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Merged
Artiprocher merged 11 commits into from
Feb 3, 2026
Merged

LTX-2 #1242

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00da4b6
add video_vae and dit for ltx-2
mi804 Jan 27, 2026
8d303b4
add audio_vae, audio_vocoder, text_encoder, connector and upsampler f…
mi804 Jan 28, 2026
b1a2782
support ltx2 one-stage pipeline
mi804 Jan 29, 2026
4f23caa
support ltx2 two stage pipeline & vram
mi804 Jan 30, 2026
9f07d65
support ltx2 distilled pipeline
mi804 Jan 30, 2026
1c8a0f8
refactor patchify
mi804 Jan 31, 2026
f4f991d
support ltx-2 t2v and i2v
mi804 Feb 2, 2026
2a7ac73
minor fix
mi804 Feb 2, 2026
25a9e75
final fix for ltx-2
mi804 Feb 3, 2026
49bc84f
add comment for tuple noise_pred
mi804 Feb 3, 2026
25c3a3d
Merge branch 'main' into ltx-2
Artiprocher Feb 3, 2026
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89 changes: 89 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -532,6 +532,95 @@ Example code for FLUX.1 is available at: [/examples/flux/](/examples/flux/)

https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314

#### LTX-2: [/docs/en/Model_Details/LTX-2.md](/docs/en/Model_Details/LTX-2.md)

<details>

<summary>Quick Start</summary>

Running the following code will quickly load the [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) model for inference. VRAM management is enabled, and the framework automatically adjusts model parameter loading based on available GPU memory. The model can run with as little as 8GB of VRAM.

```python
import torch
from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig
from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2

vram_config = {
"offload_dtype": torch.float8_e5m2,
"offload_device": "cpu",
"onload_dtype": torch.float8_e5m2,
"onload_device": "cpu",
"preparing_dtype": torch.float8_e5m2,
"preparing_device": "cuda",
"computation_dtype": torch.bfloat16,
"computation_device": "cuda",
}
pipe = LTX2AudioVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors", **vram_config),
ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
],
tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

prompt = "A girl is very happy, she is speaking: \"I enjoy working with Diffsynth-Studio, it's a perfect framework.\""
negative_prompt = (
"blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
"grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
"deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
"wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
"field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
"lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
"valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
"mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
"off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
"pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
"inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
)
height, width, num_frames = 512 * 2, 768 * 2, 121
video, audio = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
seed=43,
height=height,
width=width,
num_frames=num_frames,
tiled=True,
use_two_stage_pipeline=True,
)
write_video_audio_ltx2(
video=video,
audio=audio,
output_path='ltx2_twostage.mp4',
fps=24,
audio_sample_rate=24000,
)
```

</details>

<details>

<summary>Examples</summary>

Example code for LTX-2 is available at: [/examples/ltx2/](/examples/ltx2/)

| Model ID | Extra Args | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
|-|-|-|-|-|-|-|-|
|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
|[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
|[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
|[Lightricks/LTX-2: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-TwoStage.py)|-|-|-|-|
|[Lightricks/LTX-2: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|

</details>

#### Wan: [/docs/en/Model_Details/Wan.md](/docs/en/Model_Details/Wan.md)

<details>
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89 changes: 89 additions & 0 deletions README_zh.md
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Original file line number Diff line number Diff line change
Expand Up @@ -532,6 +532,95 @@ FLUX.1 的示例代码位于：[/examples/flux/](/examples/flux/)

https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314

#### LTX-2: [/docs/zh/Model_Details/LTX-2.md](/docs/zh/Model_Details/LTX-2.md)

<details>

<summary>快速开始</summary>

运行以下代码可以快速加载 [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 8GB 显存即可运行。

```python
import torch
from diffsynth.pipelines.ltx2_audio_video import LTX2AudioVideoPipeline, ModelConfig
from diffsynth.utils.data.media_io_ltx2 import write_video_audio_ltx2

vram_config = {
"offload_dtype": torch.float8_e5m2,
"offload_device": "cpu",
"onload_dtype": torch.float8_e5m2,
"onload_device": "cpu",
"preparing_dtype": torch.float8_e5m2,
"preparing_device": "cuda",
"computation_dtype": torch.bfloat16,
"computation_device": "cuda",
}
pipe = LTX2AudioVideoPipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors", **vram_config),
ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors", **vram_config),
ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-spatial-upscaler-x2-1.0.safetensors", **vram_config),
],
tokenizer_config=ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
stage2_lora_config=ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-distilled-lora-384.safetensors"),
vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

prompt = "A girl is very happy, she is speaking: \"I enjoy working with Diffsynth-Studio, it's a perfect framework.\""
negative_prompt = (
"blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, "
"grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, "
"deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, "
"wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of "
"field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent "
"lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny "
"valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, "
"mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, "
"off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward "
"pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, "
"inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
)
height, width, num_frames = 512 * 2, 768 * 2, 121
video, audio = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
seed=43,
height=height,
width=width,
num_frames=num_frames,
tiled=True,
use_two_stage_pipeline=True,
)
write_video_audio_ltx2(
video=video,
audio=audio,
output_path='ltx2_twostage.mp4',
fps=24,
audio_sample_rate=24000,
)
```

</details>

<details>

<summary>示例代码</summary>

LTX-2 的示例代码位于：[/examples/ltx2/](/examples/ltx2/)

|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
|-|-|-|-|-|-|-|-|
|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
|[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
|[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
|[Lightricks/LTX-2: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-TwoStage.py)|-|-|-|-|
|[Lightricks/LTX-2: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|

</details>

#### Wan: [/docs/zh/Model_Details/Wan.md](/docs/zh/Model_Details/Wan.md)

<details>
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66 changes: 65 additions & 1 deletion diffsynth/configs/model_configs.py
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Expand Up @@ -599,4 +599,68 @@
},
]

MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series
ltx2_series = [
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@gemini-code-assist gemini-code-assist bot Jan 30, 2026

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medium

To improve maintainability and reduce redundancy, consider defining the repeated model hashes as constants. This makes it easier to update them in the future as they only need to be changed in one place.

For example:

LTX2_DEV_HASH = "aca7b0bbf8415e9c98360750268915fc"
ltx2_series = [
    {
        "model_hash": LTX2_DEV_HASH,
        # ...
    },
    # ...
]

{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_dit",
"model_class": "diffsynth.models.ltx2_dit.LTXModel",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_dit.LTXModelStateDictConverter",
},
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_video_vae_encoder",
"model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoEncoderStateDictConverter",
},
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_video_vae_decoder",
"model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoDecoderStateDictConverter",
},
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_audio_vae_decoder",
"model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioDecoder",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioDecoderStateDictConverter",
},
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_audio_vocoder",
"model_class": "diffsynth.models.ltx2_audio_vae.LTX2Vocoder",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
},
# { # not used currently
# # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
# "model_hash": "aca7b0bbf8415e9c98360750268915fc",
# "model_name": "ltx2_audio_vae_encoder",
# "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioEncoder",
# "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioEncoderStateDictConverter",
# },
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "aca7b0bbf8415e9c98360750268915fc",
"model_name": "ltx2_text_encoder_post_modules",
"model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderPostModulesStateDictConverter",
},
{
# Example: ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized", origin_file_pattern="model-*.safetensors")
"model_hash": "33917f31c4a79196171154cca39f165e",
"model_name": "ltx2_text_encoder",
"model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoder",
"state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderStateDictConverter",
},
{
# Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
"model_hash": "c79c458c6e99e0e14d47e676761732d2",
"model_name": "ltx2_latent_upsampler",
"model_class": "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler",
},
]
MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series
33 changes: 33 additions & 0 deletions diffsynth/configs/vram_management_module_maps.py
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Expand Up @@ -210,4 +210,37 @@
"torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
},
"diffsynth.models.ltx2_dit.LTXModel": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler": {
"torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.GroupNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_video_vae.LTX2VideoEncoder": {
"torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_video_vae.LTX2VideoDecoder": {
"torch.nn.Conv3d": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_audio_vae.LTX2AudioDecoder": {
"torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_audio_vae.LTX2Vocoder": {
"torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
"torch.nn.ConvTranspose1d": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
"diffsynth.models.ltx2_text_encoder.Embeddings1DConnector": "diffsynth.core.vram.layers.AutoWrappedModule",
},
"diffsynth.models.ltx2_text_encoder.LTX2TextEncoder": {
"torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
"transformers.models.gemma3.modeling_gemma3.Gemma3MultiModalProjector": "diffsynth.core.vram.layers.AutoWrappedModule",
"transformers.models.gemma3.modeling_gemma3.Gemma3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
"transformers.models.gemma3.modeling_gemma3.Gemma3TextScaledWordEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
},
}
2 changes: 1 addition & 1 deletion diffsynth/core/attention/attention.py
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Expand Up @@ -52,7 +52,7 @@ def rearrange_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, q_pattern="
if k_pattern != required_in_pattern:
k = rearrange(k, f"{k_pattern} -> {required_in_pattern}", **dims)
if v_pattern != required_in_pattern:
v = rearrange(v, f"{q_pattern} -> {required_in_pattern}", **dims)
v = rearrange(v, f"{v_pattern} -> {required_in_pattern}", **dims)
return q, k, v


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9 changes: 8 additions & 1 deletion diffsynth/diffusion/base_pipeline.py
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Expand Up @@ -318,7 +318,14 @@ def cfg_guided_model_fn(self, model_fn, cfg_scale, inputs_shared, inputs_posi, i
if inputs_shared.get("positive_only_lora", None) is not None:
self.clear_lora(verbose=0)
noise_pred_nega = model_fn(**inputs_nega, **inputs_shared, **inputs_others)
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
if isinstance(noise_pred_posi, tuple):
# Separately handling different output types of latents, eg. video and audio latents.
noise_pred = tuple(
n_nega + cfg_scale * (n_posi - n_nega)
for n_posi, n_nega in zip(noise_pred_posi, noise_pred_nega)
)
else:
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
else:
noise_pred = noise_pred_posi
return noise_pred
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