Add AnyFlow Any-Step Video Diffusion Pipelines (Bidirectional + FAR Causal)

docs/source/en/_toctree.yml

@@ -198,6 +198,8 @@
   title: Model accelerators and hardware
 - isExpanded: false
   sections:
+  - local: using-diffusers/anyflow
+    title: AnyFlow
   - local: using-diffusers/helios
     title: Helios
   - local: using-diffusers/consisid
@@ -328,6 +330,10 @@
         title: AceStepTransformer1DModel
       - local: api/models/allegro_transformer3d
         title: AllegroTransformer3DModel
+      - local: api/models/anyflow_transformer3d
+        title: AnyFlowTransformer3DModel
+      - local: api/models/anyflow_far_transformer3d
+        title: AnyFlowFARTransformer3DModel
       - local: api/models/aura_flow_transformer2d
         title: AuraFlowTransformer2DModel
       - local: api/models/transformer_bria_fibo
@@ -504,6 +510,8 @@
     - sections:
       - local: api/pipelines/animatediff
         title: AnimateDiff
+      - local: api/pipelines/anyflow
+        title: AnyFlow
       - local: api/pipelines/aura_flow
         title: AuraFlow
       - local: api/pipelines/bria_3_2
@@ -731,6 +739,8 @@
       title: EulerAncestralDiscreteScheduler
     - local: api/schedulers/euler
       title: EulerDiscreteScheduler
+    - local: api/schedulers/flow_map_euler_discrete
+      title: FlowMapEulerDiscreteScheduler
     - local: api/schedulers/flow_match_euler_discrete
       title: FlowMatchEulerDiscreteScheduler
     - local: api/schedulers/flow_match_heun_discrete

docs/source/en/api/models/anyflow_far_transformer3d.md

@@ -0,0 +1,45 @@
+<!-- Copyright 2026 The AnyFlow Team, NVIDIA Corp., and 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.
+-->
+
+# AnyFlowFARTransformer3DModel
+
+The causal (FAR) 3D Transformer used by [`AnyFlowFARPipeline`](../pipelines/anyflow#anyflowfarpipeline) —
+the FAR variant of [AnyFlow](https://huggingface.co/papers/2605.13724) (Yuchao Gu, Guian Fang et al., NUS
+ShowLab × NVIDIA). It extends the v0.35.1 Wan2.1 backbone with three additions:
+
+1. **FAR causal block-mask** via `torch.nn.attention.flex_attention`, supporting frame-level autoregressive
+   generation as introduced in [FAR (Gu et al., 2025)](https://arxiv.org/abs/2503.19325).
+2. **Compressed-frame patch embedding** (`far_patch_embedding`) for context (already-generated) frames,
+   warm-started from the full-resolution `patch_embedding` at construction time via trilinear interpolation.
+3. **Dual-timestep flow-map embedding** (same as
+   [`AnyFlowTransformer3DModel`](anyflow_transformer3d)) — every forward call conditions on both the source
+   timestep ``t`` and the target timestep ``r``.
+
+The chunk schedule (`chunk_partition`) is **not** baked into the model config. It is a per-call argument to
+`forward`, so the same checkpoint handles different `num_frames` configurations without retraining.
+
+```python
+from diffusers import AnyFlowFARTransformer3DModel
+
+# Causal AnyFlow checkpoint (FAR):
+transformer = AnyFlowFARTransformer3DModel.from_pretrained(
+    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", subfolder="transformer"
+)
+```
+
+## AnyFlowFARTransformer3DModel
+
+[[autodoc]] AnyFlowFARTransformer3DModel
+
+## AnyFlowFARTransformerOutput
+
+[[autodoc]] models.transformers.transformer_anyflow.AnyFlowFARTransformerOutput

docs/source/en/api/models/anyflow_transformer3d.md

@@ -0,0 +1,36 @@
+<!-- Copyright 2026 The AnyFlow Team, NVIDIA Corp., and 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.
+-->
+
+# AnyFlowTransformer3DModel
+
+The bidirectional 3D Transformer used by [`AnyFlowPipeline`](../pipelines/anyflow#anyflowpipeline). It is the
+v0.35.1 Wan2.1 backbone with one structural change: the timestep embedder is replaced by
+``AnyFlowDualTimestepTextImageEmbedding``, so every forward call conditions on both the source timestep
+``t`` and the target timestep ``r``. This is the embedding required to learn the flow map
+:math:`\Phi_{r\leftarrow t}` introduced in
+[AnyFlow](https://huggingface.co/papers/2605.13724) (Yuchao Gu, Guian Fang et al., NUS ShowLab × NVIDIA).
+
+For frame-level autoregressive (FAR causal) generation, use
+[`AnyFlowFARTransformer3DModel`](anyflow_far_transformer3d) instead.
+
+```python
+from diffusers import AnyFlowTransformer3DModel
+
+# Bidirectional AnyFlow checkpoint (T2V):
+transformer = AnyFlowTransformer3DModel.from_pretrained(
+    "nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers", subfolder="transformer"
+)
+```
+
+## AnyFlowTransformer3DModel
+
+[[autodoc]] AnyFlowTransformer3DModel

docs/source/en/api/pipelines/anyflow.md

@@ -0,0 +1,216 @@
+<!-- Copyright 2026 The AnyFlow Team, NVIDIA Corp., and 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.
+-->
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders/lora_pipeline.py">
+            <img alt="LoRA" src="https://img.shields.io/badge/LoRA-supported-green">
+        </a>
+    </div>
+</div>
+
+# AnyFlow
+
+[AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation](https://huggingface.co/papers/2605.13724) by Yuchao Gu, Guian Fang and collaborators at [NUS ShowLab](https://sites.google.com/view/showlab) in collaboration with NVIDIA.
+
+*Few-step video generation has been significantly advanced by consistency models. However, their performance often degrades in any-step video diffusion models due to the fixed-point formulation. To address this limitation, we present AnyFlow, the first any-step video diffusion distillation framework built on flow maps. Instead of learning only the mapping z_t → z_0, AnyFlow learns transitions z_t → z_r over arbitrary time intervals, enabling a single model to adapt to different inference budgets. We design an improved forward flow map training recipe that fine-tunes pretrained video diffusion models into flow map models, and introduce Flow Map Backward Simulation to enable on-policy distillation for flow map models. Extensive experiments across both bidirectional and causal architectures, at scales ranging from 1.3B to 14B, on text-to-video and image-to-video tasks demonstrate that AnyFlow outperforms consistency-based baselines while preserving high fidelity and flexible sampling under varying step budgets.*
+
+The original training code is at [`NVlabs/AnyFlow`](https://github.com/NVlabs/AnyFlow). The project page is at [nvlabs.github.io/AnyFlow](https://nvlabs.github.io/AnyFlow).
+
+The following AnyFlow checkpoints are supported:
+
+| Checkpoint | Backbone | Description |
+|------------|----------|-------------|
+| [`nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers) | Wan2.1 1.3B | Bidirectional T2V, lightweight |
+| [`nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers) | Wan2.1 14B | Bidirectional T2V, full quality |
+| [`nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers) | FAR + Wan2.1 1.3B | Causal T2V / I2V / V2V |
+| [`nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers`](https://huggingface.co/nvidia/AnyFlow-FAR-Wan2.1-14B-Diffusers) | FAR + Wan2.1 14B | Causal T2V / I2V / V2V |
+
+All four are grouped under the [`nvidia/anyflow`](https://huggingface.co/collections/nvidia/anyflow) Hugging Face collection.
+
+> [!TIP]
+> Choose `AnyFlowPipeline` for traditional bidirectional text-to-video generation. Choose `AnyFlowFARPipeline` for streaming I2V, video continuation (V2V), or any setup that benefits from frame-by-frame autoregressive sampling.
+
+> [!TIP]
+> AnyFlow supports any-step sampling: a single distilled checkpoint can be evaluated at 1, 2, 4, 8, 16... NFE without retraining. Quality scales monotonically with steps in our benchmarks.
+
+### Optimizing Memory and Inference Speed
+
+<hfoptions id="optimization">
+<hfoption id="memory">
+
+```py
+import torch
+from diffusers import AnyFlowPipeline
+from diffusers.hooks import apply_group_offloading
+
+pipe = AnyFlowPipeline.from_pretrained(
+    "nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers", torch_dtype=torch.bfloat16
+)
+apply_group_offloading(pipe.transformer, onload_device="cuda", offload_type="leaf_level")
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
+```
+
+</hfoption>
+<hfoption id="inference speed">
+
+```py
+import torch
+from diffusers import AnyFlowPipeline
+
+pipe = AnyFlowPipeline.from_pretrained(
+    "nvidia/AnyFlow-Wan2.1-T2V-14B-Diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune-no-cudagraphs")
+```
+
+</hfoption>
+</hfoptions>
+
+### Generation with AnyFlow (Bidirectional T2V)
+
+<hfoptions id="anyflow-bidi">
+<hfoption id="usage">
+
+```py
+import torch
+from diffusers import AnyFlowPipeline
+from diffusers.utils import export_to_video
+
+pipe = AnyFlowPipeline.from_pretrained(
+    "nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+
+prompt = "A red panda eating bamboo in a forest, cinematic lighting"
+video = pipe(prompt, num_inference_steps=4, num_frames=33).frames[0]
+export_to_video(video, "out.mp4", fps=16)
+```
+
+</hfoption>
+</hfoptions>
+
+### Generation with AnyFlow (FAR Causal)
+
+The causal pipeline selects between T2V / I2V / V2V via the ``context_sequence`` argument: pass ``None``
+for plain text-to-video, or a dict with a ``"raw"`` key holding a video tensor of shape
+``(B, C, T, H, W)`` with ``T = 4n + 1`` to condition on existing frames. Use a single conditioning frame
+for I2V and a longer clip for V2V continuation.
+
+> [!IMPORTANT]
+> `AnyFlowFARPipeline.default_chunk_partition = [1, 3, 3, 3, 3, 3, 3, 2]` (sum 21) is matched to the
+> released checkpoints' canonical 81 raw frames (21 latent frames at the VAE temporal stride of 4). When
+> you change `num_frames`, you must also pass a matching `chunk_partition` summing to
+> `(num_frames - 1) // 4 + 1`, otherwise the pipeline raises an `AssertionError`.
+
+<hfoptions id="anyflow-far">
+<hfoption id="t2v">
+
+```py
+import torch
+from diffusers import AnyFlowFARPipeline
+from diffusers.utils import export_to_video
+
+pipe = AnyFlowFARPipeline.from_pretrained(
+    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+
+video = pipe(
+    prompt="A cat surfing a wave, sunset",
+    num_inference_steps=4,
+    num_frames=81,
+).frames[0]
+export_to_video(video, "out.mp4", fps=16)
+```
+
+</hfoption>
+<hfoption id="i2v">
+
+```py
+import numpy as np
+import torch
+from diffusers import AnyFlowFARPipeline
+from diffusers.utils import export_to_video, load_image
+
+pipe = AnyFlowFARPipeline.from_pretrained(
+    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+
+# Wrap the conditioning image as a one-frame video tensor: (1, 3, 1, H, W) in [0, 1].
+first_frame = load_image("path/to/first_frame.png").resize((832, 480))
+arr = np.asarray(first_frame).astype("float32") / 255.0  # (480, 832, 3)
+context_tensor = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).unsqueeze(2).to("cuda")
+
+video = pipe(
+    prompt="a cat walks across a sunlit lawn",
+    context_sequence={"raw": context_tensor},
+    num_inference_steps=4,
+    num_frames=81,
+).frames[0]
+export_to_video(video, "out.mp4", fps=16)
+```
+
+</hfoption>
+<hfoption id="v2v">
+
+```py
+import numpy as np
+import torch
+from diffusers import AnyFlowFARPipeline
+from diffusers.utils import export_to_video, load_video
+
+pipe = AnyFlowFARPipeline.from_pretrained(
+    "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers", torch_dtype=torch.bfloat16
+).to("cuda")
+
+# Context clip — 9 raw frames map to 3 latent frames (9 = 4·2 + 1, 3 = 2 + 1).
+context_frames = load_video("path/to/context.mp4")[:9]
+arr = np.stack([np.asarray(f.resize((832, 480))) for f in context_frames]).astype("float32") / 255.0
+context_tensor = torch.from_numpy(arr).permute(3, 0, 1, 2).unsqueeze(0).to("cuda")  # (1, 3, 9, 480, 832)
+
+video = pipe(
+    prompt="continue the story",
+    context_sequence={"raw": context_tensor},
+    num_inference_steps=4,
+    num_frames=81,
+    # Override chunk_partition so the first chunk covers exactly the 3 latent context frames.
+    chunk_partition=[3, 3, 3, 3, 3, 3, 3],
+).frames[0]
+export_to_video(video, "out.mp4", fps=16)
+```
+
+</hfoption>
+</hfoptions>
+
+## Notes
+
+- Classifier-free guidance is fused into the released checkpoints, so inference does not run a second guided forward pass. Keep the default `guidance_scale=1.0` unless your own checkpoint requires otherwise.
+- `FlowMapEulerDiscreteScheduler` is general-purpose. You can attach it to any flow-map-distilled checkpoint via `from_pretrained(..., scheduler=FlowMapEulerDiscreteScheduler.from_config(...))`.
+- `AnyFlowPipeline` uses [`AnyFlowTransformer3DModel`](../models/anyflow_transformer3d) (bidirectional). `AnyFlowFARPipeline` uses [`AnyFlowFARTransformer3DModel`](../models/anyflow_far_transformer3d), which adds a compressed-frame patch embedding and the FAR causal block-mask.
+- LoRA loading is supported via `WanLoraLoaderMixin`, the same mixin used by the upstream Wan pipelines.
+- For training recipes (forward flow-map training and on-policy distillation), refer to the original AnyFlow training framework at [`NVlabs/AnyFlow`](https://github.com/NVlabs/AnyFlow); training is out of scope for diffusers.
+
+## AnyFlowPipeline
+
+[[autodoc]] AnyFlowPipeline
+  - all
+  - __call__
+
+## AnyFlowFARPipeline
+
+[[autodoc]] AnyFlowFARPipeline
+  - all
+  - __call__
+
+## AnyFlowPipelineOutput
+
+[[autodoc]] pipelines.anyflow.pipeline_output.AnyFlowPipelineOutput

docs/source/en/api/schedulers/flow_map_euler_discrete.md

@@ -0,0 +1,28 @@
+<!-- Copyright 2026 The AnyFlow Team, NVIDIA Corp., and 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.
+-->
+
+# FlowMapEulerDiscreteScheduler
+
+`FlowMapEulerDiscreteScheduler` is an Euler-style sampler designed for flow-map-distilled diffusion
+models. Flow-map models learn arbitrary-interval transitions $\mathbf{z}_t \to \mathbf{z}_r$ rather than
+the fixed $\mathbf{z}_t \to \mathbf{z}_0$ mapping of consistency models. Both endpoints of the step are
+caller-provided, which is what enables any-step sampling: a single distilled checkpoint can be evaluated at
+1, 2, 4, 8, 16... NFE without retraining.
+
+The scheduler was introduced in
+[AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation](https://huggingface.co/papers/2605.13724)
+and ships with the `AnyFlowPipeline` and `AnyFlowFARPipeline` integrations, but it is not
+AnyFlow-specific — any flow-map-distilled checkpoint can use it.
+
+## FlowMapEulerDiscreteScheduler
+
+[[autodoc]] FlowMapEulerDiscreteScheduler