Merge pull request #1307 from mi804/ltx2-train

Support LTX-2 training.

.github/workflows/publish.yaml

@@ -22,7 +22,7 @@ jobs:
       - name: Install wheel
         run: pip install wheel==0.44.0 && pip install -r requirements.txt
       - name: Build DiffSynth
-        run: python setup.py sdist bdist_wheel
+        run: python -m build
       - name: Publish package to PyPI
         run: |
           pip install twine

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
@@ -32,8 +32,21 @@ 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 DiffSynth-Studio has undergone major version updates, and some old features are no longer maintained.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.
+- **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.
 
-- **December 9, 2025** We release a wild model based on DiffSynth-Studio 2.0: [Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L) (Image-to-LoRA). This model takes an image as input and outputs a LoRA. Although this version still has significant room for improvement in terms of generalization, detail preservation, and other aspects, we are open-sourcing these models to inspire more innovative research.
+- **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).
+
+- **January 19, 2026**: Added support for [FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) and [FLUX.2-klein-9B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B) models, including training and inference capabilities. [Documentation](/docs/en/Model_Details/FLUX2.md) and [example code](/examples/flux2/) are now available.
+
+- **January 12, 2026**: We trained and open-sourced a text-guided image layer separation model ([Model Link](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)). Given an input image and a textual description, the model isolates the image layer corresponding to the described content. For more details, please refer to our blog post ([Chinese version](https://modelscope.cn/learn/4938), [English version](https://huggingface.co/blog/kelseye/qwen-image-layered-control)).
+
+- **December 24, 2025**: Based on Qwen-Image-Edit-2511, we trained an In-Context Editing LoRA model ([Model Link](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)). This model takes three images as input (Image A, Image B, and Image C), and automatically analyzes the transformation from Image A to Image B, then applies the same transformation to Image C to generate Image D. For more details, please refer to our blog post ([Chinese version](https://mp.weixin.qq.com/s/41aEiN3lXKGCJs1-we4Q2g), [English version](https://huggingface.co/blog/kelseye/qwen-image-edit-2511-icedit-lora)).
+
+- **December 9, 2025** We release a wild model based on DiffSynth-Studio 2.0: [Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L) (Image-to-LoRA). This model takes an image as input and outputs a LoRA. Although this version still has significant room for improvement in terms of generalization, detail preservation, and other aspects, we are open-sourcing these models to inspire more innovative research. For more details, please refer to our [blog](https://huggingface.co/blog/kelseye/qwen-image-i2l).
 
 - **December 4, 2025** DiffSynth-Studio 2.0 released! Many new features online
   - [Documentation](/docs/en/README.md) online: Our documentation is still continuously being optimized and updated
@@ -263,9 +276,14 @@ image.save("image.jpg")
 
 Example code for Z-Image is available at: [/examples/z_image/](/examples/z_image/)
 
-| Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|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)|
 
 </details>
 
@@ -315,9 +333,13 @@ image.save("image.jpg")
 
 Example code for FLUX.2 is available at: [/examples/flux2/](/examples/flux2/)
 
-| Model ID | Inference | Low-VRAM Inference | LoRA Training | LoRA Training Validation |
-|-|-|-|-|-|
-|[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)|
+| Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
+|-|-|-|-|-|-|-|
+|[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)|
 
 </details>
 
@@ -396,8 +418,14 @@ Example code for Qwen-Image is available at: [/examples/qwen_image/](/examples/q
 | Model ID | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
 |-|-|-|-|-|-|-|
 |[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)|
+|[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)|
+|[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-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)|
@@ -508,6 +536,130 @@ 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: 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: 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>
@@ -647,6 +799,37 @@ 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: coming soon
+
+|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>
@@ -657,7 +840,7 @@ 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>
 
@@ -672,10 +855,10 @@ 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>
 
@@ -766,4 +949,3 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-47
 https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea
 
 </details>
-

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
@@ -32,8 +32,21 @@ 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)。
 
-- **2025年12月9日** 我们基于 DiffSynth-Studio 2.0 训练了一个疯狂的模型：[Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)（Image to LoRA）。这一模型以图像为输入，以 LoRA 为输出。尽管这个版本的模型在泛化能力、细节保持能力等方面还有很大改进空间，我们将这些模型开源，以启发更多创新性的研究工作。
+- **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)。
+
+- **2026年1月19日** 新增对 [FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) 和 [FLUX.2-klein-9B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-9B) 模型的支持，包括完整的训练和推理功能。[文档](/docs/zh/Model_Details/FLUX2.md)和[示例代码](/examples/flux2/)现已可用。
+
+- **2026年1月12日** 我们训练并开源了一个文本引导的图层拆分模型（[模型链接](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Layered-Control)），这一模型输入一张图与一段文本描述，模型会将图像中与文本描述相关的图层拆分出来。更多细节请阅读我们的 blog（[中文版](https://modelscope.cn/learn/4938)、[英文版](https://huggingface.co/blog/kelseye/qwen-image-layered-control)）。
+
+- **2025年12月24日** 我们基于 Qwen-Image-Edit-2511 训练了一个 In-Context Editing LoRA 模型（[模型链接](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)），这个模型可以输入三张图：图A、图B、图C，模型会自行分析图A到图B的变化，并将这样的变化应用到图C，生成图D。更多细节请阅读我们的 blog（[中文版](https://mp.weixin.qq.com/s/41aEiN3lXKGCJs1-we4Q2g)、[英文版](https://huggingface.co/blog/kelseye/qwen-image-edit-2511-icedit-lora)）。
+
+- **2025年12月9日** 我们基于 DiffSynth-Studio 2.0 训练了一个疯狂的模型：[Qwen-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-i2L)（Image to LoRA）。这一模型以图像为输入，以 LoRA 为输出。尽管这个版本的模型在泛化能力、细节保持能力等方面还有很大改进空间，我们将这些模型开源，以启发更多创新性的研究工作。更多细节，请参考我们的 [blog](https://huggingface.co/blog/kelseye/qwen-image-i2l)。
 
 - **2025年12月4日** DiffSynth-Studio 2.0 发布！众多新功能上线
   - [文档](/docs/zh/README.md)上线：我们的文档还在持续优化更新中
@@ -265,7 +278,12 @@ Z-Image 的示例代码位于：[/examples/z_image/](/examples/z_image/)
 
 |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|
 |-|-|-|-|-|-|-|
+|[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)|
 
 </details>
 
@@ -315,9 +333,13 @@ image.save("image.jpg")
 
 FLUX.2 的示例代码位于：[/examples/flux2/](/examples/flux2/)
 
-|模型 ID|推理|低显存推理|LoRA 训练|LoRA 训练后验证|
-|-|-|-|-|-|
-|[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)|
+|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|
+|-|-|-|-|-|-|-|
+|[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)|
 
 </details>
 
@@ -396,8 +418,14 @@ Qwen-Image 的示例代码位于：[/examples/qwen_image/](/examples/qwen_image/
 |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|
 |-|-|-|-|-|-|-|
 |[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)|
+|[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)|
+|[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-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)|
@@ -508,6 +536,130 @@ 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: 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: 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>
@@ -647,6 +799,37 @@ 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：coming soon
+
+|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>
@@ -658,7 +841,7 @@ 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>
 
@@ -674,10 +857,10 @@ 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>
 

diffsynth/configs/model_configs.py

@@ -63,6 +63,20 @@ qwen_image_series = [
         "model_class": "diffsynth.models.qwen_image_image2lora.QwenImageImage2LoRAModel",
         "extra_kwargs": {"compress_dim": 64, "use_residual": False}
     },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors")
+        "model_hash": "8dc8cda05de16c73afa755e2c1ce2839",
+        "model_name": "qwen_image_dit",
+        "model_class": "diffsynth.models.qwen_image_dit.QwenImageDiT",
+        "extra_kwargs": {"use_layer3d_rope": True, "use_additional_t_cond": True}
+    },
+    {
+        # Example: ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors")
+        "model_hash": "44b39ddc499e027cfb24f7878d7416b9",
+        "model_name": "qwen_image_vae",
+        "model_class": "diffsynth.models.qwen_image_vae.QwenImageVAE",
+        "extra_kwargs": {"image_channels": 4}
+    },
 ]
 
 wan_series = [
@@ -303,6 +317,13 @@ flux_series = [
         "model_class": "diffsynth.models.flux_dit.FluxDiT",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
     },
+    {
+        # Supported due to historical reasons.
+        "model_hash": "605c56eab23e9e2af863ad8f0813a25d",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverterFromDiffusers",
+    },
     {
         # Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors")
         "model_hash": "94eefa3dac9cec93cb1ebaf1747d7b78",
@@ -460,6 +481,13 @@ flux_series = [
         "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
         "extra_kwargs": {"disable_guidance_embedder": True},
     },
+    {
+        # Example: ModelConfig(model_id="MAILAND/majicflus_v1", origin_file_pattern="majicflus_v134.safetensors")
+        "model_hash": "3394f306c4cbf04334b712bf5aaed95f",
+        "model_name": "flux_dit",
+        "model_class": "diffsynth.models.flux_dit.FluxDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
+    },
 ]
 
 flux2_series = [
@@ -482,6 +510,28 @@ flux2_series = [
         "model_name": "flux2_vae",
         "model_class": "diffsynth.models.flux2_vae.Flux2VAE",
     },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-4B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "3bde7b817fec8143028b6825a63180df",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 7680, "num_attention_heads": 24, "num_layers": 5, "num_single_layers": 20}
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="text_encoder/*.safetensors")
+        "model_hash": "9195f3ea256fcd0ae6d929c203470754",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "8B"},
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
+    },
+    {
+        # Example: ModelConfig(model_id="black-forest-labs/FLUX.2-klein-9B", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "39c6fc48f07bebecedbbaa971ff466c8",
+        "model_name": "flux2_dit",
+        "model_class": "diffsynth.models.flux2_dit.Flux2DiT",
+        "extra_kwargs": {"guidance_embeds": False, "joint_attention_dim": 12288, "num_attention_heads": 32, "num_layers": 8, "num_single_layers": 24}
+    },
 ]
 
 z_image_series = [
@@ -513,6 +563,160 @@ z_image_series = [
         "state_dict_converter": "diffsynth.utils.state_dict_converters.flux_vae.FluxVAEDecoderStateDictConverterDiffusers",
         "extra_kwargs": {"use_conv_attention": False},
     },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="transformer/*.safetensors")
+        "model_hash": "aa3563718e5c3ecde3dfbb020ca61180",
+        "model_name": "z_image_dit",
+        "model_class": "diffsynth.models.z_image_dit.ZImageDiT",
+        "extra_kwargs": {"siglip_feat_dim": 1152},
+    },
+    {
+        # Example: ModelConfig(model_id="Tongyi-MAI/Z-Image-Omni-Base", origin_file_pattern="siglip/model.safetensors")
+        "model_hash": "89d48e420f45cff95115a9f3e698d44a",
+        "model_name": "siglip_vision_model_428m",
+        "model_class": "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M",
+    },
+    {
+        # Example: ModelConfig(model_id="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors")
+        "model_hash": "1677708d40029ab380a95f6c731a57d7",
+        "model_name": "z_image_controlnet",
+        "model_class": "diffsynth.models.z_image_controlnet.ZImageControlNet",
+    },
+    {
+        # Example: ???
+        "model_hash": "9510cb8cd1dd34ee0e4f111c24905510",
+        "model_name": "z_image_image2lora_style",
+        "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",
+    },
 ]
-
-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series
+"""
+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",
+    },
+]
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series

diffsynth/configs/vram_management_module_maps.py

@@ -13,6 +13,7 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
     "diffsynth.models.qwen_image_dit.QwenImageDiT": {
         "diffsynth.models.qwen_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
     "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": {
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
@@ -194,4 +195,52 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
         "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
         "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
+    "diffsynth.models.z_image_controlnet.ZImageControlNet": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "diffsynth.models.z_image_dit.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+    "diffsynth.models.z_image_image2lora.ZImageImage2LoRAModel": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+    },
+    "diffsynth.models.siglip2_image_encoder.Siglip2ImageEncoder428M": {
+        "transformers.models.siglip2.modeling_siglip2.Siglip2VisionEmbeddings": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "transformers.models.siglip2.modeling_siglip2.Siglip2MultiheadAttentionPoolingHead": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Conv2d": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "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/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
 
 

diffsynth/core/data/operators.py

@@ -53,12 +53,14 @@ class ToStr(DataProcessingOperator):
 
 
 class LoadImage(DataProcessingOperator):
-    def __init__(self, convert_RGB=True):
+    def __init__(self, convert_RGB=True, convert_RGBA=False):
         self.convert_RGB = convert_RGB
+        self.convert_RGBA = convert_RGBA
     
     def __call__(self, data: str):
         image = Image.open(data)
         if self.convert_RGB: image = image.convert("RGB")
+        if self.convert_RGBA: image = image.convert("RGBA")
         return image
 
 
@@ -216,3 +218,20 @@ class LoadAudio(DataProcessingOperator):
         import librosa
         input_audio, sample_rate = librosa.load(data, sr=self.sr)
         return input_audio
+
+
+class LoadAudioWithTorchaudio(DataProcessingOperator):
+    def __init__(self, duration=5):
+        self.duration = duration
+
+    def __call__(self, data: str):
+        import torchaudio
+        waveform, sample_rate = torchaudio.load(data)
+        target_samples = int(self.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

diffsynth/core/data/unified_dataset.py

@@ -10,6 +10,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
         data_file_keys=tuple(),
         main_data_operator=lambda x: x,
         special_operator_map=None,
+        max_data_items=None,
     ):
         self.base_path = base_path
         self.metadata_path = metadata_path
@@ -18,6 +19,7 @@ class UnifiedDataset(torch.utils.data.Dataset):
         self.main_data_operator = main_data_operator
         self.cached_data_operator = LoadTorchPickle()
         self.special_operator_map = {} if special_operator_map is None else special_operator_map
+        self.max_data_items = max_data_items
         self.data = []
         self.cached_data = []
         self.load_from_cache = metadata_path is None
@@ -97,7 +99,9 @@ class UnifiedDataset(torch.utils.data.Dataset):
         return data
 
     def __len__(self):
-        if self.load_from_cache:
+        if self.max_data_items is not None:
+            return self.max_data_items
+        elif self.load_from_cache:
             return len(self.cached_data) * self.repeat
         else:
             return len(self.data) * self.repeat

diffsynth/core/device/__init__.py

@@ -1 +1,2 @@
-from .npu_compatible_device import parse_device_type, parse_nccl_backend, get_available_device_type
+from .npu_compatible_device import parse_device_type, parse_nccl_backend, get_available_device_type, get_device_name
+from .npu_compatible_device import IS_NPU_AVAILABLE, IS_CUDA_AVAILABLE

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 + "*"
@@ -97,7 +98,8 @@ class ModelConfig:
         self.reset_local_model_path()
         if self.require_downloading():
             self.download()
-            if self.origin_file_pattern is None or self.origin_file_pattern == "":
+        if self.path is None:
+            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))

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"):

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

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)

diffsynth/core/vram/layers.py

@@ -2,7 +2,7 @@ import torch, copy
 from typing import Union
 from .initialization import skip_model_initialization
 from .disk_map import DiskMap
-from ..device import parse_device_type
+from ..device import parse_device_type, get_device_name, IS_NPU_AVAILABLE
 
 
 class AutoTorchModule(torch.nn.Module):
@@ -63,7 +63,8 @@ class AutoTorchModule(torch.nn.Module):
         return r
 
     def check_free_vram(self):
-        gpu_mem_state = getattr(torch, self.computation_device_type).mem_get_info(self.computation_device)
+        device = self.computation_device if not IS_NPU_AVAILABLE else get_device_name()
+        gpu_mem_state = getattr(torch, self.computation_device_type).mem_get_info(device)
         used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
         return used_memory < self.vram_limit
 
@@ -309,6 +310,7 @@ class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
         self.lora_B_weights = []
         self.lora_merger = None
         self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz]
+        self.computation_device_type = parse_device_type(self.computation_device)
         
         if offload_dtype == "disk":
             self.disk_map = disk_map

diffsynth/diffusion/base_pipeline.py

@@ -4,9 +4,11 @@ import numpy as np
 from einops import repeat, reduce
 from typing import Union
 from ..core import AutoTorchModule, AutoWrappedLinear, load_state_dict, ModelConfig, parse_device_type
+from ..core.device.npu_compatible_device import get_device_type
 from ..utils.lora import GeneralLoRALoader
 from ..models.model_loader import ModelPool
 from ..utils.controlnet import ControlNetInput
+from ..core.device import get_device_name, IS_NPU_AVAILABLE
 
 
 class PipelineUnit:
@@ -60,7 +62,7 @@ class BasePipeline(torch.nn.Module):
 
     def __init__(
         self,
-        device="cuda", torch_dtype=torch.float16,
+        device=get_device_type(), torch_dtype=torch.float16,
         height_division_factor=64, width_division_factor=64,
         time_division_factor=None, time_division_remainder=None,
     ):
@@ -177,7 +179,8 @@ class BasePipeline(torch.nn.Module):
 
         
     def get_vram(self):
-        return getattr(torch, self.device_type).mem_get_info(self.device)[1] / (1024 ** 3)
+        device = self.device if not IS_NPU_AVAILABLE else get_device_name()
+        return getattr(torch, self.device_type).mem_get_info(device)[1] / (1024 ** 3)
     
     def get_module(self, model, name):
         if "." in name:
@@ -234,6 +237,7 @@ class BasePipeline(torch.nn.Module):
         alpha=1,
         hotload=None,
         state_dict=None,
+        verbose=1,
     ):
         if state_dict is None:
             if isinstance(lora_config, str):
@@ -260,12 +264,13 @@ class BasePipeline(torch.nn.Module):
                         updated_num += 1
                         module.lora_A_weights.append(lora[lora_a_name] * alpha)
                         module.lora_B_weights.append(lora[lora_b_name])
-            print(f"{updated_num} tensors are patched by LoRA. You can use `pipe.clear_lora()` to clear all LoRA layers.")
+            if verbose >= 1:
+                print(f"{updated_num} tensors are patched by LoRA. You can use `pipe.clear_lora()` to clear all LoRA layers.")
         else:
             lora_loader.fuse_lora_to_base_model(module, lora, alpha=alpha)
             
             
-    def clear_lora(self):
+    def clear_lora(self, verbose=1):
         cleared_num = 0
         for name, module in self.named_modules():
             if isinstance(module, AutoWrappedLinear):
@@ -275,7 +280,8 @@ class BasePipeline(torch.nn.Module):
                     module.lora_A_weights.clear()
                 if hasattr(module, "lora_B_weights"):
                     module.lora_B_weights.clear()
-        print(f"{cleared_num} LoRA layers are cleared.")
+        if verbose >= 1:
+            print(f"{cleared_num} LoRA layers are cleared.")
         
     
     def download_and_load_models(self, model_configs: list[ModelConfig] = [], vram_limit: float = None):
@@ -290,6 +296,7 @@ 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
     
@@ -303,10 +310,22 @@ class BasePipeline(torch.nn.Module):
     
     
     def cfg_guided_model_fn(self, model_fn, cfg_scale, inputs_shared, inputs_posi, inputs_nega, **inputs_others):
+        if inputs_shared.get("positive_only_lora", None) is not None:
+            self.clear_lora(verbose=0)
+            self.load_lora(self.dit, state_dict=inputs_shared["positive_only_lora"], verbose=0)
         noise_pred_posi = model_fn(**inputs_posi, **inputs_shared, **inputs_others)
         if cfg_scale != 1.0:
+            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

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
@@ -89,13 +113,18 @@ class FlowMatchScheduler():
         return float(mu)
     
     @staticmethod
-    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=1024//16*1024//16):
+    def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
         sigma_min = 1 / num_inference_steps
         sigma_max = 1.0
         num_train_timesteps = 1000
         sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
         sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
-        mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
+        if dynamic_shift_len is None:
+            # If you ask me why I set mu=0.8,
+            # I can only say that it yields better training results.
+            mu = 0.8
+        else:
+            mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
         sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
         timesteps = sigmas * num_train_timesteps
         return sigmas, timesteps
@@ -117,6 +146,34 @@ class FlowMatchScheduler():
                 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

diffsynth/diffusion/logger.py

@@ -10,7 +10,7 @@ class ModelLogger:
         self.num_steps = 0
 
 
-    def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None):
+    def on_step_end(self, accelerator: Accelerator, model: torch.nn.Module, save_steps=None, **kwargs):
         self.num_steps += 1
         if save_steps is not None and self.num_steps % save_steps == 0:
             self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors")
@@ -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)

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

diffsynth/diffusion/runner.py

@@ -27,7 +27,7 @@ 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)
     
     for epoch_id in range(num_epochs):
@@ -40,7 +40,7 @@ def launch_training_task(
                     loss = model(data)
                 accelerator.backward(loss)
                 optimizer.step()
-                model_logger.on_step_end(accelerator, model, save_steps)
+                model_logger.on_step_end(accelerator, model, save_steps, loss=loss)
                 scheduler.step()
         if save_steps is None:
             model_logger.on_epoch_end(accelerator, model, epoch_id)
@@ -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)):

diffsynth/diffusion/training_module.py

@@ -1,4 +1,4 @@
-import torch, json
+import torch, json, os
 from ..core import ModelConfig, load_state_dict
 from ..utils.controlnet import ControlNetInput
 from peft import LoraConfig, inject_adapter_in_model
@@ -127,16 +127,67 @@ class DiffusionTrainingModule(torch.nn.Module):
         if model_id_with_origin_paths is not None:
             model_id_with_origin_paths = model_id_with_origin_paths.split(",")
             for model_id_with_origin_path in model_id_with_origin_paths:
-                model_id, origin_file_pattern = model_id_with_origin_path.split(":")
                 vram_config = self.parse_vram_config(
                     fp8=model_id_with_origin_path in fp8_models,
                     offload=model_id_with_origin_path in offload_models,
                     device=device
                 )
-                model_configs.append(ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern, **vram_config))
+                config = self.parse_path_or_model_id(model_id_with_origin_path)
+                model_configs.append(ModelConfig(model_id=config.model_id, origin_file_pattern=config.origin_file_pattern, **vram_config))
         return model_configs
     
 
+    def parse_path_or_model_id(self, model_id_with_origin_path, default_value=None):
+        if model_id_with_origin_path is None:
+            return default_value
+        elif os.path.exists(model_id_with_origin_path):
+            return ModelConfig(path=model_id_with_origin_path)
+        else:
+            if ":" not in model_id_with_origin_path:
+                raise ValueError(f"Failed to parse model config: {model_id_with_origin_path}. This is neither a valid path nor in the format of `model_id/origin_file_pattern`.")
+            split_id = model_id_with_origin_path.rfind(":")
+            model_id = model_id_with_origin_path[:split_id]
+            origin_file_pattern = model_id_with_origin_path[split_id + 1:]
+            return ModelConfig(model_id=model_id, origin_file_pattern=origin_file_pattern)
+
+
+    def auto_detect_lora_target_modules(
+        self,
+        model: torch.nn.Module,
+        search_for_linear=False,
+        linear_detector=lambda x: min(x.weight.shape) >= 512,
+        block_list_detector=lambda x: isinstance(x, torch.nn.ModuleList) and len(x) > 1,
+        name_prefix="",
+    ):
+        lora_target_modules = []
+        if search_for_linear:
+            for name, module in model.named_modules():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                if isinstance(module, torch.nn.Linear) and linear_detector(module):
+                    lora_target_modules.append(module_name)
+        else:
+            for name, module in model.named_children():
+                module_name = name_prefix + ["", "."][name_prefix != ""] + name
+                lora_target_modules += self.auto_detect_lora_target_modules(
+                    module,
+                    search_for_linear=block_list_detector(module),
+                    linear_detector=linear_detector,
+                    block_list_detector=block_list_detector,
+                    name_prefix=module_name,
+                )
+        return lora_target_modules
+    
+
+    def parse_lora_target_modules(self, model, lora_target_modules):
+        if lora_target_modules == "":
+            print("No LoRA target modules specified. The framework will automatically search for them.")
+            lora_target_modules = self.auto_detect_lora_target_modules(model)
+            print(f"LoRA will be patched at {lora_target_modules}.")
+        else:
+            lora_target_modules = lora_target_modules.split(",")
+        return lora_target_modules
+
+
     def switch_pipe_to_training_mode(
         self,
         pipe,
@@ -166,7 +217,7 @@ class DiffusionTrainingModule(torch.nn.Module):
                 return
             model = self.add_lora_to_model(
                 getattr(pipe, lora_base_model),
-                target_modules=lora_target_modules.split(","),
+                target_modules=self.parse_lora_target_modules(getattr(pipe, lora_base_model), lora_target_modules),
                 lora_rank=lora_rank,
                 upcast_dtype=pipe.torch_dtype,
             )

diffsynth/models/dinov3_image_encoder.py

@@ -2,6 +2,8 @@ from transformers import DINOv3ViTModel, DINOv3ViTImageProcessorFast
 from transformers.models.dinov3_vit.modeling_dinov3_vit import DINOv3ViTConfig
 import torch
 
+from ..core.device.npu_compatible_device import get_device_type
+
 
 class DINOv3ImageEncoder(DINOv3ViTModel):
     def __init__(self):
@@ -70,7 +72,7 @@ class DINOv3ImageEncoder(DINOv3ViTModel):
             }
         )
         
-    def forward(self, image, torch_dtype=torch.bfloat16, device="cuda"):
+    def forward(self, image, torch_dtype=torch.bfloat16, device=get_device_type()):
         inputs = self.processor(images=image, return_tensors="pt")
         pixel_values = inputs["pixel_values"].to(dtype=torch_dtype, device=device)
         bool_masked_pos = None

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,
@@ -823,7 +825,13 @@ class Flux2PosEmbed(nn.Module):
 
 
 class Flux2TimestepGuidanceEmbeddings(nn.Module):
-    def __init__(self, in_channels: int = 256, embedding_dim: int = 6144, bias: bool = False):
+    def __init__(
+        self,
+        in_channels: int = 256,
+        embedding_dim: int = 6144,
+        bias: bool = False,
+        guidance_embeds: bool = True,
+    ):
         super().__init__()
 
         self.time_proj = Timesteps(num_channels=in_channels, flip_sin_to_cos=True, downscale_freq_shift=0)
@@ -831,20 +839,24 @@ class Flux2TimestepGuidanceEmbeddings(nn.Module):
             in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
         )
 
-        self.guidance_embedder = TimestepEmbedding(
-            in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
-        )
+        if guidance_embeds:
+            self.guidance_embedder = TimestepEmbedding(
+                in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
+            )
+        else:
+            self.guidance_embedder = None
 
     def forward(self, timestep: torch.Tensor, guidance: torch.Tensor) -> torch.Tensor:
         timesteps_proj = self.time_proj(timestep)
         timesteps_emb = self.timestep_embedder(timesteps_proj.to(timestep.dtype))  # (N, D)
 
-        guidance_proj = self.time_proj(guidance)
-        guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
-
-        time_guidance_emb = timesteps_emb + guidance_emb
-
-        return time_guidance_emb
+        if guidance is not None and self.guidance_embedder is not None:
+            guidance_proj = self.time_proj(guidance)
+            guidance_emb = self.guidance_embedder(guidance_proj.to(guidance.dtype))  # (N, D)
+            time_guidance_emb = timesteps_emb + guidance_emb
+            return time_guidance_emb
+        else:
+            return timesteps_emb
 
 
 class Flux2Modulation(nn.Module):
@@ -882,6 +894,7 @@ class Flux2DiT(torch.nn.Module):
         axes_dims_rope: Tuple[int, ...] = (32, 32, 32, 32),
         rope_theta: int = 2000,
         eps: float = 1e-6,
+        guidance_embeds: bool = True,
     ):
         super().__init__()
         self.out_channels = out_channels or in_channels
@@ -892,7 +905,10 @@ class Flux2DiT(torch.nn.Module):
 
         # 2. Combined timestep + guidance embedding
         self.time_guidance_embed = Flux2TimestepGuidanceEmbeddings(
-            in_channels=timestep_guidance_channels, embedding_dim=self.inner_dim, bias=False
+            in_channels=timestep_guidance_channels,
+            embedding_dim=self.inner_dim,
+            bias=False,
+            guidance_embeds=guidance_embeds,
         )
 
         # 3. Modulation (double stream and single stream blocks share modulation parameters, resp.)
@@ -953,34 +969,9 @@ class Flux2DiT(torch.nn.Module):
         txt_ids: torch.Tensor = None,
         guidance: torch.Tensor = None,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
         use_gradient_checkpointing=False,
         use_gradient_checkpointing_offload=False,
-    ) -> Union[torch.Tensor]:
-        """
-        The [`FluxTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.Tensor` of shape `(batch_size, image_sequence_length, in_channels)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, text_sequence_length, joint_attention_dim)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
-                A list of tensors that if specified are added to the residuals of transformer blocks.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
+    ):
         # 0. Handle input arguments
         if joint_attention_kwargs is not None:
             joint_attention_kwargs = joint_attention_kwargs.copy()
@@ -992,7 +983,9 @@ class Flux2DiT(torch.nn.Module):
 
         # 1. Calculate timestep embedding and modulation parameters
         timestep = timestep.to(hidden_states.dtype) * 1000
-        guidance = guidance.to(hidden_states.dtype) * 1000
+
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
 
         temb = self.time_guidance_embed(timestep, guidance)
 

diffsynth/models/general_modules.py

@@ -19,7 +19,7 @@ def get_timestep_embedding(
     )
     exponent = exponent / (half_dim - downscale_freq_shift)
 
-    emb = torch.exp(exponent).to(timesteps.device)
+    emb = torch.exp(exponent)
     if align_dtype_to_timestep:
         emb = emb.to(timesteps.dtype)
     emb = timesteps[:, None].float() * emb[None, :]
@@ -78,7 +78,7 @@ class DiffusersCompatibleTimestepProj(torch.nn.Module):
 
 
 class TimestepEmbeddings(torch.nn.Module):
-    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False):
+    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False, use_additional_t_cond=False):
         super().__init__()
         self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device, scale=scale, align_dtype_to_timestep=align_dtype_to_timestep)
         if diffusers_compatible_format:
@@ -87,10 +87,17 @@ class TimestepEmbeddings(torch.nn.Module):
             self.timestep_embedder = torch.nn.Sequential(
                 torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
             )
+        self.use_additional_t_cond = use_additional_t_cond
+        if use_additional_t_cond:
+            self.addition_t_embedding = torch.nn.Embedding(2, dim_out)
 
-    def forward(self, timestep, dtype):
+    def forward(self, timestep, dtype, addition_t_cond=None):
         time_emb = self.time_proj(timestep).to(dtype)
         time_emb = self.timestep_embedder(time_emb)
+        if addition_t_cond is not None:
+            addition_t_emb = self.addition_t_embedding(addition_t_cond)
+            addition_t_emb = addition_t_emb.to(dtype=dtype)
+            time_emb = time_emb + addition_t_emb
         return time_emb
 
 

diffsynth/models/longcat_video_dit.py

@@ -9,6 +9,7 @@ import numpy as np
 import torch.nn.functional as F
 from einops import rearrange, repeat
 from .wan_video_dit import flash_attention
+from ..core.device.npu_compatible_device import get_device_type
 from ..core.gradient import gradient_checkpoint_forward
 
 
@@ -373,7 +374,7 @@ class FinalLayer_FP32(nn.Module):
         B, N, C = x.shape
         T, _, _ = latent_shape
 
-        with amp.autocast('cuda', dtype=torch.float32):
+        with amp.autocast(get_device_type(), dtype=torch.float32):
             shift, scale = self.adaLN_modulation(t).unsqueeze(2).chunk(2, dim=-1) # [B, T, 1, C]
             x = modulate_fp32(self.norm_final, x.view(B, T, -1, C), shift, scale).view(B, N, C)
             x = self.linear(x)
@@ -583,7 +584,7 @@ class LongCatSingleStreamBlock(nn.Module):
         T, _, _ = latent_shape # S != T*H*W in case of CP split on H*W.
 
         # compute modulation params in fp32
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
             shift_msa, scale_msa, gate_msa, \
             shift_mlp, scale_mlp, gate_mlp = \
                 self.adaLN_modulation(t).unsqueeze(2).chunk(6, dim=-1) # [B, T, 1, C]
@@ -602,7 +603,7 @@ class LongCatSingleStreamBlock(nn.Module):
         else:
             x_s = attn_outputs
 
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
             x = x + (gate_msa * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
         x = x.to(x_dtype)
 
@@ -615,7 +616,7 @@ class LongCatSingleStreamBlock(nn.Module):
         # ffn with modulation
         x_m = modulate_fp32(self.mod_norm_ffn, x.view(B, -1, N//T, C), shift_mlp, scale_mlp).view(B, -1, C)
         x_s = self.ffn(x_m)
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
             x = x + (gate_mlp * x_s.view(B, -1, N//T, C)).view(B, -1, C) # [B, N, C]
         x = x.to(x_dtype)
 
@@ -797,7 +798,7 @@ class LongCatVideoTransformer3DModel(torch.nn.Module):
 
         hidden_states = self.x_embedder(hidden_states)  # [B, N, C]
 
-        with amp.autocast(device_type='cuda', dtype=torch.float32):
+        with amp.autocast(device_type=get_device_type(), dtype=torch.float32):
             t = self.t_embedder(timestep.float().flatten(), dtype=torch.float32).reshape(B, N_t, -1)  # [B, T, C_t]
 
         encoder_hidden_states = self.y_embedder(encoder_hidden_states)  # [B, 1, N_token, C]

diffsynth/models/ltx2_common.py

@@ -0,0 +1,371 @@
+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.
+    """
+
+    latent: (
+        torch.Tensor
+    )  # Shape: (B, T, D) where B is the batch size, T is the number of tokens, and D is input dimension
+    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
+
+
+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

diffsynth/models/ltx2_text_encoder.py

@@ -0,0 +1,366 @@
+import torch
+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(torch.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 (torch.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 = torch.nn.Linear(3840 * 49, 3840, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the feature extractor.
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, 3840 * 49).
+        Returns:
+            torch.Tensor: Output tensor of shape (batch_size, 3840).
+        """
+        return self.aggregate_embed(x)
+
+
+class _BasicTransformerBlock1D(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        dim_head: int,
+        rope_type: LTXRopeType = LTXRopeType.INTERLEAVED,
+    ):
+        super().__init__()
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            rope_type=rope_type,
+        )
+
+        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(torch.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,
+    ):
+        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 = torch.nn.ModuleList(
+            [
+                _BasicTransformerBlock1D(
+                    dim=self.inner_dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    rope_type=rope_type,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.num_learnable_registers = num_learnable_registers
+        if self.num_learnable_registers:
+            self.learnable_registers = torch.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 = torch.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(torch.nn.Module):
+    def __init__(self,):
+        super().__init__()
+        self.feature_extractor_linear = GemmaFeaturesExtractorProjLinear()
+        self.embeddings_connector = Embeddings1DConnector()
+        self.audio_embeddings_connector = Embeddings1DConnector()

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

diffsynth/models/model_loader.py

@@ -29,7 +29,7 @@ class ModelPool:
             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 +43,7 @@ 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 +60,7 @@ 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 +68,7 @@ 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"]

diffsynth/models/nexus_gen_ar_model.py

@@ -583,7 +583,7 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
             is_compileable = model_kwargs["past_key_values"].is_compileable and self._supports_static_cache
             is_compileable = is_compileable and not self.generation_config.disable_compile
             if is_compileable and (
-                self.device.type == "cuda" or generation_config.compile_config._compile_all_devices
+                self.device.type in ["cuda", "npu"] or generation_config.compile_config._compile_all_devices
             ):
                 os.environ["TOKENIZERS_PARALLELISM"] = "0"
                 model_forward = self.get_compiled_call(generation_config.compile_config)

diffsynth/models/qwen_image_dit.py

@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, functools
 import torch.nn as nn
 from typing import Tuple, Optional, Union, List
 from einops import rearrange
@@ -225,6 +225,121 @@ class QwenEmbedRope(nn.Module):
         return vid_freqs, txt_freqs
 
 
+class QwenEmbedLayer3DRope(nn.Module):
+    def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat(
+            [
+                self.rope_params(pos_index, self.axes_dim[0], self.theta),
+                self.rope_params(pos_index, self.axes_dim[1], self.theta),
+                self.rope_params(pos_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+        self.neg_freqs = torch.cat(
+            [
+                self.rope_params(neg_index, self.axes_dim[0], self.theta),
+                self.rope_params(neg_index, self.axes_dim[1], self.theta),
+                self.rope_params(neg_index, self.axes_dim[2], self.theta),
+            ],
+            dim=1,
+        )
+
+        self.scale_rope = scale_rope
+
+    def rope_params(self, index, dim, theta=10000):
+        """
+        Args:
+            index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
+        """
+        assert dim % 2 == 0
+        freqs = torch.outer(index, 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim)))
+        freqs = torch.polar(torch.ones_like(freqs), freqs)
+        return freqs
+
+    def forward(self, video_fhw, txt_seq_lens, device):
+        """
+        Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args:
+        txt_length: [bs] a list of 1 integers representing the length of the text
+        """
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        video_fhw = [video_fhw]
+        if isinstance(video_fhw, list):
+            video_fhw = video_fhw[0]
+        if not isinstance(video_fhw, list):
+            video_fhw = [video_fhw]
+
+        vid_freqs = []
+        max_vid_index = 0
+        layer_num = len(video_fhw) - 1
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            if idx != layer_num:
+                video_freq = self._compute_video_freqs(frame, height, width, idx)
+            else:
+                ### For the condition image, we set the layer index to -1
+                video_freq = self._compute_condition_freqs(frame, height, width)
+            video_freq = video_freq.to(device)
+            vid_freqs.append(video_freq)
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_vid_index = max(max_vid_index, layer_num)
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+    @functools.lru_cache(maxsize=None)
+    def _compute_video_freqs(self, frame, height, width, idx=0):
+        seq_lens = frame * height * width
+        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+
+        freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        if self.scale_rope:
+            freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
+            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+        else:
+            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+        return freqs.clone().contiguous()
+
+    @functools.lru_cache(maxsize=None)
+    def _compute_condition_freqs(self, frame, height, width):
+        seq_lens = frame * height * width
+        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+
+        freqs_frame = freqs_neg[0][-1:].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+        if self.scale_rope:
+            freqs_height = torch.cat([freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0)
+            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+        else:
+            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+        return freqs.clone().contiguous()
+
+
 class QwenFeedForward(nn.Module):
     def __init__(
         self,
@@ -352,9 +467,38 @@ class QwenImageTransformerBlock(nn.Module):
         self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
         self.txt_mlp = QwenFeedForward(dim=dim, dim_out=dim)
     
-    def _modulate(self, x, mod_params):
+    def _modulate(self, x, mod_params, index=None):
         shift, scale, gate = mod_params.chunk(3, dim=-1)
-        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)    
+        if index is not None:
+            # Assuming mod_params batch dim is 2*actual_batch (chunked into 2 parts)
+            # So shift, scale, gate have shape [2*actual_batch, d]
+            actual_batch = shift.size(0) // 2
+            shift_0, shift_1 = shift[:actual_batch], shift[actual_batch:]  # each: [actual_batch, d]
+            scale_0, scale_1 = scale[:actual_batch], scale[actual_batch:]
+            gate_0, gate_1 = gate[:actual_batch], gate[actual_batch:]
+
+            # index: [b, l] where b is actual batch size
+            # Expand to [b, l, 1] to match feature dimension
+            index_expanded = index.unsqueeze(-1)  # [b, l, 1]
+
+            # Expand chunks to [b, 1, d] then broadcast to [b, l, d]
+            shift_0_exp = shift_0.unsqueeze(1)  # [b, 1, d]
+            shift_1_exp = shift_1.unsqueeze(1)  # [b, 1, d]
+            scale_0_exp = scale_0.unsqueeze(1)
+            scale_1_exp = scale_1.unsqueeze(1)
+            gate_0_exp = gate_0.unsqueeze(1)
+            gate_1_exp = gate_1.unsqueeze(1)
+
+            # Use torch.where to select based on index
+            shift_result = torch.where(index_expanded == 0, shift_0_exp, shift_1_exp)
+            scale_result = torch.where(index_expanded == 0, scale_0_exp, scale_1_exp)
+            gate_result = torch.where(index_expanded == 0, gate_0_exp, gate_1_exp)
+        else:
+            shift_result = shift.unsqueeze(1)
+            scale_result = scale.unsqueeze(1)
+            gate_result = gate.unsqueeze(1)
+
+        return x * (1 + scale_result) + shift_result, gate_result
 
     def forward(
         self,
@@ -364,13 +508,16 @@ class QwenImageTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_mask: Optional[torch.Tensor] = None,
         enable_fp8_attention = False,
+        modulate_index: Optional[List[int]] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
 
         img_mod_attn, img_mod_mlp = self.img_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
+        if modulate_index is not None:
+            temb = torch.chunk(temb, 2, dim=0)[0]
         txt_mod_attn, txt_mod_mlp = self.txt_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
 
         img_normed = self.img_norm1(image)
-        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn)
+        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn, index=modulate_index)
 
         txt_normed = self.txt_norm1(text)
         txt_modulated, txt_gate = self._modulate(txt_normed, txt_mod_attn)
@@ -387,7 +534,7 @@ class QwenImageTransformerBlock(nn.Module):
         text = text + txt_gate * txt_attn_out
 
         img_normed_2 = self.img_norm2(image)
-        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp)
+        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp, index=modulate_index)
 
         txt_normed_2 = self.txt_norm2(text)
         txt_modulated_2, txt_gate_2 = self._modulate(txt_normed_2, txt_mod_mlp)
@@ -405,12 +552,17 @@ class QwenImageDiT(torch.nn.Module):
     def __init__(
         self,
         num_layers: int = 60,
+        use_layer3d_rope: bool = False,
+        use_additional_t_cond: bool = False,
     ):
         super().__init__()
 
-        self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True) 
+        if not use_layer3d_rope:
+            self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True)
+        else:
+            self.pos_embed = QwenEmbedLayer3DRope(theta=10000, axes_dim=[16,56,56], scale_rope=True)
 
-        self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=True)
+        self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=False, use_additional_t_cond=use_additional_t_cond)
         self.txt_norm = RMSNorm(3584, eps=1e-6)
 
         self.img_in = nn.Linear(64, 3072)

diffsynth/models/qwen_image_vae.py

@@ -366,6 +366,7 @@ class QwenImageEncoder3d(nn.Module):
         temperal_downsample=[True, True, False],
         dropout=0.0,
         non_linearity: str = "silu",
+        image_channels=3
     ):
         super().__init__()
         self.dim = dim
@@ -381,7 +382,7 @@ class QwenImageEncoder3d(nn.Module):
         scale = 1.0
 
         # init block
-        self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1)
+        self.conv_in = QwenImageCausalConv3d(image_channels, dims[0], 3, padding=1)
 
         # downsample blocks
         self.down_blocks = torch.nn.ModuleList([])
@@ -544,6 +545,7 @@ class QwenImageDecoder3d(nn.Module):
         temperal_upsample=[False, True, True],
         dropout=0.0,
         non_linearity: str = "silu",
+        image_channels=3,
     ):
         super().__init__()
         self.dim = dim
@@ -594,7 +596,7 @@ class QwenImageDecoder3d(nn.Module):
 
         # output blocks
         self.norm_out = QwenImageRMS_norm(out_dim, images=False)
-        self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1)
+        self.conv_out = QwenImageCausalConv3d(out_dim, image_channels, 3, padding=1)
 
         self.gradient_checkpointing = False
 
@@ -647,6 +649,7 @@ class QwenImageVAE(torch.nn.Module):
         attn_scales: List[float] = [],
         temperal_downsample: List[bool] = [False, True, True],
         dropout: float = 0.0,
+        image_channels: int = 3,
     ) -> None:
         super().__init__()
 
@@ -655,13 +658,13 @@ class QwenImageVAE(torch.nn.Module):
         self.temperal_upsample = temperal_downsample[::-1]
 
         self.encoder = QwenImageEncoder3d(
-            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
+            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout, image_channels=image_channels,
         )
         self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1)
 
         self.decoder = QwenImageDecoder3d(
-            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
+            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout, image_channels=image_channels,
         )
 
         mean = [

diffsynth/models/siglip2_image_encoder.py

@@ -1,7 +1,9 @@
 from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer, SiglipVisionConfig
-from transformers import SiglipImageProcessor
+from transformers import SiglipImageProcessor, Siglip2VisionModel, Siglip2VisionConfig, Siglip2ImageProcessorFast
 import torch
 
+from diffsynth.core.device.npu_compatible_device import get_device_type
+
 
 class Siglip2ImageEncoder(SiglipVisionTransformer):
     def __init__(self):
@@ -47,7 +49,7 @@ class Siglip2ImageEncoder(SiglipVisionTransformer):
             }
         )
         
-    def forward(self, image, torch_dtype=torch.bfloat16, device="cuda"):
+    def forward(self, image, torch_dtype=torch.bfloat16, device=get_device_type()):
         pixel_values = self.processor(images=[image], return_tensors="pt")["pixel_values"]
         pixel_values = pixel_values.to(device=device, dtype=torch_dtype)
         output_attentions = False
@@ -68,3 +70,65 @@ class Siglip2ImageEncoder(SiglipVisionTransformer):
         pooler_output = self.head(last_hidden_state) if self.use_head else None
 
         return pooler_output
+
+
+class Siglip2ImageEncoder428M(Siglip2VisionModel):
+    def __init__(self):
+        config = Siglip2VisionConfig(
+            attention_dropout = 0.0,
+            dtype = "bfloat16",
+            hidden_act = "gelu_pytorch_tanh",
+            hidden_size = 1152,
+            intermediate_size = 4304,
+            layer_norm_eps = 1e-06,
+            model_type = "siglip2_vision_model",
+            num_attention_heads = 16,
+            num_channels = 3,
+            num_hidden_layers = 27,
+            num_patches = 256,
+            patch_size = 16,
+            transformers_version = "4.57.1"
+        )
+        super().__init__(config)
+        self.processor = Siglip2ImageProcessorFast(
+            **{
+                "data_format": "channels_first",
+                "default_to_square": True,
+                "device": None,
+                "disable_grouping": None,
+                "do_convert_rgb": None,
+                "do_normalize": True,
+                "do_pad": None,
+                "do_rescale": True,
+                "do_resize": True,
+                "image_mean": [
+                    0.5,
+                    0.5,
+                    0.5
+                ],
+                "image_processor_type": "Siglip2ImageProcessorFast",
+                "image_std": [
+                    0.5,
+                    0.5,
+                    0.5
+                ],
+                "input_data_format": None,
+                "max_num_patches": 256,
+                "pad_size": None,
+                "patch_size": 16,
+                "processor_class": "Siglip2Processor",
+                "resample": 2,
+                "rescale_factor": 0.00392156862745098,
+                "return_tensors": None,
+            }
+        )
+        
+    def forward(self, image, torch_dtype=torch.bfloat16, device="cuda"):
+        siglip_inputs = self.processor(images=[image], return_tensors="pt").to(device)
+        shape = siglip_inputs.spatial_shapes[0]
+        hidden_state = super().forward(**siglip_inputs).last_hidden_state
+        B, N, C = hidden_state.shape
+        hidden_state = hidden_state[:, : shape[0] * shape[1]]
+        hidden_state = hidden_state.view(shape[0], shape[1], C)
+        hidden_state = hidden_state.to(torch_dtype)
+        return hidden_state

diffsynth/models/step1x_text_encoder.py

@@ -1,10 +1,11 @@
 import torch
 from typing import Optional, Union
 from .qwen_image_text_encoder import QwenImageTextEncoder
+from ..core.device.npu_compatible_device import get_device_type, get_torch_device
 
 
 class Step1xEditEmbedder(torch.nn.Module):
-    def __init__(self, model: QwenImageTextEncoder, processor, max_length=640, dtype=torch.bfloat16, device="cuda"):
+    def __init__(self, model: QwenImageTextEncoder, processor, max_length=640, dtype=torch.bfloat16, device=get_device_type()):
         super().__init__()
         self.max_length = max_length
         self.dtype = dtype
@@ -77,13 +78,13 @@ User Prompt:'''
             self.max_length,
             self.model.config.hidden_size,
             dtype=torch.bfloat16,
-            device=torch.cuda.current_device(),
+            device=get_torch_device().current_device(),
         )
         masks = torch.zeros(
             len(text_list),
             self.max_length,
             dtype=torch.long,
-            device=torch.cuda.current_device(),
+            device=get_torch_device().current_device(),
         )
 
         def split_string(s):
@@ -158,7 +159,7 @@ User Prompt:'''
                 else:
                     token_list.append(token_each)
 
-            new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
+            new_txt_ids = torch.cat(token_list, dim=1).to(get_device_type())
 
             new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
 
@@ -167,15 +168,15 @@ User Prompt:'''
             inputs.input_ids = (
                 torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
                 .unsqueeze(0)
-                .to("cuda")
+                .to(get_device_type())
             )
-            inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
+            inputs.attention_mask = (inputs.input_ids > 0).long().to(get_device_type())
             outputs = self.model_forward(
                 self.model,
                 input_ids=inputs.input_ids,
                 attention_mask=inputs.attention_mask,
-                pixel_values=inputs.pixel_values.to("cuda"),
-                image_grid_thw=inputs.image_grid_thw.to("cuda"),
+                pixel_values=inputs.pixel_values.to(get_device_type()),
+                image_grid_thw=inputs.image_grid_thw.to(get_device_type()),
                 output_hidden_states=True,
             )
 
@@ -188,7 +189,7 @@ User Prompt:'''
             masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones(
                 (min(self.max_length, emb.shape[1] - 217)),
                 dtype=torch.long,
-                device=torch.cuda.current_device(),
+                device=get_torch_device().current_device(),
             )
 
         return embs, masks

diffsynth/models/wan_video_dit.py

@@ -5,6 +5,8 @@ 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
     FLASH_ATTN_3_AVAILABLE = True
@@ -92,6 +94,7 @@ 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.type == "npu" else freqs
     x_out = torch.view_as_real(x_out * freqs).flatten(2)
     return x_out.to(x.dtype)
 
@@ -378,26 +381,14 @@ class WanModel(torch.nn.Module):
             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)
 

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])

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

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):
@@ -471,7 +471,7 @@ class Down_ResidualBlock(nn.Module):
         for module in self.downsamples:
             x = 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):
@@ -511,7 +511,7 @@ class Up_ResidualBlock(nn.Module):
             x_shortcut = self.avg_shortcut(x, first_chunk)
             return x_main + x_shortcut
         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)

diffsynth/models/z_image_controlnet.py

@@ -0,0 +1,154 @@
+from .z_image_dit import ZImageTransformerBlock
+from ..core.gradient import gradient_checkpoint_forward
+from torch.nn.utils.rnn import pad_sequence
+import torch
+from torch import nn
+
+
+class ZImageControlTransformerBlock(ZImageTransformerBlock):
+    def __init__(
+        self, 
+        layer_id: int = 1000,
+        dim: int = 3840,
+        n_heads: int = 30,
+        n_kv_heads: int = 30,
+        norm_eps: float = 1e-5,
+        qk_norm: bool = True,
+        modulation = True,
+        block_id = 0
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm, modulation)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = nn.Linear(self.dim, self.dim)
+        self.after_proj = nn.Linear(self.dim, self.dim)
+
+    def forward(self, c, x, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+            all_c = []
+        else:
+            all_c = list(torch.unbind(c))
+            c = all_c.pop(-1)
+
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        all_c += [c_skip, c]
+        c = torch.stack(all_c)
+        return c
+
+
+class ZImageControlNet(torch.nn.Module):
+    def __init__(
+        self,
+        control_layers_places=(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28),
+        control_in_dim=33,
+        dim=3840,
+        n_refiner_layers=2,
+    ):
+        super().__init__()
+        self.control_layers = nn.ModuleList([ZImageControlTransformerBlock(layer_id=i, block_id=i) for i in control_layers_places])
+        self.control_all_x_embedder = nn.ModuleDict({"2-1": nn.Linear(1 * 2 * 2 * control_in_dim, dim, bias=True)})
+        self.control_noise_refiner = nn.ModuleList([ZImageControlTransformerBlock(block_id=layer_id) for layer_id in range(n_refiner_layers)])
+        self.control_layers_mapping = {0: 0, 2: 1, 4: 2, 6: 3, 8: 4, 10: 5, 12: 6, 14: 7, 16: 8, 18: 9, 20: 10, 22: 11, 24: 12, 26: 13, 28: 14}
+
+    def forward_layers(
+        self,
+        x,
+        cap_feats,
+        control_context,
+        control_context_item_seqlens,
+        kwargs,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        bsz = len(control_context)
+        # unified
+        cap_item_seqlens = [len(_) for _ in cap_feats]
+        control_context_unified = []
+        for i in range(bsz):
+            control_context_len = control_context_item_seqlens[i]
+            cap_len = cap_item_seqlens[i]
+            control_context_unified.append(torch.cat([control_context[i][:control_context_len], cap_feats[i][:cap_len]]))
+        c = pad_sequence(control_context_unified, batch_first=True, padding_value=0.0)
+
+        # arguments
+        new_kwargs = dict(x=x)
+        new_kwargs.update(kwargs)
+        
+        for layer in self.control_layers:
+            c = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                c=c, **new_kwargs
+            )
+ 
+        hints = torch.unbind(c)[:-1]
+        return hints
+    
+    def forward_refiner(
+        self,
+        dit,
+        x,
+        cap_feats,
+        control_context,
+        kwargs,
+        t=None,
+        patch_size=2,
+        f_patch_size=1,
+        use_gradient_checkpointing=False,
+        use_gradient_checkpointing_offload=False,
+    ):
+        # embeddings
+        bsz = len(control_context)
+        device = control_context[0].device
+        (
+            control_context,
+            control_context_size,
+            control_context_pos_ids,
+            control_context_inner_pad_mask,
+        ) = dit.patchify_controlnet(control_context, patch_size, f_patch_size, cap_feats[0].size(0))
+
+        # control_context embed & refine
+        control_context_item_seqlens = [len(_) for _ in control_context]
+        assert all(_ % 2 == 0 for _ in control_context_item_seqlens)
+        control_context_max_item_seqlen = max(control_context_item_seqlens)
+
+        control_context = torch.cat(control_context, dim=0)
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context)
+
+        # Match t_embedder output dtype to control_context for layerwise casting compatibility
+        adaln_input = t.type_as(control_context)
+        control_context[torch.cat(control_context_inner_pad_mask)] = dit.x_pad_token.to(dtype=control_context.dtype, device=control_context.device)
+        control_context = list(control_context.split(control_context_item_seqlens, dim=0))
+        control_context_freqs_cis = list(dit.rope_embedder(torch.cat(control_context_pos_ids, dim=0)).split(control_context_item_seqlens, dim=0))
+
+        control_context = pad_sequence(control_context, batch_first=True, padding_value=0.0)
+        control_context_freqs_cis = pad_sequence(control_context_freqs_cis, batch_first=True, padding_value=0.0)
+        control_context_attn_mask = torch.zeros((bsz, control_context_max_item_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(control_context_item_seqlens):
+            control_context_attn_mask[i, :seq_len] = 1
+        c = control_context
+
+        # arguments
+        new_kwargs = dict(
+            x=x, 
+            attn_mask=control_context_attn_mask,
+            freqs_cis=control_context_freqs_cis, 
+            adaln_input=adaln_input,
+        )
+        new_kwargs.update(kwargs)
+        
+        for layer in self.control_noise_refiner:
+            c = gradient_checkpoint_forward(
+                layer,
+                use_gradient_checkpointing=use_gradient_checkpointing,
+                use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                c=c, **new_kwargs
+            )
+ 
+        hints = torch.unbind(c)[:-1]
+        control_context = torch.unbind(c)[-1]
+
+        return hints, control_context, control_context_item_seqlens

diffsynth/models/z_image_dit.py

@@ -6,13 +6,15 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn.utils.rnn import pad_sequence
 
-from torch.nn import RMSNorm
+from .general_modules import RMSNorm
 from ..core.attention import attention_forward
+from ..core.device.npu_compatible_device import IS_NPU_AVAILABLE, get_device_type
 from ..core.gradient import gradient_checkpoint_forward
 
 
 ADALN_EMBED_DIM = 256
 SEQ_MULTI_OF = 32
+X_PAD_DIM = 64
 
 
 class TimestepEmbedder(nn.Module):
@@ -38,7 +40,7 @@ class TimestepEmbedder(nn.Module):
 
     @staticmethod
     def timestep_embedding(t, dim, max_period=10000):
-        with torch.amp.autocast("cuda", enabled=False):
+        with torch.amp.autocast(get_device_type(), enabled=False):
             half = dim // 2
             freqs = torch.exp(
                 -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
@@ -86,7 +88,15 @@ class Attention(torch.nn.Module):
         self.norm_q = RMSNorm(head_dim, eps=1e-5)
         self.norm_k = RMSNorm(head_dim, eps=1e-5)
     
-    def forward(self, hidden_states, freqs_cis):
+    # 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)
         value = self.to_v(hidden_states)
@@ -101,17 +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("cuda", 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
@@ -123,6 +125,7 @@ class Attention(torch.nn.Module):
             key,
             value,
             q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s n d",
+            attn_mask=attention_mask,
         )
 
         # Reshape back
@@ -136,6 +139,20 @@ class Attention(torch.nn.Module):
         return output
 
 
+def select_per_token(
+    value_noisy: torch.Tensor,
+    value_clean: torch.Tensor,
+    noise_mask: torch.Tensor,
+    seq_len: int,
+) -> torch.Tensor:
+    noise_mask_expanded = noise_mask.unsqueeze(-1)  # (batch, seq_len, 1)
+    return torch.where(
+        noise_mask_expanded == 1,
+        value_noisy.unsqueeze(1).expand(-1, seq_len, -1),
+        value_clean.unsqueeze(1).expand(-1, seq_len, -1),
+    )
+
+
 class ZImageTransformerBlock(nn.Module):
     def __init__(
         self,
@@ -180,40 +197,53 @@ class ZImageTransformerBlock(nn.Module):
         attn_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor] = None,
+        noise_mask: Optional[torch.Tensor] = None,
+        adaln_noisy: Optional[torch.Tensor] = None,
+        adaln_clean: Optional[torch.Tensor] = None,
     ):
         if self.modulation:
-            assert adaln_input is not None
-            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
-            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
-            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
+            seq_len = x.shape[1]
+
+            if noise_mask is not None:
+                # Per-token modulation: different modulation for noisy/clean tokens
+                mod_noisy = self.adaLN_modulation(adaln_noisy)
+                mod_clean = self.adaLN_modulation(adaln_clean)
+
+                scale_msa_noisy, gate_msa_noisy, scale_mlp_noisy, gate_mlp_noisy = mod_noisy.chunk(4, dim=1)
+                scale_msa_clean, gate_msa_clean, scale_mlp_clean, gate_mlp_clean = mod_clean.chunk(4, dim=1)
+
+                gate_msa_noisy, gate_mlp_noisy = gate_msa_noisy.tanh(), gate_mlp_noisy.tanh()
+                gate_msa_clean, gate_mlp_clean = gate_msa_clean.tanh(), gate_mlp_clean.tanh()
+
+                scale_msa_noisy, scale_mlp_noisy = 1.0 + scale_msa_noisy, 1.0 + scale_mlp_noisy
+                scale_msa_clean, scale_mlp_clean = 1.0 + scale_msa_clean, 1.0 + scale_mlp_clean
+
+                scale_msa = select_per_token(scale_msa_noisy, scale_msa_clean, noise_mask, seq_len)
+                scale_mlp = select_per_token(scale_mlp_noisy, scale_mlp_clean, noise_mask, seq_len)
+                gate_msa = select_per_token(gate_msa_noisy, gate_msa_clean, noise_mask, seq_len)
+                gate_mlp = select_per_token(gate_mlp_noisy, gate_mlp_clean, noise_mask, seq_len)
+            else:
+                # Global modulation: same modulation for all tokens (avoid double select)
+                mod = self.adaLN_modulation(adaln_input)
+                scale_msa, gate_msa, scale_mlp, gate_mlp = mod.unsqueeze(1).chunk(4, dim=2)
+                gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
+                scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
 
             # Attention block
             attn_out = self.attention(
-                self.attention_norm1(x) * scale_msa,
-                freqs_cis=freqs_cis,
+                self.attention_norm1(x) * scale_msa, attention_mask=attn_mask, freqs_cis=freqs_cis
             )
             x = x + gate_msa * self.attention_norm2(attn_out)
 
             # FFN block
-            x = x + gate_mlp * self.ffn_norm2(
-                self.feed_forward(
-                    self.ffn_norm1(x) * scale_mlp,
-                )
-            )
+            x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp))
         else:
             # Attention block
-            attn_out = self.attention(
-                self.attention_norm1(x),
-                freqs_cis=freqs_cis,
-            )
+            attn_out = self.attention(self.attention_norm1(x), attention_mask=attn_mask, freqs_cis=freqs_cis)
             x = x + self.attention_norm2(attn_out)
 
             # FFN block
-            x = x + self.ffn_norm2(
-                self.feed_forward(
-                    self.ffn_norm1(x),
-                )
-            )
+            x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x)))
 
         return x
 
@@ -229,9 +259,21 @@ class FinalLayer(nn.Module):
             nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
         )
 
-    def forward(self, x, c):
-        scale = 1.0 + self.adaLN_modulation(c)
-        x = self.norm_final(x) * scale.unsqueeze(1)
+    def forward(self, x, c=None, noise_mask=None, c_noisy=None, c_clean=None):
+        seq_len = x.shape[1]
+
+        if noise_mask is not None:
+            # Per-token modulation
+            scale_noisy = 1.0 + self.adaLN_modulation(c_noisy)
+            scale_clean = 1.0 + self.adaLN_modulation(c_clean)
+            scale = select_per_token(scale_noisy, scale_clean, noise_mask, seq_len)
+        else:
+            # Original global modulation
+            assert c is not None, "Either c or (c_noisy, c_clean) must be provided"
+            scale = 1.0 + self.adaLN_modulation(c)
+            scale = scale.unsqueeze(1)
+
+        x = self.norm_final(x) * scale
         x = self.linear(x)
         return x
 
@@ -274,7 +316,10 @@ class RopeEmbedder:
         result = []
         for i in range(len(self.axes_dims)):
             index = ids[:, i]
-            result.append(self.freqs_cis[i][index])
+            if IS_NPU_AVAILABLE:
+                result.append(torch.index_select(self.freqs_cis[i], 0, index))
+            else:
+                result.append(self.freqs_cis[i][index])
         return torch.cat(result, dim=-1)
 
 
@@ -299,6 +344,7 @@ class ZImageDiT(nn.Module):
         t_scale=1000.0,
         axes_dims=[32, 48, 48],
         axes_lens=[1024, 512, 512],
+        siglip_feat_dim=None,
     ) -> None:
         super().__init__()
         self.in_channels = in_channels
@@ -359,6 +405,32 @@ class ZImageDiT(nn.Module):
             nn.Linear(cap_feat_dim, dim, bias=True),
         )
 
+        # Optional SigLIP components (for Omni variant)
+        self.siglip_feat_dim = siglip_feat_dim
+        if siglip_feat_dim is not None:
+            self.siglip_embedder = nn.Sequential(
+                RMSNorm(siglip_feat_dim, eps=norm_eps), nn.Linear(siglip_feat_dim, dim, bias=True)
+            )
+            self.siglip_refiner = nn.ModuleList(
+                [
+                    ZImageTransformerBlock(
+                        2000 + layer_id,
+                        dim,
+                        n_heads,
+                        n_kv_heads,
+                        norm_eps,
+                        qk_norm,
+                        modulation=False,
+                    )
+                    for layer_id in range(n_refiner_layers)
+                ]
+            )
+            self.siglip_pad_token = nn.Parameter(torch.empty((1, dim)))
+        else:
+            self.siglip_embedder = None
+            self.siglip_refiner = None
+            self.siglip_pad_token = None
+
         self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
         self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))
 
@@ -375,22 +447,57 @@ class ZImageDiT(nn.Module):
 
         self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
 
-    def unpatchify(self, x: List[torch.Tensor], size: List[Tuple], patch_size, f_patch_size) -> List[torch.Tensor]:
+    def unpatchify(
+        self,
+        x: List[torch.Tensor],
+        size: List[Tuple],
+        patch_size = 2,
+        f_patch_size = 1,
+        x_pos_offsets: Optional[List[Tuple[int, int]]] = None,
+    ) -> List[torch.Tensor]:
         pH = pW = patch_size
         pF = f_patch_size
         bsz = len(x)
         assert len(size) == bsz
-        for i in range(bsz):
-            F, H, W = size[i]
-            ori_len = (F // pF) * (H // pH) * (W // pW)
-            # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
-            x[i] = (
-                x[i][:ori_len]
-                .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
-                .permute(6, 0, 3, 1, 4, 2, 5)
-                .reshape(self.out_channels, F, H, W)
-            )
-        return x
+
+        if x_pos_offsets is not None:
+            # Omni: extract target image from unified sequence (cond_images + target)
+            result = []
+            for i in range(bsz):
+                unified_x = x[i][x_pos_offsets[i][0] : x_pos_offsets[i][1]]
+                cu_len = 0
+                x_item = None
+                for j in range(len(size[i])):
+                    if size[i][j] is None:
+                        ori_len = 0
+                        pad_len = SEQ_MULTI_OF
+                        cu_len += pad_len + ori_len
+                    else:
+                        F, H, W = size[i][j]
+                        ori_len = (F // pF) * (H // pH) * (W // pW)
+                        pad_len = (-ori_len) % SEQ_MULTI_OF
+                        x_item = (
+                            unified_x[cu_len : cu_len + ori_len]
+                            .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                            .permute(6, 0, 3, 1, 4, 2, 5)
+                            .reshape(self.out_channels, F, H, W)
+                        )
+                        cu_len += ori_len + pad_len
+                result.append(x_item)  # Return only the last (target) image
+            return result
+        else:
+            # Original mode: simple unpatchify
+            for i in range(bsz):
+                F, H, W = size[i]
+                ori_len = (F // pF) * (H // pH) * (W // pW)
+                # "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
+                x[i] = (
+                    x[i][:ori_len]
+                    .view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
+                    .permute(6, 0, 3, 1, 4, 2, 5)
+                    .reshape(self.out_channels, F, H, W)
+                )
+            return x
 
     @staticmethod
     def create_coordinate_grid(size, start=None, device=None):
@@ -405,8 +512,8 @@ class ZImageDiT(nn.Module):
         self,
         all_image: List[torch.Tensor],
         all_cap_feats: List[torch.Tensor],
-        patch_size: int,
-        f_patch_size: int,
+        patch_size: int = 2,
+        f_patch_size: int = 1,
     ):
         pH = pW = patch_size
         pF = f_patch_size
@@ -490,90 +597,487 @@ class ZImageDiT(nn.Module):
             image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
             all_image_out.append(image_padded_feat)
 
+        return all_image_out, all_cap_feats_out, {
+            "x_size": all_image_size,
+            "x_pos_ids": all_image_pos_ids,
+            "cap_pos_ids": all_cap_pos_ids,
+            "x_pad_mask": all_image_pad_mask,
+            "cap_pad_mask": all_cap_pad_mask
+        }
+    # (
+    #         all_img_out,
+    #         all_cap_out,
+    #         all_img_size,
+    #         all_img_pos_ids,
+    #         all_cap_pos_ids,
+    #         all_img_pad_mask,
+    #         all_cap_pad_mask,
+    #     )
+
+    def patchify_controlnet(
+        self,
+        all_image: List[torch.Tensor],
+        patch_size: int = 2,
+        f_patch_size: int = 1,
+        cap_padding_len: int = None,
+    ):
+        pH = pW = patch_size
+        pF = f_patch_size
+        device = all_image[0].device
+
+        all_image_out = []
+        all_image_size = []
+        all_image_pos_ids = []
+        all_image_pad_mask = []
+
+        for i, image in enumerate(all_image):
+            ### Process Image
+            C, F, H, W = image.size()
+            all_image_size.append((F, H, W))
+            F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+
+            image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+            # "c f pf h ph w pw -> (f h w) (pf ph pw c)"
+            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+
+            image_ori_len = len(image)
+            image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
+
+            image_ori_pos_ids = self.create_coordinate_grid(
+                size=(F_tokens, H_tokens, W_tokens),
+                start=(cap_padding_len + 1, 0, 0),
+                device=device,
+            ).flatten(0, 2)
+            image_padding_pos_ids = (
+                self.create_coordinate_grid(
+                    size=(1, 1, 1),
+                    start=(0, 0, 0),
+                    device=device,
+                )
+                .flatten(0, 2)
+                .repeat(image_padding_len, 1)
+            )
+            image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
+            all_image_pos_ids.append(image_padded_pos_ids)
+            # pad mask
+            all_image_pad_mask.append(
+                torch.cat(
+                    [
+                        torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
+                        torch.ones((image_padding_len,), dtype=torch.bool, device=device),
+                    ],
+                    dim=0,
+                )
+            )
+            # padded feature
+            image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
+            all_image_out.append(image_padded_feat)
+
         return (
             all_image_out,
-            all_cap_feats_out,
             all_image_size,
             all_image_pos_ids,
-            all_cap_pos_ids,
             all_image_pad_mask,
-            all_cap_pad_mask,
         )
     
+    def _prepare_sequence(
+        self,
+        feats: List[torch.Tensor],
+        pos_ids: List[torch.Tensor],
+        inner_pad_mask: List[torch.Tensor],
+        pad_token: torch.nn.Parameter,
+        noise_mask: Optional[List[List[int]]] = None,
+        device: torch.device = None,
+    ):
+        """Prepare sequence: apply pad token, RoPE embed, pad to batch, create attention mask."""
+        item_seqlens = [len(f) for f in feats]
+        max_seqlen = max(item_seqlens)
+        bsz = len(feats)
+
+        # Pad token
+        feats_cat = torch.cat(feats, dim=0)
+        feats_cat[torch.cat(inner_pad_mask)] = pad_token.to(dtype=feats_cat.dtype, device=feats_cat.device)
+        feats = list(feats_cat.split(item_seqlens, dim=0))
+
+        # RoPE
+        freqs_cis = list(self.rope_embedder(torch.cat(pos_ids, dim=0)).split([len(p) for p in pos_ids], dim=0))
+
+        # Pad to batch
+        feats = pad_sequence(feats, batch_first=True, padding_value=0.0)
+        freqs_cis = pad_sequence(freqs_cis, batch_first=True, padding_value=0.0)[:, : feats.shape[1]]
+
+        # Attention mask
+        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(item_seqlens):
+            attn_mask[i, :seq_len] = 1
+
+        # Noise mask
+        noise_mask_tensor = None
+        if noise_mask is not None:
+            noise_mask_tensor = pad_sequence(
+                [torch.tensor(m, dtype=torch.long, device=device) for m in noise_mask],
+                batch_first=True,
+                padding_value=0,
+            )[:, : feats.shape[1]]
+
+        return feats, freqs_cis, attn_mask, item_seqlens, noise_mask_tensor
+    
+    def _build_unified_sequence(
+        self,
+        x: torch.Tensor,
+        x_freqs: torch.Tensor,
+        x_seqlens: List[int],
+        x_noise_mask: Optional[List[List[int]]],
+        cap: torch.Tensor,
+        cap_freqs: torch.Tensor,
+        cap_seqlens: List[int],
+        cap_noise_mask: Optional[List[List[int]]],
+        siglip: Optional[torch.Tensor],
+        siglip_freqs: Optional[torch.Tensor],
+        siglip_seqlens: Optional[List[int]],
+        siglip_noise_mask: Optional[List[List[int]]],
+        omni_mode: bool,
+        device: torch.device,
+    ):
+        """Build unified sequence: x, cap, and optionally siglip.
+        Basic mode order: [x, cap]; Omni mode order: [cap, x, siglip]
+        """
+        bsz = len(x_seqlens)
+        unified = []
+        unified_freqs = []
+        unified_noise_mask = []
+
+        for i in range(bsz):
+            x_len, cap_len = x_seqlens[i], cap_seqlens[i]
+
+            if omni_mode:
+                # Omni: [cap, x, siglip]
+                if siglip is not None and siglip_seqlens is not None:
+                    sig_len = siglip_seqlens[i]
+                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len], siglip[i][:sig_len]]))
+                    unified_freqs.append(
+                        torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len], siglip_freqs[i][:sig_len]])
+                    )
+                    unified_noise_mask.append(
+                        torch.tensor(
+                            cap_noise_mask[i] + x_noise_mask[i] + siglip_noise_mask[i], dtype=torch.long, device=device
+                        )
+                    )
+                else:
+                    unified.append(torch.cat([cap[i][:cap_len], x[i][:x_len]]))
+                    unified_freqs.append(torch.cat([cap_freqs[i][:cap_len], x_freqs[i][:x_len]]))
+                    unified_noise_mask.append(
+                        torch.tensor(cap_noise_mask[i] + x_noise_mask[i], dtype=torch.long, device=device)
+                    )
+            else:
+                # Basic: [x, cap]
+                unified.append(torch.cat([x[i][:x_len], cap[i][:cap_len]]))
+                unified_freqs.append(torch.cat([x_freqs[i][:x_len], cap_freqs[i][:cap_len]]))
+
+        # Compute unified seqlens
+        if omni_mode:
+            if siglip is not None and siglip_seqlens is not None:
+                unified_seqlens = [a + b + c for a, b, c in zip(cap_seqlens, x_seqlens, siglip_seqlens)]
+            else:
+                unified_seqlens = [a + b for a, b in zip(cap_seqlens, x_seqlens)]
+        else:
+            unified_seqlens = [a + b for a, b in zip(x_seqlens, cap_seqlens)]
+
+        max_seqlen = max(unified_seqlens)
+
+        # Pad to batch
+        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
+        unified_freqs = pad_sequence(unified_freqs, batch_first=True, padding_value=0.0)
+
+        # Attention mask
+        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+        for i, seq_len in enumerate(unified_seqlens):
+            attn_mask[i, :seq_len] = 1
+
+        # Noise mask
+        noise_mask_tensor = None
+        if omni_mode:
+            noise_mask_tensor = pad_sequence(unified_noise_mask, batch_first=True, padding_value=0)[
+                :, : unified.shape[1]
+            ]
+
+        return unified, unified_freqs, attn_mask, noise_mask_tensor
+    
+    def _pad_with_ids(
+        self,
+        feat: torch.Tensor,
+        pos_grid_size: Tuple,
+        pos_start: Tuple,
+        device: torch.device,
+        noise_mask_val: Optional[int] = None,
+    ):
+        """Pad feature to SEQ_MULTI_OF, create position IDs and pad mask."""
+        ori_len = len(feat)
+        pad_len = (-ori_len) % SEQ_MULTI_OF
+        total_len = ori_len + pad_len
+
+        # Pos IDs
+        ori_pos_ids = self.create_coordinate_grid(size=pos_grid_size, start=pos_start, device=device).flatten(0, 2)
+        if pad_len > 0:
+            pad_pos_ids = (
+                self.create_coordinate_grid(size=(1, 1, 1), start=(0, 0, 0), device=device)
+                .flatten(0, 2)
+                .repeat(pad_len, 1)
+            )
+            pos_ids = torch.cat([ori_pos_ids, pad_pos_ids], dim=0)
+            padded_feat = torch.cat([feat, feat[-1:].repeat(pad_len, 1)], dim=0)
+            pad_mask = torch.cat(
+                [
+                    torch.zeros(ori_len, dtype=torch.bool, device=device),
+                    torch.ones(pad_len, dtype=torch.bool, device=device),
+                ]
+            )
+        else:
+            pos_ids = ori_pos_ids
+            padded_feat = feat
+            pad_mask = torch.zeros(ori_len, dtype=torch.bool, device=device)
+
+        noise_mask = [noise_mask_val] * total_len if noise_mask_val is not None else None  # token level
+        return padded_feat, pos_ids, pad_mask, total_len, noise_mask
+    
+    def _patchify_image(self, image: torch.Tensor, patch_size: int, f_patch_size: int):
+        """Patchify a single image tensor: (C, F, H, W) -> (num_patches, patch_dim)."""
+        pH, pW, pF = patch_size, patch_size, f_patch_size
+        C, F, H, W = image.size()
+        F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
+        image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
+        image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
+        return image, (F, H, W), (F_tokens, H_tokens, W_tokens)
+    
+    def patchify_and_embed_omni(
+        self,
+        all_x: List[List[torch.Tensor]],
+        all_cap_feats: List[List[torch.Tensor]],
+        all_siglip_feats: List[List[torch.Tensor]],
+        patch_size: int = 2,
+        f_patch_size: int = 1,
+        images_noise_mask: List[List[int]] = None,
+    ):
+        """Patchify for omni mode: multiple images per batch item with noise masks."""
+        bsz = len(all_x)
+        device = all_x[0][-1].device
+        dtype = all_x[0][-1].dtype
+
+        all_x_out, all_x_size, all_x_pos_ids, all_x_pad_mask, all_x_len, all_x_noise_mask = [], [], [], [], [], []
+        all_cap_out, all_cap_pos_ids, all_cap_pad_mask, all_cap_len, all_cap_noise_mask = [], [], [], [], []
+        all_sig_out, all_sig_pos_ids, all_sig_pad_mask, all_sig_len, all_sig_noise_mask = [], [], [], [], []
+
+        for i in range(bsz):
+            num_images = len(all_x[i])
+            cap_feats_list, cap_pos_list, cap_mask_list, cap_lens, cap_noise = [], [], [], [], []
+            cap_end_pos = []
+            cap_cu_len = 1
+
+            # Process captions
+            for j, cap_item in enumerate(all_cap_feats[i]):
+                noise_val = images_noise_mask[i][j] if j < len(images_noise_mask[i]) else 1
+                cap_out, cap_pos, cap_mask, cap_len, cap_nm = self._pad_with_ids(
+                    cap_item,
+                    (len(cap_item) + (-len(cap_item)) % SEQ_MULTI_OF, 1, 1),
+                    (cap_cu_len, 0, 0),
+                    device,
+                    noise_val,
+                )
+                cap_feats_list.append(cap_out)
+                cap_pos_list.append(cap_pos)
+                cap_mask_list.append(cap_mask)
+                cap_lens.append(cap_len)
+                cap_noise.extend(cap_nm)
+                cap_cu_len += len(cap_item)
+                cap_end_pos.append(cap_cu_len)
+                cap_cu_len += 2  # for image vae and siglip tokens
+
+            all_cap_out.append(torch.cat(cap_feats_list, dim=0))
+            all_cap_pos_ids.append(torch.cat(cap_pos_list, dim=0))
+            all_cap_pad_mask.append(torch.cat(cap_mask_list, dim=0))
+            all_cap_len.append(cap_lens)
+            all_cap_noise_mask.append(cap_noise)
+
+            # Process images
+            x_feats_list, x_pos_list, x_mask_list, x_lens, x_size, x_noise = [], [], [], [], [], []
+            for j, x_item in enumerate(all_x[i]):
+                noise_val = images_noise_mask[i][j]
+                if x_item is not None:
+                    x_patches, size, (F_t, H_t, W_t) = self._patchify_image(x_item, patch_size, f_patch_size)
+                    x_out, x_pos, x_mask, x_len, x_nm = self._pad_with_ids(
+                        x_patches, (F_t, H_t, W_t), (cap_end_pos[j], 0, 0), device, noise_val
+                    )
+                    x_size.append(size)
+                else:
+                    x_len = SEQ_MULTI_OF
+                    x_out = torch.zeros((x_len, X_PAD_DIM), dtype=dtype, device=device)
+                    x_pos = self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(x_len, 1)
+                    x_mask = torch.ones(x_len, dtype=torch.bool, device=device)
+                    x_nm = [noise_val] * x_len
+                    x_size.append(None)
+                x_feats_list.append(x_out)
+                x_pos_list.append(x_pos)
+                x_mask_list.append(x_mask)
+                x_lens.append(x_len)
+                x_noise.extend(x_nm)
+
+            all_x_out.append(torch.cat(x_feats_list, dim=0))
+            all_x_pos_ids.append(torch.cat(x_pos_list, dim=0))
+            all_x_pad_mask.append(torch.cat(x_mask_list, dim=0))
+            all_x_size.append(x_size)
+            all_x_len.append(x_lens)
+            all_x_noise_mask.append(x_noise)
+
+            # Process siglip
+            if all_siglip_feats[i] is None:
+                all_sig_len.append([0] * num_images)
+                all_sig_out.append(None)
+            else:
+                sig_feats_list, sig_pos_list, sig_mask_list, sig_lens, sig_noise = [], [], [], [], []
+                for j, sig_item in enumerate(all_siglip_feats[i]):
+                    noise_val = images_noise_mask[i][j]
+                    if sig_item is not None:
+                        sig_H, sig_W, sig_C = sig_item.size()
+                        sig_flat = sig_item.permute(2, 0, 1).reshape(sig_H * sig_W, sig_C)
+                        sig_out, sig_pos, sig_mask, sig_len, sig_nm = self._pad_with_ids(
+                            sig_flat, (1, sig_H, sig_W), (cap_end_pos[j] + 1, 0, 0), device, noise_val
+                        )
+                        # Scale position IDs to match x resolution
+                        if x_size[j] is not None:
+                            sig_pos = sig_pos.float()
+                            sig_pos[..., 1] = sig_pos[..., 1] / max(sig_H - 1, 1) * (x_size[j][1] - 1)
+                            sig_pos[..., 2] = sig_pos[..., 2] / max(sig_W - 1, 1) * (x_size[j][2] - 1)
+                            sig_pos = sig_pos.to(torch.int32)
+                    else:
+                        sig_len = SEQ_MULTI_OF
+                        sig_out = torch.zeros((sig_len, self.siglip_feat_dim), dtype=dtype, device=device)
+                        sig_pos = (
+                            self.create_coordinate_grid((1, 1, 1), (0, 0, 0), device).flatten(0, 2).repeat(sig_len, 1)
+                        )
+                        sig_mask = torch.ones(sig_len, dtype=torch.bool, device=device)
+                        sig_nm = [noise_val] * sig_len
+                    sig_feats_list.append(sig_out)
+                    sig_pos_list.append(sig_pos)
+                    sig_mask_list.append(sig_mask)
+                    sig_lens.append(sig_len)
+                    sig_noise.extend(sig_nm)
+
+                all_sig_out.append(torch.cat(sig_feats_list, dim=0))
+                all_sig_pos_ids.append(torch.cat(sig_pos_list, dim=0))
+                all_sig_pad_mask.append(torch.cat(sig_mask_list, dim=0))
+                all_sig_len.append(sig_lens)
+                all_sig_noise_mask.append(sig_noise)
+
+        # Compute x position offsets
+        all_x_pos_offsets = [(sum(all_cap_len[i]), sum(all_cap_len[i]) + sum(all_x_len[i])) for i in range(bsz)]
+
+        return (
+            all_x_out,
+            all_cap_out,
+            all_sig_out,
+            all_x_size,
+            all_x_pos_ids,
+            all_cap_pos_ids,
+            all_sig_pos_ids,
+            all_x_pad_mask,
+            all_cap_pad_mask,
+            all_sig_pad_mask,
+            all_x_pos_offsets,
+            all_x_noise_mask,
+            all_cap_noise_mask,
+            all_sig_noise_mask,
+        )
+        return all_x_out, all_cap_out, all_sig_out, {
+            "x_size": x_size,
+            "x_pos_ids": all_x_pos_ids,
+            "cap_pos_ids": all_cap_pos_ids,
+            "sig_pos_ids": all_sig_pos_ids,
+            "x_pad_mask": all_x_pad_mask,
+            "cap_pad_mask": all_cap_pad_mask,
+            "sig_pad_mask": all_sig_pad_mask,
+            "x_pos_offsets": all_x_pos_offsets,
+            "x_noise_mask": all_x_noise_mask,
+            "cap_noise_mask": all_cap_noise_mask,
+            "sig_noise_mask": all_sig_noise_mask,
+        }
+
     def forward(
         self,
         x: List[torch.Tensor],
         t,
         cap_feats: List[torch.Tensor],
+        siglip_feats = None,
+        image_noise_mask = None,
         patch_size=2,
         f_patch_size=1,
         use_gradient_checkpointing=False,
         use_gradient_checkpointing_offload=False,
     ):
-        assert patch_size in self.all_patch_size
-        assert f_patch_size in self.all_f_patch_size
+        assert patch_size in self.all_patch_size and f_patch_size in self.all_f_patch_size
+        omni_mode = isinstance(x[0], list)
+        device = x[0][-1].device if omni_mode else x[0].device
 
-        bsz = len(x)
-        device = x[0].device
-        t = t * self.t_scale
-        t = self.t_embedder(t)
+        if omni_mode:
+            # Dual embeddings: noisy (t) and clean (t=1)
+            t_noisy = self.t_embedder(t * self.t_scale).type_as(x[0][-1])
+            t_clean = self.t_embedder(torch.ones_like(t) * self.t_scale).type_as(x[0][-1])
+            adaln_input = None
+        else:
+            # Single embedding for all tokens
+            adaln_input = self.t_embedder(t * self.t_scale).type_as(x[0])
+            t_noisy = t_clean = None
 
-        adaln_input = t
-
-        (
-            x,
-            cap_feats,
-            x_size,
-            x_pos_ids,
-            cap_pos_ids,
-            x_inner_pad_mask,
-            cap_inner_pad_mask,
-        ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+        # Patchify
+        if omni_mode:
+            (
+                x,
+                cap_feats,
+                siglip_feats,
+                x_size,
+                x_pos_ids,
+                cap_pos_ids,
+                siglip_pos_ids,
+                x_pad_mask,
+                cap_pad_mask,
+                siglip_pad_mask,
+                x_pos_offsets,
+                x_noise_mask,
+                cap_noise_mask,
+                siglip_noise_mask,
+            ) = self.patchify_and_embed_omni(x, cap_feats, siglip_feats, patch_size, f_patch_size, image_noise_mask)
+        else:
+            (
+                x,
+                cap_feats,
+                x_size,
+                x_pos_ids,
+                cap_pos_ids,
+                x_pad_mask,
+                cap_pad_mask,
+            ) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
+            x_pos_offsets = x_noise_mask = cap_noise_mask = siglip_noise_mask = None
 
         # x embed & refine
-        x_item_seqlens = [len(_) for _ in x]
-        assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
-        x_max_item_seqlen = max(x_item_seqlens)
-
-        x = torch.cat(x, dim=0)
-        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
-        x[torch.cat(x_inner_pad_mask)] = self.x_pad_token.to(dtype=x.dtype, device=x.device)
-        x = list(x.split(x_item_seqlens, dim=0))
-        x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
-
-        x = pad_sequence(x, batch_first=True, padding_value=0.0)
-        x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
-        x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(x_item_seqlens):
-            x_attn_mask[i, :seq_len] = 1
+        x_seqlens = [len(xi) for xi in x]
+        x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](torch.cat(x, dim=0))  # embed
+        x, x_freqs, x_mask, _, x_noise_tensor = self._prepare_sequence(
+            list(x.split(x_seqlens, dim=0)), x_pos_ids, x_pad_mask, self.x_pad_token, x_noise_mask, device
+        )
 
         for layer in self.noise_refiner:
             x = gradient_checkpoint_forward(
                 layer,
                 use_gradient_checkpointing=use_gradient_checkpointing,
                 use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-                x=x,
-                attn_mask=x_attn_mask,
-                freqs_cis=x_freqs_cis,
-                adaln_input=adaln_input,
+                x=x, attn_mask=x_mask, freqs_cis=x_freqs, adaln_input=adaln_input, noise_mask=x_noise_tensor, adaln_noisy=t_noisy, adaln_clean=t_clean,
             )
 
-        # cap embed & refine
-        cap_item_seqlens = [len(_) for _ in cap_feats]
-        assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
-        cap_max_item_seqlen = max(cap_item_seqlens)
-
-        cap_feats = torch.cat(cap_feats, dim=0)
-        cap_feats = self.cap_embedder(cap_feats)
-        cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token.to(dtype=x.dtype, device=x.device)
-        cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
-        cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
-
-        cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
-        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
-        cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(cap_item_seqlens):
-            cap_attn_mask[i, :seq_len] = 1
+        # Cap embed & refine
+        cap_seqlens = [len(ci) for ci in cap_feats]
+        cap_feats = self.cap_embedder(torch.cat(cap_feats, dim=0))  # embed
+        cap_feats, cap_freqs, cap_mask, _, _ = self._prepare_sequence(
+            list(cap_feats.split(cap_seqlens, dim=0)), cap_pos_ids, cap_pad_mask, self.cap_pad_token, None, device
+        )
 
         for layer in self.context_refiner:
             cap_feats = gradient_checkpoint_forward(
@@ -581,41 +1085,68 @@ class ZImageDiT(nn.Module):
                 use_gradient_checkpointing=use_gradient_checkpointing,
                 use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
                 x=cap_feats,
-                attn_mask=cap_attn_mask,
-                freqs_cis=cap_freqs_cis,
+                attn_mask=cap_mask,
+                freqs_cis=cap_freqs,
             )
 
-        # unified
-        unified = []
-        unified_freqs_cis = []
-        for i in range(bsz):
-            x_len = x_item_seqlens[i]
-            cap_len = cap_item_seqlens[i]
-            unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
-            unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
-        unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
-        assert unified_item_seqlens == [len(_) for _ in unified]
-        unified_max_item_seqlen = max(unified_item_seqlens)
+        # Siglip embed & refine
+        siglip_seqlens = siglip_freqs = None
+        if omni_mode and siglip_feats[0] is not None and self.siglip_embedder is not None:
+            siglip_seqlens = [len(si) for si in siglip_feats]
+            siglip_feats = self.siglip_embedder(torch.cat(siglip_feats, dim=0))  # embed
+            siglip_feats, siglip_freqs, siglip_mask, _, _ = self._prepare_sequence(
+                list(siglip_feats.split(siglip_seqlens, dim=0)),
+                siglip_pos_ids,
+                siglip_pad_mask,
+                self.siglip_pad_token,
+                None,
+                device,
+            )
 
-        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
-        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
-        unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(unified_item_seqlens):
-            unified_attn_mask[i, :seq_len] = 1
+            for layer in self.siglip_refiner:
+                siglip_feats = gradient_checkpoint_forward(
+                    layer,
+                    use_gradient_checkpointing=use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+                    x=siglip_feats, attn_mask=siglip_mask, freqs_cis=siglip_freqs,
+                )
 
-        for layer in self.layers:
+        # Unified sequence
+        unified, unified_freqs, unified_mask, unified_noise_tensor = self._build_unified_sequence(
+            x,
+            x_freqs,
+            x_seqlens,
+            x_noise_mask,
+            cap_feats,
+            cap_freqs,
+            cap_seqlens,
+            cap_noise_mask,
+            siglip_feats,
+            siglip_freqs,
+            siglip_seqlens,
+            siglip_noise_mask,
+            omni_mode,
+            device,
+        )
+
+        # Main transformer layers
+        for layer_idx, layer in enumerate(self.layers):
             unified = gradient_checkpoint_forward(
                 layer,
                 use_gradient_checkpointing=use_gradient_checkpointing,
                 use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-                x=unified,
-                attn_mask=unified_attn_mask,
-                freqs_cis=unified_freqs_cis,
-                adaln_input=adaln_input,
+                x=unified, attn_mask=unified_mask, freqs_cis=unified_freqs, adaln_input=adaln_input, noise_mask=unified_noise_tensor, adaln_noisy=t_noisy, adaln_clean=t_clean
             )
 
-        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
-        unified = list(unified.unbind(dim=0))
-        x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
+        unified = (
+            self.all_final_layer[f"{patch_size}-{f_patch_size}"](
+                unified, noise_mask=unified_noise_tensor, c_noisy=t_noisy, c_clean=t_clean
+            )
+            if omni_mode
+            else self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, c=adaln_input)
+        )
 
-        return x, {}
+        # Unpatchify
+        x = self.unpatchify(list(unified.unbind(dim=0)), x_size, patch_size, f_patch_size, x_pos_offsets)
+
+        return x

diffsynth/models/z_image_image2lora.py

@@ -0,0 +1,189 @@
+import torch
+from .qwen_image_image2lora import ImageEmbeddingToLoraMatrix, SequencialMLP
+
+
+class LoRATrainerBlock(torch.nn.Module):
+    def __init__(self, lora_patterns, in_dim=1536+4096, compress_dim=128, rank=4, block_id=0, use_residual=True, residual_length=64+7, residual_dim=3584, residual_mid_dim=1024, prefix="transformer_blocks"):
+        super().__init__()
+        self.prefix = prefix
+        self.lora_patterns = lora_patterns
+        self.block_id = block_id
+        self.layers = []
+        for name, lora_a_dim, lora_b_dim in self.lora_patterns:
+            self.layers.append(ImageEmbeddingToLoraMatrix(in_dim, compress_dim, lora_a_dim, lora_b_dim, rank))
+        self.layers = torch.nn.ModuleList(self.layers)
+        if use_residual:
+            self.proj_residual = SequencialMLP(residual_length, residual_dim, residual_mid_dim, compress_dim)
+        else:
+            self.proj_residual = None
+    
+    def forward(self, x, residual=None):
+        lora = {}
+        if self.proj_residual is not None: residual = self.proj_residual(residual)
+        for lora_pattern, layer in zip(self.lora_patterns, self.layers):
+            name = lora_pattern[0]
+            lora_a, lora_b = layer(x, residual=residual)
+            lora[f"{self.prefix}.{self.block_id}.{name}.lora_A.default.weight"] = lora_a
+            lora[f"{self.prefix}.{self.block_id}.{name}.lora_B.default.weight"] = lora_b
+        return lora
+
+
+class ZImageImage2LoRAComponent(torch.nn.Module):
+    def __init__(self, lora_patterns, prefix, num_blocks=60, use_residual=True, compress_dim=128, rank=4, residual_length=64+7, residual_mid_dim=1024):
+        super().__init__()
+        self.lora_patterns = lora_patterns
+        self.num_blocks = num_blocks
+        self.blocks = []
+        for lora_patterns in self.lora_patterns:
+            for block_id in range(self.num_blocks):
+                self.blocks.append(LoRATrainerBlock(lora_patterns, block_id=block_id, use_residual=use_residual, compress_dim=compress_dim, rank=rank, residual_length=residual_length, residual_mid_dim=residual_mid_dim, prefix=prefix))
+        self.blocks = torch.nn.ModuleList(self.blocks)
+        self.residual_scale = 0.05
+        self.use_residual = use_residual
+        
+    def forward(self, x, residual=None):
+        if residual is not None:
+            if self.use_residual:
+                residual = residual * self.residual_scale
+            else:
+                residual = None
+        lora = {}
+        for block in self.blocks:
+            lora.update(block(x, residual))
+        return lora
+
+
+class ZImageImage2LoRAModel(torch.nn.Module):
+    def __init__(self, use_residual=False, compress_dim=64, rank=4, residual_length=64+7, residual_mid_dim=1024):
+        super().__init__()
+        lora_patterns = [
+            [
+                ("attention.to_q", 3840, 3840),
+                ("attention.to_k", 3840, 3840),
+                ("attention.to_v", 3840, 3840),
+                ("attention.to_out.0", 3840, 3840),
+            ],
+            [
+                ("feed_forward.w1", 3840, 10240),
+                ("feed_forward.w2", 10240, 3840),
+                ("feed_forward.w3", 3840, 10240),
+            ],
+        ]
+        config = {
+            "lora_patterns": lora_patterns,
+            "use_residual": use_residual,
+            "compress_dim": compress_dim,
+            "rank": rank,
+            "residual_length": residual_length,
+            "residual_mid_dim": residual_mid_dim,
+        }
+        self.layers_lora = ZImageImage2LoRAComponent(
+            prefix="layers",
+            num_blocks=30,
+            **config,
+        )
+        self.context_refiner_lora = ZImageImage2LoRAComponent(
+            prefix="context_refiner",
+            num_blocks=2,
+            **config,
+        )
+        self.noise_refiner_lora = ZImageImage2LoRAComponent(
+            prefix="noise_refiner",
+            num_blocks=2,
+            **config,
+        )
+        
+    def forward(self, x, residual=None):
+        lora = {}
+        lora.update(self.layers_lora(x, residual=residual))
+        lora.update(self.context_refiner_lora(x, residual=residual))
+        lora.update(self.noise_refiner_lora(x, residual=residual))
+        return lora
+
+    def initialize_weights(self):
+        state_dict = self.state_dict()
+        for name in state_dict:
+            if ".proj_a." in name:
+                state_dict[name] = state_dict[name] * 0.3
+            elif ".proj_b.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0
+            elif ".proj_residual.proj_out." in name:
+                state_dict[name] = state_dict[name] * 0.3
+        self.load_state_dict(state_dict)
+
+
+class ImageEmb2LoRAWeightCompressed(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, emb_dim, rank):
+        super().__init__()
+        self.lora_a = torch.nn.Parameter(torch.randn((rank, in_dim)))
+        self.lora_b = torch.nn.Parameter(torch.randn((out_dim, rank)))
+        self.proj = torch.nn.Linear(emb_dim, rank * rank, bias=True)
+        self.rank = rank
+    
+    def forward(self, x):
+        x = self.proj(x).view(self.rank, self.rank)
+        lora_a = x @ self.lora_a
+        lora_b = self.lora_b
+        return lora_a, lora_b
+
+
+class ZImageImage2LoRAModelCompressed(torch.nn.Module):
+    def __init__(self, emb_dim=1536+4096, rank=32):
+        super().__init__()
+        target_layers = [
+            ("attention.to_q", 3840, 3840),
+            ("attention.to_k", 3840, 3840),
+            ("attention.to_v", 3840, 3840),
+            ("attention.to_out.0", 3840, 3840),
+            ("feed_forward.w1", 3840, 10240),
+            ("feed_forward.w2", 10240, 3840),
+            ("feed_forward.w3", 3840, 10240),
+        ]
+        self.lora_patterns = [
+            {
+                "prefix": "layers",
+                "num_layers": 30,
+                "target_layers": target_layers,
+            },
+            {
+                "prefix": "context_refiner",
+                "num_layers": 2,
+                "target_layers": target_layers,
+            },
+            {
+                "prefix": "noise_refiner",
+                "num_layers": 2,
+                "target_layers": target_layers,
+            },
+        ]
+        module_dict = {}
+        for lora_pattern in self.lora_patterns:
+            prefix, num_layers, target_layers = lora_pattern["prefix"], lora_pattern["num_layers"], lora_pattern["target_layers"]
+            for layer_id in range(num_layers):
+                for layer_name, in_dim, out_dim in target_layers:
+                    name = f"{prefix}.{layer_id}.{layer_name}".replace(".", "___")
+                    model = ImageEmb2LoRAWeightCompressed(in_dim, out_dim, emb_dim, rank)
+                    module_dict[name] = model
+        self.module_dict = torch.nn.ModuleDict(module_dict)
+
+    def forward(self, x, residual=None):
+        lora = {}
+        for name, module in self.module_dict.items():
+            name = name.replace("___", ".")
+            name_a, name_b = f"{name}.lora_A.default.weight", f"{name}.lora_B.default.weight"
+            lora_a, lora_b = module(x)
+            lora[name_a] = lora_a
+            lora[name_b] = lora_b
+        return lora
+
+    def initialize_weights(self):
+        state_dict = self.state_dict()
+        for name in state_dict:
+            if "lora_b" in name:
+                state_dict[name] = state_dict[name] * 0
+            elif "lora_a" in name:
+                state_dict[name] = state_dict[name] * 0.2
+            elif "proj.weight" in name:
+                print(name)
+                state_dict[name] = state_dict[name] * 0.2
+        self.load_state_dict(state_dict)

diffsynth/models/z_image_text_encoder.py

@@ -3,38 +3,101 @@ import torch
 
 
 class ZImageTextEncoder(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, model_size="4B"):
         super().__init__()
-        config = 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": 2560,
-            "initializer_range": 0.02,
-            "intermediate_size": 9728,
-            "max_position_embeddings": 40960,
-            "max_window_layers": 36,
-            "model_type": "qwen3",
-            "num_attention_heads": 32,
-            "num_hidden_layers": 36,
-            "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
-        })
+        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"
+                ],
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 2560,
+                "initializer_range": 0.02,
+                "intermediate_size": 9728,
+                "max_position_embeddings": 40960,
+                "max_window_layers": 36,
+                "model_type": "qwen3",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 36,
+                "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
+            }),
+            "8B": Qwen3Config(**{
+                "architectures": [
+                    "Qwen3ForCausalLM"
+                ],
+                "attention_bias": False,
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "dtype": "bfloat16",
+                "eos_token_id": 151645,
+                "head_dim": 128,
+                "hidden_act": "silu",
+                "hidden_size": 4096,
+                "initializer_range": 0.02,
+                "intermediate_size": 12288,
+                "max_position_embeddings": 40960,
+                "max_window_layers": 36,
+                "model_type": "qwen3",
+                "num_attention_heads": 32,
+                "num_hidden_layers": 36,
+                "num_key_value_heads": 8,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": None,
+                "rope_theta": 1000000,
+                "sliding_window": None,
+                "tie_word_embeddings": False,
+                "transformers_version": "4.56.1",
+                "use_cache": True,
+                "use_sliding_window": False,
+                "vocab_size": 151936
+            })
+        }
+        config = config_dict[model_size]
         self.model = Qwen3Model(config)
     
     def forward(self, *args, **kwargs):

diffsynth/pipelines/flux2_image.py

@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, torchvision
 from PIL import Image
 from typing import Union
 from tqdm import tqdm
@@ -6,25 +6,28 @@ from einops import rearrange
 import numpy as np
 from typing import Union, List, Optional, Tuple
 
+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 transformers import AutoProcessor
+from transformers import AutoProcessor, AutoTokenizer
 from ..models.flux2_text_encoder import Flux2TextEncoder
 from ..models.flux2_dit import Flux2DiT
 from ..models.flux2_vae import Flux2VAE
+from ..models.z_image_text_encoder import ZImageTextEncoder
 
 
 class Flux2ImagePipeline(BasePipeline):
 
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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("FLUX.2")
         self.text_encoder: Flux2TextEncoder = None
+        self.text_encoder_qwen3: ZImageTextEncoder = None
         self.dit: Flux2DiT = None
         self.vae: Flux2VAE = None
         self.tokenizer: AutoProcessor = None
@@ -32,8 +35,10 @@ class Flux2ImagePipeline(BasePipeline):
         self.units = [
             Flux2Unit_ShapeChecker(),
             Flux2Unit_PromptEmbedder(),
+            Flux2Unit_Qwen3PromptEmbedder(),
             Flux2Unit_NoiseInitializer(),
             Flux2Unit_InputImageEmbedder(),
+            Flux2Unit_EditImageEmbedder(),
             Flux2Unit_ImageIDs(),
         ]
         self.model_fn = model_fn_flux2
@@ -42,7 +47,7 @@ class Flux2ImagePipeline(BasePipeline):
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
         model_configs: list[ModelConfig] = [],
         tokenizer_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.2-dev", origin_file_pattern="tokenizer/"),
         vram_limit: float = None,
@@ -53,11 +58,12 @@ class Flux2ImagePipeline(BasePipeline):
         
         # Fetch models
         pipe.text_encoder = model_pool.fetch_model("flux2_text_encoder")
+        pipe.text_encoder_qwen3 = model_pool.fetch_model("z_image_text_encoder")
         pipe.dit = model_pool.fetch_model("flux2_dit")
         pipe.vae = model_pool.fetch_model("flux2_vae")
         if tokenizer_config is not None:
             tokenizer_config.download_if_necessary()
-            pipe.tokenizer = AutoProcessor.from_pretrained(tokenizer_config.path)
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
         
         # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()
@@ -75,6 +81,9 @@ class Flux2ImagePipeline(BasePipeline):
         # Image
         input_image: Image.Image = None,
         denoising_strength: float = 1.0,
+        # Edit
+        edit_image: Union[Image.Image, List[Image.Image]] = None,
+        edit_image_auto_resize: bool = True,
         # Shape
         height: int = 1024,
         width: int = 1024,
@@ -98,6 +107,7 @@ class Flux2ImagePipeline(BasePipeline):
         inputs_shared = {
             "cfg_scale": cfg_scale, "embedded_guidance": embedded_guidance,
             "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,
             "num_inference_steps": num_inference_steps,
@@ -275,6 +285,10 @@ class Flux2Unit_PromptEmbedder(PipelineUnit):
         return prompt_embeds, text_ids
 
     def process(self, pipe: Flux2ImagePipeline, prompt):
+        # Skip if Qwen3 text encoder is available (handled by Qwen3PromptEmbedder)
+        if pipe.text_encoder_qwen3 is not None:
+            return {}
+        
         pipe.load_models_to_device(self.onload_model_names)
         prompt_embeds, text_ids = self.encode_prompt(
             pipe.text_encoder, pipe.tokenizer, prompt,
@@ -283,6 +297,135 @@ class Flux2Unit_PromptEmbedder(PipelineUnit):
         return {"prompt_embeds": prompt_embeds, "text_ids": text_ids}
 
 
+class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_emb", "prompt_emb_mask"),
+            onload_model_names=("text_encoder_qwen3",)
+        )
+        self.hidden_states_layers = (9, 18, 27)  # Qwen3 layers
+
+    def get_qwen3_prompt_embeds(
+        self,
+        text_encoder: ZImageTextEncoder,
+        tokenizer: AutoTokenizer,
+        prompt: Union[str, List[str]],
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+    ):
+        dtype = text_encoder.dtype if dtype is None else dtype
+        device = text_encoder.device if device is None else device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        all_input_ids = []
+        all_attention_masks = []
+
+        for single_prompt in prompt:
+            messages = [{"role": "user", "content": single_prompt}]
+            text = tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+                enable_thinking=False,
+            )
+            inputs = tokenizer(
+                text,
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=max_sequence_length,
+            )
+
+            all_input_ids.append(inputs["input_ids"])
+            all_attention_masks.append(inputs["attention_mask"])
+
+        input_ids = torch.cat(all_input_ids, dim=0).to(device)
+        attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
+
+        # Forward pass through the model
+        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)
+        out = out.to(dtype=dtype, device=device)
+
+        batch_size, num_channels, seq_len, hidden_dim = out.shape
+        prompt_embeds = out.permute(0, 2, 1, 3).reshape(batch_size, seq_len, num_channels * hidden_dim)
+        return prompt_embeds
+
+    def prepare_text_ids(
+        self,
+        x: torch.Tensor,  # (B, L, D) or (L, D)
+        t_coord: Optional[torch.Tensor] = None,
+    ):
+        B, L, _ = x.shape
+        out_ids = []
+
+        for i in range(B):
+            t = torch.arange(1) if t_coord is None else t_coord[i]
+            h = torch.arange(1)
+            w = torch.arange(1)
+            l = torch.arange(L)
+
+            coords = torch.cartesian_prod(t, h, w, l)
+            out_ids.append(coords)
+
+        return torch.stack(out_ids)
+
+    def encode_prompt(
+        self,
+        text_encoder: ZImageTextEncoder,
+        tokenizer: AutoTokenizer,
+        prompt: Union[str, List[str]],
+        dtype = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 512,
+    ):
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt_embeds is None:
+            prompt_embeds = self.get_qwen3_prompt_embeds(
+                text_encoder=text_encoder,
+                tokenizer=tokenizer,
+                prompt=prompt,
+                dtype=dtype,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
+
+        batch_size, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        text_ids = self.prepare_text_ids(prompt_embeds)
+        text_ids = text_ids.to(device)
+        return prompt_embeds, text_ids
+
+    def process(self, pipe: Flux2ImagePipeline, prompt):
+        # Check if Qwen3 text encoder is available
+        if pipe.text_encoder_qwen3 is None:
+            return {}
+        
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, text_ids = self.encode_prompt(
+            pipe.text_encoder_qwen3, pipe.tokenizer, prompt,
+            dtype=pipe.torch_dtype, device=pipe.device,
+        )
+        return {"prompt_embeds": prompt_embeds, "text_ids": text_ids}
+
+
 class Flux2Unit_NoiseInitializer(PipelineUnit):
     def __init__(self):
         super().__init__(
@@ -318,6 +461,75 @@ class Flux2Unit_InputImageEmbedder(PipelineUnit):
             return {"latents": latents, "input_latents": input_latents}
 
 
+class Flux2Unit_EditImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image", "edit_image_auto_resize"),
+            output_params=("edit_latents", "edit_image_ids"),
+            onload_model_names=("vae",)
+        )
+
+    def calculate_dimensions(self, target_area, ratio):
+        import math
+        width = math.sqrt(target_area * ratio)
+        height = width / ratio
+        width = round(width / 32) * 32
+        height = round(height / 32) * 32
+        return width, height
+    
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+
+    def edit_image_auto_resize(self, edit_image):
+        calculated_width, calculated_height = self.calculate_dimensions(1024 * 1024, edit_image.size[0] / edit_image.size[1])
+        return self.crop_and_resize(edit_image, calculated_height, calculated_width)
+    
+    def process_image_ids(self, image_latents, scale=10):
+        t_coords = [scale + scale * t for t in torch.arange(0, len(image_latents))]
+        t_coords = [t.view(-1) for t in t_coords]
+
+        image_latent_ids = []
+        for x, t in zip(image_latents, t_coords):
+            x = x.squeeze(0)
+            _, height, width = x.shape
+
+            x_ids = torch.cartesian_prod(t, torch.arange(height), torch.arange(width), torch.arange(1))
+            image_latent_ids.append(x_ids)
+
+        image_latent_ids = torch.cat(image_latent_ids, dim=0)
+        image_latent_ids = image_latent_ids.unsqueeze(0)
+
+        return image_latent_ids
+
+    def process(self, pipe: Flux2ImagePipeline, edit_image, edit_image_auto_resize):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if isinstance(edit_image, Image.Image):
+            edit_image = [edit_image]
+        resized_edit_image, edit_latents = [], []
+        for image in edit_image:
+            # Preprocess
+            if edit_image_auto_resize is None or edit_image_auto_resize:
+                image = self.edit_image_auto_resize(image)
+            resized_edit_image.append(image)
+            # Encode
+            image = pipe.preprocess_image(image)
+            latents = pipe.vae.encode(image)
+            edit_latents.append(latents)
+        edit_image_ids = self.process_image_ids(edit_latents).to(pipe.device)
+        edit_latents = torch.concat([rearrange(latents, "B C H W -> B (H W) C") for latents in edit_latents], dim=1)
+        return {"edit_latents": edit_latents, "edit_image_ids": edit_image_ids}
+
+
 class Flux2Unit_ImageIDs(PipelineUnit):
     def __init__(self):
         super().__init__(
@@ -352,10 +564,17 @@ def model_fn_flux2(
     prompt_embeds=None,
     text_ids=None,
     image_ids=None,
+    edit_latents=None,
+    edit_image_ids=None,
     use_gradient_checkpointing=False,
     use_gradient_checkpointing_offload=False,
     **kwargs,
 ):
+    image_seq_len = latents.shape[1]
+    if edit_latents is not None:
+        image_seq_len = latents.shape[1]
+        latents = torch.concat([latents, edit_latents], dim=1)
+        image_ids = torch.concat([image_ids, edit_image_ids], dim=1)
     embedded_guidance = torch.tensor([embedded_guidance], device=latents.device)
     model_output = dit(
         hidden_states=latents,
@@ -367,4 +586,5 @@ def model_fn_flux2(
         use_gradient_checkpointing=use_gradient_checkpointing,
         use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
     )
+    model_output = model_output[:, :image_seq_len]
     return model_output

diffsynth/pipelines/flux_image.py

@@ -6,6 +6,7 @@ from einops import rearrange, repeat
 import numpy as np
 from transformers import CLIPTokenizer, T5TokenizerFast
 
+from ..core.device.npu_compatible_device import get_device_type
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward, load_state_dict
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
@@ -55,7 +56,7 @@ class MultiControlNet(torch.nn.Module):
 
 class FluxImagePipeline(BasePipeline):
 
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -117,7 +118,7 @@ class FluxImagePipeline(BasePipeline):
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
         model_configs: list[ModelConfig] = [],
         tokenizer_1_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer/"),
         tokenizer_2_config: ModelConfig = ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="tokenizer_2/"),
@@ -377,7 +378,7 @@ class FluxImageUnit_PromptEmbedder(PipelineUnit):
         text_encoder_2,
         prompt,
         positive=True,
-        device="cuda",
+        device=get_device_type(),
         t5_sequence_length=512,
     ):
         pooled_prompt_emb = self.encode_prompt_using_clip(prompt, text_encoder_1, tokenizer_1, 77, device)
@@ -558,7 +559,7 @@ class FluxImageUnit_EntityControl(PipelineUnit):
         text_encoder_2,
         prompt,
         positive=True,
-        device="cuda",
+        device=get_device_type(),
         t5_sequence_length=512,
     ):
         pooled_prompt_emb = self.encode_prompt_using_clip(prompt, text_encoder_1, tokenizer_1, 77, device)
@@ -793,7 +794,7 @@ class FluxImageUnit_ValueControl(PipelineUnit):
 
 
 class InfinitYou(torch.nn.Module):
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
         super().__init__()
         from facexlib.recognition import init_recognition_model
         from insightface.app import FaceAnalysis

diffsynth/pipelines/ltx2_audio_video.py

@@ -0,0 +1,584 @@
+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, gradient_checkpoint_forward
+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
+
+
+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_InputAudioEmbedder(),
+            LTX2AudioVideoUnit_InputVideoEmbedder(),
+            LTX2AudioVideoUnit_InputImagesEmbedder(),
+        ]
+        self.model_fn = model_fn_ltx2
+
+    @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,
+        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")
+
+        # Stage 2
+        if stage2_lora_config is not None:
+            stage2_lora_config.download_if_necessary()
+            pipe.stage2_lora_path = stage2_lora_config.path
+        # Optional, currently not used
+        pipe.audio_vae_encoder = model_pool.fetch_model("ltx2_audio_vae_encoder")
+
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+
+    def stage2_denoise(self, inputs_shared, inputs_posi, inputs_nega, progress_bar_cmd=tqdm):
+        if inputs_shared["use_two_stage_pipeline"]:
+            latent = self.video_vae_encoder.per_channel_statistics.un_normalize(inputs_shared["video_latents"])
+            self.load_models_to_device('upsampler',)
+            latent = self.upsampler(latent)
+            latent = self.video_vae_encoder.per_channel_statistics.normalize(latent)
+            self.scheduler.set_timesteps(special_case="stage2")
+            inputs_shared.update({k.replace("stage2_", ""): v for k, v in inputs_shared.items() if k.startswith("stage2_")})
+            denoise_mask_video = 1.0
+            if inputs_shared.get("input_images", None) is not None:
+                latent, denoise_mask_video, initial_latents = self.apply_input_images_to_latents(
+                    latent, inputs_shared.pop("input_latents"), inputs_shared["input_images_indexes"],
+                    inputs_shared["input_images_strength"], latent.clone())
+                inputs_shared.update({"input_latents_video": initial_latents, "denoise_mask_video": denoise_mask_video})
+            inputs_shared["video_latents"] = self.scheduler.sigmas[0] * denoise_mask_video * inputs_shared[
+                "video_noise"] + (1 - self.scheduler.sigmas[0] * denoise_mask_video) * latent
+            inputs_shared["audio_latents"] = self.scheduler.sigmas[0] * inputs_shared["audio_noise"] + (
+                1 - self.scheduler.sigmas[0]) * inputs_shared["audio_latents"]
+
+            self.load_models_to_device(self.in_iteration_models)
+            if not inputs_shared["use_distilled_pipeline"]:
+                self.load_lora(self.dit, self.stage2_lora_path, alpha=0.8)
+            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, 1.0, 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, **inputs_shared)
+        return inputs_shared
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: Optional[str] = "",
+        # Image-to-video
+        denoising_strength: float = 1.0,
+        input_images: Optional[list[Image.Image]] = None,
+        input_images_indexes: Optional[list[int]] = None,
+        input_images_strength: Optional[float] = 1.0,
+        # Randomness
+        seed: Optional[int] = None,
+        rand_device: Optional[str] = "cpu",
+        # Shape
+        height: Optional[int] = 512,
+        width: Optional[int] = 768,
+        num_frames=121,
+        # Classifier-free guidance
+        cfg_scale: Optional[float] = 3.0,
+        # Scheduler
+        num_inference_steps: Optional[int] = 40,
+        # 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,
+        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,
+            "seed": seed, "rand_device": rand_device,
+            "height": height, "width": width, "num_frames": num_frames,
+            "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,
+            "video_patchifier": self.video_patchifier, "audio_patchifier": self.audio_patchifier,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise Stage 1
+        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, **inputs_shared)
+
+        # Denoise Stage 2
+        inputs_shared = self.stage2_denoise(inputs_shared, inputs_posi, inputs_nega, progress_bar_cmd)
+
+        # 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).squeeze(0).float()
+        return video, decoded_audio
+
+    def apply_input_images_to_latents(self, latents, input_latents, input_indexes, input_strength, initial_latents=None, num_frames=121):
+        b, _, f, h, w = latents.shape
+        denoise_mask = torch.ones((b, 1, f, h, w), dtype=latents.dtype, device=latents.device)
+        initial_latents = torch.zeros_like(latents) if initial_latents is None else initial_latents
+        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)
+            initial_latents[:, :, idx:idx + input_latent.shape[2], :, :] = input_latent
+            denoise_mask[:, :, idx:idx + input_latent.shape[2], :, :] = 1.0 - input_strength
+        latents = latents * denoise_mask + initial_latents * (1.0 - denoise_mask)
+        return latents, denoise_mask, initial_latents
+
+
+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_path") and pipe.stage2_lora_path is not None):
+                    raise ValueError("Two-stage pipeline requested, but stage2_lora_path 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.
+    """
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "num_frames"),
+            output_params=("height", "width", "num_frames"),
+        )
+
+    def process(self, pipe: LTX2AudioVideoPipeline, height, width, num_frames, use_two_stage_pipeline=False):
+        if use_two_stage_pipeline:
+            self.width_division_factor = 64
+            self.height_division_factor = 64
+        height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames)
+        if use_two_stage_pipeline:
+            self.width_division_factor = 32
+            self.height_division_factor = 32
+        return {"height": height, "width": width, "num_frames": num_frames}
+
+
+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 _convert_to_additive_mask(self, 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 _run_connectors(self, pipe, encoded_input: torch.Tensor,
+                        attention_mask: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        connector_attention_mask = self._convert_to_additive_mask(attention_mask, encoded_input.dtype)
+
+        encoded, encoded_connector_attention_mask = pipe.text_encoder_post_modules.embeddings_connector(
+            encoded_input,
+            connector_attention_mask,
+        )
+
+        # restore the mask values to int64
+        attention_mask = (encoded_connector_attention_mask < 0.000001).to(torch.int64)
+        attention_mask = attention_mask.reshape([encoded.shape[0], encoded.shape[1], 1])
+        encoded = encoded * attention_mask
+
+        encoded_for_audio, _ = pipe.text_encoder_post_modules.audio_embeddings_connector(
+            encoded_input, connector_attention_mask)
+
+        return encoded, encoded_for_audio, attention_mask.squeeze(-1)
+
+    def _norm_and_concat_padded_batch(
+        self,
+        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 _run_feature_extractor(self,
+                               pipe,
+                               hidden_states: torch.Tensor,
+                               attention_mask: torch.Tensor,
+                               padding_side: str = "right") -> torch.Tensor:
+        encoded_text_features = torch.stack(hidden_states, dim=-1)
+        encoded_text_features_dtype = encoded_text_features.dtype
+        sequence_lengths = attention_mask.sum(dim=-1)
+        normed_concated_encoded_text_features = self._norm_and_concat_padded_batch(encoded_text_features,
+                                                                                   sequence_lengths,
+                                                                                   padding_side=padding_side)
+
+        return pipe.text_encoder_post_modules.feature_extractor_linear(
+            normed_concated_encoded_text_features.to(encoded_text_features_dtype))
+
+    def _preprocess_text(
+        self,
+        pipe,
+        text: str,
+        padding_side: str = "left",
+    ) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
+        """
+        Encode a given string into feature tensors suitable for downstream tasks.
+        Args:
+            text (str): Input string to encode.
+        Returns:
+            tuple[torch.Tensor, dict[str, torch.Tensor]]: Encoded features and a dictionary with attention mask.
+        """
+        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)
+        projected = self._run_feature_extractor(pipe,
+                                                hidden_states=outputs.hidden_states,
+                                                attention_mask=attention_mask,
+                                                padding_side=padding_side)
+        return projected, attention_mask
+
+    def encode_prompt(self, pipe, text, padding_side="left"):
+        encoded_inputs, attention_mask = self._preprocess_text(pipe, text, padding_side)
+        video_encoding, audio_encoding, attention_mask = self._run_connectors(pipe, encoded_inputs, attention_mask)
+        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", "use_two_stage_pipeline"),
+            output_params=("video_noise", "audio_noise",),
+        )
+
+    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, use_two_stage_pipeline=False):
+        if use_two_stage_pipeline:
+            stage1_dict = self.process_stage(pipe, height // 2, width // 2, num_frames, seed, rand_device, frame_rate)
+            stage2_dict = self.process_stage(pipe, height, width, num_frames, seed, rand_device, frame_rate)
+            initial_dict = stage1_dict
+            initial_dict.update({"stage2_" + k: v for k, v in stage2_dict.items()})
+            return initial_dict
+        else:
+            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:
+            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)
+            if pipe.scheduler.training:
+                return {"video_latents": input_latents, "input_latents": input_latents}
+            else:
+                # TODO: implement video-to-video
+                raise NotImplementedError("Video-to-video not implemented yet.")
+
+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:
+            return {"audio_latents": audio_noise}
+        else:
+            input_audio, sample_rate = 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)
+            if pipe.scheduler.training:
+                return {"audio_latents": audio_input_latents, "audio_input_latents": audio_input_latents, "audio_positions": audio_positions, "audio_latent_shape": audio_latent_shape}
+            else:
+                # TODO: implement video-to-video
+                raise NotImplementedError("Video-to-video not implemented yet.")
+
+class LTX2AudioVideoUnit_InputImagesEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_images", "input_images_indexes", "input_images_strength", "video_latents", "height", "width", "num_frames", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels", "use_two_stage_pipeline"),
+            output_params=("video_latents"),
+            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)
+        latent = pipe.video_vae_encoder.encode(image, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(pipe.device)
+        return latent
+
+    def process(self, pipe: LTX2AudioVideoPipeline, input_images, input_images_indexes, input_images_strength, video_latents, height, width, num_frames, tiled, tile_size_in_pixels, tile_overlap_in_pixels, use_two_stage_pipeline=False):
+        if input_images is None or len(input_images) == 0:
+            return {"video_latents": video_latents}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            output_dicts = {}
+            stage1_height = height // 2 if use_two_stage_pipeline else height
+            stage1_width = width // 2 if use_two_stage_pipeline else width
+            stage1_latents = [
+                self.get_image_latent(pipe, img, stage1_height, stage1_width, tiled, tile_size_in_pixels,
+                                      tile_overlap_in_pixels) for img in input_images
+            ]
+            video_latents, denoise_mask_video, initial_latents = pipe.apply_input_images_to_latents(video_latents, stage1_latents, input_images_indexes, input_images_strength, num_frames=num_frames)
+            output_dicts.update({"video_latents": video_latents, "denoise_mask_video": denoise_mask_video, "input_latents_video": initial_latents})
+            if use_two_stage_pipeline:
+                stage2_latents = [
+                    self.get_image_latent(pipe, img, height, width, tiled, tile_size_in_pixels,
+                                          tile_overlap_in_pixels) for img in input_images
+                ]
+                output_dicts.update({"stage2_input_latents": stage2_latents})
+            return output_dicts
+
+
+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,
+    denoise_mask_video=None,
+    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)
+    video_timesteps = timestep.repeat(1, video_latents.shape[1], 1)
+    if denoise_mask_video is not None:
+        video_timesteps = video_patchifier.patchify(denoise_mask_video) * video_timesteps
+    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
+    #TODO: support gradient checkpointing in training
+    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,
+    )
+    # 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

diffsynth/pipelines/qwen_image.py

@@ -4,7 +4,9 @@ from typing import Union
 from tqdm import tqdm
 from einops import rearrange
 import numpy as np
+from math import prod
 
+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
@@ -21,7 +23,7 @@ from ..models.qwen_image_image2lora import QwenImageImage2LoRAModel
 
 class QwenImagePipeline(BasePipeline):
 
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -47,6 +49,7 @@ class QwenImagePipeline(BasePipeline):
             QwenImageUnit_InputImageEmbedder(),
             QwenImageUnit_Inpaint(),
             QwenImageUnit_EditImageEmbedder(),
+            QwenImageUnit_LayerInputImageEmbedder(),
             QwenImageUnit_ContextImageEmbedder(),
             QwenImageUnit_PromptEmbedder(),
             QwenImageUnit_EntityControl(),
@@ -58,7 +61,7 @@ class QwenImagePipeline(BasePipeline):
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
         model_configs: list[ModelConfig] = [],
         tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
         processor_config: ModelConfig = None,
@@ -125,6 +128,11 @@ class QwenImagePipeline(BasePipeline):
         edit_image: Image.Image = None,
         edit_image_auto_resize: bool = True,
         edit_rope_interpolation: bool = False,
+        # Qwen-Image-Edit-2511
+        zero_cond_t: bool = False,
+        # Qwen-Image-Layered
+        layer_input_image: Image.Image = None,
+        layer_num: int = None,
         # In-context control
         context_image: Image.Image = None,
         # Tile
@@ -156,6 +164,9 @@ class QwenImagePipeline(BasePipeline):
             "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative,
             "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, 
             "context_image": context_image,
+            "zero_cond_t": zero_cond_t,
+            "layer_input_image": layer_input_image,
+            "layer_num": layer_num,
         }
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -175,7 +186,10 @@ class QwenImagePipeline(BasePipeline):
         # Decode
         self.load_models_to_device(['vae'])
         image = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
-        image = self.vae_output_to_image(image)
+        if layer_num is None:
+            image = self.vae_output_to_image(image)
+        else:
+            image = [self.vae_output_to_image(i, pattern="C H W") for i in image]
         self.load_models_to_device([])
 
         return image
@@ -226,12 +240,15 @@ class QwenImageUnit_ShapeChecker(PipelineUnit):
 class QwenImageUnit_NoiseInitializer(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("height", "width", "seed", "rand_device"),
+            input_params=("height", "width", "seed", "rand_device", "layer_num"),
             output_params=("noise",),
         )
 
-    def process(self, pipe: QwenImagePipeline, 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)
+    def process(self, pipe: QwenImagePipeline, height, width, seed, rand_device, layer_num):
+        if layer_num is None:
+            noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        else:
+            noise = pipe.generate_noise((layer_num + 1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
         return {"noise": noise}
 
 
@@ -248,8 +265,15 @@ class QwenImageUnit_InputImageEmbedder(PipelineUnit):
         if input_image is None:
             return {"latents": noise, "input_latents": None}
         pipe.load_models_to_device(['vae'])
-        image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
-        input_latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        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, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride))
+            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, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
         if pipe.scheduler.training:
             return {"latents": noise, "input_latents": input_latents}
         else:
@@ -257,6 +281,22 @@ class QwenImageUnit_InputImageEmbedder(PipelineUnit):
             return {"latents": latents, "input_latents": input_latents}
 
 
+class QwenImageUnit_LayerInputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("layer_input_image", "tiled", "tile_size", "tile_stride"),
+            output_params=("layer_input_latents",),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: QwenImagePipeline, layer_input_image, tiled, tile_size, tile_stride):
+        if layer_input_image is None:
+            return {}
+        pipe.load_models_to_device(['vae'])
+        image = pipe.preprocess_image(layer_input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+        latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return {"layer_input_latents": latents}
+
 
 class QwenImageUnit_Inpaint(PipelineUnit):
     def __init__(self):
@@ -673,18 +713,26 @@ def model_fn_qwen_image(
     entity_prompt_emb_mask=None,
     entity_masks=None,
     edit_latents=None,
+    layer_input_latents=None,
+    layer_num=None,
     context_latents=None,
     enable_fp8_attention=False,
     use_gradient_checkpointing=False,
     use_gradient_checkpointing_offload=False,
     edit_rope_interpolation=False,
+    zero_cond_t=False,
     **kwargs
 ):
-    img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
+    if layer_num is None:
+        layer_num = 1
+        img_shapes = [(1, latents.shape[2]//2, latents.shape[3]//2)]
+    else:
+        layer_num = layer_num + 1
+        img_shapes = [(1, latents.shape[2]//2, latents.shape[3]//2)] * layer_num
     txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
     timestep = timestep / 1000
     
-    image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
+    image = rearrange(latents, "(B N) C (H P) (W Q) -> B (N H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2, N=layer_num)
     image_seq_len = image.shape[1]
 
     if context_latents is not None:
@@ -696,9 +744,27 @@ def model_fn_qwen_image(
         img_shapes += [(e.shape[0], e.shape[2]//2, e.shape[3]//2) for e in edit_latents_list]
         edit_image = [rearrange(e, "B C (H P) (W Q) -> B (H W) (C P Q)", H=e.shape[2]//2, W=e.shape[3]//2, P=2, Q=2) for e in edit_latents_list]
         image = torch.cat([image] + edit_image, dim=1)
+    if layer_input_latents is not None:
+        layer_num = layer_num + 1
+        img_shapes += [(layer_input_latents.shape[0], layer_input_latents.shape[2]//2, layer_input_latents.shape[3]//2)]
+        layer_input_latents = rearrange(layer_input_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        image = torch.cat([image, layer_input_latents], dim=1)
 
     image = dit.img_in(image)
-    conditioning = dit.time_text_embed(timestep, image.dtype)
+    if zero_cond_t:
+        timestep = torch.cat([timestep, timestep * 0], dim=0)
+        modulate_index = torch.tensor(
+            [[0] * prod(sample[0]) + [1] * sum([prod(s) for s in sample[1:]]) for sample in [img_shapes]],
+            device=timestep.device,
+            dtype=torch.int,
+        )
+    else:
+        modulate_index = None
+    conditioning = dit.time_text_embed(
+        timestep,
+        image.dtype,
+        addition_t_cond=None if not dit.time_text_embed.use_additional_t_cond else torch.tensor([0]).to(device=image.device, dtype=torch.long)
+    )
 
     if entity_prompt_emb is not None:
         text, image_rotary_emb, attention_mask = dit.process_entity_masks(
@@ -728,6 +794,7 @@ def model_fn_qwen_image(
             image_rotary_emb=image_rotary_emb,
             attention_mask=attention_mask,
             enable_fp8_attention=enable_fp8_attention,
+            modulate_index=modulate_index,
         )
         if blockwise_controlnet_conditioning is not None:
             image_slice = image[:, :image_seq_len].clone()
@@ -738,9 +805,11 @@ def model_fn_qwen_image(
             )
             image[:, :image_seq_len] = image_slice + controlnet_output
     
+    if zero_cond_t:
+        conditioning = conditioning.chunk(2, dim=0)[0]
     image = dit.norm_out(image, conditioning)
     image = dit.proj_out(image)
     image = image[:, :image_seq_len]
     
-    latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
+    latents = rearrange(image, "B (N H W) (C P Q) -> (B N) C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2, B=1)
     return latents

diffsynth/pipelines/wan_video.py

@@ -11,6 +11,7 @@ from typing import Optional
 from typing_extensions import Literal
 from transformers import Wav2Vec2Processor
 
+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
@@ -30,7 +31,7 @@ from ..models.longcat_video_dit import LongCatVideoTransformer3DModel
 
 class WanVideoPipeline(BasePipeline):
 
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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
@@ -98,7 +99,7 @@ class WanVideoPipeline(BasePipeline):
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
         model_configs: list[ModelConfig] = [],
         tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
         audio_processor_config: ModelConfig = None,
@@ -122,11 +123,15 @@ class WanVideoPipeline(BasePipeline):
                     model_config.model_id = redirect_dict[model_config.origin_file_pattern][0]
                     model_config.origin_file_pattern = redirect_dict[model_config.origin_file_pattern][1]
         
-        # Initialize pipeline
-        pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
         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 = WanVideoPipeline(device=device, torch_dtype=torch_dtype)
         model_pool = pipe.download_and_load_models(model_configs, vram_limit)
         
         # Fetch models
@@ -241,6 +246,7 @@ class WanVideoPipeline(BasePipeline):
         tea_cache_model_id: Optional[str] = "",
         # progress_bar
         progress_bar_cmd=tqdm,
+        output_type: Optional[Literal["quantized", "floatpoint"]] = "quantized",
     ):
         # Scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
@@ -320,9 +326,11 @@ class WanVideoPipeline(BasePipeline):
         # Decode
         self.load_models_to_device(['vae'])
         video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
-        video = self.vae_output_to_video(video)
+        if output_type == "quantized":
+            video = self.vae_output_to_video(video)
+        elif output_type == "floatpoint":
+            pass
         self.load_models_to_device([])
-
         return video
 
 
@@ -823,9 +831,9 @@ class WanVideoUnit_S2V(PipelineUnit):
         pipe.load_models_to_device(["vae"])
         motion_frames = 73
         kwargs = {}
-        if motion_video is not None and len(motion_video) > 0:
-            assert len(motion_video) == motion_frames, f"motion video must have {motion_frames} frames, but got {len(motion_video)}"
-            motion_latents = pipe.preprocess_video(motion_video)
+        if motion_video is not None:
+            assert motion_video.shape[2] == motion_frames, f"motion video must have {motion_frames} frames, but got {motion_video.shape[2]}"
+            motion_latents = motion_video
             kwargs["drop_motion_frames"] = False
         else:
             motion_latents = torch.zeros([1, 3, motion_frames, height, width], dtype=pipe.torch_dtype, device=pipe.device)
@@ -957,7 +965,7 @@ class WanVideoUnit_AnimateInpaint(PipelineUnit):
             onload_model_names=("vae",)
         )
         
-    def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"):
+    def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device=get_device_type()):
         if mask_pixel_values is None:
             msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device)
         else:
@@ -1313,11 +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)
@@ -1331,32 +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:
@@ -1479,32 +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)

diffsynth/pipelines/z_image.py

@@ -1,24 +1,32 @@
-import torch, math
+import torch, math, warnings
 from PIL import Image
 from typing import Union
 from tqdm import tqdm
 from einops import rearrange
 import numpy as np
-from typing import Union, List, Optional, Tuple
+from typing import Union, List, Optional, Tuple, Iterable, Dict
 
+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
 from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora import merge_lora
 
 from transformers import AutoTokenizer
 from ..models.z_image_text_encoder import ZImageTextEncoder
 from ..models.z_image_dit import ZImageDiT
 from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from ..models.siglip2_image_encoder import Siglip2ImageEncoder428M
+from ..models.z_image_controlnet import ZImageControlNet
+from ..models.siglip2_image_encoder import Siglip2ImageEncoder
+from ..models.dinov3_image_encoder import DINOv3ImageEncoder
+from ..models.z_image_image2lora import ZImageImage2LoRAModel
 
 
 class ZImagePipeline(BasePipeline):
 
-    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
+    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,
@@ -28,13 +36,22 @@ class ZImagePipeline(BasePipeline):
         self.dit: ZImageDiT = None
         self.vae_encoder: FluxVAEEncoder = None
         self.vae_decoder: FluxVAEDecoder = None
+        self.image_encoder: Siglip2ImageEncoder428M = None
+        self.controlnet: ZImageControlNet = None
+        self.siglip2_image_encoder: Siglip2ImageEncoder = None
+        self.dinov3_image_encoder: DINOv3ImageEncoder = None
+        self.image2lora_style: ZImageImage2LoRAModel = None
         self.tokenizer: AutoTokenizer = None
-        self.in_iteration_models = ("dit",)
+        self.in_iteration_models = ("dit", "controlnet")
         self.units = [
             ZImageUnit_ShapeChecker(),
             ZImageUnit_PromptEmbedder(),
             ZImageUnit_NoiseInitializer(),
             ZImageUnit_InputImageEmbedder(),
+            ZImageUnit_EditImageAutoResize(),
+            ZImageUnit_EditImageEmbedderVAE(),
+            ZImageUnit_EditImageEmbedderSiglip(),
+            ZImageUnit_PAIControlNet(),
         ]
         self.model_fn = model_fn_z_image
     
@@ -42,10 +59,11 @@ class ZImagePipeline(BasePipeline):
     @staticmethod
     def from_pretrained(
         torch_dtype: torch.dtype = torch.bfloat16,
-        device: Union[str, torch.device] = "cuda",
+        device: Union[str, torch.device] = get_device_type(),
         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)
@@ -56,12 +74,19 @@ class ZImagePipeline(BasePipeline):
         pipe.dit = model_pool.fetch_model("z_image_dit")
         pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
         pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
+        pipe.image_encoder = model_pool.fetch_model("siglip_vision_model_428m")
+        pipe.controlnet = model_pool.fetch_model("z_image_controlnet")
+        pipe.siglip2_image_encoder = model_pool.fetch_model("siglip2_image_encoder")
+        pipe.dinov3_image_encoder = model_pool.fetch_model("dinov3_image_encoder")
+        pipe.image2lora_style = model_pool.fetch_model("z_image_image2lora_style")
         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()
+        # NPU patch
+        apply_npu_patch(enable_npu_patch)
         return pipe
     
     
@@ -75,6 +100,9 @@ class ZImagePipeline(BasePipeline):
         # Image
         input_image: Image.Image = None,
         denoising_strength: float = 1.0,
+        # Edit
+        edit_image: Image.Image = None,
+        edit_image_auto_resize: bool = True,
         # Shape
         height: int = 1024,
         width: int = 1024,
@@ -83,11 +111,17 @@ class ZImagePipeline(BasePipeline):
         rand_device: str = "cpu",
         # Steps
         num_inference_steps: int = 8,
+        sigma_shift: float = None,
+        # ControlNet
+        controlnet_inputs: List[ControlNetInput] = None,
+        # Image to LoRA
+        image2lora_images: List[Image.Image] = None,
+        positive_only_lora: Dict[str, torch.Tensor] = None,
         # Progress bar
         progress_bar_cmd = tqdm,
     ):
         # Scheduler
-        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
         
         # Parameters
         inputs_posi = {
@@ -102,6 +136,9 @@ class ZImagePipeline(BasePipeline):
             "height": height, "width": width,
             "seed": seed, "rand_device": rand_device,
             "num_inference_steps": num_inference_steps,
+            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize,
+            "controlnet_inputs": controlnet_inputs,
+            "image2lora_images": image2lora_images, "positive_only_lora": positive_only_lora,
         }
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -143,6 +180,7 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):
     def __init__(self):
         super().__init__(
             seperate_cfg=True,
+            input_params=("edit_image",),
             input_params_posi={"prompt": "prompt"},
             input_params_nega={"prompt": "negative_prompt"},
             output_params=("prompt_embeds",),
@@ -195,9 +233,80 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):
 
         return embeddings_list
     
-    def process(self, pipe: ZImagePipeline, prompt):
+    def encode_prompt_omni(
+        self,
+        pipe,
+        prompt: Union[str, List[str]],
+        edit_image=None,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 512,
+    ) -> List[torch.FloatTensor]:
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        if edit_image is None:
+            num_condition_images = 0
+        elif isinstance(edit_image, list):
+            num_condition_images = len(edit_image)
+        else:
+            num_condition_images = 1
+
+        for i, prompt_item in enumerate(prompt):
+            if num_condition_images == 0:
+                prompt[i] = ["<|im_start|>user\n" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n"]
+            elif num_condition_images > 0:
+                prompt_list = ["<|im_start|>user\n<|vision_start|>"]
+                prompt_list += ["<|vision_end|><|vision_start|>"] * (num_condition_images - 1)
+                prompt_list += ["<|vision_end|>" + prompt_item + "<|im_end|>\n<|im_start|>assistant\n<|vision_start|>"]
+                prompt_list += ["<|vision_end|><|im_end|>"]
+                prompt[i] = prompt_list
+
+        flattened_prompt = []
+        prompt_list_lengths = []
+
+        for i in range(len(prompt)):
+            prompt_list_lengths.append(len(prompt[i]))
+            flattened_prompt.extend(prompt[i])
+
+        text_inputs = pipe.tokenizer(
+            flattened_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[-2]
+
+        embeddings_list = []
+        start_idx = 0
+        for i in range(len(prompt_list_lengths)):
+            batch_embeddings = []
+            end_idx = start_idx + prompt_list_lengths[i]
+            for j in range(start_idx, end_idx):
+                batch_embeddings.append(prompt_embeds[j][prompt_masks[j]])
+            embeddings_list.append(batch_embeddings)
+            start_idx = end_idx
+
+        return embeddings_list
+
+    def process(self, pipe: ZImagePipeline, prompt, edit_image):
         pipe.load_models_to_device(self.onload_model_names)
-        prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
+        if hasattr(pipe, "dit") 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,
+            # please use `--offload_models` instead of skipping the DiT model loading.
+            prompt_embeds = self.encode_prompt_omni(pipe, prompt, edit_image, pipe.device)
+        else:
+            prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
         return {"prompt_embeds": prompt_embeds}
 
 
@@ -234,24 +343,346 @@ class ZImageUnit_InputImageEmbedder(PipelineUnit):
             return {"latents": latents, "input_latents": input_latents}
 
 
+class ZImageUnit_EditImageAutoResize(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image", "edit_image_auto_resize"),
+            output_params=("edit_image",),
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image, edit_image_auto_resize):
+        if edit_image is None:
+            return {}
+        if edit_image_auto_resize is None or not edit_image_auto_resize:
+            return {}
+        operator = ImageCropAndResize(max_pixels=1024*1024, height_division_factor=16, width_division_factor=16)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        edit_image = [operator(i) for i in edit_image]
+        return {"edit_image": edit_image}
+
+
+class ZImageUnit_EditImageEmbedderSiglip(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image",),
+            output_params=("image_embeds",),
+            onload_model_names=("image_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        image_emb = []
+        for image_ in edit_image:
+            image_emb.append(pipe.image_encoder(image_, device=pipe.device))
+        return {"image_embeds": image_emb}
+
+
+class ZImageUnit_EditImageEmbedderVAE(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image",),
+            output_params=("image_latents",),
+            onload_model_names=("vae_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, edit_image):
+        if edit_image is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        if not isinstance(edit_image, list):
+            edit_image = [edit_image]
+        image_latents = []
+        for image_ in edit_image:
+            image_ = pipe.preprocess_image(image_)
+            image_latents.append(pipe.vae_encoder(image_))
+        return {"image_latents": image_latents}
+
+
+class ZImageUnit_PAIControlNet(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("controlnet_inputs", "height", "width"),
+            output_params=("control_context", "control_scale"),
+            onload_model_names=("vae_encoder",)
+        )
+
+    def process(self, pipe: ZImagePipeline, controlnet_inputs: List[ControlNetInput], height, width):
+        if controlnet_inputs is None:
+            return {}
+        if len(controlnet_inputs) != 1:
+            print("Z-Image ControlNet doesn't support multi-ControlNet. Only one image will be used.")
+        controlnet_input = controlnet_inputs[0]
+        pipe.load_models_to_device(self.onload_model_names)
+
+        control_image = controlnet_input.image
+        if control_image is not None:
+            control_image = pipe.preprocess_image(control_image)
+            control_latents = pipe.vae_encoder(control_image)
+        else:
+            control_latents = torch.ones((1, 16, height // 8, width // 8), dtype=pipe.torch_dtype, device=pipe.device) * -1
+        
+        inpaint_mask = controlnet_input.inpaint_mask
+        if inpaint_mask is not None:
+            inpaint_mask = pipe.preprocess_image(inpaint_mask, min_value=0, max_value=1)
+            inpaint_image = controlnet_input.inpaint_image
+            inpaint_image = pipe.preprocess_image(inpaint_image)
+            inpaint_image = inpaint_image * (inpaint_mask < 0.5)
+            inpaint_mask = torch.nn.functional.interpolate(1 - inpaint_mask, (height // 8, width // 8), mode='nearest')[:, :1]
+        else:
+            inpaint_mask = torch.zeros((1, 1, height // 8, width // 8), dtype=pipe.torch_dtype, device=pipe.device)
+            inpaint_image = torch.zeros((1, 3, height, width), dtype=pipe.torch_dtype, device=pipe.device)
+        inpaint_latent = pipe.vae_encoder(inpaint_image)
+
+        control_context = torch.concat([control_latents, inpaint_mask, inpaint_latent], dim=1)
+        control_context = rearrange(control_context, "B C H W -> B C 1 H W")
+        return {"control_context": control_context, "control_scale": controlnet_input.scale}
+
+
 def model_fn_z_image(
     dit: ZImageDiT,
+    controlnet: ZImageControlNet = None,
     latents=None,
     timestep=None,
     prompt_embeds=None,
+    image_embeds=None,
+    image_latents=None,
     use_gradient_checkpointing=False,
     use_gradient_checkpointing_offload=False,
     **kwargs,
 ):
+    # Due to the complex and verbose codebase of Z-Image,
+    # we are temporarily using this inelegant structure.
+    # We will refactor this part in the future (if time permits).
+    if dit.siglip_embedder is None:
+        return model_fn_z_image_turbo(
+            dit,
+            controlnet=controlnet,
+            latents=latents,
+            timestep=timestep,
+            prompt_embeds=prompt_embeds,
+            image_embeds=image_embeds,
+            image_latents=image_latents,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            **kwargs,
+        )
     latents = [rearrange(latents, "B C H W -> C B H W")]
+    if dit.siglip_embedder is not None:
+        if image_latents is not None:
+            image_latents = [rearrange(image_latent, "B C H W -> C B H W") for image_latent in image_latents]
+            latents = [image_latents + latents]
+            image_noise_mask = [[0] * len(image_latents) + [1]]
+        else:
+            latents = [latents]
+            image_noise_mask = [[1]]
+        image_embeds = [image_embeds]
+    else:
+        image_noise_mask = None
     timestep = (1000 - timestep) / 1000
     model_output = dit(
         latents,
         timestep,
         prompt_embeds,
+        siglip_feats=image_embeds,
+        image_noise_mask=image_noise_mask,
         use_gradient_checkpointing=use_gradient_checkpointing,
         use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
-    )[0][0]
+    )[0]
     model_output = -model_output
     model_output = rearrange(model_output, "C B H W -> B C H W")
     return model_output
+
+
+class ZImageUnit_Image2LoRAEncode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_images",),
+            output_params=("image2lora_x",),
+            onload_model_names=("siglip2_image_encoder", "dinov3_image_encoder",),
+        )
+        from ..core.data.operators import ImageCropAndResize
+        self.processor_highres = ImageCropAndResize(height=1024, width=1024)
+    
+    def encode_images_using_siglip2(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["siglip2_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.siglip2_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+    
+    def encode_images_using_dinov3(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        pipe.load_models_to_device(["dinov3_image_encoder"])
+        embs = []
+        for image in images:
+            image = self.processor_highres(image)
+            embs.append(pipe.dinov3_image_encoder(image).to(pipe.torch_dtype))
+        embs = torch.stack(embs)
+        return embs
+
+    def encode_images(self, pipe: ZImagePipeline, images: list[Image.Image]):
+        if images is None:
+            return {}
+        if not isinstance(images, list):
+            images = [images]
+        embs_siglip2 = self.encode_images_using_siglip2(pipe, images)
+        embs_dinov3 = self.encode_images_using_dinov3(pipe, images)
+        x = torch.concat([embs_siglip2, embs_dinov3], dim=-1)
+        return x
+
+    def process(self, pipe: ZImagePipeline, image2lora_images):
+        if image2lora_images is None:
+            return {}
+        x = self.encode_images(pipe, image2lora_images)
+        return {"image2lora_x": x}
+
+
+class ZImageUnit_Image2LoRADecode(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("image2lora_x",),
+            output_params=("lora",),
+            onload_model_names=("image2lora_style",),
+        )
+    
+    def process(self, pipe: ZImagePipeline, image2lora_x):
+        if image2lora_x is None:
+            return {}
+        loras = []
+        if pipe.image2lora_style is not None:
+            pipe.load_models_to_device(["image2lora_style"])
+            for x in image2lora_x:
+                loras.append(pipe.image2lora_style(x=x, residual=None))
+        lora = merge_lora(loras, alpha=1 / len(image2lora_x))
+        return {"lora": lora}
+
+
+def model_fn_z_image_turbo(
+    dit: ZImageDiT,
+    controlnet: ZImageControlNet = None,
+    latents=None,
+    timestep=None,
+    prompt_embeds=None,
+    image_embeds=None,
+    image_latents=None,
+    control_context=None,
+    control_scale=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs,
+):
+    while isinstance(prompt_embeds, list):
+        prompt_embeds = prompt_embeds[0]
+    while isinstance(latents, list):
+        latents = latents[0]
+    while isinstance(image_embeds, list):
+        image_embeds = image_embeds[0]
+
+    # Timestep
+    timestep = 1000 - timestep
+    t_noisy = dit.t_embedder(timestep)
+    t_clean = dit.t_embedder(torch.ones_like(timestep) * 1000)
+
+    # Patchify
+    latents = rearrange(latents, "B C H W -> C B H W")
+    x, cap_feats, patch_metadata = dit.patchify_and_embed([latents], [prompt_embeds])
+    x = x[0]
+    cap_feats = cap_feats[0]
+
+    # Noise refine
+    x = dit.all_x_embedder["2-1"](x)
+    x[torch.cat(patch_metadata.get("x_pad_mask"))] = dit.x_pad_token.to(dtype=x.dtype, device=x.device)
+    x_freqs_cis = dit.rope_embedder(torch.cat(patch_metadata.get("x_pos_ids"), dim=0))
+    x = rearrange(x, "L C -> 1 L C")
+    x_freqs_cis = rearrange(x_freqs_cis, "L C -> 1 L C")
+
+    if control_context is not None:
+        kwargs = dict(attn_mask=None, freqs_cis=x_freqs_cis, adaln_input=t_noisy)
+        refiner_hints, control_context, control_context_item_seqlens = controlnet.forward_refiner(
+            dit, x, [cap_feats], control_context, kwargs, t=t_noisy, patch_size=2, f_patch_size=1,
+            use_gradient_checkpointing=use_gradient_checkpointing, use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+    
+    for layer_id, layer in enumerate(dit.noise_refiner):
+        x = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=x,
+            attn_mask=None,
+            freqs_cis=x_freqs_cis,
+            adaln_input=t_noisy,
+        )
+        if control_context is not None:
+            x = x + refiner_hints[layer_id] * control_scale
+
+    # Prompt refine
+    cap_feats = dit.cap_embedder(cap_feats)
+    cap_feats[torch.cat(patch_metadata.get("cap_pad_mask"))] = dit.cap_pad_token.to(dtype=x.dtype, device=x.device)
+    cap_freqs_cis = dit.rope_embedder(torch.cat(patch_metadata.get("cap_pos_ids"), dim=0))
+    cap_feats = rearrange(cap_feats, "L C -> 1 L C")
+    cap_freqs_cis = rearrange(cap_freqs_cis, "L C -> 1 L C")
+    
+    for layer in dit.context_refiner:
+        cap_feats = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=cap_feats,
+            attn_mask=None,
+            freqs_cis=cap_freqs_cis,
+        )
+
+    # Unified
+    unified = torch.cat([x, cap_feats], dim=1)
+    unified_freqs_cis = torch.cat([x_freqs_cis, cap_freqs_cis], dim=1)
+
+    if control_context is not None:
+        kwargs = dict(attn_mask=None, freqs_cis=unified_freqs_cis, adaln_input=t_noisy)
+        hints = controlnet.forward_layers(
+            unified, cap_feats, control_context, control_context_item_seqlens, kwargs,
+            use_gradient_checkpointing=use_gradient_checkpointing, use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+
+    for layer_id, layer in enumerate(dit.layers):
+        unified = gradient_checkpoint_forward(
+            layer,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            x=unified,
+            attn_mask=None,
+            freqs_cis=unified_freqs_cis,
+            adaln_input=t_noisy,
+        )
+        if control_context is not None:
+            if layer_id in controlnet.control_layers_mapping:
+                unified = unified + hints[controlnet.control_layers_mapping[layer_id]] * control_scale
+    
+    # Output
+    unified = dit.all_final_layer["2-1"](unified, t_noisy)
+    x = dit.unpatchify([unified[0]], patch_metadata.get("x_size"))[0]
+    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

diffsynth/utils/controlnet/annotator.py

@@ -1,12 +1,13 @@
 from typing_extensions import Literal, TypeAlias
 
+from diffsynth.core.device.npu_compatible_device import get_device_type
 
 Processor_id: TypeAlias = Literal[
     "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint"
 ]
 
 class Annotator:
-    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False):
+    def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device=get_device_type(), skip_processor=False):
         if not skip_processor:
             if processor_id == "canny":
                 from controlnet_aux.processor import CannyDetector

diffsynth/utils/controlnet/controlnet_input.py

@@ -9,5 +9,6 @@ class ControlNetInput:
     start: float = 1.0
     end: float = 0.0
     image: Image.Image = None
+    inpaint_image: Image.Image = None
     inpaint_mask: Image.Image = None
     processor_id: str = None

diffsynth/utils/data/media_io_ltx2.py

@@ -0,0 +1,149 @@
+
+from fractions import Fraction
+import torch
+import av
+from tqdm import tqdm
+from PIL import Image
+import numpy as np
+from io import BytesIO
+from collections.abc import Generator, Iterator
+
+
+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[:, None]
+
+    if samples.shape[1] != 2 and samples.shape[0] == 2:
+        samples = samples.T
+
+    if samples.shape[1] != 2:
+        raise ValueError(f"Expected samples with 2 channels; got shape {samples.shape}.")
+
+    # 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", rate=audio_sample_rate)
+    audio_stream.codec_context.sample_rate = audio_sample_rate
+    audio_stream.codec_context.layout = "stereo"
+    audio_stream.codec_context.time_base = Fraction(1, audio_sample_rate)
+    return audio_stream
+
+def write_video_audio_ltx2(
+    video: list[Image.Image],
+    audio: torch.Tensor | None,
+    output_path: str,
+    fps: int = 24,
+    audio_sample_rate: int | None = 24000,
+) -> None:
+
+    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()
+
+
+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

diffsynth/utils/lora/flux.py

@@ -149,6 +149,8 @@ class FluxLoRALoader(GeneralLoRALoader):
                                         dtype=state_dict_[name].dtype)
                     else:
                         state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+
+                    mlp = mlp.to(device=state_dict_[name].device)
                     if 'lora_A' in name:
                         param = torch.concat([
                             state_dict_.pop(name),

diffsynth/utils/state_dict_converters/flux_dit.py

@@ -90,3 +90,108 @@ def FluxDiTStateDictConverter(state_dict):
         else:
             pass
     return state_dict_
+
+
+def FluxDiTStateDictConverterFromDiffusers(state_dict):
+    global_rename_dict = {
+        "context_embedder": "context_embedder",
+        "x_embedder": "x_embedder",
+        "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+        "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+        "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+        "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+        "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+        "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+        "norm_out.linear": "final_norm_out.linear",
+        "proj_out": "final_proj_out",
+    }
+    rename_dict = {
+        "proj_out": "proj_out",
+        "norm1.linear": "norm1_a.linear",
+        "norm1_context.linear": "norm1_b.linear",
+        "attn.to_q": "attn.a_to_q",
+        "attn.to_k": "attn.a_to_k",
+        "attn.to_v": "attn.a_to_v",
+        "attn.to_out.0": "attn.a_to_out",
+        "attn.add_q_proj": "attn.b_to_q",
+        "attn.add_k_proj": "attn.b_to_k",
+        "attn.add_v_proj": "attn.b_to_v",
+        "attn.to_add_out": "attn.b_to_out",
+        "ff.net.0.proj": "ff_a.0",
+        "ff.net.2": "ff_a.2",
+        "ff_context.net.0.proj": "ff_b.0",
+        "ff_context.net.2": "ff_b.2",
+        "attn.norm_q": "attn.norm_q_a",
+        "attn.norm_k": "attn.norm_k_a",
+        "attn.norm_added_q": "attn.norm_q_b",
+        "attn.norm_added_k": "attn.norm_k_b",
+    }
+    rename_dict_single = {
+        "attn.to_q": "a_to_q",
+        "attn.to_k": "a_to_k",
+        "attn.to_v": "a_to_v",
+        "attn.norm_q": "norm_q_a",
+        "attn.norm_k": "norm_k_a",
+        "norm.linear": "norm.linear",
+        "proj_mlp": "proj_in_besides_attn",
+        "proj_out": "proj_out",
+    }
+    state_dict_ = {}
+    for name in state_dict:
+        param = state_dict[name]
+        if name.endswith(".weight") or name.endswith(".bias"):
+            suffix = ".weight" if name.endswith(".weight") else ".bias"
+            prefix = name[:-len(suffix)]
+            if prefix in global_rename_dict:
+                if global_rename_dict[prefix] == "final_norm_out.linear":
+                    param = torch.concat([param[3072:], param[:3072]], dim=0)
+                state_dict_[global_rename_dict[prefix] + suffix] = param
+            elif prefix.startswith("transformer_blocks."):
+                names = prefix.split(".")
+                names[0] = "blocks"
+                middle = ".".join(names[2:])
+                if middle in rename_dict:
+                    name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                    state_dict_[name_] = param
+            elif prefix.startswith("single_transformer_blocks."):
+                names = prefix.split(".")
+                names[0] = "single_blocks"
+                middle = ".".join(names[2:])
+                if middle in rename_dict_single:
+                    name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                    state_dict_[name_] = param
+                else:
+                    pass
+            else:
+                pass
+    for name in list(state_dict_.keys()):
+        if "single_blocks." in name and ".a_to_q." in name:
+            mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
+            if mlp is None:
+                mlp = torch.zeros(4 * state_dict_[name].shape[0],
+                                    *state_dict_[name].shape[1:],
+                                    dtype=state_dict_[name].dtype)
+            else:
+                state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
+            param = torch.concat([
+                state_dict_.pop(name),
+                state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
+                state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
+                mlp,
+            ], dim=0)
+            name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
+            state_dict_[name_] = param
+    for name in list(state_dict_.keys()):
+        for component in ["a", "b"]:
+            if f".{component}_to_q." in name:
+                name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
+                param = torch.concat([
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
+                    state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
+                state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
+    return state_dict_

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.replace("vocoder.", "")
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_

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_

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_

diffsynth/utils/state_dict_converters/ltx2_video_vae.py

@@ -0,0 +1,22 @@
+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.")
+            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.")
+            state_dict_[new_name] = state_dict[name]
+    return state_dict_

diffsynth/utils/state_dict_converters/z_image_text_encoder.py

@@ -0,0 +1,6 @@
+def ZImageTextEncoderStateDictConverter(state_dict):
+    state_dict_ = {}
+    for name in state_dict:
+        if name != "lm_head.weight":
+            state_dict_[name] = state_dict[name]
+    return state_dict_

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.type == "npu" else freqs_rank
     x_out = torch.view_as_real(x_out * freqs_rank).flatten(2)
     return x_out.to(x.dtype)
 
@@ -82,11 +89,6 @@ def usp_dit_forward(self,
         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)
     pad_shape = chunks[0].shape[1] - chunks[-1].shape[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,

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'

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

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

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:
 

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).

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.
 

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.
 

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.

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
 

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)

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
@@ -198,4 +198,4 @@ We have built a sample image dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_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/).

docs/en/Model_Details/FLUX2.md

@@ -2,6 +2,15 @@
 
 FLUX.2 is an image generation model trained and open-sourced by Black Forest Labs.
 
+## Model Lineage
+
+```mermaid
+graph LR;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-dev;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-4B;
+    FLUX.2-Series-->black-forest-labs/FLUX.2-klein-9B;
+```
+
 ## Installation
 
 Before using this project for model inference and training, please install DiffSynth-Studio first.
@@ -12,7 +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
 
@@ -50,20 +59,24 @@ image.save("image.jpg")
 
 ## Model Overview
 
-| Model ID | Inference | Low VRAM Inference | 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) |
+| 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](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), [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 `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:
 
@@ -82,11 +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
@@ -135,4 +148,4 @@ We have built a sample image dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_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/).

docs/en/Model_Details/LTX-2.md

@@ -0,0 +1,211 @@
+# 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: 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: 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/example_video_dataset --local_dir ./data/example_video_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/).

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/)

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
 
@@ -80,31 +80,42 @@ 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) | - | - | - | - |
-|[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)|
+|[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-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:
 
@@ -135,11 +146,11 @@ 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
@@ -189,4 +200,4 @@ We have built a sample image dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_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/).

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 +106,50 @@ 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/)
+
+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 +199,11 @@ 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
@@ -249,4 +254,4 @@ We have built a sample video dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_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/).

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
 
@@ -50,18 +50,23 @@ image.save("image.jpg")
 
 ## Model Overview
 
-| Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
-| - | - | - | - | - | - | - |
-| [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) |
+|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](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:
 
@@ -75,12 +80,15 @@ Input parameters for `ZImagePipeline` inference include:
 * `seed`: Random seed. Default is `None`, meaning completely random.
 * `rand_device`: Computing device for generating random Gaussian noise matrix, default is `"cpu"`. When set to `cuda`, different GPUs will produce different generation results.
 * `num_inference_steps`: Number of inference steps, default value is 8.
+* `controlnet_inputs`: Inputs for ControlNet models.
+* `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
@@ -129,13 +137,13 @@ We have built a sample image dataset for your testing. You can download this dat
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_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

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`
 

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
 
@@ -13,7 +13,7 @@ All sample code provided by this project supports NVIDIA GPUs by default, requir
 AMD provides PyTorch packages based on ROCm, so most models can run without code changes. A small number of models may not be compatible due to their reliance on CUDA-specific instructions.
 
 ## Ascend NPU
-
+### Inference
 When using Ascend NPU, you need to replace `"cuda"` with `"npu"` in your code.
 
 For example, here is the inference code for **Wan2.1-T2V-1.3B**, modified for Ascend NPU:
@@ -22,6 +22,7 @@ For example, here is the inference code for **Wan2.1-T2V-1.3B**, modified for As
 import torch
 from diffsynth.utils.data import save_video, VideoData
 from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
+from diffsynth.core.device.npu_compatible_device import get_device_name
 
 vram_config = {
     "offload_dtype": "disk",
@@ -46,7 +47,7 @@ pipe = WanVideoPipeline.from_pretrained(
     ],
     tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
 -   vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 2,
-+   vram_limit=torch.npu.mem_get_info("npu")[1] / (1024 ** 3) - 2,
++   vram_limit=torch.npu.mem_get_info(get_device_name())[1] / (1024 ** 3) - 2,
 )
 
 video = pipe(
@@ -56,3 +57,37 @@ 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`.
+
+In the NPU training scripts, NPU specific environment variables that can optimize performance have been added, and relevant parameters have been enabled for specific models.
+
+#### Environment variables
+```shell
+export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
+```
+`expandable_segments:<value>`: Enable the memory pool expansion segment function, which is the virtual memory feature.
+
+```shell
+export CPU_AFFINITY_CONF=1
+```
+Set 0 or not set: indicates not enabling the binding function
+
+1: Indicates enabling coarse-grained kernel binding
+
+2: Indicates enabling fine-grained kernel binding
+
+#### Parameters for specific models
+| Model          | Parameter                 | Note              |
+|----------------|---------------------------|-------------------|
+| 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 |

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()
+```

docs/en/Pipeline_Usage/Model_Training.md

@@ -65,7 +65,7 @@ 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>
 
@@ -93,7 +93,7 @@ We have built sample datasets for your testing. To understand how the universal
 
 ## 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>
 
@@ -115,9 +115,9 @@ 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>
 
@@ -237,11 +237,11 @@ 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.

docs/en/Pipeline_Usage/Setup.md

@@ -30,13 +30,18 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
 
 * **Ascend NPU**
 
-Ascend NPU support is provided via the `torch-npu` package. Taking version `2.1.0.post17` (as of the article update date: December 15, 2025) as an example, run the following command:
+1. Install [CANN](https://www.hiascend.com/document/detail/zh/canncommercial/83RC1/softwareinst/instg/instg_quick.html?Mode=PmIns&InstallType=local&OS=openEuler&Software=cannToolKit) through official documentation.
 
-```shell
-pip install torch-npu==2.1.0.post17
-```
+2. Install from source
+   ```shell
+   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"
+   # x86
+   pip install -e .[npu]
 
-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
 

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
 

docs/en/QA.md

@@ -26,3 +26,10 @@ Even with suitable hardware conditions, we currently have no plans to support na
 * 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.
+
+## 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()`.

docs/en/README.md

@@ -26,58 +26,58 @@ 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)
 
 ## 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】
+* [Training models from scratch](./Research_Tutorial/train_from_scratch.md)
 * Inference improvement techniques 【coming soon】
 * Designing controllable generation models 【coming soon】
 * Creating new training paradigms 【coming soon】
@@ -86,4 +86,4 @@ 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)

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!

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,
+    )

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:
 

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
@@ -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
 

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:
 

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 \

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

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)

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',
+]

docs/en/index.rst

@@ -0,0 +1,77 @@
+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
+
+.. 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
+
+.. toctree::
+   :maxdepth: 2
+   :caption: FAQ
+
+   QA
+
+Indices and tables
+==================
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

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

docs/zh/.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/zh/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

docs/zh/API_Reference/core/attention.md

@@ -1,6 +1,6 @@
 # `diffsynth.core.attention`: 注意力机制实现
 
-`diffsynth.core.attention` 提供了注意力机制实现的路由机制，根据 `Python` 环境中的可用包和[环境变量](/docs/zh/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation)自动选择高效的注意力机制实现。
+`diffsynth.core.attention` 提供了注意力机制实现的路由机制，根据 `Python` 环境中的可用包和[环境变量](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation)自动选择高效的注意力机制实现。
 
 ## 注意力机制
 
@@ -46,7 +46,7 @@ output_1 = attention(query, key, value)
 * xFormers：[GitHub](https://github.com/facebookresearch/xformers)、[文档](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops)
 * PyTorch：[GitHub](https://github.com/pytorch/pytorch)、[文档](https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html)
 
-如需调用除 `PyTorch` 外的其他注意力实现，请按照其 GitHub 页面的指引安装对应的包。`DiffSynth-Studio` 会自动根据 Python 环境中的可用包路由到对应的实现上，也可通过[环境变量](/docs/zh/Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation)控制。
+如需调用除 `PyTorch` 外的其他注意力实现，请按照其 GitHub 页面的指引安装对应的包。`DiffSynth-Studio` 会自动根据 Python 环境中的可用包路由到对应的实现上，也可通过[环境变量](../../Pipeline_Usage/Environment_Variables.md#diffsynth_attention_implementation)控制。
 
 ```python
 from diffsynth.core.attention import attention_forward

docs/zh/API_Reference/core/loader.md

@@ -8,9 +8,9 @@
 
 ### 从远程下载并加载模型
 
-以模型[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) 为例，在 `ModelConfig` 中填写 `model_id` 和 `origin_file_pattern` 后即可自动下载模型。默认下载到 `./models` 路径，该路径可通过[环境变量 DIFFSYNTH_MODEL_BASE_PATH](/docs/zh/Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path) 修改。
+以模型[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny) 为例，在 `ModelConfig` 中填写 `model_id` 和 `origin_file_pattern` 后即可自动下载模型。默认下载到 `./models` 路径，该路径可通过[环境变量 DIFFSYNTH_MODEL_BASE_PATH](../../Pipeline_Usage/Environment_Variables.md#diffsynth_model_base_path) 修改。
 
-默认情况下，即使模型已经下载完毕，程序仍会向远程查询是否有遗漏文件，如果要完全关闭远程请求，请将[环境变量 DIFFSYNTH_SKIP_DOWNLOAD](/docs/zh/Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) 设置为 `True`。
+默认情况下，即使模型已经下载完毕，程序仍会向远程查询是否有遗漏文件，如果要完全关闭远程请求，请将[环境变量 DIFFSYNTH_SKIP_DOWNLOAD](../../Pipeline_Usage/Environment_Variables.md#diffsynth_skip_download) 设置为 `True`。
 
 ```python
 from diffsynth.core import ModelConfig
@@ -51,7 +51,7 @@ config = ModelConfig(path=[
 
 ### 显存管理配置
 
-`ModelConfig` 也包含了显存管理配置信息，详见[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md#更多使用方式)。
+`ModelConfig` 也包含了显存管理配置信息，详见[显存管理](../../Pipeline_Usage/VRAM_management.md#更多使用方式)。
 
 ## 模型文件加载
 
@@ -103,11 +103,11 @@ print(hash_model_file([
 
 模型哈希值只与模型文件中 state dict 的 keys 和 tensor shape 有关，与模型参数的数值、文件保存时间等信息无关。在计算 `.safetensors` 格式文件的模型哈希值时，`hash_model_file` 是几乎瞬间完成的，无需读取模型的参数；但在计算 `.bin`、`.pth`、`.ckpt` 等二进制文件的模型哈希值时，则需要读取全部模型参数，因此**我们不建议开发者继续使用这些格式的文件。**
 
-通过[编写模型 Config](/docs/zh/Developer_Guide/Integrating_Your_Model.md#step-3-编写模型-config)并将模型哈希值等信息填入 `diffsynth/configs/model_configs.py`，开发者可以让 `DiffSynth-Studio` 自动识别模型类型并加载。
+通过[编写模型 Config](../../Developer_Guide/Integrating_Your_Model.md#step-3-编写模型-config)并将模型哈希值等信息填入 `diffsynth/configs/model_configs.py`，开发者可以让 `DiffSynth-Studio` 自动识别模型类型并加载。
 
 ## 模型加载
 
-`load_model` 是 `diffsynth.core.loader` 中加载模型的外部入口，它会调用 [skip_model_initialization](/docs/zh/API_Reference/core/vram.md#跳过模型参数初始化) 跳过模型参数初始化。如果启用了 [Disk Offload](/docs/zh/Pipeline_Usage/VRAM_management.md#disk-offload)，则调用 [DiskMap](/docs/zh/API_Reference/core/vram.md#state-dict-硬盘映射) 进行惰性加载；如果没有启用 Disk Offload，则调用 [load_state_dict](#模型文件加载) 加载模型参数。如果需要的话，还会调用 [state dict converter](/docs/zh/Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换) 进行模型格式转换。最后调用 `model.eval()` 将其切换到推理模式。
+`load_model` 是 `diffsynth.core.loader` 中加载模型的外部入口，它会调用 [skip_model_initialization](../../API_Reference/core/vram.md#跳过模型参数初始化) 跳过模型参数初始化。如果启用了 [Disk Offload](../../Pipeline_Usage/VRAM_management.md#disk-offload)，则调用 [DiskMap](../../API_Reference/core/vram.md#state-dict-硬盘映射) 进行惰性加载；如果没有启用 Disk Offload，则调用 [load_state_dict](#模型文件加载) 加载模型参数。如果需要的话，还会调用 [state dict converter](../../Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换) 进行模型格式转换。最后调用 `model.eval()` 将其切换到推理模式。
 
 以下是一个启用了 Disk Offload 的使用案例：