prefix = f"{transformer_prefix}.blocks.{transformer_block_idx}" + + # ada norm block + diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm1" + ada_norm_prefix = f"{transformer_block_prefix}.ln1" + + diffusers_checkpoint.update( + transformer_ada_norm_to_diffusers_checkpoint( + checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix + ) + ) + + # attention block + diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn1" + attention_prefix = f"{transformer_block_prefix}.attn1" + + diffusers_checkpoint.update( + transformer_attention_to_diffusers_checkpoint( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # ada norm block + diffusers_ada_norm_prefix = f"{diffusers_transformer_block_prefix}.norm2" + ada_norm_prefix = f"{transformer_block_prefix}.ln1_1" + + diffusers_checkpoint.update( + transformer_ada_norm_to_diffusers_checkpoint( + checkpoint, diffusers_ada_norm_prefix=diffusers_ada_norm_prefix, ada_norm_prefix=ada_norm_prefix + ) + ) + + # attention block + diffusers_attention_prefix = f"{diffusers_transformer_block_prefix}.attn2" + attention_prefix = f"{transformer_block_prefix}.attn2" + + diffusers_checkpoint.update( + transformer_attention_to_diffusers_checkpoint( + checkpoint, diffusers_attention_prefix=diffusers_attention_prefix, attention_prefix=attention_prefix + ) + ) + + # norm block + diffusers_norm_block_prefix = f"{diffusers_transformer_block_prefix}.norm3" + norm_block_prefix = f"{transformer_block_prefix}.ln2" + + diffusers_checkpoint.update( + { + f"{diffusers_norm_block_prefix}.weight": checkpoint[f"{norm_block_prefix}.weight"], + f"{diffusers_norm_block_prefix}.bias": checkpoint[f"{norm_block_prefix}.bias"], + } + ) + + # feedforward block + diffusers_feedforward_prefix = f"{diffusers_transformer_block_prefix}.ff" + feedforward_prefix = f"{transformer_block_prefix}.mlp" + + diffusers_checkpoint.update( + transformer_feedforward_to_diffusers_checkpoint( + checkpoint, + diffusers_feedforward_prefix=diffusers_feedforward_prefix, + feedforward_prefix=feedforward_prefix, + ) + ) + + # to logits + + diffusers_norm_out_prefix = "norm_out" + norm_out_prefix = f"{transformer_prefix}.to_logits.0" + + diffusers_checkpoint.update( + { + f"{diffusers_norm_out_prefix}.weight": checkpoint[f"{norm_out_prefix}.weight"], + f"{diffusers_norm_out_prefix}.bias": checkpoint[f"{norm_out_prefix}.bias"], + } + ) + + diffusers_out_prefix = "out" + out_prefix = f"{transformer_prefix}.to_logits.1" + + diffusers_checkpoint.update( + { + f"{diffusers_out_prefix}.weight": checkpoint[f"{out_prefix}.weight"], + f"{diffusers_out_prefix}.bias": checkpoint[f"{out_prefix}.bias"], + } + ) + + return diffusers_checkpoint + + +def transformer_ada_norm_to_diffusers_checkpoint(checkpoint, *, diffusers_ada_norm_prefix, ada_norm_prefix): + return { + f"{diffusers_ada_norm_prefix}.emb.weight": checkpoint[f"{ada_norm_prefix}.emb.weight"], + f"{diffusers_ada_norm_prefix}.linear.weight": checkpoint[f"{ada_norm_prefix}.linear.weight"], + f"{diffusers_ada_norm_prefix}.linear.bias": checkpoint[f"{ada_norm_prefix}.linear.bias"], + } + + +def transformer_attention_to_diffusers_checkpoint(checkpoint, *, diffusers_attention_prefix, attention_prefix): + return { + # key + f"{diffusers_attention_prefix}.to_k.weight": checkpoint[f"{attention_prefix}.key.weight"], + f"{diffusers_attention_prefix}.to_k.bias": checkpoint[f"{attention_prefix}.key.bias"], + # query + f"{diffusers_attention_prefix}.to_q.weight": checkpoint[f"{attention_prefix}.query.weight"], + f"{diffusers_attention_prefix}.to_q.bias": checkpoint[f"{attention_prefix}.query.bias"], + # value + f"{diffusers_attention_prefix}.to_v.weight": checkpoint[f"{attention_prefix}.value.weight"], + f"{diffusers_attention_prefix}.to_v.bias": checkpoint[f"{attention_prefix}.value.bias"], + # linear out + f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{attention_prefix}.proj.weight"], + f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{attention_prefix}.proj.bias"], + } + + +def transformer_feedforward_to_diffusers_checkpoint(checkpoint, *, diffusers_feedforward_prefix, feedforward_prefix): + return { + f"{diffusers_feedforward_prefix}.net.0.proj.weight": checkpoint[f"{feedforward_prefix}.0.weight"], + f"{diffusers_feedforward_prefix}.net.0.proj.bias": checkpoint[f"{feedforward_prefix}.0.bias"], + f"{diffusers_feedforward_prefix}.net.2.weight": checkpoint[f"{feedforward_prefix}.2.weight"], + f"{diffusers_feedforward_prefix}.net.2.bias": checkpoint[f"{feedforward_prefix}.2.bias"], + } + + +# done transformer checkpoint + + +def read_config_file(filename): + # The yaml file contains annotations that certain values should + # loaded as tuples. + with open(filename) as f: + original_config = yaml.load(f, FullLoader) + + return original_config + + +# We take separate arguments for the vqvae because the ITHQ vqvae config file +# is separate from the config file for the rest of the model. +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--vqvae_checkpoint_path", + default=None, + type=str, + required=True, + help="Path to the vqvae checkpoint to convert.", + ) + + parser.add_argument( + "--vqvae_original_config_file", + default=None, + type=str, + required=True, + help="The YAML config file corresponding to the original architecture for the vqvae.", + ) + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + + parser.add_argument( + "--original_config_file", + default=None, + type=str, + required=True, + help="The YAML config file corresponding to the original architecture.", + ) + + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + + parser.add_argument( + "--checkpoint_load_device", + default="cpu", + type=str, + required=False, + help="The device passed to `map_location` when loading checkpoints.", + ) + + # See link for how ema weights are always selected + # https://github.com/microsoft/VQ-Diffusion/blob/3c98e77f721db7c787b76304fa2c96a36c7b00af/inference_VQ_Diffusion.py#L65 + parser.add_argument( + "--no_use_ema", + action="store_true", + required=False, + help=( + "Set to not use the ema weights from the original VQ-Diffusion checkpoint. You probably do not want to set" + " it as the original VQ-Diffusion always uses the ema weights when loading models." + ), + ) + + args = parser.parse_args() + + use_ema = not args.no_use_ema + + print(f"loading checkpoints to {args.checkpoint_load_device}") + + checkpoint_map_location = torch.device(args.checkpoint_load_device) + + # vqvae_model + + print(f"loading vqvae, config: {args.vqvae_original_config_file}, checkpoint: {args.vqvae_checkpoint_path}") + + vqvae_original_config = read_config_file(args.vqvae_original_config_file).model + vqvae_checkpoint = torch.load(args.vqvae_checkpoint_path, map_location=checkpoint_map_location)["model"] + + with init_empty_weights(): + vqvae_model = vqvae_model_from_original_config(vqvae_original_config) + + vqvae_diffusers_checkpoint = vqvae_original_checkpoint_to_diffusers_checkpoint(vqvae_model, vqvae_checkpoint) + + with tempfile.NamedTemporaryFile() as vqvae_diffusers_checkpoint_file: + torch.save(vqvae_diffusers_checkpoint, vqvae_diffusers_checkpoint_file.name) + del vqvae_diffusers_checkpoint + del vqvae_checkpoint + load_checkpoint_and_dispatch(vqvae_model, vqvae_diffusers_checkpoint_file.name, device_map="auto") + + print("done loading vqvae") + + # done vqvae_model + + # transformer_model + + print( + f"loading transformer, config: {args.original_config_file}, checkpoint: {args.checkpoint_path}, use ema:" + f" {use_ema}" + ) + + original_config = read_config_file(args.original_config_file).model + + diffusion_config = original_config["params"]["diffusion_config"] + transformer_config = original_config["params"]["diffusion_config"]["params"]["transformer_config"] + content_embedding_config = original_config["params"]["diffusion_config"]["params"]["content_emb_config"] + + pre_checkpoint = torch.load(args.checkpoint_path, map_location=checkpoint_map_location) + + if use_ema: + if "ema" in pre_checkpoint: + checkpoint = {} + for k, v in pre_checkpoint["model"].items(): + checkpoint[k] = v + + for k, v in pre_checkpoint["ema"].items(): + # The ema weights are only used on the transformer. To mimic their key as if they came + # from the state_dict for the top level model, we prefix with an additional "transformer." + # See the source linked in the args.use_ema config for more information. + checkpoint[f"transformer.{k}"] = v + else: + print("attempted to load ema weights but no ema weights are specified in the loaded checkpoint.") + checkpoint = pre_checkpoint["model"] + else: + checkpoint = pre_checkpoint["model"] + + del pre_checkpoint + + with init_empty_weights(): + transformer_model = transformer_model_from_original_config( + diffusion_config, transformer_config, content_embedding_config + ) + + diffusers_transformer_checkpoint = transformer_original_checkpoint_to_diffusers_checkpoint( + transformer_model, checkpoint + ) + + # classifier free sampling embeddings interlude + + # The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate + # model, so we pull them off the checkpoint before the checkpoint is deleted. + + learnable_classifier_free_sampling_embeddings = diffusion_config["params"].learnable_cf + + if learnable_classifier_free_sampling_embeddings: + learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"] + else: + learned_classifier_free_sampling_embeddings_embeddings = None + + # done classifier free sampling embeddings interlude + + with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file: + torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name) + del diffusers_transformer_checkpoint + del checkpoint + load_checkpoint_and_dispatch(transformer_model, diffusers_transformer_checkpoint_file.name, device_map="auto") + + print("done loading transformer") + + # done transformer_model + + # text encoder + + print("loading CLIP text encoder") + + clip_name = "openai/clip-vit-base-patch32" + + # The original VQ-Diffusion specifies the pad value by the int used in the + # returned tokens. Each model uses `0` as the pad value. The transformers clip api + # specifies the pad value via the token before it has been tokenized. The `!` pad + # token is the same as padding with the `0` pad value. + pad_token = "!" + + tokenizer_model = CLIPTokenizer.from_pretrained(clip_name, pad_token=pad_token, device_map="auto") + + assert tokenizer_model.convert_tokens_to_ids(pad_token) == 0 + + text_encoder_model = CLIPTextModel.from_pretrained( + clip_name, + # `CLIPTextModel` does not support device_map="auto" + # device_map="auto" + ) + + print("done loading CLIP text encoder") + + # done text encoder + + # scheduler + + scheduler_model = VQDiffusionScheduler( + # the scheduler has the same number of embeddings as the transformer + num_vec_classes=transformer_model.num_vector_embeds + ) + + # done scheduler + + # learned classifier free sampling embeddings + + with init_empty_weights(): + learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings( + learnable_classifier_free_sampling_embeddings, + hidden_size=text_encoder_model.config.hidden_size, + length=tokenizer_model.model_max_length, + ) + + learned_classifier_free_sampling_checkpoint = { + "embeddings": learned_classifier_free_sampling_embeddings_embeddings.float() + } + + with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file: + torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name) + del learned_classifier_free_sampling_checkpoint + del learned_classifier_free_sampling_embeddings_embeddings + load_checkpoint_and_dispatch( + learned_classifier_free_sampling_embeddings_model, + learned_classifier_free_sampling_checkpoint_file.name, + device_map="auto", + ) + + # done learned classifier free sampling embeddings + + print(f"saving VQ diffusion model, path: {args.dump_path}") + + pipe = VQDiffusionPipeline( + vqvae=vqvae_model, + transformer=transformer_model, + tokenizer=tokenizer_model, + text_encoder=text_encoder_model, + learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model, + scheduler=scheduler_model, + ) + pipe.save_pretrained(args.dump_path) + + print("done writing VQ diffusion model") diff --git a/scripts/convert_wan_to_diffusers.py b/scripts/convert_wan_to_diffusers.py new file mode 100644 index 000000000000..eca1af61ae69 --- /dev/null +++ b/scripts/convert_wan_to_diffusers.py @@ -0,0 +1,1287 @@ +import argparse +import pathlib +from typing import Any, Dict, Tuple + +import torch +from accelerate import init_empty_weights +from huggingface_hub import hf_hub_download, snapshot_download +from safetensors.torch import load_file +from transformers import ( + AutoProcessor, + AutoTokenizer, + CLIPImageProcessor, + CLIPVisionModel, + CLIPVisionModelWithProjection, + UMT5EncoderModel, +) + +from diffusers import ( + AutoencoderKLWan, + UniPCMultistepScheduler, + WanAnimatePipeline, + WanAnimateTransformer3DModel, + WanImageToVideoPipeline, + WanPipeline, + WanTransformer3DModel, + WanVACEPipeline, + WanVACETransformer3DModel, +) + + +TRANSFORMER_KEYS_RENAME_DICT = { + "time_embedding.0": "condition_embedder.time_embedder.linear_1", + "time_embedding.2": "condition_embedder.time_embedder.linear_2", + "text_embedding.0": "condition_embedder.text_embedder.linear_1", + "text_embedding.2": "condition_embedder.text_embedder.linear_2", + "time_projection.1": "condition_embedder.time_proj", + "head.modulation": "scale_shift_table", + "head.head": "proj_out", + "modulation": "scale_shift_table", + "ffn.0": "ffn.net.0.proj", + "ffn.2": "ffn.net.2", + # Hack to swap the layer names + # The original model calls the norms in following order: norm1, norm3, norm2 + # We convert it to: norm1, norm2, norm3 + "norm2": "norm__placeholder", + "norm3": "norm2", + "norm__placeholder": "norm3", + # For the I2V model + "img_emb.proj.0": "condition_embedder.image_embedder.norm1", + "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", + "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", + "img_emb.proj.4": "condition_embedder.image_embedder.norm2", + # for the FLF2V model + "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", + # Add attention component mappings + "self_attn.q": "attn1.to_q", + "self_attn.k": "attn1.to_k", + "self_attn.v": "attn1.to_v", + "self_attn.o": "attn1.to_out.0", + "self_attn.norm_q": "attn1.norm_q", + "self_attn.norm_k": "attn1.norm_k", + "cross_attn.q": "attn2.to_q", + "cross_attn.k": "attn2.to_k", + "cross_attn.v": "attn2.to_v", + "cross_attn.o": "attn2.to_out.0", + "cross_attn.norm_q": "attn2.norm_q", + "cross_attn.norm_k": "attn2.norm_k", + "attn2.to_k_img": "attn2.add_k_proj", + "attn2.to_v_img": "attn2.add_v_proj", + "attn2.norm_k_img": "attn2.norm_added_k", +} + +VACE_TRANSFORMER_KEYS_RENAME_DICT = { + "time_embedding.0": "condition_embedder.time_embedder.linear_1", + "time_embedding.2": "condition_embedder.time_embedder.linear_2", + "text_embedding.0": "condition_embedder.text_embedder.linear_1", + "text_embedding.2": "condition_embedder.text_embedder.linear_2", + "time_projection.1": "condition_embedder.time_proj", + "head.modulation": "scale_shift_table", + "head.head": "proj_out", + "modulation": "scale_shift_table", + "ffn.0": "ffn.net.0.proj", + "ffn.2": "ffn.net.2", + # Hack to swap the layer names + # The original model calls the norms in following order: norm1, norm3, norm2 + # We convert it to: norm1, norm2, norm3 + "norm2": "norm__placeholder", + "norm3": "norm2", + "norm__placeholder": "norm3", + # # For the I2V model + # "img_emb.proj.0": "condition_embedder.image_embedder.norm1", + # "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", + # "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", + # "img_emb.proj.4": "condition_embedder.image_embedder.norm2", + # # for the FLF2V model + # "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed", + # Add attention component mappings + "self_attn.q": "attn1.to_q", + "self_attn.k": "attn1.to_k", + "self_attn.v": "attn1.to_v", + "self_attn.o": "attn1.to_out.0", + "self_attn.norm_q": "attn1.norm_q", + "self_attn.norm_k": "attn1.norm_k", + "cross_attn.q": "attn2.to_q", + "cross_attn.k": "attn2.to_k", + "cross_attn.v": "attn2.to_v", + "cross_attn.o": "attn2.to_out.0", + "cross_attn.norm_q": "attn2.norm_q", + "cross_attn.norm_k": "attn2.norm_k", + "attn2.to_k_img": "attn2.add_k_proj", + "attn2.to_v_img": "attn2.add_v_proj", + "attn2.norm_k_img": "attn2.norm_added_k", + "before_proj": "proj_in", + "after_proj": "proj_out", +} + +ANIMATE_TRANSFORMER_KEYS_RENAME_DICT = { + "time_embedding.0": "condition_embedder.time_embedder.linear_1", + "time_embedding.2": "condition_embedder.time_embedder.linear_2", + "text_embedding.0": "condition_embedder.text_embedder.linear_1", + "text_embedding.2": "condition_embedder.text_embedder.linear_2", + "time_projection.1": "condition_embedder.time_proj", + "head.modulation": "scale_shift_table", + "head.head": "proj_out", + "modulation": "scale_shift_table", + "ffn.0": "ffn.net.0.proj", + "ffn.2": "ffn.net.2", + # Hack to swap the layer names + # The original model calls the norms in following order: norm1, norm3, norm2 + # We convert it to: norm1, norm2, norm3 + "norm2": "norm__placeholder", + "norm3": "norm2", + "norm__placeholder": "norm3", + "img_emb.proj.0": "condition_embedder.image_embedder.norm1", + "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj", + "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2", + "img_emb.proj.4": "condition_embedder.image_embedder.norm2", + # Add attention component mappings + "self_attn.q": "attn1.to_q", + "self_attn.k": "attn1.to_k", + "self_attn.v": "attn1.to_v", + "self_attn.o": "attn1.to_out.0", + "self_attn.norm_q": "attn1.norm_q", + "self_attn.norm_k": "attn1.norm_k", + "cross_attn.q": "attn2.to_q", + "cross_attn.k": "attn2.to_k", + "cross_attn.v": "attn2.to_v", + "cross_attn.o": "attn2.to_out.0", + "cross_attn.norm_q": "attn2.norm_q", + "cross_attn.norm_k": "attn2.norm_k", + "cross_attn.k_img": "attn2.to_k_img", + "cross_attn.v_img": "attn2.to_v_img", + "cross_attn.norm_k_img": "attn2.norm_k_img", + # After cross_attn -> attn2 rename, we need to rename the img keys + "attn2.to_k_img": "attn2.add_k_proj", + "attn2.to_v_img": "attn2.add_v_proj", + "attn2.norm_k_img": "attn2.norm_added_k", + # Wan Animate-specific mappings (motion encoder, face encoder, face adapter) + # Motion encoder mappings + # The name mapping is complicated for the convolutional part so we handle that in its own function + "motion_encoder.enc.fc": "motion_encoder.motion_network", + "motion_encoder.dec.direction.weight": "motion_encoder.motion_synthesis_weight", + # Face encoder mappings - CausalConv1d has a .conv submodule that we need to flatten + "face_encoder.conv1_local.conv": "face_encoder.conv1_local", + "face_encoder.conv2.conv": "face_encoder.conv2", + "face_encoder.conv3.conv": "face_encoder.conv3", + # Face adapter mappings are handled in a separate function +} + + +# TODO: Verify this and simplify if possible. +def convert_animate_motion_encoder_weights(key: str, state_dict: Dict[str, Any], final_conv_idx: int = 8) -> None: + """ + Convert all motion encoder weights for Animate model. + + In the original model: + - All Linear layers in fc use EqualLinear + - All Conv2d layers in convs use EqualConv2d (except blur_conv which is initialized separately) + - Blur kernels are stored as buffers in Sequential modules + - ConvLayer is nn.Sequential with indices: [Blur (optional), EqualConv2d, FusedLeakyReLU (optional)] + + Conversion strategy: + 1. Drop .kernel buffers (blur kernels) + 2. Rename sequential indices to named components (e.g., 0 -> conv2d, 1 -> bias_leaky_relu) + """ + # Skip if not a weight, bias, or kernel + if ".weight" not in key and ".bias" not in key and ".kernel" not in key: + return + + # Handle Blur kernel buffers from original implementation. + # After renaming, these appear under: motion_encoder.res_blocks.*.conv{2,skip}.blur_kernel + # Diffusers constructs blur kernels as a non-persistent buffer so we must drop these keys + if ".kernel" in key and "motion_encoder" in key: + # Remove unexpected blur kernel buffers to avoid strict load errors + state_dict.pop(key, None) + return + + # Rename Sequential indices to named components in ConvLayer and ResBlock + if ".enc.net_app.convs." in key and (".weight" in key or ".bias" in key): + parts = key.split(".") + + # Find the sequential index (digit) after convs or after conv1/conv2/skip + # Examples: + # - enc.net_app.convs.0.0.weight -> conv_in.weight (initial conv layer weight) + # - enc.net_app.convs.0.1.bias -> conv_in.act_fn.bias (initial conv layer bias) + # - enc.net_app.convs.{n:1-7}.conv1.0.weight -> res_blocks.{(n-1):0-6}.conv1.weight (conv1 weight) + # - e.g. enc.net_app.convs.1.conv1.0.weight -> res_blocks.0.conv1.weight + # - enc.net_app.convs.{n:1-7}.conv1.1.bias -> res_blocks.{(n-1):0-6}.conv1.act_fn.bias (conv1 bias) + # - e.g. enc.net_app.convs.1.conv1.1.bias -> res_blocks.0.conv1.act_fn.bias + # - enc.net_app.convs.{n:1-7}.conv2.1.weight -> res_blocks.{(n-1):0-6}.conv2.weight (conv2 weight) + # - enc.net_app.convs.1.conv2.2.bias -> res_blocks.0.conv2.act_fn.bias (conv2 bias) + # - enc.net_app.convs.{n:1-7}.skip.1.weight -> res_blocks.{(n-1):0-6}.conv_skip.weight (skip conv weight) + # - enc.net_app.convs.8 -> conv_out (final conv layer) + + convs_idx = parts.index("convs") if "convs" in parts else -1 + if convs_idx >= 0 and len(parts) - convs_idx >= 2: + bias = False + # The nn.Sequential index will always follow convs + sequential_idx = int(parts[convs_idx + 1]) + if sequential_idx == 0: + if key.endswith(".weight"): + new_key = "motion_encoder.conv_in.weight" + elif key.endswith(".bias"): + new_key = "motion_encoder.conv_in.act_fn.bias" + bias = True + elif sequential_idx == final_conv_idx: + if key.endswith(".weight"): + new_key = "motion_encoder.conv_out.weight" + else: + # Intermediate .convs. layers, which get mapped to .res_blocks. + prefix = "motion_encoder.res_blocks." + + layer_name = parts[convs_idx + 2] + if layer_name == "skip": + layer_name = "conv_skip" + + if key.endswith(".weight"): + param_name = "weight" + elif key.endswith(".bias"): + param_name = "act_fn.bias" + bias = True + + suffix_parts = [str(sequential_idx - 1), layer_name, param_name] + suffix = ".".join(suffix_parts) + new_key = prefix + suffix + + param = state_dict.pop(key) + if bias: + param = param.squeeze() + state_dict[new_key] = param + return + return + return + + +def convert_animate_face_adapter_weights(key: str, state_dict: Dict[str, Any]) -> None: + """ + Convert face adapter weights for the Animate model. + + The original model uses a fused KV projection but the diffusers models uses separate K and V projections. + """ + # Skip if not a weight or bias + if ".weight" not in key and ".bias" not in key: + return + + prefix = "face_adapter." + if ".fuser_blocks." in key: + parts = key.split(".") + + module_list_idx = parts.index("fuser_blocks") if "fuser_blocks" in parts else -1 + if module_list_idx >= 0 and (len(parts) - 1) - module_list_idx == 3: + block_idx = parts[module_list_idx + 1] + layer_name = parts[module_list_idx + 2] + param_name = parts[module_list_idx + 3] + + if layer_name == "linear1_kv": + layer_name_k = "to_k" + layer_name_v = "to_v" + + suffix_k = ".".join([block_idx, layer_name_k, param_name]) + suffix_v = ".".join([block_idx, layer_name_v, param_name]) + new_key_k = prefix + suffix_k + new_key_v = prefix + suffix_v + + kv_proj = state_dict.pop(key) + k_proj, v_proj = torch.chunk(kv_proj, 2, dim=0) + state_dict[new_key_k] = k_proj + state_dict[new_key_v] = v_proj + return + else: + if layer_name == "q_norm": + new_layer_name = "norm_q" + elif layer_name == "k_norm": + new_layer_name = "norm_k" + elif layer_name == "linear1_q": + new_layer_name = "to_q" + elif layer_name == "linear2": + new_layer_name = "to_out" + + suffix_parts = [block_idx, new_layer_name, param_name] + suffix = ".".join(suffix_parts) + new_key = prefix + suffix + state_dict[new_key] = state_dict.pop(key) + return + return + + +TRANSFORMER_SPECIAL_KEYS_REMAP = {} +VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {} +ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP = { + "motion_encoder": convert_animate_motion_encoder_weights, + "face_adapter": convert_animate_face_adapter_weights, +} + + +def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def load_sharded_safetensors(dir: pathlib.Path): + file_paths = list(dir.glob("diffusion_pytorch_model*.safetensors")) + state_dict = {} + for path in file_paths: + state_dict.update(load_file(path)) + return state_dict + + +def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]: + if model_type == "Wan-T2V-1.3B": + config = { + "model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 12, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-T2V-14B": + config = { + "model_id": "StevenZhang/Wan2.1-T2V-14B-Diff", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-I2V-14B-480p": + config = { + "model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff", + "diffusers_config": { + "image_dim": 1280, + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-I2V-14B-720p": + config = { + "model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff", + "diffusers_config": { + "image_dim": 1280, + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-FLF2V-14B-720P": + config = { + "model_id": "ypyp/Wan2.1-FLF2V-14B-720P", # This is just a placeholder + "diffusers_config": { + "image_dim": 1280, + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "rope_max_seq_len": 1024, + "pos_embed_seq_len": 257 * 2, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-VACE-1.3B": + config = { + "model_id": "Wan-AI/Wan2.1-VACE-1.3B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 8960, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 12, + "num_layers": 30, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28], + "vace_in_channels": 96, + }, + } + RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan-VACE-14B": + config = { + "model_id": "Wan-AI/Wan2.1-VACE-14B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35], + "vace_in_channels": 96, + }, + } + RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan2.2-VACE-Fun-14B": + config = { + "model_id": "alibaba-pai/Wan2.2-VACE-Fun-A14B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35], + "vace_in_channels": 96, + }, + } + RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan2.2-I2V-14B-720p": + config = { + "model_id": "Wan-AI/Wan2.2-I2V-A14B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan2.2-T2V-A14B": + config = { + "model_id": "Wan-AI/Wan2.2-T2V-A14B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 16, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan2.2-TI2V-5B": + config = { + "model_id": "Wan-AI/Wan2.2-TI2V-5B", + "diffusers_config": { + "added_kv_proj_dim": None, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 14336, + "freq_dim": 256, + "in_channels": 48, + "num_attention_heads": 24, + "num_layers": 30, + "out_channels": 48, + "patch_size": [1, 2, 2], + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + }, + } + RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP + elif model_type == "Wan2.2-Animate-14B": + config = { + "model_id": "Wan-AI/Wan2.2-Animate-14B", + "diffusers_config": { + "image_dim": 1280, + "added_kv_proj_dim": 5120, + "attention_head_dim": 128, + "cross_attn_norm": True, + "eps": 1e-06, + "ffn_dim": 13824, + "freq_dim": 256, + "in_channels": 36, + "num_attention_heads": 40, + "num_layers": 40, + "out_channels": 16, + "patch_size": (1, 2, 2), + "qk_norm": "rms_norm_across_heads", + "text_dim": 4096, + "rope_max_seq_len": 1024, + "pos_embed_seq_len": None, + "motion_encoder_size": 512, # Start of Wan Animate-specific configs + "motion_style_dim": 512, + "motion_dim": 20, + "motion_encoder_dim": 512, + "face_encoder_hidden_dim": 1024, + "face_encoder_num_heads": 4, + "inject_face_latents_blocks": 5, + }, + } + RENAME_DICT = ANIMATE_TRANSFORMER_KEYS_RENAME_DICT + SPECIAL_KEYS_REMAP = ANIMATE_TRANSFORMER_SPECIAL_KEYS_REMAP + return config, RENAME_DICT, SPECIAL_KEYS_REMAP + + +def convert_transformer(model_type: str, stage: str = None): + config, RENAME_DICT, SPECIAL_KEYS_REMAP = get_transformer_config(model_type) + + diffusers_config = config["diffusers_config"] + model_id = config["model_id"] + model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model")) + + if stage is not None: + model_dir = model_dir / stage + + original_state_dict = load_sharded_safetensors(model_dir) + + with init_empty_weights(): + if "Animate" in model_type: + transformer = WanAnimateTransformer3DModel.from_config(diffusers_config) + elif "VACE" in model_type: + transformer = WanVACETransformer3DModel.from_config(diffusers_config) + else: + transformer = WanTransformer3DModel.from_config(diffusers_config) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + # Load state dict into the meta model, which will materialize the tensors + transformer.load_state_dict(original_state_dict, strict=True, assign=True) + + # Move to CPU to ensure all tensors are materialized + transformer = transformer.to("cpu") + + return transformer + + +def convert_vae(): + vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.1-T2V-14B", "Wan2.1_VAE.pth") + old_state_dict = torch.load(vae_ckpt_path, weights_only=True) + new_state_dict = {} + + # Create mappings for specific components + middle_key_mapping = { + # Encoder middle block + "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", + "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", + "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", + "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", + "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", + "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", + "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", + "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", + "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", + "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", + "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", + "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", + # Decoder middle block + "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", + "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", + "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", + "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", + "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", + "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", + "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", + "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", + "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", + "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", + "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", + "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", + } + + # Create a mapping for attention blocks + attention_mapping = { + # Encoder middle attention + "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", + "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", + "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", + "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", + "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", + # Decoder middle attention + "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", + "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", + "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", + "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", + "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", + } + + # Create a mapping for the head components + head_mapping = { + # Encoder head + "encoder.head.0.gamma": "encoder.norm_out.gamma", + "encoder.head.2.bias": "encoder.conv_out.bias", + "encoder.head.2.weight": "encoder.conv_out.weight", + # Decoder head + "decoder.head.0.gamma": "decoder.norm_out.gamma", + "decoder.head.2.bias": "decoder.conv_out.bias", + "decoder.head.2.weight": "decoder.conv_out.weight", + } + + # Create a mapping for the quant components + quant_mapping = { + "conv1.weight": "quant_conv.weight", + "conv1.bias": "quant_conv.bias", + "conv2.weight": "post_quant_conv.weight", + "conv2.bias": "post_quant_conv.bias", + } + + # Process each key in the state dict + for key, value in old_state_dict.items(): + # Handle middle block keys using the mapping + if key in middle_key_mapping: + new_key = middle_key_mapping[key] + new_state_dict[new_key] = value + # Handle attention blocks using the mapping + elif key in attention_mapping: + new_key = attention_mapping[key] + new_state_dict[new_key] = value + # Handle head keys using the mapping + elif key in head_mapping: + new_key = head_mapping[key] + new_state_dict[new_key] = value + # Handle quant keys using the mapping + elif key in quant_mapping: + new_key = quant_mapping[key] + new_state_dict[new_key] = value + # Handle encoder conv1 + elif key == "encoder.conv1.weight": + new_state_dict["encoder.conv_in.weight"] = value + elif key == "encoder.conv1.bias": + new_state_dict["encoder.conv_in.bias"] = value + # Handle decoder conv1 + elif key == "decoder.conv1.weight": + new_state_dict["decoder.conv_in.weight"] = value + elif key == "decoder.conv1.bias": + new_state_dict["decoder.conv_in.bias"] = value + # Handle encoder downsamples + elif key.startswith("encoder.downsamples."): + # Convert to down_blocks + new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") + + # Convert residual block naming but keep the original structure + if ".residual.0.gamma" in new_key: + new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") + elif ".residual.2.bias" in new_key: + new_key = new_key.replace(".residual.2.bias", ".conv1.bias") + elif ".residual.2.weight" in new_key: + new_key = new_key.replace(".residual.2.weight", ".conv1.weight") + elif ".residual.3.gamma" in new_key: + new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") + elif ".residual.6.bias" in new_key: + new_key = new_key.replace(".residual.6.bias", ".conv2.bias") + elif ".residual.6.weight" in new_key: + new_key = new_key.replace(".residual.6.weight", ".conv2.weight") + elif ".shortcut.bias" in new_key: + new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") + elif ".shortcut.weight" in new_key: + new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") + + new_state_dict[new_key] = value + + # Handle decoder upsamples + elif key.startswith("decoder.upsamples."): + # Convert to up_blocks + parts = key.split(".") + block_idx = int(parts[2]) + + # Group residual blocks + if "residual" in key: + if block_idx in [0, 1, 2]: + new_block_idx = 0 + resnet_idx = block_idx + elif block_idx in [4, 5, 6]: + new_block_idx = 1 + resnet_idx = block_idx - 4 + elif block_idx in [8, 9, 10]: + new_block_idx = 2 + resnet_idx = block_idx - 8 + elif block_idx in [12, 13, 14]: + new_block_idx = 3 + resnet_idx = block_idx - 12 + else: + # Keep as is for other blocks + new_state_dict[key] = value + continue + + # Convert residual block naming + if ".residual.0.gamma" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma" + elif ".residual.2.bias" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias" + elif ".residual.2.weight" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight" + elif ".residual.3.gamma" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma" + elif ".residual.6.bias" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias" + elif ".residual.6.weight" in key: + new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight" + else: + new_key = key + + new_state_dict[new_key] = value + + # Handle shortcut connections + elif ".shortcut." in key: + if block_idx == 4: + new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.") + new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1") + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + new_key = new_key.replace(".shortcut.", ".conv_shortcut.") + + new_state_dict[new_key] = value + + # Handle upsamplers + elif ".resample." in key or ".time_conv." in key: + if block_idx == 3: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0") + elif block_idx == 7: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0") + elif block_idx == 11: + new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0") + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + + new_state_dict[new_key] = value + else: + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + new_state_dict[new_key] = value + else: + # Keep other keys unchanged + new_state_dict[key] = value + + with init_empty_weights(): + vae = AutoencoderKLWan() + vae.load_state_dict(new_state_dict, strict=True, assign=True) + return vae + + +vae22_diffusers_config = { + "base_dim": 160, + "z_dim": 48, + "is_residual": True, + "in_channels": 12, + "out_channels": 12, + "decoder_base_dim": 256, + "scale_factor_temporal": 4, + "scale_factor_spatial": 16, + "patch_size": 2, + "latents_mean": [ + -0.2289, + -0.0052, + -0.1323, + -0.2339, + -0.2799, + 0.0174, + 0.1838, + 0.1557, + -0.1382, + 0.0542, + 0.2813, + 0.0891, + 0.1570, + -0.0098, + 0.0375, + -0.1825, + -0.2246, + -0.1207, + -0.0698, + 0.5109, + 0.2665, + -0.2108, + -0.2158, + 0.2502, + -0.2055, + -0.0322, + 0.1109, + 0.1567, + -0.0729, + 0.0899, + -0.2799, + -0.1230, + -0.0313, + -0.1649, + 0.0117, + 0.0723, + -0.2839, + -0.2083, + -0.0520, + 0.3748, + 0.0152, + 0.1957, + 0.1433, + -0.2944, + 0.3573, + -0.0548, + -0.1681, + -0.0667, + ], + "latents_std": [ + 0.4765, + 1.0364, + 0.4514, + 1.1677, + 0.5313, + 0.4990, + 0.4818, + 0.5013, + 0.8158, + 1.0344, + 0.5894, + 1.0901, + 0.6885, + 0.6165, + 0.8454, + 0.4978, + 0.5759, + 0.3523, + 0.7135, + 0.6804, + 0.5833, + 1.4146, + 0.8986, + 0.5659, + 0.7069, + 0.5338, + 0.4889, + 0.4917, + 0.4069, + 0.4999, + 0.6866, + 0.4093, + 0.5709, + 0.6065, + 0.6415, + 0.4944, + 0.5726, + 1.2042, + 0.5458, + 1.6887, + 0.3971, + 1.0600, + 0.3943, + 0.5537, + 0.5444, + 0.4089, + 0.7468, + 0.7744, + ], + "clip_output": False, +} + + +def convert_vae_22(): + vae_ckpt_path = hf_hub_download("Wan-AI/Wan2.2-TI2V-5B", "Wan2.2_VAE.pth") + old_state_dict = torch.load(vae_ckpt_path, weights_only=True) + new_state_dict = {} + + # Create mappings for specific components + middle_key_mapping = { + # Encoder middle block + "encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma", + "encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias", + "encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight", + "encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma", + "encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias", + "encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight", + "encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma", + "encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias", + "encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight", + "encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma", + "encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias", + "encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight", + # Decoder middle block + "decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma", + "decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias", + "decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight", + "decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma", + "decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias", + "decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight", + "decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma", + "decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias", + "decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight", + "decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma", + "decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias", + "decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight", + } + + # Create a mapping for attention blocks + attention_mapping = { + # Encoder middle attention + "encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma", + "encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight", + "encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias", + "encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight", + "encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias", + # Decoder middle attention + "decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma", + "decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight", + "decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias", + "decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight", + "decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias", + } + + # Create a mapping for the head components + head_mapping = { + # Encoder head + "encoder.head.0.gamma": "encoder.norm_out.gamma", + "encoder.head.2.bias": "encoder.conv_out.bias", + "encoder.head.2.weight": "encoder.conv_out.weight", + # Decoder head + "decoder.head.0.gamma": "decoder.norm_out.gamma", + "decoder.head.2.bias": "decoder.conv_out.bias", + "decoder.head.2.weight": "decoder.conv_out.weight", + } + + # Create a mapping for the quant components + quant_mapping = { + "conv1.weight": "quant_conv.weight", + "conv1.bias": "quant_conv.bias", + "conv2.weight": "post_quant_conv.weight", + "conv2.bias": "post_quant_conv.bias", + } + + # Process each key in the state dict + for key, value in old_state_dict.items(): + # Handle middle block keys using the mapping + if key in middle_key_mapping: + new_key = middle_key_mapping[key] + new_state_dict[new_key] = value + # Handle attention blocks using the mapping + elif key in attention_mapping: + new_key = attention_mapping[key] + new_state_dict[new_key] = value + # Handle head keys using the mapping + elif key in head_mapping: + new_key = head_mapping[key] + new_state_dict[new_key] = value + # Handle quant keys using the mapping + elif key in quant_mapping: + new_key = quant_mapping[key] + new_state_dict[new_key] = value + # Handle encoder conv1 + elif key == "encoder.conv1.weight": + new_state_dict["encoder.conv_in.weight"] = value + elif key == "encoder.conv1.bias": + new_state_dict["encoder.conv_in.bias"] = value + # Handle decoder conv1 + elif key == "decoder.conv1.weight": + new_state_dict["decoder.conv_in.weight"] = value + elif key == "decoder.conv1.bias": + new_state_dict["decoder.conv_in.bias"] = value + # Handle encoder downsamples + elif key.startswith("encoder.downsamples."): + # Change encoder.downsamples to encoder.down_blocks + new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.") + + # Handle residual blocks - change downsamples to resnets and rename components + if "residual" in new_key or "shortcut" in new_key: + # Change the second downsamples to resnets + new_key = new_key.replace(".downsamples.", ".resnets.") + + # Rename residual components + if ".residual.0.gamma" in new_key: + new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") + elif ".residual.2.weight" in new_key: + new_key = new_key.replace(".residual.2.weight", ".conv1.weight") + elif ".residual.2.bias" in new_key: + new_key = new_key.replace(".residual.2.bias", ".conv1.bias") + elif ".residual.3.gamma" in new_key: + new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") + elif ".residual.6.weight" in new_key: + new_key = new_key.replace(".residual.6.weight", ".conv2.weight") + elif ".residual.6.bias" in new_key: + new_key = new_key.replace(".residual.6.bias", ".conv2.bias") + elif ".shortcut.weight" in new_key: + new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") + elif ".shortcut.bias" in new_key: + new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") + + # Handle resample blocks - change downsamples to downsampler and remove index + elif "resample" in new_key or "time_conv" in new_key: + # Change the second downsamples to downsampler and remove the index + parts = new_key.split(".") + # Find the pattern: encoder.down_blocks.X.downsamples.Y.resample... + # We want to change it to: encoder.down_blocks.X.downsampler.resample... + if len(parts) >= 4 and parts[3] == "downsamples": + # Remove the index (parts[4]) and change downsamples to downsampler + new_parts = parts[:3] + ["downsampler"] + parts[5:] + new_key = ".".join(new_parts) + + new_state_dict[new_key] = value + + # Handle decoder upsamples + elif key.startswith("decoder.upsamples."): + # Change decoder.upsamples to decoder.up_blocks + new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.") + + # Handle residual blocks - change upsamples to resnets and rename components + if "residual" in new_key or "shortcut" in new_key: + # Change the second upsamples to resnets + new_key = new_key.replace(".upsamples.", ".resnets.") + + # Rename residual components + if ".residual.0.gamma" in new_key: + new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma") + elif ".residual.2.weight" in new_key: + new_key = new_key.replace(".residual.2.weight", ".conv1.weight") + elif ".residual.2.bias" in new_key: + new_key = new_key.replace(".residual.2.bias", ".conv1.bias") + elif ".residual.3.gamma" in new_key: + new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma") + elif ".residual.6.weight" in new_key: + new_key = new_key.replace(".residual.6.weight", ".conv2.weight") + elif ".residual.6.bias" in new_key: + new_key = new_key.replace(".residual.6.bias", ".conv2.bias") + elif ".shortcut.weight" in new_key: + new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight") + elif ".shortcut.bias" in new_key: + new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias") + + # Handle resample blocks - change upsamples to upsampler and remove index + elif "resample" in new_key or "time_conv" in new_key: + # Change the second upsamples to upsampler and remove the index + parts = new_key.split(".") + # Find the pattern: encoder.down_blocks.X.downsamples.Y.resample... + # We want to change it to: encoder.down_blocks.X.downsampler.resample... + if len(parts) >= 4 and parts[3] == "upsamples": + # Remove the index (parts[4]) and change upsamples to upsampler + new_parts = parts[:3] + ["upsampler"] + parts[5:] + new_key = ".".join(new_parts) + + new_state_dict[new_key] = value + else: + # Keep other keys unchanged + new_state_dict[key] = value + + with init_empty_weights(): + vae = AutoencoderKLWan(**vae22_diffusers_config) + vae.load_state_dict(new_state_dict, strict=True, assign=True) + return vae + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--model_type", type=str, default=None) + parser.add_argument("--output_path", type=str, required=True) + parser.add_argument("--dtype", default="fp32", choices=["fp32", "fp16", "bf16", "none"]) + return parser.parse_args() + + +DTYPE_MAPPING = { + "fp32": torch.float32, + "fp16": torch.float16, + "bf16": torch.bfloat16, +} + + +if __name__ == "__main__": + args = get_args() + + if "Wan2.2" in args.model_type and "TI2V" not in args.model_type and "Animate" not in args.model_type: + transformer = convert_transformer(args.model_type, stage="high_noise_model") + transformer_2 = convert_transformer(args.model_type, stage="low_noise_model") + else: + transformer = convert_transformer(args.model_type) + transformer_2 = None + + if "Wan2.2" in args.model_type and "TI2V" in args.model_type: + vae = convert_vae_22() + else: + vae = convert_vae() + + text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16) + tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") + if "FLF2V" in args.model_type: + flow_shift = 16.0 + elif "TI2V" in args.model_type or "Animate" in args.model_type: + flow_shift = 5.0 + else: + flow_shift = 3.0 + scheduler = UniPCMultistepScheduler( + prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift + ) + + # If user has specified "none", we keep the original dtypes of the state dict without any conversion + if args.dtype != "none": + dtype = DTYPE_MAPPING[args.dtype] + transformer.to(dtype) + if transformer_2 is not None: + transformer_2.to(dtype) + + if "Wan2.2" and "I2V" in args.model_type and "TI2V" not in args.model_type: + pipe = WanImageToVideoPipeline( + transformer=transformer, + transformer_2=transformer_2, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + boundary_ratio=0.9, + ) + elif "Wan2.2" and "T2V" in args.model_type: + pipe = WanPipeline( + transformer=transformer, + transformer_2=transformer_2, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + boundary_ratio=0.875, + ) + elif "Wan2.2" and "TI2V" in args.model_type: + pipe = WanPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + expand_timesteps=True, + ) + elif "I2V" in args.model_type or "FLF2V" in args.model_type: + image_encoder = CLIPVisionModelWithProjection.from_pretrained( + "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16 + ) + image_processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + pipe = WanImageToVideoPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + image_encoder=image_encoder, + image_processor=image_processor, + ) + elif "Wan2.2-VACE" in args.model_type: + pipe = WanVACEPipeline( + transformer=transformer, + transformer_2=transformer_2, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + boundary_ratio=0.875, + ) + elif "Wan-VACE" in args.model_type: + pipe = WanVACEPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + ) + elif "Animate" in args.model_type: + image_encoder = CLIPVisionModel.from_pretrained( + "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16 + ) + image_processor = CLIPImageProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + + pipe = WanAnimatePipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + image_encoder=image_encoder, + image_processor=image_processor, + ) + else: + pipe = WanPipeline( + transformer=transformer, + text_encoder=text_encoder, + tokenizer=tokenizer, + vae=vae, + scheduler=scheduler, + ) + + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") diff --git a/scripts/convert_wuerstchen.py b/scripts/convert_wuerstchen.py new file mode 100644 index 000000000000..826b9b208181 --- /dev/null +++ b/scripts/convert_wuerstchen.py @@ -0,0 +1,115 @@ +# Run inside root directory of official source code: https://github.com/dome272/wuerstchen/ +import os + +import torch +from transformers import AutoTokenizer, CLIPTextModel +from vqgan import VQModel + +from diffusers import ( + DDPMWuerstchenScheduler, + WuerstchenCombinedPipeline, + WuerstchenDecoderPipeline, + WuerstchenPriorPipeline, +) +from diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior + + +model_path = "models/" +device = "cpu" + +paella_vqmodel = VQModel() +state_dict = torch.load(os.path.join(model_path, "vqgan_f4_v1_500k.pt"), map_location=device)["state_dict"] +paella_vqmodel.load_state_dict(state_dict) + +state_dict["vquantizer.embedding.weight"] = state_dict["vquantizer.codebook.weight"] +state_dict.pop("vquantizer.codebook.weight") +vqmodel = PaellaVQModel(num_vq_embeddings=paella_vqmodel.codebook_size, latent_channels=paella_vqmodel.c_latent) +vqmodel.load_state_dict(state_dict) + +# Clip Text encoder and tokenizer +text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") +tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k") + +# Generator +gen_text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K").to("cpu") +gen_tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K") + +orig_state_dict = torch.load(os.path.join(model_path, "model_v2_stage_b.pt"), map_location=device)["state_dict"] +state_dict = {} +for key in orig_state_dict.keys(): + if key.endswith("in_proj_weight"): + weights = orig_state_dict[key].chunk(3, 0) + state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0] + state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1] + state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2] + elif key.endswith("in_proj_bias"): + weights = orig_state_dict[key].chunk(3, 0) + state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0] + state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1] + state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2] + elif key.endswith("out_proj.weight"): + weights = orig_state_dict[key] + state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights + elif key.endswith("out_proj.bias"): + weights = orig_state_dict[key] + state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights + else: + state_dict[key] = orig_state_dict[key] +decoder = WuerstchenDiffNeXt() +decoder.load_state_dict(state_dict) + +# Prior +orig_state_dict = torch.load(os.path.join(model_path, "model_v3_stage_c.pt"), map_location=device)["ema_state_dict"] +state_dict = {} +for key in orig_state_dict.keys(): + if key.endswith("in_proj_weight"): + weights = orig_state_dict[key].chunk(3, 0) + state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0] + state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1] + state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2] + elif key.endswith("in_proj_bias"): + weights = orig_state_dict[key].chunk(3, 0) + state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0] + state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1] + state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2] + elif key.endswith("out_proj.weight"): + weights = orig_state_dict[key] + state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights + elif key.endswith("out_proj.bias"): + weights = orig_state_dict[key] + state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights + else: + state_dict[key] = orig_state_dict[key] +prior_model = WuerstchenPrior(c_in=16, c=1536, c_cond=1280, c_r=64, depth=32, nhead=24).to(device) +prior_model.load_state_dict(state_dict) + +# scheduler +scheduler = DDPMWuerstchenScheduler() + +# Prior pipeline +prior_pipeline = WuerstchenPriorPipeline( + prior=prior_model, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler +) + +prior_pipeline.save_pretrained("warp-ai/wuerstchen-prior") + +decoder_pipeline = WuerstchenDecoderPipeline( + text_encoder=gen_text_encoder, tokenizer=gen_tokenizer, vqgan=vqmodel, decoder=decoder, scheduler=scheduler +) +decoder_pipeline.save_pretrained("warp-ai/wuerstchen") + +# Wuerstchen pipeline +wuerstchen_pipeline = WuerstchenCombinedPipeline( + # Decoder + text_encoder=gen_text_encoder, + tokenizer=gen_tokenizer, + decoder=decoder, + scheduler=scheduler, + vqgan=vqmodel, + # Prior + prior_tokenizer=tokenizer, + prior_text_encoder=text_encoder, + prior=prior_model, + prior_scheduler=scheduler, +) +wuerstchen_pipeline.save_pretrained("warp-ai/WuerstchenCombinedPipeline") diff --git a/scripts/convert_zero123_to_diffusers.py b/scripts/convert_zero123_to_diffusers.py new file mode 100644 index 000000000000..b46633fae7ff --- /dev/null +++ b/scripts/convert_zero123_to_diffusers.py @@ -0,0 +1,807 @@ +""" +This script modified from +https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py + +Convert original Zero1to3 checkpoint to diffusers checkpoint. + +# run the convert script +$ python convert_zero123_to_diffusers.py \ + --checkpoint_path /path/zero123/105000.ckpt \ + --dump_path ./zero1to3 \ + --original_config_file /path/zero123/configs/sd-objaverse-finetune-c_concat-256.yaml +``` +""" + +import argparse + +import torch +import yaml +from accelerate import init_empty_weights +from accelerate.utils import set_module_tensor_to_device +from pipeline_zero1to3 import CCProjection, Zero1to3StableDiffusionPipeline +from transformers import ( + CLIPImageProcessor, + CLIPVisionModelWithProjection, +) + +from diffusers.models import ( + AutoencoderKL, + UNet2DConditionModel, +) +from diffusers.schedulers import DDIMScheduler +from diffusers.utils import logging + + +logger = logging.get_logger(__name__) + + +def create_unet_diffusers_config(original_config, image_size: int, controlnet=False): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + if controlnet: + unet_params = original_config["model"]["params"]["control_stage_config"]["params"] + else: + if ( + "unet_config" in original_config["model"]["params"] + and original_config["model"]["params"]["unet_config"] is not None + ): + unet_params = original_config["model"]["params"]["unet_config"]["params"] + else: + unet_params = original_config["model"]["params"]["network_config"]["params"] + + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + + block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]] + + down_block_types = [] + resolution = 1 + for i in range(len(block_out_channels)): + block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D" + down_block_types.append(block_type) + if i != len(block_out_channels) - 1: + resolution *= 2 + + up_block_types = [] + for i in range(len(block_out_channels)): + block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D" + up_block_types.append(block_type) + resolution //= 2 + + if unet_params["transformer_depth"] is not None: + transformer_layers_per_block = ( + unet_params["transformer_depth"] + if isinstance(unet_params["transformer_depth"], int) + else list(unet_params["transformer_depth"]) + ) + else: + transformer_layers_per_block = 1 + + vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1) + + head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None + use_linear_projection = ( + unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False + ) + if use_linear_projection: + # stable diffusion 2-base-512 and 2-768 + if head_dim is None: + head_dim_mult = unet_params["model_channels"] // unet_params["num_head_channels"] + head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])] + + class_embed_type = None + addition_embed_type = None + addition_time_embed_dim = None + projection_class_embeddings_input_dim = None + context_dim = None + + if unet_params["context_dim"] is not None: + context_dim = ( + unet_params["context_dim"] + if isinstance(unet_params["context_dim"], int) + else unet_params["context_dim"][0] + ) + + if "num_classes" in unet_params: + if unet_params["num_classes"] == "sequential": + if context_dim in [2048, 1280]: + # SDXL + addition_embed_type = "text_time" + addition_time_embed_dim = 256 + else: + class_embed_type = "projection" + assert "adm_in_channels" in unet_params + projection_class_embeddings_input_dim = unet_params["adm_in_channels"] + else: + raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params['num_classes']}") + + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params["in_channels"], + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params["num_res_blocks"], + "cross_attention_dim": context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "addition_embed_type": addition_embed_type, + "addition_time_embed_dim": addition_time_embed_dim, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "transformer_layers_per_block": transformer_layers_per_block, + } + + if controlnet: + config["conditioning_channels"] = unet_params["hint_channels"] + else: + config["out_channels"] = unet_params["out_channels"] + config["up_block_types"] = tuple(up_block_types) + + return config + + +def assign_to_checkpoint( + paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None +): + """ + This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits + attention layers, and takes into account additional replacements that may arise. + + Assigns the weights to the new checkpoint. + """ + assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys." + + # Splits the attention layers into three variables. + if attention_paths_to_split is not None: + for path, path_map in attention_paths_to_split.items(): + old_tensor = old_checkpoint[path] + channels = old_tensor.shape[0] // 3 + + target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1) + + num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3 + + old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]) + query, key, value = old_tensor.split(channels // num_heads, dim=1) + + checkpoint[path_map["query"]] = query.reshape(target_shape) + checkpoint[path_map["key"]] = key.reshape(target_shape) + checkpoint[path_map["value"]] = value.reshape(target_shape) + + for path in paths: + new_path = path["new"] + + # These have already been assigned + if attention_paths_to_split is not None and new_path in attention_paths_to_split: + continue + + # Global renaming happens here + new_path = new_path.replace("middle_block.0", "mid_block.resnets.0") + new_path = new_path.replace("middle_block.1", "mid_block.attentions.0") + new_path = new_path.replace("middle_block.2", "mid_block.resnets.1") + + if additional_replacements is not None: + for replacement in additional_replacements: + new_path = new_path.replace(replacement["old"], replacement["new"]) + + # proj_attn.weight has to be converted from conv 1D to linear + is_attn_weight = "proj_attn.weight" in new_path or ("attentions" in new_path and "to_" in new_path) + shape = old_checkpoint[path["old"]].shape + if is_attn_weight and len(shape) == 3: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0] + elif is_attn_weight and len(shape) == 4: + checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0, 0] + else: + checkpoint[new_path] = old_checkpoint[path["old"]] + + +def shave_segments(path, n_shave_prefix_segments=1): + """ + Removes segments. Positive values shave the first segments, negative shave the last segments. + """ + if n_shave_prefix_segments >= 0: + return ".".join(path.split(".")[n_shave_prefix_segments:]) + else: + return ".".join(path.split(".")[:n_shave_prefix_segments]) + + +def renew_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item.replace("in_layers.0", "norm1") + new_item = new_item.replace("in_layers.2", "conv1") + + new_item = new_item.replace("out_layers.0", "norm2") + new_item = new_item.replace("out_layers.3", "conv2") + + new_item = new_item.replace("emb_layers.1", "time_emb_proj") + new_item = new_item.replace("skip_connection", "conv_shortcut") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + # new_item = new_item.replace('norm.weight', 'group_norm.weight') + # new_item = new_item.replace('norm.bias', 'group_norm.bias') + + # new_item = new_item.replace('proj_out.weight', 'proj_attn.weight') + # new_item = new_item.replace('proj_out.bias', 'proj_attn.bias') + + # new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def convert_ldm_unet_checkpoint( + checkpoint, config, path=None, extract_ema=False, controlnet=False, skip_extract_state_dict=False +): + """ + Takes a state dict and a config, and returns a converted checkpoint. + """ + + if skip_extract_state_dict: + unet_state_dict = checkpoint + else: + # extract state_dict for UNet + unet_state_dict = {} + keys = list(checkpoint.keys()) + + if controlnet: + unet_key = "control_model." + else: + unet_key = "model.diffusion_model." + + # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA + if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: + logger.warning(f"Checkpoint {path} has both EMA and non-EMA weights.") + logger.warning( + "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" + " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." + ) + for key in keys: + if key.startswith("model.diffusion_model"): + flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) + unet_state_dict[key.replace(unet_key, "")] = checkpoint[flat_ema_key] + else: + if sum(k.startswith("model_ema") for k in keys) > 100: + logger.warning( + "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" + " weights (usually better for inference), please make sure to add the `--extract_ema` flag." + ) + + for key in keys: + if key.startswith(unet_key): + unet_state_dict[key.replace(unet_key, "")] = checkpoint[key] + + new_checkpoint = {} + + new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] + new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] + new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] + new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] + + if config["class_embed_type"] is None: + # No parameters to port + ... + elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection": + new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + else: + raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}") + + if config["addition_embed_type"] == "text_time": + new_checkpoint["add_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"] + new_checkpoint["add_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"] + new_checkpoint["add_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"] + new_checkpoint["add_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"] + + new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] + new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] + + if not controlnet: + new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] + new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] + new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] + new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] + + # Retrieves the keys for the input blocks only + num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) + input_blocks = { + layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] + for layer_id in range(num_input_blocks) + } + + # Retrieves the keys for the middle blocks only + num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) + middle_blocks = { + layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] + for layer_id in range(num_middle_blocks) + } + + # Retrieves the keys for the output blocks only + num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) + output_blocks = { + layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] + for layer_id in range(num_output_blocks) + } + + for i in range(1, num_input_blocks): + block_id = (i - 1) // (config["layers_per_block"] + 1) + layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) + + resnets = [ + key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key + ] + attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] + + if f"input_blocks.{i}.0.op.weight" in unet_state_dict: + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.weight" + ) + new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( + f"input_blocks.{i}.0.op.bias" + ) + + paths = renew_resnet_paths(resnets) + meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + resnet_0 = middle_blocks[0] + attentions = middle_blocks[1] + resnet_1 = middle_blocks[2] + + resnet_0_paths = renew_resnet_paths(resnet_0) + assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) + + resnet_1_paths = renew_resnet_paths(resnet_1) + assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) + + attentions_paths = renew_attention_paths(attentions) + meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} + assign_to_checkpoint( + attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + for i in range(num_output_blocks): + block_id = i // (config["layers_per_block"] + 1) + layer_in_block_id = i % (config["layers_per_block"] + 1) + output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] + output_block_list = {} + + for layer in output_block_layers: + layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) + if layer_id in output_block_list: + output_block_list[layer_id].append(layer_name) + else: + output_block_list[layer_id] = [layer_name] + + if len(output_block_list) > 1: + resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] + attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] + + resnet_0_paths = renew_resnet_paths(resnets) + paths = renew_resnet_paths(resnets) + + meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + + output_block_list = {k: sorted(v) for k, v in output_block_list.items()} + if ["conv.bias", "conv.weight"] in output_block_list.values(): + index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.weight" + ] + new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ + f"output_blocks.{i}.{index}.conv.bias" + ] + + # Clear attentions as they have been attributed above. + if len(attentions) == 2: + attentions = [] + + if len(attentions): + paths = renew_attention_paths(attentions) + meta_path = { + "old": f"output_blocks.{i}.1", + "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", + } + assign_to_checkpoint( + paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config + ) + else: + resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) + for path in resnet_0_paths: + old_path = ".".join(["output_blocks", str(i), path["old"]]) + new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) + + new_checkpoint[new_path] = unet_state_dict[old_path] + + if controlnet: + # conditioning embedding + + orig_index = 0 + + new_checkpoint["controlnet_cond_embedding.conv_in.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_in.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + orig_index += 2 + + diffusers_index = 0 + + while diffusers_index < 6: + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint[f"controlnet_cond_embedding.blocks.{diffusers_index}.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + diffusers_index += 1 + orig_index += 2 + + new_checkpoint["controlnet_cond_embedding.conv_out.weight"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.weight" + ) + new_checkpoint["controlnet_cond_embedding.conv_out.bias"] = unet_state_dict.pop( + f"input_hint_block.{orig_index}.bias" + ) + + # down blocks + for i in range(num_input_blocks): + new_checkpoint[f"controlnet_down_blocks.{i}.weight"] = unet_state_dict.pop(f"zero_convs.{i}.0.weight") + new_checkpoint[f"controlnet_down_blocks.{i}.bias"] = unet_state_dict.pop(f"zero_convs.{i}.0.bias") + + # mid block + new_checkpoint["controlnet_mid_block.weight"] = unet_state_dict.pop("middle_block_out.0.weight") + new_checkpoint["controlnet_mid_block.bias"] = unet_state_dict.pop("middle_block_out.0.bias") + + return new_checkpoint + + +def create_vae_diffusers_config(original_config, image_size: int): + """ + Creates a config for the diffusers based on the config of the LDM model. + """ + vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"] + _ = original_config["model"]["params"]["first_stage_config"]["params"]["embed_dim"] + + block_out_channels = [vae_params["ch"] * mult for mult in vae_params["ch_mult"]] + down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) + up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) + + config = { + "sample_size": image_size, + "in_channels": vae_params["in_channels"], + "out_channels": vae_params["out_ch"], + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params["z_channels"], + "layers_per_block": vae_params["num_res_blocks"], + } + return config + + +def convert_ldm_vae_checkpoint(checkpoint, config): + # extract state dict for VAE + vae_state_dict = {} + vae_key = "first_stage_model." + keys = list(checkpoint.keys()) + for key in keys: + if key.startswith(vae_key): + vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) + + new_checkpoint = {} + + new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] + new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] + new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] + new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] + new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] + new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] + + new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] + new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] + new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] + new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] + new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] + new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] + + new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] + new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] + new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] + new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] + + # Retrieves the keys for the encoder down blocks only + num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) + down_blocks = { + layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) + } + + # Retrieves the keys for the decoder up blocks only + num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) + up_blocks = { + layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) + } + + for i in range(num_down_blocks): + resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] + + if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.weight" + ) + new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( + f"encoder.down.{i}.downsample.conv.bias" + ) + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + + for i in range(num_up_blocks): + block_id = num_up_blocks - 1 - i + resnets = [ + key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key + ] + + if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.weight" + ] + new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ + f"decoder.up.{block_id}.upsample.conv.bias" + ] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] + num_mid_res_blocks = 2 + for i in range(1, num_mid_res_blocks + 1): + resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] + + paths = renew_vae_resnet_paths(resnets) + meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + + mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] + paths = renew_vae_attention_paths(mid_attentions) + meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} + assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) + conv_attn_to_linear(new_checkpoint) + return new_checkpoint + + +def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside resnets to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("nin_shortcut", "conv_shortcut") + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0): + """ + Updates paths inside attentions to the new naming scheme (local renaming) + """ + mapping = [] + for old_item in old_list: + new_item = old_item + + new_item = new_item.replace("norm.weight", "group_norm.weight") + new_item = new_item.replace("norm.bias", "group_norm.bias") + + new_item = new_item.replace("q.weight", "to_q.weight") + new_item = new_item.replace("q.bias", "to_q.bias") + + new_item = new_item.replace("k.weight", "to_k.weight") + new_item = new_item.replace("k.bias", "to_k.bias") + + new_item = new_item.replace("v.weight", "to_v.weight") + new_item = new_item.replace("v.bias", "to_v.bias") + + new_item = new_item.replace("proj_out.weight", "to_out.0.weight") + new_item = new_item.replace("proj_out.bias", "to_out.0.bias") + + new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments) + + mapping.append({"old": old_item, "new": new_item}) + + return mapping + + +def conv_attn_to_linear(checkpoint): + keys = list(checkpoint.keys()) + attn_keys = ["query.weight", "key.weight", "value.weight"] + for key in keys: + if ".".join(key.split(".")[-2:]) in attn_keys: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0, 0] + elif "proj_attn.weight" in key: + if checkpoint[key].ndim > 2: + checkpoint[key] = checkpoint[key][:, :, 0] + + +def convert_from_original_zero123_ckpt(checkpoint_path, original_config_file, extract_ema, device): + ckpt = torch.load(checkpoint_path, map_location=device) + ckpt["global_step"] + checkpoint = ckpt["state_dict"] + del ckpt + torch.cuda.empty_cache() + + original_config = yaml.safe_load(original_config_file) + original_config["model"]["params"]["cond_stage_config"]["target"].split(".")[-1] + num_in_channels = 8 + original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels + prediction_type = "epsilon" + image_size = 256 + num_train_timesteps = getattr(original_config["model"]["params"], "timesteps", None) or 1000 + + beta_start = getattr(original_config["model"]["params"], "linear_start", None) or 0.02 + beta_end = getattr(original_config["model"]["params"], "linear_end", None) or 0.085 + scheduler = DDIMScheduler( + beta_end=beta_end, + beta_schedule="scaled_linear", + beta_start=beta_start, + num_train_timesteps=num_train_timesteps, + steps_offset=1, + clip_sample=False, + set_alpha_to_one=False, + prediction_type=prediction_type, + ) + scheduler.register_to_config(clip_sample=False) + + # Convert the UNet2DConditionModel model. + upcast_attention = None + unet_config = create_unet_diffusers_config(original_config, image_size=image_size) + unet_config["upcast_attention"] = upcast_attention + with init_empty_weights(): + unet = UNet2DConditionModel(**unet_config) + converted_unet_checkpoint = convert_ldm_unet_checkpoint( + checkpoint, unet_config, path=None, extract_ema=extract_ema + ) + for param_name, param in converted_unet_checkpoint.items(): + set_module_tensor_to_device(unet, param_name, "cpu", value=param) + + # Convert the VAE model. + vae_config = create_vae_diffusers_config(original_config, image_size=image_size) + converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config) + + if ( + "model" in original_config + and "params" in original_config["model"] + and "scale_factor" in original_config["model"]["params"] + ): + vae_scaling_factor = original_config["model"]["params"]["scale_factor"] + else: + vae_scaling_factor = 0.18215 # default SD scaling factor + + vae_config["scaling_factor"] = vae_scaling_factor + + with init_empty_weights(): + vae = AutoencoderKL(**vae_config) + + for param_name, param in converted_vae_checkpoint.items(): + set_module_tensor_to_device(vae, param_name, "cpu", value=param) + + feature_extractor = CLIPImageProcessor.from_pretrained( + "lambdalabs/sd-image-variations-diffusers", subfolder="feature_extractor" + ) + image_encoder = CLIPVisionModelWithProjection.from_pretrained( + "lambdalabs/sd-image-variations-diffusers", subfolder="image_encoder" + ) + + cc_projection = CCProjection() + cc_projection.load_state_dict( + { + "projection.weight": checkpoint["cc_projection.weight"].cpu(), + "projection.bias": checkpoint["cc_projection.bias"].cpu(), + } + ) + + pipe = Zero1to3StableDiffusionPipeline( + vae, image_encoder, unet, scheduler, None, feature_extractor, cc_projection, requires_safety_checker=False + ) + + return pipe + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--original_config_file", + default=None, + type=str, + help="The YAML config file corresponding to the original architecture.", + ) + parser.add_argument( + "--extract_ema", + action="store_true", + help=( + "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" + " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" + " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." + ), + ) + parser.add_argument( + "--to_safetensors", + action="store_true", + help="Whether to store pipeline in safetensors format or not.", + ) + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") + parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") + args = parser.parse_args() + + pipe = convert_from_original_zero123_ckpt( + checkpoint_path=args.checkpoint_path, + original_config_file=args.original_config_file, + extract_ema=args.extract_ema, + device=args.device, + ) + + if args.half: + pipe.to(dtype=torch.float16) + + pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/scripts/extract_lora_from_model.py b/scripts/extract_lora_from_model.py new file mode 100644 index 000000000000..0e01ddea47f9 --- /dev/null +++ b/scripts/extract_lora_from_model.py @@ -0,0 +1,151 @@ +""" +This script demonstrates how to extract a LoRA checkpoint from a fully finetuned model with the CogVideoX model. + +To make it work for other models: + +* Change the model class. Here we use `CogVideoXTransformer3DModel`. For Flux, it would be `FluxTransformer2DModel`, +for example. (TODO: more reason to add `AutoModel`). +* Spply path to the base checkpoint via `base_ckpt_path`. +* Supply path to the fully fine-tuned checkpoint via `--finetune_ckpt_path`. +* Change the `--rank` as needed. + +Example usage: + +```bash +python extract_lora_from_model.py \ + --base_ckpt_path=THUDM/CogVideoX-5b \ + --finetune_ckpt_path=finetrainers/cakeify-v0 \ + --lora_out_path=cakeify_lora.safetensors +``` + +Script is adapted from +https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py +""" + +import argparse + +import torch +from safetensors.torch import save_file +from tqdm.auto import tqdm + +from diffusers import CogVideoXTransformer3DModel + + +RANK = 64 +CLAMP_QUANTILE = 0.99 + + +# Comes from +# https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py#L9 +def extract_lora(diff, rank): + # Important to use CUDA otherwise, very slow! + if torch.cuda.is_available(): + diff = diff.to("cuda") + + is_conv2d = len(diff.shape) == 4 + kernel_size = None if not is_conv2d else diff.size()[2:4] + is_conv2d_3x3 = is_conv2d and kernel_size != (1, 1) + out_dim, in_dim = diff.size()[0:2] + rank = min(rank, in_dim, out_dim) + + if is_conv2d: + if is_conv2d_3x3: + diff = diff.flatten(start_dim=1) + else: + diff = diff.squeeze() + + U, S, Vh = torch.linalg.svd(diff.float()) + U = U[:, :rank] + S = S[:rank] + U = U @ torch.diag(S) + Vh = Vh[:rank, :] + + dist = torch.cat([U.flatten(), Vh.flatten()]) + hi_val = torch.quantile(dist, CLAMP_QUANTILE) + low_val = -hi_val + + U = U.clamp(low_val, hi_val) + Vh = Vh.clamp(low_val, hi_val) + if is_conv2d: + U = U.reshape(out_dim, rank, 1, 1) + Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1]) + return (U.cpu(), Vh.cpu()) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--base_ckpt_path", + default=None, + type=str, + required=True, + help="Base checkpoint path from which the model was finetuned. Can be a model ID on the Hub.", + ) + parser.add_argument( + "--base_subfolder", + default="transformer", + type=str, + help="subfolder to load the base checkpoint from if any.", + ) + parser.add_argument( + "--finetune_ckpt_path", + default=None, + type=str, + required=True, + help="Fully fine-tuned checkpoint path. Can be a model ID on the Hub.", + ) + parser.add_argument( + "--finetune_subfolder", + default=None, + type=str, + help="subfolder to load the fulle finetuned checkpoint from if any.", + ) + parser.add_argument("--rank", default=64, type=int) + parser.add_argument("--lora_out_path", default=None, type=str, required=True) + args = parser.parse_args() + + if not args.lora_out_path.endswith(".safetensors"): + raise ValueError("`lora_out_path` must end with `.safetensors`.") + + return args + + +@torch.no_grad() +def main(args): + model_finetuned = CogVideoXTransformer3DModel.from_pretrained( + args.finetune_ckpt_path, subfolder=args.finetune_subfolder, torch_dtype=torch.bfloat16 + ) + state_dict_ft = model_finetuned.state_dict() + + # Change the `subfolder` as needed. + base_model = CogVideoXTransformer3DModel.from_pretrained( + args.base_ckpt_path, subfolder=args.base_subfolder, torch_dtype=torch.bfloat16 + ) + state_dict = base_model.state_dict() + output_dict = {} + + for k in tqdm(state_dict, desc="Extracting LoRA..."): + original_param = state_dict[k] + finetuned_param = state_dict_ft[k] + if len(original_param.shape) >= 2: + diff = finetuned_param.float() - original_param.float() + out = extract_lora(diff, RANK) + name = k + + if name.endswith(".weight"): + name = name[: -len(".weight")] + down_key = "{}.lora_A.weight".format(name) + up_key = "{}.lora_B.weight".format(name) + + output_dict[up_key] = out[0].contiguous().to(finetuned_param.dtype) + output_dict[down_key] = out[1].contiguous().to(finetuned_param.dtype) + + prefix = "transformer" if "transformer" in base_model.__class__.__name__.lower() else "unet" + output_dict = {f"{prefix}.{k}": v for k, v in output_dict.items()} + save_file(output_dict, args.lora_out_path) + print(f"LoRA saved and it contains {len(output_dict)} keys.") + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/scripts/generate_logits.py b/scripts/generate_logits.py new file mode 100644 index 000000000000..99d46d6628a6 --- /dev/null +++ b/scripts/generate_logits.py @@ -0,0 +1,127 @@ +import random + +import torch +from huggingface_hub import HfApi + +from diffusers import UNet2DModel + + +api = HfApi() + +results = {} +# fmt: off +results["google_ddpm_cifar10_32"] = torch.tensor([ + -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467, + 1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189, + -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839, + 0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557 +]) +results["google_ddpm_ema_bedroom_256"] = torch.tensor([ + -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436, + 1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208, + -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948, + 2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365 +]) +results["CompVis_ldm_celebahq_256"] = torch.tensor([ + -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869, + -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304, + -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925, + 0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943 +]) +results["google_ncsnpp_ffhq_1024"] = torch.tensor([ + 0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172, + -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309, + 0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805, + -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505 +]) +results["google_ncsnpp_bedroom_256"] = torch.tensor([ + 0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133, + -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395, + 0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559, + -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386 +]) +results["google_ncsnpp_celebahq_256"] = torch.tensor([ + 0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078, + -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330, + 0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683, + -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431 +]) +results["google_ncsnpp_church_256"] = torch.tensor([ + 0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042, + -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398, + 0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574, + -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390 +]) +results["google_ncsnpp_ffhq_256"] = torch.tensor([ + 0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042, + -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290, + 0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746, + -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473 +]) +results["google_ddpm_cat_256"] = torch.tensor([ + -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330, + 1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243, + -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810, + 1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251]) +results["google_ddpm_celebahq_256"] = torch.tensor([ + -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324, + 0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181, + -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259, + 1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266 +]) +results["google_ddpm_ema_celebahq_256"] = torch.tensor([ + -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212, + 0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027, + -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131, + 1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355 +]) +results["google_ddpm_church_256"] = torch.tensor([ + -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959, + 1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351, + -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341, + 3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066 +]) +results["google_ddpm_bedroom_256"] = torch.tensor([ + -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740, + 1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398, + -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395, + 2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243 +]) +results["google_ddpm_ema_church_256"] = torch.tensor([ + -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336, + 1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908, + -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560, + 3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343 +]) +results["google_ddpm_ema_cat_256"] = torch.tensor([ + -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344, + 1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391, + -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439, + 1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219 +]) +# fmt: on + +models = api.list_models(filter="diffusers") +for mod in models: + if "google" in mod.author or mod.id == "CompVis/ldm-celebahq-256": + local_checkpoint = "/home/patrick/google_checkpoints/" + mod.id.split("/")[-1] + + print(f"Started running {mod.id}!!!") + + if mod.id.startswith("CompVis"): + model = UNet2DModel.from_pretrained(local_checkpoint, subfolder="unet") + else: + model = UNet2DModel.from_pretrained(local_checkpoint) + + torch.manual_seed(0) + random.seed(0) + + noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size) + time_step = torch.tensor([10] * noise.shape[0]) + with torch.no_grad(): + logits = model(noise, time_step).sample + + assert torch.allclose( + logits[0, 0, 0, :30], results["_".join("_".join(mod.id.split("/")).split("-"))], atol=1e-3 + ) + print(f"{mod.id} has passed successfully!!!") From 970753e9db3f2d1a9434b470fc649fb1398fe0a1 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:04:08 +0000 Subject: [PATCH 36/44] remove sample_bd3lm.py from git tracking --- examples/discrete_diffusion/sample_bd3lm.py | 193 -------------------- 1 file changed, 193 deletions(-) delete mode 100644 examples/discrete_diffusion/sample_bd3lm.py diff --git a/examples/discrete_diffusion/sample_bd3lm.py b/examples/discrete_diffusion/sample_bd3lm.py deleted file mode 100644 index dbf9b7999ac6..000000000000 --- a/examples/discrete_diffusion/sample_bd3lm.py +++ /dev/null @@ -1,193 +0,0 @@ -#!/usr/bin/env python -# Copyright 2025 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -""" -Sample script for BD3LM-style block diffusion decoding. - -Example: - python sample_bd3lm.py \ - --model_id kuleshov-group/bd3-lm \ - --prompt "Explain what reinforcement learning is in simple terms." \ - --max_new_tokens 256 -""" - -import argparse - -import torch -from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer -from transformers.dynamic_module_utils import get_class_from_dynamic_module - -from diffusers import BD3LMModel, BD3LMPipeline - - -def main(): - parser = argparse.ArgumentParser(description="Run BD3LM block diffusion decoding.") - parser.add_argument( - "--model_id", - type=str, - default="kuleshov-group/bd3-lm", - help="Model ID or local path.", - ) - parser.add_argument( - "--use_diffusers_model", - action="store_true", - help="Load a diffusers-native BD3LM checkpoint instead of transformers.", - ) - parser.add_argument( - "--tokenizer_id", - type=str, - default=None, - help="Tokenizer ID or local path (defaults to `model_id`, falls back to `gpt2`).", - ) - parser.add_argument( - "--prompt", - type=str, - default="Explain what reinforcement learning is in simple terms.", - help="Prompt text to generate from.", - ) - parser.add_argument("--max_new_tokens", type=int, default=256) - parser.add_argument("--block_length", type=int, default=32) - parser.add_argument("--denoising_steps", type=int, default=64) - parser.add_argument("--p_nucleus", type=float, default=1.0) - parser.add_argument("--first_hitting", action="store_true") - parser.add_argument("--no_first_hitting", action="store_true") - parser.add_argument("--use_kv_cache", action="store_true") - parser.add_argument("--max_window", type=int, default=1024) - parser.add_argument("--entropy_threshold", type=float, default=4.0) - parser.add_argument("--var_length", action="store_true") - parser.add_argument("--mask_token_id", type=int, default=None) - parser.add_argument( - "--use_chat_template", - action="store_true", - help="Use the tokenizer chat template for the prompt.", - ) - parser.add_argument( - "--add_generation_prompt", - action="store_true", - help="Add the generation prompt when using the chat template.", - ) - parser.add_argument( - "--device", - type=str, - default="cuda" if torch.cuda.is_available() else "cpu", - help="Device to run inference on.", - ) - parser.add_argument( - "--dtype", - type=str, - default="auto", - choices=["auto", "float32", "float16", "bfloat16"], - help="Model dtype.", - ) - - args = parser.parse_args() - - dtype_map = {"float32": torch.float32, "float16": torch.float16, "bfloat16": torch.bfloat16} - torch_dtype = dtype_map.get(args.dtype) - - print(f"Loading model: {args.model_id}") - if args.use_diffusers_model: - model_kwargs = {} - if torch_dtype is not None: - model_kwargs["torch_dtype"] = torch_dtype - model = BD3LMModel.from_pretrained(args.model_id, **model_kwargs) - config = model.config - else: - config = AutoConfig.from_pretrained(args.model_id, trust_remote_code=True) - if not hasattr(config, "get"): - config.get = lambda key, default=None: getattr(config, key, default) - - try: - model = AutoModelForMaskedLM.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype if torch_dtype is not None else "auto", - config=config, - ) - except AttributeError as err: - if "all_tied_weights_keys" not in str(err): - raise - class_ref = config.auto_map.get("AutoModelForMaskedLM") - if class_ref is None: - raise ValueError("BD3LM config does not define AutoModelForMaskedLM in auto_map.") from err - model_class = get_class_from_dynamic_module( - class_ref, args.model_id, revision=None, trust_remote_code=True - ) - if not hasattr(model_class, "all_tied_weights_keys"): - model_class.all_tied_weights_keys = {} - model = model_class.from_pretrained( - args.model_id, - trust_remote_code=True, - dtype=torch_dtype if torch_dtype is not None else "auto", - config=config, - ) - model = model.to(args.device) - tokenizer = None - tokenizer_id = args.tokenizer_id or args.model_id - tokenizer_kwargs = {} - if not args.use_diffusers_model: - tokenizer_kwargs["config"] = config - try: - tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True, **tokenizer_kwargs) - except Exception: - tokenizer_id = "gpt2" if args.tokenizer_id is None else args.tokenizer_id - tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, trust_remote_code=True, **tokenizer_kwargs) - - if tokenizer.mask_token_id is None: - if getattr(config, "vocab_size", None) == len(tokenizer) + 1: - tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) - elif getattr(config, "vocab_size", None) == len(tokenizer): - raise ValueError( - "Tokenizer has no mask token but matches the model vocab size. " - "Provide a tokenizer that already includes a mask token." - ) - else: - tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) - - if tokenizer.pad_token_id is None and tokenizer.eos_token_id is not None: - tokenizer.pad_token = tokenizer.eos_token - - first_hitting = args.first_hitting or not args.no_first_hitting - - pipe = BD3LMPipeline(model=model, tokenizer=tokenizer) - - print(f"\nPrompt: {args.prompt}") - output = pipe( - prompt=args.prompt, - max_new_tokens=args.max_new_tokens, - block_length=args.block_length, - denoising_steps=args.denoising_steps, - p_nucleus=args.p_nucleus, - first_hitting=first_hitting, - use_kv_cache=args.use_kv_cache, - max_window=args.max_window, - entropy_threshold=args.entropy_threshold, - var_length=args.var_length, - mask_token_id=args.mask_token_id, - use_chat_template=args.use_chat_template, - add_generation_prompt=args.add_generation_prompt, - ) - - print("\nGenerated text:") - print( - output.texts[0] - if output.texts is not None - else tokenizer.decode(output.sequences[0], skip_special_tokens=True) - ) - print(f"\nGenerated {output.sequences.shape[1]} tokens") - - -if __name__ == "__main__": - main() From ef1f521cb11e5b3849372c9a874e25270f460b69 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:05:13 +0000 Subject: [PATCH 37/44] remove bd3lm.md from git tracking --- docs/source/en/api/pipelines/bd3lm.md | 23 ----------------------- 1 file changed, 23 deletions(-) delete mode 100644 docs/source/en/api/pipelines/bd3lm.md diff --git a/docs/source/en/api/pipelines/bd3lm.md b/docs/source/en/api/pipelines/bd3lm.md deleted file mode 100644 index 545f6f31db42..000000000000 --- a/docs/source/en/api/pipelines/bd3lm.md +++ /dev/null @@ -1,23 +0,0 @@ -<!--Copyright 2025 The HuggingFace Team. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with -the License. You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on -an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the -specific language governing permissions and limitations under the License. ---> - -# BD3LM - -`BD3LMPipeline` performs block diffusion decoding in the style of BD3LM models. - -## BD3LMPipeline -[[autodoc]] BD3LMPipeline - - all - - __call__ - -## BD3LMPipelineOutput -[[autodoc]] pipelines.BD3LMPipelineOutput From 1e7ebc49156c4542d6ab2388d3877869d09592b2 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 12:05:40 +0000 Subject: [PATCH 38/44] remove bd3lm_token_diffusion.md from git tracking --- .../api/schedulers/bd3lm_token_diffusion.md | 21 ------------------- 1 file changed, 21 deletions(-) delete mode 100644 docs/source/en/api/schedulers/bd3lm_token_diffusion.md diff --git a/docs/source/en/api/schedulers/bd3lm_token_diffusion.md b/docs/source/en/api/schedulers/bd3lm_token_diffusion.md deleted file mode 100644 index 650b89031ca3..000000000000 --- a/docs/source/en/api/schedulers/bd3lm_token_diffusion.md +++ /dev/null @@ -1,21 +0,0 @@ -<!--Copyright 2025 The HuggingFace Team. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with -the License. You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on -an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the -specific language governing permissions and limitations under the License. ---> - -# BD3LMTokenDiffusionScheduler - -`BD3LMTokenDiffusionScheduler` implements block diffusion remasking and sampling for BD3LM-style decoding. - -## BD3LMTokenDiffusionScheduler -[[autodoc]] BD3LMTokenDiffusionScheduler - -## BD3LMTokenDiffusionSchedulerOutput -[[autodoc]] schedulers.scheduling_bd3lm_token_diffusion.BD3LMTokenDiffusionSchedulerOutput From 6b1bb4b8be044b55230627cea66666455f085168 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 13:54:26 +0000 Subject: [PATCH 39/44] WIP: Add BD3LM pipeline and scheduler (needs scheduler integration in pipeline) --- src/diffusers/__init__.py | 8 + src/diffusers/pipelines/__init__.py | 2 + src/diffusers/pipelines/bd3lm/__init__.py | 47 ++ .../pipelines/bd3lm/pipeline_bd3lm.py | 482 ++++++++++++++++++ src/diffusers/schedulers/__init__.py | 5 + .../scheduling_bd3lm_token_diffusion.py | 351 +++++++++++++ src/diffusers/utils/dummy_pt_objects.py | 60 +++ 7 files changed, 955 insertions(+) create mode 100644 src/diffusers/pipelines/bd3lm/__init__.py create mode 100644 src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py create mode 100644 src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 83962ffc41f6..5eb1fa40dd06 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -342,6 +342,8 @@ ) _import_structure["pipelines"].extend( [ + "BD3LMPipeline", + "BD3LMPipelineOutput", "HybridTokenDiffusionPipeline", "TokenDiffusionPipeline", "TokenDiffusionPipelineOutput", @@ -351,6 +353,8 @@ _import_structure["schedulers"].extend( [ "AmusedScheduler", + "BD3LMTokenDiffusionScheduler", + "BD3LMTokenDiffusionSchedulerOutput", "BlockRefinementScheduler", "BlockRefinementSchedulerOutput", "CMStochasticIterativeScheduler", @@ -1125,6 +1129,8 @@ AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image, + BD3LMPipeline, + BD3LMPipelineOutput, BlipDiffusionControlNetPipeline, BlipDiffusionPipeline, CLIPImageProjection, @@ -1149,6 +1155,8 @@ from .quantizers import DiffusersQuantizer from .schedulers import ( AmusedScheduler, + BD3LMTokenDiffusionScheduler, + BD3LMTokenDiffusionSchedulerOutput, BlockRefinementScheduler, BlockRefinementSchedulerOutput, CMStochasticIterativeScheduler, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index fcd61ecb0ebd..2c9da4570cdf 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -47,6 +47,7 @@ "AutoPipelineForInpainting", "AutoPipelineForText2Image", ] + _import_structure["bd3lm"] = ["BD3LMPipeline", "BD3LMPipelineOutput"] _import_structure["consistency_models"] = ["ConsistencyModelPipeline"] _import_structure["dance_diffusion"] = ["DanceDiffusionPipeline"] _import_structure["ddim"] = ["DDIMPipeline"] @@ -548,6 +549,7 @@ AutoPipelineForInpainting, AutoPipelineForText2Image, ) + from .bd3lm import BD3LMPipeline, BD3LMPipelineOutput from .consistency_models import ConsistencyModelPipeline from .dance_diffusion import DanceDiffusionPipeline from .ddim import DDIMPipeline diff --git a/src/diffusers/pipelines/bd3lm/__init__.py b/src/diffusers/pipelines/bd3lm/__init__.py new file mode 100644 index 000000000000..18f81b69d58d --- /dev/null +++ b/src/diffusers/pipelines/bd3lm/__init__.py @@ -0,0 +1,47 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_bd3lm"] = ["BD3LMPipeline", "BD3LMPipelineOutput"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 + else: + from .pipeline_bd3lm import BD3LMPipeline, BD3LMPipelineOutput +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py new file mode 100644 index 000000000000..b355d9c4d547 --- /dev/null +++ b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py @@ -0,0 +1,482 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Callable + +import torch +from tqdm.auto import tqdm + +from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...utils import BaseOutput, logging, replace_example_docstring +from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin + + +logger = logging.get_logger(__name__) + + +EXAMPLE_DOC_STRING = """ + Examples: + ```python + >>> import torch + >>> from transformers import AutoModelForMaskedLM, AutoTokenizer + >>> from diffusers import BD3LMPipeline + + >>> model_id = "kuleshov-group/bd3lm-owt-block_size4" + >>> model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True) + >>> tokenizer = AutoTokenizer.from_pretrained("gpt2") + >>> if tokenizer.mask_token_id is None: + ... tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) + + >>> pipe = BD3LMPipeline(model=model, tokenizer=tokenizer) + >>> pipe = pipe.to("cuda") + >>> output = pipe(gen_length=256, num_inference_steps=64) + >>> print(output.texts[0]) + ``` +""" + + +def _sample_categorical(categorical_probs: torch.Tensor) -> torch.LongTensor: + """Sample from categorical distributions using Gumbel-max trick.""" + gumbel_norm = 1e-10 - (torch.rand_like(categorical_probs) + 1e-10).log() + return (categorical_probs / gumbel_norm).argmax(dim=-1) + + +def _nucleus_filter( + p_x0: torch.Tensor, + nucleus_p: float, +) -> torch.Tensor: + """Apply nucleus (top-p) filtering to probability distributions. + + Args: + p_x0: Probability tensor of shape `(batch, seq_len, vocab_size)`. + nucleus_p: Cumulative probability threshold for nucleus sampling. + + Returns: + Filtered and renormalised probability tensor of the same shape. + """ + if nucleus_p >= 1.0: + return p_x0 + + sorted_probs, sorted_indices = torch.sort(p_x0, dim=-1, descending=True) + cum_probs = torch.cumsum(sorted_probs, dim=-1) + nucleus_mask = cum_probs <= nucleus_p + # Always keep at least the top-1 token. + nucleus_mask[..., 0] = True + sorted_probs = sorted_probs * nucleus_mask + + # Scatter filtered probabilities back to original positions. + filtered = torch.zeros_like(p_x0) + filtered.scatter_(-1, sorted_indices, sorted_probs) + filtered = filtered / filtered.sum(dim=-1, keepdim=True) + return filtered + + +@dataclass +class BD3LMPipelineOutput(BaseOutput): + """ + Output class for the BD3LM pipeline. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Generated token ID sequences. + texts (`list[str]` or `None`): + Decoded text strings when `output_type="text"` and a tokenizer is available. + """ + + sequences: torch.LongTensor + texts: list[str] | None = None + + +class BD3LMPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): + r""" + Pipeline for BD3LM (Block Discrete Denoising Diffusion Language Model) text generation via semi-autoregressive + block diffusion. + + BD3LM generates text by autoregressively appending masked blocks and denoising each block via discrete DDPM + updates. At each stride, a new block of mask tokens is appended and iteratively denoised using the model's + predicted token probabilities. + + The model is expected to accept `(input_ids, timesteps, sample_mode)` and return logits of shape `[batch, + block_length, vocab_size]` when in sample mode. + """ + + model: Any + tokenizer: Any + + _callback_tensor_inputs = ["x_accum", "block_logits"] + + def __init__( + self, + model: Any, + scheduler: Any, + tokenizer: Any | None = None, + ): + super().__init__() + self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) + + # Resolve mask token ID from model config or tokenizer. + self.mask_token_id: int | None = None + if hasattr(self.model, "config"): + self.mask_token_id = getattr(self.model.config, "mask_index", None) + if self.mask_token_id is None and self.tokenizer is not None: + self.mask_token_id = getattr(self.tokenizer, "mask_token_id", None) + + self.eos_token_id = getattr(self.tokenizer, "eos_token_id", None) if self.tokenizer is not None else None + + @property + def num_timesteps(self): + return self._num_timesteps + + def check_inputs( + self, + prompt: str | list[str] | None, + input_ids: torch.LongTensor | None, + gen_length: int, + block_length: int, + num_inference_steps: int, + nucleus_p: float, + output_type: str, + callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, + callback_on_step_end_tensor_inputs: list[str] | None, + ): + # Input source validation + if prompt is None and input_ids is None: + # No prompt provided -- unconditional generation starting from BOS. + pass + if input_ids is not None: + if input_ids.ndim not in (1, 2): + raise ValueError(f"`input_ids` must be 1D or 2D, got shape {tuple(input_ids.shape)}.") + if input_ids.dtype != torch.long: + raise ValueError(f"`input_ids` must be int64 token IDs, got dtype={input_ids.dtype}.") + if prompt is not None and input_ids is None and self.tokenizer is None: + raise ValueError("Tokenizer is required when `input_ids` is not provided.") + + # Generation parameter validation + if gen_length <= 0: + raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") + if block_length <= 0: + raise ValueError(f"`block_length` must be > 0, got {block_length}.") + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + if not (0.0 < nucleus_p <= 1.0): + raise ValueError(f"`nucleus_p` must be in (0, 1], got {nucleus_p}.") + if output_type not in {"seq", "text"}: + raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") + + # Callback validation + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found " + f"{[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: str | list[str] | None = None, + input_ids: torch.LongTensor | None = None, + gen_length: int = 256, + block_length: int | None = None, + num_inference_steps: int = 64, + nucleus_p: float = 1.0, + mask_token_id: int | None = None, + eos_token_id: int | None = None, + eos_early_stop: bool = True, + generator: torch.Generator | None = None, + output_type: str = "text", + return_dict: bool = True, + callback_on_step_end: Callable[[int, int, dict], None] + | PipelineCallback + | MultiPipelineCallbacks + | None = None, + callback_on_step_end_tensor_inputs: list[str] | None = None, + ) -> BD3LMPipelineOutput | tuple[torch.LongTensor, list[str] | None]: + """ + Generate text with BD3LM semi-autoregressive block diffusion. + + Args: + prompt (`str` or `List[str]`, *optional*): + Prompt text. When provided, it is tokenized and used as a prefix for generation. + input_ids (`torch.LongTensor`, *optional*): + Pre-tokenized input IDs. Takes precedence over `prompt`. + gen_length (`int`, defaults to `256`): + Total number of new tokens to generate. + block_length (`int`, *optional*): + Block size for diffusion. If not provided, reads `model.config.block_size`. + num_inference_steps (`int`, defaults to `64`): + Number of DDPM denoising steps per block. + nucleus_p (`float`, defaults to `1.0`): + Nucleus sampling probability threshold. Set to `1.0` to disable nucleus filtering. + mask_token_id (`int`, *optional*): + Mask token ID. Resolved from `model.config.mask_index` or tokenizer if not provided. + eos_token_id (`int`, *optional*): + EOS token ID for early stopping. + eos_early_stop (`bool`, defaults to `True`): + Whether to stop generation when EOS is produced. + generator (`torch.Generator`, *optional*): + RNG for reproducibility (currently unused; sampling uses Gumbel-max). + output_type (`str`, defaults to `"text"`): + Output format. `"text"` decodes sequences into strings. `"seq"` returns raw token IDs. + return_dict (`bool`, defaults to `True`): + Whether to return a [`BD3LMPipelineOutput`] instead of a tuple. + callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): + Callback executed after each denoising step. + callback_on_step_end_tensor_inputs (`List[str]`, *optional*): + Tensor keys to pass to the callback. Allowed keys: `x_accum`, `block_logits`. + + Examples: + """ + # 0. Resolve defaults + if block_length is None: + block_length = getattr(getattr(self.model, "config", None), "block_size", None) + if block_length is None: + raise ValueError("`block_length` must be provided or available as `model.config.block_size`.") + + if mask_token_id is None: + mask_token_id = self.mask_token_id + if mask_token_id is None: + raise ValueError( + "`mask_token_id` must be provided (or available via `model.config.mask_index` or tokenizer)." + ) + + if eos_token_id is None: + eos_token_id = self.eos_token_id + + # 1. Handle callbacks + if callback_on_step_end is not None and isinstance( + callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) + ): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + if callback_on_step_end_tensor_inputs is None: + callback_on_step_end_tensor_inputs = ["x_accum"] + + self.check_inputs( + prompt=prompt, + input_ids=input_ids, + gen_length=gen_length, + block_length=block_length, + num_inference_steps=num_inference_steps, + nucleus_p=nucleus_p, + output_type=output_type, + callback_on_step_end=callback_on_step_end, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + ) + + device = self._execution_device + + # 2. Prepare prompt IDs (prefix) + if prompt is not None or input_ids is not None: + prompt_ids = self._prepare_input_ids( + prompt=prompt, + messages=None, + input_ids=input_ids, + use_chat_template=False, + add_generation_prompt=False, + chat_template_kwargs=None, + ) + if prompt_ids.ndim == 1: + prompt_ids = prompt_ids.unsqueeze(0) + prompt_ids = prompt_ids.to(device=device) + batch_size = prompt_ids.shape[0] + else: + # Unconditional generation: start with BOS token. + batch_size = 1 + bos_id = self._resolve_start_token_id() + if bos_id is None: + raise ValueError( + "No prompt provided and no BOS token found on the tokenizer. " + "Provide `prompt`, `input_ids`, or a tokenizer with a BOS token." + ) + prompt_ids = torch.tensor([[bos_id]], device=device, dtype=torch.long) + + prompt_length = prompt_ids.shape[1] + + # 3. Compute number of strides (blocks to generate) + num_strides = (gen_length + block_length - 1) // block_length + + self._num_timesteps = num_inference_steps * num_strides + + # 4. Log-linear noise schedule helpers (matching BD3LM's LogLinearNoise). + # The noise schedule gives move_chance = t (probability a token is masked at time t). + # sigma(t) = -log(1 - t), capped at sigma_max = -log(eps) for eps=1e-3. + noise_eps = 1e-3 + sigma_max = -torch.log(torch.tensor(noise_eps, device=device, dtype=torch.float64)) + + def _move_chance(t: torch.Tensor) -> torch.Tensor: + """Compute masking probability at time t (log-linear schedule: move_chance = t).""" + return t + + def _sigma_from_move_chance(p: torch.Tensor) -> torch.Tensor: + """Convert move_chance to sigma, clamped at sigma_max.""" + return torch.min(-torch.log(1.0 - p), sigma_max) + + # 5. Semi-autoregressive block diffusion loop + finished = torch.zeros((batch_size,), device=device, dtype=torch.bool) + global_step = 0 + + block_progress_bar_config = getattr(self, "_progress_bar_config", {}).copy() + block_progress_bar_config["position"] = 0 + block_progress_bar_config["desc"] = "Blocks" + + x_accum: torch.LongTensor | None = None + + for stride_num in tqdm(range(num_strides), **block_progress_bar_config): + # -- Extend x_accum with a new masked block -- + if stride_num == 0: + # First block: prompt + masked block + masked_block = torch.full((batch_size, block_length), mask_token_id, device=device, dtype=torch.long) + x_accum = torch.cat([prompt_ids, masked_block], dim=1) + # Set BOS token at position 0 if prompt is just BOS + if prompt_length == 1: + bos_id = self._resolve_start_token_id() + if bos_id is not None: + x_accum[:, 0] = bos_id + else: + masked_block = torch.full((batch_size, block_length), mask_token_id, device=device, dtype=torch.long) + x_accum = torch.cat([x_accum, masked_block], dim=1) + + # -- Determine the forward window indices -- + # The model sees the last (stride_num + 1) * block_length + prompt_length tokens, + # but BD3LM in sample_mode only processes the current block. + # We pass the full accumulated sequence as context. + end_idx = prompt_length + (stride_num + 1) * block_length + start_idx = 0 # Use full context + fwd_indices = torch.arange(start_idx, end_idx, device=device) + + # -- DDPM denoising steps within this block -- + dt = 1.0 / num_inference_steps + p_x0_cache = None + timesteps = torch.linspace(1.0, 0.0, num_inference_steps, device=device, dtype=torch.float64) + + self.set_progress_bar_config(position=1, leave=False, desc=f"Block {stride_num} Denoising") + progress_bar = self.progress_bar(total=num_inference_steps) + + for step_idx in range(num_inference_steps): + # Check if any mask tokens remain in the current block + current_block = x_accum[:, -block_length:] + if (current_block != mask_token_id).all(): + progress_bar.update(num_inference_steps - step_idx) + break + + t = timesteps[step_idx] + + # -- Compute move chances and sigma -- + t_tensor = t.unsqueeze(0).expand(batch_size).to(torch.float64) + s_tensor = (t - dt).clamp(min=0.0).unsqueeze(0).expand(batch_size).to(torch.float64) + + move_chance_t = _move_chance(t_tensor) + move_chance_s = _move_chance(s_tensor) + sigma_t = _sigma_from_move_chance(move_chance_t) + + # mask_prob = move_chance_s / move_chance_t (probability token stays masked) + mask_prob = (move_chance_s / move_chance_t).unsqueeze(-1) # (batch, 1) + + # -- Get model predictions -- + x_window = x_accum[:, fwd_indices] + # Only recompute logits if cache was invalidated + if p_x0_cache is None: + # Pass only the current block to the model in sample_mode + model_input = x_window[:, -block_length:] + sigma_input = sigma_t.to(model_input.device).float() + model_output = self.model( + input_ids=model_input, + timesteps=sigma_input, + sample_mode=True, + ) + logits = model_output.logits if hasattr(model_output, "logits") else model_output + logits = logits.to(torch.float64) + + # Convert logits to probabilities + p_x0 = logits.exp() + p_x0 = _nucleus_filter(p_x0, nucleus_p) + p_x0_cache = p_x0 + + # -- DDPM update: construct transition distribution -- + # q(x_s | x_t, x_0): with probability (1 - mask_prob) sample from p(x_0), + # with probability mask_prob stay as mask token. + q_xs = p_x0_cache * (1.0 - mask_prob) + q_xs[:, :, mask_token_id] = mask_prob.squeeze(-1) + + # Sample from the transition distribution + x_block_new = _sample_categorical(q_xs) + + # Preserve already-unmasked tokens (copy flag) + current_block = x_accum[:, -block_length:] + copy_flag = (current_block != mask_token_id).to(x_block_new.dtype) + x_block_new = copy_flag * current_block + (1 - copy_flag) * x_block_new + + # Check if the block changed (for cache invalidation) + if not torch.equal(x_block_new, current_block): + p_x0_cache = None # Invalidate cache + x_accum = torch.cat([x_accum[:, :-block_length], x_block_new], dim=1) + else: + # Block unchanged, keep cache for next step + pass + + # -- Callback -- + if callback_on_step_end is not None: + callback_kwargs = {} + block_logits = logits if "block_logits" in callback_on_step_end_tensor_inputs else None + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, global_step, step_idx, callback_kwargs) + x_accum = callback_outputs.pop("x_accum", x_accum) + + global_step += 1 + progress_bar.update(1) + + progress_bar.close() + + # -- EOS early stopping -- + if eos_early_stop and eos_token_id is not None: + for b in range(batch_size): + generated_so_far = x_accum[b, prompt_length:] + eos_positions = (generated_so_far == eos_token_id).nonzero(as_tuple=True)[0] + if len(eos_positions) > 0: + finished[b] = True + if finished.all(): + break + + # 6. Post-process output + # Trim to prompt_length + gen_length + total_generated = x_accum.shape[1] - prompt_length + trim_length = min(total_generated, gen_length) + sequences = x_accum[:, prompt_length : prompt_length + trim_length] + + # Truncate at first EOS if present + if eos_token_id is not None and batch_size == 1: + eos_positions = (sequences[0] == eos_token_id).nonzero(as_tuple=True)[0] + if len(eos_positions) > 0: + sequences = sequences[:, : int(eos_positions[0].item()) + 1] + + texts = None + if output_type == "text" and self.tokenizer is not None: + texts = self.tokenizer.batch_decode(sequences, skip_special_tokens=True) + + if not return_dict: + return sequences.to(device=device), texts + return BD3LMPipelineOutput(sequences=sequences.to(device=device), texts=texts) + + +__all__ = ["BD3LMPipeline", "BD3LMPipelineOutput"] diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 74196f778bba..e0954aec37f5 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -40,6 +40,10 @@ else: _import_structure["deprecated"] = ["KarrasVeScheduler", "ScoreSdeVpScheduler"] _import_structure["scheduling_amused"] = ["AmusedScheduler"] + _import_structure["scheduling_bd3lm_token_diffusion"] = [ + "BD3LMTokenDiffusionScheduler", + "BD3LMTokenDiffusionSchedulerOutput", + ] _import_structure["scheduling_block_refinement"] = ["BlockRefinementScheduler", "BlockRefinementSchedulerOutput"] _import_structure["scheduling_consistency_decoder"] = ["ConsistencyDecoderScheduler"] _import_structure["scheduling_consistency_models"] = ["CMStochasticIterativeScheduler"] @@ -159,6 +163,7 @@ else: from .deprecated import KarrasVeScheduler, ScoreSdeVpScheduler from .scheduling_amused import AmusedScheduler + from .scheduling_bd3lm_token_diffusion import BD3LMTokenDiffusionScheduler, BD3LMTokenDiffusionSchedulerOutput from .scheduling_block_refinement import BlockRefinementScheduler, BlockRefinementSchedulerOutput from .scheduling_consistency_decoder import ConsistencyDecoderScheduler from .scheduling_consistency_models import CMStochasticIterativeScheduler diff --git a/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py new file mode 100644 index 000000000000..e57c0d9042ed --- /dev/null +++ b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py @@ -0,0 +1,351 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import math +from dataclasses import dataclass + +import torch + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput +from .scheduling_utils import SchedulerMixin + + +@dataclass +class BD3LMTokenDiffusionSchedulerOutput(BaseOutput): + """ + Output class for BD3LM token diffusion scheduling. + + Args: + prev_sample (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Updated token sequence after the current denoising step. + p_x0_cache (`torch.Tensor` of shape `(batch_size, block_size, vocab_size)` or `None`): + Cached clean-token probability distribution. When `None`, the model should be called again at the next + step; when not `None`, the cached distribution can be reused. + """ + + prev_sample: torch.LongTensor + p_x0_cache: torch.Tensor | None + + +class BD3LMTokenDiffusionScheduler(SchedulerMixin, ConfigMixin): + """ + Scheduler for Block Discrete Denoising Diffusion Language Models (BD3LM). + + Implements the DDPM-style caching update from BD3LM, which iteratively denoises masked token sequences block by + block. At each step the scheduler computes posterior transition probabilities q(x_s | x_t, x_0) and samples new + tokens for currently masked positions while preserving already-unmasked tokens. + + Supports multiple noise schedules: loglinear, cosine, square, square_root, and log. + """ + + order = 1 + + @register_to_config + def __init__( + self, + block_size: int = 1024, + num_inference_steps: int = 1024, + noise_type: str = "loglinear", + nucleus_p: float = 1.0, + mask_token_id: int = 32000, + ): + self.num_inference_steps = num_inference_steps + self.timesteps: torch.Tensor | None = None + self._dt: float | None = None + + # ------------------------------------------------------------------ + # Timestep management + # ------------------------------------------------------------------ + + def set_timesteps(self, num_inference_steps: int, device: str | torch.device | None = None) -> None: + """ + Create linearly spaced timesteps from 1 to 0 (exclusive). + + Args: + num_inference_steps (`int`): + Number of denoising steps. + device (`str` or `torch.device`, *optional*): + Device for the timestep tensor. + """ + if num_inference_steps <= 0: + raise ValueError(f"`num_inference_steps` must be > 0, got {num_inference_steps}.") + self.num_inference_steps = num_inference_steps + self.timesteps = torch.linspace(1.0, 0.0, num_inference_steps, device=device) + self._dt = 1.0 / num_inference_steps + + # ------------------------------------------------------------------ + # Noise schedule utilities + # ------------------------------------------------------------------ + + def _compute_move_chance(self, t: torch.Tensor) -> torch.Tensor: + """ + Compute the probability that a token has been masked (move chance) at continuous time *t*. + + The move chance depends on the configured ``noise_type``: + - **loglinear**: ``move_chance = t`` + - **cosine**: ``move_chance = 1 - (1 - eps) * cos(t * pi / 2)`` + - **square**: ``move_chance = t ** 2`` + - **square_root**: ``move_chance = t ** 0.5`` + - **log**: ``move_chance = log(1 + t) / log(2)`` + + Args: + t (`torch.Tensor`): + Continuous timestep values in [0, 1]. + + Returns: + `torch.Tensor`: Move chance at each timestep value, same shape as *t*. + """ + noise_type = self.config.noise_type + eps = 1e-3 + if noise_type == "loglinear": + return t + elif noise_type == "cosine": + return 1.0 - (1.0 - eps) * torch.cos(t * math.pi / 2.0) + elif noise_type == "square": + return torch.clamp(t**2, min=eps) + elif noise_type == "square_root": + return torch.clamp(t**0.5, min=eps) + elif noise_type == "log": + return torch.log1p(t) / math.log(2.0) + else: + raise ValueError( + f"Unknown noise_type '{noise_type}'. Must be one of: loglinear, cosine, square, square_root, log." + ) + + # ------------------------------------------------------------------ + # Nucleus (top-p) filtering + # ------------------------------------------------------------------ + + @staticmethod + def _nucleus_filtering(probs: torch.Tensor, nucleus_p: float) -> torch.Tensor: + """ + Apply nucleus (top-p) filtering to a probability distribution. + + Tokens outside the top-p cumulative probability mass are zeroed out and the distribution is renormalised. + + Args: + probs (`torch.Tensor` of shape `(*, vocab_size)`): + Token probability distributions (already softmaxed). + nucleus_p (`float`): + Cumulative probability threshold. Use 1.0 to disable filtering. + + Returns: + `torch.Tensor`: Filtered and renormalised probability distributions. + """ + if nucleus_p >= 1.0: + return probs + sorted_probs, sorted_indices = probs.sort(dim=-1, descending=True) + cumulative_probs = sorted_probs.cumsum(dim=-1) + nucleus_mask = cumulative_probs <= nucleus_p + # Always keep at least the top-1 token + nucleus_mask[..., 0] = True + sorted_probs = sorted_probs * nucleus_mask + # Scatter back to original order + filtered = torch.zeros_like(probs) + filtered.scatter_(-1, sorted_indices, sorted_probs) + filtered = filtered / filtered.sum(dim=-1, keepdim=True) + return filtered + + # ------------------------------------------------------------------ + # Core step + # ------------------------------------------------------------------ + + def step( + self, + model_output: torch.Tensor, + timestep: float | torch.Tensor, + sample: torch.LongTensor, + *, + mask_token_id: int | None = None, + nucleus_p: float | None = None, + generator: torch.Generator | None = None, + return_dict: bool = True, + ) -> BD3LMTokenDiffusionSchedulerOutput | tuple[torch.LongTensor, torch.Tensor | None]: + """ + Perform a single DDPM caching denoising step. + + The method implements the BD3LM reverse-process update: given predicted clean-token logits from the model, it + computes the posterior q(x_s | x_t, x_0), samples new tokens for masked positions, and copies through tokens + that are already unmasked. + + Args: + model_output (`torch.Tensor` of shape `(batch_size, seq_len, vocab_size)`): + Raw logits from the model. Softmax and nucleus filtering are applied internally. + timestep (`float` or `torch.Tensor`): + Current continuous timestep *t* (in [0, 1], starting at 1 and decreasing toward 0). + sample (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Current noisy token sequence. Masked positions contain ``mask_token_id``. + mask_token_id (`int`, *optional*): + Token ID used for masked positions. Defaults to the value from the scheduler config. + nucleus_p (`float`, *optional*): + Nucleus sampling threshold. Defaults to the value from the scheduler config. + generator (`torch.Generator`, *optional*): + Random number generator for reproducible sampling. + return_dict (`bool`): + Whether to return a [`BD3LMTokenDiffusionSchedulerOutput`] or a plain tuple. + + Returns: + [`BD3LMTokenDiffusionSchedulerOutput`] or `tuple`: + The denoised sample and the p_x0 cache (``None`` when the sample changed, meaning the cache is + invalidated and the model must be called again at the next step). + """ + if mask_token_id is None: + mask_token_id = self.config.mask_token_id + if nucleus_p is None: + nucleus_p = self.config.nucleus_p + + block_size = self.config.block_size + dt = self._dt if self._dt is not None else 1.0 / self.num_inference_steps + + # Ensure timestep is a tensor + if not isinstance(timestep, torch.Tensor): + t = torch.tensor([timestep], device=sample.device, dtype=torch.float64) + else: + t = timestep.to(dtype=torch.float64) + if t.dim() == 0: + t = t.unsqueeze(0) + + # ------------------------------------------------------------------ + # Compute move chances at t and s = t - dt + # ------------------------------------------------------------------ + move_chance_t = self._compute_move_chance(t).to(dtype=torch.float64) + move_chance_s = self._compute_move_chance(t - dt).to(dtype=torch.float64) + + # Expand to (batch, 1) for broadcasting against (batch, seq_len) + if move_chance_t.dim() == 1: + move_chance_t = move_chance_t.unsqueeze(-1) + move_chance_s = move_chance_s.unsqueeze(-1) + + # mask_prob: probability that a token stays masked at s given it was masked at t + mask_prob = move_chance_s / move_chance_t # (batch, 1) + + # ------------------------------------------------------------------ + # Convert model logits -> p(x_0) with nucleus filtering + # ------------------------------------------------------------------ + logits = model_output[:, -block_size:].to(dtype=torch.float64) + p_x0 = torch.softmax(logits, dim=-1) + p_x0 = self._nucleus_filtering(p_x0, nucleus_p) + + # ------------------------------------------------------------------ + # Compute posterior q(x_s | x_t, x_0) and sample + # ------------------------------------------------------------------ + # For non-mask tokens: q_xs = p_x0 * (1 - mask_prob), q_xs[mask] = mask_prob + q_xs = p_x0 * (1.0 - mask_prob.unsqueeze(-1)) + q_xs[..., mask_token_id] = mask_prob.squeeze(-1) + + # Gumbel-argmax categorical sampling + gumbel_noise = -(torch.rand_like(q_xs, generator=generator) + 1e-10).log() + gumbel_noise = (1e-10 + gumbel_noise).clamp(min=1e-30) + x_block = (q_xs / gumbel_noise).argmax(dim=-1) + + # ------------------------------------------------------------------ + # Copy flag: preserve tokens that are already unmasked + # ------------------------------------------------------------------ + x_current_block = sample[:, -block_size:] + is_masked = (x_current_block == mask_token_id).to(dtype=x_block.dtype) + x_block = (1 - is_masked) * x_current_block + is_masked * x_block + + # Assemble full sequence + if sample.shape[-1] > block_size: + prev_sample = torch.cat([sample[:, :-block_size], x_block], dim=-1) + else: + prev_sample = x_block + + # ------------------------------------------------------------------ + # Determine p_x0 cache validity + # ------------------------------------------------------------------ + # If any token changed, invalidate the cache so the model is called again. + if not torch.equal(prev_sample, sample): + p_x0_cache = None + else: + p_x0_cache = p_x0 + + if not return_dict: + return prev_sample, p_x0_cache + return BD3LMTokenDiffusionSchedulerOutput( + prev_sample=prev_sample, + p_x0_cache=p_x0_cache, + ) + + # ------------------------------------------------------------------ + # Forward (noising) process + # ------------------------------------------------------------------ + + def add_noise( + self, + original_samples: torch.LongTensor, + timesteps: torch.Tensor, + mask_token_id: int | None = None, + generator: torch.Generator | None = None, + ) -> torch.LongTensor: + """ + Apply the forward noising process: randomly mask tokens with probability determined by the noise schedule. + + Args: + original_samples (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Clean token IDs. + timesteps (`torch.Tensor` of shape `(batch_size,)` or `(batch_size, seq_len)`): + Continuous timestep values in [0, 1] controlling the amount of noise. + mask_token_id (`int`, *optional*): + Token ID to use for masked positions. Defaults to the value from the scheduler config. + generator (`torch.Generator`, *optional*): + Random number generator for reproducibility. + + Returns: + `torch.LongTensor`: Noisy token sequence with the same shape as *original_samples*. + """ + if mask_token_id is None: + mask_token_id = self.config.mask_token_id + + move_chance = self._compute_move_chance(timesteps) + # Expand move_chance to match sample dimensions for broadcasting + if move_chance.dim() == 1: + move_chance = move_chance.unsqueeze(-1) # (batch, 1) + + # Sample uniform noise and mask tokens where noise < move_chance + uniform_noise = torch.rand( + original_samples.shape, + device=original_samples.device, + dtype=move_chance.dtype, + generator=generator, + ) + mask = uniform_noise < move_chance + noisy_samples = torch.where(mask, mask_token_id, original_samples) + return noisy_samples + + # ------------------------------------------------------------------ + # Stopping criterion + # ------------------------------------------------------------------ + + @staticmethod + def check_should_stop(sequences: torch.LongTensor, mask_token_id: int) -> bool: + """ + Check whether all mask tokens have been resolved. + + Args: + sequences (`torch.LongTensor`): + Current token sequences. + mask_token_id (`int`): + Token ID used for masked positions. + + Returns: + `bool`: `True` if no mask tokens remain in *sequences*. + """ + return (sequences == mask_token_id).sum().item() == 0 + + +__all__ = ["BD3LMTokenDiffusionScheduler", "BD3LMTokenDiffusionSchedulerOutput"] diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 4d6e5ba9c7d1..dff111db538e 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2338,6 +2338,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class BD3LMPipeline(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class BD3LMPipelineOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class DiffusionPipeline(metaclass=DummyObject): _backends = ["torch"] @@ -2563,6 +2593,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class BD3LMTokenDiffusionScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + +class BD3LMTokenDiffusionSchedulerOutput(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class BlockRefinementScheduler(metaclass=DummyObject): _backends = ["torch"] From 223197785d3572efdbe4afc863d4ee20fcaa21a6 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Wed, 25 Mar 2026 18:34:27 +0000 Subject: [PATCH 40/44] BD3LM pipeline and scheduler: working end-to-end with scheduler.step integration --- .../pipelines/bd3lm/pipeline_bd3lm.py | 251 ++++++------------ src/diffusers/utils/dummy_pt_objects.py | 18 +- 2 files changed, 90 insertions(+), 179 deletions(-) diff --git a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py index b355d9c4d547..5bd5d3a8ada7 100644 --- a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py +++ b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py @@ -21,6 +21,7 @@ from tqdm.auto import tqdm from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...schedulers import BD3LMTokenDiffusionScheduler from ...utils import BaseOutput, logging, replace_example_docstring from ..pipeline_utils import DiffusionPipeline, DiscreteDiffusionPipelineMixin @@ -32,71 +33,26 @@ Examples: ```python >>> import torch - >>> from transformers import AutoModelForMaskedLM, AutoTokenizer - >>> from diffusers import BD3LMPipeline + >>> from transformers import AutoModelForMaskedLM, GPT2TokenizerFast + >>> from diffusers import BD3LMPipeline, BD3LMTokenDiffusionScheduler >>> model_id = "kuleshov-group/bd3lm-owt-block_size4" - >>> model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True) - >>> tokenizer = AutoTokenizer.from_pretrained("gpt2") - >>> if tokenizer.mask_token_id is None: - ... tokenizer.add_special_tokens({"mask_token": "<|MASK|>"}) - - >>> pipe = BD3LMPipeline(model=model, tokenizer=tokenizer) - >>> pipe = pipe.to("cuda") - >>> output = pipe(gen_length=256, num_inference_steps=64) + >>> model = AutoModelForMaskedLM.from_pretrained(model_id, trust_remote_code=True, dtype=torch.bfloat16).cuda() + >>> tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + >>> scheduler = BD3LMTokenDiffusionScheduler( + ... block_size=model.config.block_size, + ... mask_token_id=model.config.vocab_size, + ... ) + + >>> pipe = BD3LMPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) + >>> output = pipe(gen_length=64, num_inference_steps=64) >>> print(output.texts[0]) ``` """ -def _sample_categorical(categorical_probs: torch.Tensor) -> torch.LongTensor: - """Sample from categorical distributions using Gumbel-max trick.""" - gumbel_norm = 1e-10 - (torch.rand_like(categorical_probs) + 1e-10).log() - return (categorical_probs / gumbel_norm).argmax(dim=-1) - - -def _nucleus_filter( - p_x0: torch.Tensor, - nucleus_p: float, -) -> torch.Tensor: - """Apply nucleus (top-p) filtering to probability distributions. - - Args: - p_x0: Probability tensor of shape `(batch, seq_len, vocab_size)`. - nucleus_p: Cumulative probability threshold for nucleus sampling. - - Returns: - Filtered and renormalised probability tensor of the same shape. - """ - if nucleus_p >= 1.0: - return p_x0 - - sorted_probs, sorted_indices = torch.sort(p_x0, dim=-1, descending=True) - cum_probs = torch.cumsum(sorted_probs, dim=-1) - nucleus_mask = cum_probs <= nucleus_p - # Always keep at least the top-1 token. - nucleus_mask[..., 0] = True - sorted_probs = sorted_probs * nucleus_mask - - # Scatter filtered probabilities back to original positions. - filtered = torch.zeros_like(p_x0) - filtered.scatter_(-1, sorted_indices, sorted_probs) - filtered = filtered / filtered.sum(dim=-1, keepdim=True) - return filtered - - @dataclass class BD3LMPipelineOutput(BaseOutput): - """ - Output class for the BD3LM pipeline. - - Args: - sequences (`torch.LongTensor` of shape `(batch_size, seq_len)`): - Generated token ID sequences. - texts (`list[str]` or `None`): - Decoded text strings when `output_type="text"` and a tokenizer is available. - """ - sequences: torch.LongTensor texts: list[str] | None = None @@ -115,23 +71,27 @@ class BD3LMPipeline(DiffusionPipeline, DiscreteDiffusionPipelineMixin): """ model: Any + scheduler: BD3LMTokenDiffusionScheduler tokenizer: Any - _callback_tensor_inputs = ["x_accum", "block_logits"] + _callback_tensor_inputs = ["x_accum"] def __init__( self, model: Any, - scheduler: Any, + scheduler: BD3LMTokenDiffusionScheduler, tokenizer: Any | None = None, ): super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) - # Resolve mask token ID from model config or tokenizer. + # Resolve mask token ID: model.config.mask_index > model.config.vocab_size > tokenizer self.mask_token_id: int | None = None if hasattr(self.model, "config"): self.mask_token_id = getattr(self.model.config, "mask_index", None) + if self.mask_token_id is None: + # BD3LM convention: mask_token_id = vocab_size (appended mask token) + self.mask_token_id = getattr(self.model.config, "vocab_size", None) if self.mask_token_id is None and self.tokenizer is not None: self.mask_token_id = getattr(self.tokenizer, "mask_token_id", None) @@ -153,10 +113,6 @@ def check_inputs( callback_on_step_end: Callable | PipelineCallback | MultiPipelineCallbacks | None, callback_on_step_end_tensor_inputs: list[str] | None, ): - # Input source validation - if prompt is None and input_ids is None: - # No prompt provided -- unconditional generation starting from BOS. - pass if input_ids is not None: if input_ids.ndim not in (1, 2): raise ValueError(f"`input_ids` must be 1D or 2D, got shape {tuple(input_ids.shape)}.") @@ -165,7 +121,6 @@ def check_inputs( if prompt is not None and input_ids is None and self.tokenizer is None: raise ValueError("Tokenizer is required when `input_ids` is not provided.") - # Generation parameter validation if gen_length <= 0: raise ValueError(f"`gen_length` must be > 0, got {gen_length}.") if block_length <= 0: @@ -177,7 +132,6 @@ def check_inputs( if output_type not in {"seq", "text"}: raise ValueError(f"`output_type` must be 'seq' or 'text', got {output_type!r}.") - # Callback validation if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -229,13 +183,13 @@ def __call__( nucleus_p (`float`, defaults to `1.0`): Nucleus sampling probability threshold. Set to `1.0` to disable nucleus filtering. mask_token_id (`int`, *optional*): - Mask token ID. Resolved from `model.config.mask_index` or tokenizer if not provided. + Mask token ID. Resolved from model config or scheduler config if not provided. eos_token_id (`int`, *optional*): EOS token ID for early stopping. eos_early_stop (`bool`, defaults to `True`): Whether to stop generation when EOS is produced. generator (`torch.Generator`, *optional*): - RNG for reproducibility (currently unused; sampling uses Gumbel-max). + RNG for reproducibility. output_type (`str`, defaults to `"text"`): Output format. `"text"` decodes sequences into strings. `"seq"` returns raw token IDs. return_dict (`bool`, defaults to `True`): @@ -243,7 +197,7 @@ def __call__( callback_on_step_end (`Callable` or `PipelineCallback`, *optional*): Callback executed after each denoising step. callback_on_step_end_tensor_inputs (`List[str]`, *optional*): - Tensor keys to pass to the callback. Allowed keys: `x_accum`, `block_logits`. + Tensor keys to pass to the callback. Allowed keys: `x_accum`. Examples: """ @@ -256,9 +210,9 @@ def __call__( if mask_token_id is None: mask_token_id = self.mask_token_id if mask_token_id is None: - raise ValueError( - "`mask_token_id` must be provided (or available via `model.config.mask_index` or tokenizer)." - ) + mask_token_id = self.scheduler.config.mask_token_id + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available via model config or scheduler config).") if eos_token_id is None: eos_token_id = self.eos_token_id @@ -312,132 +266,92 @@ def __call__( prompt_length = prompt_ids.shape[1] - # 3. Compute number of strides (blocks to generate) + # 3. Set up scheduler timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) num_strides = (gen_length + block_length - 1) // block_length - self._num_timesteps = num_inference_steps * num_strides - # 4. Log-linear noise schedule helpers (matching BD3LM's LogLinearNoise). - # The noise schedule gives move_chance = t (probability a token is masked at time t). - # sigma(t) = -log(1 - t), capped at sigma_max = -log(eps) for eps=1e-3. + # Compute sigma from move_chance for model input noise_eps = 1e-3 sigma_max = -torch.log(torch.tensor(noise_eps, device=device, dtype=torch.float64)) - def _move_chance(t: torch.Tensor) -> torch.Tensor: - """Compute masking probability at time t (log-linear schedule: move_chance = t).""" - return t - - def _sigma_from_move_chance(p: torch.Tensor) -> torch.Tensor: - """Convert move_chance to sigma, clamped at sigma_max.""" - return torch.min(-torch.log(1.0 - p), sigma_max) - - # 5. Semi-autoregressive block diffusion loop + # 4. Semi-autoregressive block diffusion loop finished = torch.zeros((batch_size,), device=device, dtype=torch.bool) global_step = 0 + x_accum: torch.LongTensor = prompt_ids block_progress_bar_config = getattr(self, "_progress_bar_config", {}).copy() block_progress_bar_config["position"] = 0 block_progress_bar_config["desc"] = "Blocks" - x_accum: torch.LongTensor | None = None - for stride_num in tqdm(range(num_strides), **block_progress_bar_config): - # -- Extend x_accum with a new masked block -- - if stride_num == 0: - # First block: prompt + masked block - masked_block = torch.full((batch_size, block_length), mask_token_id, device=device, dtype=torch.long) - x_accum = torch.cat([prompt_ids, masked_block], dim=1) - # Set BOS token at position 0 if prompt is just BOS - if prompt_length == 1: - bos_id = self._resolve_start_token_id() - if bos_id is not None: - x_accum[:, 0] = bos_id - else: - masked_block = torch.full((batch_size, block_length), mask_token_id, device=device, dtype=torch.long) - x_accum = torch.cat([x_accum, masked_block], dim=1) - - # -- Determine the forward window indices -- - # The model sees the last (stride_num + 1) * block_length + prompt_length tokens, - # but BD3LM in sample_mode only processes the current block. - # We pass the full accumulated sequence as context. - end_idx = prompt_length + (stride_num + 1) * block_length - start_idx = 0 # Use full context - fwd_indices = torch.arange(start_idx, end_idx, device=device) - - # -- DDPM denoising steps within this block -- - dt = 1.0 / num_inference_steps + # Append a new masked block + masked_block = torch.full((batch_size, block_length), mask_token_id, device=device, dtype=torch.long) + x_accum = torch.cat([x_accum, masked_block], dim=1) + + # DDPM denoising steps within this block p_x0_cache = None - timesteps = torch.linspace(1.0, 0.0, num_inference_steps, device=device, dtype=torch.float64) self.set_progress_bar_config(position=1, leave=False, desc=f"Block {stride_num} Denoising") progress_bar = self.progress_bar(total=num_inference_steps) for step_idx in range(num_inference_steps): - # Check if any mask tokens remain in the current block - current_block = x_accum[:, -block_length:] - if (current_block != mask_token_id).all(): + # Check if all mask tokens resolved + if self.scheduler.check_should_stop(x_accum[:, -block_length:], mask_token_id): progress_bar.update(num_inference_steps - step_idx) break - t = timesteps[step_idx] - - # -- Compute move chances and sigma -- - t_tensor = t.unsqueeze(0).expand(batch_size).to(torch.float64) - s_tensor = (t - dt).clamp(min=0.0).unsqueeze(0).expand(batch_size).to(torch.float64) - - move_chance_t = _move_chance(t_tensor) - move_chance_s = _move_chance(s_tensor) - sigma_t = _sigma_from_move_chance(move_chance_t) + t = self.scheduler.timesteps[step_idx] - # mask_prob = move_chance_s / move_chance_t (probability token stays masked) - mask_prob = (move_chance_s / move_chance_t).unsqueeze(-1) # (batch, 1) - - # -- Get model predictions -- - x_window = x_accum[:, fwd_indices] - # Only recompute logits if cache was invalidated + # Get model predictions (only if cache was invalidated) if p_x0_cache is None: - # Pass only the current block to the model in sample_mode - model_input = x_window[:, -block_length:] - sigma_input = sigma_t.to(model_input.device).float() + # Compute sigma for the model + t_tensor = t.unsqueeze(0).expand(batch_size).to(torch.float64) + move_chance_t = self.scheduler._compute_move_chance(t_tensor) + sigma_t = torch.min(-torch.log(1.0 - move_chance_t), sigma_max).float() + + model_input = x_accum[:, -block_length:] model_output = self.model( input_ids=model_input, - timesteps=sigma_input, + timesteps=sigma_t.to(model_input.device), sample_mode=True, ) logits = model_output.logits if hasattr(model_output, "logits") else model_output - logits = logits.to(torch.float64) - - # Convert logits to probabilities - p_x0 = logits.exp() - p_x0 = _nucleus_filter(p_x0, nucleus_p) - p_x0_cache = p_x0 - - # -- DDPM update: construct transition distribution -- - # q(x_s | x_t, x_0): with probability (1 - mask_prob) sample from p(x_0), - # with probability mask_prob stay as mask token. - q_xs = p_x0_cache * (1.0 - mask_prob) - q_xs[:, :, mask_token_id] = mask_prob.squeeze(-1) - - # Sample from the transition distribution - x_block_new = _sample_categorical(q_xs) - - # Preserve already-unmasked tokens (copy flag) - current_block = x_accum[:, -block_length:] - copy_flag = (current_block != mask_token_id).to(x_block_new.dtype) - x_block_new = copy_flag * current_block + (1 - copy_flag) * x_block_new - - # Check if the block changed (for cache invalidation) - if not torch.equal(x_block_new, current_block): - p_x0_cache = None # Invalidate cache - x_accum = torch.cat([x_accum[:, :-block_length], x_block_new], dim=1) + + # Scheduler step: DDPM update + scheduler_output = self.scheduler.step( + model_output=logits, + timestep=t, + sample=x_accum, + mask_token_id=mask_token_id, + nucleus_p=nucleus_p, + generator=generator, + return_dict=True, + ) + + x_accum = scheduler_output.prev_sample + p_x0_cache = scheduler_output.p_x0_cache else: - # Block unchanged, keep cache for next step - pass + # Cache is valid — reuse p_x0. We still need to run the scheduler step + # but with the cached logits. Convert p_x0_cache back to logits. + logits = p_x0_cache.log() + + scheduler_output = self.scheduler.step( + model_output=logits, + timestep=t, + sample=x_accum, + mask_token_id=mask_token_id, + nucleus_p=nucleus_p, + generator=generator, + return_dict=True, + ) + + x_accum = scheduler_output.prev_sample + p_x0_cache = scheduler_output.p_x0_cache - # -- Callback -- + # Callback if callback_on_step_end is not None: callback_kwargs = {} - block_logits = logits if "block_logits" in callback_on_step_end_tensor_inputs else None for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, global_step, step_idx, callback_kwargs) @@ -448,23 +362,20 @@ def _sigma_from_move_chance(p: torch.Tensor) -> torch.Tensor: progress_bar.close() - # -- EOS early stopping -- + # EOS early stopping if eos_early_stop and eos_token_id is not None: for b in range(batch_size): - generated_so_far = x_accum[b, prompt_length:] - eos_positions = (generated_so_far == eos_token_id).nonzero(as_tuple=True)[0] - if len(eos_positions) > 0: + generated = x_accum[b, prompt_length:] + if (generated == eos_token_id).any(): finished[b] = True if finished.all(): break - # 6. Post-process output - # Trim to prompt_length + gen_length + # 5. Post-process output total_generated = x_accum.shape[1] - prompt_length trim_length = min(total_generated, gen_length) sequences = x_accum[:, prompt_length : prompt_length + trim_length] - # Truncate at first EOS if present if eos_token_id is not None and batch_size == 1: eos_positions = (sequences[0] == eos_token_id).nonzero(as_tuple=True)[0] if len(eos_positions) > 0: diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index dff111db538e..d3274c6e9f49 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -2233,7 +2233,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BlipDiffusionControlNetPipeline(metaclass=DummyObject): +class BD3LMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2248,7 +2248,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BlipDiffusionPipeline(metaclass=DummyObject): +class BD3LMPipelineOutput(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2263,7 +2263,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class CLIPImageProjection(metaclass=DummyObject): +class BlipDiffusionControlNetPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2278,7 +2278,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class ConsistencyModelPipeline(metaclass=DummyObject): +class BlipDiffusionPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2293,7 +2293,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DanceDiffusionPipeline(metaclass=DummyObject): +class CLIPImageProjection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2308,7 +2308,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDIMPipeline(metaclass=DummyObject): +class ConsistencyModelPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2323,7 +2323,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class DDPMPipeline(metaclass=DummyObject): +class DanceDiffusionPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2338,7 +2338,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BD3LMPipeline(metaclass=DummyObject): +class DDIMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): @@ -2353,7 +2353,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) -class BD3LMPipelineOutput(metaclass=DummyObject): +class DDPMPipeline(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): From 47e6af880ca932e0f81f6076db573fc9c03ae5c7 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Fri, 27 Mar 2026 08:58:18 +0000 Subject: [PATCH 41/44] BD3LM: fix sampling with kv_cache and subs parameterization - Add kv_cache support: reset_kv_cache() at start, store_kv=True after each block - Apply subs parameterization in scheduler: mask token -> -inf, log_softmax, identity for unmasked - These match the official BD3LM sampling code --- .../pipelines/bd3lm/pipeline_bd3lm.py | 21 +++++++++++++++++-- .../scheduling_bd3lm_token_diffusion.py | 16 ++++++++++++-- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py index 5bd5d3a8ada7..976a7746f784 100644 --- a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py +++ b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py @@ -280,6 +280,10 @@ def __call__( global_step = 0 x_accum: torch.LongTensor = prompt_ids + # Initialize KV cache for cross-block context + if hasattr(self.model, "reset_kv_cache"): + self.model.reset_kv_cache(eval_batch_size=batch_size) + block_progress_bar_config = getattr(self, "_progress_bar_config", {}).copy() block_progress_bar_config["position"] = 0 block_progress_bar_config["desc"] = "Blocks" @@ -310,6 +314,7 @@ def __call__( move_chance_t = self.scheduler._compute_move_chance(t_tensor) sigma_t = torch.min(-torch.log(1.0 - move_chance_t), sigma_max).float() + # Pass current block to model in sample_mode (KV cache provides cross-block context). model_input = x_accum[:, -block_length:] model_output = self.model( input_ids=model_input, @@ -332,8 +337,7 @@ def __call__( x_accum = scheduler_output.prev_sample p_x0_cache = scheduler_output.p_x0_cache else: - # Cache is valid — reuse p_x0. We still need to run the scheduler step - # but with the cached logits. Convert p_x0_cache back to logits. + # Cache is valid — reuse p_x0. Run scheduler step with cached distribution. logits = p_x0_cache.log() scheduler_output = self.scheduler.step( @@ -362,6 +366,19 @@ def __call__( progress_bar.close() + # Store denoised block's KVs into cache for cross-block context + if hasattr(self.model, "reset_kv_cache"): + denoised_block = x_accum[:, -block_length:] + t_tensor = self.scheduler.timesteps[0].unsqueeze(0).expand(batch_size).to(torch.float64) + move_chance_t = self.scheduler._compute_move_chance(t_tensor) + sigma_store = torch.min(-torch.log(1.0 - move_chance_t), sigma_max).float() + self.model( + input_ids=denoised_block, + timesteps=sigma_store.to(denoised_block.device), + sample_mode=True, + store_kv=True, + ) + # EOS early stopping if eos_early_stop and eos_token_id is not None: for b in range(batch_size): diff --git a/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py index e57c0d9042ed..27271effde9b 100644 --- a/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py @@ -234,10 +234,22 @@ def step( mask_prob = move_chance_s / move_chance_t # (batch, 1) # ------------------------------------------------------------------ - # Convert model logits -> p(x_0) with nucleus filtering + # Apply subs parameterization and convert to p(x_0) # ------------------------------------------------------------------ logits = model_output[:, -block_size:].to(dtype=torch.float64) - p_x0 = torch.softmax(logits, dim=-1) + + # Subs parameterization: mask token gets -inf, then log_softmax normalizes. + # For unmasked positions, the distribution is forced to be the identity. + logits[..., mask_token_id] = -1e9 + logits = logits - torch.logsumexp(logits, dim=-1, keepdim=True) + + x_current_block = sample[:, -block_size:] + unmasked = x_current_block != mask_token_id + logits[unmasked] = -1e9 + logits[unmasked, x_current_block[unmasked]] = 0.0 + + # Convert log-probs to probs and apply nucleus filtering + p_x0 = logits.exp() p_x0 = self._nucleus_filtering(p_x0, nucleus_p) # ------------------------------------------------------------------ From 99a1b2294b3483c8bf234cb36c45059f23a9f6bc Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Fri, 27 Mar 2026 09:17:01 +0000 Subject: [PATCH 42/44] BD3LM: apply all LLaDA2 PR conventions, add tests - check_inputs called first, before any processing - mask_token_id: vocab_size - 1 (not vocab_size) - Sigma computation moved to scheduler.compute_sigma() - EOS checking moved to scheduler.check_eos_finished() - Deduplicated p_x0_cache handling - Added 36 tests (10 pipeline + 26 scheduler) --- .../pipelines/bd3lm/pipeline_bd3lm.py | 80 ++--- .../scheduling_bd3lm_token_diffusion.py | 67 +++- tests/pipelines/bd3lm/__init__.py | 0 tests/pipelines/bd3lm/test_bd3lm.py | 226 ++++++++++++ .../test_scheduler_bd3lm_token_diffusion.py | 338 ++++++++++++++++++ 5 files changed, 656 insertions(+), 55 deletions(-) create mode 100644 tests/pipelines/bd3lm/__init__.py create mode 100644 tests/pipelines/bd3lm/test_bd3lm.py create mode 100644 tests/schedulers/test_scheduler_bd3lm_token_diffusion.py diff --git a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py index 976a7746f784..0459d26a0ac4 100644 --- a/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py +++ b/src/diffusers/pipelines/bd3lm/pipeline_bd3lm.py @@ -41,11 +41,11 @@ >>> tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") >>> scheduler = BD3LMTokenDiffusionScheduler( ... block_size=model.config.block_size, - ... mask_token_id=model.config.vocab_size, + ... mask_token_id=model.config.vocab_size - 1, ... ) >>> pipe = BD3LMPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) - >>> output = pipe(gen_length=64, num_inference_steps=64) + >>> output = pipe(gen_length=64, num_inference_steps=64, nucleus_p=0.9) >>> print(output.texts[0]) ``` """ @@ -85,13 +85,14 @@ def __init__( super().__init__() self.register_modules(model=model, scheduler=scheduler, tokenizer=tokenizer) - # Resolve mask token ID: model.config.mask_index > model.config.vocab_size > tokenizer + # Resolve mask token ID: model.config.mask_index > vocab_size - 1 (BD3LM convention) > tokenizer self.mask_token_id: int | None = None if hasattr(self.model, "config"): self.mask_token_id = getattr(self.model.config, "mask_index", None) if self.mask_token_id is None: - # BD3LM convention: mask_token_id = vocab_size (appended mask token) - self.mask_token_id = getattr(self.model.config, "vocab_size", None) + vocab_size = getattr(self.model.config, "vocab_size", None) + if vocab_size is not None: + self.mask_token_id = vocab_size - 1 if self.mask_token_id is None and self.tokenizer is not None: self.mask_token_id = getattr(self.tokenizer, "mask_token_id", None) @@ -201,7 +202,7 @@ def __call__( Examples: """ - # 0. Resolve defaults + # 1. Resolve defaults and check inputs early if block_length is None: block_length = getattr(getattr(self.model, "config", None), "block_size", None) if block_length is None: @@ -217,7 +218,6 @@ def __call__( if eos_token_id is None: eos_token_id = self.eos_token_id - # 1. Handle callbacks if callback_on_step_end is not None and isinstance( callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks) ): @@ -254,7 +254,6 @@ def __call__( prompt_ids = prompt_ids.to(device=device) batch_size = prompt_ids.shape[0] else: - # Unconditional generation: start with BOS token. batch_size = 1 bos_id = self._resolve_start_token_id() if bos_id is None: @@ -271,10 +270,6 @@ def __call__( num_strides = (gen_length + block_length - 1) // block_length self._num_timesteps = num_inference_steps * num_strides - # Compute sigma from move_chance for model input - noise_eps = 1e-3 - sigma_max = -torch.log(torch.tensor(noise_eps, device=device, dtype=torch.float64)) - # 4. Semi-autoregressive block diffusion loop finished = torch.zeros((batch_size,), device=device, dtype=torch.bool) global_step = 0 @@ -300,21 +295,16 @@ def __call__( progress_bar = self.progress_bar(total=num_inference_steps) for step_idx in range(num_inference_steps): - # Check if all mask tokens resolved + # Check if all mask tokens in current block are resolved if self.scheduler.check_should_stop(x_accum[:, -block_length:], mask_token_id): progress_bar.update(num_inference_steps - step_idx) break t = self.scheduler.timesteps[step_idx] - # Get model predictions (only if cache was invalidated) + # Get model predictions only when p_x0 cache is invalidated if p_x0_cache is None: - # Compute sigma for the model - t_tensor = t.unsqueeze(0).expand(batch_size).to(torch.float64) - move_chance_t = self.scheduler._compute_move_chance(t_tensor) - sigma_t = torch.min(-torch.log(1.0 - move_chance_t), sigma_max).float() - - # Pass current block to model in sample_mode (KV cache provides cross-block context). + sigma_t = self.scheduler.compute_sigma(t, batch_size) model_input = x_accum[:, -block_length:] model_output = self.model( input_ids=model_input, @@ -322,36 +312,23 @@ def __call__( sample_mode=True, ) logits = model_output.logits if hasattr(model_output, "logits") else model_output - - # Scheduler step: DDPM update - scheduler_output = self.scheduler.step( - model_output=logits, - timestep=t, - sample=x_accum, - mask_token_id=mask_token_id, - nucleus_p=nucleus_p, - generator=generator, - return_dict=True, - ) - - x_accum = scheduler_output.prev_sample - p_x0_cache = scheduler_output.p_x0_cache else: - # Cache is valid — reuse p_x0. Run scheduler step with cached distribution. + # Reuse cached p_x0 distribution (convert back to log-probs for scheduler) logits = p_x0_cache.log() - scheduler_output = self.scheduler.step( - model_output=logits, - timestep=t, - sample=x_accum, - mask_token_id=mask_token_id, - nucleus_p=nucleus_p, - generator=generator, - return_dict=True, - ) + # Scheduler step: DDPM update with subs parameterization + scheduler_output = self.scheduler.step( + model_output=logits, + timestep=t, + sample=x_accum, + mask_token_id=mask_token_id, + nucleus_p=nucleus_p, + generator=generator, + return_dict=True, + ) - x_accum = scheduler_output.prev_sample - p_x0_cache = scheduler_output.p_x0_cache + x_accum = scheduler_output.prev_sample + p_x0_cache = scheduler_output.p_x0_cache # Callback if callback_on_step_end is not None: @@ -369,9 +346,7 @@ def __call__( # Store denoised block's KVs into cache for cross-block context if hasattr(self.model, "reset_kv_cache"): denoised_block = x_accum[:, -block_length:] - t_tensor = self.scheduler.timesteps[0].unsqueeze(0).expand(batch_size).to(torch.float64) - move_chance_t = self.scheduler._compute_move_chance(t_tensor) - sigma_store = torch.min(-torch.log(1.0 - move_chance_t), sigma_max).float() + sigma_store = self.scheduler.compute_sigma(self.scheduler.timesteps[0], batch_size) self.model( input_ids=denoised_block, timesteps=sigma_store.to(denoised_block.device), @@ -379,12 +354,9 @@ def __call__( store_kv=True, ) - # EOS early stopping + # EOS early stopping (delegated to scheduler) if eos_early_stop and eos_token_id is not None: - for b in range(batch_size): - generated = x_accum[b, prompt_length:] - if (generated == eos_token_id).any(): - finished[b] = True + finished = self.scheduler.check_eos_finished(x_accum, prompt_length, eos_token_id, finished) if finished.all(): break diff --git a/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py index 27271effde9b..f484939ea725 100644 --- a/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py +++ b/src/diffusers/schedulers/scheduling_bd3lm_token_diffusion.py @@ -340,9 +340,74 @@ def add_noise( return noisy_samples # ------------------------------------------------------------------ - # Stopping criterion + # Sigma computation (for model input) # ------------------------------------------------------------------ + def compute_sigma(self, t: float | torch.Tensor, batch_size: int = 1) -> torch.Tensor: + """ + Compute the sigma value (noise level) for a given timestep. + + Sigma is derived from the noise schedule's move chance: ``sigma = -log(1 - move_chance)``, clamped at + ``sigma_max = -log(eps)`` where ``eps = 1e-3``. + + Args: + t (`float` or `torch.Tensor`): + Continuous timestep value in [0, 1]. + batch_size (`int`): + Batch size for expanding the result. + + Returns: + `torch.Tensor`: Sigma values of shape ``(batch_size,)`` in float32. + """ + if not isinstance(t, torch.Tensor): + t = torch.tensor([t], dtype=torch.float64) + t = t.to(dtype=torch.float64) + if t.dim() == 0: + t = t.unsqueeze(0) + t = t.expand(batch_size) + + eps = 1e-3 + sigma_max = -torch.log(torch.tensor(eps, device=t.device, dtype=torch.float64)) + move_chance = self._compute_move_chance(t) + sigma = torch.min(-torch.log(1.0 - move_chance), sigma_max) + return sigma.float() + + # ------------------------------------------------------------------ + # Stopping criteria + # ------------------------------------------------------------------ + + @staticmethod + def check_eos_finished( + sequences: torch.LongTensor, + prompt_length: int, + eos_token_id: int, + finished: torch.BoolTensor, + ) -> torch.BoolTensor: + """ + Update per-batch finished flags when EOS tokens appear in the generated portion. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, seq_len)`): + Full accumulated sequence including prompt. + prompt_length (`int`): + Number of prompt tokens at the start. + eos_token_id (`int`): + EOS token ID. + finished (`torch.BoolTensor` of shape `(batch_size,)`): + Current per-batch finished flags. + + Returns: + `torch.BoolTensor`: Updated finished flags. + """ + batch_size = sequences.shape[0] + for b in range(batch_size): + if finished[b]: + continue + generated = sequences[b, prompt_length:] + if (generated == eos_token_id).any(): + finished[b] = True + return finished + @staticmethod def check_should_stop(sequences: torch.LongTensor, mask_token_id: int) -> bool: """ diff --git a/tests/pipelines/bd3lm/__init__.py b/tests/pipelines/bd3lm/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/bd3lm/test_bd3lm.py b/tests/pipelines/bd3lm/test_bd3lm.py new file mode 100644 index 000000000000..d31c91a11f5e --- /dev/null +++ b/tests/pipelines/bd3lm/test_bd3lm.py @@ -0,0 +1,226 @@ +import unittest + +import torch + +from diffusers import BD3LMPipeline, BD3LMTokenDiffusionScheduler + + +class _DummyConfig: + def __init__(self, block_size, vocab_size, mask_index): + self.block_size = block_size + self.vocab_size = vocab_size + self.mask_index = mask_index + + +class _DummyBD3LMModel(torch.nn.Module): + """Minimal model that satisfies BD3LMPipeline's interface.""" + + def __init__(self, vocab_size=32, block_size=4): + super().__init__() + self.config = _DummyConfig( + block_size=block_size, + vocab_size=vocab_size, + mask_index=vocab_size - 1, + ) + self.backbone = torch.nn.Linear(1, vocab_size, bias=False) + self.register_buffer("_device_anchor", torch.empty(0)) + + @property + def dtype(self): + return torch.float32 + + @property + def device(self): + return self._device_anchor.device + + def reset_kv_cache(self, eval_batch_size): + pass + + def forward(self, input_ids, timesteps, sample_mode=False, store_kv=False): + batch_size, seq_len = input_ids.shape + logits = torch.zeros( + (batch_size, seq_len, self.config.vocab_size), + device=input_ids.device, + dtype=torch.float32, + ) + # Make logits vary by position so denoising is deterministic. + positions = torch.arange(seq_len, device=input_ids.device, dtype=torch.float32).view(1, seq_len, 1) + token_ids = (torch.arange(seq_len, device=input_ids.device) % (self.config.vocab_size - 2)).view(1, seq_len, 1) + logits.scatter_( + 2, + token_ids.expand(batch_size, -1, -1), + 1.0 + positions.expand(batch_size, -1, -1) * 0.1, + ) + return logits + + +def _make_pipeline(tokenizer=None, vocab_size=32, block_size=4): + model = _DummyBD3LMModel(vocab_size=vocab_size, block_size=block_size) + scheduler = BD3LMTokenDiffusionScheduler( + block_size=block_size, + mask_token_id=vocab_size - 1, + ) + return BD3LMPipeline(model=model, scheduler=scheduler, tokenizer=tokenizer) + + +class BD3LMPipelineTest(unittest.TestCase): + def test_pipeline_runs(self): + """Basic end-to-end generation with input_ids.""" + pipe = _make_pipeline().to("cpu") + + input_ids = torch.tensor([[5, 6, 7, 8], [1, 2, 3, 4]], dtype=torch.long) + out = pipe( + input_ids=input_ids, + gen_length=8, + num_inference_steps=8, + nucleus_p=1.0, + output_type="seq", + ) + + self.assertEqual(out.sequences.shape[0], 2) + self.assertGreater(out.sequences.shape[1], 0) + self.assertLessEqual(out.sequences.shape[1], 8) + + def test_output_type_seq(self): + """output_type='seq' returns sequences but no texts.""" + pipe = _make_pipeline().to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="seq", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + def test_output_type_text_with_tokenizer(self): + """output_type='text' with a tokenizer should return decoded texts.""" + tok = type( + "Tok", + (), + { + "eos_token_id": None, + "mask_token_id": 31, + "batch_decode": lambda self, seqs, **kw: [f"decoded_{len(s)}" for s in seqs], + }, + )() + pipe = _make_pipeline(tokenizer=tok).to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNotNone(out.texts) + self.assertEqual(len(out.texts), 1) + self.assertTrue(out.texts[0].startswith("decoded_")) + + def test_output_type_text_without_tokenizer(self): + """output_type='text' without a tokenizer should return texts=None.""" + pipe = _make_pipeline(tokenizer=None).to("cpu") + + out = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="text", + ) + + self.assertIsNotNone(out.sequences) + self.assertIsNone(out.texts) + + def test_output_type_invalid_raises(self): + """Invalid output_type should raise ValueError.""" + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="invalid", + ) + + def test_return_dict_false(self): + """return_dict=False should return a plain tuple.""" + pipe = _make_pipeline().to("cpu") + + result = pipe( + input_ids=torch.tensor([[5, 6, 7, 8]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="seq", + return_dict=False, + ) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 2) + sequences, texts = result + self.assertIsInstance(sequences, torch.Tensor) + self.assertIsNone(texts) + + def test_check_inputs_bad_gen_length(self): + """gen_length <= 0 should raise ValueError.""" + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + gen_length=0, + num_inference_steps=4, + nucleus_p=1.0, + output_type="seq", + ) + + def test_check_inputs_bad_num_inference_steps(self): + """num_inference_steps <= 0 should raise ValueError.""" + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + gen_length=8, + num_inference_steps=0, + nucleus_p=1.0, + output_type="seq", + ) + + def test_check_inputs_bad_nucleus_p(self): + """nucleus_p out of (0, 1] should raise ValueError.""" + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=0.0, + output_type="seq", + ) + + def test_check_inputs_bad_output_type(self): + """output_type not in {'seq', 'text'} should raise ValueError.""" + pipe = _make_pipeline().to("cpu") + + with self.assertRaises(ValueError): + pipe( + input_ids=torch.tensor([[1, 2]], dtype=torch.long), + gen_length=8, + num_inference_steps=4, + nucleus_p=1.0, + output_type="bad", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/schedulers/test_scheduler_bd3lm_token_diffusion.py b/tests/schedulers/test_scheduler_bd3lm_token_diffusion.py new file mode 100644 index 000000000000..a13f063753b8 --- /dev/null +++ b/tests/schedulers/test_scheduler_bd3lm_token_diffusion.py @@ -0,0 +1,338 @@ +import math +import tempfile +import unittest + +import torch + +from diffusers import BD3LMTokenDiffusionScheduler + + +class BD3LMTokenDiffusionSchedulerTest(unittest.TestCase): + def get_scheduler(self, **kwargs): + config = { + "block_size": 8, + "num_inference_steps": 8, + "noise_type": "loglinear", + "nucleus_p": 1.0, + "mask_token_id": 31, + } + config.update(kwargs) + return BD3LMTokenDiffusionScheduler(**config) + + # ------------------------------------------------------------------ + # Timestep management + # ------------------------------------------------------------------ + + def test_set_timesteps(self): + scheduler = self.get_scheduler() + scheduler.set_timesteps(8) + self.assertEqual(scheduler.num_inference_steps, 8) + self.assertEqual(len(scheduler.timesteps), 8) + # Should go from 1.0 down to near 0.0 + self.assertAlmostEqual(scheduler.timesteps[0].item(), 1.0, places=4) + self.assertAlmostEqual(scheduler.timesteps[-1].item(), 0.0, places=4) + + def test_set_timesteps_invalid(self): + scheduler = self.get_scheduler() + with self.assertRaises(ValueError): + scheduler.set_timesteps(0) + + # ------------------------------------------------------------------ + # Noise schedule: _compute_move_chance + # ------------------------------------------------------------------ + + def test_compute_move_chance_loglinear(self): + scheduler = self.get_scheduler(noise_type="loglinear") + t = torch.tensor([0.0, 0.5, 1.0]) + mc = scheduler._compute_move_chance(t) + self.assertTrue(torch.allclose(mc, t)) + + def test_compute_move_chance_cosine(self): + scheduler = self.get_scheduler(noise_type="cosine") + t = torch.tensor([0.0, 0.5, 1.0]) + mc = scheduler._compute_move_chance(t) + eps = 1e-3 + expected_0 = 1.0 - (1.0 - eps) * math.cos(0.0) + expected_half = 1.0 - (1.0 - eps) * math.cos(0.5 * math.pi / 2.0) + expected_1 = 1.0 - (1.0 - eps) * math.cos(math.pi / 2.0) + self.assertAlmostEqual(mc[0].item(), expected_0, places=5) + self.assertAlmostEqual(mc[1].item(), expected_half, places=5) + self.assertAlmostEqual(mc[2].item(), expected_1, places=5) + + def test_compute_move_chance_square(self): + scheduler = self.get_scheduler(noise_type="square") + t = torch.tensor([0.0, 0.5, 1.0]) + mc = scheduler._compute_move_chance(t) + eps = 1e-3 + self.assertAlmostEqual(mc[0].item(), eps, places=5) + self.assertAlmostEqual(mc[1].item(), 0.25, places=5) + self.assertAlmostEqual(mc[2].item(), 1.0, places=5) + + def test_compute_move_chance_square_root(self): + scheduler = self.get_scheduler(noise_type="square_root") + t = torch.tensor([0.0, 0.25, 1.0]) + mc = scheduler._compute_move_chance(t) + eps = 1e-3 + self.assertAlmostEqual(mc[0].item(), eps, places=5) + self.assertAlmostEqual(mc[1].item(), 0.5, places=5) + self.assertAlmostEqual(mc[2].item(), 1.0, places=5) + + def test_compute_move_chance_log(self): + scheduler = self.get_scheduler(noise_type="log") + t = torch.tensor([0.0, 1.0]) + mc = scheduler._compute_move_chance(t) + self.assertAlmostEqual(mc[0].item(), 0.0, places=5) + self.assertAlmostEqual(mc[1].item(), 1.0, places=5) + + # ------------------------------------------------------------------ + # Sigma computation + # ------------------------------------------------------------------ + + def test_compute_sigma(self): + scheduler = self.get_scheduler(noise_type="loglinear") + sigma = scheduler.compute_sigma(0.5, batch_size=2) + self.assertEqual(sigma.shape, (2,)) + # sigma = -log(1 - move_chance) = -log(1 - 0.5) = log(2) + expected = math.log(2.0) + self.assertAlmostEqual(sigma[0].item(), expected, places=4) + self.assertAlmostEqual(sigma[1].item(), expected, places=4) + + def test_compute_sigma_clamps_at_max(self): + scheduler = self.get_scheduler(noise_type="loglinear") + # At t=1.0, move_chance=1.0, so -log(0) -> inf, should be clamped. + sigma = scheduler.compute_sigma(1.0, batch_size=1) + eps = 1e-3 + sigma_max = -math.log(eps) + self.assertAlmostEqual(sigma[0].item(), sigma_max, places=3) + + # ------------------------------------------------------------------ + # Config save/load + # ------------------------------------------------------------------ + + def test_save_load_config_round_trip(self): + scheduler = self.get_scheduler(block_size=16, noise_type="cosine", nucleus_p=0.9, mask_token_id=99) + with tempfile.TemporaryDirectory() as tmpdir: + scheduler.save_config(tmpdir) + loaded = BD3LMTokenDiffusionScheduler.from_pretrained(tmpdir) + + self.assertEqual(loaded.config.block_size, 16) + self.assertEqual(loaded.config.noise_type, "cosine") + self.assertAlmostEqual(loaded.config.nucleus_p, 0.9) + self.assertEqual(loaded.config.mask_token_id, 99) + + def test_from_config(self): + scheduler = self.get_scheduler(block_size=16, noise_type="square") + new_scheduler = BD3LMTokenDiffusionScheduler.from_config(scheduler.config) + self.assertEqual(new_scheduler.config.block_size, 16) + self.assertEqual(new_scheduler.config.noise_type, "square") + + # ------------------------------------------------------------------ + # step() + # ------------------------------------------------------------------ + + def test_step_commits_tokens(self): + """Running enough steps should commit masked tokens to non-mask values.""" + scheduler = self.get_scheduler(block_size=4) + scheduler.set_timesteps(8) + + batch_size, block_size, vocab_size = 1, 4, 32 + mask_id = 31 + + sample = torch.full((batch_size, block_size), mask_id, dtype=torch.long) + + # Create logits with strong preference for non-mask tokens. + logits = torch.zeros(batch_size, block_size, vocab_size) + for i in range(block_size): + logits[0, i, i] = 10.0 + + # Run all denoising steps. + for step_idx in range(scheduler.num_inference_steps): + t = scheduler.timesteps[step_idx] + out = scheduler.step( + model_output=logits, + timestep=t, + sample=sample, + mask_token_id=mask_id, + return_dict=True, + ) + sample = out.prev_sample + + # After all steps, no mask tokens should remain. + self.assertFalse((sample == mask_id).any().item()) + + def test_step_preserves_unmasked_tokens(self): + """Already-unmasked positions must be preserved (copy flag).""" + scheduler = self.get_scheduler(block_size=4) + scheduler.set_timesteps(4) + + batch_size, block_size, vocab_size = 1, 4, 32 + mask_id = 31 + + # Positions 0,1 are already unmasked; positions 2,3 are masked. + sample = torch.tensor([[5, 10, mask_id, mask_id]], dtype=torch.long) + logits = torch.zeros(batch_size, block_size, vocab_size) + for i in range(block_size): + logits[0, i, i % (vocab_size - 2)] = 10.0 + + out = scheduler.step( + model_output=logits, + timestep=scheduler.timesteps[0], + sample=sample, + mask_token_id=mask_id, + return_dict=True, + ) + + # Unmasked positions must be unchanged. + self.assertEqual(out.prev_sample[0, 0].item(), 5) + self.assertEqual(out.prev_sample[0, 1].item(), 10) + + def test_step_return_tuple(self): + """return_dict=False should return a plain tuple.""" + scheduler = self.get_scheduler(block_size=4) + scheduler.set_timesteps(4) + + vocab_size = 32 + sample = torch.full((1, 4), 31, dtype=torch.long) + logits = torch.randn(1, 4, vocab_size) + + result = scheduler.step( + model_output=logits, + timestep=scheduler.timesteps[0], + sample=sample, + mask_token_id=31, + return_dict=False, + ) + + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 2) + + def test_step_batched(self): + """step works with batch_size > 1.""" + scheduler = self.get_scheduler(block_size=4) + scheduler.set_timesteps(4) + + batch_size, vocab_size = 3, 32 + mask_id = 31 + sample = torch.full((batch_size, 4), mask_id, dtype=torch.long) + logits = torch.randn(batch_size, 4, vocab_size) + + out = scheduler.step( + model_output=logits, + timestep=scheduler.timesteps[0], + sample=sample, + mask_token_id=mask_id, + return_dict=True, + ) + + self.assertEqual(out.prev_sample.shape, (batch_size, 4)) + + # ------------------------------------------------------------------ + # Nucleus filtering + # ------------------------------------------------------------------ + + def test_nucleus_filtering_passthrough(self): + """nucleus_p=1.0 should not alter the distribution.""" + probs = torch.softmax(torch.randn(1, 4, 32), dim=-1) + filtered = BD3LMTokenDiffusionScheduler._nucleus_filtering(probs, nucleus_p=1.0) + self.assertTrue(torch.allclose(probs, filtered)) + + def test_nucleus_filtering_truncates(self): + """nucleus_p < 1.0 should zero out low-probability tokens.""" + probs = torch.zeros(1, 1, 4) + probs[0, 0] = torch.tensor([0.5, 0.3, 0.15, 0.05]) + filtered = BD3LMTokenDiffusionScheduler._nucleus_filtering(probs, nucleus_p=0.8) + # Token with prob 0.05 should be zeroed out. + self.assertAlmostEqual(filtered[0, 0, 3].item(), 0.0, places=5) + # Filtered probs should still sum to ~1. + self.assertAlmostEqual(filtered.sum().item(), 1.0, places=4) + + def test_nucleus_filtering_keeps_top1(self): + """Nucleus filtering always keeps at least the top-1 token.""" + probs = torch.zeros(1, 1, 4) + probs[0, 0] = torch.tensor([0.1, 0.1, 0.1, 0.7]) + filtered = BD3LMTokenDiffusionScheduler._nucleus_filtering(probs, nucleus_p=0.01) + # Top-1 (index 3) must be kept. + self.assertGreater(filtered[0, 0, 3].item(), 0.0) + + # ------------------------------------------------------------------ + # Stopping criteria + # ------------------------------------------------------------------ + + def test_check_should_stop_all_unmasked(self): + mask_id = 31 + sequences = torch.tensor([[1, 2, 3, 4]], dtype=torch.long) + self.assertTrue(BD3LMTokenDiffusionScheduler.check_should_stop(sequences, mask_id)) + + def test_check_should_stop_has_masks(self): + mask_id = 31 + sequences = torch.tensor([[1, 31, 3, 4]], dtype=torch.long) + self.assertFalse(BD3LMTokenDiffusionScheduler.check_should_stop(sequences, mask_id)) + + def test_check_eos_finished(self): + eos_id = 2 + prompt_length = 2 + sequences = torch.tensor([[10, 11, 5, eos_id, 7, 8]], dtype=torch.long) + finished = torch.tensor([False]) + + finished = BD3LMTokenDiffusionScheduler.check_eos_finished(sequences, prompt_length, eos_id, finished) + self.assertTrue(finished[0].item()) + + def test_check_eos_finished_no_eos(self): + eos_id = 2 + prompt_length = 2 + sequences = torch.tensor([[10, 11, 5, 6, 7, 8]], dtype=torch.long) + finished = torch.tensor([False]) + + finished = BD3LMTokenDiffusionScheduler.check_eos_finished(sequences, prompt_length, eos_id, finished) + self.assertFalse(finished[0].item()) + + def test_check_eos_finished_already_finished(self): + eos_id = 2 + sequences = torch.tensor([[10, 11, 5, 6]], dtype=torch.long) + finished = torch.tensor([True]) + + finished = BD3LMTokenDiffusionScheduler.check_eos_finished(sequences, 2, eos_id, finished) + self.assertTrue(finished[0].item()) + + # ------------------------------------------------------------------ + # add_noise + # ------------------------------------------------------------------ + + def test_add_noise(self): + scheduler = self.get_scheduler() + original = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=torch.long) + mask_id = 31 + + gen = torch.Generator().manual_seed(42) + # At t=1.0 (loglinear), all tokens should be masked. + t_full = torch.tensor([1.0]) + noisy = scheduler.add_noise(original, t_full, mask_token_id=mask_id, generator=gen) + self.assertTrue((noisy == mask_id).all().item()) + + def test_add_noise_zero(self): + scheduler = self.get_scheduler() + original = torch.tensor([[1, 2, 3, 4]], dtype=torch.long) + mask_id = 31 + + # At t=0.0 (loglinear), no tokens should be masked. + t_zero = torch.tensor([0.0]) + noisy = scheduler.add_noise(original, t_zero, mask_token_id=mask_id) + self.assertTrue(torch.equal(noisy, original)) + + def test_add_noise_partial(self): + scheduler = self.get_scheduler() + original = torch.arange(100).unsqueeze(0).long() + mask_id = 999 + + gen = torch.Generator().manual_seed(0) + t_half = torch.tensor([0.5]) + noisy = scheduler.add_noise(original, t_half, mask_token_id=mask_id, generator=gen) + + num_masked = (noisy == mask_id).sum().item() + # With 100 tokens and move_chance=0.5, we expect roughly 50 masked. + self.assertGreater(num_masked, 20) + self.assertLess(num_masked, 80) + + +if __name__ == "__main__": + unittest.main() From f7a0eb1ff7c85afae3a9ea8e502ca3912a919db2 Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Fri, 27 Mar 2026 09:58:01 +0000 Subject: [PATCH 43/44] style: formatting fixes from make style --- .../diffusion_dpo/train_diffusion_dpo.py | 2 +- .../diffusion_dpo/train_diffusion_dpo_sdxl.py | 2 +- .../train_diffusion_orpo_sdxl_lora.py | 2 +- .../train_diffusion_orpo_sdxl_lora_wds.py | 2 +- .../unified_latents/README.md | 189 ++++++ .../unified_latents/eval_ul.py | 412 ++++++++++++ .../unified_latents/sample_ul_stage2.py | 131 ++++ .../unified_latents/train_ul_stage1.py | 588 ++++++++++++++++++ .../unified_latents/train_ul_stage2_base.py | 462 ++++++++++++++ .../unified_latents/ul_models.py | 61 ++ src/diffusers/pipelines/pipeline_utils.py | 2 +- tests/pipelines/dflash/test_dflash.py | 28 +- .../test_scheduler_dflash_token_diffusion.py | 10 +- 13 files changed, 1877 insertions(+), 14 deletions(-) create mode 100644 examples/research_projects/unified_latents/README.md create mode 100644 examples/research_projects/unified_latents/eval_ul.py create mode 100644 examples/research_projects/unified_latents/sample_ul_stage2.py create mode 100644 examples/research_projects/unified_latents/train_ul_stage1.py create mode 100644 examples/research_projects/unified_latents/train_ul_stage2_base.py create mode 100644 examples/research_projects/unified_latents/ul_models.py diff --git a/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py b/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py index a65767d084b6..4c8951a4400e 100644 --- a/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py +++ b/examples/research_projects/diffusion_dpo/train_diffusion_dpo.py @@ -28,7 +28,6 @@ import torch.nn.functional as F import torch.utils.checkpoint import transformers -import wandb from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed @@ -43,6 +42,7 @@ from transformers import AutoTokenizer, PretrainedConfig import diffusers +import wandb from diffusers import ( AutoencoderKL, DDPMScheduler, diff --git a/examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py b/examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py index 756b20bb8d26..a7af773e2fb7 100644 --- a/examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py +++ b/examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py @@ -29,7 +29,6 @@ import torch.nn.functional as F import torch.utils.checkpoint import transformers -import wandb from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed @@ -45,6 +44,7 @@ from transformers import AutoTokenizer, PretrainedConfig import diffusers +import wandb from diffusers import ( AutoencoderKL, DDPMScheduler, diff --git a/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py b/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py index 5a1b26f88604..f8850f591aec 100644 --- a/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py +++ b/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py @@ -29,7 +29,6 @@ import torch.nn.functional as F import torch.utils.checkpoint import transformers -import wandb from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed @@ -45,6 +44,7 @@ from transformers import AutoTokenizer, PretrainedConfig import diffusers +import wandb from diffusers import ( AutoencoderKL, DDPMScheduler, diff --git a/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py b/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py index f1bfaa2fb551..1fc89191b212 100644 --- a/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py +++ b/examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py @@ -28,7 +28,6 @@ import torch.nn.functional as F import torch.utils.checkpoint import transformers -import wandb import webdataset as wds from accelerate import Accelerator from accelerate.logging import get_logger @@ -43,6 +42,7 @@ from transformers import AutoTokenizer, PretrainedConfig import diffusers +import wandb from diffusers import ( AutoencoderKL, DDPMScheduler, diff --git a/examples/research_projects/unified_latents/README.md b/examples/research_projects/unified_latents/README.md new file mode 100644 index 000000000000..97ef95ca033e --- /dev/null +++ b/examples/research_projects/unified_latents/README.md @@ -0,0 +1,189 @@ +# Unified Latents (UL) Training (Diffusers Research Scripts) + +This folder contains a Diffusers-based implementation of Unified Latents from `2602.17270`. + +Current scripts: +- `train_ul_stage1.py`: joint UL stage-1 training (`encoder + prior + decoder`) +- `train_ul_stage2_base.py`: UL stage-2 base model training on frozen stage-1 encoder latents + +## What Is Implemented + +- Stage 1 (UL latent learning): + - deterministic ResNet-like encoder (`AutoencoderULEncoder`) with Section 5.1-style stage depths `[2,2,2,3]` + - latent prior denoiser (DiT-style) + - decoder denoiser (UViT-style approximation) +- Stage 2 (UL base model): + - two-stage ViT-like latent denoiser approximation + - trained with stage-1 encoder frozen + +Notes on architecture fidelity: +- Section 5.1 defaults are used where practical. +- Decoder is an approximation (concat-conditioned conv+attention) rather than the paper's exact dedicated UViT implementation. + +## Requirements + +Run from repo root with Diffusers import path available: + +```bash +cd /path/to/diffusers +export PYTHONPATH=src +``` + +Use `accelerate launch ...` for training. + +## Dataset Format + +Both stages expect `torchvision.datasets.ImageFolder` layout: + +```text +data_root/ + class_a/ + img1.png + img2.png + class_b/ + img3.png +``` + +## Stage 1 Training + +Stage-1 objective implementation follows UL Algorithm 1: +- prior term uses `(-d lambda_z/dt) * exp(lambda_z) / 2 * ||z_clean - z_hat||^2` +- decoder term uses `(-d lambda_x/dt) * exp(lambda_x) / 2 * w(lambda_x) * ||x - x_hat||^2` with `w(lambda)=sigmoid(lambda-b)` +- prior terminal KL `KL[q(z1|x)||N(0,I)]` is always included (paper Algorithm 1) +- `||.||^2` uses true squared sums and losses are reported in bits-per-pixel via division by `num_pixels * ln(2)` + +Command: + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ + --train_data_dir /path/to/imagefolder \ + --output_dir ul-stage1 \ + --resolution 256 \ + --train_batch_size 8 \ + --max_train_steps 10000 +``` + +Recommended useful options: +- `--report_to tensorboard|wandb` +- `--tracker_project_name unified-latents-stage1` +- `--checkpoints_total_limit 3` +- `--resume_from_checkpoint latest` +- `--num_workers 0` (useful in restricted environments) + +Stage 1 outputs: +- `ul-stage1/checkpoint-*/`: + - accelerate state files + - `encoder/`, `prior/`, `decoder/` (Diffusers `save_pretrained` format) +- `ul-stage1/final/`: + - `encoder/`, `prior/`, `decoder/` (Diffusers format) + - `encoder.pt`, `prior.pt`, `decoder.pt` (raw state_dict) + +## Stage 2 Training + +Train the base model using the frozen stage-1 encoder: + +Stage-2 objective uses paper-style weighted ELBO on latents: +- diffusion target is the clean encoder mean latent `z_clean` +- training target is clean encoder latent (`z_clean`) for lower variance +- base model uses v-prediction parameterization and computes weighted ELBO in x-space +- loss uses `(-d lambda_z/dt) * exp(lambda_z) / 2 * w(lambda_z) * ||z_clean - z_hat||^2` with `w(lambda)=sigmoid(lambda-b)` +- sampling stops at `logsnr_0` and passes the resulting noisy latent `z0` to the decoder conditioning path + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ + --train_data_dir /path/to/imagefolder \ + --stage1_encoder_path /path/to/ul-stage1/final/encoder \ + --output_dir ul-stage2-base \ + --resolution 256 \ + --train_batch_size 8 \ + --max_train_steps 10000 +``` + +`--stage1_encoder_path` supports: +- Diffusers encoder directory (recommended), e.g. `.../final/encoder` +- raw checkpoint file, e.g. `.../final/encoder.pt` + +Recommended useful options: +- `--report_to tensorboard|wandb` +- `--tracker_project_name unified-latents-stage2` +- `--checkpoints_total_limit 3` +- `--resume_from_checkpoint latest` +- `--num_workers 0` (useful in restricted environments) + +Stage 2 outputs: +- `ul-stage2-base/checkpoint-*/`: + - accelerate state files + - `base_model/` (Diffusers `save_pretrained` format) +- `ul-stage2-base/final/`: + - `base_model/` (Diffusers format) + - `base_model.pt` (raw state_dict) + +## Resume Examples + +Stage 1 resume: + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ + --train_data_dir /path/to/imagefolder \ + --output_dir ul-stage1 \ + --resume_from_checkpoint latest +``` + +Stage 2 resume: + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ + --train_data_dir /path/to/imagefolder \ + --stage1_encoder_path /path/to/ul-stage1/final/encoder \ + --output_dir ul-stage2-base \ + --resume_from_checkpoint latest +``` + +## Quick Smoke-Test Settings + +For a fast sanity run on a tiny dataset: + +- `--resolution 64` +- `--train_batch_size 2` +- `--max_train_steps 1` +- `--save_steps 1` +- `--num_workers 0` +- `--mixed_precision no` + +These settings validate training loop, checkpointing, and serialization paths. + + +## ImageNet-512 + Weights & Biases (wandb) + +Use either a Hub dataset (`--dataset_name`) or local ImageNet-512 imagefolder (`--train_data_dir`). + +Stage 1 example (local ImageNet-512): + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage1.py \ + --train_data_dir /path/to/imagenet512_imagefolder \ + --output_dir ul-stage1-imagenet512 \ + --resolution 512 \ + --train_batch_size 8 \ + --max_train_steps 100000 \ + --report_to wandb \ + --tracker_project_name ul-imagenet512-stage1 +``` + +Stage 2 example: + +```bash +accelerate launch examples/research_projects/unified_latents/train_ul_stage2_base.py \ + --train_data_dir /path/to/imagenet512_imagefolder \ + --stage1_encoder_path /path/to/ul-stage1-imagenet512/final/encoder \ + --output_dir ul-stage2-imagenet512 \ + --resolution 512 \ + --train_batch_size 8 \ + --max_train_steps 100000 \ + --report_to wandb \ + --tracker_project_name ul-imagenet512-stage2 +``` + +Hub dataset variant: +- replace `--train_data_dir ...` with `--dataset_name <your_dataset>` +- optionally set `--dataset_config_name ...` and `--image_column ...` diff --git a/examples/research_projects/unified_latents/eval_ul.py b/examples/research_projects/unified_latents/eval_ul.py new file mode 100644 index 000000000000..98003d9d0170 --- /dev/null +++ b/examples/research_projects/unified_latents/eval_ul.py @@ -0,0 +1,412 @@ +#!/usr/bin/env python +# coding=utf-8 + +import argparse +import json +from pathlib import Path + +import torch +import torch.nn.functional as F +from torchvision import transforms +from torchvision.datasets import ImageFolder +from torchvision.models import Inception_V3_Weights, inception_v3 +from torchvision.utils import make_grid, save_image +from ul_models import ULTwoStageBaseModel + +from diffusers import UNet2DModel +from diffusers.models.autoencoders import AutoencoderULEncoder +from diffusers.training_utils import ul_alpha_sigma_from_logsnr, ul_logsnr_schedule + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Evaluate Unified Latents stage-1 reconstruction and stage-2 realism." + ) + parser.add_argument("--train_data_dir", type=str, required=True, help="ImageFolder root with real images.") + parser.add_argument("--stage1_encoder_path", type=str, required=True, help="Path to stage-1 encoder directory.") + parser.add_argument("--stage1_decoder_path", type=str, required=True, help="Path to stage-1 decoder directory.") + parser.add_argument("--stage2_base_path", type=str, required=True, help="Path to stage-2 base model directory.") + parser.add_argument("--output_dir", type=str, default="ul-eval", help="Directory to store evaluation outputs.") + parser.add_argument("--resolution", type=int, default=256) + parser.add_argument("--batch_size", type=int, default=8) + parser.add_argument("--num_workers", type=int, default=0) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", type=str, default="cuda") + + parser.add_argument("--num_recon_samples", type=int, default=256) + parser.add_argument("--num_gen_samples", type=int, default=1024) + parser.add_argument("--num_sampling_steps", type=int, default=30) + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--latent_channels", type=int, default=4) + parser.add_argument("--latent_downsample_factor", type=int, default=8) + + parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--lambda_z_min", type=float, default=-10.0) + parser.add_argument("--lambda_z_max", type=float, default=5.0) + parser.add_argument("--lambda_x_min", type=float, default=-10.0) + parser.add_argument("--lambda_x_max", type=float, default=10.0) + + parser.add_argument( + "--recon_use_noisy_z0", action="store_true", help="Use noisy z0 for recon instead of deterministic z0." + ) + parser.add_argument("--decoder_prediction_type", type=str, default="x0", choices=["epsilon", "x0"]) + parser.add_argument("--save_recon_grid", type=str, default="stage1_recon_grid.png") + parser.add_argument("--save_stage2_grid", type=str, default="stage2_samples_grid.png") + parser.add_argument("--grid_items", type=int, default=8) + + parser.add_argument("--kid_subset_size", type=int, default=256) + parser.add_argument("--kid_subsets", type=int, default=10) + + parser.add_argument("--pass_psnr", type=float, default=18.0) + parser.add_argument("--pass_mae", type=float, default=0.10) + parser.add_argument("--pass_kid", type=float, default=0.02) + + return parser.parse_args() + + +def _build_loader(data_dir: str, resolution: int, batch_size: int, num_workers: int): + image_transform = transforms.Compose( + [ + transforms.Resize(resolution, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(resolution), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + ] + ) + dataset = ImageFolder(root=data_dir, transform=image_transform) + loader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=True, + drop_last=False, + ) + return dataset, loader + + +def _decode_image_diffusion( + decoder: UNet2DModel, + z0: torch.Tensor, + *, + resolution: int, + num_sampling_steps: int, + num_train_timesteps: int, + schedule: str, + lambda_min: float, + lambda_max: float, + decoder_prediction_type: str, +) -> torch.Tensor: + bsz = z0.shape[0] + z0_up = F.interpolate(z0, size=(resolution, resolution), mode="bilinear", align_corners=False) + + x_t = torch.randn(bsz, 3, resolution, resolution, device=z0.device, dtype=z0.dtype) + t_grid = torch.linspace(1.0, 0.0, num_sampling_steps + 1, device=z0.device, dtype=z0.dtype) + for i in range(num_sampling_steps): + t_cur = t_grid[i].repeat(bsz) + t_nxt = t_grid[i + 1].repeat(bsz) + + lambda_cur = ul_logsnr_schedule(t_cur, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) + lambda_nxt = ul_logsnr_schedule(t_nxt, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) + alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) + alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) + + timestep_idx = (t_cur * (num_train_timesteps - 1)).long().clamp(0, num_train_timesteps - 1) + decoder_pred = decoder(torch.cat([x_t, z0_up], dim=1), timestep_idx).sample + if decoder_prediction_type == "epsilon": + eps_hat = decoder_pred + x0_hat = (x_t - sigma_cur[:, None, None, None] * eps_hat) / alpha_cur[:, None, None, None].clamp_min(1e-5) + else: + x0_hat = decoder_pred + eps_hat = (x_t - alpha_cur[:, None, None, None] * x0_hat) / sigma_cur[:, None, None, None].clamp_min(1e-5) + x_t = alpha_nxt[:, None, None, None] * x0_hat + sigma_nxt[:, None, None, None] * eps_hat + + return x_t.clamp(-1, 1) + + +def _sample_stage2_latent( + base_model: ULTwoStageBaseModel, + *, + batch_size: int, + latent_channels: int, + latent_size: int, + num_sampling_steps: int, + num_train_timesteps: int, + schedule: str, + lambda_min: float, + lambda_max: float, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + z_t = torch.randn(batch_size, latent_channels, latent_size, latent_size, device=device, dtype=dtype) + t_grid = torch.linspace(1.0, 0.0, num_sampling_steps + 1, device=device, dtype=dtype) + for i in range(num_sampling_steps): + t_cur = t_grid[i].repeat(batch_size) + t_nxt = t_grid[i + 1].repeat(batch_size) + + lambda_cur = ul_logsnr_schedule(t_cur, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) + lambda_nxt = ul_logsnr_schedule(t_nxt, schedule_type=schedule, lambda_min=lambda_min, lambda_max=lambda_max) + alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) + alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) + + timestep_idx = (t_cur * (num_train_timesteps - 1)).long().clamp(0, num_train_timesteps - 1) + dummy_labels = torch.zeros((batch_size,), device=device, dtype=torch.long) + v_pred = base_model(z_t, timestep_idx, dummy_labels) + + z0_hat = alpha_cur[:, None, None, None] * z_t - sigma_cur[:, None, None, None] * v_pred + eps_hat = sigma_cur[:, None, None, None] * z_t + alpha_cur[:, None, None, None] * v_pred + z_t = alpha_nxt[:, None, None, None] * z0_hat + sigma_nxt[:, None, None, None] * eps_hat + + # Paper-ground-truth: hand off noisy latent at logsnr_0. + return z_t + + +def _collect_inception_features( + images_m11: torch.Tensor, + inception: torch.nn.Module, + *, + device: torch.device, +) -> torch.Tensor: + x = (images_m11 + 1.0) / 2.0 + x = x.clamp(0.0, 1.0) + x = F.interpolate(x, size=(299, 299), mode="bilinear", align_corners=False) + mean = torch.tensor([0.485, 0.456, 0.406], device=device, dtype=x.dtype)[None, :, None, None] + std = torch.tensor([0.229, 0.224, 0.225], device=device, dtype=x.dtype)[None, :, None, None] + x = (x - mean) / std + with torch.no_grad(): + feats = inception(x) + return feats.float().cpu() + + +def _kid_mmd2_unbiased(x: torch.Tensor, y: torch.Tensor) -> float: + # Polynomial kernel used in KID. + # x: [n, d], y: [m, d] + n = x.shape[0] + m = y.shape[0] + d = x.shape[1] + k_xx = ((x @ x.T) / d + 1.0) ** 3 + k_yy = ((y @ y.T) / d + 1.0) ** 3 + k_xy = ((x @ y.T) / d + 1.0) ** 3 + + sum_xx = (k_xx.sum() - torch.diagonal(k_xx).sum()) / (n * (n - 1)) + sum_yy = (k_yy.sum() - torch.diagonal(k_yy).sum()) / (m * (m - 1)) + sum_xy = k_xy.mean() + return (sum_xx + sum_yy - 2.0 * sum_xy).item() + + +def _compute_kid( + real_feats: torch.Tensor, + fake_feats: torch.Tensor, + *, + subset_size: int, + subsets: int, + seed: int, +) -> tuple[float, float]: + g = torch.Generator(device="cpu") + g.manual_seed(seed) + + n = min(real_feats.shape[0], fake_feats.shape[0]) + subset_size = min(subset_size, n) + if subset_size < 2: + raise ValueError("Need at least 2 samples to compute KID.") + + values = [] + for _ in range(subsets): + idx_r = torch.randperm(real_feats.shape[0], generator=g)[:subset_size] + idx_f = torch.randperm(fake_feats.shape[0], generator=g)[:subset_size] + values.append(_kid_mmd2_unbiased(real_feats[idx_r], fake_feats[idx_f])) + + vals = torch.tensor(values, dtype=torch.float32) + return vals.mean().item(), vals.std(unbiased=False).item() + + +def main(): + args = parse_args() + out_dir = Path(args.output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + torch.manual_seed(args.seed) + device = torch.device(args.device if torch.cuda.is_available() else "cpu") + + dataset, loader = _build_loader(args.train_data_dir, args.resolution, args.batch_size, args.num_workers) + + encoder = AutoencoderULEncoder.from_pretrained(args.stage1_encoder_path).to(device).eval() + decoder = UNet2DModel.from_pretrained(args.stage1_decoder_path).to(device).eval() + base_model = ULTwoStageBaseModel.from_pretrained(args.stage2_base_path).to(device).eval() + + latent_size = int(getattr(base_model.config, "latent_size", args.resolution // args.latent_downsample_factor)) + + # Stage-1 reconstruction metrics. + recon_count = 0 + recon_mae = [] + recon_mse = [] + vis_pairs = [] + + for x, _ in loader: + if recon_count >= args.num_recon_samples: + break + x = x.to(device) + if recon_count + x.shape[0] > args.num_recon_samples: + x = x[: args.num_recon_samples - recon_count] + + with torch.no_grad(): + z_clean = encoder.encode(x).latent + lambda0 = torch.full((x.shape[0],), args.lambda_z_max, device=device, dtype=x.dtype) + alpha0, sigma0 = ul_alpha_sigma_from_logsnr(lambda0) + if args.recon_use_noisy_z0: + eps0 = torch.randn_like(z_clean) + else: + eps0 = torch.zeros_like(z_clean) + z0 = alpha0[:, None, None, None] * z_clean + sigma0[:, None, None, None] * eps0 + + recon = _decode_image_diffusion( + decoder, + z0, + resolution=args.resolution, + num_sampling_steps=args.num_sampling_steps, + num_train_timesteps=args.num_train_timesteps, + schedule=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + decoder_prediction_type=args.decoder_prediction_type, + ) + + diff = recon - x + mse = diff.square().mean(dim=(1, 2, 3)) + mae = diff.abs().mean(dim=(1, 2, 3)) + recon_mse.append(mse.cpu()) + recon_mae.append(mae.cpu()) + + if len(vis_pairs) < args.grid_items: + take = min(args.grid_items - len(vis_pairs), x.shape[0]) + for i in range(take): + vis_pairs.append(torch.cat([x[i], recon[i]], dim=2).detach().cpu()) + + recon_count += x.shape[0] + + recon_mse = torch.cat(recon_mse) + recon_mae = torch.cat(recon_mae) + recon_psnr = -10.0 * torch.log10(recon_mse.clamp_min(1e-12)) + + if vis_pairs: + recon_grid = make_grid([(p + 1.0) / 2.0 for p in vis_pairs], nrow=2) + save_image(recon_grid, out_dir / args.save_recon_grid) + + # Stage-2 generation and realism metrics. + gen_images = [] + gen_count = 0 + while gen_count < args.num_gen_samples: + bsz = min(args.batch_size, args.num_gen_samples - gen_count) + with torch.no_grad(): + z0_sampled = _sample_stage2_latent( + base_model, + batch_size=bsz, + latent_channels=args.latent_channels, + latent_size=latent_size, + num_sampling_steps=args.num_sampling_steps, + num_train_timesteps=args.num_train_timesteps, + schedule=args.base_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + device=device, + dtype=torch.float32, + ) + x_gen = _decode_image_diffusion( + decoder, + z0_sampled, + resolution=args.resolution, + num_sampling_steps=args.num_sampling_steps, + num_train_timesteps=args.num_train_timesteps, + schedule=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + decoder_prediction_type=args.decoder_prediction_type, + ) + gen_images.append(x_gen.cpu()) + gen_count += bsz + + gen_images = torch.cat(gen_images, dim=0)[: args.num_gen_samples] + + stage2_vis = make_grid(((gen_images[: args.grid_items] + 1.0) / 2.0), nrow=2) + save_image(stage2_vis, out_dir / args.save_stage2_grid) + + # Real images for comparison features. + real_images = [] + real_count = 0 + for x, _ in loader: + if real_count >= args.num_gen_samples: + break + if real_count + x.shape[0] > args.num_gen_samples: + x = x[: args.num_gen_samples - real_count] + real_images.append(x) + real_count += x.shape[0] + real_images = torch.cat(real_images, dim=0) + + inception = inception_v3(weights=Inception_V3_Weights.IMAGENET1K_V1, aux_logits=True, transform_input=False) + inception.aux_logits = False + inception.AuxLogits = None + inception.fc = torch.nn.Identity() + inception = inception.to(device).eval() + + real_feats = [] + fake_feats = [] + with torch.no_grad(): + for i in range(0, real_images.shape[0], args.batch_size): + real_feats.append( + _collect_inception_features(real_images[i : i + args.batch_size].to(device), inception, device=device) + ) + fake_feats.append( + _collect_inception_features(gen_images[i : i + args.batch_size].to(device), inception, device=device) + ) + real_feats = torch.cat(real_feats, dim=0) + fake_feats = torch.cat(fake_feats, dim=0) + + kid_mean, kid_std = _compute_kid( + real_feats, + fake_feats, + subset_size=args.kid_subset_size, + subsets=args.kid_subsets, + seed=args.seed, + ) + + metrics = { + "dataset_size": len(dataset), + "num_recon_samples": int(recon_mse.numel()), + "num_gen_samples": int(gen_images.shape[0]), + "stage1": { + "mae_mean": float(recon_mae.mean().item()), + "mse_mean": float(recon_mse.mean().item()), + "psnr_mean_db": float(recon_psnr.mean().item()), + "psnr_median_db": float(recon_psnr.median().item()), + "recon_uses_noisy_z0": bool(args.recon_use_noisy_z0), + }, + "stage2": { + "kid_mean": float(kid_mean), + "kid_std": float(kid_std), + }, + "passes": { + "stage1_psnr": bool(recon_psnr.mean().item() >= args.pass_psnr), + "stage1_mae": bool(recon_mae.mean().item() <= args.pass_mae), + "stage2_kid": bool(kid_mean <= args.pass_kid), + }, + "thresholds": { + "pass_psnr": float(args.pass_psnr), + "pass_mae": float(args.pass_mae), + "pass_kid": float(args.pass_kid), + }, + "artifacts": { + "recon_grid": str((out_dir / args.save_recon_grid).resolve()), + "stage2_grid": str((out_dir / args.save_stage2_grid).resolve()), + }, + } + metrics["passes"]["all"] = all(metrics["passes"].values()) + + metrics_path = out_dir / "metrics.json" + metrics_path.write_text(json.dumps(metrics, indent=2)) + print(json.dumps(metrics, indent=2)) + print(f"Saved metrics to {metrics_path}") + + +if __name__ == "__main__": + main() diff --git a/examples/research_projects/unified_latents/sample_ul_stage2.py b/examples/research_projects/unified_latents/sample_ul_stage2.py new file mode 100644 index 000000000000..34d4cb8e7613 --- /dev/null +++ b/examples/research_projects/unified_latents/sample_ul_stage2.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python +# coding=utf-8 + +import argparse +from pathlib import Path + +import torch +import torch.nn.functional as F +from torchvision.utils import save_image +from ul_models import ULTwoStageBaseModel + +from diffusers import UNet2DModel +from diffusers.training_utils import ( + ul_alpha_sigma_from_logsnr, + ul_logsnr_schedule, +) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Sample UL Stage-2 latents and decode to images.") + parser.add_argument("--stage2_base_path", type=str, required=True, help="Path to stage2 base_model directory.") + parser.add_argument("--stage1_decoder_path", type=str, required=True, help="Path to stage1 decoder directory.") + parser.add_argument("--output_path", type=str, required=True, help="Output image path.") + parser.add_argument("--resolution", type=int, default=256) + parser.add_argument("--latent_channels", type=int, default=4) + parser.add_argument("--latent_downsample_factor", type=int, default=8) + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--num_sampling_steps", type=int, default=50) + parser.add_argument("--batch_size", type=int, default=4) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", type=str, default="cuda") + parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--lambda_z_min", type=float, default=-10.0) + parser.add_argument("--lambda_z_max", type=float, default=5.0) + parser.add_argument("--lambda_x_min", type=float, default=-10.0) + parser.add_argument("--lambda_x_max", type=float, default=10.0) + parser.add_argument("--decoder_prediction_type", type=str, default="x0", choices=["epsilon", "x0"]) + return parser.parse_args() + + +@torch.no_grad() +def main(): + args = parse_args() + device = torch.device(args.device if torch.cuda.is_available() else "cpu") + torch.manual_seed(args.seed) + latent_size = args.resolution // args.latent_downsample_factor + + base_model = ULTwoStageBaseModel.from_pretrained(args.stage2_base_path).to(device).eval() + decoder = UNet2DModel.from_pretrained(args.stage1_decoder_path).to(device).eval() + + # Stage-2 latent sampling (DDIM-like deterministic update with v-pred model). + z_t = torch.randn(args.batch_size, args.latent_channels, latent_size, latent_size, device=device) + t_grid = torch.linspace(1.0, 0.0, args.num_sampling_steps + 1, device=device) + + for i in range(args.num_sampling_steps): + t_cur = t_grid[i].repeat(args.batch_size) + t_nxt = t_grid[i + 1].repeat(args.batch_size) + + lambda_cur = ul_logsnr_schedule( + t_cur, + schedule_type=args.base_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + lambda_nxt = ul_logsnr_schedule( + t_nxt, + schedule_type=args.base_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) + alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) + + timestep_idx = (t_cur * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) + dummy_labels = torch.zeros((args.batch_size,), device=device, dtype=torch.long) + v_pred = base_model(z_t, timestep_idx, dummy_labels) + + z0_hat = alpha_cur[:, None, None, None] * z_t - sigma_cur[:, None, None, None] * v_pred + eps_hat = sigma_cur[:, None, None, None] * z_t + alpha_cur[:, None, None, None] * v_pred + + z_t = alpha_nxt[:, None, None, None] * z0_hat + sigma_nxt[:, None, None, None] * eps_hat + + # Paper Sec. 3.3: hand off the noisy latent at logsnr_0 to the decoder. + z_handoff = z_t + + # Decoder sampling conditioned on final stage-2 latent. + z0_up = F.interpolate(z_handoff, size=(args.resolution, args.resolution), mode="bilinear", align_corners=False) + + x_t = torch.randn(args.batch_size, 3, args.resolution, args.resolution, device=device) + x_grid = torch.linspace(1.0, 0.0, args.num_sampling_steps + 1, device=device) + + for i in range(args.num_sampling_steps): + t_cur = x_grid[i].repeat(args.batch_size) + t_nxt = x_grid[i + 1].repeat(args.batch_size) + + lambda_cur = ul_logsnr_schedule( + t_cur, + schedule_type=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + ) + lambda_nxt = ul_logsnr_schedule( + t_nxt, + schedule_type=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + ) + alpha_cur, sigma_cur = ul_alpha_sigma_from_logsnr(lambda_cur) + alpha_nxt, sigma_nxt = ul_alpha_sigma_from_logsnr(lambda_nxt) + + timestep_idx = (t_cur * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) + decoder_input = torch.cat([x_t, z0_up], dim=1) + decoder_pred = decoder(decoder_input, timestep_idx).sample + if args.decoder_prediction_type == "epsilon": + eps_hat = decoder_pred + x0_hat = (x_t - sigma_cur[:, None, None, None] * eps_hat) / alpha_cur[:, None, None, None].clamp_min(1e-5) + else: + x0_hat = decoder_pred + eps_hat = (x_t - alpha_cur[:, None, None, None] * x0_hat) / sigma_cur[:, None, None, None].clamp_min(1e-5) + x_t = alpha_nxt[:, None, None, None] * x0_hat + sigma_nxt[:, None, None, None] * eps_hat + + x_final = x_t.clamp(-1, 1) + output_path = Path(args.output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + save_image((x_final + 1.0) / 2.0, output_path, nrow=2) + print(f"Saved samples to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/examples/research_projects/unified_latents/train_ul_stage1.py b/examples/research_projects/unified_latents/train_ul_stage1.py new file mode 100644 index 000000000000..db6f445c032c --- /dev/null +++ b/examples/research_projects/unified_latents/train_ul_stage1.py @@ -0,0 +1,588 @@ +#!/usr/bin/env python +# coding=utf-8 + +import argparse +import logging +import math +import os +import shutil +from pathlib import Path + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch import nn +from torch.utils.data import DataLoader, IterableDataset +from torchvision import transforms +from torchvision.utils import save_image +from tqdm.auto import tqdm + +from diffusers.models.autoencoders import AutoencoderULEncoder +from diffusers.models.transformers.dit_transformer_2d import DiTTransformer2DModel +from diffusers.models.unets.unet_2d import UNet2DModel +from diffusers.optimization import get_scheduler +from diffusers.training_utils import ( + ul_alpha_sigma_from_logsnr, + ul_decoder_loss_weight, + ul_dlogsnr_dt, + ul_elbo_prefactor, + ul_logsnr_schedule, + ul_sample_t, + ul_terminal_gaussian_kl, +) + + +logger = get_logger(__name__, log_level="INFO") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Train Unified Latents Stage-1 (UL) with Diffusers.") + + parser.add_argument("--dataset_name", type=str, default=None, help="Dataset name on the Hub.") + parser.add_argument("--dataset_config_name", type=str, default=None, help="Dataset config name.") + parser.add_argument( + "--train_data_dir", type=str, default=None, help="Local dataset directory (imagefolder style)." + ) + parser.add_argument("--cache_dir", type=str, default=None, help="Hugging Face datasets cache directory.") + parser.add_argument("--image_column", type=str, default="image", help="Column containing images.") + parser.add_argument("--streaming", action="store_true", help="Stream dataset from the Hub/local files.") + parser.add_argument("--output_dir", type=str, default="ul-stage1", help="Where to save checkpoints and samples.") + parser.add_argument("--logging_dir", type=str, default="logs") + parser.add_argument("--report_to", type=str, default="tensorboard") + parser.add_argument("--tracker_project_name", type=str, default="unified-latents-stage1") + parser.add_argument("--resolution", type=int, default=256) + parser.add_argument("--center_crop", action="store_true") + parser.add_argument("--random_flip", action="store_true") + parser.add_argument("--num_workers", type=int, default=4) + + parser.add_argument("--train_batch_size", type=int, default=8) + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument("--max_train_steps", type=int, default=10000) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + parser.add_argument("--learning_rate", type=float, default=1e-4) + parser.add_argument("--adam_beta1", type=float, default=0.9) + parser.add_argument("--adam_beta2", type=float, default=0.999) + parser.add_argument("--adam_weight_decay", type=float, default=1e-2) + parser.add_argument("--adam_epsilon", type=float, default=1e-8) + parser.add_argument("--max_grad_norm", type=float, default=1.0) + parser.add_argument("--lr_scheduler", type=str, default="cosine") + parser.add_argument("--lr_warmup_steps", type=int, default=500) + + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--lambda_z_min", type=float, default=-10.0) + parser.add_argument("--lambda_z_max", type=float, default=5.0) + parser.add_argument("--lambda_x_min", type=float, default=-10.0) + parser.add_argument("--lambda_x_max", type=float, default=10.0) + parser.add_argument("--prior_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--decoder_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--decoder_loss_factor", type=float, default=1.6) + parser.add_argument("--decoder_sigmoid_bias", type=float, default=0.0) + parser.add_argument( + "--decoder_prediction_type", + type=str, + default="x0", + choices=["epsilon", "x0"], + help="Decoder target parameterization.", + ) + parser.add_argument("--latent_channels", type=int, default=4) + parser.add_argument( + "--latent_downsample_factor", + type=int, + default=8, + help="Downsample factor of encoder output. Current encoder config is 8x; paper target is 16x with 2x2 patching.", + ) + + # Prior (paper Sec. 5.1: single-level ViT, 8 blocks, 1024 channels) + parser.add_argument("--prior_num_layers", type=int, default=8) + parser.add_argument("--prior_num_heads", type=int, default=16) + parser.add_argument("--prior_head_dim", type=int, default=64) # 16 * 64 = 1024 + parser.add_argument("--prior_patch_size", type=int, default=1) + + # Decoder approximation (paper Sec. 5.1: UViT conv channels [128, 256, 512], dropout 0.1) + parser.add_argument("--decoder_block_out_channels", type=str, default="128,256,512") + parser.add_argument("--decoder_layers_per_block", type=int, default=2) + parser.add_argument("--decoder_dropout", type=float, default=0.1) + + parser.add_argument("--save_steps", type=int, default=500) + parser.add_argument("--checkpoints_total_limit", type=int, default=None) + parser.add_argument("--resume_from_checkpoint", type=str, default=None) + parser.add_argument("--sample_steps", type=int, default=250) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"]) + + return parser.parse_args() + + +def build_transforms(args): + transform_list = [ + transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), + ] + if args.center_crop: + transform_list.append(transforms.CenterCrop(args.resolution)) + else: + transform_list.append(transforms.RandomCrop(args.resolution)) + + if args.random_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list.extend( + [ + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + ] + ) + return transforms.Compose(transform_list) + + +def parse_channels(channels_str: str) -> tuple[int, ...]: + values = [int(x.strip()) for x in channels_str.split(",") if x.strip()] + if not values: + raise ValueError("`decoder_block_out_channels` must contain at least one integer.") + return tuple(values) + + +class HFStreamingImageDataset(IterableDataset): + def __init__(self, hf_iterable, image_column: str, image_transform): + super().__init__() + self.hf_iterable = hf_iterable + self.image_column = image_column + self.image_transform = image_transform + + def __iter__(self): + for example in self.hf_iterable: + image = example[self.image_column].convert("RGB") + yield {"pixel_values": self.image_transform(image)} + + +def get_train_dataset_and_collate(args, image_transform): + if args.dataset_name is not None: + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + data_dir=args.train_data_dir, + streaming=args.streaming, + ) + else: + if args.train_data_dir is None: + raise ValueError("Provide either `--dataset_name` or `--train_data_dir`.") + data_files = {"train": os.path.join(args.train_data_dir, "**")} + dataset = load_dataset( + "imagefolder", data_files=data_files, cache_dir=args.cache_dir, streaming=args.streaming + ) + + train_dataset = dataset["train"] + + if args.streaming: + image_column = args.image_column + train_dataset = HFStreamingImageDataset( + train_dataset, image_column=image_column, image_transform=image_transform + ) + else: + column_names = train_dataset.column_names + image_column = args.image_column if args.image_column in column_names else column_names[0] + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + examples["pixel_values"] = [image_transform(image) for image in images] + return examples + + train_dataset = train_dataset.with_transform(preprocess_train) + + def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + return {"pixel_values": pixel_values.contiguous().float()} + + return train_dataset, collate_fn + + +def _save_pretrained_components( + accelerator: Accelerator, encoder: nn.Module, prior: nn.Module, decoder: nn.Module, output_dir +): + output_dir = Path(output_dir) + accelerator.unwrap_model(encoder).save_pretrained(output_dir / "encoder") + accelerator.unwrap_model(prior).save_pretrained(output_dir / "prior") + accelerator.unwrap_model(decoder).save_pretrained(output_dir / "decoder") + + +def main(): + args = parse_args() + if args.seed is not None: + set_seed(args.seed) + + output_dir = Path(args.output_dir) + logging_dir = output_dir / args.logging_dir + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_dir)) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=args.report_to, + project_config=accelerator_project_config, + ) + + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state) + + if accelerator.is_main_process: + output_dir.mkdir(parents=True, exist_ok=True) + (output_dir / "samples").mkdir(exist_ok=True) + logger.info("Starting UL Stage-1 training") + logger.info(f"Output dir: {output_dir}") + + image_transform = build_transforms(args) + train_dataset, collate_fn = get_train_dataset_and_collate(args, image_transform) + train_dataloader = DataLoader( + train_dataset, + batch_size=args.train_batch_size, + shuffle=not args.streaming, + collate_fn=collate_fn, + num_workers=args.num_workers, + pin_memory=True, + drop_last=True, + ) + + latent_size = args.resolution // args.latent_downsample_factor + if latent_size < 1: + raise ValueError("`resolution // latent_downsample_factor` must be >= 1.") + + encoder = AutoencoderULEncoder(in_channels=3, latent_channels=args.latent_channels) + + prior = DiTTransformer2DModel( + num_attention_heads=args.prior_num_heads, + attention_head_dim=args.prior_head_dim, + in_channels=args.latent_channels, + out_channels=args.latent_channels, + num_layers=args.prior_num_layers, + sample_size=latent_size, + patch_size=args.prior_patch_size, + num_embeds_ada_norm=args.num_train_timesteps, + ) + + decoder_channels = parse_channels(args.decoder_block_out_channels) + decoder = UNet2DModel( + sample_size=args.resolution, + in_channels=3 + args.latent_channels, + out_channels=3, + down_block_types=("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), + up_block_types=("AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), + block_out_channels=decoder_channels, + layers_per_block=args.decoder_layers_per_block, + dropout=args.decoder_dropout, + attention_head_dim=8, + add_attention=True, + num_train_timesteps=args.num_train_timesteps, + ) + + params = list(encoder.parameters()) + list(prior.parameters()) + list(decoder.parameters()) + optimizer = torch.optim.AdamW( + params, + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + has_dataloader_length = True + try: + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + except TypeError: + has_dataloader_length = False + num_update_steps_per_epoch = None + if args.max_train_steps is None or args.max_train_steps <= 0: + if not has_dataloader_length: + raise ValueError("For streaming datasets, set `--max_train_steps` to a positive value.") + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + + lr_scheduler = get_scheduler( + name=args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps * accelerator.num_processes, + ) + + encoder, prior, decoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + encoder, prior, decoder, optimizer, train_dataloader, lr_scheduler + ) + trainable_params = list(encoder.parameters()) + list(prior.parameters()) + list(decoder.parameters()) + + # Recompute schedule values after `accelerator.prepare`. + if has_dataloader_length: + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + else: + args.num_train_epochs = max(args.num_train_epochs, 1) + + if accelerator.is_main_process: + accelerator.init_trackers(args.tracker_project_name, config=vars(args)) + + logger.info("***** Running training *****") + if has_dataloader_length: + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num batches each epoch = {len(train_dataloader)}") + else: + logger.info(" Num examples = streaming (unknown)") + logger.info(" Num batches each epoch = streaming (unknown)") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info( + " Total train batch size (w. parallel, distributed & accumulation) = " + f"{args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps}" + ) + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + + global_step = 0 + first_epoch = 0 + + if args.resume_from_checkpoint: + if args.resume_from_checkpoint != "latest": + path = os.path.basename(args.resume_from_checkpoint) + else: + dirs = os.listdir(args.output_dir) if os.path.exists(args.output_dir) else [] + dirs = [d for d in dirs if d.startswith("checkpoint-")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] if len(dirs) > 0 else None + + if path is None: + logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.") + else: + logger.info(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(args.output_dir, path)) + global_step = int(path.split("-")[1]) + first_epoch = global_step // num_update_steps_per_epoch if has_dataloader_length else 0 + + progress_bar = tqdm( + range(0, args.max_train_steps), + initial=global_step, + desc="Steps", + disable=not accelerator.is_local_main_process, + ) + + train_loss = 0.0 + fixed_eval_batch_cpu = None + for epoch in range(first_epoch, args.num_train_epochs): + encoder.train() + prior.train() + decoder.train() + + for step, batch in enumerate(train_dataloader): + with accelerator.accumulate(encoder, prior, decoder): + x = batch["pixel_values"].to(accelerator.device) + bsz = x.shape[0] + if fixed_eval_batch_cpu is None: + # Keep a fixed mini-batch for deterministic reconstruction snapshots. + fixed_eval_batch_cpu = x[: min(4, bsz)].detach().float().cpu() + + # Encode deterministic latent z_clean + z_clean = encoder.encode(x).latent + + # Prior branch: z_t -> z_clean with unweighted ELBO-style denoising loss + t_z = ul_sample_t(bsz, x.device) + lambda_z = ul_logsnr_schedule( + t_z, + schedule_type=args.prior_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + dlogsnr_dt_z = ul_dlogsnr_dt( + t_z, + schedule_type=args.prior_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + prefactor_z = ul_elbo_prefactor(lambda_z, dlogsnr_dt_z) + alpha_z, sigma_z = ul_alpha_sigma_from_logsnr(lambda_z) + eps_z = torch.randn_like(z_clean) + z_t = alpha_z[:, None, None, None] * z_clean + sigma_z[:, None, None, None] * eps_z + t_z_idx = (t_z * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) + dummy_labels = torch.zeros((bsz,), device=x.device, dtype=torch.long) + z_pred = prior(z_t, timestep=t_z_idx, class_labels=dummy_labels).sample + prior_per_sample = F.mse_loss(z_pred.float(), z_clean.float(), reduction="none").sum(dim=(1, 2, 3)) + prior_loss = (prefactor_z * prior_per_sample).mean() + prior_loss = prior_loss + ul_terminal_gaussian_kl(z_clean, logsnr_terminal=args.lambda_z_min).mean() + + # Decoder branch: x_t conditioned on fixed-noise z0 + lambda0 = torch.full((bsz,), args.lambda_z_max, device=x.device, dtype=x.dtype) + alpha0, sigma0 = ul_alpha_sigma_from_logsnr(lambda0) + eps_0 = torch.randn_like(z_clean) + z0 = alpha0[:, None, None, None] * z_clean + sigma0[:, None, None, None] * eps_0 + + t_x = ul_sample_t(bsz, x.device) + lambda_x = ul_logsnr_schedule( + t_x, + schedule_type=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + ) + dlogsnr_dt_x = ul_dlogsnr_dt( + t_x, + schedule_type=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + ) + prefactor_x = ul_elbo_prefactor(lambda_x, dlogsnr_dt_x) + alpha_x, sigma_x = ul_alpha_sigma_from_logsnr(lambda_x) + eps_x = torch.randn_like(x) + x_t = alpha_x[:, None, None, None] * x + sigma_x[:, None, None, None] * eps_x + t_x_idx = (t_x * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) + + z0_up = F.interpolate(z0, size=x.shape[-2:], mode="bilinear", align_corners=False) + decoder_input = torch.cat([x_t, z0_up], dim=1) + decoder_pred = decoder(decoder_input, t_x_idx).sample + if args.decoder_prediction_type == "epsilon": + decoder_target = eps_x + decoder_prefactor = 0.5 * (-dlogsnr_dt_x) + else: + decoder_target = x + decoder_prefactor = prefactor_x + + decoder_per_sample = F.mse_loss(decoder_pred.float(), decoder_target.float(), reduction="none").sum( + dim=(1, 2, 3) + ) + decoder_w = ul_decoder_loss_weight( + lambda_x, + bias=args.decoder_sigmoid_bias, + loss_factor=args.decoder_loss_factor, + invert=False, + ) + decoder_loss = (decoder_prefactor * decoder_w * decoder_per_sample).mean() + + num_pixels = x.shape[-2] * x.shape[-1] + bpp_denom = num_pixels * math.log(2.0) + prior_bpp = prior_loss / bpp_denom + decoder_bpp = decoder_loss / bpp_denom + loss = prior_bpp + decoder_bpp + accelerator.backward(loss) + + if accelerator.sync_gradients: + accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm) + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + avg_loss = accelerator.gather(loss.detach().repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + if accelerator.sync_gradients: + global_step += 1 + progress_bar.update(1) + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if accelerator.is_main_process and global_step % 50 == 0: + logger.info( + f"step={global_step} loss_bpp={loss.item():.4f} prior_bpp={prior_bpp.item():.4f} " + f"decoder_bpp={decoder_bpp.item():.4f} lr={lr_scheduler.get_last_lr()[0]:.2e}" + ) + + if accelerator.is_main_process and global_step % args.sample_steps == 0: + with torch.no_grad(): + encoder.eval() + decoder.eval() + + eval_x = fixed_eval_batch_cpu.to(accelerator.device) + eval_z_clean = encoder.encode(eval_x).latent + eval_z_up = F.interpolate( + eval_z_clean, size=eval_x.shape[-2:], mode="bilinear", align_corners=False + ) + + # Deterministic decode from encoder latents at t=0 with zero x-noise input. + eval_x_t = torch.zeros_like(eval_x) + eval_t_idx = torch.zeros((eval_x.shape[0],), device=eval_x.device, dtype=torch.long) + eval_decoder_input = torch.cat([eval_x_t, eval_z_up], dim=1) + eval_decoder_out = decoder(eval_decoder_input, eval_t_idx).sample + if args.decoder_prediction_type == "epsilon": + eval_lambda0 = ul_logsnr_schedule( + torch.zeros((eval_x.shape[0],), device=eval_x.device), + schedule_type=args.decoder_schedule, + lambda_min=args.lambda_x_min, + lambda_max=args.lambda_x_max, + ) + eval_alpha0, eval_sigma0 = ul_alpha_sigma_from_logsnr(eval_lambda0) + eval_recon = ( + (eval_x_t - eval_sigma0[:, None, None, None] * eval_decoder_out) + / eval_alpha0[:, None, None, None].clamp_min(1e-5) + ).clamp(-1, 1) + else: + eval_recon = eval_decoder_out.clamp(-1, 1) + + # Save side-by-side: left=input, right=reconstruction. + side_by_side = torch.cat([eval_x.clamp(-1, 1), eval_recon], dim=3).detach().float().cpu() + save_image( + (side_by_side + 1.0) / 2.0, + output_dir / "samples" / f"step_{global_step:07d}.png", + nrow=2, + ) + + encoder.train() + decoder.train() + + if accelerator.is_main_process and global_step % args.save_steps == 0: + if args.checkpoints_total_limit is not None: + checkpoints = os.listdir(args.output_dir) + checkpoints = [d for d in checkpoints if d.startswith("checkpoint-")] + checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) + if len(checkpoints) >= args.checkpoints_total_limit: + num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 + removing_checkpoints = checkpoints[:num_to_remove] + logger.info( + f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)}" + ) + for removing_checkpoint in removing_checkpoints: + shutil.rmtree(os.path.join(args.output_dir, removing_checkpoint)) + + save_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}") + accelerator.save_state(save_dir) + _save_pretrained_components( + accelerator=accelerator, + encoder=encoder, + prior=prior, + decoder=decoder, + output_dir=save_dir, + ) + logger.info(f"Saved state to {save_dir}") + + logs = { + "step_loss_bpp": loss.detach().item(), + "prior_loss_bpp": prior_bpp.detach().item(), + "decoder_loss_bpp": decoder_bpp.detach().item(), + "lr": lr_scheduler.get_last_lr()[0], + } + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + if global_step >= args.max_train_steps: + break + + if global_step >= args.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = output_dir / "final" + final_dir.mkdir(parents=True, exist_ok=True) + torch.save(accelerator.unwrap_model(encoder).state_dict(), final_dir / "encoder.pt") + torch.save(accelerator.unwrap_model(prior).state_dict(), final_dir / "prior.pt") + torch.save(accelerator.unwrap_model(decoder).state_dict(), final_dir / "decoder.pt") + _save_pretrained_components( + accelerator=accelerator, + encoder=encoder, + prior=prior, + decoder=decoder, + output_dir=final_dir, + ) + logger.info(f"Training finished. Saved final checkpoints to {final_dir}") + accelerator.end_training() + + +if __name__ == "__main__": + main() diff --git a/examples/research_projects/unified_latents/train_ul_stage2_base.py b/examples/research_projects/unified_latents/train_ul_stage2_base.py new file mode 100644 index 000000000000..373a0bb5ffa7 --- /dev/null +++ b/examples/research_projects/unified_latents/train_ul_stage2_base.py @@ -0,0 +1,462 @@ +#!/usr/bin/env python +# coding=utf-8 + +import argparse +import logging +import math +import os +import shutil +from pathlib import Path + +import torch +import torch.nn.functional as F +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration, set_seed +from datasets import load_dataset +from torch import nn +from torch.utils.data import DataLoader, IterableDataset +from torchvision import transforms +from tqdm.auto import tqdm +from ul_models import ULTwoStageBaseModel + +from diffusers.models.autoencoders import AutoencoderULEncoder +from diffusers.optimization import get_scheduler +from diffusers.training_utils import ( + ul_alpha_sigma_from_logsnr, + ul_decoder_loss_weight, + ul_dlogsnr_dt, + ul_elbo_prefactor, + ul_logsnr_schedule, + ul_sample_t, +) + + +logger = get_logger(__name__, log_level="INFO") + + +def parse_args(): + parser = argparse.ArgumentParser(description="Train Unified Latents Stage-2 base model with Diffusers.") + + parser.add_argument("--dataset_name", type=str, default=None, help="Dataset name on the Hub.") + parser.add_argument("--dataset_config_name", type=str, default=None, help="Dataset config name.") + parser.add_argument( + "--train_data_dir", type=str, default=None, help="Local dataset directory (imagefolder style)." + ) + parser.add_argument("--cache_dir", type=str, default=None, help="Hugging Face datasets cache directory.") + parser.add_argument("--image_column", type=str, default="image", help="Column containing images.") + parser.add_argument("--streaming", action="store_true", help="Stream dataset from the Hub/local files.") + parser.add_argument( + "--stage1_encoder_path", + type=str, + required=True, + help="Path to stage-1 encoder checkpoint. Supports a `.pt` file or a `save_pretrained` directory.", + ) + + parser.add_argument("--output_dir", type=str, default="ul-stage2-base") + parser.add_argument("--logging_dir", type=str, default="logs") + parser.add_argument("--report_to", type=str, default="tensorboard") + parser.add_argument("--tracker_project_name", type=str, default="unified-latents-stage2") + + parser.add_argument("--resolution", type=int, default=256) + parser.add_argument("--center_crop", action="store_true") + parser.add_argument("--random_flip", action="store_true") + parser.add_argument("--num_workers", type=int, default=4) + + parser.add_argument("--train_batch_size", type=int, default=8) + parser.add_argument("--num_train_epochs", type=int, default=1) + parser.add_argument("--max_train_steps", type=int, default=10000) + parser.add_argument("--gradient_accumulation_steps", type=int, default=1) + + parser.add_argument("--learning_rate", type=float, default=1e-4) + parser.add_argument("--adam_beta1", type=float, default=0.9) + parser.add_argument("--adam_beta2", type=float, default=0.999) + parser.add_argument("--adam_weight_decay", type=float, default=1e-2) + parser.add_argument("--adam_epsilon", type=float, default=1e-8) + parser.add_argument("--max_grad_norm", type=float, default=1.0) + parser.add_argument("--lr_scheduler", type=str, default="cosine") + parser.add_argument("--lr_warmup_steps", type=int, default=500) + + parser.add_argument("--num_train_timesteps", type=int, default=1000) + parser.add_argument("--base_schedule", type=str, default="linear", choices=["linear", "cosine"]) + parser.add_argument("--lambda_z_min", type=float, default=-10.0) + parser.add_argument("--lambda_z_max", type=float, default=5.0) + + parser.add_argument("--loss_factor", type=float, default=1.0) + parser.add_argument("--sigmoid_bias", type=float, default=0.0) + + parser.add_argument("--latent_channels", type=int, default=4) + parser.add_argument("--latent_downsample_factor", type=int, default=8) + parser.add_argument("--base_patch_size", type=int, default=1) + parser.add_argument("--base_stage_a_layers", type=int, default=6) + parser.add_argument("--base_stage_b_layers", type=int, default=16) + parser.add_argument("--base_stage_a_heads", type=int, default=8) + parser.add_argument("--base_stage_a_head_dim", type=int, default=64) + parser.add_argument("--base_stage_b_heads", type=int, default=16) + parser.add_argument("--base_stage_b_head_dim", type=int, default=64) + + parser.add_argument("--save_steps", type=int, default=500) + parser.add_argument("--checkpoints_total_limit", type=int, default=None) + parser.add_argument("--resume_from_checkpoint", type=str, default=None) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"]) + + return parser.parse_args() + + +def build_transforms(args): + transform_list = [ + transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), + ] + if args.center_crop: + transform_list.append(transforms.CenterCrop(args.resolution)) + else: + transform_list.append(transforms.RandomCrop(args.resolution)) + + if args.random_flip: + transform_list.append(transforms.RandomHorizontalFlip()) + + transform_list.extend( + [ + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + ] + ) + return transforms.Compose(transform_list) + + +class HFStreamingImageDataset(IterableDataset): + def __init__(self, hf_iterable, image_column: str, image_transform): + super().__init__() + self.hf_iterable = hf_iterable + self.image_column = image_column + self.image_transform = image_transform + + def __iter__(self): + for example in self.hf_iterable: + image = example[self.image_column].convert("RGB") + yield {"pixel_values": self.image_transform(image)} + + +def get_train_dataset_and_collate(args, image_transform): + if args.dataset_name is not None: + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + data_dir=args.train_data_dir, + streaming=args.streaming, + ) + else: + if args.train_data_dir is None: + raise ValueError("Provide either `--dataset_name` or `--train_data_dir`.") + data_files = {"train": os.path.join(args.train_data_dir, "**")} + dataset = load_dataset( + "imagefolder", data_files=data_files, cache_dir=args.cache_dir, streaming=args.streaming + ) + + train_dataset = dataset["train"] + + if args.streaming: + image_column = args.image_column + train_dataset = HFStreamingImageDataset( + train_dataset, image_column=image_column, image_transform=image_transform + ) + else: + column_names = train_dataset.column_names + image_column = args.image_column if args.image_column in column_names else column_names[0] + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + examples["pixel_values"] = [image_transform(image) for image in images] + return examples + + train_dataset = train_dataset.with_transform(preprocess_train) + + def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + return {"pixel_values": pixel_values.contiguous().float()} + + return train_dataset, collate_fn + + +def _save_pretrained_base_model(accelerator: Accelerator, base_model: nn.Module, output_dir): + output_dir = Path(output_dir) + accelerator.unwrap_model(base_model).save_pretrained(output_dir / "base_model") + + +def main(): + args = parse_args() + if args.seed is not None: + set_seed(args.seed) + + output_dir = Path(args.output_dir) + logging_dir = output_dir / args.logging_dir + accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_dir)) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=args.report_to, + project_config=accelerator_project_config, + ) + + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state) + + if accelerator.is_main_process: + output_dir.mkdir(parents=True, exist_ok=True) + logger.info("Starting UL Stage-2 base training") + logger.info(f"Output dir: {output_dir}") + + image_transform = build_transforms(args) + train_dataset, collate_fn = get_train_dataset_and_collate(args, image_transform) + train_dataloader = DataLoader( + train_dataset, + batch_size=args.train_batch_size, + shuffle=not args.streaming, + collate_fn=collate_fn, + num_workers=args.num_workers, + pin_memory=True, + drop_last=True, + ) + + latent_size = args.resolution // args.latent_downsample_factor + if latent_size < 1: + raise ValueError("`resolution // latent_downsample_factor` must be >= 1.") + + encoder_path = Path(args.stage1_encoder_path) + if encoder_path.is_dir(): + encoder = AutoencoderULEncoder.from_pretrained(str(encoder_path)) + else: + encoder = AutoencoderULEncoder(in_channels=3, latent_channels=args.latent_channels) + state_dict = torch.load(args.stage1_encoder_path, map_location="cpu") + missing, unexpected = encoder.load_state_dict(state_dict, strict=False) + if len(missing) > 0: + logger.warning(f"Missing keys when loading stage-1 encoder: {missing}") + if len(unexpected) > 0: + logger.warning(f"Unexpected keys when loading stage-1 encoder: {unexpected}") + encoder.requires_grad_(False) + encoder.eval() + + base_model = ULTwoStageBaseModel( + latent_channels=args.latent_channels, + latent_size=latent_size, + num_train_timesteps=args.num_train_timesteps, + stage_a_layers=args.base_stage_a_layers, + stage_b_layers=args.base_stage_b_layers, + stage_a_heads=args.base_stage_a_heads, + stage_a_head_dim=args.base_stage_a_head_dim, + stage_b_heads=args.base_stage_b_heads, + stage_b_head_dim=args.base_stage_b_head_dim, + patch_size=args.base_patch_size, + ) + + optimizer = torch.optim.AdamW( + base_model.parameters(), + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + has_dataloader_length = True + try: + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + except TypeError: + has_dataloader_length = False + num_update_steps_per_epoch = None + if args.max_train_steps is None or args.max_train_steps <= 0: + if not has_dataloader_length: + raise ValueError("For streaming datasets, set `--max_train_steps` to a positive value.") + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + + lr_scheduler = get_scheduler( + name=args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, + num_training_steps=args.max_train_steps * accelerator.num_processes, + ) + + encoder, base_model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + encoder, base_model, optimizer, train_dataloader, lr_scheduler + ) + + if has_dataloader_length: + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + else: + args.num_train_epochs = max(args.num_train_epochs, 1) + + if accelerator.is_main_process: + accelerator.init_trackers(args.tracker_project_name, config=vars(args)) + + logger.info("***** Running stage-2 base training *****") + if has_dataloader_length: + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num batches each epoch = {len(train_dataloader)}") + else: + logger.info(" Num examples = streaming (unknown)") + logger.info(" Num batches each epoch = streaming (unknown)") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info( + " Total train batch size (w. parallel, distributed & accumulation) = " + f"{args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps}" + ) + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + + global_step = 0 + first_epoch = 0 + + if args.resume_from_checkpoint: + if args.resume_from_checkpoint != "latest": + path = os.path.basename(args.resume_from_checkpoint) + else: + dirs = os.listdir(args.output_dir) if os.path.exists(args.output_dir) else [] + dirs = [d for d in dirs if d.startswith("checkpoint-")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] if len(dirs) > 0 else None + + if path is None: + logger.info(f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run.") + else: + logger.info(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(args.output_dir, path)) + global_step = int(path.split("-")[1]) + first_epoch = global_step // num_update_steps_per_epoch if has_dataloader_length else 0 + + progress_bar = tqdm( + range(0, args.max_train_steps), + initial=global_step, + desc="Steps", + disable=not accelerator.is_local_main_process, + ) + + train_loss = 0.0 + + for epoch in range(first_epoch, args.num_train_epochs): + base_model.train() + + for _, batch in enumerate(train_dataloader): + with accelerator.accumulate(base_model): + x = batch["pixel_values"].to(accelerator.device) + bsz = x.shape[0] + + with torch.no_grad(): + z_clean = encoder.encode(x).latent + + # Stage-2 uses clean encoder means as training targets. + z_target = z_clean + t = ul_sample_t(bsz, x.device) + lambda_t = ul_logsnr_schedule( + t, + schedule_type=args.base_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + dlogsnr_dt = ul_dlogsnr_dt( + t, + schedule_type=args.base_schedule, + lambda_min=args.lambda_z_min, + lambda_max=args.lambda_z_max, + ) + prefactor = ul_elbo_prefactor(lambda_t, dlogsnr_dt) + alpha_t, sigma_t = ul_alpha_sigma_from_logsnr(lambda_t) + eps = torch.randn_like(z_target) + z_t = alpha_t[:, None, None, None] * z_target + sigma_t[:, None, None, None] * eps + + timesteps = (t * (args.num_train_timesteps - 1)).long().clamp(0, args.num_train_timesteps - 1) + dummy_labels = torch.zeros((bsz,), device=x.device, dtype=torch.long) + + # Preferred stage-2 parameterization: predict velocity v. + v_pred = base_model(z_t, timesteps, dummy_labels) + z_target_hat = alpha_t[:, None, None, None] * z_t - sigma_t[:, None, None, None] * v_pred + + per_sample = F.mse_loss(z_target_hat.float(), z_target.float(), reduction="none").sum(dim=(1, 2, 3)) + weights = ul_decoder_loss_weight( + lambda_t, + bias=args.sigmoid_bias, + loss_factor=args.loss_factor, + invert=False, + ) + loss_raw = (prefactor * weights * per_sample).mean() + num_pixels = x.shape[-2] * x.shape[-1] + bpp_denom = num_pixels * math.log(2.0) + loss = loss_raw / bpp_denom + + accelerator.backward(loss) + if accelerator.sync_gradients: + accelerator.clip_grad_norm_(base_model.parameters(), args.max_grad_norm) + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad(set_to_none=True) + + avg_loss = accelerator.gather(loss.detach().repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + if accelerator.sync_gradients: + global_step += 1 + progress_bar.update(1) + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if accelerator.is_main_process and global_step % args.save_steps == 0: + if args.checkpoints_total_limit is not None: + checkpoints = os.listdir(args.output_dir) + checkpoints = [d for d in checkpoints if d.startswith("checkpoint-")] + checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) + if len(checkpoints) >= args.checkpoints_total_limit: + num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 + removing_checkpoints = checkpoints[:num_to_remove] + logger.info( + f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)}" + ) + for removing_checkpoint in removing_checkpoints: + shutil.rmtree(os.path.join(args.output_dir, removing_checkpoint)) + + save_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}") + accelerator.save_state(save_dir) + _save_pretrained_base_model( + accelerator=accelerator, + base_model=base_model, + output_dir=save_dir, + ) + logger.info(f"Saved state to {save_dir}") + + logs = { + "step_loss": loss.detach().item(), + "lr": lr_scheduler.get_last_lr()[0], + } + progress_bar.set_postfix(**logs) + accelerator.log(logs, step=global_step) + + if global_step >= args.max_train_steps: + break + + if global_step >= args.max_train_steps: + break + + accelerator.wait_for_everyone() + if accelerator.is_main_process: + final_dir = output_dir / "final" + final_dir.mkdir(parents=True, exist_ok=True) + torch.save(accelerator.unwrap_model(base_model).state_dict(), final_dir / "base_model.pt") + _save_pretrained_base_model( + accelerator=accelerator, + base_model=base_model, + output_dir=final_dir, + ) + logger.info(f"Training finished. Saved final checkpoint to {final_dir / 'base_model.pt'}") + + accelerator.end_training() + + +if __name__ == "__main__": + main() diff --git a/examples/research_projects/unified_latents/ul_models.py b/examples/research_projects/unified_latents/ul_models.py new file mode 100644 index 000000000000..0c01d00ffa97 --- /dev/null +++ b/examples/research_projects/unified_latents/ul_models.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# coding=utf-8 + +import torch + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.models.modeling_utils import ModelMixin +from diffusers.models.transformers.dit_transformer_2d import DiTTransformer2DModel + + +class ULTwoStageBaseModel(ModelMixin, ConfigMixin): + """ + Approximation of UL Sec. 5.1 stage-2 base model: + - Stage A: ViT-like latent denoiser, width ~512, 6 layers + - Stage B: ViT-like latent denoiser, width ~1024, 16 layers + + Both stages are DiT blocks and predict residual denoised latents. + """ + + @register_to_config + def __init__( + self, + latent_channels: int, + latent_size: int, + num_train_timesteps: int, + stage_a_layers: int = 6, + stage_b_layers: int = 16, + stage_a_heads: int = 8, + stage_a_head_dim: int = 64, # 512 width + stage_b_heads: int = 16, + stage_b_head_dim: int = 64, # 1024 width + patch_size: int = 1, + ): + super().__init__() + self.stage_a = DiTTransformer2DModel( + num_attention_heads=stage_a_heads, + attention_head_dim=stage_a_head_dim, + in_channels=latent_channels, + out_channels=latent_channels, + num_layers=stage_a_layers, + sample_size=latent_size, + patch_size=patch_size, + num_embeds_ada_norm=num_train_timesteps, + dropout=0.1, + ) + self.stage_b = DiTTransformer2DModel( + num_attention_heads=stage_b_heads, + attention_head_dim=stage_b_head_dim, + in_channels=latent_channels, + out_channels=latent_channels, + num_layers=stage_b_layers, + sample_size=latent_size, + patch_size=patch_size, + num_embeds_ada_norm=num_train_timesteps, + dropout=0.1, + ) + + def forward(self, z_t: torch.Tensor, timesteps: torch.LongTensor, class_labels: torch.LongTensor) -> torch.Tensor: + h = self.stage_a(z_t, timestep=timesteps, class_labels=class_labels).sample + out = self.stage_b(h, timestep=timesteps, class_labels=class_labels).sample + return out diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index b2b6ccf0d123..ba854532a9e3 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -22,7 +22,7 @@ import types from dataclasses import dataclass from pathlib import Path -from typing import Any, Callable, Dict, List, Optional, Union, get_args, get_origin, get_type_hints +from typing import Any, Callable, Dict, List, Union, get_args, get_origin, get_type_hints import httpx import numpy as np diff --git a/tests/pipelines/dflash/test_dflash.py b/tests/pipelines/dflash/test_dflash.py index 288865844721..dcb3249ca68c 100644 --- a/tests/pipelines/dflash/test_dflash.py +++ b/tests/pipelines/dflash/test_dflash.py @@ -36,8 +36,16 @@ def get_input_embeddings(self): def get_output_embeddings(self): return self.lm_head - def forward(self, input_ids, position_ids=None, past_key_values=None, - use_cache=False, output_hidden_states=False, logits_to_keep=None, **kwargs): + def forward( + self, + input_ids, + position_ids=None, + past_key_values=None, + use_cache=False, + output_hidden_states=False, + logits_to_keep=None, + **kwargs, + ): bsz, seq_len = input_ids.shape h = self.embed(input_ids) # Create hidden_states list: one entry per layer + 1 for the embedding layer @@ -71,8 +79,16 @@ def __init__(self, hidden_dim: int, num_target_layers: int, block_size: int): def device(self): return self._device_anchor.device - def forward(self, target_hidden, noise_embedding, position_ids=None, - past_key_values=None, use_cache=False, is_causal=False, **kwargs): + def forward( + self, + target_hidden, + noise_embedding, + position_ids=None, + past_key_values=None, + use_cache=False, + is_causal=False, + **kwargs, + ): # Return a tensor with shape (batch, seq_len, hidden_dim) bsz = noise_embedding.shape[0] seq_len = position_ids.shape[1] if position_ids is not None else noise_embedding.shape[1] @@ -365,7 +381,9 @@ def test_prepare_input_ids_from_1d_tensor(self): def test_prepare_latents(self): pipe = _make_pipeline() mask_token_id = 99 - latents = pipe.prepare_latents(max_length=10, block_size=4, mask_token_id=mask_token_id, device=torch.device("cpu")) + latents = pipe.prepare_latents( + max_length=10, block_size=4, mask_token_id=mask_token_id, device=torch.device("cpu") + ) self.assertEqual(latents.shape, (1, 14)) # 10 + 4 self.assertTrue((latents == mask_token_id).all().item()) self.assertEqual(latents.dtype, torch.long) diff --git a/tests/schedulers/test_scheduler_dflash_token_diffusion.py b/tests/schedulers/test_scheduler_dflash_token_diffusion.py index 00f4ccad221f..9cbb52b28193 100644 --- a/tests/schedulers/test_scheduler_dflash_token_diffusion.py +++ b/tests/schedulers/test_scheduler_dflash_token_diffusion.py @@ -66,10 +66,12 @@ def test_sample_greedy(self): def test_sample_greedy_batched(self): scheduler = self.get_scheduler() - logits = torch.tensor([ - [[10.0, 0.0], [0.0, 10.0]], - [[0.0, 10.0], [10.0, 0.0]], - ]) # (2, 2, 2) + logits = torch.tensor( + [ + [[10.0, 0.0], [0.0, 10.0]], + [[0.0, 10.0], [10.0, 0.0]], + ] + ) # (2, 2, 2) tokens = scheduler.sample(logits, temperature=0.0) self.assertEqual(tokens.shape, (2, 2)) self.assertEqual(tokens[0, 0].item(), 0) From 1afd5efad0e708821e3be99dc22bd83894d92eca Mon Sep 17 00:00:00 2001 From: Kashif Rasul <kashif.rasul@gmail.com> Date: Fri, 27 Mar 2026 13:42:58 +0000 Subject: [PATCH 44/44] DFlash: resolve mask_token_id from config.dflash_config --- src/diffusers/pipelines/dflash/pipeline_dflash.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/dflash/pipeline_dflash.py b/src/diffusers/pipelines/dflash/pipeline_dflash.py index a61431fbbf5f..544f6b8f62ee 100644 --- a/src/diffusers/pipelines/dflash/pipeline_dflash.py +++ b/src/diffusers/pipelines/dflash/pipeline_dflash.py @@ -289,7 +289,12 @@ def __call__( if mask_token_id is None: mask_token_id = getattr(getattr(self, "tokenizer", None), "mask_token_id", None) if mask_token_id is None: - raise ValueError("`mask_token_id` must be provided (or available on the tokenizer).") + # DFlash models store mask_token_id in config.dflash_config + dflash_config = getattr(getattr(self.draft_model, "config", None), "dflash_config", None) + if dflash_config is not None: + mask_token_id = dflash_config.get("mask_token_id", None) + if mask_token_id is None: + raise ValueError("`mask_token_id` must be provided (or available on the tokenizer/model config).") if input_ids.shape[0] != 1: raise ValueError("DFlashPipeline currently supports batch_size=1 input_ids.")

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