add example_dataset in training scripts (#1358 )

* add example_dataset in training scripts * fix example datasets
Merge pull request #1354 from mi804/low_vram_training_ds
2026-03-19 14:58:12 +00:00 · 2026-03-18 15:37:03 +08:00 · 2026-03-17 16:09:52 +08:00 · 2026-03-13 19:36:59 +08:00 · 2026-03-13 19:36:33 +08:00 · 2026-03-13 17:57:14 +08:00
430 changed files with 25602 additions and 1315 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 /models
 /scripts
 /diffusers
+/.vscode
 *.pkl
 *.safetensors
 *.pth
--- a/README.md
+++ b/README.md
@@ -12,6 +12,8 @@

 ## Introduction

+> DiffSynth-Studio Documentation: [中文版](https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/)、[English version](https://diffsynth-studio-doc.readthedocs.io/en/latest/)
+
 Welcome to the magical world of Diffusion models! DiffSynth-Studio is an open-source Diffusion model engine developed and maintained by the [ModelScope Community](https://www.modelscope.cn/). We hope to foster technological innovation through framework construction, aggregate the power of the open-source community, and explore the boundaries of generative model technology!

 DiffSynth currently includes two open-source projects:
@@ -23,8 +25,6 @@ DiffSynth currently includes two open-source projects:
 * ModelScope AIGC Zone (for Chinese users): https://modelscope.cn/aigc/home
 * ModelScope Civision (for global users): https://modelscope.ai/civision/home

-> DiffSynth-Studio Documentation: [中文版](/docs/zh/README.md)、[English version](/docs/en/README.md)
-
 We believe that a well-developed open-source code framework can lower the threshold for technical exploration. We have achieved many [interesting technologies](#innovative-achievements) based on this codebase. Perhaps you also have many wild ideas, and with DiffSynth-Studio, you can quickly realize these ideas. For this reason, we have prepared detailed documentation for developers. We hope that through these documents, developers can understand the principles of Diffusion models, and we look forward to expanding the boundaries of technology together with you.

 ## Update History
@@ -32,6 +32,19 @@ We believe that a well-developed open-source code framework can lower the thresh
 > DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained. If you need to use old features, please switch to the [last historical version](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3) before the major version update.

 > Currently, the development personnel of this project are limited, with most of the work handled by [Artiprocher](https://github.com/Artiprocher). Therefore, the progress of new feature development will be relatively slow, and the speed of responding to and resolving issues is limited. We apologize for this and ask developers to understand.
+- **January 19, 2026**: Added support for [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) and [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/Wan.md) and [example code](/examples/mova/) are now available.
+
+- **March 12, 2026**: We have added support for the [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) audio-video generation model. The features includes text-to-audio/video, image-to-audio/video, IC-LoRA control, audio-to-video, and audio-video inpainting. We have supported the complete inference and training functionalities. For details, please refer to the [documentation](/docs/en/Model_Details/LTX-2.md) and [code](/examples/ltx2/).
+
+- **March 3, 2026**: We released the [DiffSynth-Studio/Qwen-Image-Layered-Control-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control-V2) model, which is an updated version of Qwen-Image-Layered-Control. In addition to the originally supported text-guided functionality, it adds brush-controlled layer separation capabilities.
+
+- **March 2, 2026** Added support for [Anima](https://modelscope.cn/models/circlestone-labs/Anima). For details, please refer to the [documentation](docs/en/Model_Details/Anima.md). This is an interesting anime-style image generation model. We look forward to its future updates.
+
+- **February 26, 2026** Added full and lora training support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details.
+
+- **February 10, 2026** Added inference support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details. Support for model training will be implemented in the future.
+
+- **February 2, 2026** The first document of the Research Tutorial series is now available, guiding you through training a small 0.1B text-to-image model from scratch. For details, see the [documentation](/docs/en/Research_Tutorial/train_from_scratch.md) and [model](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel). We hope DiffSynth-Studio can evolve into a more powerful training framework for Diffusion models.

 - **January 27, 2026**: [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) is released, and our [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) model is released concurrently. You can use it in [ModelScope Studios](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L). For details, see the [documentation](/docs/zh/Model_Details/Z-Image.md).

@@ -338,6 +351,60 @@ Example code for FLUX.2 is available at: [/examples/flux2/](/examples/flux2/)

 </details>

+#### Anima: [/docs/en/Model_Details/Anima.md](/docs/en/Model_Details/Anima.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Run the following code to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model and perform inference. VRAM management is enabled, and the framework will automatically control the loading of model parameters based on available VRAM. The model can run with a minimum of 8GB VRAM.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for Anima is located at: [/examples/anima/](/examples/anima/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Validation after Full Training | LoRA Training | Validation after LoRA Training |
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](/examples/anima/model_inference/anima-preview.py)|[code](/examples/anima/model_inference_low_vram/anima-preview.py)|[code](/examples/anima/model_training/full/anima-preview.sh)|[code](/examples/anima/model_training/validate_full/anima-preview.py)|[code](/examples/anima/model_training/lora/anima-preview.sh)|[code](/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/en/Model_Details/Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)

 <details>
@@ -417,8 +484,12 @@ Example code for Qwen-Image is available at: [/examples/qwen_image/](/examples/q
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
 |[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.0](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.0)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.1](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.1)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py)|
+|[lightx2v/Qwen-Image-Edit-2511-Lightning](https://modelscope.cn/models/lightx2v/Qwen-Image-Edit-2511-Lightning)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py)|-|-|-|-|
 |[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
 |[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
@@ -529,6 +600,142 @@ 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",
+}
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
+# use the repackaged modelconfig from "DiffSynth-Studio/LTX-2-Repackage" to avoid redundant model loading
+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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.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,
+)
+
+# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
+# 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-2.3: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: A2V](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](/examples/ltx2/model_inference/LTX-2.3-A2V-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-A2V-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: Retake](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_video`,`retake_video_regions`,`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage-Retake.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py)|-|-|-|-|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[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)|[code](/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.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)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
+
+</details>
+
 #### Wan: [/docs/en/Model_Details/Wan.md](/docs/en/Model_Details/Wan.md)

 <details>
@@ -661,6 +868,8 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)
 |[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)|
 |[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)|
 |[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
+| [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) | `input_image` | [code](/examples/mova/model_inference/MOVA-360p-I2AV.py) | [code](/examples/mova/model_training/full/MOVA-360P-I2AV.sh) | [code](/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py) | [code](/examples/mova/model_training/lora/MOVA-360P-I2AV.sh) | [code](/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py) |
+| [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) | `input_image` | [code](/examples/mova/model_inference/MOVA-720p-I2AV.py) | [code](/examples/mova/model_training/full/MOVA-720P-I2AV.sh) | [code](/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py) | [code](/examples/mova/model_training/lora/MOVA-720P-I2AV.sh) | [code](/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py) |

 </details>

@@ -668,6 +877,37 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)

 DiffSynth-Studio is not just an engineered model framework, but also an incubator for innovative achievements.

+<details>
+
+<summary>Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation</summary>
+
+- Paper: [Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
+](https://arxiv.org/abs/2602.03208)
+- Sample Code: [/docs/en/Research_Tutorial/inference_time_scaling.md](/docs/en/Research_Tutorial/inference_time_scaling.md)
+
+|FLUX.1-dev|FLUX.1-dev + SES|Qwen-Image|Qwen-Image + SES|
+|-|-|-|-|
+|![Image](https://github.com/user-attachments/assets/5be15dc6-2805-4822-b04c-2573fc0f45f0)|![Image](https://github.com/user-attachments/assets/e71b8c20-1629-41d9-b0ff-185805c1da4e)|![Image](https://github.com/user-attachments/assets/7a73c968-133a-4545-9aa2-205533861cd4)|![Image](https://github.com/user-attachments/assets/c8390b22-14fe-48a0-a6e6-d6556d31235e)|
+
+</details>
+
+
+<details>
+
+<summary>VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers</summary>
+
+- Paper: [VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
+](https://arxiv.org/abs/2602.03210)
+- Sample code: [/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py)
+- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)
+
+|Example 1|Example 2|Query|Output|
+|-|-|-|-|
+|![Image](https://github.com/user-attachments/assets/380d2670-47bf-41cd-b5c9-37110cc4a943)|![Image](https://github.com/user-attachments/assets/7ceaf345-0992-46e6-b38f-394c2065b165)|![Image](https://github.com/user-attachments/assets/f7c26c21-6894-4d9e-b570-f1d44ca7c1de)|![Image](https://github.com/user-attachments/assets/c2bebe3b-5984-41ba-94bf-9509f6a8a990)|
+
+</details>
+
+
 <details>

 <summary>AttriCtrl: Attribute Intensity Control for Image Generation Models</summary>
@@ -678,7 +918,7 @@ DiffSynth-Studio is not just an engineered model framework, but also an incubato

 |brightness scale = 0.1|brightness scale = 0.3|brightness scale = 0.5|brightness scale = 0.7|brightness scale = 0.9|
 |-|-|-|-|-|
-|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.5.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.7.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.9.jpg)|
+|![Image](https://github.com/user-attachments/assets/e74b32a5-5b2e-4c87-9df8-487c0f8366b7)|![Image](https://github.com/user-attachments/assets/bfe8bec2-9e55-493d-9a26-7e9cce28e03d)|![Image](https://github.com/user-attachments/assets/b099dfe3-ff1f-4b96-894c-d48bbe92db7a)|![Image](https://github.com/user-attachments/assets/0a6b2982-deab-4b0d-91ad-888782de01c9)|![Image](https://github.com/user-attachments/assets/fcecb755-7d03-4020-b83a-13ad2b38705c)|

 </details>

@@ -693,10 +933,10 @@ DiffSynth-Studio is not just an engineered model framework, but also an incubato

 ||[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)|
 |-|-|-|-|-|
-|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)                              |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_0.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_3.jpg)|
-|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)                       |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_3.jpg)|
-|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)  |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_3.jpg)|
-|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)                          |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_3_3.jpg)|
+|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)                              |![Image](https://github.com/user-attachments/assets/01c54d5a-4f00-4c2e-982a-4ec0a4c6a6e3)|![Image](https://github.com/user-attachments/assets/e6621457-b9f1-437c-bcc8-3e12e41646de)|![Image](https://github.com/user-attachments/assets/4b7f721f-a2e5-416c-af2c-b53ef236c321)|![Image](https://github.com/user-attachments/assets/802d554e-0402-482c-9f28-87605f8fe318)|
+|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)                       |![Image](https://github.com/user-attachments/assets/e6621457-b9f1-437c-bcc8-3e12e41646de)|![Image](https://github.com/user-attachments/assets/43720a9f-aa27-4918-947d-545389375d46)|![Image](https://github.com/user-attachments/assets/418c725b-6d35-41f4-b18f-c7e3867cc142)|![Image](https://github.com/user-attachments/assets/8c8f22fa-9643-4019-b6d7-396d8b7fed9a)|
+|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)  |![Image](https://github.com/user-attachments/assets/4b7f721f-a2e5-416c-af2c-b53ef236c321)|![Image](https://github.com/user-attachments/assets/418c725b-6d35-41f4-b18f-c7e3867cc142)|![Image](https://github.com/user-attachments/assets/041a3f9a-c7b4-4311-8582-cb71a7226d80)|![Image](https://github.com/user-attachments/assets/b54ebaa4-31a7-4536-a2c1-496adba0c013)|
+|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)                          |![Image](https://github.com/user-attachments/assets/802d554e-0402-482c-9f28-87605f8fe318)|![Image](https://github.com/user-attachments/assets/8c8f22fa-9643-4019-b6d7-396d8b7fed9a)|![Image](https://github.com/user-attachments/assets/b54ebaa4-31a7-4536-a2c1-496adba0c013)|![Image](https://github.com/user-attachments/assets/a640fd54-3192-49a0-9281-b43d9ba64f09)|

 </details>

--- a/README_zh.md
+++ b/README_zh.md
@@ -12,6 +12,8 @@

 ## 简介

+> DiffSynth-Studio 文档：[中文版](https://diffsynth-studio-doc.readthedocs.io/zh-cn/latest/)、[English version](https://diffsynth-studio-doc.readthedocs.io/en/latest/)
+
 欢迎来到 Diffusion 模型的魔法世界！DiffSynth-Studio 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！

 DiffSynth 目前包括两个开源项目：
@@ -23,8 +25,6 @@ DiffSynth 目前包括两个开源项目：
 * 魔搭社区 AIGC 专区 (面向中国用户): https://modelscope.cn/aigc/home
 * ModelScope Civision (for global users): https://modelscope.ai/civision/home

-> DiffSynth-Studio 文档：[中文版](/docs/zh/README.md)、[English version](/docs/en/README.md)
-
 我们相信，一个完善的开源代码框架能够降低技术探索的门槛，我们基于这个代码库搞出了不少[有意思的技术](#创新成果)。或许你也有许多天马行空的构想，借助 DiffSynth-Studio，你可以快速实现这些想法。为此，我们为开发者准备了详细的文档，我们希望通过这些文档，帮助开发者理解 Diffusion 模型的原理，更期待与你一同拓展技术的边界。

 ## 更新历史
@@ -32,6 +32,19 @@ DiffSynth 目前包括两个开源项目：
 > DiffSynth-Studio 经历了大版本更新，部分旧功能已停止维护，如需使用旧版功能，请切换到大版本更新前的[最后一个历史版本](https://github.com/modelscope/DiffSynth-Studio/tree/afd101f3452c9ecae0c87b79adfa2e22d65ffdc3)。

 > 目前本项目的开发人员有限，大部分工作由 [Artiprocher](https://github.com/Artiprocher) 负责，因此新功能的开发进展会比较缓慢，issue 的回复和解决速度有限，我们对此感到非常抱歉，请各位开发者理解。
+- **2026年1月19日** 新增对 [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) 和 [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/Wan.md)和[示例代码](/examples/mova/)现已可用。
+
+- **2026年3月12日** 我们新增了 [LTX-2.3](https://modelscope.cn/models/Lightricks/LTX-2.3) 音视频生成模型的支持，模型支持的功能包括文生音视频、图生音视频、IC-LoRA控制、音频生视频、音视频局部Inpainting，框架支持完整的推理和训练功能。详细信息请参考 [文档](/docs/zh/Model_Details/LTX-2.md) 和 [示例代码](/examples/ltx2/)。
+
+- **2026年3月3日** 我们发布了 [DiffSynth-Studio/Qwen-Image-Layered-Control-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control-V2) 模型，这是 Qwen-Image-Layered-Control 的更新版本。除了原本就支持的文本引导功能，新增了画笔控制的图层拆分能力。
+
+- **2026年3月2日** 新增对[Anima](https://modelscope.cn/models/circlestone-labs/Anima)的支持，详见[文档](docs/zh/Model_Details/Anima.md)。这是一个有趣的动漫风格图像生成模型，我们期待其后续的模型更新。
+
+- **2026年2月26日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型全量微调与LoRA训练支持，详见[文档](docs/zh/Model_Details/LTX-2.md)。
+
+- **2026年2月10日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型的推理支持，详见[文档](docs/zh/Model_Details/LTX-2.md)，后续将推进模型训练的支持。
+
+- **2026年2月2日** Research Tutorial 的第一篇文档上线，带你从零开始训练一个 0.1B 的小型文生图模型，详见[文档](/docs/zh/Research_Tutorial/train_from_scratch.md)、[模型](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel)，我们希望 DiffSynth-Studio 能够成为一个更强大的 Diffusion 模型训练框架。

 - **2026年1月27日** [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) 发布，我们的 [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) 模型同步发布，在[魔搭创空间](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L)可直接体验，详见[文档](/docs/zh/Model_Details/Z-Image.md)。

@@ -338,6 +351,60 @@ FLUX.2 的示例代码位于：[/examples/flux2/](/examples/flux2/)

 </details>

+#### Anima: [/docs/zh/Model_Details/Anima.md](/docs/zh/Model_Details/Anima.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) 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 8G 显存即可运行。
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+Anima 的示例代码位于：[/examples/anima/](/examples/anima/)
+
+|模型 ID|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|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](/examples/anima/model_inference/anima-preview.py)|[code](/examples/anima/model_inference_low_vram/anima-preview.py)|[code](/examples/anima/model_training/full/anima-preview.sh)|[code](/examples/anima/model_training/validate_full/anima-preview.py)|[code](/examples/anima/model_training/lora/anima-preview.sh)|[code](/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/zh/Model_Details/Qwen-Image.md](/docs/zh/Model_Details/Qwen-Image.md)

 <details>
@@ -417,8 +484,12 @@ Qwen-Image 的示例代码位于：[/examples/qwen_image/](/examples/qwen_image/
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
 |[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.0](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.0)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.1](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.1)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py)|
+|[lightx2v/Qwen-Image-Edit-2511-Lightning](https://modelscope.cn/models/lightx2v/Qwen-Image-Edit-2511-Lightning)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py)|-|-|-|-|
 |[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
 |[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
 |[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)|
@@ -529,6 +600,142 @@ 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",
+}
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
+# use the repackaged modelconfig from "DiffSynth-Studio/LTX-2-Repackage" to avoid redundant model loading
+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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.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,
+)
+
+# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
+# 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-2.3: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2.3-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: A2V](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](/examples/ltx2/model_inference/LTX-2.3-A2V-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-A2V-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: Retake](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_video`,`retake_video_regions`,`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage-Retake.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py)|-|-|-|-|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[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)|[code](/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.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)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
+
+</details>
+
 #### Wan: [/docs/zh/Model_Details/Wan.md](/docs/zh/Model_Details/Wan.md)

 <details>
@@ -661,6 +868,8 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)
 |[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)|
 |[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)|
 |[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
+| [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) | `input_image` | [code](/examples/mova/model_inference/MOVA-360p-I2AV.py) | [code](/examples/mova/model_training/full/MOVA-360P-I2AV.sh) | [code](/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py) | [code](/examples/mova/model_training/lora/MOVA-360P-I2AV.sh) | [code](/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py) |
+| [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) | `input_image` | [code](/examples/mova/model_inference/MOVA-720p-I2AV.py) | [code](/examples/mova/model_training/full/MOVA-720P-I2AV.sh) | [code](/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py) | [code](/examples/mova/model_training/lora/MOVA-720P-I2AV.sh) | [code](/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py) |

 </details>

@@ -668,6 +877,37 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)

 DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。

+<details>
+
+<summary>Spectral Evolution Search: 用于奖励对齐图像生成的高效推理阶段缩放</summary>
+
+- 论文：[Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
+](https://arxiv.org/abs/2602.03208)
+- 代码样例：[/docs/en/Research_Tutorial/inference_time_scaling.md](/docs/en/Research_Tutorial/inference_time_scaling.md)
+
+|FLUX.1-dev|FLUX.1-dev + SES|Qwen-Image|Qwen-Image + SES|
+|-|-|-|-|
+|![Image](https://github.com/user-attachments/assets/5be15dc6-2805-4822-b04c-2573fc0f45f0)|![Image](https://github.com/user-attachments/assets/e71b8c20-1629-41d9-b0ff-185805c1da4e)|![Image](https://github.com/user-attachments/assets/7a73c968-133a-4545-9aa2-205533861cd4)|![Image](https://github.com/user-attachments/assets/c8390b22-14fe-48a0-a6e6-d6556d31235e)|
+
+</details>
+
+
+<details>
+
+<summary>VIRAL：基于DiT模型的类比视觉上下文推理</summary>
+
+- 论文：[VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
+](https://arxiv.org/abs/2602.03210)
+- 代码样例：[/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py)
+- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)
+
+|Example 1|Example 2|Query|Output|
+|-|-|-|-|
+|![Image](https://github.com/user-attachments/assets/380d2670-47bf-41cd-b5c9-37110cc4a943)|![Image](https://github.com/user-attachments/assets/7ceaf345-0992-46e6-b38f-394c2065b165)|![Image](https://github.com/user-attachments/assets/f7c26c21-6894-4d9e-b570-f1d44ca7c1de)|![Image](https://github.com/user-attachments/assets/c2bebe3b-5984-41ba-94bf-9509f6a8a990)|
+
+</details>
+
+
 <details>

 <summary>AttriCtrl: 图像生成模型的属性强度控制</summary>
@@ -679,7 +919,7 @@ DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果

 |brightness scale = 0.1|brightness scale = 0.3|brightness scale = 0.5|brightness scale = 0.7|brightness scale = 0.9|
 |-|-|-|-|-|
-|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.5.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.7.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/AttriCtrl-FLUX.1-Dev/resolve/master/assets/brightness/value_control_0.9.jpg)|
+|![Image](https://github.com/user-attachments/assets/e74b32a5-5b2e-4c87-9df8-487c0f8366b7)|![Image](https://github.com/user-attachments/assets/bfe8bec2-9e55-493d-9a26-7e9cce28e03d)|![Image](https://github.com/user-attachments/assets/b099dfe3-ff1f-4b96-894c-d48bbe92db7a)|![Image](https://github.com/user-attachments/assets/0a6b2982-deab-4b0d-91ad-888782de01c9)|![Image](https://github.com/user-attachments/assets/fcecb755-7d03-4020-b83a-13ad2b38705c)|

 </details>

@@ -695,10 +935,10 @@ DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果

 ||[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)|
 |-|-|-|-|-|
-|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)                              |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_0.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_3.jpg)|
-|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)                       |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_1.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_3.jpg)|
-|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)  |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_2.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_3.jpg)|
-|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)                          |![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_0_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_1_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_2_3.jpg)|![](https://www.modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev/resolve/master/assets/car/image_3_3.jpg)|
+|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)                              |![Image](https://github.com/user-attachments/assets/01c54d5a-4f00-4c2e-982a-4ec0a4c6a6e3)|![Image](https://github.com/user-attachments/assets/e6621457-b9f1-437c-bcc8-3e12e41646de)|![Image](https://github.com/user-attachments/assets/4b7f721f-a2e5-416c-af2c-b53ef236c321)|![Image](https://github.com/user-attachments/assets/802d554e-0402-482c-9f28-87605f8fe318)|
+|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)                       |![Image](https://github.com/user-attachments/assets/e6621457-b9f1-437c-bcc8-3e12e41646de)|![Image](https://github.com/user-attachments/assets/43720a9f-aa27-4918-947d-545389375d46)|![Image](https://github.com/user-attachments/assets/418c725b-6d35-41f4-b18f-c7e3867cc142)|![Image](https://github.com/user-attachments/assets/8c8f22fa-9643-4019-b6d7-396d8b7fed9a)|
+|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)  |![Image](https://github.com/user-attachments/assets/4b7f721f-a2e5-416c-af2c-b53ef236c321)|![Image](https://github.com/user-attachments/assets/418c725b-6d35-41f4-b18f-c7e3867cc142)|![Image](https://github.com/user-attachments/assets/041a3f9a-c7b4-4311-8582-cb71a7226d80)|![Image](https://github.com/user-attachments/assets/b54ebaa4-31a7-4536-a2c1-496adba0c013)|
+|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)                          |![Image](https://github.com/user-attachments/assets/802d554e-0402-482c-9f28-87605f8fe318)|![Image](https://github.com/user-attachments/assets/8c8f22fa-9643-4019-b6d7-396d8b7fed9a)|![Image](https://github.com/user-attachments/assets/b54ebaa4-31a7-4536-a2c1-496adba0c013)|![Image](https://github.com/user-attachments/assets/a640fd54-3192-49a0-9281-b43d9ba64f09)|

 </details>

--- a/diffsynth/configs/init.py
+++ b/diffsynth/configs/init.py
@@ -1,2 +1,2 @@
 from .model_configs import MODEL_CONFIGS
-from .vram_management_module_maps import VRAM_MANAGEMENT_MODULE_MAPS
+from .vram_management_module_maps import VRAM_MANAGEMENT_MODULE_MAPS, VERSION_CHECKER_MAPS
--- a/diffsynth/configs/model_configs.py
+++ b/diffsynth/configs/model_configs.py
@@ -589,6 +589,285 @@ z_image_series = [
        "model_class": "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel",
        "extra_kwargs": {"compress_dim": 128},
    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors")
+        "model_hash": "1392adecee344136041e70553f875f31",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
+    },
+]
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
+ltx2_series = [
+    {
+        # 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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors")
+        "model_hash": "c567aaa37d5ed7454c73aa6024458661",
+        "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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.safetensors")
+        "model_hash": "7f7e904a53260ec0351b05f32153754b",
+        "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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors")
+        "model_hash": "dc6029ca2825147872b45e35a2dc3a97",
+        "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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors")
+        "model_hash": "7d7823dde8f1ea0b50fb07ac329dd4cb",
+        "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",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors")
+        "model_hash": "f471360f6b24bef702ab73133d9f8bb9",
+        "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",
+    },
+    {
+        # 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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_encoder.safetensors")
+        "model_hash": "29338f3b95e7e312a3460a482e4f4554",
+        "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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors")
+        "model_hash": "981629689c8be92a712ab3c5eb4fc3f6",
+        "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",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "model_name": "ltx2_dit",
+        "model_class": "diffsynth.models.ltx2_dit.LTXModel",
+        "extra_kwargs": {"apply_gated_attention": True, "cross_attention_adaln": True, "caption_channels": None},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_dit.LTXModelStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "model_name": "ltx2_video_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder",
+        "extra_kwargs": {"encoder_version": "ltx-2.3"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "model_name": "ltx2_video_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder",
+        "extra_kwargs": {"decoder_version": "ltx-2.3"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "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-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "model_name": "ltx2_audio_vocoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2VocoderWithBWE",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "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-2.3", origin_file_pattern="ltx-2.3-22b-dev.safetensors")
+        "model_hash": "f3a83ecf3995dcc4fae2d27e08ad5767",
+        "model_name": "ltx2_text_encoder_post_modules",
+        "model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules",
+        "extra_kwargs": {"separated_audio_video": True, "embedding_dim_gemma": 3840, "num_layers_gemma": 49, "video_attention_heads": 32, "video_attention_head_dim": 128, "audio_attention_heads": 32, "audio_attention_head_dim": 64, "num_connector_layers": 8, "apply_gated_attention": True},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderPostModulesStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="Lightricks/LTX-2.3", origin_file_pattern="ltx-2.3-spatial-upscaler-x2-1.0.safetensors")
+        "model_hash": "aed408774d694a2452f69936c32febb5",
+        "model_name": "ltx2_latent_upsampler",
+        "model_class": "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler",
+        "extra_kwargs": {"rational_resampler": False},
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="transformer.safetensors")
+        "model_hash": "1c55afad76ed33c112a2978550b524d1",
+        "model_name": "ltx2_dit",
+        "model_class": "diffsynth.models.ltx2_dit.LTXModel",
+        "extra_kwargs": {"apply_gated_attention": True, "cross_attention_adaln": True, "caption_channels": None},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_dit.LTXModelStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_encoder.safetensors")
+        "model_hash": "eecdc07c2ec30863b8a2b8b2134036cf",
+        "model_name": "ltx2_video_vae_encoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoEncoder",
+        "extra_kwargs": {"encoder_version": "ltx-2.3"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="video_vae_decoder.safetensors")
+        "model_hash": "deda2f542e17ee25bc8c38fd605316ea",
+        "model_name": "ltx2_video_vae_decoder",
+        "model_class": "diffsynth.models.ltx2_video_vae.LTX2VideoDecoder",
+        "extra_kwargs": {"decoder_version": "ltx-2.3"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_video_vae.LTX2VideoDecoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors")
+        "model_hash": "7d7823dde8f1ea0b50fb07ac329dd4cb",
+        "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="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vae_encoder.safetensors")
+        "model_hash": "29338f3b95e7e312a3460a482e4f4554",
+        "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="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="audio_vocoder.safetensors")
+        "model_hash": "cd436c99e69ec5c80f050f0944f02a15",
+        "model_name": "ltx2_audio_vocoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2VocoderWithBWE",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2.3-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors")
+        "model_hash": "05da2aab1c4b061f72c426311c165a43",
+        "model_name": "ltx2_text_encoder_post_modules",
+        "model_class": "diffsynth.models.ltx2_text_encoder.LTX2TextEncoderPostModules",
+        "extra_kwargs": {"separated_audio_video": True, "embedding_dim_gemma": 3840, "num_layers_gemma": 49, "video_attention_heads": 32, "video_attention_head_dim": 128, "audio_attention_heads": 32, "audio_attention_head_dim": 64, "num_connector_layers": 8, "apply_gated_attention": True},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_text_encoder.LTX2TextEncoderPostModulesStateDictConverter",
+    },
+]
+anima_series = [
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors")
+        "model_hash": "a9995952c2d8e63cf82e115005eb61b9",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+    },
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors")
+        "model_hash": "417673936471e79e31ed4d186d7a3f4a",
+        "model_name": "anima_dit",
+        "model_class": "diffsynth.models.anima_dit.AnimaDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.anima_dit.AnimaDiTStateDictConverter",
+    }
 ]

-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series
+mova_series = [
+    # Example: ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_dit/diffusion_pytorch_model.safetensors")
+    {
+        "model_hash": "8c57e12790e2c45a64817e0ce28cde2f",
+        "model_name": "mova_audio_dit",
+        "model_class": "diffsynth.models.mova_audio_dit.MovaAudioDit",
+        "extra_kwargs": {'has_image_input': False, 'patch_size': [1], 'in_dim': 128, 'dim': 1536, 'ffn_dim': 8960, 'freq_dim': 256, 'text_dim': 4096, 'out_dim': 128, 'num_heads': 12, 'num_layers': 30, 'eps': 1e-06}
+    },
+    # Example: ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="audio_vae/diffusion_pytorch_model.safetensors")
+    {
+        "model_hash": "418517fb2b4e919d2cac8f314fcf82ac",
+        "model_name": "mova_audio_vae",
+        "model_class": "diffsynth.models.mova_audio_vae.DacVAE",
+    },
+    # Example: ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="dual_tower_bridge/diffusion_pytorch_model.safetensors")
+    {
+        "model_hash": "d1139dbbc8b4ab53cf4b4243d57bbceb",
+        "model_name": "mova_dual_tower_bridge",
+        "model_class": "diffsynth.models.mova_dual_tower_bridge.DualTowerConditionalBridge",
+    },
+]
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series + mova_series
--- a/diffsynth/configs/vram_management_module_maps.py
+++ b/diffsynth/configs/vram_management_module_maps.py
@@ -210,4 +210,75 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
        "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",
+    },
+    "diffsynth.models.anima_dit.AnimaDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.mova_audio_dit.MovaAudioDit": {
+        "diffsynth.models.wan_video_dit.DiTBlock": "diffsynth.core.vram.layers.AutoWrappedNonRecurseModule",
+        "diffsynth.models.wan_video_dit.Head": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.mova_dual_tower_bridge.DualTowerConditionalBridge": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "diffsynth.models.wan_video_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.mova_audio_vae.DacVAE": {
+        "diffsynth.models.mova_audio_vae.Snake1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.ConvTranspose1d": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
 }
+
+def QwenImageTextEncoder_Module_Map_Updater():
+    current = VRAM_MANAGEMENT_MODULE_MAPS["diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder"]
+    from packaging import version
+    import transformers
+    if version.parse(transformers.__version__) >= version.parse("5.2.0"):
+        # The Qwen2RMSNorm in transformers 5.2.0+ has been renamed to Qwen2_5_VLRMSNorm, so we need to update the module map accordingly
+        current.pop("transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2RMSNorm", None)
+        current["transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLRMSNorm"] = "diffsynth.core.vram.layers.AutoWrappedModule"
+    return current
+
+VERSION_CHECKER_MAPS = {
+    "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": QwenImageTextEncoder_Module_Map_Updater,
+}
--- a/diffsynth/core/attention/attention.py
+++ b/diffsynth/core/attention/attention.py
@@ -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


--- a/diffsynth/core/data/operators.py
+++ b/diffsynth/core/data/operators.py
@@ -1,6 +1,8 @@
+import math
 import torch, torchvision, imageio, os
 import imageio.v3 as iio
 from PIL import Image
+import torchaudio


 class DataProcessingPipeline:
@@ -105,27 +107,59 @@ class ToList(DataProcessingOperator):
        return [data]
    

-class LoadVideo(DataProcessingOperator):
-    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
+class FrameSamplerByRateMixin:
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_rate=24, fix_frame_rate=False):
        self.num_frames = num_frames
        self.time_division_factor = time_division_factor
        self.time_division_remainder = time_division_remainder
-        # frame_processor is build in the video loader for high efficiency.
-        self.frame_processor = frame_processor
-        
+        self.frame_rate = frame_rate
+        self.fix_frame_rate = fix_frame_rate
+
+    def get_reader(self, data: str):
+        return imageio.get_reader(data)
+
+    def get_available_num_frames(self, reader):
+        if not self.fix_frame_rate:
+            return reader.count_frames()
+        meta_data = reader.get_meta_data()
+        total_original_frames = int(reader.count_frames())
+        duration = meta_data["duration"] if "duration" in meta_data else total_original_frames / meta_data['fps']
+        total_available_frames = math.floor(duration * self.frame_rate)
+        return int(total_available_frames)
+
    def get_num_frames(self, reader):
        num_frames = self.num_frames
-        if int(reader.count_frames()) < num_frames:
-            num_frames = int(reader.count_frames())
+        total_frames = self.get_available_num_frames(reader)
+        if int(total_frames) < num_frames:
+            num_frames = total_frames
            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
                num_frames -= 1
        return num_frames
-        
+
+    def map_single_frame_id(self, new_sequence_id: int, raw_frame_rate: float, total_raw_frames: int) -> int:
+        if not self.fix_frame_rate:
+            return new_sequence_id
+        target_time_in_seconds = new_sequence_id / self.frame_rate
+        raw_frame_index_float = target_time_in_seconds * raw_frame_rate
+        frame_id = int(round(raw_frame_index_float))        
+        frame_id = min(frame_id, total_raw_frames - 1)
+        return frame_id
+
+
+class LoadVideo(DataProcessingOperator, FrameSamplerByRateMixin):
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x, frame_rate=24, fix_frame_rate=False):
+        FrameSamplerByRateMixin.__init__(self, num_frames, time_division_factor, time_division_remainder, frame_rate, fix_frame_rate)
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+
    def __call__(self, data: str):
-        reader = imageio.get_reader(data)
+        reader = self.get_reader(data)
+        raw_frame_rate = reader.get_meta_data()['fps']
        num_frames = self.get_num_frames(reader)
+        total_raw_frames = reader.count_frames()
        frames = []
        for frame_id in range(num_frames):
+            frame_id = self.map_single_frame_id(frame_id, raw_frame_rate, total_raw_frames)
            frame = reader.get_data(frame_id)
            frame = Image.fromarray(frame)
            frame = self.frame_processor(frame)
@@ -149,7 +183,7 @@ class LoadGIF(DataProcessingOperator):
        self.time_division_remainder = time_division_remainder
        # frame_processor is build in the video loader for high efficiency.
        self.frame_processor = frame_processor
-        
+
    def get_num_frames(self, path):
        num_frames = self.num_frames
        images = iio.imread(path, mode="RGB")
@@ -218,3 +252,23 @@ class LoadAudio(DataProcessingOperator):
        import librosa
        input_audio, sample_rate = librosa.load(data, sr=self.sr)
        return input_audio
+
+
+class LoadAudioWithTorchaudio(DataProcessingOperator, FrameSamplerByRateMixin):
+
+    def __init__(self, num_frames=121, time_division_factor=8, time_division_remainder=1, frame_rate=24, fix_frame_rate=True):
+        FrameSamplerByRateMixin.__init__(self, num_frames, time_division_factor, time_division_remainder, frame_rate, fix_frame_rate)
+
+    def __call__(self, data: str):
+        reader = self.get_reader(data)
+        num_frames = self.get_num_frames(reader)
+        duration = num_frames / self.frame_rate
+        waveform, sample_rate = torchaudio.load(data)
+        target_samples = int(duration * sample_rate)
+        current_samples = waveform.shape[-1]
+        if current_samples > target_samples:
+            waveform = waveform[..., :target_samples]
+        elif current_samples < target_samples:
+            padding = target_samples - current_samples
+            waveform = torch.nn.functional.pad(waveform, (0, padding))
+        return waveform, sample_rate
--- a/diffsynth/core/data/unified_dataset.py
+++ b/diffsynth/core/data/unified_dataset.py
@@ -42,6 +42,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
        max_pixels=1920*1080, height=None, width=None,
        height_division_factor=16, width_division_factor=16,
        num_frames=81, time_division_factor=4, time_division_remainder=1,
+        frame_rate=24, fix_frame_rate=False,
    ):
        return RouteByType(operator_map=[
            (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[
@@ -53,6 +54,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
                (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo(
                    num_frames, time_division_factor, time_division_remainder,
                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
+                    frame_rate=frame_rate, fix_frame_rate=fix_frame_rate,
                )),
            ])),
        ])
--- a/diffsynth/core/gradient/gradient_checkpoint.py
+++ b/diffsynth/core/gradient/gradient_checkpoint.py
@@ -1,12 +1,32 @@
 import torch


+try:
+    import deepspeed
+    _HAS_DEEPSPEED = True
+except ModuleNotFoundError:
+    _HAS_DEEPSPEED = False
+
+
 def create_custom_forward(module):
    def custom_forward(*inputs, **kwargs):
        return module(*inputs, **kwargs)
    return custom_forward


+def create_custom_forward_use_reentrant(module):
+    def custom_forward(*inputs):
+        return module(*inputs)
+    return custom_forward
+
+
+def judge_args_requires_grad(*args):
+    for arg in args:
+        if isinstance(arg, torch.Tensor) and arg.requires_grad:
+            return True
+    return False
+
+
 def gradient_checkpoint_forward(
    model,
    use_gradient_checkpointing,
@@ -14,6 +34,17 @@ def gradient_checkpoint_forward(
    *args,
    **kwargs,
 ):
+    if use_gradient_checkpointing and _HAS_DEEPSPEED and deepspeed.checkpointing.is_configured():
+        all_args = args + tuple(kwargs.values())
+        if not judge_args_requires_grad(*all_args):
+            # get the first grad_enabled tensor from un_checkpointed forward
+            model_output = model(*args, **kwargs)
+        else:
+            model_output = deepspeed.checkpointing.checkpoint(
+                create_custom_forward_use_reentrant(model),
+                *all_args,
+            )
+        return model_output
    if use_gradient_checkpointing_offload:
        with torch.autograd.graph.save_on_cpu():
            model_output = torch.utils.checkpoint.checkpoint(
--- a/diffsynth/core/loader/config.py
+++ b/diffsynth/core/loader/config.py
@@ -1,5 +1,5 @@
 import torch, glob, os
-from typing import Optional, Union
+from typing import Optional, Union, Dict
 from dataclasses import dataclass
 from modelscope import snapshot_download
 from huggingface_hub import snapshot_download as hf_snapshot_download
@@ -23,13 +23,14 @@ class ModelConfig:
    computation_device: Optional[Union[str, torch.device]] = None
    computation_dtype: Optional[torch.dtype] = None
    clear_parameters: bool = False
+    state_dict: Dict[str, torch.Tensor] = None
    
    def check_input(self):
        if self.path is None and self.model_id is None:
            raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`. `skip_download=True` only supports the first one.""")
    
    def parse_original_file_pattern(self):
-        if self.origin_file_pattern is None or self.origin_file_pattern == "":
+        if self.origin_file_pattern in [None, "", "./"]:
            return "*"
        elif self.origin_file_pattern.endswith("/"):
            return self.origin_file_pattern + "*"
@@ -98,7 +99,7 @@ class ModelConfig:
        if self.require_downloading():
            self.download()
        if self.path is None:
-            if self.origin_file_pattern is None or self.origin_file_pattern == "":
+            if self.origin_file_pattern in [None, "", "./"]:
                self.path = os.path.join(self.local_model_path, self.model_id)
            else:
                self.path = glob.glob(os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern))
--- a/diffsynth/core/loader/file.py
+++ b/diffsynth/core/loader/file.py
@@ -2,16 +2,25 @@ from safetensors import safe_open
 import torch, hashlib


-def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+def load_state_dict(file_path, torch_dtype=None, device="cpu", pin_memory=False, verbose=0):
    if isinstance(file_path, list):
        state_dict = {}
        for file_path_ in file_path:
-            state_dict.update(load_state_dict(file_path_, torch_dtype, device))
-        return state_dict
-    if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+            state_dict.update(load_state_dict(file_path_, torch_dtype, device, pin_memory=pin_memory, verbose=verbose))
    else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        if verbose >= 1:
+            print(f"Loading file [started]: {file_path}")
+        if file_path.endswith(".safetensors"):
+            state_dict = load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+        else:
+            state_dict = load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        # If load state dict in CPU memory, `pin_memory=True` will make `model.to("cuda")` faster.
+        if pin_memory:
+            for i in state_dict:
+                state_dict[i] = state_dict[i].pin_memory()
+        if verbose >= 1:
+            print(f"Loading file [done]: {file_path}")
+    return state_dict


 def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
--- a/diffsynth/core/loader/model.py
+++ b/diffsynth/core/loader/model.py
@@ -3,14 +3,14 @@ from ..vram.disk_map import DiskMap
 from ..vram.layers import enable_vram_management
 from .file import load_state_dict
 import torch
+from contextlib import contextmanager
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.utils import ContextManagers


-def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None):
+def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None, state_dict=None):
    config = {} if config is None else config
-    # Why do we use `skip_model_initialization`?
-    # It skips the random initialization of model parameters,
-    # thereby speeding up model loading and avoiding excessive memory usage.
-    with skip_model_initialization():
+    with ContextManagers(get_init_context(torch_dtype=torch_dtype, device=device)):
        model = model_class(**config)
    # What is `module_map`?
    # This is a module mapping table for VRAM management.
@@ -20,7 +20,7 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
        dtypes = [vram_config["offload_dtype"], vram_config["onload_dtype"], vram_config["preparing_dtype"], vram_config["computation_dtype"]]
        dtype = [d for d in dtypes if d != "disk"][0]
        if vram_config["offload_device"] != "disk":
-            state_dict = DiskMap(path, device, torch_dtype=dtype)
+            if state_dict is None: state_dict = DiskMap(path, device, torch_dtype=dtype)
            if state_dict_converter is not None:
                state_dict = state_dict_converter(state_dict)
            else:
@@ -35,7 +35,9 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
        # Sometimes a model file contains multiple models,
        # and DiskMap can load only the parameters of a single model,
        # avoiding the need to load all parameters in the file.
-        if use_disk_map:
+        if state_dict is not None:
+            pass
+        elif use_disk_map:
            state_dict = DiskMap(path, device, torch_dtype=torch_dtype)
        else:
            state_dict = load_state_dict(path, torch_dtype, device)
@@ -46,7 +48,14 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
            state_dict = state_dict_converter(state_dict)
        else:
            state_dict = {i: state_dict[i] for i in state_dict}
-        model.load_state_dict(state_dict, assign=True)
+        # Why does DeepSpeed ZeRO Stage 3 need to be handled separately?
+        # Because at this stage, model parameters are partitioned across multiple GPUs.
+        # Loading them directly could lead to excessive GPU memory consumption.
+        if is_deepspeed_zero3_enabled():
+            from transformers.integrations.deepspeed import _load_state_dict_into_zero3_model
+            _load_state_dict_into_zero3_model(model, state_dict)
+        else:
+            model.load_state_dict(state_dict, assign=True)
        # Why do we call `to()`?
        # Because some models override the behavior of `to()`,
        # especially those from libraries like Transformers.
@@ -77,3 +86,20 @@ def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=tor
    }
    enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=80)
    return model
+
+
+def get_init_context(torch_dtype, device):
+    if is_deepspeed_zero3_enabled():
+        from transformers.modeling_utils import set_zero3_state
+        import deepspeed
+        # Why do we use "deepspeed.zero.Init"?
+        # Weight segmentation of the model can be performed on the CPU side
+        # and loading the segmented weights onto the computing card
+        init_contexts = [deepspeed.zero.Init(remote_device=device, dtype=torch_dtype), set_zero3_state()]
+    else:
+        # Why do we use `skip_model_initialization`?
+        # It skips the random initialization of model parameters,
+        # thereby speeding up model loading and avoiding excessive memory usage.
+        init_contexts = [skip_model_initialization()]
+
+    return init_contexts
--- a/diffsynth/core/npu_patch/npu_fused_operator.py
+++ b/diffsynth/core/npu_patch/npu_fused_operator.py
@@ -0,0 +1,30 @@
+import torch
+from ..device.npu_compatible_device import get_device_type
+try:
+    import torch_npu
+except:
+    pass
+
+
+def rms_norm_forward_npu(self, hidden_states):
+    "npu rms fused operator for RMSNorm.forward from diffsynth\models\general_modules.py"
+    if hidden_states.dtype != self.weight.dtype:
+        hidden_states = hidden_states.to(self.weight.dtype)
+    return torch_npu.npu_rms_norm(hidden_states, self.weight, self.eps)[0]
+
+
+def rms_norm_forward_transformers_npu(self, hidden_states):
+    "npu rms fused operator for transformers"
+    if hidden_states.dtype != self.weight.dtype:
+        hidden_states = hidden_states.to(self.weight.dtype)
+    return torch_npu.npu_rms_norm(hidden_states, self.weight, self.variance_epsilon)[0]
+
+
+def rotary_emb_Zimage_npu(self, x_in: torch.Tensor, freqs_cis: torch.Tensor):
+    "npu rope fused operator for Zimage"
+    with torch.amp.autocast(get_device_type(), enabled=False):
+        freqs_cis = freqs_cis.unsqueeze(2)
+        cos, sin = torch.chunk(torch.view_as_real(freqs_cis), 2, dim=-1)
+        cos = cos.expand(-1, -1, -1, -1, 2).flatten(-2)
+        sin = sin.expand(-1, -1, -1, -1, 2).flatten(-2)
+        return torch_npu.npu_rotary_mul(x_in, cos, sin, rotary_mode="interleave").to(x_in)
--- a/diffsynth/core/vram/layers.py
+++ b/diffsynth/core/vram/layers.py
@@ -417,7 +417,7 @@ class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
    def lora_forward(self, x, out):
        if self.lora_merger is None:
            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
-                out = out + x @ lora_A.T @ lora_B.T
+                out = out + x @ lora_A.T.to(device=x.device, dtype=x.dtype) @ lora_B.T.to(device=x.device, dtype=x.dtype)
        else:
            lora_output = []
            for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
--- a/diffsynth/diffusion/base_pipeline.py
+++ b/diffsynth/diffusion/base_pipeline.py
@@ -94,20 +94,23 @@ class BasePipeline(torch.nn.Module):
        return self


-    def check_resize_height_width(self, height, width, num_frames=None):
+    def check_resize_height_width(self, height, width, num_frames=None, verbose=1):
        # Shape check
        if height % self.height_division_factor != 0:
            height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
-            print(f"height % {self.height_division_factor} != 0. We round it up to {height}.")
+            if verbose > 0:
+                print(f"height % {self.height_division_factor} != 0. We round it up to {height}.")
        if width % self.width_division_factor != 0:
            width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor
-            print(f"width % {self.width_division_factor} != 0. We round it up to {width}.")
+            if verbose > 0:
+                print(f"width % {self.width_division_factor} != 0. We round it up to {width}.")
        if num_frames is None:
            return height, width
        else:
            if num_frames % self.time_division_factor != self.time_division_remainder:
                num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder
-                print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
+                if verbose > 0:
+                    print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
            return height, width, num_frames


@@ -144,6 +147,12 @@ class BasePipeline(torch.nn.Module):
        video = [self.vae_output_to_image(image, pattern="H W C", min_value=min_value, max_value=max_value) for image in vae_output]
        return video

+    def output_audio_format_check(self, audio_output):
+        # output standard foramt: [C, T], output dtype: float()
+        # remove batch dim
+        if audio_output.ndim == 3:
+            audio_output = audio_output.squeeze(0)
+        return audio_output.float()

    def load_models_to_device(self, model_names):
        if self.vram_management_enabled:
@@ -296,6 +305,7 @@ class BasePipeline(torch.nn.Module):
                vram_config=vram_config,
                vram_limit=vram_limit,
                clear_parameters=model_config.clear_parameters,
+                state_dict=model_config.state_dict,
            )
        return model_pool
    
@@ -317,7 +327,14 @@ class BasePipeline(torch.nn.Module):
            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
--- a/diffsynth/diffusion/flow_match.py
+++ b/diffsynth/diffusion/flow_match.py
@@ -4,13 +4,15 @@ from typing_extensions import Literal

 class FlowMatchScheduler():

-    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image"] = "FLUX.1"):
+    def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning"] = "FLUX.1"):
        self.set_timesteps_fn = {
            "FLUX.1": FlowMatchScheduler.set_timesteps_flux,
            "Wan": FlowMatchScheduler.set_timesteps_wan,
            "Qwen-Image": FlowMatchScheduler.set_timesteps_qwen_image,
            "FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
            "Z-Image": FlowMatchScheduler.set_timesteps_z_image,
+            "LTX-2": FlowMatchScheduler.set_timesteps_ltx2,
+            "Qwen-Image-Lightning": FlowMatchScheduler.set_timesteps_qwen_image_lightning,
        }.get(template, FlowMatchScheduler.set_timesteps_flux)
        self.num_train_timesteps = 1000

@@ -70,6 +72,28 @@ class FlowMatchScheduler():
        timesteps = sigmas * num_train_timesteps
        return sigmas, timesteps
    
+    @staticmethod
+    def set_timesteps_qwen_image_lightning(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
+        sigma_min = 0.0
+        sigma_max = 1.0
+        num_train_timesteps = 1000
+        base_shift = math.log(3)
+        max_shift = math.log(3)
+        # Sigmas
+        sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+        sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+        # Mu
+        if exponential_shift_mu is not None:
+            mu = exponential_shift_mu
+        elif dynamic_shift_len is not None:
+            mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len, base_shift=base_shift, max_shift=max_shift)
+        else:
+            mu = 0.8
+        sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
+        # Timesteps
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+    
    @staticmethod
    def compute_empirical_mu(image_seq_len, num_steps):
        a1, b1 = 8.73809524e-05, 1.89833333
@@ -121,7 +145,35 @@ class FlowMatchScheduler():
                timestep_id = torch.argmin((timesteps - timestep).abs())
                timesteps[timestep_id] = timestep
        return sigmas, timesteps
-    
+
+    @staticmethod
+    def set_timesteps_ltx2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None, terminal=0.1, special_case=None):
+        num_train_timesteps = 1000
+        if special_case == "stage2":
+            sigmas = torch.Tensor([0.909375, 0.725, 0.421875])
+        elif special_case == "ditilled_stage1":
+            sigmas = torch.Tensor([1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875])
+        else:
+            dynamic_shift_len = dynamic_shift_len or 4096
+            sigma_shift = FlowMatchScheduler._calculate_shift_qwen_image(
+                image_seq_len=dynamic_shift_len,
+                base_seq_len=1024,
+                max_seq_len=4096,
+                base_shift=0.95,
+                max_shift=2.05,
+            )
+            sigma_min = 0.0
+            sigma_max = 1.0
+            sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
+            sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
+            sigmas = math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1))
+            # Shift terminal
+            one_minus_z = 1.0 - sigmas
+            scale_factor = one_minus_z[-1] / (1 - terminal)
+            sigmas = 1.0 - (one_minus_z / scale_factor)
+        timesteps = sigmas * num_train_timesteps
+        return sigmas, timesteps
+
    def set_training_weight(self):
        steps = 1000
        x = self.timesteps
--- a/diffsynth/diffusion/logger.py
+++ b/diffsynth/diffusion/logger.py
@@ -18,8 +18,8 @@ class ModelLogger:

    def on_epoch_end(self, accelerator: Accelerator, model: torch.nn.Module, epoch_id):
        accelerator.wait_for_everyone()
+        state_dict = accelerator.get_state_dict(model)
        if accelerator.is_main_process:
-            state_dict = accelerator.get_state_dict(model)
            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
            state_dict = self.state_dict_converter(state_dict)
            os.makedirs(self.output_path, exist_ok=True)
@@ -34,8 +34,8 @@ class ModelLogger:

    def save_model(self, accelerator: Accelerator, model: torch.nn.Module, file_name):
        accelerator.wait_for_everyone()
+        state_dict = accelerator.get_state_dict(model)
        if accelerator.is_main_process:
-            state_dict = accelerator.get_state_dict(model)
            state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
            state_dict = self.state_dict_converter(state_dict)
            os.makedirs(self.output_path, exist_ok=True)
--- a/diffsynth/diffusion/loss.py
+++ b/diffsynth/diffusion/loss.py
@@ -13,14 +13,51 @@ def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
    inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
    training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
    
+    if "first_frame_latents" in inputs:
+        inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
+    
    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
    noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
    
+    if "first_frame_latents" in inputs:
+        noise_pred = noise_pred[:, :, 1:]
+        training_target = training_target[:, :, 1:]
+    
    loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
    loss = loss * pipe.scheduler.training_weight(timestep)
    return loss


+def FlowMatchSFTAudioVideoLoss(pipe: BasePipeline, **inputs):
+    max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
+    min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
+
+    timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
+    timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
+    
+    # video
+    noise = torch.randn_like(inputs["input_latents"])
+    inputs["video_latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
+    training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
+    
+    # audio
+    if inputs.get("audio_input_latents") is not None:
+        audio_noise = torch.randn_like(inputs["audio_input_latents"])
+        inputs["audio_latents"] = pipe.scheduler.add_noise(inputs["audio_input_latents"], audio_noise, timestep)
+        training_target_audio = pipe.scheduler.training_target(inputs["audio_input_latents"], audio_noise, timestep)
+
+    models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
+    noise_pred, noise_pred_audio = pipe.model_fn(**models, **inputs, timestep=timestep)
+
+    loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
+    loss = loss * pipe.scheduler.training_weight(timestep)
+    if inputs.get("audio_input_latents") is not None:
+        loss_audio = torch.nn.functional.mse_loss(noise_pred_audio.float(), training_target_audio.float())
+        loss_audio = loss_audio * pipe.scheduler.training_weight(timestep)
+        loss = loss + loss_audio
+    return loss
+
+
 def DirectDistillLoss(pipe: BasePipeline, **inputs):
    pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
    pipe.scheduler.training = True
@@ -84,7 +121,9 @@ class TrajectoryImitationLoss(torch.nn.Module):
                progress_id_teacher = torch.argmin((timesteps_teacher - pipe.scheduler.timesteps[progress_id + 1]).abs())
                latents_ = trajectory_teacher[progress_id_teacher]
            
-            target = (latents_ - inputs_shared["latents"]) / (sigma_ - sigma)
+            denom = sigma_ - sigma
+            denom = torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6)
+            target = (latents_ - inputs_shared["latents"]) / denom
            loss = loss + torch.nn.functional.mse_loss(noise_pred.float(), target.float()) * pipe.scheduler.training_weight(timestep)
        return loss
    
--- a/diffsynth/diffusion/runner.py
+++ b/diffsynth/diffusion/runner.py
@@ -27,9 +27,9 @@ def launch_training_task(
    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
-    
+    model.to(device=accelerator.device)
    model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
-    
+    initialize_deepspeed_gradient_checkpointing(accelerator)
    for epoch_id in range(num_epochs):
        for data in tqdm(dataloader):
            with accelerator.accumulate(model):
@@ -59,6 +59,7 @@ def launch_data_process_task(
        num_workers = args.dataset_num_workers
        
    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
+    model.to(device=accelerator.device)
    model, dataloader = accelerator.prepare(model, dataloader)
    
    for data_id, data in enumerate(tqdm(dataloader)):
@@ -69,3 +70,19 @@ def launch_data_process_task(
                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
                data = model(data)
                torch.save(data, save_path)
+
+
+def initialize_deepspeed_gradient_checkpointing(accelerator: Accelerator):
+    if getattr(accelerator.state, "deepspeed_plugin", None) is not None:
+        ds_config = accelerator.state.deepspeed_plugin.deepspeed_config
+        if "activation_checkpointing" in ds_config:
+            import deepspeed
+            act_config = ds_config["activation_checkpointing"]
+            deepspeed.checkpointing.configure(
+                mpu_=None, 
+                partition_activations=act_config.get("partition_activations", False),
+                checkpoint_in_cpu=act_config.get("cpu_checkpointing", False),
+                contiguous_checkpointing=act_config.get("contiguous_memory_optimization", False)
+            )
+        else:
+            print("Do not find activation_checkpointing config in deepspeed config, skip initializing deepspeed gradient checkpointing.")
--- a/diffsynth/diffusion/training_module.py
+++ b/diffsynth/diffusion/training_module.py
@@ -1,9 +1,32 @@
-import torch, json, os
+import torch, json, os, inspect
 from ..core import ModelConfig, load_state_dict
 from ..utils.controlnet import ControlNetInput
+from .base_pipeline import PipelineUnit
 from peft import LoraConfig, inject_adapter_in_model


+class GeneralUnit_RemoveCache(PipelineUnit):
+    def __init__(self, required_params=tuple(), force_remove_params_shared=tuple(), force_remove_params_posi=tuple(), force_remove_params_nega=tuple()):
+        super().__init__(take_over=True)
+        self.required_params = required_params
+        self.force_remove_params_shared = force_remove_params_shared
+        self.force_remove_params_posi = force_remove_params_posi
+        self.force_remove_params_nega = force_remove_params_nega
+
+    def process_params(self, inputs, required_params, force_remove_params):
+        inputs_ = {}
+        for name, param in inputs.items():
+            if name in required_params and name not in force_remove_params:
+                inputs_[name] = param
+        return inputs_
+
+    def process(self, pipe, inputs_shared, inputs_posi, inputs_nega):
+        inputs_shared = self.process_params(inputs_shared, self.required_params, self.force_remove_params_shared)
+        inputs_posi = self.process_params(inputs_posi, self.required_params, self.force_remove_params_posi)
+        inputs_nega = self.process_params(inputs_nega, self.required_params, self.force_remove_params_nega)
+        return inputs_shared, inputs_posi, inputs_nega
+
+
 class DiffusionTrainingModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
@@ -231,14 +254,30 @@ class DiffusionTrainingModule(torch.nn.Module):
            setattr(pipe, lora_base_model, model)


-    def split_pipeline_units(self, task, pipe, trainable_models=None, lora_base_model=None):
+    def split_pipeline_units(
+        self, task, pipe,
+        trainable_models=None, lora_base_model=None,
+        # TODO: set `remove_unnecessary_params` to `True` by default
+        remove_unnecessary_params=False,
+        # TODO: move `loss_required_params` to `loss.py`
+        loss_required_params=("input_latents", "max_timestep_boundary", "min_timestep_boundary", "first_frame_latents", "video_latents", "audio_input_latents", "num_inference_steps"),
+        force_remove_params_shared=tuple(),
+        force_remove_params_posi=tuple(),
+        force_remove_params_nega=tuple(),
+    ):
        models_require_backward = []
        if trainable_models is not None:
            models_require_backward += trainable_models.split(",")
        if lora_base_model is not None:
            models_require_backward += [lora_base_model]
        if task.endswith(":data_process"):
-            _, pipe.units = pipe.split_pipeline_units(models_require_backward)
+            other_units, pipe.units = pipe.split_pipeline_units(models_require_backward)
+            if remove_unnecessary_params:
+                required_params = list(loss_required_params) + [i for i in inspect.signature(self.pipe.model_fn).parameters]
+                for unit in other_units:
+                    required_params.extend(unit.fetch_input_params())
+                required_params = sorted(list(set(required_params)))
+                pipe.units.append(GeneralUnit_RemoveCache(required_params, force_remove_params_shared, force_remove_params_posi, force_remove_params_nega))
        elif task.endswith(":train"):
            pipe.units, _ = pipe.split_pipeline_units(models_require_backward)
        return pipe
--- a/diffsynth/models/anima_dit.py
+++ b/diffsynth/models/anima_dit.py
--- a/diffsynth/models/flux2_dit.py
+++ b/diffsynth/models/flux2_dit.py
@@ -407,6 +407,7 @@ class Flux2AttnProcessor:
            query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
            key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)

+        query, key, value = query.to(hidden_states.dtype), key.to(hidden_states.dtype), value.to(hidden_states.dtype)
        hidden_states = attention_forward(
            query,
            key,
@@ -536,6 +537,7 @@ class Flux2ParallelSelfAttnProcessor:
            query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
            key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)

+        query, key, value = query.to(hidden_states.dtype), key.to(hidden_states.dtype), value.to(hidden_states.dtype)
        hidden_states = attention_forward(
            query,
            key,
--- a/diffsynth/models/ltx2_audio_vae.py
+++ b/diffsynth/models/ltx2_audio_vae.py
--- a/diffsynth/models/ltx2_common.py
+++ b/diffsynth/models/ltx2_common.py
@@ -0,0 +1,388 @@
+from dataclasses import dataclass
+from typing import NamedTuple, Protocol, Tuple
+import torch
+from torch import nn
+from enum import Enum
+
+
+class VideoPixelShape(NamedTuple):
+    """
+    Shape of the tensor representing the video pixel array. Assumes BGR channel format.
+    """
+
+    batch: int
+    frames: int
+    height: int
+    width: int
+    fps: float
+
+
+class SpatioTemporalScaleFactors(NamedTuple):
+    """
+    Describes the spatiotemporal downscaling between decoded video space and
+    the corresponding VAE latent grid.
+    """
+
+    time: int
+    width: int
+    height: int
+
+    @classmethod
+    def default(cls) -> "SpatioTemporalScaleFactors":
+        return cls(time=8, width=32, height=32)
+
+
+VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default()
+
+
+class VideoLatentShape(NamedTuple):
+    """
+    Shape of the tensor representing video in VAE latent space.
+    The latent representation is a 5D tensor with dimensions ordered as
+    (batch, channels, frames, height, width). Spatial and temporal dimensions
+    are downscaled relative to pixel space according to the VAE's scale factors.
+    """
+
+    batch: int
+    channels: int
+    frames: int
+    height: int
+    width: int
+
+    def to_torch_shape(self) -> torch.Size:
+        return torch.Size([self.batch, self.channels, self.frames, self.height, self.width])
+
+    @staticmethod
+    def from_torch_shape(shape: torch.Size) -> "VideoLatentShape":
+        return VideoLatentShape(
+            batch=shape[0],
+            channels=shape[1],
+            frames=shape[2],
+            height=shape[3],
+            width=shape[4],
+        )
+
+    def mask_shape(self) -> "VideoLatentShape":
+        return self._replace(channels=1)
+
+    @staticmethod
+    def from_pixel_shape(
+        shape: VideoPixelShape,
+        latent_channels: int = 128,
+        scale_factors: SpatioTemporalScaleFactors = VIDEO_SCALE_FACTORS,
+    ) -> "VideoLatentShape":
+        frames = (shape.frames - 1) // scale_factors[0] + 1
+        height = shape.height // scale_factors[1]
+        width = shape.width // scale_factors[2]
+
+        return VideoLatentShape(
+            batch=shape.batch,
+            channels=latent_channels,
+            frames=frames,
+            height=height,
+            width=width,
+        )
+
+    def upscale(self, scale_factors: SpatioTemporalScaleFactors = VIDEO_SCALE_FACTORS) -> "VideoLatentShape":
+        return self._replace(
+            channels=3,
+            frames=(self.frames - 1) * scale_factors.time + 1,
+            height=self.height * scale_factors.height,
+            width=self.width * scale_factors.width,
+        )
+
+
+class AudioLatentShape(NamedTuple):
+    """
+    Shape of audio in VAE latent space: (batch, channels, frames, mel_bins).
+    mel_bins is the number of frequency bins from the mel-spectrogram encoding.
+    """
+
+    batch: int
+    channels: int
+    frames: int
+    mel_bins: int
+
+    def to_torch_shape(self) -> torch.Size:
+        return torch.Size([self.batch, self.channels, self.frames, self.mel_bins])
+
+    def mask_shape(self) -> "AudioLatentShape":
+        return self._replace(channels=1, mel_bins=1)
+
+    @staticmethod
+    def from_torch_shape(shape: torch.Size) -> "AudioLatentShape":
+        return AudioLatentShape(
+            batch=shape[0],
+            channels=shape[1],
+            frames=shape[2],
+            mel_bins=shape[3],
+        )
+
+    @staticmethod
+    def from_duration(
+        batch: int,
+        duration: float,
+        channels: int = 8,
+        mel_bins: int = 16,
+        sample_rate: int = 16000,
+        hop_length: int = 160,
+        audio_latent_downsample_factor: int = 4,
+    ) -> "AudioLatentShape":
+        latents_per_second = float(sample_rate) / float(hop_length) / float(audio_latent_downsample_factor)
+
+        return AudioLatentShape(
+            batch=batch,
+            channels=channels,
+            frames=round(duration * latents_per_second),
+            mel_bins=mel_bins,
+        )
+
+    @staticmethod
+    def from_video_pixel_shape(
+        shape: VideoPixelShape,
+        channels: int = 8,
+        mel_bins: int = 16,
+        sample_rate: int = 16000,
+        hop_length: int = 160,
+        audio_latent_downsample_factor: int = 4,
+    ) -> "AudioLatentShape":
+        return AudioLatentShape.from_duration(
+            batch=shape.batch,
+            duration=float(shape.frames) / float(shape.fps),
+            channels=channels,
+            mel_bins=mel_bins,
+            sample_rate=sample_rate,
+            hop_length=hop_length,
+            audio_latent_downsample_factor=audio_latent_downsample_factor,
+        )
+
+
+@dataclass(frozen=True)
+class LatentState:
+    """
+    State of latents during the diffusion denoising process.
+    Attributes:
+        latent: The current noisy latent tensor being denoised.
+        denoise_mask: Mask encoding the denoising strength for each token (1 = full denoising, 0 = no denoising).
+        positions: Positional indices for each latent element, used for positional embeddings.
+        clean_latent: Initial state of the latent before denoising, may include conditioning latents.
+    """
+
+    latent: torch.Tensor
+    denoise_mask: torch.Tensor
+    positions: torch.Tensor
+    clean_latent: torch.Tensor
+
+    def clone(self) -> "LatentState":
+        return LatentState(
+            latent=self.latent.clone(),
+            denoise_mask=self.denoise_mask.clone(),
+            positions=self.positions.clone(),
+            clean_latent=self.clean_latent.clone(),
+        )
+
+
+class NormType(Enum):
+    """Normalization layer types: GROUP (GroupNorm) or PIXEL (per-location RMS norm)."""
+
+    GROUP = "group"
+    PIXEL = "pixel"
+
+
+class PixelNorm(nn.Module):
+    """
+    Per-pixel (per-location) RMS normalization layer.
+    For each element along the chosen dimension, this layer normalizes the tensor
+    by the root-mean-square of its values across that dimension:
+        y = x / sqrt(mean(x^2, dim=dim, keepdim=True) + eps)
+    """
+
+    def __init__(self, dim: int = 1, eps: float = 1e-8) -> None:
+        """
+        Args:
+            dim: Dimension along which to compute the RMS (typically channels).
+            eps: Small constant added for numerical stability.
+        """
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Apply RMS normalization along the configured dimension.
+        """
+        # Compute mean of squared values along `dim`, keep dimensions for broadcasting.
+        mean_sq = torch.mean(x**2, dim=self.dim, keepdim=True)
+        # Normalize by the root-mean-square (RMS).
+        rms = torch.sqrt(mean_sq + self.eps)
+        return x / rms
+
+
+def build_normalization_layer(
+    in_channels: int, *, num_groups: int = 32, normtype: NormType = NormType.GROUP
+) -> nn.Module:
+    """
+    Create a normalization layer based on the normalization type.
+    Args:
+        in_channels: Number of input channels
+        num_groups: Number of groups for group normalization
+        normtype: Type of normalization: "group" or "pixel"
+    Returns:
+        A normalization layer
+    """
+    if normtype == NormType.GROUP:
+        return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    if normtype == NormType.PIXEL:
+        return PixelNorm(dim=1, eps=1e-6)
+    raise ValueError(f"Invalid normalization type: {normtype}")
+
+
+def rms_norm(x: torch.Tensor, weight: torch.Tensor | None = None, eps: float = 1e-6) -> torch.Tensor:
+    """Root-mean-square (RMS) normalize `x` over its last dimension.
+    Thin wrapper around `torch.nn.functional.rms_norm` that infers the normalized
+    shape and forwards `weight` and `eps`.
+    """
+    return torch.nn.functional.rms_norm(x, (x.shape[-1],), weight=weight, eps=eps)
+
+
+@dataclass(frozen=True)
+class Modality:
+    """
+    Input data for a single modality (video or audio) in the transformer.
+    Bundles the latent tokens, timestep embeddings, positional information,
+    and text conditioning context for processing by the diffusion transformer.
+    Attributes:
+        latent: Patchified latent tokens, shape ``(B, T, D)`` where *B* is
+            the batch size, *T* is the total number of tokens (noisy +
+            conditioning), and *D* is the input dimension.
+        timesteps: Per-token timestep embeddings, shape ``(B, T)``.
+        positions: Positional coordinates, shape ``(B, 3, T)`` for video
+            (time, height, width) or ``(B, 1, T)`` for audio.
+        context: Text conditioning embeddings from the prompt encoder.
+        enabled: Whether this modality is active in the current forward pass.
+        context_mask: Optional mask for the text context tokens.
+        attention_mask: Optional 2-D self-attention mask, shape ``(B, T, T)``.
+            Values in ``[0, 1]`` where ``1`` = full attention and ``0`` = no
+            attention. ``None`` means unrestricted (full) attention between
+            all tokens. Built incrementally by conditioning items; see
+            :class:`~ltx_core.conditioning.types.attention_strength_wrapper.ConditioningItemAttentionStrengthWrapper`.
+    """
+
+    latent: (
+        torch.Tensor
+    )  # Shape: (B, T, D) where B is the batch size, T is the number of tokens, and D is input dimension
+    sigma: torch.Tensor  # Shape: (B,). Current sigma value, used for cross-attention timestep calculation.
+    timesteps: torch.Tensor  # Shape: (B, T) where T is the number of timesteps
+    positions: (
+        torch.Tensor
+    )  # Shape: (B, 3, T) for video, where 3 is the number of dimensions and T is the number of tokens
+    context: torch.Tensor
+    enabled: bool = True
+    context_mask: torch.Tensor | None = None
+    attention_mask: torch.Tensor | None = None
+
+
+def to_denoised(
+    sample: torch.Tensor,
+    velocity: torch.Tensor,
+    sigma: float | torch.Tensor,
+    calc_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """
+    Convert the sample and its denoising velocity to denoised sample.
+    Returns:
+        Denoised sample
+    """
+    if isinstance(sigma, torch.Tensor):
+        sigma = sigma.to(calc_dtype)
+    return (sample.to(calc_dtype) - velocity.to(calc_dtype) * sigma).to(sample.dtype)
+
+
+
+class Patchifier(Protocol):
+    """
+    Protocol for patchifiers that convert latent tensors into patches and assemble them back.
+    """
+
+    def patchify(
+        self,
+        latents: torch.Tensor,
+    ) -> torch.Tensor:
+        ...
+        """
+        Convert latent tensors into flattened patch tokens.
+        Args:
+            latents: Latent tensor to patchify.
+        Returns:
+            Flattened patch tokens tensor.
+        """
+
+    def unpatchify(
+        self,
+        latents: torch.Tensor,
+        output_shape: AudioLatentShape | VideoLatentShape,
+    ) -> torch.Tensor:
+        """
+        Converts latent tensors between spatio-temporal formats and flattened sequence representations.
+        Args:
+            latents: Patch tokens that must be rearranged back into the latent grid constructed by `patchify`.
+            output_shape: Shape of the output tensor. Note that output_shape is either AudioLatentShape or
+            VideoLatentShape.
+        Returns:
+            Dense latent tensor restored from the flattened representation.
+        """
+
+    @property
+    def patch_size(self) -> Tuple[int, int, int]:
+        ...
+        """
+        Returns the patch size as a tuple of (temporal, height, width) dimensions
+        """
+
+    def get_patch_grid_bounds(
+        self,
+        output_shape: AudioLatentShape | VideoLatentShape,
+        device: torch.device | None = None,
+    ) -> torch.Tensor:
+        ...
+        """
+        Compute metadata describing where each latent patch resides within the
+        grid specified by `output_shape`.
+        Args:
+            output_shape: Target grid layout for the patches.
+            device: Target device for the returned tensor.
+        Returns:
+            Tensor containing patch coordinate metadata such as spatial or temporal intervals.
+        """
+
+
+def get_pixel_coords(
+    latent_coords: torch.Tensor,
+    scale_factors: SpatioTemporalScaleFactors,
+    causal_fix: bool = False,
+) -> torch.Tensor:
+    """
+    Map latent-space `[start, end)` coordinates to their pixel-space equivalents by scaling
+    each axis (frame/time, height, width) with the corresponding VAE downsampling factors.
+    Optionally compensate for causal encoding that keeps the first frame at unit temporal scale.
+    Args:
+        latent_coords: Tensor of latent bounds shaped `(batch, 3, num_patches, 2)`.
+        scale_factors: SpatioTemporalScaleFactors tuple `(temporal, height, width)` with integer scale factors applied
+        per axis.
+        causal_fix: When True, rewrites the temporal axis of the first frame so causal VAEs
+            that treat frame zero differently still yield non-negative timestamps.
+    """
+    # Broadcast the VAE scale factors so they align with the `(batch, axis, patch, bound)` layout.
+    broadcast_shape = [1] * latent_coords.ndim
+    broadcast_shape[1] = -1  # axis dimension corresponds to (frame/time, height, width)
+    scale_tensor = torch.tensor(scale_factors, device=latent_coords.device).view(*broadcast_shape)
+
+    # Apply per-axis scaling to convert latent bounds into pixel-space coordinates.
+    pixel_coords = latent_coords * scale_tensor
+
+    if causal_fix:
+        # VAE temporal stride for the very first frame is 1 instead of `scale_factors[0]`.
+        # Shift and clamp to keep the first-frame timestamps causal and non-negative.
+        pixel_coords[:, 0, ...] = (pixel_coords[:, 0, ...] + 1 - scale_factors[0]).clamp(min=0)
+
+    return pixel_coords
--- a/diffsynth/models/ltx2_dit.py
+++ b/diffsynth/models/ltx2_dit.py
--- a/diffsynth/models/ltx2_text_encoder.py
+++ b/diffsynth/models/ltx2_text_encoder.py
@@ -0,0 +1,549 @@
+import math
+import torch
+import torch.nn as nn
+from einops import rearrange
+from transformers import Gemma3ForConditionalGeneration, Gemma3Config, AutoTokenizer
+from .ltx2_dit import (LTXRopeType, generate_freq_grid_np, generate_freq_grid_pytorch, precompute_freqs_cis, Attention,
+                       FeedForward)
+from .ltx2_common import rms_norm
+
+
+class LTX2TextEncoder(Gemma3ForConditionalGeneration):
+    def __init__(self):
+        config = Gemma3Config(
+            **{
+                "architectures": ["Gemma3ForConditionalGeneration"],
+                "boi_token_index": 255999,
+                "dtype": "bfloat16",
+                "eoi_token_index": 256000,
+                "eos_token_id": [1, 106],
+                "image_token_index": 262144,
+                "initializer_range": 0.02,
+                "mm_tokens_per_image": 256,
+                "model_type": "gemma3",
+                "text_config": {
+                    "_sliding_window_pattern": 6,
+                    "attention_bias": False,
+                    "attention_dropout": 0.0,
+                    "attn_logit_softcapping": None,
+                    "cache_implementation": "hybrid",
+                    "dtype": "bfloat16",
+                    "final_logit_softcapping": None,
+                    "head_dim": 256,
+                    "hidden_activation": "gelu_pytorch_tanh",
+                    "hidden_size": 3840,
+                    "initializer_range": 0.02,
+                    "intermediate_size": 15360,
+                    "layer_types": [
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "full_attention", "sliding_attention", "sliding_attention",
+                        "sliding_attention", "sliding_attention", "sliding_attention", "full_attention"
+                    ],
+                    "max_position_embeddings": 131072,
+                    "model_type": "gemma3_text",
+                    "num_attention_heads": 16,
+                    "num_hidden_layers": 48,
+                    "num_key_value_heads": 8,
+                    "query_pre_attn_scalar": 256,
+                    "rms_norm_eps": 1e-06,
+                    "rope_local_base_freq": 10000,
+                    "rope_scaling": {
+                        "factor": 8.0,
+                        "rope_type": "linear"
+                    },
+                    "rope_theta": 1000000,
+                    "sliding_window": 1024,
+                    "sliding_window_pattern": 6,
+                    "use_bidirectional_attention": False,
+                    "use_cache": True,
+                    "vocab_size": 262208
+                },
+                "transformers_version": "4.57.3",
+                "vision_config": {
+                    "attention_dropout": 0.0,
+                    "dtype": "bfloat16",
+                    "hidden_act": "gelu_pytorch_tanh",
+                    "hidden_size": 1152,
+                    "image_size": 896,
+                    "intermediate_size": 4304,
+                    "layer_norm_eps": 1e-06,
+                    "model_type": "siglip_vision_model",
+                    "num_attention_heads": 16,
+                    "num_channels": 3,
+                    "num_hidden_layers": 27,
+                    "patch_size": 14,
+                    "vision_use_head": False
+                }
+            })
+        super().__init__(config)
+
+
+class LTXVGemmaTokenizer:
+    """
+    Tokenizer wrapper for Gemma models compatible with LTXV processes.
+    This class wraps HuggingFace's `AutoTokenizer` for use with Gemma text encoders,
+    ensuring correct settings and output formatting for downstream consumption.
+    """
+
+    def __init__(self, tokenizer_path: str, max_length: int = 1024):
+        """
+        Initialize the tokenizer.
+        Args:
+            tokenizer_path (str): Path to the pretrained tokenizer files or model directory.
+            max_length (int, optional): Max sequence length for encoding. Defaults to 256.
+        """
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_path, local_files_only=True, model_max_length=max_length
+        )
+        # Gemma expects left padding for chat-style prompts; for plain text it doesn't matter much.
+        self.tokenizer.padding_side = "left"
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+
+        self.max_length = max_length
+
+    def tokenize_with_weights(self, text: str, return_word_ids: bool = False) -> dict[str, list[tuple[int, int]]]:
+        """
+        Tokenize the given text and return token IDs and attention weights.
+        Args:
+            text (str): The input string to tokenize.
+            return_word_ids (bool, optional): If True, includes the token's position (index) in the output tuples.
+                                              If False (default), omits the indices.
+        Returns:
+            dict[str, list[tuple[int, int]]] OR dict[str, list[tuple[int, int, int]]]:
+                A dictionary with a "gemma" key mapping to:
+                    - a list of (token_id, attention_mask) tuples if return_word_ids is False;
+                    - a list of (token_id, attention_mask, index) tuples if return_word_ids is True.
+        Example:
+            >>> tokenizer = LTXVGemmaTokenizer("path/to/tokenizer", max_length=8)
+            >>> tokenizer.tokenize_with_weights("hello world")
+            {'gemma': [(1234, 1), (5678, 1), (2, 0), ...]}
+        """
+        text = text.strip()
+        encoded = self.tokenizer(
+            text,
+            padding="max_length",
+            max_length=self.max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        input_ids = encoded.input_ids
+        attention_mask = encoded.attention_mask
+        tuples = [
+            (token_id, attn, i) for i, (token_id, attn) in enumerate(zip(input_ids[0], attention_mask[0], strict=True))
+        ]
+        out = {"gemma": tuples}
+
+        if not return_word_ids:
+            # Return only (token_id, attention_mask) pairs, omitting token position
+            out = {k: [(t, w) for t, w, _ in v] for k, v in out.items()}
+
+        return out
+
+
+class GemmaFeaturesExtractorProjLinear(nn.Module):
+    """
+    Feature extractor module for Gemma models.
+    This module applies a single linear projection to the input tensor.
+    It expects a flattened feature tensor of shape (batch_size, 3840*49).
+    The linear layer maps this to a (batch_size, 3840) embedding.
+    Attributes:
+        aggregate_embed (nn.Linear): Linear projection layer.
+    """
+
+    def __init__(self) -> None:
+        """
+        Initialize the GemmaFeaturesExtractorProjLinear module.
+        The input dimension is expected to be 3840 * 49, and the output is 3840.
+        """
+        super().__init__()
+        self.aggregate_embed = nn.Linear(3840 * 49, 3840, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        padding_side: str = "left",
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        encoded = torch.stack(hidden_states, dim=-1) if isinstance(hidden_states, (list, tuple)) else hidden_states
+        dtype = encoded.dtype
+        sequence_lengths = attention_mask.sum(dim=-1)
+        normed = _norm_and_concat_padded_batch(encoded, sequence_lengths, padding_side)
+        features = self.aggregate_embed(normed.to(dtype))
+        return features, features
+
+
+class GemmaSeperatedFeaturesExtractorProjLinear(nn.Module):
+    """22B: per-token RMS norm → rescale → dual aggregate embeds"""
+
+    def __init__(
+        self,
+        num_layers: int,
+        embedding_dim: int,
+        video_inner_dim: int,
+        audio_inner_dim: int,
+    ):
+        super().__init__()
+        in_dim = embedding_dim * num_layers
+        self.video_aggregate_embed = torch.nn.Linear(in_dim, video_inner_dim, bias=True)
+        self.audio_aggregate_embed = torch.nn.Linear(in_dim, audio_inner_dim, bias=True)
+        self.embedding_dim = embedding_dim
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        padding_side: str = "left",  # noqa: ARG002
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        encoded = torch.stack(hidden_states, dim=-1) if isinstance(hidden_states, (list, tuple)) else hidden_states
+        normed = norm_and_concat_per_token_rms(encoded, attention_mask)
+        normed = normed.to(encoded.dtype)
+        v_dim = self.video_aggregate_embed.out_features
+        video = self.video_aggregate_embed(_rescale_norm(normed, v_dim, self.embedding_dim))
+        audio = None
+        if self.audio_aggregate_embed is not None:
+            a_dim = self.audio_aggregate_embed.out_features
+            audio = self.audio_aggregate_embed(_rescale_norm(normed, a_dim, self.embedding_dim))
+        return video, audio
+
+
+
+class _BasicTransformerBlock1D(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        dim_head: int,
+        rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
+        apply_gated_attention: bool = False,
+    ):
+        super().__init__()
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            rope_type=rope_type,
+            apply_gated_attention=apply_gated_attention,
+        )
+
+        self.ff = FeedForward(
+            dim,
+            dim_out=dim,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        pe: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+
+        # 1. Normalization Before Self-Attention
+        norm_hidden_states = rms_norm(hidden_states)
+
+        norm_hidden_states = norm_hidden_states.squeeze(1)
+
+        # 2. Self-Attention
+        attn_output = self.attn1(norm_hidden_states, mask=attention_mask, pe=pe)
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 3. Normalization before Feed-Forward
+        norm_hidden_states = rms_norm(hidden_states)
+
+        # 4. Feed-forward
+        ff_output = self.ff(norm_hidden_states)
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class Embeddings1DConnector(nn.Module):
+    """
+    Embeddings1DConnector applies a 1D transformer-based processing to sequential embeddings (e.g., for video, audio, or
+    other modalities). It supports rotary positional encoding (rope), optional causal temporal positioning, and can
+    substitute padded positions with learnable registers. The module is highly configurable for head size, number of
+    layers, and register usage.
+    Args:
+        attention_head_dim (int): Dimension of each attention head (default=128).
+        num_attention_heads (int): Number of attention heads (default=30).
+        num_layers (int): Number of transformer layers (default=2).
+        positional_embedding_theta (float): Scaling factor for position embedding (default=10000.0).
+        positional_embedding_max_pos (list[int] | None): Max positions for positional embeddings (default=[1]).
+        causal_temporal_positioning (bool): If True, uses causal attention (default=False).
+        num_learnable_registers (int | None): Number of learnable registers to replace padded tokens. If None, disables
+            register replacement. (default=128)
+        rope_type (LTXRopeType): The RoPE variant to use (default=DEFAULT_ROPE_TYPE).
+        double_precision_rope (bool): Use double precision rope calculation (default=False).
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 30,
+        num_layers: int = 2,
+        positional_embedding_theta: float = 10000.0,
+        positional_embedding_max_pos: list[int] | None = [4096],
+        causal_temporal_positioning: bool = False,
+        num_learnable_registers: int | None = 128,
+        rope_type: LTXRopeType = LTXRopeType.SPLIT,
+        double_precision_rope: bool = True,
+        apply_gated_attention: bool = False,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.causal_temporal_positioning = causal_temporal_positioning
+        self.positional_embedding_theta = positional_embedding_theta
+        self.positional_embedding_max_pos = (
+            positional_embedding_max_pos if positional_embedding_max_pos is not None else [1]
+        )
+        self.rope_type = rope_type
+        self.double_precision_rope = double_precision_rope
+        self.transformer_1d_blocks = nn.ModuleList(
+            [
+                _BasicTransformerBlock1D(
+                    dim=self.inner_dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    rope_type=rope_type,
+                    apply_gated_attention=apply_gated_attention,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.num_learnable_registers = num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = nn.Parameter(
+                torch.rand(self.num_learnable_registers, self.inner_dim, dtype=torch.bfloat16) * 2.0 - 1.0
+            )
+
+    def _replace_padded_with_learnable_registers(
+        self, hidden_states: torch.Tensor, attention_mask: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert hidden_states.shape[1] % self.num_learnable_registers == 0, (
+            f"Hidden states sequence length {hidden_states.shape[1]} must be divisible by num_learnable_registers "
+            f"{self.num_learnable_registers}."
+        )
+
+        num_registers_duplications = hidden_states.shape[1] // self.num_learnable_registers
+        learnable_registers = torch.tile(self.learnable_registers, (num_registers_duplications, 1))
+        attention_mask_binary = (attention_mask.squeeze(1).squeeze(1).unsqueeze(-1) >= -9000.0).int()
+
+        non_zero_hidden_states = hidden_states[:, attention_mask_binary.squeeze().bool(), :]
+        non_zero_nums = non_zero_hidden_states.shape[1]
+        pad_length = hidden_states.shape[1] - non_zero_nums
+        adjusted_hidden_states = nn.functional.pad(non_zero_hidden_states, pad=(0, 0, 0, pad_length), value=0)
+        flipped_mask = torch.flip(attention_mask_binary, dims=[1])
+        hidden_states = flipped_mask * adjusted_hidden_states + (1 - flipped_mask) * learnable_registers
+
+        attention_mask = torch.full_like(
+            attention_mask,
+            0.0,
+            dtype=attention_mask.dtype,
+            device=attention_mask.device,
+        )
+
+        return hidden_states, attention_mask
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Forward pass of Embeddings1DConnector.
+        Args:
+            hidden_states (torch.Tensor): Input tensor of embeddings (shape [batch, seq_len, feature_dim]).
+            attention_mask (torch.Tensor|None): Optional mask for valid tokens (shape compatible with hidden_states).
+        Returns:
+            tuple[torch.Tensor, torch.Tensor]: Processed features and the corresponding (possibly modified) mask.
+        """
+        if self.num_learnable_registers:
+            hidden_states, attention_mask = self._replace_padded_with_learnable_registers(hidden_states, attention_mask)
+
+        indices_grid = torch.arange(hidden_states.shape[1], dtype=torch.float32, device=hidden_states.device)
+        indices_grid = indices_grid[None, None, :]
+        freq_grid_generator = generate_freq_grid_np if self.double_precision_rope else generate_freq_grid_pytorch
+        freqs_cis = precompute_freqs_cis(
+            indices_grid=indices_grid,
+            dim=self.inner_dim,
+            out_dtype=hidden_states.dtype,
+            theta=self.positional_embedding_theta,
+            max_pos=self.positional_embedding_max_pos,
+            num_attention_heads=self.num_attention_heads,
+            rope_type=self.rope_type,
+            freq_grid_generator=freq_grid_generator,
+        )
+
+        for block in self.transformer_1d_blocks:
+            hidden_states = block(hidden_states, attention_mask=attention_mask, pe=freqs_cis)
+
+        hidden_states = rms_norm(hidden_states)
+
+        return hidden_states, attention_mask
+
+
+class LTX2TextEncoderPostModules(nn.Module):
+    def __init__(
+        self,
+        separated_audio_video: bool = False,
+        embedding_dim_gemma: int = 3840,
+        num_layers_gemma: int = 49,
+        video_attention_heads: int = 32,
+        video_attention_head_dim: int = 128,
+        audio_attention_heads: int = 32,
+        audio_attention_head_dim: int = 64,
+        num_connector_layers: int = 2,
+        apply_gated_attention: bool = False,
+    ):
+        super().__init__()
+        if not separated_audio_video:
+            self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear()
+            self.embeddings_connector = Embeddings1DConnector()
+            self.audio_embeddings_connector = Embeddings1DConnector()
+        else:
+            # LTX-2.3
+            self.feature_extractor_linear = GemmaSeperatedFeaturesExtractorProjLinear(
+                num_layers_gemma, embedding_dim_gemma, video_attention_heads * video_attention_head_dim,
+                audio_attention_heads * audio_attention_head_dim)
+            self.embeddings_connector = Embeddings1DConnector(
+                attention_head_dim=video_attention_head_dim,
+                num_attention_heads=video_attention_heads,
+                num_layers=num_connector_layers,
+                apply_gated_attention=apply_gated_attention,
+            )
+            self.audio_embeddings_connector = Embeddings1DConnector(
+                attention_head_dim=audio_attention_head_dim,
+                num_attention_heads=audio_attention_heads,
+                num_layers=num_connector_layers,
+                apply_gated_attention=apply_gated_attention,
+            )
+
+    def create_embeddings(
+        self,
+        video_features: torch.Tensor,
+        audio_features: torch.Tensor | None,
+        additive_attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor]:
+        video_encoded, video_mask = self.embeddings_connector(video_features, additive_attention_mask)
+        video_encoded, binary_mask = _to_binary_mask(video_encoded, video_mask)
+        audio_encoded, _ = self.audio_embeddings_connector(audio_features, additive_attention_mask)
+
+        return video_encoded, audio_encoded, binary_mask
+
+    def process_hidden_states(
+        self,
+        hidden_states: tuple[torch.Tensor, ...],
+        attention_mask: torch.Tensor,
+        padding_side: str = "left",
+    ):
+        video_feats, audio_feats = self.feature_extractor_linear(hidden_states, attention_mask, padding_side)
+        additive_mask = _convert_to_additive_mask(attention_mask, video_feats.dtype)
+        video_enc, audio_enc, binary_mask = self.create_embeddings(video_feats, audio_feats, additive_mask)
+        return video_enc, audio_enc, binary_mask
+
+
+def _norm_and_concat_padded_batch(
+    encoded_text: torch.Tensor,
+    sequence_lengths: torch.Tensor,
+    padding_side: str = "right",
+) -> torch.Tensor:
+    """Normalize and flatten multi-layer hidden states, respecting padding.
+    Performs per-batch, per-layer normalization using masked mean and range,
+    then concatenates across the layer dimension.
+    Args:
+        encoded_text: Hidden states of shape [batch, seq_len, hidden_dim, num_layers].
+        sequence_lengths: Number of valid (non-padded) tokens per batch item.
+        padding_side: Whether padding is on "left" or "right".
+    Returns:
+        Normalized tensor of shape [batch, seq_len, hidden_dim * num_layers],
+        with padded positions zeroed out.
+    """
+    b, t, d, l = encoded_text.shape  # noqa: E741
+    device = encoded_text.device
+    # Build mask: [B, T, 1, 1]
+    token_indices = torch.arange(t, device=device)[None, :]  # [1, T]
+    if padding_side == "right":
+        # For right padding, valid tokens are from 0 to sequence_length-1
+        mask = token_indices < sequence_lengths[:, None]  # [B, T]
+    elif padding_side == "left":
+        # For left padding, valid tokens are from (T - sequence_length) to T-1
+        start_indices = t - sequence_lengths[:, None]  # [B, 1]
+        mask = token_indices >= start_indices  # [B, T]
+    else:
+        raise ValueError(f"padding_side must be 'left' or 'right', got {padding_side}")
+    mask = rearrange(mask, "b t -> b t 1 1")
+    eps = 1e-6
+    # Compute masked mean: [B, 1, 1, L]
+    masked = encoded_text.masked_fill(~mask, 0.0)
+    denom = (sequence_lengths * d).view(b, 1, 1, 1)
+    mean = masked.sum(dim=(1, 2), keepdim=True) / (denom + eps)
+    # Compute masked min/max: [B, 1, 1, L]
+    x_min = encoded_text.masked_fill(~mask, float("inf")).amin(dim=(1, 2), keepdim=True)
+    x_max = encoded_text.masked_fill(~mask, float("-inf")).amax(dim=(1, 2), keepdim=True)
+    range_ = x_max - x_min
+    # Normalize only the valid tokens
+    normed = 8 * (encoded_text - mean) / (range_ + eps)
+    # concat to be [Batch, T,  D * L] - this preserves the original structure
+    normed = normed.reshape(b, t, -1)  # [B, T, D * L]
+    # Apply mask to preserve original padding (set padded positions to 0)
+    mask_flattened = rearrange(mask, "b t 1 1 -> b t 1").expand(-1, -1, d * l)
+    normed = normed.masked_fill(~mask_flattened, 0.0)
+
+    return normed
+
+
+def _convert_to_additive_mask(attention_mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    return (attention_mask - 1).to(dtype).reshape(
+        (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(dtype).max
+
+def _to_binary_mask(encoded: torch.Tensor, encoded_mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert connector output mask to binary mask and apply to encoded tensor."""
+    binary_mask = (encoded_mask < 0.000001).to(torch.int64)
+    binary_mask = binary_mask.reshape([encoded.shape[0], encoded.shape[1], 1])
+    encoded = encoded * binary_mask
+    return encoded, binary_mask
+
+
+def norm_and_concat_per_token_rms(
+    encoded_text: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> torch.Tensor:
+    """Per-token RMSNorm normalization for V2 models.
+    Args:
+        encoded_text: [B, T, D, L]
+        attention_mask: [B, T] binary mask
+    Returns:
+        [B, T, D*L] normalized tensor with padding zeroed out.
+    """
+    B, T, D, L = encoded_text.shape  # noqa: N806
+    variance = torch.mean(encoded_text**2, dim=2, keepdim=True)  # [B,T,1,L]
+    normed = encoded_text * torch.rsqrt(variance + 1e-6)
+    normed = normed.reshape(B, T, D * L)
+    mask_3d = attention_mask.bool().unsqueeze(-1)  # [B, T, 1]
+    return torch.where(mask_3d, normed, torch.zeros_like(normed))
+
+
+def _rescale_norm(x: torch.Tensor, target_dim: int, source_dim: int) -> torch.Tensor:
+    """Rescale normalization: x * sqrt(target_dim / source_dim)."""
+    return x * math.sqrt(target_dim / source_dim)
--- a/diffsynth/models/ltx2_upsampler.py
+++ b/diffsynth/models/ltx2_upsampler.py
@@ -0,0 +1,313 @@
+import math
+from typing import Optional, Tuple
+import torch
+from einops import rearrange
+import torch.nn.functional as F
+from .ltx2_video_vae import LTX2VideoEncoder
+
+class PixelShuffleND(torch.nn.Module):
+    """
+    N-dimensional pixel shuffle operation for upsampling tensors.
+    Args:
+        dims (int): Number of dimensions to apply pixel shuffle to.
+            - 1: Temporal (e.g., frames)
+            - 2: Spatial (e.g., height and width)
+            - 3: Spatiotemporal (e.g., depth, height, width)
+        upscale_factors (tuple[int, int, int], optional): Upscaling factors for each dimension.
+            For dims=1, only the first value is used.
+            For dims=2, the first two values are used.
+            For dims=3, all three values are used.
+    The input tensor is rearranged so that the channel dimension is split into
+    smaller channels and upscaling factors, and the upscaling factors are moved
+    into the corresponding spatial/temporal dimensions.
+    Note:
+    This operation is equivalent to the patchifier operation in for the models. Consider
+    using this class instead.
+    """
+
+    def __init__(self, dims: int, upscale_factors: tuple[int, int, int] = (2, 2, 2)):
+        super().__init__()
+        assert dims in [1, 2, 3], "dims must be 1, 2, or 3"
+        self.dims = dims
+        self.upscale_factors = upscale_factors
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dims == 3:
+            return rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+                p3=self.upscale_factors[2],
+            )
+        elif self.dims == 2:
+            return rearrange(
+                x,
+                "b (c p1 p2) h w -> b c (h p1) (w p2)",
+                p1=self.upscale_factors[0],
+                p2=self.upscale_factors[1],
+            )
+        elif self.dims == 1:
+            return rearrange(
+                x,
+                "b (c p1) f h w -> b c (f p1) h w",
+                p1=self.upscale_factors[0],
+            )
+        else:
+            raise ValueError(f"Unsupported dims: {self.dims}")
+
+
+class ResBlock(torch.nn.Module):
+    """
+    Residual block with two convolutional layers, group normalization, and SiLU activation.
+    Args:
+        channels (int): Number of input and output channels.
+        mid_channels (Optional[int]): Number of channels in the intermediate convolution layer. Defaults to `channels`
+        if not specified.
+        dims (int): Dimensionality of the convolution (2 for Conv2d, 3 for Conv3d). Defaults to 3.
+    """
+
+    def __init__(self, channels: int, mid_channels: Optional[int] = None, dims: int = 3):
+        super().__init__()
+        if mid_channels is None:
+            mid_channels = channels
+
+        conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+
+        self.conv1 = conv(channels, mid_channels, kernel_size=3, padding=1)
+        self.norm1 = torch.nn.GroupNorm(32, mid_channels)
+        self.conv2 = conv(mid_channels, channels, kernel_size=3, padding=1)
+        self.norm2 = torch.nn.GroupNorm(32, channels)
+        self.activation = torch.nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.activation(x)
+        x = self.conv2(x)
+        x = self.norm2(x)
+        x = self.activation(x + residual)
+        return x
+
+
+class BlurDownsample(torch.nn.Module):
+    """
+    Anti-aliased spatial downsampling by integer stride using a fixed separable binomial kernel.
+    Applies only on H,W. Works for dims=2 or dims=3 (per-frame).
+    """
+
+    def __init__(self, dims: int, stride: int, kernel_size: int = 5) -> None:
+        super().__init__()
+        assert dims in (2, 3)
+        assert isinstance(stride, int)
+        assert stride >= 1
+        assert kernel_size >= 3
+        assert kernel_size % 2 == 1
+        self.dims = dims
+        self.stride = stride
+        self.kernel_size = kernel_size
+
+        # 5x5 separable binomial kernel using binomial coefficients [1, 4, 6, 4, 1] from
+        # the 4th row of Pascal's triangle. This kernel is used for anti-aliasing and
+        # provides a smooth approximation of a Gaussian filter (often called a "binomial filter").
+        # The 2D kernel is constructed as the outer product and normalized.
+        k = torch.tensor([math.comb(kernel_size - 1, k) for k in range(kernel_size)])
+        k2d = k[:, None] @ k[None, :]
+        k2d = (k2d / k2d.sum()).float()  # shape (kernel_size, kernel_size)
+        self.register_buffer("kernel", k2d[None, None, :, :])  # (1, 1, kernel_size, kernel_size)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.stride == 1:
+            return x
+
+        if self.dims == 2:
+            return self._apply_2d(x)
+        else:
+            # dims == 3: apply per-frame on H,W
+            b, _, f, _, _ = x.shape
+            x = rearrange(x, "b c f h w -> (b f) c h w")
+            x = self._apply_2d(x)
+            h2, w2 = x.shape[-2:]
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f, h=h2, w=w2)
+            return x
+
+    def _apply_2d(self, x2d: torch.Tensor) -> torch.Tensor:
+        c = x2d.shape[1]
+        weight = self.kernel.expand(c, 1, self.kernel_size, self.kernel_size)  # depthwise
+        x2d = F.conv2d(x2d, weight=weight, bias=None, stride=self.stride, padding=self.kernel_size // 2, groups=c)
+        return x2d
+
+
+def _rational_for_scale(scale: float) -> Tuple[int, int]:
+    mapping = {0.75: (3, 4), 1.5: (3, 2), 2.0: (2, 1), 4.0: (4, 1)}
+    if float(scale) not in mapping:
+        raise ValueError(f"Unsupported scale {scale}. Choose from {list(mapping.keys())}")
+    return mapping[float(scale)]
+
+
+class SpatialRationalResampler(torch.nn.Module):
+    """
+    Fully-learned rational spatial scaling: up by 'num' via PixelShuffle, then anti-aliased
+    downsample by 'den' using fixed blur + stride. Operates on H,W only.
+    For dims==3, work per-frame for spatial scaling (temporal axis untouched).
+    Args:
+        mid_channels (`int`): Number of intermediate channels for the convolution layer
+        scale (`float`): Spatial scaling factor. Supported values are:
+            - 0.75: Downsample by 3/4 (reduce spatial size)
+            - 1.5: Upsample by 3/2 (increase spatial size)
+            - 2.0: Upsample by 2x (double spatial size)
+            - 4.0: Upsample by 4x (quadruple spatial size)
+            Any other value will raise a ValueError.
+    """
+
+    def __init__(self, mid_channels: int, scale: float):
+        super().__init__()
+        self.scale = float(scale)
+        self.num, self.den = _rational_for_scale(self.scale)
+        self.conv = torch.nn.Conv2d(mid_channels, (self.num**2) * mid_channels, kernel_size=3, padding=1)
+        self.pixel_shuffle = PixelShuffleND(2, upscale_factors=(self.num, self.num))
+        self.blur_down = BlurDownsample(dims=2, stride=self.den)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        b, _, f, _, _ = x.shape
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.blur_down(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        return x
+
+
+class LTX2LatentUpsampler(torch.nn.Module):
+    """
+    Model to upsample VAE latents spatially and/or temporally.
+    Args:
+        in_channels (`int`): Number of channels in the input latent
+        mid_channels (`int`): Number of channels in the middle layers
+        num_blocks_per_stage (`int`): Number of ResBlocks to use in each stage (pre/post upsampling)
+        dims (`int`): Number of dimensions for convolutions (2 or 3)
+        spatial_upsample (`bool`): Whether to spatially upsample the latent
+        temporal_upsample (`bool`): Whether to temporally upsample the latent
+        spatial_scale (`float`): Scale factor for spatial upsampling
+        rational_resampler (`bool`): Whether to use a rational resampler for spatial upsampling
+    """
+    def __init__(
+        self,
+        in_channels: int = 128,
+        mid_channels: int = 1024,
+        num_blocks_per_stage: int = 4,
+        dims: int = 3,
+        spatial_upsample: bool = True,
+        temporal_upsample: bool = False,
+        spatial_scale: float = 2.0,
+        rational_resampler: bool = True,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.mid_channels = mid_channels
+        self.num_blocks_per_stage = num_blocks_per_stage
+        self.dims = dims
+        self.spatial_upsample = spatial_upsample
+        self.temporal_upsample = temporal_upsample
+        self.spatial_scale = float(spatial_scale)
+        self.rational_resampler = rational_resampler
+
+        conv = torch.nn.Conv2d if dims == 2 else torch.nn.Conv3d
+
+        self.initial_conv = conv(in_channels, mid_channels, kernel_size=3, padding=1)
+        self.initial_norm = torch.nn.GroupNorm(32, mid_channels)
+        self.initial_activation = torch.nn.SiLU()
+
+        self.res_blocks = torch.nn.ModuleList([ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)])
+
+        if spatial_upsample and temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 8 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(3),
+            )
+        elif spatial_upsample:
+            if rational_resampler:
+                self.upsampler = SpatialRationalResampler(mid_channels=mid_channels, scale=self.spatial_scale)
+            else:
+                self.upsampler = torch.nn.Sequential(
+                    torch.nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1),
+                    PixelShuffleND(2),
+                )
+        elif temporal_upsample:
+            self.upsampler = torch.nn.Sequential(
+                torch.nn.Conv3d(mid_channels, 2 * mid_channels, kernel_size=3, padding=1),
+                PixelShuffleND(1),
+            )
+        else:
+            raise ValueError("Either spatial_upsample or temporal_upsample must be True")
+
+        self.post_upsample_res_blocks = torch.nn.ModuleList(
+            [ResBlock(mid_channels, dims=dims) for _ in range(num_blocks_per_stage)]
+        )
+
+        self.final_conv = conv(mid_channels, in_channels, kernel_size=3, padding=1)
+
+    def forward(self, latent: torch.Tensor) -> torch.Tensor:
+        b, _, f, _, _ = latent.shape
+
+        if self.dims == 2:
+            x = rearrange(latent, "b c f h w -> (b f) c h w")
+            x = self.initial_conv(x)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            x = self.upsampler(x)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+            x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+        else:
+            x = self.initial_conv(latent)
+            x = self.initial_norm(x)
+            x = self.initial_activation(x)
+
+            for block in self.res_blocks:
+                x = block(x)
+
+            if self.temporal_upsample:
+                x = self.upsampler(x)
+                # remove the first frame after upsampling.
+                # This is done because the first frame encodes one pixel frame.
+                x = x[:, :, 1:, :, :]
+            elif isinstance(self.upsampler, SpatialRationalResampler):
+                x = self.upsampler(x)
+            else:
+                x = rearrange(x, "b c f h w -> (b f) c h w")
+                x = self.upsampler(x)
+                x = rearrange(x, "(b f) c h w -> b c f h w", b=b, f=f)
+
+            for block in self.post_upsample_res_blocks:
+                x = block(x)
+
+            x = self.final_conv(x)
+
+        return x
+
+
+def upsample_video(latent: torch.Tensor, video_encoder: LTX2VideoEncoder, upsampler: "LTX2LatentUpsampler") -> torch.Tensor:
+    """
+    Apply upsampling to the latent representation using the provided upsampler,
+    with normalization and un-normalization based on the video encoder's per-channel statistics.
+    Args:
+        latent: Input latent tensor of shape [B, C, F, H, W].
+        video_encoder: VideoEncoder with per_channel_statistics for normalization.
+        upsampler: LTX2LatentUpsampler module to perform upsampling.
+    Returns:
+        torch.Tensor: Upsampled and re-normalized latent tensor.
+    """
+    latent = video_encoder.per_channel_statistics.un_normalize(latent)
+    latent = upsampler(latent)
+    latent = video_encoder.per_channel_statistics.normalize(latent)
+    return latent
--- a/diffsynth/models/ltx2_video_vae.py
+++ b/diffsynth/models/ltx2_video_vae.py
--- a/diffsynth/models/model_loader.py
+++ b/diffsynth/models/model_loader.py
@@ -1,6 +1,6 @@
 from ..core.loader import load_model, hash_model_file
 from ..core.vram import AutoWrappedModule
-from ..configs import MODEL_CONFIGS, VRAM_MANAGEMENT_MODULE_MAPS
+from ..configs import MODEL_CONFIGS, VRAM_MANAGEMENT_MODULE_MAPS, VERSION_CHECKER_MAPS
 import importlib, json, torch


@@ -22,14 +22,15 @@ class ModelPool:
    def fetch_module_map(self, model_class, vram_config):
        if self.need_to_enable_vram_management(vram_config):
            if model_class in VRAM_MANAGEMENT_MODULE_MAPS:
-                module_map = {self.import_model_class(source): self.import_model_class(target) for source, target in VRAM_MANAGEMENT_MODULE_MAPS[model_class].items()}
+                vram_module_map = VRAM_MANAGEMENT_MODULE_MAPS[model_class] if model_class not in VERSION_CHECKER_MAPS else VERSION_CHECKER_MAPS[model_class]()
+                module_map = {self.import_model_class(source): self.import_model_class(target) for source, target in vram_module_map.items()}
            else:
                module_map = {self.import_model_class(model_class): AutoWrappedModule}
        else:
            module_map = None
        return module_map
    
-    def load_model_file(self, config, path, vram_config, vram_limit=None):
+    def load_model_file(self, config, path, vram_config, vram_limit=None, state_dict=None):
        model_class = self.import_model_class(config["model_class"])
        model_config = config.get("extra_kwargs", {})
        if "state_dict_converter" in config:
@@ -43,6 +44,7 @@ class ModelPool:
            state_dict_converter,
            use_disk_map=True,
            vram_config=vram_config, module_map=module_map, vram_limit=vram_limit,
+            state_dict=state_dict,
        )
        return model
    
@@ -59,7 +61,7 @@ class ModelPool:
        }
        return vram_config
    
-    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False):
+    def auto_load_model(self, path, vram_config=None, vram_limit=None, clear_parameters=False, state_dict=None):
        print(f"Loading models from: {json.dumps(path, indent=4)}")
        if vram_config is None:
            vram_config = self.default_vram_config()
@@ -67,7 +69,7 @@ class ModelPool:
        loaded = False
        for config in MODEL_CONFIGS:
            if config["model_hash"] == model_hash:
-                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit)
+                model = self.load_model_file(config, path, vram_config, vram_limit=vram_limit, state_dict=state_dict)
                if clear_parameters: self.clear_parameters(model)
                self.model.append(model)
                model_name = config["model_name"]
--- a/diffsynth/models/mova_audio_dit.py
+++ b/diffsynth/models/mova_audio_dit.py
@@ -0,0 +1,57 @@
+import torch
+import torch.nn as nn
+from .wan_video_dit import WanModel, precompute_freqs_cis, sinusoidal_embedding_1d
+from einops import rearrange
+from ..core import gradient_checkpoint_forward
+
+def precompute_freqs_cis_1d(dim: int, end: int = 16384, theta: float = 10000.0):
+    f_freqs_cis = precompute_freqs_cis(dim, end, theta)
+    return f_freqs_cis.chunk(3, dim=-1)
+
+class MovaAudioDit(WanModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        head_dim = kwargs.get("dim", 1536) // kwargs.get("num_heads", 12)
+        self.freqs = precompute_freqs_cis_1d(head_dim)
+        self.patch_embedding = nn.Conv1d(
+            kwargs.get("in_dim", 128), kwargs.get("dim", 1536), kernel_size=[1], stride=[1]
+        )
+
+    def precompute_freqs_cis(self, dim: int, end: int = 16384, theta: float = 10000.0):
+        self.f_freqs_cis = precompute_freqs_cis_1d(dim, end, theta)
+
+    def forward(self,
+                x: torch.Tensor,
+                timestep: torch.Tensor,
+                context: torch.Tensor,
+                use_gradient_checkpointing: bool = False,
+                use_gradient_checkpointing_offload: bool = False,
+                **kwargs,
+                ):
+        t = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, timestep))
+        t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
+        context = self.text_embedding(context)
+        x, (f, ) = self.patchify(x)
+        freqs = torch.cat([
+            self.freqs[0][:f].view(f, -1).expand(f, -1),
+            self.freqs[1][:f].view(f, -1).expand(f, -1),
+            self.freqs[2][:f].view(f, -1).expand(f, -1),
+        ], dim=-1).reshape(f, 1, -1).to(x.device)
+
+        for block in self.blocks:
+            x = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                x, context, t_mod, freqs,
+            )
+        x = self.head(x, t)
+        x = self.unpatchify(x, (f, ))
+        return x
+
+    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
+        return rearrange(
+            x, 'b f (p c) -> b c (f p)',
+            f=grid_size[0],
+            p=self.patch_size[0]
+        )
--- a/diffsynth/models/mova_audio_vae.py
+++ b/diffsynth/models/mova_audio_vae.py
@@ -0,0 +1,796 @@
+import math
+from typing import List, Union
+import numpy as np
+import torch
+from torch import nn
+from torch.nn.utils import weight_norm
+import torch.nn.functional as F
+from einops import rearrange
+
+def WNConv1d(*args, **kwargs):
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def WNConvTranspose1d(*args, **kwargs):
+    return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
+
+
+# Scripting this brings model speed up 1.4x
+@torch.jit.script
+def snake(x, alpha):
+    shape = x.shape
+    x = x.reshape(shape[0], shape[1], -1)
+    x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
+    x = x.reshape(shape)
+    return x
+
+
+class Snake1d(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, x):
+        return snake(x, self.alpha)
+
+
+class VectorQuantize(nn.Module):
+    """
+    Implementation of VQ similar to Karpathy's repo:
+    https://github.com/karpathy/deep-vector-quantization
+    Additionally uses following tricks from Improved VQGAN
+    (https://arxiv.org/pdf/2110.04627.pdf):
+        1. Factorized codes: Perform nearest neighbor lookup in low-dimensional space
+            for improved codebook usage
+        2. l2-normalized codes: Converts euclidean distance to cosine similarity which
+            improves training stability
+    """
+
+    def __init__(self, input_dim: int, codebook_size: int, codebook_dim: int):
+        super().__init__()
+        self.codebook_size = codebook_size
+        self.codebook_dim = codebook_dim
+
+        self.in_proj = WNConv1d(input_dim, codebook_dim, kernel_size=1)
+        self.out_proj = WNConv1d(codebook_dim, input_dim, kernel_size=1)
+        self.codebook = nn.Embedding(codebook_size, codebook_dim)
+
+    def forward(self, z):
+        """Quantized the input tensor using a fixed codebook and returns
+        the corresponding codebook vectors
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        Tensor[1]
+            Commitment loss to train encoder to predict vectors closer to codebook
+            entries
+        Tensor[1]
+            Codebook loss to update the codebook
+        Tensor[B x T]
+            Codebook indices (quantized discrete representation of input)
+        Tensor[B x D x T]
+            Projected latents (continuous representation of input before quantization)
+        """
+
+        # Factorized codes (ViT-VQGAN) Project input into low-dimensional space
+        z_e = self.in_proj(z)  # z_e : (B x D x T)
+        z_q, indices = self.decode_latents(z_e)
+
+        commitment_loss = F.mse_loss(z_e, z_q.detach(), reduction="none").mean([1, 2])
+        codebook_loss = F.mse_loss(z_q, z_e.detach(), reduction="none").mean([1, 2])
+
+        z_q = (
+            z_e + (z_q - z_e).detach()
+        )  # noop in forward pass, straight-through gradient estimator in backward pass
+
+        z_q = self.out_proj(z_q)
+
+        return z_q, commitment_loss, codebook_loss, indices, z_e
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.codebook.weight)
+
+    def decode_code(self, embed_id):
+        return self.embed_code(embed_id).transpose(1, 2)
+
+    def decode_latents(self, latents):
+        encodings = rearrange(latents, "b d t -> (b t) d")
+        codebook = self.codebook.weight  # codebook: (N x D)
+
+        # L2 normalize encodings and codebook (ViT-VQGAN)
+        encodings = F.normalize(encodings)
+        codebook = F.normalize(codebook)
+
+        # Compute euclidean distance with codebook
+        dist = (
+            encodings.pow(2).sum(1, keepdim=True)
+            - 2 * encodings @ codebook.t()
+            + codebook.pow(2).sum(1, keepdim=True).t()
+        )
+        indices = rearrange((-dist).max(1)[1], "(b t) -> b t", b=latents.size(0))
+        z_q = self.decode_code(indices)
+        return z_q, indices
+
+
+class ResidualVectorQuantize(nn.Module):
+    """
+    Introduced in SoundStream: An end2end neural audio codec
+    https://arxiv.org/abs/2107.03312
+    """
+
+    def __init__(
+        self,
+        input_dim: int = 512,
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: float = 0.0,
+    ):
+        super().__init__()
+        if isinstance(codebook_dim, int):
+            codebook_dim = [codebook_dim for _ in range(n_codebooks)]
+
+        self.n_codebooks = n_codebooks
+        self.codebook_dim = codebook_dim
+        self.codebook_size = codebook_size
+
+        self.quantizers = nn.ModuleList(
+            [
+                VectorQuantize(input_dim, codebook_size, codebook_dim[i])
+                for i in range(n_codebooks)
+            ]
+        )
+        self.quantizer_dropout = quantizer_dropout
+
+    def forward(self, z, n_quantizers: int = None):
+        """Quantized the input tensor using a fixed set of `n` codebooks and returns
+        the corresponding codebook vectors
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+        n_quantizers : int, optional
+            No. of quantizers to use
+            (n_quantizers < self.n_codebooks ex: for quantizer dropout)
+            Note: if `self.quantizer_dropout` is True, this argument is ignored
+                when in training mode, and a random number of quantizers is used.
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+        """
+        z_q = 0
+        residual = z
+        commitment_loss = 0
+        codebook_loss = 0
+
+        codebook_indices = []
+        latents = []
+
+        if n_quantizers is None:
+            n_quantizers = self.n_codebooks
+        if self.training:
+            n_quantizers = torch.ones((z.shape[0],)) * self.n_codebooks + 1
+            dropout = torch.randint(1, self.n_codebooks + 1, (z.shape[0],))
+            n_dropout = int(z.shape[0] * self.quantizer_dropout)
+            n_quantizers[:n_dropout] = dropout[:n_dropout]
+            n_quantizers = n_quantizers.to(z.device)
+
+        for i, quantizer in enumerate(self.quantizers):
+            if self.training is False and i >= n_quantizers:
+                break
+
+            z_q_i, commitment_loss_i, codebook_loss_i, indices_i, z_e_i = quantizer(
+                residual
+            )
+
+            # Create mask to apply quantizer dropout
+            mask = (
+                torch.full((z.shape[0],), fill_value=i, device=z.device) < n_quantizers
+            )
+            z_q = z_q + z_q_i * mask[:, None, None]
+            residual = residual - z_q_i
+
+            # Sum losses
+            commitment_loss += (commitment_loss_i * mask).mean()
+            codebook_loss += (codebook_loss_i * mask).mean()
+
+            codebook_indices.append(indices_i)
+            latents.append(z_e_i)
+
+        codes = torch.stack(codebook_indices, dim=1)
+        latents = torch.cat(latents, dim=1)
+
+        return z_q, codes, latents, commitment_loss, codebook_loss
+
+    def from_codes(self, codes: torch.Tensor):
+        """Given the quantized codes, reconstruct the continuous representation
+        Parameters
+        ----------
+        codes : Tensor[B x N x T]
+            Quantized discrete representation of input
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized continuous representation of input
+        """
+        z_q = 0.0
+        z_p = []
+        n_codebooks = codes.shape[1]
+        for i in range(n_codebooks):
+            z_p_i = self.quantizers[i].decode_code(codes[:, i, :])
+            z_p.append(z_p_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+        return z_q, torch.cat(z_p, dim=1), codes
+
+    def from_latents(self, latents: torch.Tensor):
+        """Given the unquantized latents, reconstruct the
+        continuous representation after quantization.
+
+        Parameters
+        ----------
+        latents : Tensor[B x N x T]
+            Continuous representation of input after projection
+
+        Returns
+        -------
+        Tensor[B x D x T]
+            Quantized representation of full-projected space
+        Tensor[B x D x T]
+            Quantized representation of latent space
+        """
+        z_q = 0
+        z_p = []
+        codes = []
+        dims = np.cumsum([0] + [q.codebook_dim for q in self.quantizers])
+
+        n_codebooks = np.where(dims <= latents.shape[1])[0].max(axis=0, keepdims=True)[
+            0
+        ]
+        for i in range(n_codebooks):
+            j, k = dims[i], dims[i + 1]
+            z_p_i, codes_i = self.quantizers[i].decode_latents(latents[:, j:k, :])
+            z_p.append(z_p_i)
+            codes.append(codes_i)
+
+            z_q_i = self.quantizers[i].out_proj(z_p_i)
+            z_q = z_q + z_q_i
+
+        return z_q, torch.cat(z_p, dim=1), torch.stack(codes, dim=1)
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        else:
+            if other is None:
+                return 0.5 * torch.mean(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2],
+                )
+            else:
+                return 0.5 * torch.mean(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var
+                    - 1.0
+                    - self.logvar
+                    + other.logvar,
+                    dim=[1, 2],
+                )
+
+    def nll(self, sample, dims=[1, 2]):
+        if self.deterministic:
+            return torch.Tensor([0.0])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) for x in (logvar1, logvar2)]
+
+    return 0.5 * (
+        -1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )
+
+
+def init_weights(m):
+    if isinstance(m, nn.Conv1d):
+        nn.init.trunc_normal_(m.weight, std=0.02)
+        nn.init.constant_(m.bias, 0)
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, dim: int = 16, dilation: int = 1):
+        super().__init__()
+        pad = ((7 - 1) * dilation) // 2
+        self.block = nn.Sequential(
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
+            Snake1d(dim),
+            WNConv1d(dim, dim, kernel_size=1),
+        )
+
+    def forward(self, x):
+        y = self.block(x)
+        pad = (x.shape[-1] - y.shape[-1]) // 2
+        if pad > 0:
+            x = x[..., pad:-pad]
+        return x + y
+
+
+class EncoderBlock(nn.Module):
+    def __init__(self, dim: int = 16, stride: int = 1):
+        super().__init__()
+        self.block = nn.Sequential(
+            ResidualUnit(dim // 2, dilation=1),
+            ResidualUnit(dim // 2, dilation=3),
+            ResidualUnit(dim // 2, dilation=9),
+            Snake1d(dim // 2),
+            WNConv1d(
+                dim // 2,
+                dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+            ),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        d_model: int = 64,
+        strides: list = [2, 4, 8, 8],
+        d_latent: int = 64,
+    ):
+        super().__init__()
+        # Create first convolution
+        self.block = [WNConv1d(1, d_model, kernel_size=7, padding=3)]
+
+        # Create EncoderBlocks that double channels as they downsample by `stride`
+        for stride in strides:
+            d_model *= 2
+            self.block += [EncoderBlock(d_model, stride=stride)]
+
+        # Create last convolution
+        self.block += [
+            Snake1d(d_model),
+            WNConv1d(d_model, d_latent, kernel_size=3, padding=1),
+        ]
+
+        # Wrap black into nn.Sequential
+        self.block = nn.Sequential(*self.block)
+        self.enc_dim = d_model
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class DecoderBlock(nn.Module):
+    def __init__(self, input_dim: int = 16, output_dim: int = 8, stride: int = 1):
+        super().__init__()
+        self.block = nn.Sequential(
+            Snake1d(input_dim),
+            WNConvTranspose1d(
+                input_dim,
+                output_dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+                output_padding=stride % 2,
+            ),
+            ResidualUnit(output_dim, dilation=1),
+            ResidualUnit(output_dim, dilation=3),
+            ResidualUnit(output_dim, dilation=9),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        input_channel,
+        channels,
+        rates,
+        d_out: int = 1,
+    ):
+        super().__init__()
+
+        # Add first conv layer
+        layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]
+
+        # Add upsampling + MRF blocks
+        for i, stride in enumerate(rates):
+            input_dim = channels // 2**i
+            output_dim = channels // 2 ** (i + 1)
+            layers += [DecoderBlock(input_dim, output_dim, stride)]
+
+        # Add final conv layer
+        layers += [
+            Snake1d(output_dim),
+            WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
+            nn.Tanh(),
+        ]
+
+        self.model = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class DacVAE(nn.Module):
+
+    def __init__(
+        self,
+        encoder_dim: int = 128,
+        encoder_rates: List[int] = [2, 3, 4, 5, 8],
+        latent_dim: int = 128,
+        decoder_dim: int = 2048,
+        decoder_rates: List[int] = [8, 5, 4, 3, 2],
+        n_codebooks: int = 9,
+        codebook_size: int = 1024,
+        codebook_dim: Union[int, list] = 8,
+        quantizer_dropout: bool = False,
+        sample_rate: int = 48000,
+        continuous: bool = True,
+        use_weight_norm: bool = False,
+    ):
+        super().__init__()
+
+        self.encoder_dim = encoder_dim
+        self.encoder_rates = encoder_rates
+        self.decoder_dim = decoder_dim
+        self.decoder_rates = decoder_rates
+        self.sample_rate = sample_rate
+        self.continuous = continuous
+        self.use_weight_norm = use_weight_norm
+
+        if latent_dim is None:
+            latent_dim = encoder_dim * (2 ** len(encoder_rates))
+
+        self.latent_dim = latent_dim
+
+        self.hop_length = np.prod(encoder_rates)
+        self.encoder = Encoder(encoder_dim, encoder_rates, latent_dim)
+
+        if not continuous:
+            self.n_codebooks = n_codebooks
+            self.codebook_size = codebook_size
+            self.codebook_dim = codebook_dim
+            self.quantizer = ResidualVectorQuantize(
+                input_dim=latent_dim,
+                n_codebooks=n_codebooks,
+                codebook_size=codebook_size,
+                codebook_dim=codebook_dim,
+                quantizer_dropout=quantizer_dropout,
+            )
+        else:
+            self.quant_conv = torch.nn.Conv1d(latent_dim, 2 * latent_dim, 1)
+            self.post_quant_conv = torch.nn.Conv1d(latent_dim, latent_dim, 1)
+
+        self.decoder = Decoder(
+            latent_dim,
+            decoder_dim,
+            decoder_rates,
+        )
+        self.sample_rate = sample_rate
+        self.apply(init_weights)
+
+        self.delay = self.get_delay()
+
+        if not self.use_weight_norm:
+            self.remove_weight_norm()
+
+    def get_delay(self):
+        # Any number works here, delay is invariant to input length
+        l_out = self.get_output_length(0)
+        L = l_out
+
+        layers = []
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                layers.append(layer)
+
+        for layer in reversed(layers):
+            d = layer.dilation[0]
+            k = layer.kernel_size[0]
+            s = layer.stride[0]
+
+            if isinstance(layer, nn.ConvTranspose1d):
+                L = ((L - d * (k - 1) - 1) / s) + 1
+            elif isinstance(layer, nn.Conv1d):
+                L = (L - 1) * s + d * (k - 1) + 1
+
+            L = math.ceil(L)
+
+        l_in = L
+
+        return (l_in - l_out) // 2
+
+    def get_output_length(self, input_length):
+        L = input_length
+        # Calculate output length
+        for layer in self.modules():
+            if isinstance(layer, (nn.Conv1d, nn.ConvTranspose1d)):
+                d = layer.dilation[0]
+                k = layer.kernel_size[0]
+                s = layer.stride[0]
+
+                if isinstance(layer, nn.Conv1d):
+                    L = ((L - d * (k - 1) - 1) / s) + 1
+                elif isinstance(layer, nn.ConvTranspose1d):
+                    L = (L - 1) * s + d * (k - 1) + 1
+
+                L = math.floor(L)
+        return L
+
+    @property
+    def dtype(self):
+        """Get the dtype of the model parameters."""
+        # Return the dtype of the first parameter found
+        for param in self.parameters():
+            return param.dtype
+        return torch.float32  # fallback
+
+    @property
+    def device(self):
+        """Get the device of the model parameters."""
+        # Return the device of the first parameter found
+        for param in self.parameters():
+            return param.device
+        return torch.device('cpu')  # fallback
+
+    def preprocess(self, audio_data, sample_rate):
+        if sample_rate is None:
+            sample_rate = self.sample_rate
+        assert sample_rate == self.sample_rate
+
+        length = audio_data.shape[-1]
+        right_pad = math.ceil(length / self.hop_length) * self.hop_length - length
+        audio_data = nn.functional.pad(audio_data, (0, right_pad))
+
+        return audio_data
+
+    def encode(
+        self,
+        audio_data: torch.Tensor,
+        n_quantizers: int = None,
+    ):
+        """Encode given audio data and return quantized latent codes
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+        """
+        z = self.encoder(audio_data)  # [B x D x T]
+        if not self.continuous:
+            z, codes, latents, commitment_loss, codebook_loss = self.quantizer(z, n_quantizers)
+        else:
+            z = self.quant_conv(z)  # [B x 2D x T]
+            z = DiagonalGaussianDistribution(z)
+            codes, latents, commitment_loss, codebook_loss = None, None, 0, 0
+
+        return z, codes, latents, commitment_loss, codebook_loss
+
+    def decode(self, z: torch.Tensor):
+        """Decode given latent codes and return audio data
+
+        Parameters
+        ----------
+        z : Tensor[B x D x T]
+            Quantized continuous representation of input
+        length : int, optional
+            Number of samples in output audio, by default None
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        if not self.continuous:
+            audio = self.decoder(z)
+        else:
+            z = self.post_quant_conv(z)
+            audio = self.decoder(z)
+
+        return audio
+
+    def forward(
+        self,
+        audio_data: torch.Tensor,
+        sample_rate: int = None,
+        n_quantizers: int = None,
+    ):
+        """Model forward pass
+
+        Parameters
+        ----------
+        audio_data : Tensor[B x 1 x T]
+            Audio data to encode
+        sample_rate : int, optional
+            Sample rate of audio data in Hz, by default None
+            If None, defaults to `self.sample_rate`
+        n_quantizers : int, optional
+            Number of quantizers to use, by default None.
+            If None, all quantizers are used.
+
+        Returns
+        -------
+        dict
+            A dictionary with the following keys:
+            "z" : Tensor[B x D x T]
+                Quantized continuous representation of input
+            "codes" : Tensor[B x N x T]
+                Codebook indices for each codebook
+                (quantized discrete representation of input)
+            "latents" : Tensor[B x N*D x T]
+                Projected latents (continuous representation of input before quantization)
+            "vq/commitment_loss" : Tensor[1]
+                Commitment loss to train encoder to predict vectors closer to codebook
+                entries
+            "vq/codebook_loss" : Tensor[1]
+                Codebook loss to update the codebook
+            "length" : int
+                Number of samples in input audio
+            "audio" : Tensor[B x 1 x length]
+                Decoded audio data.
+        """
+        length = audio_data.shape[-1]
+        audio_data = self.preprocess(audio_data, sample_rate)
+        if not self.continuous:
+            z, codes, latents, commitment_loss, codebook_loss = self.encode(audio_data, n_quantizers)
+
+            x = self.decode(z)
+            return {
+                "audio": x[..., :length],
+                "z": z,
+                "codes": codes,
+                "latents": latents,
+                "vq/commitment_loss": commitment_loss,
+                "vq/codebook_loss": codebook_loss,
+            }
+        else:
+            posterior, _, _, _, _ = self.encode(audio_data, n_quantizers)
+            z = posterior.sample()
+            x = self.decode(z)
+
+            kl_loss = posterior.kl()
+            kl_loss = kl_loss.mean()
+
+            return {
+                "audio": x[..., :length],
+                "z": z,
+                "kl_loss": kl_loss,
+            }
+
+    def remove_weight_norm(self):
+        """
+        Remove weight_norm from all modules in the model.
+        This fuses the weight_g and weight_v parameters into a single weight parameter.
+        Should be called before inference for better performance.
+        Returns:
+            self: The model with weight_norm removed
+        """
+        from torch.nn.utils import remove_weight_norm
+        num_removed = 0
+        for name, module in list(self.named_modules()):
+            if hasattr(module, "_forward_pre_hooks"):
+                for hook_id, hook in list(module._forward_pre_hooks.items()):
+                    if "WeightNorm" in str(type(hook)):
+                        try:
+                            remove_weight_norm(module)
+                            num_removed += 1
+                            # print(f"Removed weight_norm from: {name}")
+                        except ValueError as e:
+                            print(f"Failed to remove weight_norm from {name}: {e}")
+        if num_removed > 0:
+            # print(f"Successfully removed weight_norm from {num_removed} modules")
+            self.use_weight_norm = False
+        else:
+            print("No weight_norm found in the model")
+        return self
--- a/diffsynth/models/mova_dual_tower_bridge.py
+++ b/diffsynth/models/mova_dual_tower_bridge.py
@@ -0,0 +1,595 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Dict, List, Tuple, Optional
+from einops import rearrange
+from .wan_video_dit import AttentionModule, RMSNorm
+from ..core import gradient_checkpoint_forward
+
+class RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, base: float, dim: int, device=None):
+        super().__init__()
+        self.base = base
+        self.dim = dim
+        self.attention_scaling = 1.0
+
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@torch.compile(fullgraph=True)
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class PerFrameAttentionPooling(nn.Module):
+    """
+    Per-frame multi-head attention pooling.
+
+    Given a flattened token sequence [B, L, D] and grid size (T, H, W), perform a
+    single-query attention pooling over the H*W tokens for each time frame, producing
+    [B, T, D].
+
+    Inspired by SigLIP's Multihead Attention Pooling head (without MLP/residual stack).
+    """
+
+    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
+        super().__init__()
+        assert dim % num_heads == 0, "dim must be divisible by num_heads"
+        self.dim = dim
+        self.num_heads = num_heads
+
+        self.probe = nn.Parameter(torch.randn(1, 1, dim))
+        nn.init.normal_(self.probe, std=0.02)
+
+        self.attention = nn.MultiheadAttention(embed_dim=dim, num_heads=num_heads, batch_first=True)
+        self.layernorm = nn.LayerNorm(dim, eps=eps)
+
+    def forward(self, x: torch.Tensor, grid_size: Tuple[int, int, int]) -> torch.Tensor:
+        """
+        Args:
+            x: [B, L, D], where L = T*H*W
+            grid_size: (T, H, W)
+        Returns:
+            pooled: [B, T, D]
+        """
+        B, L, D = x.shape
+        T, H, W = grid_size
+        assert D == self.dim, f"Channel dimension mismatch: D={D} vs dim={self.dim}"
+        assert L == T * H * W, f"Flattened length mismatch: L={L} vs T*H*W={T*H*W}"
+
+        S = H * W
+        # Re-arrange tokens grouped by frame.
+        x_bt_s_d = x.view(B, T, S, D).contiguous().view(B * T, S, D)  # [B*T, S, D]
+
+        # A learnable probe as the query (one query per frame).
+        probe = self.probe.expand(B * T, -1, -1)  # [B*T, 1, D]
+
+        # Attention pooling: query=probe, key/value=H*W tokens within the frame.
+        pooled_bt_1_d = self.attention(probe, x_bt_s_d, x_bt_s_d, need_weights=False)[0]  # [B*T, 1, D]
+        pooled_bt_d = pooled_bt_1_d.squeeze(1)  # [B*T, D]
+
+        # Restore to [B, T, D].
+        pooled = pooled_bt_d.view(B, T, D)
+        pooled = self.layernorm(pooled)
+        return pooled
+
+
+class CrossModalInteractionController:
+    """
+    Strategy class that controls interactions between two towers.
+    Manages the interaction mapping between visual DiT (e.g. 30 layers) and audio DiT (e.g. 30 layers).
+    """
+
+    def __init__(self, visual_layers: int = 30, audio_layers: int = 30):
+        self.visual_layers = visual_layers
+        self.audio_layers = audio_layers
+        self.min_layers = min(visual_layers, audio_layers)
+
+    def get_interaction_layers(self, strategy: str = "shallow_focus") -> Dict[str, List[Tuple[int, int]]]:
+        """
+        Get interaction layer mappings.
+
+        Args:
+            strategy: interaction strategy
+                - "shallow_focus": emphasize shallow layers to avoid deep-layer asymmetry
+                - "distributed": distributed interactions across the network
+                - "progressive": dense shallow interactions, sparse deeper interactions
+                - "custom": custom interaction layers
+
+        Returns:
+            A dict containing mappings for 'v2a' (visual -> audio) and 'a2v' (audio -> visual).
+        """
+
+        if strategy == "shallow_focus":
+            # Emphasize the first ~1/3 layers to avoid deep-layer asymmetry.
+            num_interact = min(10, self.min_layers // 3)
+            interact_layers = list(range(0, num_interact))
+
+        elif strategy == "distributed":
+            # Distribute interactions across the network (every few layers).
+            step = 3
+            interact_layers = list(range(0, self.min_layers, step))
+
+        elif strategy == "progressive":
+            # Progressive: dense shallow interactions, sparse deeper interactions.
+            shallow = list(range(0, min(8, self.min_layers)))  # Dense for the first 8 layers.
+            if self.min_layers > 8:
+                deep = list(range(8, self.min_layers, 3))  # Every 3 layers afterwards.
+                interact_layers = shallow + deep
+            else:
+                interact_layers = shallow
+
+        elif strategy == "custom":
+            # Custom strategy: adjust as needed.
+            interact_layers = [0, 2, 4, 6, 8, 12, 16, 20]  # Explicit layer indices.
+            interact_layers = [i for i in interact_layers if i < self.min_layers]
+
+        elif strategy == "full":
+            interact_layers = list(range(0, self.min_layers))
+
+        else:
+            raise ValueError(f"Unknown interaction strategy: {strategy}")
+
+        # Build bidirectional mapping.
+        mapping = {
+            'v2a': [(i, i) for i in interact_layers],  # visual layer i -> audio layer i
+            'a2v': [(i, i) for i in interact_layers]   # audio layer i -> visual layer i
+        }
+
+        return mapping
+
+    def should_interact(self, layer_idx: int, direction: str, interaction_mapping: Dict) -> bool:
+        """
+        Check whether a given layer should interact.
+
+        Args:
+            layer_idx: current layer index
+            direction: interaction direction ('v2a' or 'a2v')
+            interaction_mapping: interaction mapping table
+
+        Returns:
+            bool: whether to interact
+        """
+        if direction not in interaction_mapping:
+            return False
+
+        return any(src == layer_idx for src, _ in interaction_mapping[direction])
+
+
+class ConditionalCrossAttention(nn.Module):
+    def __init__(self, dim: int, kv_dim: int, num_heads: int, eps: float = 1e-6):
+        super().__init__()
+        self.q_dim = dim
+        self.kv_dim = kv_dim
+        self.num_heads = num_heads
+        self.head_dim = self.q_dim // num_heads
+
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(kv_dim, dim)
+        self.v = nn.Linear(kv_dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.norm_q = RMSNorm(dim, eps=eps)
+        self.norm_k = RMSNorm(dim, eps=eps)
+
+        self.attn = AttentionModule(self.num_heads)
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor, x_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, y_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        ctx = y
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(ctx))
+        v = self.v(ctx)
+        if x_freqs is not None:
+            x_cos, x_sin = x_freqs
+            B, L, _ = q.shape
+            q_view = rearrange(q, 'b l (h d) -> b l h d', d=self.head_dim)
+            x_cos = x_cos.to(q_view.dtype).to(q_view.device)
+            x_sin = x_sin.to(q_view.dtype).to(q_view.device)
+            # Expect x_cos/x_sin shape: [B or 1, L, head_dim]
+            q_view, _ = apply_rotary_pos_emb(q_view, q_view, x_cos, x_sin, unsqueeze_dim=2)
+            q = rearrange(q_view, 'b l h d -> b l (h d)')
+        if y_freqs is not None:
+            y_cos, y_sin = y_freqs
+            Bc, Lc, _ = k.shape
+            k_view = rearrange(k, 'b l (h d) -> b l h d', d=self.head_dim)
+            y_cos = y_cos.to(k_view.dtype).to(k_view.device)
+            y_sin = y_sin.to(k_view.dtype).to(k_view.device)
+            # Expect y_cos/y_sin shape: [B or 1, L, head_dim]
+            _, k_view = apply_rotary_pos_emb(k_view, k_view, y_cos, y_sin, unsqueeze_dim=2)
+            k = rearrange(k_view, 'b l h d -> b l (h d)')
+        x = self.attn(q, k, v)
+        return self.o(x)
+
+
+# from diffusers.models.attention import AdaLayerNorm
+class AdaLayerNorm(nn.Module):
+    r"""
+    Norm layer modified to incorporate timestep embeddings.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`, *optional*): The size of the embeddings dictionary.
+        output_dim (`int`, *optional*):
+        norm_elementwise_affine (`bool`, defaults to `False):
+        norm_eps (`bool`, defaults to `False`):
+        chunk_dim (`int`, defaults to `0`):
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        num_embeddings: Optional[int] = None,
+        output_dim: Optional[int] = None,
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-5,
+        chunk_dim: int = 0,
+    ):
+        super().__init__()
+
+        self.chunk_dim = chunk_dim
+        output_dim = output_dim or embedding_dim * 2
+
+        if num_embeddings is not None:
+            self.emb = nn.Embedding(num_embeddings, embedding_dim)
+        else:
+            self.emb = None
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
+
+    def forward(
+        self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        if self.emb is not None:
+            temb = self.emb(timestep)
+
+        temb = self.linear(self.silu(temb))
+
+        if self.chunk_dim == 2:
+            scale, shift = temb.chunk(2, dim=2)
+            # print(f"{x.shape = }, {scale.shape = }, {shift.shape = }")
+        elif self.chunk_dim == 1:
+            # This is a bit weird why we have the order of "shift, scale" here and "scale, shift" in the
+            # other if-branch. This branch is specific to CogVideoX and OmniGen for now.
+            shift, scale = temb.chunk(2, dim=1)
+            shift = shift[:, None, :]
+            scale = scale[:, None, :]
+        else:
+            scale, shift = temb.chunk(2, dim=0)
+
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class ConditionalCrossAttentionBlock(nn.Module):
+    """
+    A thin wrapper around ConditionalCrossAttention.
+    Applies LayerNorm to the conditioning input `y` before cross-attention.
+    """
+    def __init__(self, dim: int, kv_dim: int, num_heads: int, eps: float = 1e-6, pooled_adaln: bool = False):
+        super().__init__()
+        self.y_norm = nn.LayerNorm(kv_dim, eps=eps)
+        self.inner = ConditionalCrossAttention(dim=dim, kv_dim=kv_dim, num_heads=num_heads, eps=eps)
+        self.pooled_adaln = pooled_adaln
+        if pooled_adaln:
+            self.per_frame_pooling = PerFrameAttentionPooling(kv_dim, num_heads=num_heads, eps=eps)
+            self.adaln = AdaLayerNorm(kv_dim, output_dim=dim*2, chunk_dim=2)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        y: torch.Tensor,
+        x_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        y_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        video_grid_size: Optional[Tuple[int, int, int]] = None,
+    ) -> torch.Tensor:
+        if self.pooled_adaln:
+            assert video_grid_size is not None, "video_grid_size must not be None"
+            pooled_y = self.per_frame_pooling(y, video_grid_size)
+            # Interpolate pooled_y along its temporal dimension to match x's sequence length.
+            if pooled_y.shape[1] != x.shape[1]:
+                pooled_y = F.interpolate(
+                    pooled_y.permute(0, 2, 1),  # [B, C, T]
+                    size=x.shape[1],
+                    mode='linear',
+                    align_corners=False,
+                ).permute(0, 2, 1)  # [B, T, C]
+            x = self.adaln(x, temb=pooled_y)
+        y = self.y_norm(y)
+        return self.inner(x=x, y=y, x_freqs=x_freqs, y_freqs=y_freqs)
+
+
+class DualTowerConditionalBridge(nn.Module):
+    """
+    Dual-tower conditional bridge.
+    """
+    def __init__(self,
+                 visual_layers: int = 40,
+                 audio_layers: int = 30,
+                 visual_hidden_dim: int = 5120,    # visual DiT hidden state dimension
+                 audio_hidden_dim: int = 1536,     # audio DiT hidden state dimension
+                 audio_fps: float = 50.0,
+                 head_dim: int = 128,              # attention head dimension
+                 interaction_strategy: str = "full",
+                 apply_cross_rope: bool = True,   # whether to apply RoPE in cross-attention
+                 apply_first_frame_bias_in_rope: bool = False,  # whether to account for 1/video_fps bias for the first frame in RoPE alignment
+                 trainable_condition_scale: bool = False,
+                 pooled_adaln: bool = False,
+                 ):
+        super().__init__()
+
+        self.visual_hidden_dim = visual_hidden_dim
+        self.audio_hidden_dim = audio_hidden_dim
+        self.audio_fps = audio_fps
+        self.head_dim = head_dim
+        self.apply_cross_rope = apply_cross_rope
+        self.apply_first_frame_bias_in_rope = apply_first_frame_bias_in_rope
+        self.trainable_condition_scale = trainable_condition_scale
+        self.pooled_adaln = pooled_adaln
+        if self.trainable_condition_scale:
+            self.condition_scale = nn.Parameter(torch.tensor([1.0], dtype=torch.float32))
+        else:
+            self.condition_scale = 1.0
+
+        self.controller = CrossModalInteractionController(visual_layers, audio_layers)
+        self.interaction_mapping = self.controller.get_interaction_layers(interaction_strategy)
+
+        # Conditional cross-attention modules operating at the DiT hidden-state level.
+        self.audio_to_video_conditioners = nn.ModuleDict()  # audio hidden states -> visual DiT conditioning
+        self.video_to_audio_conditioners = nn.ModuleDict()  # visual hidden states -> audio DiT conditioning
+
+        # Build conditioners for layers that should interact.
+        # audio hidden states condition the visual DiT
+        self.rotary = RotaryEmbedding(base=10000.0, dim=head_dim)
+        for v_layer, _ in self.interaction_mapping['a2v']:
+            self.audio_to_video_conditioners[str(v_layer)] = ConditionalCrossAttentionBlock(
+                dim=visual_hidden_dim,     # 3072 (visual DiT hidden states)
+                kv_dim=audio_hidden_dim,    # 1536 (audio DiT hidden states)
+                num_heads=visual_hidden_dim // head_dim, # derive number of heads from hidden dim
+                pooled_adaln=False # a2v typically does not need pooled AdaLN
+            )
+
+        # visual hidden states condition the audio DiT
+        for a_layer, _ in self.interaction_mapping['v2a']:
+            self.video_to_audio_conditioners[str(a_layer)] = ConditionalCrossAttentionBlock(
+                dim=audio_hidden_dim,      # 1536 (audio DiT hidden states)
+                kv_dim=visual_hidden_dim,   # 3072 (visual DiT hidden states)
+                num_heads=audio_hidden_dim // head_dim, # safe head count derivation
+                pooled_adaln=self.pooled_adaln
+            )
+
+    @torch.no_grad()
+    def build_aligned_freqs(self,
+                            video_fps: float,
+                            grid_size: Tuple[int, int, int],
+                            audio_steps: int,
+                            device: Optional[torch.device] = None,
+                            dtype: Optional[torch.dtype] = None) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Build aligned RoPE (cos, sin) pairs based on video fps, video grid size (f_v, h, w),
+        and audio sequence length `audio_steps` (with fixed audio fps = 44100/2048).
+
+        Returns:
+            visual_freqs: (cos_v, sin_v), shape [1, f_v*h*w, head_dim]
+            audio_freqs:  (cos_a, sin_a), shape [1, audio_steps, head_dim]
+        """
+        f_v, h, w = grid_size
+        L_v = f_v * h * w
+        L_a = int(audio_steps)
+
+        device = device or next(self.parameters()).device
+        dtype = dtype or torch.float32
+
+        # Audio positions: 0,1,2,...,L_a-1 (audio as reference).
+        audio_pos = torch.arange(L_a, device=device, dtype=torch.float32).unsqueeze(0)
+
+        # Video positions: align video frames to audio-step units.
+        # FIXME(dhyu): hard-coded VAE temporal stride = 4
+        if self.apply_first_frame_bias_in_rope:
+            # Account for the "first frame lasts 1/video_fps" bias.
+            video_effective_fps = float(video_fps) / 4.0
+            if f_v > 0:
+                t_starts = torch.zeros((f_v,), device=device, dtype=torch.float32)
+                if f_v > 1:
+                    t_starts[1:] = (1.0 / float(video_fps)) + torch.arange(f_v - 1, device=device, dtype=torch.float32) * (1.0 / video_effective_fps)
+            else:
+                t_starts = torch.zeros((0,), device=device, dtype=torch.float32)
+            # Convert to audio-step units.
+            video_pos_per_frame = t_starts * float(self.audio_fps)
+        else:
+            # No first-frame bias: uniform alignment.
+            scale = float(self.audio_fps) / float(video_fps / 4.0)
+            video_pos_per_frame = torch.arange(f_v, device=device, dtype=torch.float32) * scale
+        # Flatten to f*h*w; tokens within the same frame share the same time position.
+        video_pos = video_pos_per_frame.repeat_interleave(h * w).unsqueeze(0)
+
+        # print(f"video fps: {video_fps}, audio fps: {self.audio_fps}, scale: {scale}")
+        # print(f"video pos: {video_pos.shape}, audio pos: {audio_pos.shape}")
+
+        # Build dummy x to produce cos/sin, dim=head_dim.
+        dummy_v = torch.zeros((1, L_v, self.head_dim), device=device, dtype=dtype)
+        dummy_a = torch.zeros((1, L_a, self.head_dim), device=device, dtype=dtype)
+
+        cos_v, sin_v = self.rotary(dummy_v, position_ids=video_pos)
+        cos_a, sin_a = self.rotary(dummy_a, position_ids=audio_pos)
+
+        return (cos_v, sin_v), (cos_a, sin_a)
+
+    def should_interact(self, layer_idx: int, direction: str) -> bool:
+        return self.controller.should_interact(layer_idx, direction, self.interaction_mapping)
+
+    def apply_conditional_control(
+        self,
+        layer_idx: int,
+        direction: str,
+        primary_hidden_states: torch.Tensor,
+        condition_hidden_states: torch.Tensor,
+        x_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        y_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        condition_scale: Optional[float] = None,
+        video_grid_size: Optional[Tuple[int, int, int]] = None,
+        use_gradient_checkpointing: Optional[bool] = False,
+        use_gradient_checkpointing_offload: Optional[bool] = False,
+    ) -> torch.Tensor:
+        """
+        Apply conditional control (at the DiT hidden-state level).
+
+        Args:
+            layer_idx: current layer index
+            direction: conditioning direction
+                - 'a2v': audio hidden states -> visual DiT
+                - 'v2a': visual hidden states -> audio DiT
+            primary_hidden_states: primary DiT hidden states [B, L, hidden_dim]
+            condition_hidden_states: condition DiT hidden states [B, L, hidden_dim]
+            condition_scale: conditioning strength (similar to CFG scale)
+
+        Returns:
+            Conditioned primary DiT hidden states [B, L, hidden_dim]
+        """
+
+        if not self.controller.should_interact(layer_idx, direction, self.interaction_mapping):
+            return primary_hidden_states
+
+        if direction == 'a2v':
+            # audio hidden states condition the visual DiT
+            conditioner = self.audio_to_video_conditioners[str(layer_idx)]
+
+        elif direction == 'v2a':
+            # visual hidden states condition the audio DiT
+            conditioner = self.video_to_audio_conditioners[str(layer_idx)]
+        else:
+            raise ValueError(f"Invalid direction: {direction}")
+
+        conditioned_features = gradient_checkpoint_forward(
+            conditioner,
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            x=primary_hidden_states,
+            y=condition_hidden_states,
+            x_freqs=x_freqs,
+            y_freqs=y_freqs,
+            video_grid_size=video_grid_size,
+        )
+
+        if self.trainable_condition_scale and condition_scale is not None:
+            print(
+                "[WARN] This model has a trainable condition_scale, but an external "
+                f"condition_scale={condition_scale} was provided. The trainable condition_scale "
+                "will be ignored in favor of the external value."
+            )
+
+        scale = condition_scale if condition_scale is not None else self.condition_scale
+
+        primary_hidden_states = primary_hidden_states + conditioned_features * scale
+
+        return primary_hidden_states
+
+    def forward(
+        self,
+        layer_idx: int,
+        visual_hidden_states: torch.Tensor,
+        audio_hidden_states: torch.Tensor,
+        *,
+        x_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        y_freqs: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        a2v_condition_scale: Optional[float] = None,
+        v2a_condition_scale: Optional[float] = None,
+        condition_scale: Optional[float] = None,
+        video_grid_size: Optional[Tuple[int, int, int]] = None,
+        use_gradient_checkpointing: Optional[bool] = False,
+        use_gradient_checkpointing_offload: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Apply bidirectional conditional control to both visual/audio towers.
+
+        Args:
+            layer_idx: current layer index
+            visual_hidden_states: visual DiT hidden states
+            audio_hidden_states: audio DiT hidden states
+            x_freqs / y_freqs: cross-modal RoPE (cos, sin) pairs.
+                If provided, x_freqs is assumed to correspond to the primary tower and y_freqs
+                to the conditioning tower.
+            a2v_condition_scale: audio->visual conditioning strength (overrides global condition_scale)
+            v2a_condition_scale: visual->audio conditioning strength (overrides global condition_scale)
+            condition_scale: fallback conditioning strength when per-direction scale is None
+            video_grid_size: (F, H, W), used on the audio side when pooled_adaln is enabled
+
+        Returns:
+            (visual_hidden_states, audio_hidden_states), both conditioned in their respective directions.
+        """
+
+        visual_conditioned = self.apply_conditional_control(
+            layer_idx=layer_idx,
+            direction="a2v",
+            primary_hidden_states=visual_hidden_states,
+            condition_hidden_states=audio_hidden_states,
+            x_freqs=x_freqs,
+            y_freqs=y_freqs,
+            condition_scale=a2v_condition_scale if a2v_condition_scale is not None else condition_scale,
+            video_grid_size=video_grid_size,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+
+        audio_conditioned = self.apply_conditional_control(
+            layer_idx=layer_idx,
+            direction="v2a",
+            primary_hidden_states=audio_hidden_states,
+            condition_hidden_states=visual_hidden_states,
+            x_freqs=y_freqs,
+            y_freqs=x_freqs,
+            condition_scale=v2a_condition_scale if v2a_condition_scale is not None else condition_scale,
+            video_grid_size=video_grid_size,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+
+        return visual_conditioned, audio_conditioned
--- a/diffsynth/models/wan_video_dit.py
+++ b/diffsynth/models/wan_video_dit.py
@@ -5,6 +5,7 @@ import math
 from typing import Tuple, Optional
 from einops import rearrange
 from .wan_video_camera_controller import SimpleAdapter
+from ..core.gradient import gradient_checkpoint_forward

 try:
    import flash_attn_interface
@@ -93,23 +94,35 @@ def rope_apply(x, freqs, num_heads):
    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
        x.shape[0], x.shape[1], x.shape[2], -1, 2))
-    freqs = freqs.to(torch.complex64) if freqs.device == "npu" else freqs
+    freqs = freqs.to(torch.complex64) if freqs.device.type == "npu" else freqs
    x_out = torch.view_as_real(x_out * freqs).flatten(2)
    return x_out.to(x.dtype)


+def set_to_torch_norm(models):
+    for model in models:
+        for module in model.modules():
+            if isinstance(module, RMSNorm):
+                module.use_torch_norm = True
+
+
 class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))
+        self.use_torch_norm = False
+        self.normalized_shape = (dim,)

    def norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)

    def forward(self, x):
        dtype = x.dtype
-        return self.norm(x.float()).to(dtype) * self.weight
+        if self.use_torch_norm:
+            return F.rms_norm(x, self.normalized_shape, self.weight, self.eps)
+        else:        
+            return self.norm(x.float()).to(dtype) * self.weight


 class AttentionModule(nn.Module):
@@ -379,27 +392,15 @@ class WanModel(torch.nn.Module):
            self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
            self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
        ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
-        
-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward

        for block in self.blocks:
-            if self.training and use_gradient_checkpointing:
-                if use_gradient_checkpointing_offload:
-                    with torch.autograd.graph.save_on_cpu():
-                        x = torch.utils.checkpoint.checkpoint(
-                            create_custom_forward(block),
-                            x, context, t_mod, freqs,
-                            use_reentrant=False,
-                        )
-                else:
-                    x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        x, context, t_mod, freqs,
-                        use_reentrant=False,
-                    )
+            if self.training:
+                x = gradient_checkpoint_forward(
+                    block,
+                    use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload,
+                    x, context, t_mod, freqs
+                )
            else:
                x = block(x, context, t_mod, freqs)

--- a/diffsynth/models/wan_video_dit_s2v.py
+++ b/diffsynth/models/wan_video_dit_s2v.py
@@ -4,6 +4,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 from typing import Tuple
 from .wan_video_dit import rearrange, precompute_freqs_cis_3d, DiTBlock, Head, CrossAttention, modulate, sinusoidal_embedding_1d
+from ..core.gradient import gradient_checkpoint_forward


 def torch_dfs(model: nn.Module, parent_name='root'):
@@ -545,46 +546,19 @@ class WanS2VModel(torch.nn.Module):
        t = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, timestep))
        t_mod = self.time_projection(t).unflatten(1, (6, self.dim)).unsqueeze(2).transpose(0, 2)

-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward
-
        for block_id, block in enumerate(self.blocks):
-            if use_gradient_checkpointing_offload:
-                with torch.autograd.graph.save_on_cpu():
-                    x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        x,
-                        context,
-                        t_mod,
-                        seq_len_x,
-                        pre_compute_freqs[0],
-                        use_reentrant=False,
-                    )
-                    x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
-                        x,
-                        use_reentrant=False,
-                    )
-            elif use_gradient_checkpointing:
-                x = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    x,
-                    context,
-                    t_mod,
-                    seq_len_x,
-                    pre_compute_freqs[0],
-                    use_reentrant=False,
-                )
-                x = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
-                    x,
-                    use_reentrant=False,
-                )
-            else:
-                x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
-                x = self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)
+            x = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                x, context, t_mod, seq_len_x, pre_compute_freqs[0]
+            )
+            x = gradient_checkpoint_forward(
+                lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                x
+            )

        x = x[:, :seq_len_x]
        x = self.head(x, t[:-1])
--- a/diffsynth/models/wan_video_vace.py
+++ b/diffsynth/models/wan_video_vace.py
@@ -1,6 +1,6 @@
 import torch
 from .wan_video_dit import DiTBlock
-
+from ..core.gradient import gradient_checkpoint_forward

 class VaceWanAttentionBlock(DiTBlock):
    def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0):
@@ -62,26 +62,13 @@ class VaceWanModel(torch.nn.Module):
                      dim=1) for u in c
        ])
        
-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward
-        
        for block in self.vace_blocks:
-            if use_gradient_checkpointing_offload:
-                with torch.autograd.graph.save_on_cpu():
-                    c = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        c, x, context, t_mod, freqs,
-                        use_reentrant=False,
-                    )
-            elif use_gradient_checkpointing:
-                c = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    c, x, context, t_mod, freqs,
-                    use_reentrant=False,
-                )
-            else:
-                c = block(c, x, context, t_mod, freqs)
+            c = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                c, x, context, t_mod, freqs
+            )
+            
        hints = torch.unbind(c)[:-1]
        return hints
--- a/diffsynth/models/wan_video_vae.py
+++ b/diffsynth/models/wan_video_vae.py
@@ -171,7 +171,7 @@ class Resample(nn.Module):
                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1
-        return x
+        return x, feat_cache, feat_idx

    def init_weight(self, conv):
        conv_weight = conv.weight
@@ -298,7 +298,7 @@ class ResidualBlock(nn.Module):
                feat_idx[0] += 1
            else:
                x = layer(x)
-        return x + h
+        return x + h, feat_cache, feat_idx


 class AttentionBlock(nn.Module):
@@ -469,9 +469,9 @@ class Down_ResidualBlock(nn.Module):
    def forward(self, x, feat_cache=None, feat_idx=[0]):
        x_copy = x.clone()
        for module in self.downsamples:
-            x = module(x, feat_cache, feat_idx)
+            x, feat_cache, feat_idx = module(x, feat_cache, feat_idx)

-        return x + self.avg_shortcut(x_copy)
+        return x + self.avg_shortcut(x_copy), feat_cache, feat_idx


 class Up_ResidualBlock(nn.Module):
@@ -506,12 +506,12 @@ class Up_ResidualBlock(nn.Module):
    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
        x_main = x.clone()
        for module in self.upsamples:
-            x_main = module(x_main, feat_cache, feat_idx)
+            x_main, feat_cache, feat_idx = module(x_main, feat_cache, feat_idx)
        if self.avg_shortcut is not None:
            x_shortcut = self.avg_shortcut(x, first_chunk)
-            return x_main + x_shortcut
+            return x_main + x_shortcut, feat_cache, feat_idx
        else:
-            return x_main
+            return x_main, feat_cache, feat_idx


 class Encoder3d(nn.Module):
@@ -586,14 +586,14 @@ class Encoder3d(nn.Module):
        ## downsamples
        for layer in self.downsamples:
            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## middle
        for layer in self.middle:
            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

@@ -614,7 +614,7 @@ class Encoder3d(nn.Module):
                feat_idx[0] += 1
            else:
                x = layer(x)
-        return x
+        return x, feat_cache, feat_idx


 class Encoder3d_38(nn.Module):
@@ -698,14 +698,14 @@ class Encoder3d_38(nn.Module):
        ## downsamples
        for layer in self.downsamples:
            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## middle
        for layer in self.middle:
            if isinstance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

@@ -730,7 +730,7 @@ class Encoder3d_38(nn.Module):
            else:
                x = layer(x)

-        return x
+        return x, feat_cache, feat_idx


 class Decoder3d(nn.Module):
@@ -807,14 +807,14 @@ class Decoder3d(nn.Module):
        ## middle
        for layer in self.middle:
            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## upsamples
        for layer in self.upsamples:
            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

@@ -835,7 +835,7 @@ class Decoder3d(nn.Module):
                feat_idx[0] += 1
            else:
                x = layer(x)
-        return x
+        return x, feat_cache, feat_idx



@@ -906,14 +906,14 @@ class Decoder3d_38(nn.Module):

        for layer in self.middle:
            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## upsamples
        for layer in self.upsamples:
            if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx, first_chunk)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx, first_chunk)
            else:
                x = layer(x)

@@ -937,7 +937,7 @@ class Decoder3d_38(nn.Module):
                feat_idx[0] += 1
            else:
                x = layer(x)
-        return x
+        return x, feat_cache, feat_idx


 def count_conv3d(model):
@@ -990,11 +990,11 @@ class VideoVAE_(nn.Module):
        for i in range(iter_):
            self._enc_conv_idx = [0]
            if i == 0:
-                out = self.encoder(x[:, :, :1, :, :],
+                out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
                                   feat_cache=self._enc_feat_map,
                                   feat_idx=self._enc_conv_idx)
            else:
-                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
                                    feat_cache=self._enc_feat_map,
                                    feat_idx=self._enc_conv_idx)
                out = torch.cat([out, out_], 2)
@@ -1023,11 +1023,11 @@ class VideoVAE_(nn.Module):
        for i in range(iter_):
            self._conv_idx = [0]
            if i == 0:
-                out = self.decoder(x[:, :, i:i + 1, :, :],
+                out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                   feat_cache=self._feat_map,
                                   feat_idx=self._conv_idx)
            else:
-                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                    feat_cache=self._feat_map,
                                    feat_idx=self._conv_idx)
                out = torch.cat([out, out_], 2) # may add tensor offload
@@ -1303,11 +1303,11 @@ class VideoVAE38_(VideoVAE_):
        for i in range(iter_):
            self._enc_conv_idx = [0]
            if i == 0:
-                out = self.encoder(x[:, :, :1, :, :],
+                out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
                                   feat_cache=self._enc_feat_map,
                                   feat_idx=self._enc_conv_idx)
            else:
-                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
                                    feat_cache=self._enc_feat_map,
                                    feat_idx=self._enc_conv_idx)
                out = torch.cat([out, out_], 2)
@@ -1337,12 +1337,12 @@ class VideoVAE38_(VideoVAE_):
        for i in range(iter_):
            self._conv_idx = [0]
            if i == 0:
-                out = self.decoder(x[:, :, i:i + 1, :, :],
+                out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                   feat_cache=self._feat_map,
                                   feat_idx=self._conv_idx,
                                   first_chunk=True)
            else:
-                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                    feat_cache=self._feat_map,
                                    feat_idx=self._conv_idx)
                out = torch.cat([out, out_], 2)
--- a/diffsynth/models/z_image_dit.py
+++ b/diffsynth/models/z_image_dit.py
@@ -88,6 +88,14 @@ class Attention(torch.nn.Module):
        self.norm_q = RMSNorm(head_dim, eps=1e-5)
        self.norm_k = RMSNorm(head_dim, eps=1e-5)
    
+    # Apply RoPE
+    def apply_rotary_emb(self, x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+        with torch.amp.autocast(get_device_type(), enabled=False):
+            x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
+            freqs_cis = freqs_cis.unsqueeze(2)
+            x_out = torch.view_as_real(x * freqs_cis).flatten(3)
+            return x_out.type_as(x_in)  # todo
+
    def forward(self, hidden_states, freqs_cis, attention_mask):
        query = self.to_q(hidden_states)
        key = self.to_k(hidden_states)
@@ -103,17 +111,9 @@ class Attention(torch.nn.Module):
        if self.norm_k is not None:
            key = self.norm_k(key)

-        # Apply RoPE
-        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-            with torch.amp.autocast(get_device_type(), enabled=False):
-                x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
-                freqs_cis = freqs_cis.unsqueeze(2)
-                x_out = torch.view_as_real(x * freqs_cis).flatten(3)
-                return x_out.type_as(x_in)  # todo
-
        if freqs_cis is not None:
-            query = apply_rotary_emb(query, freqs_cis)
-            key = apply_rotary_emb(key, freqs_cis)
+            query = self.apply_rotary_emb(query, freqs_cis)
+            key = self.apply_rotary_emb(key, freqs_cis)

        # Cast to correct dtype
        dtype = query.dtype
--- a/diffsynth/models/z_image_text_encoder.py
+++ b/diffsynth/models/z_image_text_encoder.py
@@ -6,6 +6,36 @@ class ZImageTextEncoder(torch.nn.Module):
    def __init__(self, model_size="4B"):
        super().__init__()
        config_dict = {
+            "0.6B": Qwen3Config(**{
+                "architectures": [
+                    "Qwen3ForCausalLM"
+                ],
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 1024,
+                "initializer_range": 0.02,
+                "intermediate_size": 3072,
+                "max_position_embeddings": 40960,
+                "max_window_layers": 28,
+                "model_type": "qwen3",
+                "num_attention_heads": 16,
+                "num_hidden_layers": 28,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": None,
+                "rope_theta": 1000000,
+                "sliding_window": None,
+                "tie_word_embeddings": True,
+                "torch_dtype": "bfloat16",
+                "transformers_version": "4.51.0",
+                "use_cache": True,
+                "use_sliding_window": False,
+                "vocab_size": 151936
+            }),
            "4B": Qwen3Config(**{
                "architectures": [
                    "Qwen3ForCausalLM"
--- a/diffsynth/pipelines/anima_image.py
+++ b/diffsynth/pipelines/anima_image.py
@@ -0,0 +1,263 @@
+import torch, math
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange
+import numpy as np
+from math import prod
+from transformers import AutoTokenizer
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig, gradient_checkpoint_forward
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora.merge import merge_lora
+
+from ..models.anima_dit import AnimaDiT
+from ..models.z_image_text_encoder import ZImageTextEncoder
+from ..models.wan_video_vae import WanVideoVAE
+
+
+class AnimaImagePipeline(BasePipeline):
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler("Z-Image")
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: AnimaDiT = None
+        self.vae: WanVideoVAE = None
+        self.tokenizer: AutoTokenizer = None
+        self.tokenizer_t5xxl: AutoTokenizer = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            AnimaUnit_ShapeChecker(),
+            AnimaUnit_NoiseInitializer(),
+            AnimaUnit_InputImageEmbedder(),
+            AnimaUnit_PromptEmbedder(),
+        ]
+        self.model_fn = model_fn_anima
+    
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+        tokenizer_t5xxl_config: ModelConfig = ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = AnimaImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("anima_dit")
+        pipe.vae = model_pool.fetch_model("wan_video_vae")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        if tokenizer_t5xxl_config is not None:
+            tokenizer_t5xxl_config.download_if_necessary()
+            pipe.tokenizer_t5xxl = AutoTokenizer.from_pretrained(tokenizer_t5xxl_config.path)
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 4.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 30,
+        sigma_shift: float = None,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+        
+        # Parameters
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae.decode(inputs_shared["latents"].unsqueeze(2), device=self.device).squeeze(2)
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class AnimaUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width"),
+            output_params=("height", "width"),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width):
+        height, width = pipe.check_resize_height_width(height, width)
+        return {"height": height, "width": width}
+
+
+
+class AnimaUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+
+class AnimaUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: AnimaImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        if isinstance(input_image, list):
+            input_latents = []
+            for image in input_image:
+                image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
+                input_latents.append(pipe.vae.encode(image))
+            input_latents = torch.concat(input_latents, dim=0)
+        else:
+            image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+            input_latents = pipe.vae.encode(image.unsqueeze(2), device=pipe.device).squeeze(2)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+class AnimaUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_emb",),
+            onload_model_names=("text_encoder",)
+        )
+
+    def encode_prompt(
+        self,
+        pipe: AnimaImagePipeline,
+        prompt,
+        device = None,
+        max_sequence_length: int = 512,
+    ):
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        text_inputs = pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = pipe.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-1]
+        
+        t5xxl_text_inputs = pipe.tokenizer_t5xxl(
+            prompt,
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        t5xxl_ids = t5xxl_text_inputs.input_ids.to(device)
+
+        return prompt_embeds.to(pipe.torch_dtype), t5xxl_ids
+
+    def process(self, pipe: AnimaImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, t5xxl_ids = self.encode_prompt(pipe, prompt, pipe.device)
+        return {"prompt_emb": prompt_embeds, "t5xxl_ids": t5xxl_ids}
+
+
+def model_fn_anima(
+    dit: AnimaDiT = None,
+    latents=None,
+    timestep=None,
+    prompt_emb=None,
+    t5xxl_ids=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs
+):
+    latents = latents.unsqueeze(2)
+    timestep = timestep / 1000
+    model_output = dit(
+        x=latents,
+        timesteps=timestep,
+        context=prompt_emb,
+        t5xxl_ids=t5xxl_ids,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    model_output = model_output.squeeze(2)
+    return model_output
--- a/diffsynth/pipelines/flux2_image.py
+++ b/diffsynth/pipelines/flux2_image.py
@@ -90,6 +90,7 @@ class Flux2ImagePipeline(BasePipeline):
        # Randomness
        seed: int = None,
        rand_device: str = "cpu",
+        initial_noise: torch.Tensor = None,
        # Steps
        num_inference_steps: int = 30,
        # Progress bar
@@ -109,7 +110,7 @@ class Flux2ImagePipeline(BasePipeline):
            "input_image": input_image, "denoising_strength": denoising_strength,
            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize,
            "height": height, "width": width,
-            "seed": seed, "rand_device": rand_device,
+            "seed": seed, "rand_device": rand_device, "initial_noise": initial_noise,
            "num_inference_steps": num_inference_steps,
        }
        for unit in self.units:
@@ -348,13 +349,12 @@ class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
        attention_mask = torch.cat(all_attention_masks, dim=0).to(device)

        # Forward pass through the model
-        with torch.inference_mode():
-            output = text_encoder(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                output_hidden_states=True,
-                use_cache=False,
-            )
+        output = text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            use_cache=False,
+        )

        # Only use outputs from intermediate layers and stack them
        out = torch.stack([output.hidden_states[k] for k in self.hidden_states_layers], dim=1)
@@ -430,12 +430,15 @@ class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
 class Flux2Unit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(
-            input_params=("height", "width", "seed", "rand_device"),
+            input_params=("height", "width", "seed", "rand_device", "initial_noise"),
            output_params=("noise",),
        )

-    def process(self, pipe: Flux2ImagePipeline, height, width, seed, rand_device):
-        noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+    def process(self, pipe: Flux2ImagePipeline, height, width, seed, rand_device, initial_noise):
+        if initial_noise is not None:
+            noise = initial_noise.clone()
+        else:
+            noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
        noise = noise.reshape(1, 128, height//16 * width//16).permute(0, 2, 1)
        return {"noise": noise}

--- a/diffsynth/pipelines/ltx2_audio_video.py
+++ b/diffsynth/pipelines/ltx2_audio_video.py
@@ -0,0 +1,730 @@
+import torch, types
+import numpy as np
+from PIL import Image
+from einops import repeat
+from typing import Optional, Union
+from einops import rearrange
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from typing import Optional
+from transformers import AutoImageProcessor, Gemma3Processor
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
+
+from ..models.ltx2_text_encoder import LTX2TextEncoder, LTX2TextEncoderPostModules, LTXVGemmaTokenizer
+from ..models.ltx2_dit import LTXModel
+from ..models.ltx2_video_vae import LTX2VideoEncoder, LTX2VideoDecoder, VideoLatentPatchifier
+from ..models.ltx2_audio_vae import LTX2AudioEncoder, LTX2AudioDecoder, LTX2Vocoder, AudioPatchifier, AudioProcessor
+from ..models.ltx2_upsampler import LTX2LatentUpsampler
+from ..models.ltx2_common import VideoLatentShape, AudioLatentShape, VideoPixelShape, get_pixel_coords, VIDEO_SCALE_FACTORS
+from ..utils.data.media_io_ltx2 import ltx2_preprocess
+from ..utils.data.audio import convert_to_stereo
+
+
+class LTX2AudioVideoPipeline(BasePipeline):
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device,
+            torch_dtype=torch_dtype,
+            height_division_factor=32,
+            width_division_factor=32,
+            time_division_factor=8,
+            time_division_remainder=1,
+        )
+        self.scheduler = FlowMatchScheduler("LTX-2")
+        self.text_encoder: LTX2TextEncoder = None
+        self.tokenizer: LTXVGemmaTokenizer = None
+        self.processor: Gemma3Processor = None
+        self.text_encoder_post_modules: LTX2TextEncoderPostModules = None
+        self.dit: LTXModel = None
+        self.video_vae_encoder: LTX2VideoEncoder = None
+        self.video_vae_decoder: LTX2VideoDecoder = None
+        self.audio_vae_encoder: LTX2AudioEncoder = None
+        self.audio_vae_decoder: LTX2AudioDecoder = None
+        self.audio_vocoder: LTX2Vocoder = None
+        self.upsampler: LTX2LatentUpsampler = None
+
+        self.video_patchifier: VideoLatentPatchifier = VideoLatentPatchifier(patch_size=1)
+        self.audio_patchifier: AudioPatchifier = AudioPatchifier(patch_size=1)
+        self.audio_processor: AudioProcessor = AudioProcessor()
+
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            LTX2AudioVideoUnit_PipelineChecker(),
+            LTX2AudioVideoUnit_ShapeChecker(),
+            LTX2AudioVideoUnit_PromptEmbedder(),
+            LTX2AudioVideoUnit_NoiseInitializer(),
+            LTX2AudioVideoUnit_VideoRetakeEmbedder(),
+            LTX2AudioVideoUnit_AudioRetakeEmbedder(),
+            LTX2AudioVideoUnit_InputAudioEmbedder(),
+            LTX2AudioVideoUnit_InputVideoEmbedder(),
+            LTX2AudioVideoUnit_InputImagesEmbedder(),
+            LTX2AudioVideoUnit_InContextVideoEmbedder(),
+        ]
+        self.stage2_units = [
+            LTX2AudioVideoUnit_SwitchStage2(),
+            LTX2AudioVideoUnit_NoiseInitializer(),
+            LTX2AudioVideoUnit_LatentsUpsampler(),
+            LTX2AudioVideoUnit_VideoRetakeEmbedder(),
+            LTX2AudioVideoUnit_AudioRetakeEmbedder(),
+            LTX2AudioVideoUnit_InputImagesEmbedder(),
+            LTX2AudioVideoUnit_SetScheduleStage2(),
+        ]
+        self.model_fn = model_fn_ltx2
+
+        self.default_negative_prompt = {
+            "LTX-2": (
+                "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."
+            ),
+            "LTX-2.3": (
+                "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."
+            ),
+        }
+
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="google/gemma-3-12b-it-qat-q4_0-unquantized"),
+        stage2_lora_config: Optional[ModelConfig] = None,
+        stage2_lora_strength: float = 0.8,
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = LTX2AudioVideoPipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("ltx2_text_encoder")
+        tokenizer_config.download_if_necessary()
+        pipe.tokenizer = LTXVGemmaTokenizer(tokenizer_path=tokenizer_config.path)
+        image_processor = AutoImageProcessor.from_pretrained(tokenizer_config.path, local_files_only=True)
+        pipe.processor = Gemma3Processor(image_processor=image_processor, tokenizer=pipe.tokenizer.tokenizer)
+
+        pipe.text_encoder_post_modules = model_pool.fetch_model("ltx2_text_encoder_post_modules")
+        pipe.dit = model_pool.fetch_model("ltx2_dit")
+        pipe.video_vae_encoder = model_pool.fetch_model("ltx2_video_vae_encoder")
+        pipe.video_vae_decoder = model_pool.fetch_model("ltx2_video_vae_decoder")
+        pipe.audio_vae_decoder = model_pool.fetch_model("ltx2_audio_vae_decoder")
+        pipe.audio_vocoder = model_pool.fetch_model("ltx2_audio_vocoder")
+        pipe.upsampler = model_pool.fetch_model("ltx2_latent_upsampler")
+        pipe.audio_vae_encoder = model_pool.fetch_model("ltx2_audio_vae_encoder")
+
+        # Stage 2
+        if stage2_lora_config is not None:
+            pipe.stage2_lora_config = stage2_lora_config
+            pipe.stage2_lora_strength = stage2_lora_strength
+
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+
+    def denoise_stage(self, inputs_shared, inputs_posi, inputs_nega, units, cfg_scale=1.0, progress_bar_cmd=tqdm, skip_stage=False):
+        if skip_stage:
+            return inputs_shared, inputs_posi, inputs_nega
+        for unit in units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred_video, noise_pred_audio = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale, inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["video_latents"] = self.step(self.scheduler, inputs_shared["video_latents"], progress_id=progress_id, noise_pred=noise_pred_video,
+                                                       inpaint_mask=inputs_shared.get("denoise_mask_video", None), input_latents=inputs_shared.get("input_latents_video", None), **inputs_shared)
+            inputs_shared["audio_latents"] = self.step(self.scheduler, inputs_shared["audio_latents"], progress_id=progress_id, noise_pred=noise_pred_audio,
+                                                       inpaint_mask=inputs_shared.get("denoise_mask_audio", None), input_latents=inputs_shared.get("input_latents_audio", None), **inputs_shared)
+        return inputs_shared, inputs_posi, inputs_nega
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: Optional[str] = "",
+        denoising_strength: float = 1.0,
+        # Image-to-video
+        input_images: Optional[list[Image.Image]] = None,
+        input_images_indexes: Optional[list[int]] = [0],
+        input_images_strength: Optional[float] = 1.0,
+        # In-Context Video Control
+        in_context_videos: Optional[list[list[Image.Image]]] = None,
+        in_context_downsample_factor: Optional[int] = 2,
+        # Video-to-video
+        retake_video: Optional[list[Image.Image]] = None,
+        retake_video_regions: Optional[list[tuple[float, float]]] = None,
+        # Audio-to-video
+        retake_audio: Optional[torch.Tensor] = None,
+        audio_sample_rate: Optional[int] = 48000,
+        retake_audio_regions: Optional[list[tuple[float, float]]] = None,
+        # Randomness
+        seed: Optional[int] = None,
+        rand_device: Optional[str] = "cpu",
+        # Shape
+        height: Optional[int] = 512,
+        width: Optional[int] = 768,
+        num_frames: Optional[int] = 121,
+        frame_rate: Optional[int] = 24,
+        # Classifier-free guidance
+        cfg_scale: Optional[float] = 3.0,
+        # Scheduler
+        num_inference_steps: Optional[int] = 30,
+        # VAE tiling
+        tiled: Optional[bool] = True,
+        tile_size_in_pixels: Optional[int] = 512,
+        tile_overlap_in_pixels: Optional[int] = 128,
+        tile_size_in_frames: Optional[int] = 128,
+        tile_overlap_in_frames: Optional[int] = 24,
+        # Special Pipelines
+        use_two_stage_pipeline: Optional[bool] = False,
+        stage2_spatial_upsample_factor: Optional[int] = 2,
+        clear_lora_before_state_two: Optional[bool] = False,
+        use_distilled_pipeline: Optional[bool] = False,
+        # progress_bar
+        progress_bar_cmd=tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, special_case="ditilled_stage1" if use_distilled_pipeline else None)
+        # Inputs
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "input_images": input_images, "input_images_indexes": input_images_indexes, "input_images_strength": input_images_strength,
+            "retake_video": retake_video, "retake_video_regions": retake_video_regions,
+            "retake_audio": (retake_audio, audio_sample_rate) if retake_audio is not None else None, "retake_audio_regions": retake_audio_regions,
+            "in_context_videos": in_context_videos, "in_context_downsample_factor": in_context_downsample_factor,
+            "seed": seed, "rand_device": rand_device,
+            "height": height, "width": width, "num_frames": num_frames, "frame_rate": frame_rate,
+            "cfg_scale": cfg_scale,
+            "tiled": tiled, "tile_size_in_pixels": tile_size_in_pixels, "tile_overlap_in_pixels": tile_overlap_in_pixels,
+            "tile_size_in_frames": tile_size_in_frames, "tile_overlap_in_frames": tile_overlap_in_frames,
+            "use_two_stage_pipeline": use_two_stage_pipeline, "use_distilled_pipeline": use_distilled_pipeline, "clear_lora_before_state_two": clear_lora_before_state_two, "stage2_spatial_upsample_factor": stage2_spatial_upsample_factor,
+            "video_patchifier": self.video_patchifier, "audio_patchifier": self.audio_patchifier,
+        }
+        # Stage 1
+        inputs_shared, inputs_posi, inputs_nega = self.denoise_stage(inputs_shared, inputs_posi, inputs_nega, self.units, cfg_scale, progress_bar_cmd)
+        # Stage 2
+        inputs_shared, inputs_posi, inputs_nega = self.denoise_stage(inputs_shared, inputs_posi, inputs_nega, self.stage2_units, 1.0, progress_bar_cmd, not inputs_shared["use_two_stage_pipeline"])
+        # Decode
+        self.load_models_to_device(['video_vae_decoder'])
+        video = self.video_vae_decoder.decode(inputs_shared["video_latents"], tiled, tile_size_in_pixels, tile_overlap_in_pixels, tile_size_in_frames, tile_overlap_in_frames)
+        video = self.vae_output_to_video(video)
+        self.load_models_to_device(['audio_vae_decoder', 'audio_vocoder'])
+        decoded_audio = self.audio_vae_decoder(inputs_shared["audio_latents"])
+        decoded_audio = self.audio_vocoder(decoded_audio)
+        decoded_audio = self.output_audio_format_check(decoded_audio)
+        return video, decoded_audio
+
+
+class LTX2AudioVideoUnit_PipelineChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            take_over=True,
+            input_params=("use_distilled_pipeline", "use_two_stage_pipeline"),
+            output_params=("use_two_stage_pipeline", "cfg_scale")
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, inputs_shared, inputs_posi, inputs_nega):
+        if inputs_shared.get("use_distilled_pipeline", False):
+            inputs_shared["use_two_stage_pipeline"] = True
+            inputs_shared["cfg_scale"] = 1.0
+            print(f"Distilled pipeline requested, setting use_two_stage_pipeline to True, disable CFG by setting cfg_scale to 1.0.")
+        if inputs_shared.get("use_two_stage_pipeline", False):
+            # distill pipeline also uses two-stage, but it does not needs lora
+            if not inputs_shared.get("use_distilled_pipeline", False):
+                if not (hasattr(pipe, "stage2_lora_config") and pipe.stage2_lora_config is not None):
+                    raise ValueError("Two-stage pipeline requested, but stage2_lora_config is not set in the pipeline.")
+            if not (hasattr(pipe, "upsampler") and pipe.upsampler is not None):
+                raise ValueError("Two-stage pipeline requested, but upsampler model is not loaded in the pipeline.")
+        return inputs_shared, inputs_posi, inputs_nega
+
+
+class LTX2AudioVideoUnit_ShapeChecker(PipelineUnit):
+    """
+    For two-stage pipelines, the resolution must be divisible by 64.
+    For one-stage pipelines, the resolution must be divisible by 32.
+    This unit set height and width to stage 1 resolution, and stage_2_width and stage_2_height.
+    """
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames", "use_two_stage_pipeline", "stage2_spatial_upsample_factor"),
+            output_params=("height", "width", "num_frames", "stage_2_height", "stage_2_width"),
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, height, width, num_frames, use_two_stage_pipeline=False, stage2_spatial_upsample_factor=2):
+        if use_two_stage_pipeline:
+            height, width = height // stage2_spatial_upsample_factor, width // stage2_spatial_upsample_factor
+            height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
+            stage_2_height, stage_2_width = int(height * stage2_spatial_upsample_factor), int(width * stage2_spatial_upsample_factor)
+        else:
+            stage_2_height, stage_2_width = None, None
+            height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
+        return {"height": height, "width": width, "num_frames": num_frames, "stage_2_height": stage_2_height, "stage_2_width": stage_2_width}
+
+
+class LTX2AudioVideoUnit_PromptEmbedder(PipelineUnit):
+
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("video_context", "audio_context"),
+            onload_model_names=("text_encoder", "text_encoder_post_modules"),
+        )
+    def _preprocess_text(
+        self,
+        pipe,
+        text: str,
+    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
+        token_pairs = pipe.tokenizer.tokenize_with_weights(text)["gemma"]
+        input_ids = torch.tensor([[t[0] for t in token_pairs]], device=pipe.device)
+        attention_mask = torch.tensor([[w[1] for w in token_pairs]], device=pipe.device)
+        outputs = pipe.text_encoder(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True)
+        return outputs.hidden_states, attention_mask
+    def encode_prompt(self, pipe, text, padding_side="left"):
+        hidden_states, attention_mask = self._preprocess_text(pipe, text)
+        video_encoding, audio_encoding, attention_mask = pipe.text_encoder_post_modules.process_hidden_states(
+            hidden_states, attention_mask, padding_side)
+        return video_encoding, audio_encoding, attention_mask
+
+    def process(self, pipe: LTX2AudioVideoPipeline, prompt: str):
+        pipe.load_models_to_device(self.onload_model_names)
+        video_context, audio_context, _ = self.encode_prompt(pipe, prompt)
+        return {"video_context": video_context, "audio_context": audio_context}
+
+
+class LTX2AudioVideoUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames", "seed", "rand_device", "frame_rate"),
+            output_params=("video_noise", "audio_noise", "video_positions", "audio_positions", "video_latent_shape", "audio_latent_shape")
+        )
+
+    def process_stage(self, pipe: LTX2AudioVideoPipeline, height, width, num_frames, seed, rand_device, frame_rate=24.0):
+        video_pixel_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
+        video_latent_shape = VideoLatentShape.from_pixel_shape(shape=video_pixel_shape, latent_channels=128)
+        video_noise = pipe.generate_noise(video_latent_shape.to_torch_shape(), seed=seed, rand_device=rand_device)
+
+        latent_coords = pipe.video_patchifier.get_patch_grid_bounds(output_shape=video_latent_shape, device=pipe.device)
+        video_positions = get_pixel_coords(latent_coords, VIDEO_SCALE_FACTORS, True).float()
+        video_positions[:, 0, ...] = video_positions[:, 0, ...] / frame_rate
+        video_positions = video_positions.to(pipe.torch_dtype)
+
+        audio_latent_shape = AudioLatentShape.from_video_pixel_shape(video_pixel_shape)
+        audio_noise = pipe.generate_noise(audio_latent_shape.to_torch_shape(), seed=seed, rand_device=rand_device)
+        audio_positions = pipe.audio_patchifier.get_patch_grid_bounds(audio_latent_shape, device=pipe.device)
+        return {
+            "video_noise": video_noise,
+            "audio_noise": audio_noise,
+            "video_positions": video_positions,
+            "audio_positions": audio_positions,
+            "video_latent_shape": video_latent_shape,
+            "audio_latent_shape": audio_latent_shape
+        }
+
+    def process(self, pipe: LTX2AudioVideoPipeline, height, width, num_frames, seed, rand_device, frame_rate=24.0):
+        return self.process_stage(pipe, height, width, num_frames, seed, rand_device, frame_rate)
+
+
+class LTX2AudioVideoUnit_InputVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_video", "video_noise", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels"),
+            output_params=("video_latents", "input_latents"),
+            onload_model_names=("video_vae_encoder")
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, input_video, video_noise, tiled, tile_size_in_pixels, tile_overlap_in_pixels):
+        if input_video is None or not pipe.scheduler.training:
+            return {"video_latents": video_noise}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            input_video = pipe.preprocess_video(input_video)
+            input_latents = pipe.video_vae_encoder.encode(input_video, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(dtype=pipe.torch_dtype, device=pipe.device)
+            return {"video_latents": input_latents, "input_latents": input_latents}
+
+class LTX2AudioVideoUnit_InputAudioEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_audio", "audio_noise"),
+            output_params=("audio_latents", "audio_input_latents", "audio_positions", "audio_latent_shape"),
+            onload_model_names=("audio_vae_encoder",)
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, input_audio, audio_noise):
+        if input_audio is None or not pipe.scheduler.training:
+            return {"audio_latents": audio_noise}
+        else:
+            input_audio, sample_rate = input_audio
+            input_audio = convert_to_stereo(input_audio)
+            pipe.load_models_to_device(self.onload_model_names)
+            input_audio = pipe.audio_processor.waveform_to_mel(input_audio.unsqueeze(0), waveform_sample_rate=sample_rate).to(dtype=pipe.torch_dtype)
+            audio_input_latents = pipe.audio_vae_encoder(input_audio)
+            audio_latent_shape = AudioLatentShape.from_torch_shape(audio_input_latents.shape)
+            audio_positions = pipe.audio_patchifier.get_patch_grid_bounds(audio_latent_shape, device=pipe.device)
+            return {"audio_latents": audio_input_latents, "audio_input_latents": audio_input_latents, "audio_positions": audio_positions, "audio_latent_shape": audio_latent_shape}
+
+
+class LTX2AudioVideoUnit_VideoRetakeEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("retake_video", "height", "width", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels", "video_positions", "retake_video_regions"),
+            output_params=("input_latents_video", "denoise_mask_video"),
+            onload_model_names=("video_vae_encoder")
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, retake_video, height, width, tiled, tile_size_in_pixels, tile_overlap_in_pixels, video_positions, retake_video_regions=None):
+        if retake_video is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        resized_video = [frame.resize((width, height)) for frame in retake_video]
+        input_video = pipe.preprocess_video(resized_video)
+        input_latents_video = pipe.video_vae_encoder.encode(input_video, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(dtype=pipe.torch_dtype, device=pipe.device)
+
+        b, c, f, h, w = input_latents_video.shape
+        denoise_mask_video = torch.zeros((b, 1, f, h, w), device=input_latents_video.device, dtype=input_latents_video.dtype)
+        if retake_video_regions is not None and len(retake_video_regions) > 0:
+            for start_time, end_time in retake_video_regions:
+                t_start, t_end = video_positions[0, 0].unbind(dim=-1)
+                in_region = (t_end >= start_time) & (t_start <= end_time)
+                in_region = pipe.video_patchifier.unpatchify_video(in_region.unsqueeze(0).unsqueeze(-1), f, h, w)
+                denoise_mask_video = torch.where(in_region, torch.ones_like(denoise_mask_video), denoise_mask_video)
+
+        return {"input_latents_video": input_latents_video, "denoise_mask_video": denoise_mask_video}
+
+
+class LTX2AudioVideoUnit_AudioRetakeEmbedder(PipelineUnit):
+    """
+    Functionality of audio2video, audio retaking.
+    """
+    def __init__(self):
+        super().__init__(
+            input_params=("retake_audio", "seed", "rand_device", "retake_audio_regions"),
+            output_params=("input_latents_audio", "audio_noise", "audio_positions", "audio_latent_shape", "denoise_mask_audio"),
+            onload_model_names=("audio_vae_encoder",)
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, retake_audio, seed, rand_device, retake_audio_regions=None):
+        if retake_audio is None:
+            return {}
+        else:
+            input_audio, sample_rate = retake_audio
+            input_audio = convert_to_stereo(input_audio)
+            pipe.load_models_to_device(self.onload_model_names)
+            input_audio = pipe.audio_processor.waveform_to_mel(input_audio.unsqueeze(0), waveform_sample_rate=sample_rate).to(dtype=pipe.torch_dtype, device=pipe.device)
+            input_latents_audio = pipe.audio_vae_encoder(input_audio)
+            audio_latent_shape = AudioLatentShape.from_torch_shape(input_latents_audio.shape)
+            audio_positions = pipe.audio_patchifier.get_patch_grid_bounds(audio_latent_shape, device=pipe.device)
+            # Regenerate noise for the new shape if retake_audio is provided, to avoid shape mismatch.
+            audio_noise = pipe.generate_noise(input_latents_audio.shape, seed=seed, rand_device=rand_device)
+
+            b, c, t, f = input_latents_audio.shape
+            denoise_mask_audio = torch.zeros((b, 1, t, 1), device=input_latents_audio.device, dtype=input_latents_audio.dtype)
+            if retake_audio_regions is not None and len(retake_audio_regions) > 0:
+                for start_time, end_time in retake_audio_regions:
+                    t_start, t_end = audio_positions[:, 0, :, 0], audio_positions[:, 0, :, 1]
+                    in_region = (t_end >= start_time) & (t_start <= end_time)
+                    in_region = pipe.audio_patchifier.unpatchify_audio(in_region.unsqueeze(-1), 1, 1)
+                    denoise_mask_audio = torch.where(in_region, torch.ones_like(denoise_mask_audio), denoise_mask_audio)
+
+            return {
+                "input_latents_audio": input_latents_audio,
+                "denoise_mask_audio": denoise_mask_audio,
+                "audio_noise": audio_noise,
+                "audio_positions": audio_positions,
+                "audio_latent_shape": audio_latent_shape,
+            }
+
+
+class LTX2AudioVideoUnit_InputImagesEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_images", "input_images_indexes", "input_images_strength", "video_latents", "height", "width", "frame_rate", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels", "input_latents_video", "denoise_mask_video"),
+            output_params=("denoise_mask_video", "input_latents_video", "ref_frames_latents", "ref_frames_positions"),
+            onload_model_names=("video_vae_encoder")
+        )
+
+    def get_image_latent(self, pipe, input_image, height, width, tiled, tile_size_in_pixels, tile_overlap_in_pixels):
+        image = ltx2_preprocess(np.array(input_image.resize((width, height))))
+        image = torch.Tensor(np.array(image, dtype=np.float32)).to(dtype=pipe.torch_dtype, device=pipe.device)
+        image = image / 127.5 - 1.0
+        image = repeat(image, f"H W C -> B C F H W", B=1, F=1)
+        latents = pipe.video_vae_encoder.encode(image, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(pipe.device)
+        return latents
+
+    def apply_input_images_to_latents(self, latents, input_latents, input_indexes, input_strength=1.0, input_latents_video=None, denoise_mask_video=None):
+        b, _, f, h, w = latents.shape
+        denoise_mask = torch.ones((b, 1, f, h, w), dtype=latents.dtype, device=latents.device) if denoise_mask_video is None else denoise_mask_video
+        input_latents_video = torch.zeros_like(latents) if input_latents_video is None else input_latents_video
+        for idx, input_latent in zip(input_indexes, input_latents):
+            idx = min(max(1 + (idx-1) // 8, 0), f - 1)
+            input_latent = input_latent.to(dtype=latents.dtype, device=latents.device)
+            input_latents_video[:, :, idx:idx + input_latent.shape[2], :, :] = input_latent
+            denoise_mask[:, :, idx:idx + input_latent.shape[2], :, :] = 1.0 - input_strength
+        return input_latents_video, denoise_mask
+
+    def process(
+        self,
+        pipe: LTX2AudioVideoPipeline,
+        video_latents,
+        input_images,
+        height,
+        width,
+        frame_rate,
+        tiled,
+        tile_size_in_pixels,
+        tile_overlap_in_pixels,
+        input_images_indexes=[0],
+        input_images_strength=1.0,
+        input_latents_video=None,
+        denoise_mask_video=None,
+    ):
+        if input_images is None or len(input_images) == 0:
+            return {}
+        else:
+            if len(input_images_indexes) != len(set(input_images_indexes)):
+                raise ValueError("Input images must have unique indexes.")
+            pipe.load_models_to_device(self.onload_model_names)
+            frame_conditions = {"input_latents_video": None, "denoise_mask_video": None, "ref_frames_latents": [], "ref_frames_positions": []}
+            for img, index in zip(input_images, input_images_indexes):
+                latents = self.get_image_latent(pipe, img, height, width, tiled, tile_size_in_pixels, tile_overlap_in_pixels)
+                # first_frame by replacing latents
+                if index == 0:
+                    input_latents_video, denoise_mask_video = self.apply_input_images_to_latents(
+                        video_latents, [latents], [0], input_images_strength, input_latents_video, denoise_mask_video)
+                    frame_conditions.update({"input_latents_video": input_latents_video, "denoise_mask_video": denoise_mask_video})
+                # other frames by adding reference latents
+                else:
+                    latent_coords = pipe.video_patchifier.get_patch_grid_bounds(output_shape=VideoLatentShape.from_torch_shape(latents.shape), device=pipe.device)
+                    video_positions = get_pixel_coords(latent_coords, VIDEO_SCALE_FACTORS, False).float()
+                    video_positions[:, 0, ...] = (video_positions[:, 0, ...] + index) / frame_rate
+                    video_positions = video_positions.to(pipe.torch_dtype)
+                    frame_conditions["ref_frames_latents"].append(latents)
+                    frame_conditions["ref_frames_positions"].append(video_positions)
+            if len(frame_conditions["ref_frames_latents"]) == 0:
+                frame_conditions.update({"ref_frames_latents": None, "ref_frames_positions": None})
+            return frame_conditions
+
+
+class LTX2AudioVideoUnit_InContextVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("in_context_videos", "height", "width", "num_frames", "frame_rate", "in_context_downsample_factor", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels"),
+            output_params=("in_context_video_latents", "in_context_video_positions"),
+            onload_model_names=("video_vae_encoder")
+        )
+
+    def check_in_context_video(self, pipe, in_context_video, height, width, num_frames, in_context_downsample_factor):
+        if in_context_video is None or len(in_context_video) == 0:
+            raise ValueError("In-context video is None or empty.")
+        in_context_video = in_context_video[:num_frames]
+        expected_height = height // in_context_downsample_factor
+        expected_width = width // in_context_downsample_factor
+        current_h, current_w, current_f = in_context_video[0].size[1], in_context_video[0].size[0], len(in_context_video)
+        h, w, f = pipe.check_resize_height_width(expected_height, expected_width, current_f, verbose=0)
+        if current_h != h or current_w != w:
+            in_context_video = [img.resize((w, h)) for img in in_context_video]
+        if current_f != f:
+            # pad black frames at the end
+            in_context_video = in_context_video + [Image.new("RGB", (w, h), (0, 0, 0))] * (f - current_f)
+        return in_context_video
+
+    def process(self, pipe: LTX2AudioVideoPipeline, in_context_videos, height, width, num_frames, frame_rate, in_context_downsample_factor, tiled, tile_size_in_pixels, tile_overlap_in_pixels):
+        if in_context_videos is None or len(in_context_videos) == 0:
+            return {}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            latents, positions = [], []
+            for in_context_video in in_context_videos:
+                in_context_video = self.check_in_context_video(pipe, in_context_video, height, width, num_frames, in_context_downsample_factor)
+                in_context_video = pipe.preprocess_video(in_context_video)
+                in_context_latents = pipe.video_vae_encoder.encode(in_context_video, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(dtype=pipe.torch_dtype, device=pipe.device)
+
+                latent_coords = pipe.video_patchifier.get_patch_grid_bounds(output_shape=VideoLatentShape.from_torch_shape(in_context_latents.shape), device=pipe.device)
+                video_positions = get_pixel_coords(latent_coords, VIDEO_SCALE_FACTORS, True).float()
+                video_positions[:, 0, ...] = video_positions[:, 0, ...] / frame_rate
+                video_positions[:, 1, ...] *= in_context_downsample_factor  # height axis
+                video_positions[:, 2, ...] *= in_context_downsample_factor  # width axis
+                video_positions = video_positions.to(pipe.torch_dtype)
+
+                latents.append(in_context_latents)
+                positions.append(video_positions)
+            latents = torch.cat(latents, dim=1)
+            positions = torch.cat(positions, dim=1)
+            return {"in_context_video_latents": latents, "in_context_video_positions": positions}
+
+
+class LTX2AudioVideoUnit_SwitchStage2(PipelineUnit):
+    """
+    1. switch height and width to stage 2 resolution
+    2. clear in_context_video_latents and in_context_video_positions
+    3. switch stage 2 lora model
+    """
+    def __init__(self):
+        super().__init__(
+            input_params=("stage_2_height", "stage_2_width", "clear_lora_before_state_two", "use_distilled_pipeline"),
+            output_params=("height", "width", "in_context_video_latents", "in_context_video_positions"),
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, stage_2_height, stage_2_width, clear_lora_before_state_two, use_distilled_pipeline):
+        stage2_params = {}
+        stage2_params.update({"height": stage_2_height, "width": stage_2_width})
+        stage2_params.update({"in_context_video_latents": None, "in_context_video_positions": None})
+        stage2_params.update({"input_latents_video": None, "denoise_mask_video": None})
+        if clear_lora_before_state_two:
+            pipe.clear_lora()
+        if not use_distilled_pipeline:
+            pipe.load_lora(pipe.dit, pipe.stage2_lora_config, alpha=pipe.stage2_lora_strength, state_dict=pipe.stage2_lora_config.state_dict)
+        return stage2_params
+
+
+class LTX2AudioVideoUnit_SetScheduleStage2(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("video_latents", "video_noise", "audio_latents", "audio_noise"),
+            output_params=("video_latents", "audio_latents"),
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, video_latents, video_noise, audio_latents, audio_noise):
+        pipe.scheduler.set_timesteps(special_case="stage2")
+        video_latents = pipe.scheduler.add_noise(video_latents, video_noise, pipe.scheduler.timesteps[0])
+        audio_latents = pipe.scheduler.add_noise(audio_latents, audio_noise, pipe.scheduler.timesteps[0])
+        return {"video_latents": video_latents, "audio_latents": audio_latents}
+
+
+class LTX2AudioVideoUnit_LatentsUpsampler(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("video_latents",),
+            output_params=("video_latents",),
+            onload_model_names=("upsampler",),
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, video_latents):
+        if video_latents is None or pipe.upsampler is None:
+            raise ValueError("No upsampler or no video latents before stage 2.")
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            video_latents = pipe.video_vae_encoder.per_channel_statistics.un_normalize(video_latents)
+            video_latents = pipe.upsampler(video_latents)
+            video_latents = pipe.video_vae_encoder.per_channel_statistics.normalize(video_latents)
+            return {"video_latents": video_latents}
+
+
+def model_fn_ltx2(
+    dit: LTXModel,
+    video_latents=None,
+    video_context=None,
+    video_positions=None,
+    video_patchifier=None,
+    audio_latents=None,
+    audio_context=None,
+    audio_positions=None,
+    audio_patchifier=None,
+    timestep=None,
+    # First Frame Conditioning
+    input_latents_video=None,
+    denoise_mask_video=None,
+    # Other Frames Conditioning
+    ref_frames_latents=None,
+    ref_frames_positions=None,
+    # In-Context Conditioning
+    in_context_video_latents=None,
+    in_context_video_positions=None,
+    # Audio Inputs
+    input_latents_audio=None,
+    denoise_mask_audio=None,
+    # Gradient Checkpointing
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    timestep = timestep.float() / 1000.
+
+    # patchify
+    b, c_v, f, h, w = video_latents.shape
+    video_latents = video_patchifier.patchify(video_latents)
+    seq_len_video = video_latents.shape[1]
+    video_timesteps = timestep.repeat(1, video_latents.shape[1], 1)
+    # Frist frame conditioning by replacing the video latents
+    if input_latents_video is not None:
+        denoise_mask_video = video_patchifier.patchify(denoise_mask_video)
+        video_latents = video_latents * denoise_mask_video + video_patchifier.patchify(input_latents_video) * (1.0 - denoise_mask_video)
+        video_timesteps = denoise_mask_video * video_timesteps
+
+    # Reference conditioning by appending the reference video or frame latents
+    total_ref_latents = ref_frames_latents if ref_frames_latents is not None else []
+    total_ref_positions = ref_frames_positions if ref_frames_positions is not None else []
+    total_ref_latents += [in_context_video_latents] if in_context_video_latents is not None else []
+    total_ref_positions += [in_context_video_positions] if in_context_video_positions is not None else []
+    if len(total_ref_latents) > 0:
+        for ref_frames_latent, ref_frames_position in zip(total_ref_latents, total_ref_positions):
+            ref_frames_latent = video_patchifier.patchify(ref_frames_latent)
+            ref_frames_timestep = timestep.repeat(1, ref_frames_latent.shape[1], 1) * 0.
+            video_latents = torch.cat([video_latents, ref_frames_latent], dim=1)
+            video_positions = torch.cat([video_positions, ref_frames_position], dim=2)
+            video_timesteps = torch.cat([video_timesteps, ref_frames_timestep], dim=1)
+
+    if audio_latents is not None:
+        _, c_a, _, mel_bins  = audio_latents.shape
+        audio_latents = audio_patchifier.patchify(audio_latents)
+        audio_timesteps = timestep.repeat(1, audio_latents.shape[1], 1)
+    else:
+        audio_timesteps = None
+    if input_latents_audio is not None:
+        denoise_mask_audio = audio_patchifier.patchify(denoise_mask_audio)
+        audio_latents = audio_latents * denoise_mask_audio + audio_patchifier.patchify(input_latents_audio) * (1.0 - denoise_mask_audio)
+        audio_timesteps = denoise_mask_audio * audio_timesteps
+
+    vx, ax = dit(
+        video_latents=video_latents,
+        video_positions=video_positions,
+        video_context=video_context,
+        video_timesteps=video_timesteps,
+        audio_latents=audio_latents,
+        audio_positions=audio_positions,
+        audio_context=audio_context,
+        audio_timesteps=audio_timesteps,
+        sigma=timestep,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+
+    vx = vx[:, :seq_len_video, ...]
+    # unpatchify
+    vx = video_patchifier.unpatchify_video(vx, f, h, w)
+    ax = audio_patchifier.unpatchify_audio(ax, c_a, mel_bins) if ax is not None else None
+    return vx, ax
--- a/diffsynth/pipelines/mova_audio_video.py
+++ b/diffsynth/pipelines/mova_audio_video.py
@@ -0,0 +1,460 @@
+import sys
+import torch, types
+from PIL import Image
+from typing import Optional, Union
+from einops import rearrange
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from typing import Optional
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig, gradient_checkpoint_forward
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
+
+from ..models.wan_video_dit import WanModel, sinusoidal_embedding_1d, set_to_torch_norm
+from ..models.wan_video_text_encoder import WanTextEncoder, HuggingfaceTokenizer
+from ..models.wan_video_vae import WanVideoVAE
+from ..models.mova_audio_dit import MovaAudioDit
+from ..models.mova_audio_vae import DacVAE
+from ..models.mova_dual_tower_bridge import DualTowerConditionalBridge
+from ..utils.data.audio import convert_to_mono, resample_waveform
+
+
+class MovaAudioVideoPipeline(BasePipeline):
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1
+        )
+        self.scheduler = FlowMatchScheduler("Wan")
+        self.tokenizer: HuggingfaceTokenizer = None
+        self.text_encoder: WanTextEncoder = None
+        self.video_dit: WanModel = None # high noise model
+        self.video_dit2: WanModel = None # low noise model
+        self.audio_dit: MovaAudioDit = None
+        self.dual_tower_bridge: DualTowerConditionalBridge = None
+        self.video_vae: WanVideoVAE = None
+        self.audio_vae: DacVAE = None
+
+        self.in_iteration_models = ("video_dit", "audio_dit", "dual_tower_bridge")
+        self.in_iteration_models_2 = ("video_dit2", "audio_dit", "dual_tower_bridge")
+
+        self.units = [
+            MovaAudioVideoUnit_ShapeChecker(),
+            MovaAudioVideoUnit_NoiseInitializer(),
+            MovaAudioVideoUnit_InputVideoEmbedder(),
+            MovaAudioVideoUnit_InputAudioEmbedder(),
+            MovaAudioVideoUnit_PromptEmbedder(),
+            MovaAudioVideoUnit_ImageEmbedderVAE(),
+            MovaAudioVideoUnit_UnifiedSequenceParallel(),
+        ]
+        self.model_fn = model_fn_mova_audio_video
+
+    def enable_usp(self):
+        from ..utils.xfuser import get_sequence_parallel_world_size, usp_attn_forward
+        for block in self.video_dit.blocks + self.audio_dit.blocks + self.video_dit2.blocks:
+            block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn)
+        self.sp_size = get_sequence_parallel_world_size()
+        self.use_unified_sequence_parallel = True
+
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="openmoss/MOVA-720p", origin_file_pattern="tokenizer/"),
+        use_usp: bool = False,
+        vram_limit: float = None,
+    ):
+        if use_usp:
+            from ..utils.xfuser import initialize_usp
+            initialize_usp(device)
+            import torch.distributed as dist
+            from ..core.device.npu_compatible_device import get_device_name
+            if dist.is_available() and dist.is_initialized():
+                device = get_device_name()
+        # Initialize pipeline
+        pipe = MovaAudioVideoPipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("wan_video_text_encoder")
+        dit = model_pool.fetch_model("wan_video_dit", index=2)
+        if isinstance(dit, list):
+            pipe.video_dit, pipe.video_dit2 = dit
+        else:
+            pipe.video_dit = dit
+        pipe.audio_dit = model_pool.fetch_model("mova_audio_dit")
+        pipe.dual_tower_bridge = model_pool.fetch_model("mova_dual_tower_bridge")
+        pipe.video_vae = model_pool.fetch_model("wan_video_vae")
+        pipe.audio_vae = model_pool.fetch_model("mova_audio_vae")
+        set_to_torch_norm([pipe.video_dit, pipe.audio_dit, pipe.dual_tower_bridge] + ([pipe.video_dit2] if pipe.video_dit2 is not None else []))
+
+        # Size division factor
+        if pipe.video_vae is not None:
+            pipe.height_division_factor = pipe.video_vae.upsampling_factor * 2
+            pipe.width_division_factor = pipe.video_vae.upsampling_factor * 2
+
+        # Initialize tokenizer and processor
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = HuggingfaceTokenizer(name=tokenizer_config.path, seq_len=512, clean='whitespace')
+
+        # Unified Sequence Parallel
+        if use_usp: pipe.enable_usp()
+
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: Optional[str] = "",
+        # Image-to-video
+        input_image: Optional[Image.Image] = None,
+        # First-last-frame-to-video
+        end_image: Optional[Image.Image] = None,
+        # Video-to-video
+        denoising_strength: Optional[float] = 1.0,
+        # Randomness
+        seed: Optional[int] = None,
+        rand_device: Optional[str] = "cpu",
+        # Shape
+        height: Optional[int] = 352,
+        width: Optional[int] = 640,
+        num_frames: Optional[int] = 81,
+        frame_rate: Optional[int] = 24,
+        # Classifier-free guidance
+        cfg_scale: Optional[float] = 5.0,
+        # Boundary
+        switch_DiT_boundary: Optional[float] = 0.9,
+        # Scheduler
+        num_inference_steps: Optional[int] = 50,
+        sigma_shift: Optional[float] = 5.0,
+        # VAE tiling
+        tiled: Optional[bool] = True,
+        tile_size: Optional[tuple[int, int]] = (30, 52),
+        tile_stride: Optional[tuple[int, int]] = (15, 26),
+        # progress_bar
+        progress_bar_cmd=tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+
+        # Inputs
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "input_image": input_image,
+            "end_image": end_image,
+            "denoising_strength": denoising_strength,
+            "seed": seed, "rand_device": rand_device,
+            "height": height, "width": width, "num_frames": num_frames, "frame_rate": frame_rate,
+            "cfg_scale": cfg_scale,
+            "sigma_shift": sigma_shift,
+            "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            # Switch DiT if necessary
+            if timestep.item() < switch_DiT_boundary * 1000 and self.video_dit2 is not None and not models["video_dit"] is self.video_dit2:
+                self.load_models_to_device(self.in_iteration_models_2)
+                models["video_dit"] = self.video_dit2
+            # Timestep
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred_video, noise_pred_audio = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale, inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            # Scheduler
+            inputs_shared["video_latents"] = self.step(self.scheduler, inputs_shared["video_latents"], progress_id=progress_id, noise_pred=noise_pred_video, **inputs_shared)
+            inputs_shared["audio_latents"] = self.step(self.scheduler, inputs_shared["audio_latents"], progress_id=progress_id, noise_pred=noise_pred_audio, **inputs_shared)
+
+        # Decode
+        self.load_models_to_device(['video_vae'])
+        video = self.video_vae.decode(inputs_shared["video_latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        video = self.vae_output_to_video(video)
+        self.load_models_to_device(["audio_vae"])
+        audio = self.audio_vae.decode(inputs_shared["audio_latents"])
+        audio = self.output_audio_format_check(audio)
+        self.load_models_to_device([])
+        return video, audio
+
+
+class MovaAudioVideoUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames"),
+            output_params=("height", "width", "num_frames"),
+        )
+
+    def process(self, pipe: MovaAudioVideoPipeline, height, width, num_frames):
+        height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
+        return {"height": height, "width": width, "num_frames": num_frames}
+
+
+class MovaAudioVideoUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames", "seed", "rand_device", "frame_rate"),
+            output_params=("video_noise", "audio_noise")
+        )
+
+    def process(self, pipe: MovaAudioVideoPipeline, height, width, num_frames, seed, rand_device, frame_rate):
+        length = (num_frames - 1) // 4 + 1
+        video_shape = (1, pipe.video_vae.model.z_dim, length, height // pipe.video_vae.upsampling_factor, width // pipe.video_vae.upsampling_factor)
+        video_noise = pipe.generate_noise(video_shape, seed=seed, rand_device=rand_device)
+
+        audio_num_samples = (int(pipe.audio_vae.sample_rate * num_frames / frame_rate) - 1) // int(pipe.audio_vae.hop_length) + 1
+        audio_shape = (1, pipe.audio_vae.latent_dim, audio_num_samples)
+        audio_noise = pipe.generate_noise(audio_shape, seed=seed, rand_device=rand_device)
+        return {"video_noise": video_noise, "audio_noise": audio_noise}
+
+
+class MovaAudioVideoUnit_InputVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_video", "video_noise", "tiled", "tile_size", "tile_stride"),
+            output_params=("video_latents", "input_latents"),
+            onload_model_names=("video_vae",)
+        )
+
+    def process(self, pipe: MovaAudioVideoPipeline, input_video, video_noise, tiled, tile_size, tile_stride):
+        if input_video is None or not pipe.scheduler.training:
+            return {"video_latents": video_noise}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            input_video = pipe.preprocess_video(input_video)
+            input_latents = pipe.video_vae.encode(input_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
+            return {"input_latents": input_latents}
+
+
+class MovaAudioVideoUnit_InputAudioEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_audio", "audio_noise"),
+            output_params=("audio_latents", "audio_input_latents"),
+            onload_model_names=("audio_vae",)
+        )
+
+    def process(self, pipe: MovaAudioVideoPipeline, input_audio, audio_noise):
+        if input_audio is None or not pipe.scheduler.training:
+            return {"audio_latents": audio_noise}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            input_audio, sample_rate = input_audio
+            input_audio = convert_to_mono(input_audio)
+            input_audio = resample_waveform(input_audio, sample_rate, pipe.audio_vae.sample_rate)
+            input_audio = pipe.audio_vae.preprocess(input_audio.unsqueeze(0), pipe.audio_vae.sample_rate)
+            z, _, _, _, _ = pipe.audio_vae.encode(input_audio)
+            return {"audio_input_latents": z.mode()}
+
+
+class MovaAudioVideoUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("context",),
+            onload_model_names=("text_encoder",)
+        )
+
+    def encode_prompt(self, pipe: MovaAudioVideoPipeline, prompt):
+        ids, mask = pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=512,
+            truncation=True,
+            add_special_tokens=True,
+            return_mask=True,
+            return_tensors="pt",
+        )
+        ids = ids.to(pipe.device)
+        mask = mask.to(pipe.device)
+        seq_lens = mask.gt(0).sum(dim=1).long()
+        prompt_emb = pipe.text_encoder(ids, mask)
+        for i, v in enumerate(seq_lens):
+            prompt_emb[:, v:] = 0
+        return prompt_emb
+
+    def process(self, pipe: MovaAudioVideoPipeline, prompt) -> dict:
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_emb = self.encode_prompt(pipe, prompt)
+        return {"context": prompt_emb}
+
+
+class MovaAudioVideoUnit_ImageEmbedderVAE(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"),
+            output_params=("y",),
+            onload_model_names=("video_vae",)
+        )
+
+    def process(self, pipe: MovaAudioVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride):
+        if input_image is None or not pipe.video_dit.require_vae_embedding:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+
+        image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
+        msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+        msk[:, 1:] = 0
+        if end_image is not None:
+            end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
+            vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1)
+            msk[:, -1:] = 1
+        else:
+            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
+
+        msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
+        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+        msk = msk.transpose(1, 2)[0]
+
+        y = pipe.video_vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+        y = torch.concat([msk, y])
+        y = y.unsqueeze(0)
+        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+        return {"y": y}
+
+
+class MovaAudioVideoUnit_UnifiedSequenceParallel(PipelineUnit):
+    def __init__(self):
+        super().__init__(input_params=(), output_params=("use_unified_sequence_parallel",))
+
+    def process(self, pipe: MovaAudioVideoPipeline):
+        if hasattr(pipe, "use_unified_sequence_parallel") and pipe.use_unified_sequence_parallel:
+            return {"use_unified_sequence_parallel": True}
+        return {"use_unified_sequence_parallel": False}
+
+
+def model_fn_mova_audio_video(
+    video_dit: WanModel,
+    audio_dit: MovaAudioDit,
+    dual_tower_bridge: DualTowerConditionalBridge,
+    video_latents: torch.Tensor = None,
+    audio_latents: torch.Tensor = None,
+    timestep: torch.Tensor = None,
+    context: torch.Tensor = None,
+    y: Optional[torch.Tensor] = None,
+    frame_rate: Optional[int] = 24,
+    use_unified_sequence_parallel: bool = False,
+    use_gradient_checkpointing: bool = False,
+    use_gradient_checkpointing_offload: bool = False,
+    **kwargs,
+):
+    video_x, audio_x = video_latents, audio_latents
+    # First-Last Frame
+    if y is not None:
+        video_x = torch.cat([video_x, y], dim=1)
+
+    # Timestep
+    video_t = video_dit.time_embedding(sinusoidal_embedding_1d(video_dit.freq_dim, timestep))
+    video_t_mod = video_dit.time_projection(video_t).unflatten(1, (6, video_dit.dim))
+    audio_t = audio_dit.time_embedding(sinusoidal_embedding_1d(audio_dit.freq_dim, timestep))
+    audio_t_mod = audio_dit.time_projection(audio_t).unflatten(1, (6, audio_dit.dim))
+
+    # Context
+    video_context = video_dit.text_embedding(context)
+    audio_context = audio_dit.text_embedding(context)
+
+    # Patchify
+    video_x = video_dit.patch_embedding(video_x)
+    f_v, h, w = video_x.shape[2:]
+    video_x = rearrange(video_x, 'b c f h w -> b (f h w) c').contiguous()
+    seq_len_video = video_x.shape[1]
+
+    audio_x = audio_dit.patch_embedding(audio_x)
+    f_a = audio_x.shape[2]
+    audio_x = rearrange(audio_x, 'b c f -> b f c').contiguous()
+    seq_len_audio = audio_x.shape[1]
+
+    # Freqs
+    video_freqs = torch.cat([
+        video_dit.freqs[0][:f_v].view(f_v, 1, 1, -1).expand(f_v, h, w, -1),
+        video_dit.freqs[1][:h].view(1, h, 1, -1).expand(f_v, h, w, -1),
+        video_dit.freqs[2][:w].view(1, 1, w, -1).expand(f_v, h, w, -1)
+    ], dim=-1).reshape(f_v * h * w, 1, -1).to(video_x.device)
+    audio_freqs = torch.cat([
+        audio_dit.freqs[0][:f_a].view(f_a, -1).expand(f_a, -1),
+        audio_dit.freqs[1][:f_a].view(f_a, -1).expand(f_a, -1),
+        audio_dit.freqs[2][:f_a].view(f_a, -1).expand(f_a, -1),
+    ], dim=-1).reshape(f_a, 1, -1).to(audio_x.device)
+
+    video_rope, audio_rope = dual_tower_bridge.build_aligned_freqs(
+        video_fps=frame_rate,
+        grid_size=(f_v, h, w),
+        audio_steps=audio_x.shape[1],
+        device=video_x.device,
+        dtype=video_x.dtype,
+    )
+    # usp func
+    if use_unified_sequence_parallel:
+        from ..utils.xfuser import get_current_chunk, gather_all_chunks
+    else:
+        get_current_chunk = lambda x, dim=1: x
+        gather_all_chunks = lambda x, seq_len, dim=1: x
+    # Forward blocks
+    for block_id in range(len(audio_dit.blocks)):
+        if dual_tower_bridge.should_interact(block_id, "a2v"):
+            video_x, audio_x = dual_tower_bridge(
+                block_id,
+                video_x,
+                audio_x,
+                x_freqs=video_rope,
+                y_freqs=audio_rope,
+                condition_scale=1.0,
+                video_grid_size=(f_v, h, w),
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            )
+        video_x = get_current_chunk(video_x, dim=1)
+        video_x = gradient_checkpoint_forward(
+            video_dit.blocks[block_id],
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            video_x, video_context, video_t_mod, video_freqs
+        )
+        video_x = gather_all_chunks(video_x, seq_len=seq_len_video, dim=1)
+        audio_x = get_current_chunk(audio_x, dim=1)
+        audio_x = gradient_checkpoint_forward(
+            audio_dit.blocks[block_id],
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            audio_x, audio_context, audio_t_mod, audio_freqs
+        )
+        audio_x = gather_all_chunks(audio_x, seq_len=seq_len_audio, dim=1)
+
+    video_x = get_current_chunk(video_x, dim=1)
+    for block_id in range(len(audio_dit.blocks), len(video_dit.blocks)):
+        video_x = gradient_checkpoint_forward(
+            video_dit.blocks[block_id],
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            video_x, video_context, video_t_mod, video_freqs
+        )
+    video_x = gather_all_chunks(video_x, seq_len=seq_len_video, dim=1)
+
+    # Head
+    video_x = video_dit.head(video_x, video_t)
+    video_x = video_dit.unpatchify(video_x, (f_v, h, w))
+
+    audio_x = audio_dit.head(audio_x, audio_t)
+    audio_x = audio_dit.unpatchify(audio_x, (f_a,))
+    return video_x, audio_x
--- a/diffsynth/pipelines/qwen_image.py
+++ b/diffsynth/pipelines/qwen_image.py
@@ -682,14 +682,16 @@ class QwenImageUnit_Image2LoRADecode(PipelineUnit):
 class QwenImageUnit_ContextImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
-            input_params=("context_image", "height", "width", "tiled", "tile_size", "tile_stride"),
+            input_params=("context_image", "height", "width", "tiled", "tile_size", "tile_stride", "layer_input_image"),
            output_params=("context_latents",),
            onload_model_names=("vae",)
        )

-    def process(self, pipe: QwenImagePipeline, context_image, height, width, tiled, tile_size, tile_stride):
+    def process(self, pipe: QwenImagePipeline, context_image, height, width, tiled, tile_size, tile_stride, layer_input_image=None):
        if context_image is None:
            return {}
+        if layer_input_image is not None:
+            context_image = context_image.convert("RGBA")
        pipe.load_models_to_device(self.onload_model_names)
        context_image = pipe.preprocess_image(context_image.resize((width, height))).to(device=pipe.device, dtype=pipe.torch_dtype)
        context_latents = pipe.vae.encode(context_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
--- a/diffsynth/pipelines/wan_video.py
+++ b/diffsynth/pipelines/wan_video.py
@@ -1321,11 +1321,6 @@ def model_fn_wan_video(
    if tea_cache_update:
        x = tea_cache.update(x)
    else:
-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward
-        
        def create_custom_forward_vap(block, vap):
            def custom_forward(*inputs):
                return vap(block, *inputs)
@@ -1339,32 +1334,24 @@ def model_fn_wan_video(
                        x, x_vap = torch.utils.checkpoint.checkpoint(
                            create_custom_forward_vap(block, vap),
                            x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
-                            use_reentrant=False,
+                            use_reentrant=False
                        )
                elif use_gradient_checkpointing:
                    x, x_vap = torch.utils.checkpoint.checkpoint(
                        create_custom_forward_vap(block, vap),
                        x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
-                        use_reentrant=False,
+                        use_reentrant=False
                    )
                else:
                    x, x_vap = vap(block, x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id)
            else:
-                if use_gradient_checkpointing_offload:
-                    with torch.autograd.graph.save_on_cpu():
-                        x = torch.utils.checkpoint.checkpoint(
-                            create_custom_forward(block),
-                            x, context, t_mod, freqs,
-                            use_reentrant=False,
-                        )
-                elif use_gradient_checkpointing:
-                    x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        x, context, t_mod, freqs,
-                        use_reentrant=False,
-                    )
-                else:
-                    x = block(x, context, t_mod, freqs)
+                x = gradient_checkpoint_forward(
+                    block,
+                    use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload,
+                    x, context, t_mod, freqs
+                )
+              
            
            # VACE
            if vace_context is not None and block_id in vace.vace_layers_mapping:
@@ -1487,32 +1474,18 @@ def model_fn_wans2v(
        return custom_forward

    for block_id, block in enumerate(dit.blocks):
-        if use_gradient_checkpointing_offload:
-            with torch.autograd.graph.save_on_cpu():
-                x = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    x, context, t_mod, seq_len_x, pre_compute_freqs[0],
-                    use_reentrant=False,
-                )
-                x = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
-                    x,
-                    use_reentrant=False,
-                )
-        elif use_gradient_checkpointing:
-            x = torch.utils.checkpoint.checkpoint(
-                create_custom_forward(block),
-                x, context, t_mod, seq_len_x, pre_compute_freqs[0],
-                use_reentrant=False,
+        x = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                x, context, t_mod, seq_len_x, pre_compute_freqs[0]
            )
-            x = torch.utils.checkpoint.checkpoint(
-                create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
-                x,
-                use_reentrant=False,
-            )
-        else:
-            x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
-            x = dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x_global, use_unified_sequence_parallel)
+        x = gradient_checkpoint_forward(
+            lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
+            use_gradient_checkpointing,
+            use_gradient_checkpointing_offload,
+            x
+        )

    if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
        x = get_sp_group().all_gather(x, dim=1)
--- a/diffsynth/pipelines/z_image.py
+++ b/diffsynth/pipelines/z_image.py
@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, warnings
 from PIL import Image
 from typing import Union
 from tqdm import tqdm
@@ -6,7 +6,7 @@ from einops import rearrange
 import numpy as np
 from typing import Union, List, Optional, Tuple, Iterable, Dict

-from ..core.device.npu_compatible_device import get_device_type
+from ..core.device.npu_compatible_device import get_device_type, IS_NPU_AVAILABLE
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..core.data.operators import ImageCropAndResize
@@ -63,6 +63,7 @@ class ZImagePipeline(BasePipeline):
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
        vram_limit: float = None,
+        enable_npu_patch: bool = True,
    ):
        # Initialize pipeline
        pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
@@ -84,6 +85,8 @@ class ZImagePipeline(BasePipeline):
        
        # VRAM Management
        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        # NPU patch
+        apply_npu_patch(enable_npu_patch)
        return pipe
    
    
@@ -296,7 +299,7 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):

    def process(self, pipe: ZImagePipeline, prompt, edit_image):
        pipe.load_models_to_device(self.onload_model_names)
-        if hasattr(pipe, "dit") and pipe.dit.siglip_embedder is not None:
+        if hasattr(pipe, "dit") and pipe.dit is not None and pipe.dit.siglip_embedder is not None:
            # Z-Image-Turbo and Z-Image-Omni-Base use different prompt encoding methods.
            # We determine which encoding method to use based on the model architecture.
            # If you are using two-stage split training,
@@ -667,3 +670,19 @@ def model_fn_z_image_turbo(
    x = rearrange(x, "C B H W -> B C H W")
    x = -x
    return x
+
+
+def apply_npu_patch(enable_npu_patch: bool=True):
+    if IS_NPU_AVAILABLE and enable_npu_patch:
+        from ..models.general_modules import RMSNorm
+        from transformers.models.qwen3.modeling_qwen3 import Qwen3RMSNorm
+        from ..models.z_image_dit import Attention
+        from ..core.npu_patch.npu_fused_operator import (
+            rms_norm_forward_npu, 
+            rms_norm_forward_transformers_npu,
+            rotary_emb_Zimage_npu
+        )
+        warnings.warn("Replacing RMSNorm and Rope with NPU fusion operators to improve the performance of the model on NPU.Set enable_npu_patch=False to disable this feature.")
+        RMSNorm.forward = rms_norm_forward_npu
+        Qwen3RMSNorm.forward = rms_norm_forward_transformers_npu
+        Attention.apply_rotary_emb = rotary_emb_Zimage_npu
--- a/diffsynth/utils/data/init.py
+++ b/diffsynth/utils/data/init.py
@@ -116,7 +116,7 @@ class VideoData:
        if self.height is not None and self.width is not None:
            return self.height, self.width
        else:
-            height, width, _ = self.__getitem__(0).shape
+            width, height = self.__getitem__(0).size
            return height, width

    def __getitem__(self, item):
--- a/diffsynth/utils/data/audio.py
+++ b/diffsynth/utils/data/audio.py
@@ -0,0 +1,108 @@
+import torch
+import torchaudio
+
+
+def convert_to_mono(audio_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Convert audio to mono by averaging channels.
+    Supports [C, T] or [B, C, T]. Output shape: [1, T] or [B, 1, T].
+    """
+    return audio_tensor.mean(dim=-2, keepdim=True)
+
+
+def convert_to_stereo(audio_tensor: torch.Tensor) -> torch.Tensor:
+    """
+    Convert audio to stereo.
+    Supports [C, T] or [B, C, T]. Duplicate mono, keep stereo.
+    """
+    if audio_tensor.size(-2) == 1:
+        return audio_tensor.repeat(1, 2, 1) if audio_tensor.dim() == 3 else audio_tensor.repeat(2, 1)
+    return audio_tensor
+
+
+def resample_waveform(waveform: torch.Tensor, source_rate: int, target_rate: int) -> torch.Tensor:
+    """Resample waveform to target sample rate if needed."""
+    if source_rate == target_rate:
+        return waveform
+    resampled = torchaudio.functional.resample(waveform, source_rate, target_rate)
+    return resampled.to(dtype=waveform.dtype)
+
+
+def read_audio_with_torchcodec(
+    path: str,
+    start_time: float = 0,
+    duration: float | None = None,
+) -> tuple[torch.Tensor, int]:
+    """
+    Read audio from file natively using torchcodec, with optional start time and duration.
+    
+    Args:
+        path (str): The file path to the audio file.
+        start_time (float, optional): The start time in seconds to read from. Defaults to 0.
+        duration (float | None, optional): The duration in seconds to read. If None, reads until the end. Defaults to None.
+        
+    Returns:
+        tuple[torch.Tensor, int]: A tuple containing the audio tensor and the sample rate.
+            The audio tensor shape is [C, T] where C is the number of channels and T is the number of audio frames.
+    """
+    from torchcodec.decoders import AudioDecoder
+    decoder = AudioDecoder(path)
+    stop_seconds = None if duration is None else start_time + duration
+    waveform = decoder.get_samples_played_in_range(start_seconds=start_time, stop_seconds=stop_seconds).data
+    return waveform, decoder.metadata.sample_rate
+
+
+def read_audio(
+    path: str,
+    start_time: float = 0,
+    duration: float | None = None,
+    resample: bool = False,
+    resample_rate: int = 48000,
+    backend: str = "torchcodec",
+) -> tuple[torch.Tensor, int]:
+    """
+    Read audio from file, with optional start time, duration, and resampling.
+    
+    Args:
+        path (str): The file path to the audio file.
+        start_time (float, optional): The start time in seconds to read from. Defaults to 0.
+        duration (float | None, optional): The duration in seconds to read. If None, reads until the end. Defaults to None.
+        resample (bool, optional): Whether to resample the audio to a different sample rate. Defaults to False.
+        resample_rate (int, optional): The target sample rate for resampling if resample is True. Defaults to 48000.
+        backend (str, optional): The audio backend to use for reading. Defaults to "torchcodec".
+        
+    Returns:
+        tuple[torch.Tensor, int]: A tuple containing the audio tensor and the sample rate.
+            The audio tensor shape is [C, T] where C is the number of channels and T is the number of audio frames.
+    """
+    if backend == "torchcodec":
+        waveform, sample_rate = read_audio_with_torchcodec(path, start_time, duration)
+    else:
+        raise ValueError(f"Unsupported audio backend: {backend}")
+
+    if resample:
+        waveform = resample_waveform(waveform, sample_rate, resample_rate)
+        sample_rate = resample_rate
+
+    return waveform, sample_rate
+
+
+def save_audio(waveform: torch.Tensor, sample_rate: int, save_path: str, backend: str = "torchcodec"):
+    """
+    Save audio tensor to file.
+    
+    Args:
+        waveform (torch.Tensor): The audio tensor to save. Shape can be [C, T] or [B, C, T].
+        sample_rate (int): The sample rate of the audio.
+        save_path (str): The file path to save the audio to.
+        backend (str, optional): The audio backend to use for saving. Defaults to "torchcodec".
+    """
+    if waveform.dim() == 3:
+        waveform = waveform[0]
+
+    if backend == "torchcodec":
+        from torchcodec.encoders import AudioEncoder
+        encoder = AudioEncoder(waveform, sample_rate=sample_rate)
+        encoder.to_file(dest=save_path)
+    else:
+        raise ValueError(f"Unsupported audio backend: {backend}")
--- a/diffsynth/utils/data/audio_video.py
+++ b/diffsynth/utils/data/audio_video.py
@@ -0,0 +1,134 @@
+import av
+from fractions import Fraction
+import torch
+from PIL import Image
+from tqdm import tqdm
+from .audio import convert_to_stereo
+
+
+def _resample_audio(
+    container: av.container.Container, audio_stream: av.audio.AudioStream, frame_in: av.AudioFrame
+) -> None:
+    cc = audio_stream.codec_context
+
+    # Use the encoder's format/layout/rate as the *target*
+    target_format = cc.format or "fltp"  # AAC → usually fltp
+    target_layout = cc.layout or "stereo"
+    target_rate = cc.sample_rate or frame_in.sample_rate
+
+    audio_resampler = av.audio.resampler.AudioResampler(
+        format=target_format,
+        layout=target_layout,
+        rate=target_rate,
+    )
+
+    audio_next_pts = 0
+    for rframe in audio_resampler.resample(frame_in):
+        if rframe.pts is None:
+            rframe.pts = audio_next_pts
+        audio_next_pts += rframe.samples
+        rframe.sample_rate = frame_in.sample_rate
+        container.mux(audio_stream.encode(rframe))
+
+    # flush audio encoder
+    for packet in audio_stream.encode():
+        container.mux(packet)
+
+
+def _write_audio(
+    container: av.container.Container, audio_stream: av.audio.AudioStream, samples: torch.Tensor, audio_sample_rate: int
+) -> None:
+    if samples.ndim == 1:
+        samples = samples.unsqueeze(0)
+    samples = convert_to_stereo(samples)
+    assert samples.ndim == 2 and samples.shape[0] == 2, "audio samples must be [C, S] or [S], C must be 1 or 2"
+    samples = samples.T
+    # Convert to int16 packed for ingestion; resampler converts to encoder fmt.
+    if samples.dtype != torch.int16:
+        samples = torch.clip(samples, -1.0, 1.0)
+        samples = (samples * 32767.0).to(torch.int16)
+
+    frame_in = av.AudioFrame.from_ndarray(
+        samples.contiguous().reshape(1, -1).cpu().numpy(),
+        format="s16",
+        layout="stereo",
+    )
+    frame_in.sample_rate = audio_sample_rate
+
+    _resample_audio(container, audio_stream, frame_in)
+
+
+def _prepare_audio_stream(container: av.container.Container, audio_sample_rate: int) -> av.audio.AudioStream:
+    """
+    Prepare the audio stream for writing.
+    """
+    audio_stream = container.add_stream("aac")
+    supported_sample_rates = audio_stream.codec_context.codec.audio_rates
+    if supported_sample_rates:
+        best_rate = min(supported_sample_rates, key=lambda x: abs(x - audio_sample_rate))
+        if best_rate != audio_sample_rate:
+            print(f"Using closest supported audio sample rate: {best_rate}")
+    else:
+        best_rate = audio_sample_rate
+    audio_stream.codec_context.sample_rate = best_rate
+    audio_stream.codec_context.layout = "stereo"
+    audio_stream.codec_context.time_base = Fraction(1, best_rate)
+    return audio_stream
+
+
+def write_video_audio(
+    video: list[Image.Image],
+    audio: torch.Tensor | None,
+    output_path: str,
+    fps: int = 24,
+    audio_sample_rate: int | None = None,
+) -> None:
+    """
+    Writes a sequence of images and an audio tensor to a video file.
+
+    This function utilizes PyAV (or a similar multimedia library) to encode a list of PIL images into a video stream
+    and multiplex a PyTorch tensor as the audio stream into the output container.
+
+    Args:
+        video (list[Image.Image]): A list of PIL Image objects representing the video frames. 
+            The length of this list determines the total duration of the video based on the FPS.
+        audio (torch.Tensor | None): The audio data as a PyTorch tensor.
+            The shape is typically (channels, samples). If no audio is required, pass None.
+            channels can be 1 or 2. 1 for mono, 2 for stereo.
+        output_path (str): The file path (including extension) where the output video will be saved.
+        fps (int, optional): The frame rate (frames per second) for the video. Defaults to 24.
+        audio_sample_rate (int | None, optional): The sample rate (e.g., 44100, 48000) for the audio.
+            If the audio tensor is provided and this is None, the function attempts to infer the rate 
+            based on the audio tensor's length and the video duration.
+    Raises:
+        ValueError: If an audio tensor is provided but the sample rate cannot be determined.
+    """
+    duration = len(video) / fps
+    if audio_sample_rate is None:
+        audio_sample_rate = int(audio.shape[-1] / duration)
+
+    width, height = video[0].size
+    container = av.open(output_path, mode="w")
+    stream = container.add_stream("libx264", rate=int(fps))
+    stream.width = width
+    stream.height = height
+    stream.pix_fmt = "yuv420p"
+
+    if audio is not None:
+        if audio_sample_rate is None:
+            raise ValueError("audio_sample_rate is required when audio is provided")
+        audio_stream = _prepare_audio_stream(container, audio_sample_rate)
+
+    for frame in tqdm(video, total=len(video)):
+        frame = av.VideoFrame.from_image(frame)
+        for packet in stream.encode(frame):
+            container.mux(packet)
+
+    # Flush encoder
+    for packet in stream.encode():
+        container.mux(packet)
+
+    if audio is not None:
+        _write_audio(container, audio_stream, audio, audio_sample_rate)
+
+    container.close()
--- a/diffsynth/utils/data/media_io_ltx2.py
+++ b/diffsynth/utils/data/media_io_ltx2.py
@@ -0,0 +1,43 @@
+import av
+import numpy as np
+from io import BytesIO
+from .audio_video import write_video_audio as write_video_audio_ltx2
+
+
+def encode_single_frame(output_file: str, image_array: np.ndarray, crf: float) -> None:
+    container = av.open(output_file, "w", format="mp4")
+    try:
+        stream = container.add_stream("libx264", rate=1, options={"crf": str(crf), "preset": "veryfast"})
+        # Round to nearest multiple of 2 for compatibility with video codecs
+        height = image_array.shape[0] // 2 * 2
+        width = image_array.shape[1] // 2 * 2
+        image_array = image_array[:height, :width]
+        stream.height = height
+        stream.width = width
+        av_frame = av.VideoFrame.from_ndarray(image_array, format="rgb24").reformat(format="yuv420p")
+        container.mux(stream.encode(av_frame))
+        container.mux(stream.encode())
+    finally:
+        container.close()
+
+
+def decode_single_frame(video_file: str) -> np.array:
+    container = av.open(video_file)
+    try:
+        stream = next(s for s in container.streams if s.type == "video")
+        frame = next(container.decode(stream))
+    finally:
+        container.close()
+    return frame.to_ndarray(format="rgb24")
+
+
+def ltx2_preprocess(image: np.array, crf: float = 33) -> np.array:
+    if crf == 0:
+        return image
+
+    with BytesIO() as output_file:
+        encode_single_frame(output_file, image, crf)
+        video_bytes = output_file.getvalue()
+    with BytesIO(video_bytes) as video_file:
+        image_array = decode_single_frame(video_file)
+    return image_array
--- a/diffsynth/utils/lora/general.py
+++ b/diffsynth/utils/lora/general.py
@@ -1,4 +1,4 @@
-import torch
+import torch, warnings


 class GeneralLoRALoader:
@@ -26,7 +26,11 @@ class GeneralLoRALoader:
                keys.pop(0)
            keys.pop(-1)
            target_name = ".".join(keys)
-            lora_name_dict[target_name] = (key, key.replace(lora_B_key, lora_A_key))
+            # Alpha: Deprecated but retained for compatibility.
+            key_alpha = key.replace(lora_B_key + ".weight", "alpha").replace(lora_B_key + ".default.weight", "alpha")
+            if key_alpha == key or key_alpha not in lora_state_dict:
+                key_alpha = None
+            lora_name_dict[target_name] = (key, key.replace(lora_B_key, lora_A_key), key_alpha)
        return lora_name_dict
    
    
@@ -36,6 +40,10 @@ class GeneralLoRALoader:
        for name in name_dict:
            weight_up = state_dict[name_dict[name][0]]
            weight_down = state_dict[name_dict[name][1]]
+            if name_dict[name][2] is not None:
+                warnings.warn("Alpha detected in the LoRA file. This may be a LoRA model not trained by DiffSynth-Studio. To ensure compatibility, the LoRA weights will be converted to weight * alpha / rank.")
+                alpha = state_dict[name_dict[name][2]] / weight_down.shape[0]
+                weight_down = weight_down * alpha
            state_dict_[name + f".lora_B{suffix}"] = weight_up
            state_dict_[name + f".lora_A{suffix}"] = weight_down
        return state_dict_
--- a/diffsynth/utils/ses/README.md
+++ b/diffsynth/utils/ses/README.md
@@ -0,0 +1 @@
+Please see `docs/en/Research_Tutorial/inference_time_scaling.md` or `docs/zh/Research_Tutorial/inference_time_scaling.md` for more details.
--- a/diffsynth/utils/ses/init.py
+++ b/diffsynth/utils/ses/init.py
@@ -0,0 +1 @@
+from .ses import ses_search
--- a/diffsynth/utils/ses/ses.py
+++ b/diffsynth/utils/ses/ses.py
@@ -0,0 +1,117 @@
+import torch
+import pywt
+import numpy as np
+from tqdm import tqdm
+
+
+def split_dwt(z_tensor_cpu, wavelet_name, dwt_level):
+    all_clow_np = []
+    all_chigh_list = []
+    z_tensor_cpu = z_tensor_cpu.cpu().float()
+    
+    for i in range(z_tensor_cpu.shape[0]): 
+        z_numpy_ch = z_tensor_cpu[i].numpy()
+        
+        coeffs_ch = pywt.wavedec2(z_numpy_ch, wavelet_name, level=dwt_level, mode='symmetric', axes=(-2, -1))
+        
+        clow_np = coeffs_ch[0]
+        chigh_list = coeffs_ch[1:]
+        
+        all_clow_np.append(clow_np)
+        all_chigh_list.append(chigh_list)
+        
+    all_clow_tensor = torch.from_numpy(np.stack(all_clow_np, axis=0))
+    return all_clow_tensor, all_chigh_list
+
+
+def reconstruct_dwt(c_low_tensor_cpu, c_high_coeffs, wavelet_name, original_shape):
+    H_high, W_high = original_shape
+    c_low_tensor_cpu = c_low_tensor_cpu.cpu().float()
+    
+    clow_np = c_low_tensor_cpu.numpy()
+    
+    if clow_np.ndim == 4 and clow_np.shape[0] == 1:
+        clow_np = clow_np[0]
+
+    coeffs_combined = [clow_np] + c_high_coeffs
+    z_recon_np = pywt.waverec2(coeffs_combined, wavelet_name, mode='symmetric', axes=(-2, -1))
+    if z_recon_np.shape[-2] != H_high or z_recon_np.shape[-1] != W_high:
+        z_recon_np = z_recon_np[..., :H_high, :W_high]
+    z_recon_tensor = torch.from_numpy(z_recon_np)
+    if z_recon_tensor.ndim == 3:
+        z_recon_tensor = z_recon_tensor.unsqueeze(0)
+    return z_recon_tensor
+
+
+def ses_search(
+    base_latents,
+    objective_reward_fn,
+    total_eval_budget=30,
+    popsize=10,
+    k_elites=5,
+    wavelet_name="db1",
+    dwt_level=4,
+):
+    latent_h, latent_w = base_latents.shape[-2], base_latents.shape[-1]
+    c_low_init, c_high_fixed_batch = split_dwt(base_latents, wavelet_name, dwt_level)
+    c_high_fixed = c_high_fixed_batch[0]    
+    c_low_shape = c_low_init.shape[1:]
+    mu = torch.zeros_like(c_low_init.view(-1).cpu()) 
+    sigma_sq = torch.ones_like(mu) * 1.0 
+    
+    best_overall = {"fitness": -float('inf'), "score": -float('inf'), "c_low": c_low_init[0]}
+    eval_count = 0
+    
+    elite_db = []    
+    n_generations = (total_eval_budget // popsize) + 5
+    pbar = tqdm(total=total_eval_budget, desc="[SES] Searching", unit="img")
+
+    for gen in range(n_generations):
+        if eval_count >= total_eval_budget: break
+        
+        std = torch.sqrt(torch.clamp(sigma_sq, min=1e-9))
+        z_noise = torch.randn(popsize, mu.shape[0])
+        samples_flat = mu + z_noise * std
+        samples_reshaped = samples_flat.view(popsize, *c_low_shape) 
+        
+        batch_results = []
+        
+        for i in range(popsize):
+            if eval_count >= total_eval_budget: break
+            
+            c_low_sample = samples_reshaped[i].unsqueeze(0) 
+            z_recon = reconstruct_dwt(c_low_sample, c_high_fixed, wavelet_name, (latent_h, latent_w))
+            z_recon = z_recon.to(base_latents.device, dtype=base_latents.dtype)  
+            # img = pipeline_callback(z_recon)
+
+            # score = scorer.get_score(img, prompt)
+            score = objective_reward_fn(z_recon)
+            res = {
+                "score": score, 
+                "c_low": c_low_sample.cpu()
+            }
+            batch_results.append(res)
+            if score > best_overall['score']:
+                best_overall = res
+                
+            eval_count += 1
+            pbar.update(1)
+            
+        if not batch_results: break
+        elite_db.extend(batch_results)        
+        elite_db.sort(key=lambda x: x['score'], reverse=True)        
+        elite_db = elite_db[:k_elites]        
+        elites_flat = torch.stack([x['c_low'].view(-1) for x in elite_db])
+        mu_new = torch.mean(elites_flat, dim=0)
+        
+        if len(elite_db) > 1:
+            sigma_sq_new = torch.var(elites_flat, dim=0, unbiased=True) + 1e-7
+        else:
+            sigma_sq_new = sigma_sq
+        mu = mu_new
+        sigma_sq = sigma_sq_new
+    pbar.close()
+    best_c_low = best_overall['c_low']
+    final_latents = reconstruct_dwt(best_c_low, c_high_fixed, wavelet_name, (latent_h, latent_w))
+    
+    return final_latents.to(base_latents.device, dtype=base_latents.dtype)
--- a/diffsynth/utils/state_dict_converters/anima_dit.py
+++ b/diffsynth/utils/state_dict_converters/anima_dit.py
@@ -0,0 +1,6 @@
+def AnimaDiTStateDictConverter(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        value = state_dict[key]
+        new_state_dict[key.replace("net.", "")] = value
+    return new_state_dict
--- a/diffsynth/utils/state_dict_converters/ltx2_audio_vae.py
+++ b/diffsynth/utils/state_dict_converters/ltx2_audio_vae.py
@@ -0,0 +1,32 @@
+def LTX2AudioEncoderStateDictConverter(state_dict):
+    # Not used
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("audio_vae.encoder."):
+            new_name = name.replace("audio_vae.encoder.", "")
+            state_dict_[new_name] = state_dict[name]
+        elif name.startswith("audio_vae.per_channel_statistics."):
+            new_name = name.replace("audio_vae.per_channel_statistics.", "per_channel_statistics.")
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_
+
+
+def LTX2AudioDecoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("audio_vae.decoder."):
+            new_name = name.replace("audio_vae.decoder.", "")
+            state_dict_[new_name] = state_dict[name]
+        elif name.startswith("audio_vae.per_channel_statistics."):
+            new_name = name.replace("audio_vae.per_channel_statistics.", "per_channel_statistics.")
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_
+
+
+def LTX2VocoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("vocoder."):
+            new_name = name[len("vocoder."):]
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_
--- a/diffsynth/utils/state_dict_converters/ltx2_dit.py
+++ b/diffsynth/utils/state_dict_converters/ltx2_dit.py
@@ -0,0 +1,9 @@
+def LTXModelStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("model.diffusion_model."):
+            new_name = name.replace("model.diffusion_model.", "")
+            if new_name.startswith("audio_embeddings_connector.") or new_name.startswith("video_embeddings_connector."):
+                continue
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_
--- a/diffsynth/utils/state_dict_converters/ltx2_text_encoder.py
+++ b/diffsynth/utils/state_dict_converters/ltx2_text_encoder.py
@@ -0,0 +1,31 @@
+def LTX2TextEncoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for key in state_dict:
+        if key.startswith("language_model.model."):
+            new_key = key.replace("language_model.model.", "model.language_model.")
+        elif key.startswith("vision_tower."):
+            new_key = key.replace("vision_tower.", "model.vision_tower.")
+        elif key.startswith("multi_modal_projector."):
+            new_key = key.replace("multi_modal_projector.", "model.multi_modal_projector.")
+        elif key.startswith("language_model.lm_head."):
+            new_key = key.replace("language_model.lm_head.", "lm_head.")
+        else:
+            continue
+        state_dict_[new_key] = state_dict[key]
+    state_dict_["lm_head.weight"] = state_dict_.get("model.language_model.embed_tokens.weight")
+    return state_dict_
+
+
+def LTX2TextEncoderPostModulesStateDictConverter(state_dict):
+    state_dict_ = {}
+    for key in state_dict:
+        if key.startswith("text_embedding_projection."):
+            new_key = key.replace("text_embedding_projection.", "feature_extractor_linear.")
+        elif key.startswith("model.diffusion_model.video_embeddings_connector."):
+            new_key = key.replace("model.diffusion_model.video_embeddings_connector.", "embeddings_connector.")
+        elif key.startswith("model.diffusion_model.audio_embeddings_connector."):
+            new_key = key.replace("model.diffusion_model.audio_embeddings_connector.", "audio_embeddings_connector.")
+        else:
+            continue
+        state_dict_[new_key] = state_dict[key]
+    return state_dict_
--- a/diffsynth/utils/state_dict_converters/ltx2_video_vae.py
+++ b/diffsynth/utils/state_dict_converters/ltx2_video_vae.py
@@ -0,0 +1,24 @@
+def LTX2VideoEncoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("vae.encoder."):
+            new_name = name.replace("vae.encoder.", "")
+            state_dict_[new_name] = state_dict[name]
+        elif name.startswith("vae.per_channel_statistics."):
+            new_name = name.replace("vae.per_channel_statistics.", "per_channel_statistics.")
+            if new_name not in ["per_channel_statistics.channel", "per_channel_statistics.mean-of-stds", "per_channel_statistics.mean-of-stds_over_std-of-means"]:
+                state_dict_[new_name] = state_dict[name]
+    return state_dict_
+
+
+def LTX2VideoDecoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name.startswith("vae.decoder."):
+            new_name = name.replace("vae.decoder.", "")
+            state_dict_[new_name] = state_dict[name]
+        elif name.startswith("vae.per_channel_statistics."):
+            new_name = name.replace("vae.per_channel_statistics.", "per_channel_statistics.")
+            if new_name not in ["per_channel_statistics.channel", "per_channel_statistics.mean-of-stds", "per_channel_statistics.mean-of-stds_over_std-of-means"]:
+                state_dict_[new_name] = state_dict[name]
+    return state_dict_
--- a/diffsynth/utils/xfuser/init.py
+++ b/diffsynth/utils/xfuser/init.py
@@ -1 +1 @@
-from .xdit_context_parallel import usp_attn_forward, usp_dit_forward, get_sequence_parallel_world_size, initialize_usp
+from .xdit_context_parallel import usp_attn_forward, usp_dit_forward, get_sequence_parallel_world_size, initialize_usp, get_current_chunk, gather_all_chunks
--- a/diffsynth/utils/xfuser/xdit_context_parallel.py
+++ b/diffsynth/utils/xfuser/xdit_context_parallel.py
@@ -1,11 +1,15 @@
 import torch
 from typing import Optional
 from einops import rearrange
+from yunchang.kernels import AttnType
 from xfuser.core.distributed import (get_sequence_parallel_rank,
                                     get_sequence_parallel_world_size,
                                     get_sp_group)
 from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+
+from ... import IS_NPU_AVAILABLE
 from ...core.device import parse_nccl_backend, parse_device_type
+from ...core.gradient import gradient_checkpoint_forward


 def initialize_usp(device_type):
@@ -30,13 +34,16 @@ def sinusoidal_embedding_1d(dim, position):
 def pad_freqs(original_tensor, target_len):
    seq_len, s1, s2 = original_tensor.shape
    pad_size = target_len - seq_len
+    original_tensor_device = original_tensor.device
+    if original_tensor.device == "npu":
+        original_tensor = original_tensor.cpu()
    padding_tensor = torch.ones(
        pad_size,
        s1,
        s2,
        dtype=original_tensor.dtype,
        device=original_tensor.device)
-    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
+    padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0).to(device=original_tensor_device)
    return padded_tensor
    
 def rope_apply(x, freqs, num_heads):
@@ -50,7 +57,7 @@ def rope_apply(x, freqs, num_heads):
    sp_rank = get_sequence_parallel_rank()
    freqs = pad_freqs(freqs, s_per_rank * sp_size)
    freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :]
-    freqs_rank = freqs_rank.to(torch.complex64) if freqs_rank.device == "npu" else freqs_rank
+    freqs_rank = freqs_rank.to(torch.complex64) if freqs_rank.device.type == "npu" else freqs_rank
    x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
    return x_out.to(x.dtype)

@@ -81,11 +88,6 @@ def usp_dit_forward(self,
        self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
        self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
    ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
-    
-    def create_custom_forward(module):
-        def custom_forward(*inputs):
-            return module(*inputs)
-        return custom_forward

    # Context Parallel
    chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
@@ -94,20 +96,13 @@ def usp_dit_forward(self,
    x = chunks[get_sequence_parallel_rank()]

    for block in self.blocks:
-        if self.training and use_gradient_checkpointing:
-            if use_gradient_checkpointing_offload:
-                with torch.autograd.graph.save_on_cpu():
-                    x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(block),
-                        x, context, t_mod, freqs,
-                        use_reentrant=False,
-                    )
-            else:
-                x = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    x, context, t_mod, freqs,
-                    use_reentrant=False,
-                )
+        if self.training:
+            x = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing,
+                use_gradient_checkpointing_offload,
+                x, context, t_mod, freqs
+            )
        else:
            x = block(x, context, t_mod, freqs)

@@ -133,7 +128,12 @@ def usp_attn_forward(self, x, freqs):
    k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads)
    v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads)

-    x = xFuserLongContextAttention()(
+    attn_type = AttnType.FA
+    ring_impl_type = "basic"
+    if IS_NPU_AVAILABLE:
+        attn_type = AttnType.NPU
+        ring_impl_type = "basic_npu"
+    x = xFuserLongContextAttention(attn_type=attn_type, ring_impl_type=ring_impl_type)(
        None,
        query=q,
        key=k,
@@ -143,4 +143,31 @@ def usp_attn_forward(self, x, freqs):

    del q, k, v
    getattr(torch, parse_device_type(x.device)).empty_cache()
-    return self.o(x)
+    return self.o(x)
+
+
+def get_current_chunk(x, dim=1):
+    chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=dim)
+    ndims = len(chunks[0].shape)
+    pad_list = [0] * (2 * ndims)
+    pad_end_index = 2 * (ndims - 1 - dim) + 1
+    max_size = chunks[0].size(dim)
+    chunks = [
+        torch.nn.functional.pad(
+            chunk, 
+            tuple(pad_list[:pad_end_index] + [max_size - chunk.size(dim)] + pad_list[pad_end_index+1:]), 
+            value=0
+        ) 
+        for chunk in chunks
+    ]
+    x = chunks[get_sequence_parallel_rank()]
+    return x
+
+
+def gather_all_chunks(x, seq_len=None, dim=1):
+    x = get_sp_group().all_gather(x, dim=dim)
+    if seq_len is not None:
+        slices = [slice(None)] * x.ndim
+        slices[dim] = slice(0, seq_len)
+        x = x[tuple(slices)]
+    return x
--- a/diffsynth/version.py
+++ b/diffsynth/version.py
@@ -0,0 +1,5 @@
+# Make sure to modify __release_datetime__ to release time when making official release.
+__version__ = '2.0.0'
+# default release datetime for branches under active development is set
+# to be a time far-far-away-into-the-future
+__release_datetime__ = '2099-10-13 08:56:12'
--- a/docs/en/.readthedocs.yaml
+++ b/docs/en/.readthedocs.yaml
@@ -0,0 +1,28 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+# Build documentation in the "docs/" directory with Sphinx
+sphinx:
+  configuration: docs/en/conf.py
+
+# Optionally build your docs in additional formats such as PDF and ePub
+# formats:
+#    - pdf
+#    - epub
+
+# Optional but recommended, declare the Python requirements required
+# to build your documentation
+# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+   install:
+      - requirements: docs/requirements.txt
--- a/docs/en/API_Reference/core/attention.md
+++ b/docs/en/API_Reference/core/attention.md
@@ -1,6 +1,6 @@
 # `diffsynth.core.attention`: Attention Mechanism Implementation

-`diffsynth.core.attention` provides routing mechanisms for attention mechanism implementations, automatically selecting efficient attention implementations based on available packages in the `Python` environment and [environment variables](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
+`diffsynth.core.attention` provides routing mechanisms for attention mechanism implementations, automatically selecting efficient attention implementations based on available packages in the `Python` environment and [environment variables](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).

 ## Attention Mechanism

@@ -46,7 +46,7 @@ Note that the dimension of the Attention Score in the attention mechanism ( $\te
 * xFormers: [GitHub](https://github.com/facebookresearch/xformers), [Documentation](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops)
 * PyTorch: [GitHub](https://github.com/pytorch/pytorch), [Documentation](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)

-To call attention implementations other than `PyTorch`, please follow the instructions on their GitHub pages to install the corresponding packages. `DiffSynth-Studio` will automatically route to the corresponding implementation based on available packages in the Python environment, or can be controlled through [environment variables](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).
+To call attention implementations other than `PyTorch`, please follow the instructions on their GitHub pages to install the corresponding packages. `DiffSynth-Studio` will automatically route to the corresponding implementation based on available packages in the Python environment, or can be controlled through [environment variables](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation).

 ```python
 from diffsynth.core.attention import attention_forward
--- a/docs/en/API_Reference/core/loader.md
+++ b/docs/en/API_Reference/core/loader.md
@@ -8,9 +8,9 @@ This document introduces the model download and loading functionalities in `diff

 ### Downloading and Loading Models from Remote Sources

-Taking the model [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) as an example, after filling in `model_id` and `origin_file_pattern` in `ModelConfig`, the model can be automatically downloaded. By default, it downloads to the `./models` path, which can be modified through the [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
+Taking the model [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) as an example, after filling in `model_id` and `origin_file_pattern` in `ModelConfig`, the model can be automatically downloaded. By default, it downloads to the `./models` path, which can be modified through the [environment variable DIFFSYNTH_MODEL_BASE_PATH](../../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).

-By default, even if the model has already been downloaded, the program will still query the remote for any missing files. To completely disable remote requests, set the [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
+By default, even if the model has already been downloaded, the program will still query the remote for any missing files. To completely disable remote requests, set the [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.

 ```python
 from diffsynth.core import ModelConfig
@@ -51,7 +51,7 @@ config = ModelConfig(path=[

 ### VRAM Management Configuration

-`ModelConfig` also contains VRAM management configuration information. See [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods) for details.
+`ModelConfig` also contains VRAM management configuration information. See [VRAM Management](../../Pipeline_Usage/VRAM_management.md#more-usage-methods) for details.

 ## Model File Loading

@@ -103,11 +103,11 @@ print(hash_model_file([

 The model hash value is only related to the keys and tensor shapes in the state dict of the model file, and is unrelated to the numerical values of the model parameters, file saving time, and other information. When calculating the model hash value of `.safetensors` format files, `hash_model_file` is almost instantly completed without reading the model parameters. However, when calculating the model hash value of `.bin`, `.pth`, `.ckpt`, and other binary files, all model parameters need to be read, so **we do not recommend developers to continue using these formats of files.**

-By [writing model Config](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config) and filling in model hash value and other information into `diffsynth/configs/model_configs.py`, developers can let `DiffSynth-Studio` automatically identify the model type and load it.
+By [writing model Config](../../Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config) and filling in model hash value and other information into `diffsynth/configs/model_configs.py`, developers can let `DiffSynth-Studio` automatically identify the model type and load it.

 ## Model Loading

-`load_model` is the external entry for loading models in `diffsynth.core.loader`. It will call [skip_model_initialization](/docs/en/API_Reference/core/vram.md#skipping-model-parameter-initialization) to skip model parameter initialization. If [Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is enabled, it calls [DiskMap](/docs/en/API_Reference/core/vram.md#state-dict-disk-mapping) for lazy loading. If Disk Offload is not enabled, it calls [load_state_dict](#model-file-loading) to load model parameters. If necessary, it will also call [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) for model format conversion. Finally, it calls `model.eval()` to switch to inference mode.
+`load_model` is the external entry for loading models in `diffsynth.core.loader`. It will call [skip_model_initialization](../../API_Reference/core/vram.md#skipping-model-parameter-initialization) to skip model parameter initialization. If [Disk Offload](../../Pipeline_Usage/VRAM_management.md#disk-offload) is enabled, it calls [DiskMap](../../API_Reference/core/vram.md#state-dict-disk-mapping) for lazy loading. If Disk Offload is not enabled, it calls [load_state_dict](#model-file-loading) to load model parameters. If necessary, it will also call [state dict converter](../../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) for model format conversion. Finally, it calls `model.eval()` to switch to inference mode.

 Here is a usage example with Disk Offload enabled:

--- a/docs/en/API_Reference/core/vram.md
+++ b/docs/en/API_Reference/core/vram.md
@@ -31,7 +31,7 @@ state_dict = load_state_dict(path, device="cpu")
 model.load_state_dict(state_dict, assign=True)
 ```

-In `DiffSynth-Studio`, all pretrained models follow this loading logic. After developers [integrate models](/docs/en/Developer_Guide/Integrating_Your_Model.md), they can directly load models quickly using this approach.
+In `DiffSynth-Studio`, all pretrained models follow this loading logic. After developers [integrate models](../../Developer_Guide/Integrating_Your_Model.md), they can directly load models quickly using this approach.

 ## State Dict Disk Mapping

@@ -57,10 +57,10 @@ state_dict = DiskMap(path, device="cpu") # Fast
 print(state_dict["img_in.weight"])
 ```

-`DiskMap` is the basic component of Disk Offload in `DiffSynth-Studio`. After developers [configure fine-grained VRAM management schemes](/docs/en/Developer_Guide/Enabling_VRAM_management.md), they can directly enable Disk Offload.
+`DiskMap` is the basic component of Disk Offload in `DiffSynth-Studio`. After developers [configure fine-grained VRAM management schemes](../../Developer_Guide/Enabling_VRAM_management.md), they can directly enable Disk Offload.

 `DiskMap` is a functionality implemented using the characteristics of `.safetensors` files. Therefore, when using `.bin`, `.pth`, `.ckpt`, and other binary files, model parameters are fully loaded, which causes Disk Offload to not support these formats of files. **We do not recommend developers to continue using these formats of files.**

 ## Replacable Modules for VRAM Management

-When `DiffSynth-Studio`'s VRAM management is enabled, the modules inside the model will be replaced with replacable modules in `diffsynth.core.vram.layers`. For usage, see [Fine-grained VRAM Management Scheme](/docs/en/Developer_Guide/Enabling_VRAM_management.md#writing-fine-grained-vram-management-schemes).
+When `DiffSynth-Studio`'s VRAM management is enabled, the modules inside the model will be replaced with replacable modules in `diffsynth.core.vram.layers`. For usage, see [Fine-grained VRAM Management Scheme](../../Developer_Guide/Enabling_VRAM_management.md#writing-fine-grained-vram-management-schemes).
--- a/docs/en/Developer_Guide/Building_a_Pipeline.md
+++ b/docs/en/Developer_Guide/Building_a_Pipeline.md
@@ -1,6 +1,6 @@
 # Building a Pipeline

-After [integrating the required models for the Pipeline](/docs/en/Developer_Guide/Integrating_Your_Model.md), you also need to build a `Pipeline` for model inference. This document provides a standardized process for building a `Pipeline`. Developers can also refer to existing `Pipeline` implementations for construction.
+After [integrating the required models for the Pipeline](../Developer_Guide/Integrating_Your_Model.md), you also need to build a `Pipeline` for model inference. This document provides a standardized process for building a `Pipeline`. Developers can also refer to existing `Pipeline` implementations for construction.

 The `Pipeline` implementation is located in `diffsynth/pipelines`. Each `Pipeline` contains the following essential key components:

@@ -79,7 +79,7 @@ This includes the following parts:
        return pipe
 ```

-Developers need to implement the logic for fetching models. The corresponding model names are the `"model_name"` in the [model Config filled in during model integration](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config).
+Developers need to implement the logic for fetching models. The corresponding model names are the `"model_name"` in the [model Config filled in during model integration](../Developer_Guide/Integrating_Your_Model.md#step-3-writing-model-config).

 Some models also need to load `tokenizer`. Extra `tokenizer_config` parameters can be added to `from_pretrained` as needed, and this part can be implemented after fetching the models.

--- a/docs/en/Developer_Guide/Enabling_VRAM_management.md
+++ b/docs/en/Developer_Guide/Enabling_VRAM_management.md
@@ -1,6 +1,6 @@
 # Fine-Grained VRAM Management Scheme

-This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
+This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](../Pipeline_Usage/VRAM_management.md).

 ## How Much VRAM Does a 20B Model Need?

@@ -124,7 +124,7 @@ module_map={
 }
 ```

-In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods).
+In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](../Pipeline_Usage/VRAM_management.md#more-usage-methods).

 Call `enable_vram_management` to enable VRAM management. Note that the `device` when loading the model is `cpu`, consistent with `offload_device`:

@@ -171,7 +171,7 @@ The above code only requires 2G VRAM to run the `forward` of a 20B model.

 ## Disk Offload

-[Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
+[Disk Offload](../Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:

 ```python
 from diffsynth.core import load_model, enable_vram_management, AutoWrappedLinear, AutoWrappedModule
@@ -212,7 +212,7 @@ with torch.no_grad():
    output = model(**inputs)
 ```

-Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
+Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.

 If there are situations where Disk Offload cannot run normally but non-Disk Offload can run normally, please submit an issue to us on GitHub.

@@ -227,7 +227,7 @@ To make it easier for users to use the VRAM management function, we write the fi
 }
 ```# Fine-Grained VRAM Management Scheme

-This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
+This document introduces how to write reasonable fine-grained VRAM management schemes for models, and how to use the VRAM management functions in `DiffSynth-Studio` for other external code libraries. Before reading this document, please read the document [VRAM Management](../Pipeline_Usage/VRAM_management.md).

 ## How Much VRAM Does a 20B Model Need?

@@ -351,7 +351,7 @@ module_map={
 }
 ```

-In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md#more-usage-methods).
+In addition, `vram_config` and `vram_limit` are also required, which have been introduced in [VRAM Management](../Pipeline_Usage/VRAM_management.md#more-usage-methods).

 Call `enable_vram_management` to enable VRAM management. Note that the `device` when loading the model is `cpu`, consistent with `offload_device`:

@@ -398,7 +398,7 @@ The above code only requires 2G VRAM to run the `forward` of a 20B model.

 ## Disk Offload

-[Disk Offload](/docs/en/Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:
+[Disk Offload](../Pipeline_Usage/VRAM_management.md#disk-offload) is a special VRAM management scheme that needs to be enabled during the model loading process, not after the model is loaded. Usually, when the above code can run smoothly, Disk Offload can be directly enabled:

 ```python
 from diffsynth.core import load_model, enable_vram_management, AutoWrappedLinear, AutoWrappedModule
@@ -439,7 +439,7 @@ with torch.no_grad():
    output = model(**inputs)
 ```

-Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
+Disk Offload is an extremely special VRAM management scheme. It only supports `.safetensors` format files, not binary files such as `.bin`, `.pth`, `.ckpt`, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.

 If there are situations where Disk Offload cannot run normally but non-Disk Offload can run normally, please submit an issue to us on GitHub.

--- a/docs/en/Developer_Guide/Integrating_Your_Model.md
+++ b/docs/en/Developer_Guide/Integrating_Your_Model.md
@@ -183,4 +183,4 @@ Loaded model: {

 ## Step 5: Writing Model VRAM Management Scheme

-`DiffSynth-Studio` supports complex VRAM management. See [Enabling VRAM Management](/docs/en/Developer_Guide/Enabling_VRAM_management.md) for details.
+`DiffSynth-Studio` supports complex VRAM management. See [Enabling VRAM Management](../Developer_Guide/Enabling_VRAM_management.md) for details.
--- a/docs/en/Developer_Guide/Training_Diffusion_Models.md
+++ b/docs/en/Developer_Guide/Training_Diffusion_Models.md
@@ -1,6 +1,6 @@
 # Integrating Model Training

-After [integrating models](/docs/en/Developer_Guide/Integrating_Your_Model.md) and [implementing Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md), the next step is to integrate model training functionality.
+After [integrating models](../Developer_Guide/Integrating_Your_Model.md) and [implementing Pipeline](../Developer_Guide/Building_a_Pipeline.md), the next step is to integrate model training functionality.

 ## Training-Inference Consistent Pipeline Modification

--- a/docs/en/Makefile
+++ b/docs/en/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/en/Model_Details/Anima.md
+++ b/docs/en/Model_Details/Anima.md
@@ -0,0 +1,139 @@
+# Anima
+
+Anima is an image generation model trained and open-sourced by CircleStone Labs and Comfy Org.
+
+## Installation
+
+Before using this project for model inference and training, please install DiffSynth-Studio first.
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+For more installation information, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+The following code demonstrates how to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model for inference. VRAM management is enabled by default, allowing the framework to automatically control model parameter loading based on available VRAM. Minimum 8GB VRAM required.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+## Model Overview
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+Special training scripts:
+
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
+* Two-Stage Split Training: [doc](../Training/Split_Training.md)
+* End-to-End Direct Distillation: [doc](../Training/Direct_Distill.md)
+
+## Model Inference
+
+Models are loaded through `AnimaImagePipeline.from_pretrained`, see [Model Inference](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+Input parameters for `AnimaImagePipeline` inference include:
+
+* `prompt`: Text description of the desired image content.
+* `negative_prompt`: Content to exclude from the generated image (default: `""`).
+* `cfg_scale`: Classifier-free guidance parameter (default: 4.0).
+* `input_image`: Input image for image-to-image generation (default: `None`).
+* `denoising_strength`: Controls similarity to input image (default: 1.0).
+* `height`: Image height (must be multiple of 16, default: 1024).
+* `width`: Image width (must be multiple of 16, default: 1024).
+* `seed`: Random seed (default: `None`).
+* `rand_device`: Device for random noise generation (default: `"cpu"`).
+* `num_inference_steps`: Inference steps (default: 30).
+* `sigma_shift`: Scheduler sigma offset (default: `None`).
+* `progress_bar_cmd`: Progress bar implementation (default: `tqdm.tqdm`).
+
+For VRAM constraints, enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). Recommended low-VRAM configurations are provided in the "Model Overview" table above.
+
+## Model Training
+
+Anima models are trained through [`examples/anima/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/train.py) with parameters including:
+
+* General Training Parameters
+    * Dataset Configuration
+        * `--dataset_base_path`: Dataset root directory.
+        * `--dataset_metadata_path`: Metadata file path.
+        * `--dataset_repeat`: Dataset repetition per epoch.
+        * `--dataset_num_workers`: Dataloader worker count.
+        * `--data_file_keys`: Metadata fields to load (comma-separated).
+    * Model Loading
+        * `--model_paths`: Model paths (JSON format).
+        * `--model_id_with_origin_paths`: Model IDs with origin paths (e.g., `"anima-team/anima-1B:text_encoder/*.safetensors"`).
+        * `--extra_inputs`: Additional pipeline inputs (e.g., `controlnet_inputs` for ControlNet).
+        * `--fp8_models`: FP8-formatted models (same format as `--model_paths`).
+    * Training Configuration
+        * `--learning_rate`: Learning rate.
+        * `--num_epochs`: Training epochs.
+        * `--trainable_models`: Trainable components (e.g., `dit`, `vae`, `text_encoder`).
+        * `--find_unused_parameters`: Handle unused parameters in DDP training.
+        * `--weight_decay`: Weight decay value.
+        * `--task`: Training task (default: `sft`).
+    * Output Configuration
+        * `--output_path`: Model output directory.
+        * `--remove_prefix_in_ckpt`: Remove state dict prefixes.
+        * `--save_steps`: Model saving interval.
+    * LoRA Configuration
+        * `--lora_base_model`: Target model for LoRA.
+        * `--lora_target_modules`: Target modules for LoRA.
+        * `--lora_rank`: LoRA rank.
+        * `--lora_checkpoint`: LoRA checkpoint path.
+        * `--preset_lora_path`: Preloaded LoRA checkpoint path.
+        * `--preset_lora_model`: Model to merge LoRA with (e.g., `dit`).
+    * Gradient Configuration
+        * `--use_gradient_checkpointing`: Enable gradient checkpointing.
+        * `--use_gradient_checkpointing_offload`: Offload checkpointing to CPU.
+        * `--gradient_accumulation_steps`: Gradient accumulation steps.
+    * Image Resolution
+        * `--height`: Image height (empty for dynamic resolution).
+        * `--width`: Image width (empty for dynamic resolution).
+        * `--max_pixels`: Maximum pixel area for dynamic resolution.
+* Anima-Specific Parameters
+    * `--tokenizer_path`: Tokenizer path for text-to-image models.
+    * `--tokenizer_t5xxl_path`: T5-XXL tokenizer path.
+
+We provide a sample image dataset for testing:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
+```
+
+For training script details, refer to [Model Training](../Pipeline_Usage/Model_Training.md). For advanced training techniques, see [Training Framework Documentation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/).
--- a/docs/en/Model_Details/FLUX.md
+++ b/docs/en/Model_Details/FLUX.md
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).

 ## Quick Start

@@ -81,31 +81,31 @@ graph LR;

 | Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - | - |
-| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
-| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
-| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
-| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
-| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
-| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
-| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
-| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
-| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
-| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
-| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
-| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](/examples/flux/model_inference/Step1X-Edit.py) | [code](/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](/examples/flux/model_training/full/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
-| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](/examples/flux/model_inference/FLEX.2-preview.py) | [code](/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
-| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](/examples/flux/model_training/full/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
+| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
+| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
+| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
+| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
+| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
+| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
+| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
+| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
+| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
+| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
+| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
+| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
+| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
+| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Nexus-Gen.py) |

 Special Training Scripts:

-* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/flux/model_training/special/differential_training/)
-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/flux/model_training/special/fp8_training/)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/flux/model_training/special/split_training/)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
+* Two-stage Split Training: [doc](../Training/Split_Training.md)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)

 ## Model Inference

-Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `FluxImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).

 Input parameters for `FluxImagePipeline` inference include:

@@ -143,11 +143,11 @@ Input parameters for `FluxImagePipeline` inference include:
 * `flex_control_stop`: Flex model control stop timestep.
 * `nexus_gen_reference_image`: Nexus-Gen model reference image.

-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

 ## Model Training

-FLUX series models are uniformly trained through [`examples/flux/model_training/train.py`](/examples/flux/model_training/train.py), and the script parameters include:
+FLUX series models are uniformly trained through [`examples/flux/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/train.py), and the script parameters include:

 * General Training Parameters
    * Dataset Basic Configuration
@@ -195,7 +195,7 @@ FLUX series models are uniformly trained through [`examples/flux/model_training/
 We have built a sample image dataset for your testing. You can download this dataset with the following command:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
--- a/docs/en/Model_Details/FLUX2.md
+++ b/docs/en/Model_Details/FLUX2.md
@@ -21,7 +21,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).

 ## Quick Start

@@ -61,22 +61,22 @@ image.save("image.jpg")

 | Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
-|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
-|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
-|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
-|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|
+|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-dev.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-klein-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-4B.py)|
+|[black-forest-labs/FLUX.2-klein-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-9B.py)|
+|[black-forest-labs/FLUX.2-klein-base-4B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-4B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-base-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-base-4B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-base-4B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-4B.py)|
+|[black-forest-labs/FLUX.2-klein-base-9B](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-klein-base-9B)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|

 Special Training Scripts:

-* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md)
-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md)
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
+* Two-stage Split Training: [doc](../Training/Split_Training.md)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)

 ## Model Inference

-Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).

 Input parameters for `Flux2ImagePipeline` inference include:

@@ -95,11 +95,11 @@ Input parameters for `Flux2ImagePipeline` inference include:
 * `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
 * `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.

-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

 ## Model Training

-FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](/examples/flux2/model_training/train.py), and the script parameters include:
+FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/train.py), and the script parameters include:

 * General Training Parameters
    * Dataset Basic Configuration
@@ -145,7 +145,7 @@ FLUX.2 series models are uniformly trained through [`examples/flux2/model_traini
 We have built a sample image dataset for your testing. You can download this dataset with the following command:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
--- a/docs/en/Model_Details/LTX-2.md
+++ b/docs/en/Model_Details/LTX-2.md
@@ -0,0 +1,223 @@
+# LTX-2
+
+LTX-2 is a series of audio-video generation models developed by Lightricks.
+
+## Installation
+
+Before using this project for model inference and training, please install DiffSynth-Studio first.
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+For more information about installation, please refer to [Installation Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+Run the following code to quickly load the [Lightricks/LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2) model and perform inference. VRAM management has been enabled, and the framework will automatically control model parameter loading based on remaining VRAM. It can run with a minimum of 8GB 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",
+}
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
+# use the repackaged modelconfig from "DiffSynth-Studio/LTX-2-Repackage" to avoid redundant model loading
+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="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="transformer.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="text_encoder_post_modules.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vae_decoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors", **vram_config),
+        ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="video_vae_encoder.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,
+)
+
+# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
+# 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,
+)
+```
+
+## Model Overview
+|Model ID|Additional Parameters|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
+|-|-|-|-|-|-|-|-|
+|[Lightricks/LTX-2.3: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2.3-I2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2.3-I2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2.3-I2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2.3-I2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-I2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`input_images`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-I2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2.3-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2.3-T2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2.3-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV.py)|
+|[Lightricks/LTX-2.3: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2.3)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: A2V](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-A2V-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-A2V-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2.3: Retake](https://www.modelscope.cn/models/Lightricks/LTX-2.3)|`retake_video`,`retake_video_regions`,`retake_audio`,`audio_sample_rate`,`retake_audio_regions`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-TwoStage-Retake.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-TwoStage-Retake.py)|-|-|-|-|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control](https://www.modelscope.cn/models/Lightricks/LTX-2.3-22b-IC-LoRA-Motion-Track-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2.3-T2AV-IC-LoRA-Motion-Track-Control.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2.3-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2.3-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/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](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/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](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/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](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/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](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
+
+## Model Inference
+
+Models are loaded through `LTX2AudioVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+Input parameters for `LTX2AudioVideoPipeline` inference include:
+
+* `prompt`: Prompt describing the content appearing in the video.
+* `negative_prompt`: Negative prompt describing content that should not appear in the video, default value is `""`.
+* `cfg_scale`: Classifier-free guidance parameter, default value is 3.0.
+* `input_images`: List of input images for image-to-video generation.
+* `input_images_indexes`: Frame index list of input images in the video.
+* `input_images_strength`: Strength of input images, default value is 1.0.
+* `denoising_strength`: Denoising strength, range is 0～1, default value is 1.0.
+* `seed`: Random seed. Default is `None`, which means completely random.
+* `rand_device`: Computing device for generating random Gaussian noise matrix, default is `"cpu"`. When set to `cuda`, different results will be generated on different GPUs.
+* `height`: Video height, must be a multiple of 32 (single-stage) or 64 (two-stage).
+* `width`: Video width, must be a multiple of 32 (single-stage) or 64 (two-stage).
+* `num_frames`: Number of video frames, default value is 121, must be a multiple of 8 + 1.
+* `num_inference_steps`: Number of inference steps, default value is 40.
+* `tiled`: Whether to enable VAE tiling inference, default is `True`. When set to `True`, it can significantly reduce VRAM usage during VAE encoding/decoding stages, with slight errors and minor inference time extension.
+* `tile_size_in_pixels`: Pixel tiling size during VAE encoding/decoding stages, default is 512.
+* `tile_overlap_in_pixels`: Pixel tiling overlap size during VAE encoding/decoding stages, default is 128.
+* `tile_size_in_frames`: Frame tiling size during VAE encoding/decoding stages, default is 128.
+* `tile_overlap_in_frames`: Frame tiling overlap size during VAE encoding/decoding stages, default is 24.
+* `use_two_stage_pipeline`: Whether to use two-stage pipeline, default is `False`.
+* `use_distilled_pipeline`: Whether to use distilled pipeline, default is `False`.
+* `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be set to `lambda x:x` to hide the progress bar.
+
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the previous "Supported Inference Scripts" section.
+
+## Model Training
+
+LTX-2 series models are uniformly trained through [`examples/ltx2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/train.py), and the script parameters include:
+
+* General Training Parameters
+    * Dataset Basic Configuration
+        * `--dataset_base_path`: Root directory of the dataset.
+        * `--dataset_metadata_path`: Metadata file path of the dataset.
+        * `--dataset_repeat`: Number of times the dataset is repeated in each epoch.
+        * `--dataset_num_workers`: Number of processes for each DataLoader.
+        * `--data_file_keys`: Field names to be loaded from metadata, usually image or video file paths, separated by `,`.
+    * Model Loading Configuration
+        * `--model_paths`: Paths of models to be loaded. JSON format.
+        * `--model_id_with_origin_paths`: Model IDs with original paths, e.g., `"Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors"`. Separated by commas.
+        * `--extra_inputs`: Extra input parameters required by the model Pipeline, e.g., extra parameters when training image editing models, separated by `,`.
+        * `--fp8_models`: Models loaded in FP8 format, consistent with `--model_paths` or `--model_id_with_origin_paths` format. Currently only supports models whose parameters are not updated by gradients (no gradient backpropagation, or gradients only update their LoRA).
+    * Training Basic Configuration
+        * `--learning_rate`: Learning rate.
+        * `--num_epochs`: Number of epochs.
+        * `--trainable_models`: Trainable models, e.g., `dit`, `vae`, `text_encoder`.
+        * `--find_unused_parameters`: Whether there are unused parameters in DDP training. Some models contain redundant parameters that do not participate in gradient calculation, and this setting needs to be enabled to avoid errors in multi-GPU training.
+        * `--weight_decay`: Weight decay size, see [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html).
+        * `--task`: Training task, default is `sft`. Some models support more training modes, please refer to the documentation of each specific model.
+    * Output Configuration
+        * `--output_path`: Model saving path.
+        * `--remove_prefix_in_ckpt`: Remove prefix in the state dict of the model file.
+        * `--save_steps`: Interval of training steps to save the model. If this parameter is left blank, the model is saved once per epoch.
+    * LoRA Configuration
+        * `--lora_base_model`: Which model to add LoRA to.
+        * `--lora_target_modules`: Which layers to add LoRA to.
+        * `--lora_rank`: Rank of LoRA.
+        * `--lora_checkpoint`: Path of the LoRA checkpoint. If this path is provided, LoRA will be loaded from this checkpoint.
+        * `--preset_lora_path`: Preset LoRA checkpoint path. If this path is provided, this LoRA will be loaded in the form of being merged into the base model. This parameter is used for LoRA differential training.
+        * `--preset_lora_model`: Model that the preset LoRA is merged into, e.g., `dit`.
+    * Gradient Configuration
+        * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing.
+        * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to memory.
+        * `--gradient_accumulation_steps`: Number of gradient accumulation steps.
+    * Video Width/Height Configuration
+        * `--height`: Height of the video. Leave `height` and `width` blank to enable dynamic resolution.
+        * `--width`: Width of the video. Leave `height` and `width` blank to enable dynamic resolution.
+        * `--max_pixels`: Maximum pixel area of video frames. When dynamic resolution is enabled, video frames with resolution larger than this value will be downscaled, and video frames with resolution smaller than this value will remain unchanged.
+        * `--num_frames`: Number of frames in the video.
+* LTX-2 Series Specific Parameters
+    * `--tokenizer_path`: Path of the tokenizer, applicable to text-to-video models, leave blank to automatically download from remote.
+    * `--frame_rate`: frame rate of the training videos.
+
+We have built a sample video dataset for your testing. You can download this dataset with the following command:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
+```
+
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
--- a/docs/en/Model_Details/Overview.md
+++ b/docs/en/Model_Details/Overview.md
@@ -2,7 +2,7 @@

 ## Qwen-Image

-Documentation: [./Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)
+Documentation: [./Qwen-Image.md](../Model_Details/Qwen-Image.md)

 <details>

@@ -69,23 +69,23 @@ graph LR;

 | Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](/examples/qwen_image/model_inference/Qwen-Image.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
-| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
-| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
-| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
-| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
-| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
-| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
-| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
-| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
-| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
+| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
+| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
+| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
+| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
+| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
+| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
+| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
+| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
+| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
+| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |

 ## FLUX Series

-Documentation: [./FLUX.md](/docs/en/Model_Details/FLUX.md)
+Documentation: [./FLUX.md](../Model_Details/FLUX.md)

 <details>

@@ -149,24 +149,24 @@ graph LR;

 | Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - | - |
-| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
-| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
-| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
-| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
-| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
-| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
-| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
-| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
-| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
-| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
-| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
-| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](/examples/flux/model_inference/Step1X-Edit.py) | [code](/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](/examples/flux/model_training/full/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
-| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](/examples/flux/model_inference/FLEX.2-preview.py) | [code](/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
-| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](/examples/flux/model_training/full/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](/examples/flux/model_training/validate_lora/Nexus-Gen.py) |
+| [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev.py) |
+| [black-forest-labs/FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py) |
+| [black-forest-labs/FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) | `kontext_images` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py) |
+| [alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py) |
+| [InstantX/FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py) |
+| [jasperai/Flux.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler) | `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py) |
+| [InstantX/FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter) | `ipadapter_images`, `ipadapter_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py) |
+| [ByteDance/InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou) | `infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py) |
+| [DiffSynth-Studio/Eligen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen) | `eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py) |
+| [DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev) | `lora_encoder_inputs`, `lora_encoder_scale` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py) | - | - |
+| [DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py) | - | - | - | - | - |
+| [stepfun-ai/Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit) | `step1x_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Step1X-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Step1X-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Step1X-Edit.py) |
+| [ostris/Flex.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview) | `flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/FLEX.2-preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/FLEX.2-preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/FLEX.2-preview.py) |
+| [DiffSynth-Studio/Nexus-GenV2](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2) | `nexus_gen_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/full/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_full/Nexus-Gen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/lora/Nexus-Gen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/validate_lora/Nexus-Gen.py) |

 ## Wan Series

-Documentation: [./Wan.md](/docs/en/Model_Details/Wan.md)
+Documentation: [./Wan.md](../Model_Details/Wan.md)

 <details>

@@ -254,38 +254,38 @@ graph LR;

 | Model ID | Extra Parameters | Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
-| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
-| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
-| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
-| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
-| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
-| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
-| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
-| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
-| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
-| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
-| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
-| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
-| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
-| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](/examples/wanvideo/model_inference/LongCat-Video.py) | [code](/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
-| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
-| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
-| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
-| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
-| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
-| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
-| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
-| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
-| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
-| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
+| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
+| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
+| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
+| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
+| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
+| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
+| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
+| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
+| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
+| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
+| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
+| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
+| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
+| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
+| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
+| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
+| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
+| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
+| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
+| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
+| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
+| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
+| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
+| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
+| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |

-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/fp8_training/)
+* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/split_training/)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/direct_distill/)
--- a/docs/en/Model_Details/Qwen-Image.md
+++ b/docs/en/Model_Details/Qwen-Image.md
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).

 ## Quick Start

@@ -80,35 +80,44 @@ graph LR;

 | Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](/examples/qwen_image/model_inference/Qwen-Image.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
-|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
-| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
-| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
-|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
-|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
-|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
-| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
-| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
-| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
-| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
-| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
-| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
-| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
-| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
-|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|
+| [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image.py) |
+|[Qwen/Qwen-Image-2512](https://www.modelscope.cn/models/Qwen/Qwen-Image-2512)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-2512.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-2512.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-2512.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-2512.py)|
+| [Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py) |
+| [Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py) |
+|[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.0](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.0)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.1](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/FireRed-Image-Edit-1.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.1.py)|
+|[lightx2v/Qwen-Image-Edit-2511-Lightning](https://modelscope.cn/models/lightx2v/Qwen-Image-Edit-2511-Lightning)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-Lightning.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511-Lightning.py)|-|-|-|-|
+|[Qwen/Qwen-Image-Layered](https://www.modelscope.cn/models/Qwen/Qwen-Image-Layered)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Layered.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Layered.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Layered-Control.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Layered-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control.py)|
+|[DiffSynth-Studio/Qwen-Image-Layered-Control-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control-V2)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Layered-Control-V2.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Layered-Control-V2.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Layered-Control-V2.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Layered-Control-V2.py)|
+| [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
+| [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py) |
+| [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py) |
+| [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py) |
+| [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py) |
+| [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py) |
+| [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py) | - | - | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py) |
+| [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py) | - | - | - | - |
+|[DiffSynth-Studio/Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference/Qwen-Image-i2L.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_inference_low_vram/Qwen-Image-i2L.py)|-|-|-|-|

 Special Training Scripts:

-* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/qwen_image/model_training/special/differential_training/)
-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/qwen_image/model_training/special/fp8_training/)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_training/)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/differential_training/)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/fp8_training/)
+* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/split_training/)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
+
+DeepSpeed ZeRO Stage 3 Training: The Qwen-Image series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Qwen-Image model as an example, the following modifications are required:
+
+* `--config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml`
+* `--initialize_model_on_cpu`

 ## Model Inference

-Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).

 Input parameters for `QwenImagePipeline` inference include:

@@ -139,11 +148,11 @@ Input parameters for `QwenImagePipeline` inference include:
 * `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
 * `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.

-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

 ## Model Training

-Qwen-Image series models are uniformly trained through [`examples/qwen_image/model_training/train.py`](/examples/qwen_image/model_training/train.py), and the script parameters include:
+Qwen-Image series models are uniformly trained through [`examples/qwen_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/train.py), and the script parameters include:

 * General Training Parameters
    * Dataset Basic Configuration
@@ -190,7 +199,7 @@ Qwen-Image series models are uniformly trained through [`examples/qwen_image/mod
 We have built a sample image dataset for your testing. You can download this dataset with the following command:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
--- a/docs/en/Model_Details/Wan.md
+++ b/docs/en/Model_Details/Wan.md
@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).

 ## Quick Start

@@ -106,45 +106,52 @@ graph LR;

 | Model ID | Extra Parameters | Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
-| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
-| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
-| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
-| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
-| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
-| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
-| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
-| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
-| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
-| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
-| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
-| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
-| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
-| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
-| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
-| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](/examples/wanvideo/model_inference/LongCat-Video.py) | [code](/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
-| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
-| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
-| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
-| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
-| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
-| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
-| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
-| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
-| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
-| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
+| [Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py) |
+| [Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py) |
+| [Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py) |
+| [Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py) |
+| [Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py) |
+| [iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py) |
+| [Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py) |
+| [Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py) |
+| [PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py) |
+| [PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py) |
+| [PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py) |
+| [PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | `control_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py) |
+| [PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py) |
+| [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py) |
+| [DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1) | `motion_bucket_id` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py) |
+| [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/krea-realtime-video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/krea-realtime-video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py) |
+| [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) | `longcat_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/LongCat-Video.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/LongCat-Video.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/LongCat-Video.py) |
+| [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) | `vap_video`, `vap_prompt` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py) |
+| [Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B) | | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py) |
+| [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py) |
+| [Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py) |
+| [Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B) | `input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py) |
+| [Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B) | `input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py) |
+| [PAI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B) | `vace_control_video`, `vace_reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py) |
+| [PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP) | `input_image`, `end_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py) |
+| [PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control) | `control_video`, `reference_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py) |
+| [PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera) | `control_camera_video`, `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py) |
+| [openmoss/MOVA-360p](https://modelscope.cn/models/openmoss/MOVA-360p) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_inference/MOVA-360p-I2AV.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/full/MOVA-360P-I2AV.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/validate_full/MOVA-360p-I2AV.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/lora/MOVA-360P-I2AV.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/validate_lora/MOVA-360p-I2AV.py) |
+| [openmoss/MOVA-720p](https://modelscope.cn/models/openmoss/MOVA-720p) | `input_image` | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_inference/MOVA-720p-I2AV.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/full/MOVA-720P-I2AV.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/validate_full/MOVA-720p-I2AV.py) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/lora/MOVA-720P-I2AV.sh) | [code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/mova/model_training/validate_lora/MOVA-720p-I2AV.py) |

-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md), [code](/examples/wanvideo/model_training/special/fp8_training/)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/fp8_training/)
+* Two-stage Split Training: [doc](../Training/Split_Training.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/split_training/)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/direct_distill/)
+
+DeepSpeed ZeRO Stage 3 Training: The Wan series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Wan2.1-T2V-14B model as an example, the following modifications are required:
+
+* `--config_file examples/wanvideo/model_training/full/accelerate_config_zero3.yaml`
+* `--initialize_model_on_cpu`

 ## Model Inference

-Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).

 Input parameters for `WanVideoPipeline` inference include:

@@ -194,11 +201,11 @@ Input parameters for `WanVideoPipeline` inference include:
 * `tea_cache_model_id`: Model ID used by TeaCache.
 * `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.

-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

 ## Model Training

-Wan series models are uniformly trained through [`examples/wanvideo/model_training/train.py`](/examples/wanvideo/model_training/train.py), and the script parameters include:
+Wan series models are uniformly trained through [`examples/wanvideo/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/train.py), and the script parameters include:

 * General Training Parameters
    * Dataset Basic Configuration
@@ -246,7 +253,7 @@ Wan series models are uniformly trained through [`examples/wanvideo/model_traini
 We have built a sample video dataset for your testing. You can download this dataset with the following command:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).
--- a/docs/en/Model_Details/Z-Image.md
+++ b/docs/en/Model_Details/Z-Image.md
@@ -12,7 +12,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```

-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).

 ## Quick Start

@@ -52,21 +52,21 @@ image.save("image.jpg")

 |Model ID|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
 |-|-|-|-|-|-|-|
-|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
-|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
-|[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
-|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
-|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
-|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|
+|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[DiffSynth-Studio/Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-i2L.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-i2L.py)|-|-|-|-|
+|[Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.py)|
+|[PAI/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps](https://www.modelscope.cn/models/PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_inference_low_vram/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_full/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/validate_lora/Z-Image-Turbo-Fun-Controlnet-Tile-2.1-8steps.py)|

 Special Training Scripts:

-* Differential LoRA Training: [doc](/docs/en/Training/Differential_LoRA.md), [code](/examples/z_image/model_training/special/differential_training/)
-* Trajectory Imitation Distillation Training (Experimental Feature): [code](/examples/z_image/model_training/special/trajectory_imitation/)
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md), [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)
+* Trajectory Imitation Distillation Training (Experimental Feature): [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)

 ## Model Inference

-Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).

 Input parameters for `ZImagePipeline` inference include:

@@ -84,11 +84,11 @@ Input parameters for `ZImagePipeline` inference include:
 * `edit_image`: Edit images for image editing models, supporting multiple images.
 * `positive_only_lora`: LoRA weights used only in positive prompts.

-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.

 ## Model Training

-Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](/examples/z_image/model_training/train.py), and the script parameters include:
+Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/train.py), and the script parameters include:

 * General Training Parameters
    * Dataset Basic Configuration
@@ -134,16 +134,16 @@ Z-Image series models are uniformly trained through [`examples/z_image/model_tra
 We have built a sample image dataset for your testing. You can download this dataset with the following command:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

-We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](/docs/Training/).
+We have written recommended training scripts for each model, please refer to the table in the "Model Overview" section above. For how to write model training scripts, please refer to [Model Training](../Pipeline_Usage/Model_Training.md); for more advanced training algorithms, please refer to [Training Framework Detailed Explanation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/en/Training/).

 Training Tips:

 * [Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) is a distilled acceleration model. Therefore, direct training will quickly cause the model to lose its acceleration capability. The effect of inference with "acceleration configuration" (`num_inference_steps=8`, `cfg_scale=1`) becomes worse, while the effect of inference with "no acceleration configuration" (`num_inference_steps=30`, `cfg_scale=2`) becomes better. The following training and inference schemes can be adopted:
-    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + No Acceleration Configuration Inference
-    * Differential LoRA Training ([code](/examples/z_image/model_training/special/differential_training/)) + Acceleration Configuration Inference
+    * Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + No Acceleration Configuration Inference
+    * Differential LoRA Training ([code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)) + Acceleration Configuration Inference
        * An additional LoRA needs to be loaded in differential LoRA training, e.g., [ostris/zimage_turbo_training_adapter](https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter)
-    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Trajectory Imitation Distillation Training ([code](/examples/z_image/model_training/special/trajectory_imitation/)) + Acceleration Configuration Inference
-    * Standard SFT Training ([code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)) + Acceleration Configuration Inference
+    * Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Trajectory Imitation Distillation Training ([code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)) + Acceleration Configuration Inference
+    * Standard SFT Training ([code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)) + Load Distillation Acceleration LoRA During Inference ([model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)) + Acceleration Configuration Inference
--- a/docs/en/Pipeline_Usage/Environment_Variables.md
+++ b/docs/en/Pipeline_Usage/Environment_Variables.md
@@ -28,7 +28,7 @@ Model download root directory. Can be set to any local path. If `local_model_pat

 ## `DIFFSYNTH_ATTENTION_IMPLEMENTATION`

-Attention mechanism implementation method. Can be set to `flash_attention_3`, `flash_attention_2`, `sage_attention`, `xformers`, or `torch`. See [`./core/attention.md`](/docs/en/API_Reference/core/attention.md) for details.
+Attention mechanism implementation method. Can be set to `flash_attention_3`, `flash_attention_2`, `sage_attention`, `xformers`, or `torch`. See [`./core/attention.md`](../API_Reference/core/attention.md) for details.

 ## `DIFFSYNTH_DISK_MAP_BUFFER_SIZE`

--- a/docs/en/Pipeline_Usage/GPU_support.md
+++ b/docs/en/Pipeline_Usage/GPU_support.md
@@ -2,7 +2,7 @@

 `DiffSynth-Studio` supports various GPUs and NPUs. This document explains how to run model inference and training on these devices.

-Before you begin, please follow the [Installation Guide](/docs/en/Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.
+Before you begin, please follow the [Installation Guide](../Pipeline_Usage/Setup.md) to install the required GPU/NPU dependencies.

 ## NVIDIA GPU

@@ -58,6 +58,14 @@ video = pipe(
 save_video(video, "video.mp4", fps=15, quality=5)
 ```

+#### USP(Unified Sequence Parallel)
+If you want to use this feature on NPU, please install additional third-party libraries as follows:
+```shell
+pip install git+https://github.com/feifeibear/long-context-attention.git
+pip install git+https://github.com/xdit-project/xDiT.git
+```
+
+
 ### Training
 NPU startup script samples have been added for each type of model,the scripts are stored in the `examples/xxx/special/npu_training`, for example `examples/wanvideo/model_training/special/npu_training/Wan2.2-T2V-A14B-NPU.sh`.

@@ -81,4 +89,6 @@ Set 0 or not set: indicates not enabling the binding function
 #### Parameters for specific models
 | Model          | Parameter                 | Note              |
 |----------------|---------------------------|-------------------|
-| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
+| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
+| Qwen-Image series | --initialize_model_on_cpu | The model needs to be initialized on the CPU |
+| Z-Image series | --enable_npu_patch | Using NPU fusion operator to replace the corresponding operator in Z-image model to improve the performance of the model on NPU |
--- a/docs/en/Pipeline_Usage/Model_Inference.md
+++ b/docs/en/Pipeline_Usage/Model_Inference.md
@@ -22,7 +22,7 @@ pipe = QwenImagePipeline.from_pretrained(
 )
 ```

-Where `torch_dtype` and `device` are computation precision and computation device (not model precision and device). `model_configs` can be configured in multiple ways for model paths. For how models are loaded internally in this project, please refer to [`diffsynth.core.loader`](/docs/en/API_Reference/core/loader.md).
+Where `torch_dtype` and `device` are computation precision and computation device (not model precision and device). `model_configs` can be configured in multiple ways for model paths. For how models are loaded internally in this project, please refer to [`diffsynth.core.loader`](../API_Reference/core/loader.md).

 <details>

@@ -34,7 +34,7 @@ Where `torch_dtype` and `device` are computation precision and computation devic
 > ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
 > ```
 > 
-> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
+> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).

 </details>

@@ -61,7 +61,7 @@ Where `torch_dtype` and `device` are computation precision and computation devic

 </details>

-By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
+By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.

 ```shell
 import os
@@ -69,7 +69,7 @@ os.environ["DIFFSYNTH_SKIP_DOWNLOAD"] = "True"
 import diffsynth
 ```

-To download models from [HuggingFace](https://huggingface.co/), set [environment variable DIFFSYNTH_DOWNLOAD_SOURCE](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) to `huggingface`.
+To download models from [HuggingFace](https://huggingface.co/), set [environment variable DIFFSYNTH_DOWNLOAD_SOURCE](../Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) to `huggingface`.

 ```shell
 import os
@@ -102,4 +102,65 @@ image.save("image.jpg")

 Each model `Pipeline` has different input parameters. Please refer to the documentation for each model.

-If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md).
+If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](../Pipeline_Usage/VRAM_management.md).
+
+## Loading LoRA
+
+LoRA is a lightweight model training method that produces a small number of parameters to extend model capabilities. DiffSynth-Studio supports two ways to load LoRA: cold loading and hot loading.
+
+* Cold loading: When the base model does not have [VRAM management](../Pipeline_Usage/VRAM_management.md) enabled, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, but LoRA cannot be unloaded after loading.
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, lora, alpha=1)
+prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+```
+
+* Hot loading: When the base model has [VRAM management](../Pipeline_Usage/VRAM_management.md) enabled, LoRA will not be fused into the base model weights. In this case, inference speed will be slower, but LoRA can be unloaded through `pipe.clear_lora()` after loading.
+
+```python
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": torch.bfloat16,
+    "offload_device": "cuda",
+    "onload_dtype": torch.bfloat16,
+    "onload_device": "cuda",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, lora, alpha=1)
+prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+pipe.clear_lora()
+```
--- a/docs/en/Pipeline_Usage/Model_Training.md
+++ b/docs/en/Pipeline_Usage/Model_Training.md
@@ -65,35 +65,21 @@ image_1.jpg,"a dog"
 image_2.jpg,"a cat"
 ```

-We have built sample datasets for your testing. To understand how the universal dataset architecture is implemented, please refer to [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md).
+We have built sample datasets for your testing. To understand how the universal dataset architecture is implemented, please refer to [`diffsynth.core.data`](../API_Reference/core/data.md).

 <details>

-<summary>Sample Image Dataset</summary>
+<summary>Sample Dataset</summary>

 > ```shell
-> modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+> modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 > ```
-> 
-> Applicable to training of image generation models such as Qwen-Image and FLUX.
-
-</details>
-
-<details>
-
-<summary>Sample Video Dataset</summary>
-
-> ```shell
-> modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
-> ```
-> 
-> Applicable to training of video generation models such as Wan.

 </details>

 ## Loading Models

-Similar to [model loading during inference](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models), we support multiple ways to configure model paths, and the two methods can be mixed.
+Similar to [model loading during inference](../Pipeline_Usage/Model_Inference.md#loading-models), we support multiple ways to configure model paths, and the two methods can be mixed.

 <details>

@@ -115,15 +101,14 @@ Similar to [model loading during inference](/docs/en/Pipeline_Usage/Model_Infere
 > --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors"
 > ```
 > 
-> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
+> Model files are downloaded to the `./models` path by default, which can be modified through [environment variable DIFFSYNTH_MODEL_BASE_PATH](../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path).
 > 
-> By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](/docs/en/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.
+> By default, even after models have been downloaded, the program will still query remotely for missing files. To completely disable remote requests, set [environment variable DIFFSYNTH_SKIP_DOWNLOAD](../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) to `True`.

 </details>

 <details>

-<details>

 <summary>Load models from local file paths</summary>

@@ -237,11 +222,126 @@ accelerate launch --config_file examples/qwen_image/model_training/full/accelera

 ## Training Considerations

-* In addition to the `csv` format, dataset metadata also supports `json` and `jsonl` formats. For how to choose the best metadata format, please refer to [/docs/en/API_Reference/core/data.md#metadata](/docs/en/API_Reference/core/data.md#metadata)
+* In addition to the `csv` format, dataset metadata also supports `json` and `jsonl` formats. For how to choose the best metadata format, please refer to [../API_Reference/core/data.md#metadata](../API_Reference/core/data.md#metadata)
 * Training effectiveness is usually strongly correlated with training steps and weakly correlated with epoch count. Therefore, we recommend using the `--save_steps` parameter to save model files at training step intervals.
 * When data volume * `dataset_repeat` exceeds $10^9$, we observed that the dataset speed becomes significantly slower, which seems to be a `PyTorch` bug. We are not sure if newer versions of `PyTorch` have fixed this issue.
 * For learning rate `--learning_rate`, it is recommended to set to `1e-4` in LoRA training and `1e-5` in full training.
-* The training framework does not support batch size > 1. The reasons are complex. See [Q&A: Why doesn't the training framework support batch size > 1?](/docs/en/QA.md#why-doesnt-the-training-framework-support-batch-size--1)
+* The training framework does not support batch size > 1. The reasons are complex. See [Q&A: Why doesn't the training framework support batch size > 1?](../QA.md#why-doesnt-the-training-framework-support-batch-size--1)
 * Some models contain redundant parameters. For example, the text encoding part of the last layer of Qwen-Image's DiT part. When training these models, `--find_unused_parameters` needs to be set to avoid errors in multi-GPU training. For compatibility with community models, we do not intend to remove these redundant parameters.
 * The loss function value of Diffusion models has little relationship with actual effects. Therefore, we do not record loss function values during training. We recommend setting `--num_epochs` to a sufficiently large value, testing while training, and manually closing the training program after the effect converges.
-* `--use_gradient_checkpointing` is usually enabled unless GPU VRAM is sufficient; `--use_gradient_checkpointing_offload` is enabled as needed. See [`diffsynth.core.gradient`](/docs/en/API_Reference/core/gradient.md) for details.
+* `--use_gradient_checkpointing` is usually enabled unless GPU VRAM is sufficient; `--use_gradient_checkpointing_offload` is enabled as needed. See [`diffsynth.core.gradient`](../API_Reference/core/gradient.md) for details.
+
+## Low VRAM Training
+
+If you want to complete LoRA model training on GPU with low vram, you can combine [Two-Stage Split Training](../Training/Split_Training.md) with `deepspeed_zero3_offload` training. First, split the preprocessing steps into the first stage and store the computed results onto the hard disk. Second, read these results from the disk and train the denoising model. By using `deepspeed_zero3_offload`, the training parameters and optimizer states are offloaded to the CPU or disk. We provide examples for some models, primarily by specifying the `deepspeed` configuration via `--config_file`.
+
+Please note that the `deepspeed_zero3_offload` mode is incompatible with PyTorch's native gradient checkpointing mechanism. To address this, we have adapted the `checkpointing` interface of `deepspeed`. Users need to fill the `activation_checkpointing` field in the `deepspeed` configuration to enable gradient checkpointing.
+
+Below is the script for low VRAM model training for the Qwen-Image model:
+
+```shell
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --dataset_repeat 1 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_lora-splited-cache" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --task "sft:data_process" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters
+
+accelerate launch --config_file examples/qwen_image/model_training/special/low_vram_training/deepspeed_zero3_cpuoffload.yaml examples/qwen_image/model_training/train.py \
+  --dataset_base_path "./models/train/Qwen-Image_lora-splited-cache" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --task "sft:train" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --initialize_model_on_cpu
+```
+
+The configurations for `accelerate` and `deepspeed` are as follows:
+
+```yaml
+compute_environment: LOCAL_MACHINE
+debug: true
+deepspeed_config:
+  deepspeed_config_file: examples/qwen_image/model_training/special/low_vram_training/ds_z3_cpuoffload.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+machine_rank: 0
+main_training_function: main
+num_machines: 1
+num_processes: 1
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+```json
+{
+    "fp16": {
+        "enabled": "auto",
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "bf16": {
+        "enabled": "auto"
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "offload_optimizer": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "offload_param": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "overlap_comm": false,
+        "contiguous_gradients": true,
+        "sub_group_size": 1e9,
+        "reduce_bucket_size": 5e7,
+        "stage3_prefetch_bucket_size": 5e7,
+        "stage3_param_persistence_threshold": 1e5,
+        "stage3_max_live_parameters": 1e8,
+        "stage3_max_reuse_distance": 1e8,
+        "stage3_gather_16bit_weights_on_model_save": true
+    },
+    "activation_checkpointing": {
+        "partition_activations": false,
+        "cpu_checkpointing": false,
+        "contiguous_memory_optimization": false
+    },
+    "gradient_accumulation_steps": "auto",
+    "gradient_clipping": "auto",
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}
+```
--- a/docs/en/Pipeline_Usage/Setup.md
+++ b/docs/en/Pipeline_Usage/Setup.md
@@ -37,11 +37,11 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
   git clone https://github.com/modelscope/DiffSynth-Studio.git
   cd DiffSynth-Studio
   # aarch64/ARM
-   pip install -e .[npu_aarch64] --extra-index-url "https://download.pytorch.org/whl/cpu"
+   pip install -e .[npu_aarch64] 
   # x86
-   pip install -e .[npu]
+   pip install -e .[npu] --extra-index-url "https://download.pytorch.org/whl/cpu"

-When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](/docs/en/Pipeline_Usage/GPU_support.md#ascend-npu).
+When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](../Pipeline_Usage/GPU_support.md#ascend-npu).

 ## Other Installation Issues

--- a/docs/en/Pipeline_Usage/VRAM_management.md
+++ b/docs/en/Pipeline_Usage/VRAM_management.md
@@ -140,7 +140,7 @@ image.save("image.jpg")

 In more extreme cases, when memory is also insufficient to store the entire model, the Disk Offload feature allows lazy loading of model parameters, meaning each Layer of the model only reads the corresponding parameters from disk when the forward function is called. When enabling this feature, we recommend using high-speed SSD drives.

-Disk Offload is a very special VRAM management solution that only supports `.safetensors` format files, not `.bin`, `.pth`, `.ckpt`, or other binary files, and does not support [state dict converter](/docs/en/Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.
+Disk Offload is a very special VRAM management solution that only supports `.safetensors` format files, not `.bin`, `.pth`, `.ckpt`, or other binary files, and does not support [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-model-file-format-conversion) with Tensor reshape.

 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
@@ -196,7 +196,7 @@ Specifically, the VRAM management module divides model Layers into the following
 * Preparing: Intermediate state between Onload and Computation. A temporary storage state when VRAM allows. This state is controlled by the VRAM management mechanism and enters this state if and only if [vram_limit is set to unlimited] or [vram_limit is set and there is spare VRAM]
 * Computation: The model is being computed. This state is controlled by the VRAM management mechanism and is temporarily entered only during `forward`

-If you are a model developer and want to control the VRAM management granularity of a specific model, please refer to [../Developer_Guide/Enabling_VRAM_management.md](/docs/en/Developer_Guide/Enabling_VRAM_management.md).
+If you are a model developer and want to control the VRAM management granularity of a specific model, please refer to [../Developer_Guide/Enabling_VRAM_management.md](../Developer_Guide/Enabling_VRAM_management.md).

 ## Best Practices

--- a/docs/en/QA.md
+++ b/docs/en/QA.md
@@ -25,4 +25,11 @@ Even with suitable hardware conditions, we currently have no plans to support na
 * The main challenge of native FP8 precision training is precision overflow caused by gradient explosion. To ensure training stability, the model structure needs to be redesigned accordingly. However, no model developers are willing to do so at present.
 * Additionally, models trained with native FP8 precision can only be computed with BF16 precision during inference without Hopper architecture GPUs, theoretically resulting in generation quality inferior to FP8.

-Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
+Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
+
+## How to dynamically load LoRA models during inference?
+
+We support two loading methods for LoRA models. See [LoRA Loading](./Pipeline_Usage/Model_Inference.md#loading-lora) for details:
+
+* Cold Loading: When [VRAM Management](./Pipeline_Usage/VRAM_management.md) is not enabled for the base model, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, and LoRA cannot be unloaded after loading.
+* Hot Loading: When [VRAM Management](./Pipeline_Usage/VRAM_management.md) is enabled for the base model, LoRA will not be fused into the base model weights. In this case, inference speed will slow down, and LoRA can be unloaded after loading via `pipe.clear_lora()`.
--- a/docs/en/README.md
+++ b/docs/en/README.md
@@ -26,59 +26,61 @@ graph LR;

 This section introduces the basic usage of `DiffSynth-Studio`, including how to enable VRAM management for inference on GPUs with extremely low VRAM, and how to train various base models, LoRAs, ControlNets, and other models.

-* [Installation Dependencies](/docs/en/Pipeline_Usage/Setup.md)
-* [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md)
-* [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md)
-* [Model Training](/docs/en/Pipeline_Usage/Model_Training.md)
-* [Environment Variables](/docs/en/Pipeline_Usage/Environment_Variables.md)
-* [GPU/NPU Support](/docs/en/Pipeline_Usage/GPU_support.md)
+* [Installation Dependencies](./Pipeline_Usage/Setup.md)
+* [Model Inference](./Pipeline_Usage/Model_Inference.md)
+* [VRAM Management](./Pipeline_Usage/VRAM_management.md)
+* [Model Training](./Pipeline_Usage/Model_Training.md)
+* [Environment Variables](./Pipeline_Usage/Environment_Variables.md)
+* [GPU/NPU Support](./Pipeline_Usage/GPU_support.md)

 ## Section 2: Model Details

 This section introduces the Diffusion models supported by `DiffSynth-Studio`. Some model pipelines feature special functionalities such as controllable generation and parallel acceleration.

-* [FLUX.1](/docs/en/Model_Details/FLUX.md)
-* [Wan](/docs/en/Model_Details/Wan.md)
-* [Qwen-Image](/docs/en/Model_Details/Qwen-Image.md)
-* [FLUX.2](/docs/en/Model_Details/FLUX2.md)
-* [Z-Image](/docs/en/Model_Details/Z-Image.md)
+* [FLUX.1](./Model_Details/FLUX.md)
+* [Wan](./Model_Details/Wan.md)
+* [Qwen-Image](./Model_Details/Qwen-Image.md)
+* [FLUX.2](./Model_Details/FLUX2.md)
+* [Z-Image](./Model_Details/Z-Image.md)
+* [Anima](./Model_Details/Anima.md)
+* [LTX-2](./Model_Details/LTX-2.md)

 ## Section 3: Training Framework

 This section introduces the design philosophy of the training framework in `DiffSynth-Studio`, helping developers understand the principles of Diffusion model training algorithms.

-* [Basic Principles of Diffusion Models](/docs/en/Training/Understanding_Diffusion_models.md)
-* [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md)
-* [Enabling FP8 Precision in Training](/docs/en/Training/FP8_Precision.md)
-* [End-to-End Distillation Accelerated Training](/docs/en/Training/Direct_Distill.md)
-* [Two-Stage Split Training](/docs/en/Training/Split_Training.md)
-* [Differential LoRA Training](/docs/en/Training/Differential_LoRA.md)
+* [Basic Principles of Diffusion Models](./Training/Understanding_Diffusion_models.md)
+* [Standard Supervised Training](./Training/Supervised_Fine_Tuning.md)
+* [Enabling FP8 Precision in Training](./Training/FP8_Precision.md)
+* [End-to-End Distillation Accelerated Training](./Training/Direct_Distill.md)
+* [Two-Stage Split Training](./Training/Split_Training.md)
+* [Differential LoRA Training](./Training/Differential_LoRA.md)

 ## Section 4: Model Integration

 This section introduces how to integrate models into `DiffSynth-Studio` to utilize the framework's basic functions, helping developers provide support for new models in this project or perform inference and training of private models.

-* [Integrating Model Architecture](/docs/en/Developer_Guide/Integrating_Your_Model.md)
-* [Building a Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md)
-* [Enabling Fine-Grained VRAM Management](/docs/en/Developer_Guide/Enabling_VRAM_management.md)
-* [Model Training Integration](/docs/en/Developer_Guide/Training_Diffusion_Models.md)
+* [Integrating Model Architecture](./Developer_Guide/Integrating_Your_Model.md)
+* [Building a Pipeline](./Developer_Guide/Building_a_Pipeline.md)
+* [Enabling Fine-Grained VRAM Management](./Developer_Guide/Enabling_VRAM_management.md)
+* [Model Training Integration](./Developer_Guide/Training_Diffusion_Models.md)

 ## Section 5: API Reference

 This section introduces the independent core module `diffsynth.core` in `DiffSynth-Studio`, explaining how internal functions are designed and operate. Developers can use these functional modules in other codebase developments if needed.

-* [`diffsynth.core.attention`](/docs/en/API_Reference/core/attention.md): Attention mechanism implementation
-* [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md): Data processing operators and general datasets
-* [`diffsynth.core.gradient`](/docs/en/API_Reference/core/gradient.md): Gradient checkpointing
-* [`diffsynth.core.loader`](/docs/en/API_Reference/core/loader.md): Model download and loading
-* [`diffsynth.core.vram`](/docs/en/API_Reference/core/vram.md): VRAM management
+* [`diffsynth.core.attention`](./API_Reference/core/attention.md): Attention mechanism implementation
+* [`diffsynth.core.data`](./API_Reference/core/data.md): Data processing operators and general datasets
+* [`diffsynth.core.gradient`](./API_Reference/core/gradient.md): Gradient checkpointing
+* [`diffsynth.core.loader`](./API_Reference/core/loader.md): Model download and loading
+* [`diffsynth.core.vram`](./API_Reference/core/vram.md): VRAM management

 ## Section 6: Academic Guide

 This section introduces how to use `DiffSynth-Studio` to train new models, helping researchers explore new model technologies.

-* Training models from scratch 【coming soon】
-* Inference improvement techniques 【coming soon】
+* [Training models from scratch](./Research_Tutorial/train_from_scratch.md)
+* [Inference improvement techniques](./Research_Tutorial/inference_time_scaling.md)
 * Designing controllable generation models 【coming soon】
 * Creating new training paradigms 【coming soon】

@@ -86,4 +88,4 @@ This section introduces how to use `DiffSynth-Studio` to train new models, helpi

 This section summarizes common developer questions. If you encounter issues during usage or development, please refer to this section. If you still cannot resolve the problem, please submit an issue on GitHub.

-* [Frequently Asked Questions](/docs/en/QA.md)
+* [Frequently Asked Questions](./QA.md)
--- a/docs/en/Research_Tutorial/inference_time_scaling.ipynb
+++ b/docs/en/Research_Tutorial/inference_time_scaling.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8db54992",
+   "metadata": {},
+   "source": [
+    "# Inference Optimization Techniques\n",
+    "\n",
+    "DiffSynth-Studio aims to drive technological innovation through its foundational framework. This article demonstrates how to build a training-free image generation enhancement solution using DiffSynth-Studio, taking Inference-time scaling as an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0911cad4",
+   "metadata": {},
+   "source": [
+    "## 1. Image Quality Quantification\n",
+    "\n",
+    "First, we need to find an indicator to quantify image quality from generation models. Manual scoring is the most straightforward solution but too costly for large-scale applications. However, after collecting manual scores, training an image classification model to predict human scoring is completely feasible. PickScore [[1]](https://arxiv.org/abs/2305.01569) is such a model. Running the following code will automatically download and load the [PickScore model](https://modelscope.cn/models/AI-ModelScope/PickScore_v1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4faca4ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from modelscope import AutoProcessor, AutoModel\n",
+    "import torch\n",
+    "\n",
+    "class PickScore(torch.nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.processor = AutoProcessor.from_pretrained(\"laion/CLIP-ViT-H-14-laion2B-s32B-b79K\")\n",
+    "        self.model = AutoModel.from_pretrained(\"AI-ModelScope/PickScore_v1\").eval().to(\"cuda\")\n",
+    "\n",
+    "    def forward(self, image, prompt):\n",
+    "        image_inputs = self.processor(images=image, padding=True, truncation=True, max_length=77, return_tensors=\"pt\").to(\"cuda\")\n",
+    "        text_inputs = self.processor(text=prompt, padding=True, truncation=True, max_length=77, return_tensors=\"pt\").to(\"cuda\")\n",
+    "        with torch.inference_mode():\n",
+    "            image_embs = self.model.get_image_features(**image_inputs).pooler_output\n",
+    "            image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)\n",
+    "            text_embs = self.model.get_text_features(**text_inputs).pooler_output\n",
+    "            text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)\n",
+    "            score = (text_embs @ image_embs.T).flatten().item()\n",
+    "        return score\n",
+    "\n",
+    "reward_model = PickScore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f807cec",
+   "metadata": {},
+   "source": [
+    "## 2. Inference-time Scaling Techniques\n",
+    "\n",
+    "Inference-time Scaling [[2]](https://arxiv.org/abs/2504.00294) is an interesting technique aiming to improve generation quality by increasing computational costs during inference. For example, in language models, models like [Qwen/Qwen3.5-27B](https://modelscope.cn/models/Qwen/Qwen3.5-27B) and [deepseek-ai/DeepSeek-R1](deepseek-ai/DeepSeek-R1) use \"thinking mode\" to guide the model to spend more time considering results more carefully, producing more accurate answers. Next, we'll use the [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) model as an example to explore how to design Inference-time Scaling solutions for image generation models.\n",
+    "\n",
+    "> Before starting, we slightly modified the `Flux2ImagePipeline` code to allow initialization with specific Gaussian noise matrices for result reproducibility. See `Flux2Unit_NoiseInitializer` in [diffsynth/pipelines/flux2_image.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/pipelines/flux2_image.py).\n",
+    "\n",
+    "Run the following code to load the [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5818a87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig\n",
+    "\n",
+    "pipe = Flux2ImagePipeline.from_pretrained(\n",
+    "    torch_dtype=torch.bfloat16,\n",
+    "    device=\"cuda\",\n",
+    "    model_configs=[\n",
+    "        ModelConfig(model_id=\"black-forest-labs/FLUX.2-klein-4B\", origin_file_pattern=\"text_encoder/*.safetensors\"),\n",
+    "        ModelConfig(model_id=\"black-forest-labs/FLUX.2-klein-4B\", origin_file_pattern=\"transformer/*.safetensors\"),\n",
+    "        ModelConfig(model_id=\"black-forest-labs/FLUX.2-klein-4B\", origin_file_pattern=\"vae/diffusion_pytorch_model.safetensors\"),\n",
+    "    ],\n",
+    "    tokenizer_config=ModelConfig(model_id=\"black-forest-labs/FLUX.2-klein-4B\", origin_file_pattern=\"tokenizer/\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f58e9945",
+   "metadata": {},
+   "source": [
+    "Generate a sketch cat image using the prompt `\"sketch, a cat\"` and score it with the PickScore model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ea2d258",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_noise(noise, pipe, reward_model, prompt):\n",
+    "    # Generate an image and compute the score.\n",
+    "    image = pipe(\n",
+    "        prompt=prompt,\n",
+    "        num_inference_steps=4,\n",
+    "        initial_noise=noise,\n",
+    "        progress_bar_cmd=lambda x: x,\n",
+    "    )\n",
+    "    score = reward_model(image, prompt)\n",
+    "    return score\n",
+    "\n",
+    "torch.manual_seed(1)\n",
+    "prompt = \"sketch, a cat\"\n",
+    "noise = pipe.generate_noise((1, 128, 64, 64), rand_device=\"cuda\", rand_torch_dtype=pipe.torch_dtype)\n",
+    "\n",
+    "image_1 = pipe(prompt, num_inference_steps=4, initial_noise=noise)\n",
+    "print(\"Score:\", reward_model(image_1, prompt))\n",
+    "image_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e11694e",
+   "metadata": {},
+   "source": [
+    "### 2.1 Best-of-N Random Search\n",
+    "\n",
+    "Model generation results have inherent randomness. Different random seeds produce different images - sometimes high quality, sometimes low. This leads to a simple Inference-time scaling solution: generate images using multiple random seeds, score them with PickScore, and retain only the highest-scoring image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "241f10d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "def random_search(base_latents, objective_reward_fn, total_eval_budget):\n",
+    "    # Search for the noise randomly.\n",
+    "    best_noise = base_latents\n",
+    "    best_score = objective_reward_fn(base_latents)\n",
+    "    for it in tqdm(range(total_eval_budget - 1)):\n",
+    "        noise = pipe.generate_noise((1, 128, 64, 64), seed=None)\n",
+    "        score = objective_reward_fn(noise)\n",
+    "        if score > best_score:\n",
+    "            best_score, best_noise = score, noise\n",
+    "    return best_noise\n",
+    "\n",
+    "best_noise = random_search(\n",
+    "    base_latents=noise,\n",
+    "    objective_reward_fn=lambda noise: evaluate_noise(noise, pipe, reward_model, prompt),\n",
+    "    total_eval_budget=50,\n",
+    ")\n",
+    "image_2 = pipe(prompt, num_inference_steps=4, initial_noise=best_noise)\n",
+    "print(\"Score:\", reward_model(image_2, prompt))\n",
+    "image_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e9bf966",
+   "metadata": {},
+   "source": [
+    "We can clearly see that after multiple random searches, the final selected cat image shows richer fur details and significantly improved PickScore. However, this brute-force random search is extremely inefficient - generation time multiplies while easily hitting quality limits. Therefore, we need a more efficient search method that achieves higher scores within the same computational budget."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9578349",
+   "metadata": {},
+   "source": [
+    "### 2.2 SES Search\n",
+    "\n",
+    "To overcome random search limitations, we introduce the Spectral Evolution Search (SES) algorithm [[3]](https://arxiv.org/abs/2602.03208). Detailed code is available at [diffsynth/utils/ses](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/utils/ses).\n",
+    "\n",
+    "Image generation in diffusion models is largely determined by low-frequency components in the initial noise. The SES algorithm decomposes Gaussian noise through wavelet transforms, fixes high-frequency details, and applies an evolution search using the cross-entropy method specifically on low-frequency components to find optimal initial noise with higher efficiency.\n",
+    "\n",
+    "Run the following code to perform efficient best Gaussian noise matrix search using SES."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adeed2aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from diffsynth.utils.ses import ses_search\n",
+    "\n",
+    "best_noise = ses_search(\n",
+    "    base_latents=noise,\n",
+    "    objective_reward_fn=lambda noise: evaluate_noise(noise, pipe, reward_model, prompt),\n",
+    "    total_eval_budget=50,\n",
+    ")\n",
+    "image_3 = pipe(prompt, num_inference_steps=4, initial_noise=best_noise)\n",
+    "print(\"Score:\", reward_model(image_3, prompt))\n",
+    "image_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "940a97f1",
+   "metadata": {},
+   "source": [
+    "Observing the results, under the same computational budget, SES achieves significantly higher PickScore compared to random search. The \"sketch cat\" demonstrates more refined overall composition and more layered contrast between light and shadow.\n",
+    "\n",
+    "Inference-time scaling can achieve higher image quality at the cost of longer inference time. The generated image data can then be used to train the model itself through methods like DPO [[4]](https://arxiv.org/abs/2311.12908) or differential training [[5]](https://arxiv.org/abs/2412.12888), opening another interesting research direction."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dzj8",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/docs/en/Research_Tutorial/inference_time_scaling.md
+++ b/docs/en/Research_Tutorial/inference_time_scaling.md
@@ -0,0 +1,140 @@
+# Inference Optimization Techniques
+
+DiffSynth-Studio aims to drive technological innovation through its foundational framework. This article demonstrates how to build a training-free image generation enhancement solution using DiffSynth-Studio, taking Inference-time scaling as an example.
+
+Notebook: https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Research_Tutorial/inference_time_scaling.ipynb
+
+## 1. Image Quality Quantification
+
+First, we need to find an indicator to quantify image quality from generation models. Manual scoring is the most straightforward solution but too costly for large-scale applications. However, after collecting manual scores, training an image classification model to predict human scoring is completely feasible. PickScore [[1]](https://arxiv.org/abs/2305.01569) is such a model. Running the following code will automatically download and load the [PickScore model](https://modelscope.cn/models/AI-ModelScope/PickScore_v1).
+
+```python
+from modelscope import AutoProcessor, AutoModel
+import torch
+
+class PickScore(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+        self.model = AutoModel.from_pretrained("AI-ModelScope/PickScore_v1").eval().to("cuda")
+
+    def forward(self, image, prompt):
+        image_inputs = self.processor(images=image, padding=True, truncation=True, max_length=77, return_tensors="pt").to("cuda")
+        text_inputs = self.processor(text=prompt, padding=True, truncation=True, max_length=77, return_tensors="pt").to("cuda")
+        with torch.inference_mode():
+            image_embs = self.model.get_image_features(**image_inputs).pooler_output
+            image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
+            text_embs = self.model.get_text_features(**text_inputs).pooler_output
+            text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
+            score = (text_embs @ image_embs.T).flatten().item()
+        return score
+
+reward_model = PickScore()
+```
+
+## 2. Inference-time Scaling Techniques
+
+Inference-time Scaling [[2]](https://arxiv.org/abs/2504.00294) is an interesting technique aiming to improve generation quality by increasing computational costs during inference. For example, in language models, models like [Qwen/Qwen3.5-27B](https://modelscope.cn/models/Qwen/Qwen3.5-27B) and [deepseek-ai/DeepSeek-R1](deepseek-ai/DeepSeek-R1) use "thinking mode" to guide the model to spend more time considering results more carefully, producing more accurate answers. Next, we'll use the [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) model as an example to explore how to design Inference-time Scaling solutions for image generation models.
+
+> Before starting, we slightly modified the `Flux2ImagePipeline` code to allow initialization with specific Gaussian noise matrices for result reproducibility. See `Flux2Unit_NoiseInitializer` in [diffsynth/pipelines/flux2_image.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/pipelines/flux2_image.py).
+
+Run the following code to load the [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) model.
+
+```python
+from diffsynth.pipelines.flux2_image import Flux2ImagePipeline, ModelConfig
+
+pipe = Flux2ImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="text_encoder/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="tokenizer/"),
+)
+```
+
+Generate a sketch cat image using the prompt `"sketch, a cat"` and score it with the PickScore model.
+
+```python
+def evaluate_noise(noise, pipe, reward_model, prompt):
+    # Generate an image and compute the score.
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=4,
+        initial_noise=noise,
+        progress_bar_cmd=lambda x: x,
+    )
+    score = reward_model(image, prompt)
+    return score
+
+torch.manual_seed(1)
+prompt = "sketch, a cat"
+noise = pipe.generate_noise((1, 128, 64, 64), rand_device="cuda", rand_torch_dtype=pipe.torch_dtype)
+
+image_1 = pipe(prompt, num_inference_steps=4, initial_noise=noise)
+print("Score:", reward_model(image_1, prompt))
+image_1
+```
+
+![Image](https://github.com/user-attachments/assets/b6546c6d-b368-4463-b703-d561a9134ba0)
+
+### 2.1 Best-of-N Random Search
+
+Model generation results have inherent randomness. Different random seeds produce different images - sometimes high quality, sometimes low. This leads to a simple Inference-time scaling solution: generate images using multiple random seeds, score them with PickScore, and retain only the highest-scoring image.
+
+```python
+from tqdm import tqdm
+
+def random_search(base_latents, objective_reward_fn, total_eval_budget):
+    # Search for the noise randomly.
+    best_noise = base_latents
+    best_score = objective_reward_fn(base_latents)
+    for it in tqdm(range(total_eval_budget - 1)):
+        noise = pipe.generate_noise((1, 128, 64, 64), seed=None)
+        score = objective_reward_fn(noise)
+        if score > best_score:
+            best_score, best_noise = score, noise
+    return best_noise
+
+best_noise = random_search(
+    base_latents=noise,
+    objective_reward_fn=lambda noise: evaluate_noise(noise, pipe, reward_model, prompt),
+    total_eval_budget=50,
+)
+image_2 = pipe(prompt, num_inference_steps=4, initial_noise=best_noise)
+print("Score:", reward_model(image_2, prompt))
+image_2
+```
+
+![Image](https://github.com/user-attachments/assets/b8dba70a-daa8-4368-8f32-a6c150daecb5)
+
+We can clearly see that after multiple random searches, the final selected cat image shows richer fur details and significantly improved PickScore. However, this brute-force random search is extremely inefficient - generation time multiplies while easily hitting quality limits. Therefore, we need a more efficient search method that achieves higher scores within the same computational budget.
+
+### 2.2 SES Search
+
+To overcome random search limitations, we introduce the Spectral Evolution Search (SES) algorithm [[3]](https://arxiv.org/abs/2602.03208). Detailed code is available at [diffsynth/utils/ses](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/utils/ses).
+
+Image generation in diffusion models is largely determined by low-frequency components in the initial noise. The SES algorithm decomposes Gaussian noise through wavelet transforms, fixes high-frequency details, and applies an evolution search using the cross-entropy method specifically on low-frequency components to find optimal initial noise with higher efficiency.
+
+Run the following code to perform efficient best Gaussian noise matrix search using SES.
+
+```python
+from diffsynth.utils.ses import ses_search
+
+best_noise = ses_search(
+    base_latents=noise,
+    objective_reward_fn=lambda noise: evaluate_noise(noise, pipe, reward_model, prompt),
+    total_eval_budget=50,
+)
+image_3 = pipe(prompt, num_inference_steps=4, initial_noise=best_noise)
+print("Score:", reward_model(image_3, prompt))
+image_3
+```
+
+![Image](https://github.com/user-attachments/assets/9a3f7598-3812-46d2-b333-cd65e49886ab)
+
+Observing the results, under the same computational budget, SES achieves significantly higher PickScore compared to random search. The "sketch cat" demonstrates more refined overall composition and more layered contrast between light and shadow.
+
+Inference-time scaling can achieve higher image quality at the cost of longer inference time. The generated image data can then be used to train the model itself through methods like DPO [[4]](https://arxiv.org/abs/2311.12908) or differential training [[5]](https://arxiv.org/abs/2412.12888), opening another interesting research direction.
--- a/docs/en/Research_Tutorial/train_from_scratch.md
+++ b/docs/en/Research_Tutorial/train_from_scratch.md
@@ -0,0 +1,476 @@
+# Training Models from Scratch
+
+DiffSynth-Studio's training engine supports training foundation models from scratch. This article introduces how to train a small text-to-image model with only 0.1B parameters from scratch.
+
+## 1. Building Model Architecture
+
+### 1.1 Diffusion Model
+
+From UNet [[1]](https://arxiv.org/abs/1505.04597) [[2]](https://arxiv.org/abs/2112.10752) to DiT [[3]](https://arxiv.org/abs/2212.09748) [[4]](https://arxiv.org/abs/2403.03206), the mainstream model architectures of Diffusion have undergone multiple evolutions. Typically, a Diffusion model's inputs include:
+
+* Image tensor (`latents`): The encoding of images, generated by the VAE model, containing partial noise
+* Text tensor (`prompt_embeds`): The encoding of text, generated by the text encoder
+* Timestep (`timestep`): A scalar used to mark which stage of the Diffusion process we are currently at
+
+The model's output is a tensor with the same shape as the image tensor, representing the denoising direction predicted by the model. For details about Diffusion model theory, please refer to [Basic Principles of Diffusion Models](../Training/Understanding_Diffusion_models.md). In this article, we build a DiT model with only 0.1B parameters: `AAADiT`.
+
+<details>
+<summary>Model Architecture Code</summary>
+
+```python
+import torch, accelerate
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange, repeat
+
+from transformers import AutoProcessor, AutoTokenizer
+from diffsynth.core import ModelConfig, gradient_checkpoint_forward, attention_forward, UnifiedDataset, load_model
+from diffsynth.diffusion import FlowMatchScheduler, DiffusionTrainingModule, FlowMatchSFTLoss, ModelLogger, launch_training_task
+from diffsynth.diffusion.base_pipeline import BasePipeline, PipelineUnit
+from diffsynth.models.general_modules import TimestepEmbeddings
+from diffsynth.models.z_image_text_encoder import ZImageTextEncoder
+from diffsynth.models.flux2_vae import Flux2VAE
+
+
+class AAAPositionalEmbedding(torch.nn.Module):
+    def __init__(self, height=16, width=16, dim=1024):
+        super().__init__()
+        self.image_emb = torch.nn.Parameter(torch.randn((1, dim, height, width)))
+        self.text_emb = torch.nn.Parameter(torch.randn((dim,)))
+
+    def forward(self, image, text):
+        height, width = image.shape[-2:]
+        image_emb = self.image_emb.to(device=image.device, dtype=image.dtype)
+        image_emb = torch.nn.functional.interpolate(image_emb, size=(height, width), mode="bilinear")
+        image_emb = rearrange(image_emb, "B C H W -> B (H W) C")
+        text_emb = self.text_emb.to(device=text.device, dtype=text.dtype)
+        text_emb = repeat(text_emb, "C -> B L C", B=text.shape[0], L=text.shape[1])
+        emb = torch.concat([image_emb, text_emb], dim=1)
+        return emb
+
+
+class AAABlock(torch.nn.Module):
+    def __init__(self, dim=1024, num_heads=32):
+        super().__init__()
+        self.norm_attn = torch.nn.RMSNorm(dim, elementwise_affine=False)
+        self.to_q = torch.nn.Linear(dim, dim)
+        self.to_k = torch.nn.Linear(dim, dim)
+        self.to_v = torch.nn.Linear(dim, dim)
+        self.to_out = torch.nn.Linear(dim, dim)
+        self.norm_mlp = torch.nn.RMSNorm(dim, elementwise_affine=False)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*3),
+            torch.nn.SiLU(),
+            torch.nn.Linear(dim*3, dim),
+        )
+        self.to_gate = torch.nn.Linear(dim, dim * 2)
+        self.num_heads = num_heads
+
+    def attention(self, emb, pos_emb):
+        emb = self.norm_attn(emb + pos_emb)
+        q, k, v = self.to_q(emb), self.to_k(emb), self.to_v(emb)
+        emb = attention_forward(
+            q, k, v,
+            q_pattern="b s (n d)", k_pattern="b s (n d)", v_pattern="b s (n d)", out_pattern="b s (n d)",
+            dims={"n": self.num_heads},
+        )
+        emb = self.to_out(emb)
+        return emb
+    
+    def feed_forward(self, emb, pos_emb):
+        emb = self.norm_mlp(emb + pos_emb)
+        emb = self.ff(emb)
+        return emb
+    
+    def forward(self, emb, pos_emb, t_emb):
+        gate_attn, gate_mlp = self.to_gate(t_emb).chunk(2, dim=-1)
+        emb = emb + self.attention(emb, pos_emb) * (1 + gate_attn)
+        emb = emb + self.feed_forward(emb, pos_emb) * (1 + gate_mlp)
+        return emb
+
+
+class AAADiT(torch.nn.Module):
+    def __init__(self, dim=1024):
+        super().__init__()
+        self.pos_embedder = AAAPositionalEmbedding(dim=dim)
+        self.timestep_embedder = TimestepEmbeddings(256, dim)
+        self.image_embedder = torch.nn.Sequential(torch.nn.Linear(128, dim), torch.nn.LayerNorm(dim))
+        self.text_embedder = torch.nn.Sequential(torch.nn.Linear(1024, dim), torch.nn.LayerNorm(dim))
+        self.blocks = torch.nn.ModuleList([AAABlock(dim) for _ in range(10)])
+        self.proj_out = torch.nn.Linear(dim, 128)
+
+    def forward(
+        self,
+        latents,
+        prompt_embeds,
+        timestep,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        pos_emb = self.pos_embedder(latents, prompt_embeds)
+        t_emb = self.timestep_embedder(timestep, dtype=latents.dtype).view(1, 1, -1)
+        image = self.image_embedder(rearrange(latents, "B C H W -> B (H W) C"))
+        text = self.text_embedder(prompt_embeds)
+        emb = torch.concat([image, text], dim=1)
+        for block_id, block in enumerate(self.blocks):
+            emb = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                emb=emb,
+                pos_emb=pos_emb,
+                t_emb=t_emb,
+            )
+        emb = emb[:, :latents.shape[-1] * latents.shape[-2]]
+        emb = self.proj_out(emb)
+        emb = rearrange(emb, "B (H W) C -> B C H W", W=latents.shape[-1])
+        return emb
+```
+
+</details>
+
+### 1.2 Encoder-Decoder Models
+
+Besides the Diffusion model used for denoising, we also need two other models:
+
+* Text Encoder: Used to encode text into tensors. We adopt the [Qwen/Qwen3-0.6B](https://modelscope.cn/models/Qwen/Qwen3-0.6B) model.
+* VAE Encoder-Decoder: The encoder part is used to encode images into tensors, and the decoder part is used to decode image tensors into images. We adopt the VAE model from [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B).
+
+The architectures of these two models are already integrated in DiffSynth-Studio, located at [/diffsynth/models/z_image_text_encoder.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/z_image_text_encoder.py) and [/diffsynth/models/flux2_vae.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/flux2_vae.py), so we don't need to modify any code.
+
+## 2. Building Pipeline
+
+We introduced how to build a model Pipeline in the document [Integrating Pipeline](../Developer_Guide/Building_a_Pipeline.md). For the model in this article, we also need to build a Pipeline to connect the text encoder, Diffusion model, and VAE encoder-decoder.
+
+<details>
+<summary>Pipeline Code</summary>
+
+```python
+class AAAImagePipeline(BasePipeline):
+    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler("FLUX.2")
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: AAADiT = None
+        self.vae: Flux2VAE = None
+        self.tokenizer: AutoProcessor = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            AAAUnit_PromptEmbedder(),
+            AAAUnit_NoiseInitializer(),
+            AAAUnit_InputImageEmbedder(),
+        ]
+        self.model_fn = model_fn_aaa
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = "cuda",
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = None,
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = AAAImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("aaa_dit")
+        pipe.vae = model_pool.fetch_model("flux2_vae")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 1.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 30,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=height//16*width//16)
+
+        # Parameters
+        inputs_posi = {"prompt": prompt}
+        inputs_nega = {"negative_prompt": negative_prompt}
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae.decode(inputs_shared["latents"])
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class AAAUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_embeds",),
+            onload_model_names=("text_encoder",)
+        )
+        self.hidden_states_layers = (-1,)
+
+    def process(self, pipe: AAAImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        text = pipe.tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False,
+        )
+        inputs = pipe.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(pipe.device)
+        output = pipe.text_encoder(**inputs, output_hidden_states=True, use_cache=False)
+        prompt_embeds = torch.concat([output.hidden_states[k] for k in self.hidden_states_layers], dim=-1)
+        return {"prompt_embeds": prompt_embeds}
+
+
+class AAAUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: AAAImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+class AAAUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: AAAImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(input_image)
+        input_latents = pipe.vae.encode(image)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+def model_fn_aaa(
+    dit: AAADiT,
+    latents=None,
+    prompt_embeds=None,
+    timestep=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    model_output = dit(
+        latents,
+        prompt_embeds,
+        timestep,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    return model_output
+```
+
+</details>
+
+## 3. Preparing Dataset
+
+To quickly verify training effectiveness, we use the dataset [Pokemon-First Generation](https://modelscope.cn/datasets/DiffSynth-Studio/pokemon-gen1), which is reproduced from the open-source project [pokemon-dataset-zh](https://github.com/42arch/pokemon-dataset-zh), containing 151 first-generation Pokemon from Bulbasaur to Mew. If you want to use other datasets, please refer to the document [Preparing Datasets](../Pipeline_Usage/Model_Training.md#preparing-datasets) and [`diffsynth.core.data`](../API_Reference/core/data.md).
+
+```shell
+modelscope download --dataset DiffSynth-Studio/pokemon-gen1 --local_dir ./data
+```
+
+### 4. Start Training
+
+The training process can be quickly implemented using Pipeline. We have placed the complete code at [../Research_Tutorial/train_from_scratch.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/en/Research_Tutorial/train_from_scratch.py), which can be directly started with `python docs/en/Research_Tutorial/train_from_scratch.py` for single GPU training.
+
+To enable multi-GPU parallel training, please run `accelerate config` to set relevant parameters, then use the command `accelerate launch docs/en/Research_Tutorial/train_from_scratch.py` to start training.
+
+This training script has no stopping condition, please manually close it when needed. The model converges after training approximately 60,000 steps, requiring 10-20 hours for single GPU training.
+
+<details>
+<summary>Training Code</summary>
+
+```python
+class AAATrainingModule(DiffusionTrainingModule):
+    def __init__(self, device):
+        super().__init__()
+        self.pipe = AAAImagePipeline.from_pretrained(
+            torch_dtype=torch.bfloat16,
+            device=device,
+            model_configs=[
+                ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
+                ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+            ],
+            tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+        )
+        self.pipe.dit = AAADiT().to(dtype=torch.bfloat16, device=device)
+        self.pipe.freeze_except(["dit"])
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+
+    def forward(self, data):
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        inputs_shared = {
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            "cfg_scale": 1,
+            "use_gradient_checkpointing": False,
+            "use_gradient_checkpointing_offload": False,
+        }
+        for unit in self.pipe.units:
+            inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
+        loss = FlowMatchSFTLoss(self.pipe, **inputs_shared, **inputs_posi)
+        return loss
+
+
+if __name__ == "__main__":
+    accelerator = accelerate.Accelerator(gradient_accumulation_steps=1)
+    dataset = UnifiedDataset(
+        base_path="data/images",
+        metadata_path="data/metadata_merged.csv",
+        max_data_items=10000000,
+        data_file_keys=("image",),
+        main_data_operator=UnifiedDataset.default_image_operator(base_path="data/images", height=256, width=256)
+    )
+    model = AAATrainingModule(device=accelerator.device)
+    model_logger = ModelLogger(
+        "models/AAA/v1",
+        remove_prefix_in_ckpt="pipe.dit.",
+    )
+    launch_training_task(
+        accelerator, dataset, model, model_logger,
+        learning_rate=2e-4,
+        num_workers=4,
+        save_steps=50000,
+        num_epochs=999999,
+    )
+```
+
+</details>
+
+## 5. Verifying Training Results
+
+If you don't want to wait for the model training to complete, you can directly download [our pre-trained model](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel).
+
+```shell
+modelscope download --model DiffSynth-Studio/AAAMyModel step-600000.safetensors --local_dir models/DiffSynth-Studio/AAAMyModel
+```
+
+Loading the model
+
+```python
+from diffsynth import load_model
+
+pipe = AAAImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+)
+pipe.dit = load_model(AAADiT, "models/DiffSynth-Studio/AAAMyModel/step-600000.safetensors", torch_dtype=torch.bfloat16, device="cuda")
+```
+
+Model inference, generating the first-generation Pokemon "starter trio". At this point, the images generated by the model basically match the training data.
+
+```python
+for seed, prompt in enumerate([
+    "green, lizard, plant, Grass, Poison, seed on back, red eyes, smiling expression, short stout limbs, sharp claws",
+    "orange, cream, lizard, Fire, flame on tail tip, large eyes, smiling expression, cream-colored belly patch, sharp claws",
+    "blue, beige, brown, turtle, water type, shell, big eyes, short limbs, curled tail",
+]):
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=" ",
+        num_inference_steps=30,
+        cfg_scale=10,
+        seed=seed,
+        height=256, width=256,
+    )
+    image.save(f"image_{seed}.jpg")
+```
+
+|![Image](https://github.com/user-attachments/assets/3c620fbf-5d28-4a1a-b887-519d85ac7d1c)|![Image](https://github.com/user-attachments/assets/909efd4c-9e61-4b33-9321-39da0e499b00)|![Image](https://github.com/user-attachments/assets/f3474bcd-b474-4a90-a1ea-579f67e161e3)|
+|-|-|-|
+
+Model inference, generating Pokemon with "sharp claws". At this point, different random seeds can produce different image results.
+
+```python
+for seed, prompt in enumerate([
+    "sharp claws",
+    "sharp claws",
+    "sharp claws",
+]):
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=" ",
+        num_inference_steps=30,
+        cfg_scale=10,
+        seed=seed+4,
+        height=256, width=256,
+    )
+    image.save(f"image_sharp_claws_{seed}.jpg")
+```
+
+|![Image](https://github.com/user-attachments/assets/94862edd-96ae-4276-a38f-795249f11a13)|![Image](https://github.com/user-attachments/assets/b2291f23-20ba-42de-8bfd-76cb4afc6eea)|![Image](https://github.com/user-attachments/assets/f2aab9a4-85ec-498e-8039-648b1289796e)|
+|-|-|-|
+
+Now, we have obtained a 0.1B small text-to-image model. This model can already generate 151 Pokemon, but cannot generate other image content. If you increase the amount of data, model parameters, and number of GPUs based on this, you can train a more powerful text-to-image model!
--- a/docs/en/Research_Tutorial/train_from_scratch.py
+++ b/docs/en/Research_Tutorial/train_from_scratch.py
@@ -0,0 +1,341 @@
+import torch, accelerate
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange, repeat
+
+from transformers import AutoProcessor, AutoTokenizer
+from diffsynth.core import ModelConfig, gradient_checkpoint_forward, attention_forward, UnifiedDataset, load_model
+from diffsynth.diffusion import FlowMatchScheduler, DiffusionTrainingModule, FlowMatchSFTLoss, ModelLogger, launch_training_task
+from diffsynth.diffusion.base_pipeline import BasePipeline, PipelineUnit
+from diffsynth.models.general_modules import TimestepEmbeddings
+from diffsynth.models.z_image_text_encoder import ZImageTextEncoder
+from diffsynth.models.flux2_vae import Flux2VAE
+
+
+class AAAPositionalEmbedding(torch.nn.Module):
+    def __init__(self, height=16, width=16, dim=1024):
+        super().__init__()
+        self.image_emb = torch.nn.Parameter(torch.randn((1, dim, height, width)))
+        self.text_emb = torch.nn.Parameter(torch.randn((dim,)))
+
+    def forward(self, image, text):
+        height, width = image.shape[-2:]
+        image_emb = self.image_emb.to(device=image.device, dtype=image.dtype)
+        image_emb = torch.nn.functional.interpolate(image_emb, size=(height, width), mode="bilinear")
+        image_emb = rearrange(image_emb, "B C H W -> B (H W) C")
+        text_emb = self.text_emb.to(device=text.device, dtype=text.dtype)
+        text_emb = repeat(text_emb, "C -> B L C", B=text.shape[0], L=text.shape[1])
+        emb = torch.concat([image_emb, text_emb], dim=1)
+        return emb
+
+
+class AAABlock(torch.nn.Module):
+    def __init__(self, dim=1024, num_heads=32):
+        super().__init__()
+        self.norm_attn = torch.nn.RMSNorm(dim, elementwise_affine=False)
+        self.to_q = torch.nn.Linear(dim, dim)
+        self.to_k = torch.nn.Linear(dim, dim)
+        self.to_v = torch.nn.Linear(dim, dim)
+        self.to_out = torch.nn.Linear(dim, dim)
+        self.norm_mlp = torch.nn.RMSNorm(dim, elementwise_affine=False)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*3),
+            torch.nn.SiLU(),
+            torch.nn.Linear(dim*3, dim),
+        )
+        self.to_gate = torch.nn.Linear(dim, dim * 2)
+        self.num_heads = num_heads
+
+    def attention(self, emb, pos_emb):
+        emb = self.norm_attn(emb + pos_emb)
+        q, k, v = self.to_q(emb), self.to_k(emb), self.to_v(emb)
+        emb = attention_forward(
+            q, k, v,
+            q_pattern="b s (n d)", k_pattern="b s (n d)", v_pattern="b s (n d)", out_pattern="b s (n d)",
+            dims={"n": self.num_heads},
+        )
+        emb = self.to_out(emb)
+        return emb
+    
+    def feed_forward(self, emb, pos_emb):
+        emb = self.norm_mlp(emb + pos_emb)
+        emb = self.ff(emb)
+        return emb
+    
+    def forward(self, emb, pos_emb, t_emb):
+        gate_attn, gate_mlp = self.to_gate(t_emb).chunk(2, dim=-1)
+        emb = emb + self.attention(emb, pos_emb) * (1 + gate_attn)
+        emb = emb + self.feed_forward(emb, pos_emb) * (1 + gate_mlp)
+        return emb
+
+
+class AAADiT(torch.nn.Module):
+    def __init__(self, dim=1024):
+        super().__init__()
+        self.pos_embedder = AAAPositionalEmbedding(dim=dim)
+        self.timestep_embedder = TimestepEmbeddings(256, dim)
+        self.image_embedder = torch.nn.Sequential(torch.nn.Linear(128, dim), torch.nn.LayerNorm(dim))
+        self.text_embedder = torch.nn.Sequential(torch.nn.Linear(1024, dim), torch.nn.LayerNorm(dim))
+        self.blocks = torch.nn.ModuleList([AAABlock(dim) for _ in range(10)])
+        self.proj_out = torch.nn.Linear(dim, 128)
+
+    def forward(
+        self,
+        latents,
+        prompt_embeds,
+        timestep,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        pos_emb = self.pos_embedder(latents, prompt_embeds)
+        t_emb = self.timestep_embedder(timestep, dtype=latents.dtype).view(1, 1, -1)
+        image = self.image_embedder(rearrange(latents, "B C H W -> B (H W) C"))
+        text = self.text_embedder(prompt_embeds)
+        emb = torch.concat([image, text], dim=1)
+        for block_id, block in enumerate(self.blocks):
+            emb = gradient_checkpoint_forward(
+                block,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                emb=emb,
+                pos_emb=pos_emb,
+                t_emb=t_emb,
+            )
+        emb = emb[:, :latents.shape[-1] * latents.shape[-2]]
+        emb = self.proj_out(emb)
+        emb = rearrange(emb, "B (H W) C -> B C H W", W=latents.shape[-1])
+        return emb
+
+
+class AAAImagePipeline(BasePipeline):
+    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler("FLUX.2")
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: AAADiT = None
+        self.vae: Flux2VAE = None
+        self.tokenizer: AutoProcessor = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            AAAUnit_PromptEmbedder(),
+            AAAUnit_NoiseInitializer(),
+            AAAUnit_InputImageEmbedder(),
+        ]
+        self.model_fn = model_fn_aaa
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = "cuda",
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = None,
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = AAAImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("aaa_dit")
+        pipe.vae = model_pool.fetch_model("flux2_vae")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 1.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 30,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=height//16*width//16)
+
+        # Parameters
+        inputs_posi = {"prompt": prompt}
+        inputs_nega = {"negative_prompt": negative_prompt}
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae.decode(inputs_shared["latents"])
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class AAAUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_embeds",),
+            onload_model_names=("text_encoder",)
+        )
+        self.hidden_states_layers = (-1,)
+
+    def process(self, pipe: AAAImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        text = pipe.tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False,
+        )
+        inputs = pipe.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(pipe.device)
+        output = pipe.text_encoder(**inputs, output_hidden_states=True, use_cache=False)
+        prompt_embeds = torch.concat([output.hidden_states[k] for k in self.hidden_states_layers], dim=-1)
+        return {"prompt_embeds": prompt_embeds}
+
+
+class AAAUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: AAAImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 128, height//16, width//16), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+class AAAUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: AAAImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(input_image)
+        input_latents = pipe.vae.encode(image)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+def model_fn_aaa(
+    dit: AAADiT,
+    latents=None,
+    prompt_embeds=None,
+    timestep=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    model_output = dit(
+        latents,
+        prompt_embeds,
+        timestep,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    return model_output
+
+
+class AAATrainingModule(DiffusionTrainingModule):
+    def __init__(self, device):
+        super().__init__()
+        self.pipe = AAAImagePipeline.from_pretrained(
+            torch_dtype=torch.bfloat16,
+            device=device,
+            model_configs=[
+                ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="model.safetensors"),
+                ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
+            ],
+            tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+        )
+        self.pipe.dit = AAADiT().to(dtype=torch.bfloat16, device=device)
+        self.pipe.freeze_except(["dit"])
+        self.pipe.scheduler.set_timesteps(1000, training=True)
+
+    def forward(self, data):
+        inputs_posi = {"prompt": data["prompt"]}
+        inputs_nega = {"negative_prompt": ""}
+        inputs_shared = {
+            "input_image": data["image"],
+            "height": data["image"].size[1],
+            "width": data["image"].size[0],
+            "cfg_scale": 1,
+            "use_gradient_checkpointing": False,
+            "use_gradient_checkpointing_offload": False,
+        }
+        for unit in self.pipe.units:
+            inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega)
+        loss = FlowMatchSFTLoss(self.pipe, **inputs_shared, **inputs_posi)
+        return loss
+
+
+if __name__ == "__main__":
+    accelerator = accelerate.Accelerator(gradient_accumulation_steps=1)
+    dataset = UnifiedDataset(
+        base_path="data/images",
+        metadata_path="data/metadata_merged.csv",
+        max_data_items=10000000,
+        data_file_keys=("image",),
+        main_data_operator=UnifiedDataset.default_image_operator(base_path="data/images", height=256, width=256)
+    )
+    model = AAATrainingModule(device=accelerator.device)
+    model_logger = ModelLogger(
+        "models/AAA/v1",
+        remove_prefix_in_ckpt="pipe.dit.",
+    )
+    launch_training_task(
+        accelerator, dataset, model, model_logger,
+        learning_rate=2e-4,
+        num_workers=4,
+        save_steps=50000,
+        num_epochs=999999,
+    )
--- a/docs/en/Training/Differential_LoRA.md
+++ b/docs/en/Training/Differential_LoRA.md
@@ -8,8 +8,8 @@ We were unable to identify the original proposer of differential LoRA training,

 Assume we have two similar-content images: Image 1 and Image 2. For example, both images contain a car, but Image 1 has fewer details while Image 2 has more details. In differential LoRA training, we perform two-step training:

-* Train LoRA 1 using Image 1 as training data with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
-* Train LoRA 2 using Image 2 as training data, after integrating LoRA 1 into the base model, with [standard supervised training](/docs/en/Training/Supervised_Fine_Tuning.md)
+* Train LoRA 1 using Image 1 as training data with [standard supervised training](../Training/Supervised_Fine_Tuning.md)
+* Train LoRA 2 using Image 2 as training data, after integrating LoRA 1 into the base model, with [standard supervised training](../Training/Supervised_Fine_Tuning.md)

 In the first training step, since there is only one training image, the LoRA model easily overfits. Therefore, after training, LoRA 1 will cause the model to generate Image 1 without hesitation, regardless of the random seed. In the second training step, the LoRA model overfits again. Thus, after training, with the combined effect of LoRA 1 and LoRA 2, the model will generate Image 2 without hesitation. In short:

--- a/docs/en/Training/Direct_Distill.md
+++ b/docs/en/Training/Direct_Distill.md
@@ -44,7 +44,7 @@ Click on the model links to go to the model pages and view the model effects.

 ## Using Distillation Accelerated Training in the Training Framework

-First, you need to generate training data. Please refer to the [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md) section to write inference code and generate training data with a sufficient number of inference steps.
+First, you need to generate training data. Please refer to the [Model Inference](../Pipeline_Usage/Model_Inference.md) section to write inference code and generate training data with a sufficient number of inference steps.

 Taking Qwen-Image as an example, the following code can generate an image:

@@ -67,7 +67,7 @@ image = pipe(prompt, seed=0, num_inference_steps=40)
 image.save("image.jpg")
 ```

-Then, we compile the necessary information into [metadata files](/docs/en/API_Reference/core/data.md#metadata):
+Then, we compile the necessary information into [metadata files](../API_Reference/core/data.md#metadata):

 ```csv
 image,prompt,seed,rand_device,num_inference_steps,cfg_scale
@@ -77,7 +77,7 @@ distill_qwen/image.jpg,"精致肖像，水下少女，蓝裙飘逸，发丝轻
 This sample dataset can be downloaded directly:

 ```shell
-modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset
 ```

 Then start LoRA distillation accelerated training:
@@ -86,11 +86,11 @@ Then start LoRA distillation accelerated training:
 bash examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh
 ```

-Please note that in the [training script parameters](/docs/en/Pipeline_Usage/Model_Training.md#script-parameters), the image resolution setting for the dataset should avoid triggering scaling processing. When setting `--height` and `--width` to enable fixed resolution, all training data must be generated with exactly the same width and height. When setting `--max_pixels` to enable dynamic resolution, the value of `--max_pixels` must be greater than or equal to the pixel area of any training image.
+Please note that in the [training script parameters](../Pipeline_Usage/Model_Training.md#script-parameters), the image resolution setting for the dataset should avoid triggering scaling processing. When setting `--height` and `--width` to enable fixed resolution, all training data must be generated with exactly the same width and height. When setting `--max_pixels` to enable dynamic resolution, the value of `--max_pixels` must be greater than or equal to the pixel area of any training image.

 ## Framework Design Concept

-Compared to [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md), Direct Distillation only differs in the training loss function. The loss function for Direct Distillation is `DirectDistillLoss` in `diffsynth.diffusion.loss`.
+Compared to [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md), Direct Distillation only differs in the training loss function. The loss function for Direct Distillation is `DirectDistillLoss` in `diffsynth.diffusion.loss`.

 ## Future Work

--- a/docs/en/Training/FP8_Precision.md
+++ b/docs/en/Training/FP8_Precision.md
@@ -1,12 +1,12 @@
 # Enabling FP8 Precision in Training

-Although `DiffSynth-Studio` supports [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md) in model inference, most of the techniques for reducing VRAM usage are not suitable for training. Offloading would cause extremely slow training processes.
+Although `DiffSynth-Studio` supports [VRAM management](../Pipeline_Usage/VRAM_management.md) in model inference, most of the techniques for reducing VRAM usage are not suitable for training. Offloading would cause extremely slow training processes.

-FP8 precision is the only VRAM management strategy that can be enabled during training. However, this framework currently does not support native FP8 precision training. For reasons, see [Q&A: Why doesn't the training framework support native FP8 precision training?](/docs/en/QA.md#why-doesnt-the-training-framework-support-native-fp8-precision-training). It only supports storing models whose parameters are not updated by gradients (models that do not require gradient backpropagation, or whose gradients only update their LoRA) in FP8 precision.
+FP8 precision is the only VRAM management strategy that can be enabled during training. However, this framework currently does not support native FP8 precision training. For reasons, see [Q&A: Why doesn't the training framework support native FP8 precision training?](../QA.md#why-doesnt-the-training-framework-support-native-fp8-precision-training). It only supports storing models whose parameters are not updated by gradients (models that do not require gradient backpropagation, or whose gradients only update their LoRA) in FP8 precision.

 ## Enabling FP8

-In our provided training scripts, you can quickly set models to be stored in FP8 precision through the `--fp8_models` parameter. Taking Qwen-Image LoRA training as an example, we provide a script for enabling FP8 training located at [`/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh`](/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh). After training is completed, you can verify the training results with the script [`/examples/qwen_image/model_training/special/fp8_training/validate.py`](/examples/qwen_image/model_training/special/fp8_training/validate.py).
+In our provided training scripts, you can quickly set models to be stored in FP8 precision through the `--fp8_models` parameter. Taking Qwen-Image LoRA training as an example, we provide a script for enabling FP8 training located at [`/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh). After training is completed, you can verify the training results with the script [`/examples/qwen_image/model_training/special/fp8_training/validate.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/special/fp8_training/validate.py).

 Please note that this FP8 VRAM management strategy does not support gradient updates. When a model is set to be trainable, FP8 precision cannot be enabled for that model. Models that support FP8 include two types:

--- a/docs/en/Training/Split_Training.md
+++ b/docs/en/Training/Split_Training.md
@@ -8,7 +8,7 @@ This document introduces split training, which can automatically divide the trai

 In the training process of most models, a large amount of computation occurs in "preprocessing," i.e., "computations unrelated to the denoising model," including VAE encoding, text encoding, etc. When the corresponding model parameters are fixed, the results of these computations are repetitive. For each data sample, the computational results are identical across multiple epochs. Therefore, we provide a "split training" feature that can automatically analyze and split the training process.

-For standard supervised training of ordinary text-to-image models, the splitting process is straightforward. It only requires splitting the computation of all [`Pipeline Units`](/docs/en/Developer_Guide/Building_a_Pipeline.md#units) into the first stage, storing the computational results to disk, and then reading these results from disk in the second stage for subsequent computations. However, if gradient backpropagation is required during preprocessing, the situation becomes extremely complex. To address this, we introduced a computational graph splitting algorithm to analyze how to split the computation.
+For standard supervised training of ordinary text-to-image models, the splitting process is straightforward. It only requires splitting the computation of all [`Pipeline Units`](../Developer_Guide/Building_a_Pipeline.md#units) into the first stage, storing the computational results to disk, and then reading these results from disk in the second stage for subsequent computations. However, if gradient backpropagation is required during preprocessing, the situation becomes extremely complex. To address this, we introduced a computational graph splitting algorithm to analyze how to split the computation.

 ## Computational Graph Splitting Algorithm

@@ -16,7 +16,7 @@ For standard supervised training of ordinary text-to-image models, the splitting

 ## Using Split Training

-Split training already supports [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md) and [Direct Distillation Training](/docs/en/Training/Direct_Distill.md). The `--task` parameter in the training command controls this. Taking LoRA training of the Qwen-Image model as an example, the pre-split training command is:
+Split training already supports [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md) and [Direct Distillation Training](../Training/Direct_Distill.md). The `--task` parameter in the training command controls this. Taking LoRA training of the Qwen-Image model as an example, the pre-split training command is:

 ```shell
 accelerate launch examples/qwen_image/model_training/train.py \
--- a/docs/en/Training/Supervised_Fine_Tuning.md
+++ b/docs/en/Training/Supervised_Fine_Tuning.md
@@ -1,10 +1,10 @@
 # Standard Supervised Training

-After understanding the [Basic Principles of Diffusion Models](/docs/en/Training/Understanding_Diffusion_models.md), this document introduces how the framework implements Diffusion model training. This document explains the framework's principles to help developers write new training code. If you want to use our provided default training functions, please refer to [Model Training](/docs/en/Pipeline_Usage/Model_Training.md).
+After understanding the [Basic Principles of Diffusion Models](../Training/Understanding_Diffusion_models.md), this document introduces how the framework implements Diffusion model training. This document explains the framework's principles to help developers write new training code. If you want to use our provided default training functions, please refer to [Model Training](../Pipeline_Usage/Model_Training.md).

 Recalling the model training pseudocode from earlier, when we actually write code, the situation becomes extremely complex. Some models require additional guidance conditions and preprocessing, such as ControlNet; some models require cross-computation with the denoising model, such as VACE; some models require Gradient Checkpointing due to excessive VRAM demands, such as Qwen-Image's DiT.

-To achieve strict consistency between inference and training, we abstractly encapsulate components like `Pipeline`, reusing inference code extensively during training. Please refer to [Integrating Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md) to understand the design of `Pipeline` components. Next, we'll introduce how the training framework utilizes `Pipeline` components to build training algorithms.
+To achieve strict consistency between inference and training, we abstractly encapsulate components like `Pipeline`, reusing inference code extensively during training. Please refer to [Integrating Pipeline](../Developer_Guide/Building_a_Pipeline.md) to understand the design of `Pipeline` components. Next, we'll introduce how the training framework utilizes `Pipeline` components to build training algorithms.

 ## Framework Design Concept

@@ -48,13 +48,13 @@ In `__init__`, model initialization is required. First load the model, then swit
        )
 ```

-The logic for loading models is basically consistent with inference, supporting loading models from remote and local paths. See [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md) for details, but please note not to enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md).
+The logic for loading models is basically consistent with inference, supporting loading models from remote and local paths. See [Model Inference](../Pipeline_Usage/Model_Inference.md) for details, but please note not to enable [VRAM Management](../Pipeline_Usage/VRAM_management.md).

 `switch_pipe_to_training_mode` can switch the model to training mode. See `switch_pipe_to_training_mode` for details.

 ### `forward`

-In `forward`, the loss function value needs to be calculated. First perform preprocessing, then compute the loss function through the `Pipeline`'s [`model_fn`](/docs/en/Developer_Guide/Building_a_Pipeline.md#model_fn).
+In `forward`, the loss function value needs to be calculated. First perform preprocessing, then compute the loss function through the `Pipeline`'s [`model_fn`](../Developer_Guide/Building_a_Pipeline.md#model_fn).

 ```python
    def forward(self, data):
@@ -90,7 +90,7 @@ The loss function calculation reuses `FlowMatchSFTLoss` from `diffsynth.diffusio
 The training framework requires other modules, including:

 * accelerator: Training launcher provided by `accelerate`, see [`accelerate`](https://huggingface.co/docs/accelerate/index) for details
-* dataset: Generic dataset, see [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md) for details
+* dataset: Generic dataset, see [`diffsynth.core.data`](../API_Reference/core/data.md) for details
 * model_logger: Model logger, see `diffsynth.diffusion.logger` for details

 ```python
--- a/docs/en/Training/Understanding_Diffusion_models.md
+++ b/docs/en/Training/Understanding_Diffusion_models.md
@@ -6,7 +6,7 @@ This document introduces the basic principles of Diffusion models to help you un

 Diffusion models generate clear images or video content through iterative denoising. We start by explaining the generation process of a data sample $x_0$. Intuitively, in a complete round of denoising, we start from random Gaussian noise $x_T$ and iteratively obtain $x_{T-1}$, $x_{T-2}$, $x_{T-3}$, $\cdots$, gradually reducing the noise content at each step until we finally obtain the noise-free data sample $x_0$.

-(Figure)
+![Image](https://github.com/user-attachments/assets/6471ae4c-a635-4924-8b36-b0bd4d42043d)

 This process is intuitive, but to understand the details, we need to answer several questions:

@@ -28,7 +28,7 @@ As for the intermediate values $\sigma_{T-1}$, $\sigma_{T-2}$, $\cdots$, $\sigma

 At an intermediate step, we can directly synthesize noisy data samples $x_t=(1-\sigma_t)x_0+\sigma_t x_T$.

-(Figure)
+![Image](https://github.com/user-attachments/assets/e25a2f71-123c-4e18-8b34-3a066af15667)

 ## How is the iterative denoising computation performed?

@@ -40,8 +40,6 @@ Before understanding the iterative denoising computation, we need to clarify wha

 Among these, the guidance condition $c$ is a newly introduced parameter that is input by the user. It can be text describing the image content or a sketch outlining the image structure.

-(Figure)
-
 The model's output $\hat \epsilon(x_t,c,t)$ approximately equals $x_T-x_0$, which is the direction of the entire diffusion process (the reverse process of denoising).

 Next, we analyze the computation occurring in one iteration. At time step $t$, after the model computes an approximation of $x_T-x_0$, we calculate the next $x_{t-1}$:
@@ -91,8 +89,6 @@ After understanding the iterative denoising process, we next consider how to tra

 The training process differs from the generation process. If we retain multi-step iterations during training, the gradient would need to backpropagate through multiple steps, bringing catastrophic time and space complexity. To improve computational efficiency, we randomly select a time step $t$ for training.

-(Figure)
-
 The following is pseudocode for the training process:

 > Obtain data sample $x_0$ and guidance condition $c$ from the dataset
@@ -113,7 +109,7 @@ The following is pseudocode for the training process:

 From theory to practice, more details need to be filled in. Modern Diffusion model architectures have matured, with mainstream architectures following the "three-stage" architecture proposed by Latent Diffusion, including data encoder-decoder, guidance condition encoder, and denoising model.

-(Figure)
+![Image](https://github.com/user-attachments/assets/43855430-6427-4aca-83a0-f684e01438b1)

 ### Data Encoder-Decoder

@@ -142,4 +138,4 @@ The denoising model is the true essence of Diffusion models, with diverse model

 ## How does this project encapsulate and implement model training?

-Please read the next document: [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md)
+Please read the next document: [Standard Supervised Training](../Training/Supervised_Fine_Tuning.md)
--- a/docs/en/conf.py
+++ b/docs/en/conf.py
@@ -0,0 +1,124 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+# import sphinx_book_theme
+
+sys.path.insert(0, os.path.abspath('../../'))
+# -- Project information -----------------------------------------------------
+
+project = 'diffsynth'
+copyright = '2022-2025, Alibaba ModelScope'
+author = 'ModelScope Authors'
+version_file = '../../diffsynth/version.py'
+html_theme = 'sphinx_rtd_theme'
+language = 'en'
+
+
+def get_version():
+    with open(version_file, 'r', encoding='utf-8') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+# The full version, including alpha/beta/rc tags
+version = get_version()
+release = version
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.napoleon',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.viewcode',
+    'sphinx_markdown_tables',
+    'sphinx_copybutton',
+    "sphinx_rtd_theme",
+    'sphinx.ext.mathjax',
+    'myst_parser',
+]
+# build the templated autosummary files
+autosummary_generate = True
+numpydoc_show_class_members = False
+
+# Enable overriding of function signatures in the first line of the docstring.
+autodoc_docstring_signature = True
+
+# Disable docstring inheritance
+autodoc_inherit_docstrings = False
+
+# Show type hints in the description
+autodoc_typehints = 'description'
+
+# Add parameter types if the parameter is documented in the docstring
+autodoc_typehints_description_target = 'documented_params'
+
+autodoc_default_options = {
+    'member-order': 'bysource',
+}
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+source_suffix = ['.rst', '.md']
+
+# The master toctree document.
+root_doc = 'index'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['build']
+# A list of glob-style patterns [1] that are used to find source files.
+# They are matched against the source file names relative to the source directory,
+# using slashes as directory separators on all platforms.
+# The default is **, meaning that all files are recursively included from the source directory.
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+# html_theme = 'sphinx_book_theme'
+# html_theme_path = [sphinx_book_theme.get_html_theme_path()]
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+# html_css_files = ['css/readthedocs.css']
+
+# -- Options for HTMLHelp output ---------------------------------------------
+# Output file base name for HTML help builder.
+
+# -- Extension configuration -------------------------------------------------
+# Ignore >>> when copying code
+copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_is_regexp = True
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'https://docs.python.org/': None}
+
+myst_enable_extensions = [
+    'amsmath',
+    'dollarmath',
+    'colon_fence',
+]
--- a/docs/en/index.rst
+++ b/docs/en/index.rst
@@ -0,0 +1,80 @@
+Welcome to DiffSynth-Studio's Documentation
+==========================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Documentation Introduction
+
+   README
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Getting Started
+
+   Pipeline_Usage/Setup
+   Pipeline_Usage/Model_Inference
+   Pipeline_Usage/VRAM_management
+   Pipeline_Usage/Model_Training
+   Pipeline_Usage/Environment_Variables
+   Pipeline_Usage/GPU_support
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Model Details
+
+   Model_Details/FLUX
+   Model_Details/Wan
+   Model_Details/Qwen-Image
+   Model_Details/FLUX2
+   Model_Details/Z-Image
+   Model_Details/Anima
+   Model_Details/LTX-2
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Training Framework
+
+   Training/Understanding_Diffusion_models
+   Training/Supervised_Fine_Tuning
+   Training/FP8_Precision
+   Training/Direct_Distill
+   Training/Split_Training
+   Training/Differential_LoRA
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Model Integration
+
+   Developer_Guide/Integrating_Your_Model
+   Developer_Guide/Building_a_Pipeline
+   Developer_Guide/Enabling_VRAM_management
+   Developer_Guide/Training_Diffusion_Models
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API Reference
+
+   API_Reference/core/attention
+   API_Reference/core/data
+   API_Reference/core/gradient
+   API_Reference/core/loader
+   API_Reference/core/vram
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Research Guide
+
+   Research_Tutorial/train_from_scratch
+   Research_Tutorial/inference_time_scaling
+
+.. toctree::
+   :maxdepth: 2
+   :caption: FAQ
+
+   QA
+
+Indices and tables
+==================
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -0,0 +1,11 @@
+docutils>=0.16.0
+myst_parser
+recommonmark
+sphinx>=5.3.0
+sphinx-book-theme
+sphinx-copybutton
+sphinx-autobuild
+sphinx-rtd-theme
+sphinx_markdown_tables
+sphinxcontrib-mermaid
+pymdown-extensions
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Zhongjie Duan	ba0626e38f	add example_dataset in training scripts (#1358 ) * add example_dataset in training scripts * fix example datasets	2026-03-18 15:37:03 +08:00
Hong Zhang	4ec4d9c20a	Merge pull request #1354 from mi804/low_vram_training_ds low vram training with deepspeed zero3	2026-03-17 16:09:52 +08:00
Zhongjie Duan	7a80f10fa4	update to 2.0.6 (#1350 )	2026-03-13 19:36:59 +08:00
Artiprocher	3bd5188b3e	update to 2.0.6	2026-03-13 19:36:33 +08:00
Zhongjie Duan	7650e9381e	Update audio.py (#1349 )	2026-03-13 17:57:14 +08:00
Hong Zhang	8c9ddc9274	support loading ltx2.3 stage2lora by statedict (#1348 ) * support ltx2.3 stage2lora by statedict * bug fix * bug fix	2026-03-13 17:19:18 +08:00
Hong Zhang	681df93a85	Mova (#1337 ) * support mova inference * mova media_io * add unified audio_video api & fix bug of mono audio input for ltx * support mova train * mova docs * fix bug	2026-03-13 13:06:07 +08:00
Hong Zhang	4741542523	Ltx2.3 a2v& retake video and audio (#1346 ) * temp commit * support ltx2 a2v * support ltx2.3 retake video and audio * add news * minor fix	2026-03-12 14:16:01 +08:00
Hong Zhang	c927062546	Merge pull request #1343 from mi804/ltx2.3_multiref Ltx2.3 multiref	2026-03-10 17:31:05 +08:00
Zhongjie Duan	f3ebd6f714	Merge pull request #1342 from modelscope/ltx2-default-prompt add default negative prompt of ltx2	2026-03-10 15:10:51 +08:00
Artiprocher	959471f083	add default negative prompt of ltx2	2026-03-10 15:10:03 +08:00
Hong Zhang	d9228074bd	refactor ltx2 stage2 pipeline (#1341 ) * refactor ltx2 pipeline * fix bug	2026-03-10 13:55:40 +08:00
Hong Zhang	b272253956	Ltx2.3 i2v training and sample frames with fixed fps (#1339 ) * add 2.3 i2v training scripts * add frame resampling by fixed fps * LoadVideo: add compatibility for not fix_frame_rate * refactor frame resampler * minor fix	2026-03-09 20:32:02 +08:00
Hong Zhang	7bc5611fb8	ltx2.3 bugfix & ic lora (#1336 ) * ltx2.3 ic lora inference&train * temp commit * fix first frame train-inference consistency * minor fix	2026-03-09 16:33:19 +08:00
Zhongjie Duan	f7d23c6551	Merge pull request #1338 from modelscope/cache-remove remove unnecessary params in cache	2026-03-09 14:11:59 +08:00
Artiprocher	13eff18e7d	remove unnecessary params in cache	2026-03-09 14:09:30 +08:00
Zhongjie Duan	a38954b72c	Merge pull request #1334 from mi804/ltx2.3 ltx2.3 train	2026-03-06 18:10:13 +08:00
mi804	d40efe897f	ltx2.3 train	2026-03-06 18:08:42 +08:00
Zhongjie Duan	c9c2561791	Merge pull request #1333 from mi804/ltx2.3 ltx2.3 docs	2026-03-06 16:53:56 +08:00
mi804	0139b042e0	fix link	2026-03-06 16:48:55 +08:00
mi804	ed9e4374af	ltx2.3 docs	2026-03-06 16:45:12 +08:00
Zhongjie Duan	2a0eb9c383	support ltx2.3 inference (#1332 )	2026-03-06 16:24:53 +08:00
mi804	73b13f4c86	support ltx2.3 inference	2026-03-06 16:07:17 +08:00
lzws	75ebd797da	add FireRed-Image-Edit-1.1 (#1331 )	2026-03-06 15:08:02 +08:00
Zhongjie Duan	31ba103d8e	Merge pull request #1330 from modelscope/ses-doc Research Tutorial Sec 2	2026-03-06 14:25:45 +08:00
Zhongjie Duan	c5aaa1da41	Merge pull request #1306 from mi804/layercontrol_v2 qwen_image layercontrol v2	2026-03-03 21:06:25 +08:00
Zhongjie Duan	6bcb99fd2e	Merge branch 'main' into layercontrol_v2	2026-03-03 21:04:04 +08:00
Zhongjie Duan	ab8f455c46	Merge pull request #1322 from modelscope/vram-bugfix bugfix	2026-03-03 15:34:06 +08:00
Artiprocher	add6f88324	bugfix	2026-03-03 15:33:42 +08:00
Zhongjie Duan	430b495100	Merge pull request #1321 from mi804/bugfix fix qwen_text_encoder bug in transformers>=5.2.0	2026-03-03 13:02:45 +08:00
mi804	62ba8a3f2e	fix qwen_text_encoder bug in transformers>=5.2.0	2026-03-03 12:44:36 +08:00
Zhongjie Duan	237d178733	Fix LoRA compatibility issues. (#1320 )	2026-03-03 11:08:31 +08:00
Zhongjie Duan	b3ef224042	support Anima gradient checkpointing (#1319 )	2026-03-02 19:06:55 +08:00
Zhongjie Duan	f43b18ec21	Update docs (#1318 ) * update docs	2026-03-02 18:59:13 +08:00
Zhongjie Duan	6d671db5d2	Support Anima (#1317 ) * support Anima Co-authored-by: mi804 <1576993271@qq.com>	2026-03-02 18:49:02 +08:00
mi804	07f5d88ac9	update modelid	2026-03-02 17:41:47 +08:00
Zhongjie Duan	880231b4be	Merge pull request #1315 from modelscope/docs2.0 update ltx-2 docs	2026-03-02 11:02:20 +08:00
mi804	b3f6c3275f	update ltx-2	2026-03-02 10:58:02 +08:00
Zhongjie Duan	29cd5c7612	Merge pull request #1275 from Mr-Neutr0n/fix-dit-none-check Fix AttributeError when pipe.dit is None during split training	2026-03-02 10:25:11 +08:00
Zhongjie Duan	ff4be1c7c7	Merge pull request #1293 from Mr-Neutr0n/fix/trajectory-loss-div-by-zero fix: prevent division by zero in TrajectoryImitationLoss at final denoising step	2026-03-02 10:21:39 +08:00
Zhongjie Duan	6b0fb1601f	Merge pull request #1296 from Explorer-Dong/fix/wan_vae fix: WanVAE2.2 encode and decode error	2026-03-02 10:19:36 +08:00
Zhongjie Duan	4b400c07eb	Merge pull request #1297 from Feng0w0/npu_fused [doc][NPU]Documentation on modifications, NPU environment installation, and additional parameter	2026-03-02 10:16:01 +08:00
Zhongjie Duan	6a6ae6d791	Merge pull request #1312 from mi804/ltx2-iclora Ltx2 iclora	2026-02-28 12:45:16 +08:00
mi804	1a380a6b62	minor fix	2026-02-28 11:09:10 +08:00
mi804	5ca74923e8	add readme	2026-02-28 10:56:08 +08:00
mi804	8b9a094c1b	ltx iclora train	2026-02-27 18:43:53 +08:00
mi804	5996c2b068	support inference	2026-02-27 16:48:16 +08:00
Zhongjie Duan	8fc7e005a6	Merge pull request #1309 from mi804/ltx2-train support ltx2 gradient_checkpointing	2026-02-26 19:31:04 +08:00
mi804	a18966c300	support ltx2 gradient_checkpointing	2026-02-26 19:19:59 +08:00
Zhongjie Duan	a87910bc65	Merge pull request #1307 from mi804/ltx2-train Support LTX-2 training.	2026-02-26 11:39:09 +08:00
mi804	f48662e863	update docs	2026-02-26 11:10:00 +08:00
mi804	8d8bfc7f54	minor fix	2026-02-25 19:04:10 +08:00
mi804	8e15dcd289	support ltx2 train -2	2026-02-25 18:06:02 +08:00
mi804	586ac9d8a6	support ltx-2 training	2026-02-25 17:19:57 +08:00
Hong Zhang	625b5ff16d	Apply suggestion from @gemini-code-assist[bot] Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-24 15:26:49 +08:00
mi804	ee73a29885	qwen_image layercontrol v2	2026-02-24 15:19:16 +08:00
Zhongjie Duan	288bbc7128	Merge pull request #1299 from modelscope/firered support FireRed	2026-02-15 14:18:13 +08:00
Zhongjie Duan	5002ac74dc	Update Qwen-Image.md	2026-02-15 14:15:44 +08:00
Zhongjie Duan	863a6ba597	Merge branch 'main' into firered	2026-02-15 14:12:44 +08:00
Artiprocher	b08bc1470d	support firered	2026-02-15 14:02:50 +08:00
feng0w0	96143aa26b	Merge branch 'npu_fused' of https://github.com/Feng0w0/DiffSynth-Studio into npu_fused	2026-02-13 10:06:39 +08:00
feng0w0	71cea4371c	[doc][NPU]Documentation on modifications, NPU environment installation, and additional parameter	2026-02-13 09:58:27 +08:00
Mr_Dwj	fc11fd4297	chore: remove invalid comment code	2026-02-13 09:38:14 +08:00
Mr_Dwj	bd3c5822a1	fix: WanVAE2.2 decode error	2026-02-13 01:13:08 +08:00
Mr_Dwj	96fb0f3afe	fix: unpack Resample38 output	2026-02-12 23:51:56 +08:00
Mr-Neutr0n	b68663426f	fix: preserve sign of denominator in clamp to avoid inverting gradient direction The previous .clamp(min=1e-6) on (sigma_ - sigma) flips the sign when the denominator is negative (which is the typical case since sigmas decrease monotonically). This would invert the target and cause training divergence. Use torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6) instead, which prevents division by zero while preserving the correct sign.	2026-02-11 21:04:55 +05:30
Mr-Neutr0n	0e6976a0ae	fix: prevent division by zero in trajectory imitation loss at last step	2026-02-11 19:51:25 +05:30
Hong Zhang	94b57e9677	Fix readthedocs rendering (#1290 ) * test latex * test latex * fix conf	2026-02-11 11:32:27 +08:00
Hong Zhang	3fb037d33a	Correct hyperlinks for docs	2026-02-10 20:59:47 +08:00
Hong Zhang	b3b63fef3e	Add readthedocs for diffsynth-studio * add conf docs * add conf docs * add index * add index * update ref * test root * add en * test relative * redirect relative * add document * test_document * test_document	2026-02-10 19:51:04 +08:00
Zhongjie Duan	f6d85f3c2e	Merge pull request #1282 from mi804/ltx-2 add inference script for ltx-2 lora	2026-02-10 15:13:06 +08:00
mi804	2f22e598b7	fix load lora	2026-02-10 15:06:04 +08:00
Hong Zhang	888caf8b88	Update README_zh.md Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-10 14:48:19 +08:00
mi804	b6e39c97af	add inference script for ltx-2 lora	2026-02-10 14:32:30 +08:00
Zhongjie Duan	02124c4034	Merge pull request #1280 from modelscope/issue-fix fix mix-precision issues in low-version torch	2026-02-10 11:14:12 +08:00
Artiprocher	fddc98ff16	fix mix-precision issues in low-version torch	2026-02-10 11:12:50 +08:00
Zhongjie Duan	0dfcd25cf3	Merge pull request #1278 from modelscope/issue-fix update lora loading in docs	2026-02-10 10:50:18 +08:00
Artiprocher	ff10fde47f	update lora loading in docs	2026-02-10 10:48:44 +08:00
Zhongjie Duan	dc94614c80	Merge pull request #1256 from Feng0w0/npu_fused [model][NPU]:Add NPU fusion operator patch to Zimage model to improve performance	2026-02-09 20:08:44 +08:00
feng0w0	e56a4d5730	[model][NPU]:Add NPU fusion operator patch to Zimage model to improve performance	2026-02-09 12:31:34 +08:00
feng0w0	3f8468893a	[model][NPU]:Add NPU fusion operator patch to Zimage model to improve performance	2026-02-09 09:51:06 +08:00
Mr-Neutr0n	6383ec358c	Fix AttributeError when pipe.dit is None When using split training with 'sft:data_process' task, the DiT model is not loaded but the attribute 'dit' exists with value None. The existing hasattr check returns True but then accessing siglip_embedder fails. Add an explicit None check before accessing pipe.dit.siglip_embedder. Fixes #1246	2026-02-07 05:23:11 +05:30
Zhongjie Duan	1b47e1dc22	Merge pull request #1272 from modelscope/zero3-fix Support DeepSpeed ZeRO 3	2026-02-06 16:33:12 +08:00
Artiprocher	b0bf78e915	refine code & doc	2026-02-06 16:27:23 +08:00
Zhongjie Duan	abdf66d09e	Merge pull request #1265 from lzws/main fix wanS2V bug and update readme	2026-02-06 10:22:48 +08:00
lzws	27b1fe240b	add examples	2026-02-05 17:17:10 +08:00
lzws	1635897516	update readme	2026-02-05 16:56:39 +08:00
lzws	8d172127cd	fix wans2v bug and update readme	2026-02-05 16:52:38 +08:00
feng0w0	fccb1ecdd7	Initialize qwen-image on CPU	2026-02-05 11:54:36 +08:00
Zhongjie Duan	c0f7e1db7c	Merge pull request #1261 from modelscope/examples-update update examples	2026-02-05 11:11:35 +08:00
Artiprocher	53890bafa4	update examples	2026-02-05 11:10:55 +08:00
feng0w0	6886f7ba35	fix wan decoder bug	2026-02-05 10:31:41 +08:00
Zhongjie Duan	afd48cd706	Merge pull request #1259 from mi804/multi_controlnet add example for multiple controlnet	2026-02-04 17:04:11 +08:00
mi804	24b68c2392	add example for multiple controlnet	2026-02-04 16:52:39 +08:00
Zhongjie Duan	280ff7cca6	Merge pull request #1229 from Feng0w0/wan_rope [bugfix][NPU]:Fix bug that correctly obtains device type	2026-02-04 13:26:00 +08:00
Zhongjie Duan	b4b62e2f7c	Merge pull request #1221 from Feng0w0/usp_npu [NPU]:Support USP feature in NPU	2026-02-04 13:25:24 +08:00
feng0w0	051b957adb	[model][NPU] Add NPU fusion operator patch to Zimage model to improve performance	2026-02-03 19:50:21 +08:00
feng0w0	ca9b5e64ea	[feature]:Add adaptation of all models to zero3	2026-02-03 15:44:53 +08:00
Zhongjie Duan	6d1be405b9	Merge pull request #1242 from mi804/ltx-2 LTX-2	2026-02-03 13:07:41 +08:00
Zhongjie Duan	25c3a3d3e2	Merge branch 'main' into ltx-2	2026-02-03 13:06:44 +08:00
mi804	49bc84f78e	add comment for tuple noise_pred	2026-02-03 10:43:25 +08:00
mi804	25a9e75030	final fix for ltx-2	2026-02-03 10:39:35 +08:00
mi804	2a7ac73eb5	minor fix	2026-02-02 20:07:08 +08:00
mi804	f4f991d409	support ltx-2 t2v and i2v	2026-02-02 19:53:07 +08:00
Zhongjie Duan	a781138413	Merge pull request #1245 from modelscope/docs-update update docs	2026-02-02 17:00:04 +08:00
Artiprocher	91a5623976	update docs	2026-02-02 16:52:12 +08:00
Zhongjie Duan	28cd355aba	Merge pull request #1232 from huarzone/main fix wan i2v/ti2v train bug	2026-02-02 15:26:01 +08:00
Zhongjie Duan	005389fca7	Merge pull request #1244 from modelscope/qwen-image-edit-lightning Qwen image edit lightning	2026-02-02 15:20:11 +08:00
Artiprocher	a6282056eb	fix typo	2026-02-02 15:19:19 +08:00
Zhongjie Duan	21a6eb8e2f	Merge pull request #1243 from modelscope/research_tutorial_1 add research tutorial sec 1	2026-02-02 14:29:39 +08:00
Artiprocher	98ab238340	add research tutorial sec 1	2026-02-02 14:28:26 +08:00
feng0w0	2070bbd925	[feature]:Add adaptation of all models to zero3	2026-01-31 16:50:18 +08:00
mi804	1c8a0f8317	refactor patchify	2026-01-31 13:55:52 +08:00
mi804	9f07d65ebb	support ltx2 distilled pipeline	2026-01-30 17:40:30 +08:00
lzws	5f1d5adfce	qwen-image-edit-2511-lightning	2026-01-30 17:26:26 +08:00
mi804	4f23caa55f	support ltx2 two stage pipeline & vram	2026-01-30 16:55:40 +08:00
Zhongjie Duan	b4f6a4de6c	Merge pull request #1240 from modelscope/loader-update Loader update	2026-01-30 13:51:17 +08:00
Artiprocher	53fe42af1b	update version	2026-01-30 13:49:27 +08:00
Artiprocher	ee9a3b4405	support loading models from state dict	2026-01-30 13:47:36 +08:00
mi804	b1a2782ad7	support ltx2 one-stage pipeline	2026-01-29 16:30:15 +08:00
mi804	8d303b47e9	add audio_vae, audio_vocoder, text_encoder, connector and upsampler for ltx2	2026-01-28 16:09:22 +08:00
mi804	00da4b6c4f	add video_vae and dit for ltx-2	2026-01-27 19:34:09 +08:00
Zhongjie Duan	22695e9be0	Merge pull request #1233 from modelscope/z-image-release Z-Image and Z-Image-i2L	2026-01-27 18:41:28 +08:00
feng0w0	3140199c96	[feature]:Add adaptation of all models to zero3	2026-01-27 15:33:42 +08:00
Kared	8d0df403ca	fix wan i2v train bug	2026-01-27 03:55:36 +00:00
feng0w0	4e9db263b0	[feature]:Add adaptation of all models to zero3	2026-01-27 11:24:43 +08:00
feng0w0	35e0776022	[bugfix][NPU]:Fix bug that correctly obtains device type	2026-01-23 10:45:03 +08:00
feng0w0	b3cc652dea	[NPU]:Support USP feature in NPU	2026-01-21 10:38:27 +08:00
feng0w0	d879d66c62	[NPU]:Support USP feature in NPU	2026-01-21 10:34:09 +08:00
feng0w0	848bfd6993	[NPU]:Support USP feature in NPU	2026-01-21 10:25:31 +08:00
feng0w0	269da09f6e	Merge branch 'main' of https://github.com/modelscope/DiffSynth-Studio into usp_npu	2026-01-21 10:00:08 +08:00
feng0w0	e30514a00c	Merge branch 'main' of https://github.com/Feng0w0/DiffSynth-Studio into usp_npu	2026-01-21 09:59:18 +08:00
feng0w0	c758769a02	训练快速上手	2025-12-29 09:25:46 +08:00
feng0w0	a5935e973a	训练快速上手	2025-12-29 09:23:59 +08:00
feng0w0	9834d72e4d	文档环境安装上手	2025-12-27 16:11:27 +08:00
feng0w0	01234e59c0	文档环境安装上手	2025-12-27 15:01:10 +08:00
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				Please see `docs/en/Research_Tutorial/inference_time_scaling.md` or `docs/zh/Research_Tutorial/inference_time_scaling.md` for more details.