bugfix

fix qwen_text_encoder bug in transformers>=5.2.0

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
@@ -33,6 +33,12 @@ We believe that a well-developed open-source code framework can lower the thresh
 
 > 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.
 
+- **March 2, 2026** Added support for [Anima](https://modelscope.cn/models/circlestone-labs/Anima). For details, please refer to the [documentation](docs/en/Model_Details/Anima.md). This is an interesting anime-style image generation model. We look forward to its future updates.
+
+- **February 26, 2026** Added full and lora training support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details.
+
+- **February 10, 2026** Added inference support for the LTX-2 audio-video generation model. See the [documentation](/docs/en/Model_Details/LTX-2.md) for details. Support for model training will be implemented in the future.
+
 - **February 2, 2026** The first document of the Research Tutorial series is now available, guiding you through training a small 0.1B text-to-image model from scratch. For details, see the [documentation](/docs/en/Research_Tutorial/train_from_scratch.md) and [model](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel). We hope DiffSynth-Studio can evolve into a more powerful training framework for Diffusion models.
 
 - **January 27, 2026**: [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) is released, and our [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) model is released concurrently. You can use it in [ModelScope Studios](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L). For details, see the [documentation](/docs/zh/Model_Details/Z-Image.md).
@@ -340,6 +346,60 @@ Example code for FLUX.2 is available at: [/examples/flux2/](/examples/flux2/)
 
 </details>
 
+#### Anima: [/docs/en/Model_Details/Anima.md](/docs/en/Model_Details/Anima.md)
+
+<details>
+
+<summary>Quick Start</summary>
+
+Run the following code to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model and perform inference. VRAM management is enabled, and the framework will automatically control the loading of model parameters based on available VRAM. The model can run with a minimum of 8GB VRAM.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Examples</summary>
+
+Example code for Anima is located at: [/examples/anima/](/examples/anima/)
+
+| Model ID | Inference | Low VRAM Inference | Full Training | Validation after Full Training | LoRA Training | Validation after LoRA Training |
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/en/Model_Details/Qwen-Image.md](/docs/en/Model_Details/Qwen-Image.md)
 
 <details>
@@ -419,6 +479,7 @@ Example code for Qwen-Image is available at: [/examples/qwen_image/](/examples/q
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
 |[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.0](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.0)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py)|
 |[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)|
@@ -555,12 +616,26 @@ vram_config = {
     "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="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.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"),
@@ -568,6 +643,20 @@ pipe = LTX2AudioVideoPipeline.from_pretrained(
     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, "
@@ -612,12 +701,21 @@ Example code for LTX-2 is available at: [/examples/ltx2/](/examples/ltx2/)
 
 | Model ID | Extra Args | Inference | Low-VRAM Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
 |-|-|-|-|-|-|-|-|
-|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
+|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
 |[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
 |[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-TwoStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
 
 </details>
 
@@ -760,6 +858,37 @@ 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>
@@ -770,7 +899,7 @@ 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>
 
@@ -785,10 +914,10 @@ 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 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)                       |![](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 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)  |![](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 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)                          |![](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 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>
 

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
@@ -33,6 +33,12 @@ DiffSynth 目前包括两个开源项目：
 
 > 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3月2日** 新增对[Anima](https://modelscope.cn/models/circlestone-labs/Anima)的支持，详见[文档](docs/zh/Model_Details/Anima.md)。这是一个有趣的动漫风格图像生成模型，我们期待其后续的模型更新。
+
+- **2026年2月26日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型全量微调与LoRA训练支持，详见[文档](docs/zh/Model_Details/LTX-2.md)。
+
+- **2026年2月10日** 新增对[LTX-2](https://www.modelscope.cn/models/Lightricks/LTX-2)音视频生成模型的推理支持，详见[文档](docs/zh/Model_Details/LTX-2.md)，后续将推进模型训练的支持。
+
 - **2026年2月2日** Research Tutorial 的第一篇文档上线，带你从零开始训练一个 0.1B 的小型文生图模型，详见[文档](/docs/zh/Research_Tutorial/train_from_scratch.md)、[模型](https://modelscope.cn/models/DiffSynth-Studio/AAAMyModel)，我们希望 DiffSynth-Studio 能够成为一个更强大的 Diffusion 模型训练框架。
 
 - **2026年1月27日** [Z-Image](https://modelscope.cn/models/Tongyi-MAI/Z-Image) 发布，我们的 [Z-Image-i2L](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-i2L) 模型同步发布，在[魔搭创空间](https://modelscope.cn/studios/DiffSynth-Studio/Z-Image-i2L)可直接体验，详见[文档](/docs/zh/Model_Details/Z-Image.md)。
@@ -340,6 +346,60 @@ FLUX.2 的示例代码位于：[/examples/flux2/](/examples/flux2/)
 
 </details>
 
+#### Anima: [/docs/zh/Model_Details/Anima.md](/docs/zh/Model_Details/Anima.md)
+
+<details>
+
+<summary>快速开始</summary>
+
+运行以下代码可以快速加载 [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 8G 显存即可运行。
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>示例代码</summary>
+
+Anima 的示例代码位于：[/examples/anima/](/examples/anima/)
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+</details>
+
 #### Qwen-Image: [/docs/zh/Model_Details/Qwen-Image.md](/docs/zh/Model_Details/Qwen-Image.md)
 
 <details>
@@ -419,6 +479,7 @@ Qwen-Image 的示例代码位于：[/examples/qwen_image/](/examples/qwen_image/
 |[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
 |[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)|
 |[Qwen/Qwen-Image-Edit-2511](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2511)|[code](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[FireRedTeam/FireRed-Image-Edit-1.0](https://www.modelscope.cn/models/FireRedTeam/FireRed-Image-Edit-1.0)|[code](/examples/qwen_image/model_inference/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_inference_low_vram/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/full/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_full/FireRed-Image-Edit-1.0.py)|[code](/examples/qwen_image/model_training/lora/FireRed-Image-Edit-1.0.sh)|[code](/examples/qwen_image/model_training/validate_lora/FireRed-Image-Edit-1.0.py)|
 |[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)|
@@ -555,12 +616,26 @@ vram_config = {
     "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="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.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"),
@@ -568,6 +643,20 @@ pipe = LTX2AudioVideoPipeline.from_pretrained(
     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, "
@@ -612,12 +701,21 @@ LTX-2 的示例代码位于：[/examples/ltx2/](/examples/ltx2/)
 
 |模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|-|
-|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
+|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
+|[Lightricks/LTX-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
 |[Lightricks/LTX-2: TwoStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: DistilledPipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-DistilledPipeline.py)|-|-|-|-|
 |[Lightricks/LTX-2: OneStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-OneStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: TwoStagePipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-TwoStage.py)|-|-|-|-|
 |[Lightricks/LTX-2: DistilledPipeline-I2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)|`input_images`|[code](/examples/ltx2/model_inference/LTX-2-I2AV-DistilledPipeline.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-I2AV-DistilledPipeline.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-In)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-In.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-In.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Out.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Left)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Left.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Right)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Dolly-Right.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Up)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Up.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Up.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Jib-Down)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Jib-Down.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Jib-Down.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-LoRA-Camera-Control-Static](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-LoRA-Camera-Control-Static)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-Camera-Control-Static.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-Camera-Control-Static.py)|-|-|-|-|
 
 </details>
 
@@ -760,6 +858,37 @@ 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>
@@ -771,7 +900,7 @@ 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>
 
@@ -787,10 +916,10 @@ 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 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)                       |![](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 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)  |![](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 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)                          |![](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 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/__init__.py

@@ -1,2 +1,2 @@
 from .model_configs import MODEL_CONFIGS
-from .vram_management_module_maps import VRAM_MANAGEMENT_MODULE_MAPS
+from .vram_management_module_maps import VRAM_MANAGEMENT_MODULE_MAPS, VERSION_CHECKER_MAPS

diffsynth/configs/model_configs.py

@@ -598,7 +598,14 @@ z_image_series = [
         "state_dict_converter": "diffsynth.utils.state_dict_converters.z_image_text_encoder.ZImageTextEncoderStateDictConverter",
     },
 ]
-
+"""
+Offical model repo: https://www.modelscope.cn/models/Lightricks/LTX-2
+Repackaged model repo: https://www.modelscope.cn/models/DiffSynth-Studio/LTX-2-Repackage
+For base models of LTX-2, offical checkpoint (with model config ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors"))
+and repackaged checkpoints (with model config ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="*.safetensors")) are both supported.
+We have repackeged the official checkpoints in DiffSynth-Studio/LTX-2-Repackage repo to support separate loading of different submodules,
+and avoid redundant memory usage when users only want to use part of the model.
+"""
 ltx2_series = [
     {
         # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
@@ -607,6 +614,13 @@ ltx2_series = [
         "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",
@@ -614,6 +628,13 @@ ltx2_series = [
         "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",
@@ -621,6 +642,13 @@ ltx2_series = [
         "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",
@@ -628,6 +656,13 @@ ltx2_series = [
         "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",
@@ -635,16 +670,37 @@ ltx2_series = [
         "model_class": "diffsynth.models.ltx2_audio_vae.LTX2Vocoder",
         "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2VocoderStateDictConverter",
     },
-    # { # not used currently
+    {
-    #     # Example: ModelConfig(model_id="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.safetensors")
+        # Example: ModelConfig(model_id="DiffSynth-Studio/LTX-2-Repackage", origin_file_pattern="audio_vocoder.safetensors")
-    #     "model_hash": "aca7b0bbf8415e9c98360750268915fc",
+        "model_hash": "f471360f6b24bef702ab73133d9f8bb9",
-    #     "model_name": "ltx2_audio_vae_encoder",
+        "model_name": "ltx2_audio_vocoder",
-    #     "model_class": "diffsynth.models.ltx2_audio_vae.LTX2AudioEncoder",
+        "model_class": "diffsynth.models.ltx2_audio_vae.LTX2Vocoder",
-    #     "state_dict_converter": "diffsynth.utils.state_dict_converters.ltx2_audio_vae.LTX2AudioEncoderStateDictConverter",
+        "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",
@@ -663,4 +719,20 @@ ltx2_series = [
         "model_class": "diffsynth.models.ltx2_upsampler.LTX2LatentUpsampler",
     },
 ]
-MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series
+anima_series = [
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors")
+        "model_hash": "a9995952c2d8e63cf82e115005eb61b9",
+        "model_name": "z_image_text_encoder",
+        "model_class": "diffsynth.models.z_image_text_encoder.ZImageTextEncoder",
+        "extra_kwargs": {"model_size": "0.6B"},
+    },
+    {
+        # Example: ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors")
+        "model_hash": "417673936471e79e31ed4d186d7a3f4a",
+        "model_name": "anima_dit",
+        "model_class": "diffsynth.models.anima_dit.AnimaDiT",
+        "state_dict_converter": "diffsynth.utils.state_dict_converters.anima_dit.AnimaDiTStateDictConverter",
+    }
+]
+MODEL_CONFIGS = qwen_image_series + wan_series + flux_series + flux2_series + z_image_series + ltx2_series + anima_series

diffsynth/configs/vram_management_module_maps.py

@@ -243,4 +243,24 @@ VRAM_MANAGEMENT_MODULE_MAPS = {
         "transformers.models.gemma3.modeling_gemma3.Gemma3RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
         "transformers.models.gemma3.modeling_gemma3.Gemma3TextScaledWordEmbedding": "diffsynth.core.vram.layers.AutoWrappedModule",
     },
+    "diffsynth.models.anima_dit.AnimaDiT": {
+        "torch.nn.Linear": "diffsynth.core.vram.layers.AutoWrappedLinear",
+        "torch.nn.LayerNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.RMSNorm": "diffsynth.core.vram.layers.AutoWrappedModule",
+        "torch.nn.Embedding": "diffsynth.core.vram.layers.AutoWrappedModule",
+    },
+}
+
+def QwenImageTextEncoder_Module_Map_Updater():
+    current = VRAM_MANAGEMENT_MODULE_MAPS["diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder"]
+    from packaging import version
+    import transformers
+    if version.parse(transformers.__version__) >= version.parse("5.2.0"):
+        # The Qwen2RMSNorm in transformers 5.2.0+ has been renamed to Qwen2_5_VLRMSNorm, so we need to update the module map accordingly
+        current.pop("transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2RMSNorm", None)
+        current["transformers.models.qwen2_5_vl.modeling_qwen2_5_vl.Qwen2_5_VLRMSNorm"] = "diffsynth.core.vram.layers.AutoWrappedModule"
+    return current
+
+VERSION_CHECKER_MAPS = {
+    "diffsynth.models.qwen_image_text_encoder.QwenImageTextEncoder": QwenImageTextEncoder_Module_Map_Updater,
 }

diffsynth/core/data/operators.py

@@ -218,3 +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/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, state_dict=None):
     config = {} if config is None else config
-    # Why do we use `skip_model_initialization`?
+    with ContextManagers(get_init_context(torch_dtype=torch_dtype, device=device)):
-    # It skips the random initialization of model parameters,
-    # thereby speeding up model loading and avoiding excessive memory usage.
-    with skip_model_initialization():
         model = model_class(**config)
     # What is `module_map`?
     # This is a module mapping table for VRAM management.
@@ -48,7 +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.
@@ -79,3 +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/diffusion/base_pipeline.py

@@ -94,20 +94,23 @@ class BasePipeline(torch.nn.Module):
         return self
 
 
-    def check_resize_height_width(self, height, width, num_frames=None):
+    def check_resize_height_width(self, height, width, num_frames=None, verbose=1):
         # Shape check
         if height % self.height_division_factor != 0:
             height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor
-            print(f"height % {self.height_division_factor} != 0. We round it up to {height}.")
+            if verbose > 0:
+                print(f"height % {self.height_division_factor} != 0. We round it up to {height}.")
         if width % self.width_division_factor != 0:
             width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor
-            print(f"width % {self.width_division_factor} != 0. We round it up to {width}.")
+            if verbose > 0:
+                print(f"width % {self.width_division_factor} != 0. We round it up to {width}.")
         if num_frames is None:
             return height, width
         else:
             if num_frames % self.time_division_factor != self.time_division_remainder:
                 num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder
-                print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
+                if verbose > 0:
+                    print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.")
             return height, width, num_frames
 
 

diffsynth/diffusion/logger.py

@@ -18,8 +18,8 @@ class ModelLogger:
 
     def on_epoch_end(self, accelerator: Accelerator, model: torch.nn.Module, epoch_id):
         accelerator.wait_for_everyone()
+        state_dict = accelerator.get_state_dict(model)
         if accelerator.is_main_process:
-            state_dict = accelerator.get_state_dict(model)
             state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
             state_dict = self.state_dict_converter(state_dict)
             os.makedirs(self.output_path, exist_ok=True)
@@ -34,8 +34,8 @@ class ModelLogger:
 
     def save_model(self, accelerator: Accelerator, model: torch.nn.Module, file_name):
         accelerator.wait_for_everyone()
+        state_dict = accelerator.get_state_dict(model)
         if accelerator.is_main_process:
-            state_dict = accelerator.get_state_dict(model)
             state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
             state_dict = self.state_dict_converter(state_dict)
             os.makedirs(self.output_path, exist_ok=True)

diffsynth/diffusion/loss.py

@@ -28,6 +28,36 @@ def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
     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
@@ -91,7 +121,9 @@ class TrajectoryImitationLoss(torch.nn.Module):
                 progress_id_teacher = torch.argmin((timesteps_teacher - pipe.scheduler.timesteps[progress_id + 1]).abs())
                 latents_ = trajectory_teacher[progress_id_teacher]
             
-            target = (latents_ - inputs_shared["latents"]) / (sigma_ - sigma)
+            denom = sigma_ - sigma
+            denom = torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6)
+            target = (latents_ - inputs_shared["latents"]) / denom
             loss = loss + torch.nn.functional.mse_loss(noise_pred.float(), target.float()) * pipe.scheduler.training_weight(timestep)
         return loss
     

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):
@@ -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/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,

diffsynth/models/ltx2_audio_vae.py

@@ -5,8 +5,65 @@ import einops
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torchaudio
 from .ltx2_common import VideoLatentShape, AudioLatentShape, Patchifier, NormType, build_normalization_layer
 
+
+class AudioProcessor(nn.Module):
+    """Converts audio waveforms to log-mel spectrograms with optional resampling."""
+
+    def __init__(
+        self,
+        sample_rate: int = 16000,
+        mel_bins: int = 64,
+        mel_hop_length: int = 160,
+        n_fft: int = 1024,
+    ) -> None:
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.mel_transform = torchaudio.transforms.MelSpectrogram(
+            sample_rate=sample_rate,
+            n_fft=n_fft,
+            win_length=n_fft,
+            hop_length=mel_hop_length,
+            f_min=0.0,
+            f_max=sample_rate / 2.0,
+            n_mels=mel_bins,
+            window_fn=torch.hann_window,
+            center=True,
+            pad_mode="reflect",
+            power=1.0,
+            mel_scale="slaney",
+            norm="slaney",
+        )
+
+    def resample_waveform(
+        self,
+        waveform: torch.Tensor,
+        source_rate: int,
+        target_rate: int,
+    ) -> torch.Tensor:
+        """Resample waveform to target sample rate if needed."""
+        if source_rate == target_rate:
+            return waveform
+        resampled = torchaudio.functional.resample(waveform, source_rate, target_rate)
+        return resampled.to(device=waveform.device, dtype=waveform.dtype)
+
+    def waveform_to_mel(
+        self,
+        waveform: torch.Tensor,
+        waveform_sample_rate: int,
+    ) -> torch.Tensor:
+        """Convert waveform to log-mel spectrogram [batch, channels, time, n_mels]."""
+        waveform = self.resample_waveform(waveform, waveform_sample_rate, self.sample_rate)
+
+        mel = self.mel_transform(waveform)
+        mel = torch.log(torch.clamp(mel, min=1e-5))
+
+        mel = mel.to(device=waveform.device, dtype=waveform.dtype)
+        return mel.permute(0, 1, 3, 2).contiguous()
+
+
 class AudioPatchifier(Patchifier):
     def __init__(
         self,

diffsynth/models/ltx2_dit.py

@@ -8,6 +8,7 @@ import torch
 from einops import rearrange
 from .ltx2_common import rms_norm, Modality
 from ..core.attention.attention import attention_forward
+from ..core import gradient_checkpoint_forward
 
 
 def get_timestep_embedding(
@@ -1352,28 +1353,21 @@ class LTXModel(torch.nn.Module):
         video: TransformerArgs | None,
         audio: TransformerArgs | None,
         perturbations: BatchedPerturbationConfig,
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
     ) -> tuple[TransformerArgs, TransformerArgs]:
         """Process transformer blocks for LTXAV."""
 
         # Process transformer blocks
         for block in self.transformer_blocks:
-            if self._enable_gradient_checkpointing and self.training:
+            video, audio = gradient_checkpoint_forward(
-                # Use gradient checkpointing to save memory during training.
+                block,
-                # With use_reentrant=False, we can pass dataclasses directly -
+                use_gradient_checkpointing,
-                # PyTorch will track all tensor leaves in the computation graph.
+                use_gradient_checkpointing_offload,
-                video, audio = torch.utils.checkpoint.checkpoint(
+                video=video,
-                    block,
+                audio=audio,
-                    video,
+                perturbations=perturbations,
-                    audio,
+            )
-                    perturbations,
-                    use_reentrant=False,
-                )
-            else:
-                video, audio = block(
-                    video=video,
-                    audio=audio,
-                    perturbations=perturbations,
-                )
 
         return video, audio
 
@@ -1398,7 +1392,12 @@ class LTXModel(torch.nn.Module):
         return x
 
     def _forward(
-        self, video: Modality | None, audio: Modality | None, perturbations: BatchedPerturbationConfig
+        self,
+        video: Modality | None,
+        audio: Modality | None,
+        perturbations: BatchedPerturbationConfig,
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Forward pass for LTX models.
@@ -1417,6 +1416,8 @@ class LTXModel(torch.nn.Module):
             video=video_args,
             audio=audio_args,
             perturbations=perturbations,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
         )
 
         # Process output
@@ -1440,12 +1441,12 @@ class LTXModel(torch.nn.Module):
         )
         return vx, ax
 
-    def forward(self, video_latents, video_positions, video_context, video_timesteps, audio_latents, audio_positions, audio_context, audio_timesteps):
+    def forward(self, video_latents, video_positions, video_context, video_timesteps, audio_latents, audio_positions, audio_context, audio_timesteps, use_gradient_checkpointing=False, use_gradient_checkpointing_offload=False):
         cross_pe_max_pos = None
         if self.model_type.is_video_enabled() and self.model_type.is_audio_enabled():
             cross_pe_max_pos = max(self.positional_embedding_max_pos[0], self.audio_positional_embedding_max_pos[0])
         self._init_preprocessors(cross_pe_max_pos)
         video = Modality(video_latents, video_timesteps, video_positions, video_context)
-        audio = Modality(audio_latents, audio_timesteps, audio_positions, audio_context)
+        audio = Modality(audio_latents, audio_timesteps, audio_positions, audio_context) if audio_latents is not None else None
-        vx, ax = self._forward(video=video, audio=audio, perturbations=None)
+        vx, ax = self._forward(video=video, audio=audio, perturbations=None, use_gradient_checkpointing=use_gradient_checkpointing, use_gradient_checkpointing_offload=use_gradient_checkpointing_offload)
         return vx, ax

diffsynth/models/model_loader.py

@@ -1,6 +1,6 @@
 from ..core.loader import load_model, hash_model_file
 from ..core.vram import AutoWrappedModule
-from ..configs import MODEL_CONFIGS, VRAM_MANAGEMENT_MODULE_MAPS
+from ..configs import MODEL_CONFIGS, VRAM_MANAGEMENT_MODULE_MAPS, VERSION_CHECKER_MAPS
 import importlib, json, torch
 
 
@@ -22,7 +22,8 @@ class ModelPool:
     def fetch_module_map(self, model_class, vram_config):
         if self.need_to_enable_vram_management(vram_config):
             if model_class in VRAM_MANAGEMENT_MODULE_MAPS:
-                module_map = {self.import_model_class(source): self.import_model_class(target) for source, target in VRAM_MANAGEMENT_MODULE_MAPS[model_class].items()}
+                vram_module_map = VRAM_MANAGEMENT_MODULE_MAPS[model_class] if model_class not in VERSION_CHECKER_MAPS else VERSION_CHECKER_MAPS[model_class]()
+                module_map = {self.import_model_class(source): self.import_model_class(target) for source, target in vram_module_map.items()}
             else:
                 module_map = {self.import_model_class(model_class): AutoWrappedModule}
         else:

diffsynth/models/wan_video_dit.py

@@ -5,6 +5,7 @@ import math
 from typing import Tuple, Optional
 from einops import rearrange
 from .wan_video_camera_controller import SimpleAdapter
+from ..core.gradient import gradient_checkpoint_forward
 
 try:
     import flash_attn_interface
@@ -380,26 +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 self.training:
-                if use_gradient_checkpointing_offload:
+                x = gradient_checkpoint_forward(
-                    with torch.autograd.graph.save_on_cpu():
+                    block,
-                        x = torch.utils.checkpoint.checkpoint(
+                    use_gradient_checkpointing,
-                            create_custom_forward(block),
+                    use_gradient_checkpointing_offload,
-                            x, context, t_mod, freqs,
+                    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,
-                    )
             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:
+            x = gradient_checkpoint_forward(
-                with torch.autograd.graph.save_on_cpu():
+                block,
-                    x = torch.utils.checkpoint.checkpoint(
+                use_gradient_checkpointing,
-                        create_custom_forward(block),
+                use_gradient_checkpointing_offload,
-                        x,
+                x, context, t_mod, seq_len_x, pre_compute_freqs[0]
-                        context,
+            )
-                        t_mod,
+            x = gradient_checkpoint_forward(
-                        seq_len_x,
+                lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
-                        pre_compute_freqs[0],
+                use_gradient_checkpointing,
-                        use_reentrant=False,
+                use_gradient_checkpointing_offload,
-                    )
+                x
-                    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 = 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:
+            c = gradient_checkpoint_forward(
-                with torch.autograd.graph.save_on_cpu():
+                block,
-                    c = torch.utils.checkpoint.checkpoint(
+                use_gradient_checkpointing,
-                        create_custom_forward(block),
+                use_gradient_checkpointing_offload,
-                        c, x, context, t_mod, freqs,
+                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)
         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):
@@ -469,9 +469,9 @@ class Down_ResidualBlock(nn.Module):
     def forward(self, x, feat_cache=None, feat_idx=[0]):
         x_copy = x.clone()
         for module in self.downsamples:
-            x = module(x, feat_cache, feat_idx)
+            x, feat_cache, feat_idx = module(x, feat_cache, feat_idx)
 
-        return x + self.avg_shortcut(x_copy)
+        return x + self.avg_shortcut(x_copy), feat_cache, feat_idx
 
 
 class Up_ResidualBlock(nn.Module):
@@ -506,12 +506,12 @@ class Up_ResidualBlock(nn.Module):
     def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
         x_main = x.clone()
         for module in self.upsamples:
-            x_main = module(x_main, feat_cache, feat_idx)
+            x_main, feat_cache, feat_idx = module(x_main, feat_cache, feat_idx)
         if self.avg_shortcut is not None:
             x_shortcut = self.avg_shortcut(x, first_chunk)
-            return x_main + x_shortcut
+            return x_main + x_shortcut, feat_cache, feat_idx
         else:
-            return x_main
+            return x_main, feat_cache, feat_idx
 
 
 class Encoder3d(nn.Module):
@@ -586,14 +586,14 @@ class Encoder3d(nn.Module):
         ## downsamples
         for layer in self.downsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## middle
         for layer in self.middle:
             if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
@@ -614,7 +614,7 @@ class Encoder3d(nn.Module):
                 feat_idx[0] += 1
             else:
                 x = layer(x)
-        return x
+        return x, feat_cache, feat_idx
 
 
 class Encoder3d_38(nn.Module):
@@ -698,14 +698,14 @@ class Encoder3d_38(nn.Module):
         ## downsamples
         for layer in self.downsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## middle
         for layer in self.middle:
             if isinstance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
@@ -730,7 +730,7 @@ class Encoder3d_38(nn.Module):
             else:
                 x = layer(x)
 
-        return x
+        return x, feat_cache, feat_idx
 
 
 class Decoder3d(nn.Module):
@@ -807,14 +807,14 @@ class Decoder3d(nn.Module):
         ## middle
         for layer in self.middle:
             if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## upsamples
         for layer in self.upsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
@@ -835,7 +835,7 @@ class Decoder3d(nn.Module):
                 feat_idx[0] += 1
             else:
                 x = layer(x)
-        return x
+        return x, feat_cache, feat_idx
 
 
 
@@ -906,14 +906,14 @@ class Decoder3d_38(nn.Module):
 
         for layer in self.middle:
             if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
         ## upsamples
         for layer in self.upsamples:
             if feat_cache is not None:
-                x = layer(x, feat_cache, feat_idx, first_chunk)
+                x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx, first_chunk)
             else:
                 x = layer(x)
 
@@ -937,7 +937,7 @@ class Decoder3d_38(nn.Module):
                 feat_idx[0] += 1
             else:
                 x = layer(x)
-        return x
+        return x, feat_cache, feat_idx
 
 
 def count_conv3d(model):
@@ -990,11 +990,11 @@ class VideoVAE_(nn.Module):
         for i in range(iter_):
             self._enc_conv_idx = [0]
             if i == 0:
-                out = self.encoder(x[:, :, :1, :, :],
+                out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
                                    feat_cache=self._enc_feat_map,
                                    feat_idx=self._enc_conv_idx)
             else:
-                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
                                     feat_cache=self._enc_feat_map,
                                     feat_idx=self._enc_conv_idx)
                 out = torch.cat([out, out_], 2)
@@ -1023,11 +1023,11 @@ class VideoVAE_(nn.Module):
         for i in range(iter_):
             self._conv_idx = [0]
             if i == 0:
-                out = self.decoder(x[:, :, i:i + 1, :, :],
+                out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                    feat_cache=self._feat_map,
                                    feat_idx=self._conv_idx)
             else:
-                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                     feat_cache=self._feat_map,
                                     feat_idx=self._conv_idx)
                 out = torch.cat([out, out_], 2) # may add tensor offload
@@ -1303,11 +1303,11 @@ class VideoVAE38_(VideoVAE_):
         for i in range(iter_):
             self._enc_conv_idx = [0]
             if i == 0:
-                out = self.encoder(x[:, :, :1, :, :],
+                out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
                                    feat_cache=self._enc_feat_map,
                                    feat_idx=self._enc_conv_idx)
             else:
-                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
                                     feat_cache=self._enc_feat_map,
                                     feat_idx=self._enc_conv_idx)
                 out = torch.cat([out, out_], 2)
@@ -1337,12 +1337,12 @@ class VideoVAE38_(VideoVAE_):
         for i in range(iter_):
             self._conv_idx = [0]
             if i == 0:
-                out = self.decoder(x[:, :, i:i + 1, :, :],
+                out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                    feat_cache=self._feat_map,
                                    feat_idx=self._conv_idx,
                                    first_chunk=True)
             else:
-                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
                                     feat_cache=self._feat_map,
                                     feat_idx=self._conv_idx)
                 out = torch.cat([out, out_], 2)

diffsynth/models/z_image_dit.py

@@ -88,6 +88,14 @@ class Attention(torch.nn.Module):
         self.norm_q = RMSNorm(head_dim, eps=1e-5)
         self.norm_k = RMSNorm(head_dim, eps=1e-5)
     
+    # Apply RoPE
+    def apply_rotary_emb(self, x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
+        with torch.amp.autocast(get_device_type(), enabled=False):
+            x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
+            freqs_cis = freqs_cis.unsqueeze(2)
+            x_out = torch.view_as_real(x * freqs_cis).flatten(3)
+            return x_out.type_as(x_in)  # todo
+
     def forward(self, hidden_states, freqs_cis, attention_mask):
         query = self.to_q(hidden_states)
         key = self.to_k(hidden_states)
@@ -103,17 +111,9 @@ class Attention(torch.nn.Module):
         if self.norm_k is not None:
             key = self.norm_k(key)
 
-        # Apply RoPE
-        def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-            with torch.amp.autocast(get_device_type(), enabled=False):
-                x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
-                freqs_cis = freqs_cis.unsqueeze(2)
-                x_out = torch.view_as_real(x * freqs_cis).flatten(3)
-                return x_out.type_as(x_in)  # todo
-
         if freqs_cis is not None:
-            query = apply_rotary_emb(query, freqs_cis)
+            query = self.apply_rotary_emb(query, freqs_cis)
-            key = apply_rotary_emb(key, freqs_cis)
+            key = self.apply_rotary_emb(key, freqs_cis)
 
         # Cast to correct dtype
         dtype = query.dtype

diffsynth/pipelines/anima_image.py

@@ -0,0 +1,263 @@
+import torch, math
+from PIL import Image
+from typing import Union
+from tqdm import tqdm
+from einops import rearrange
+import numpy as np
+from math import prod
+from transformers import AutoTokenizer
+
+from ..core.device.npu_compatible_device import get_device_type
+from ..diffusion import FlowMatchScheduler
+from ..core import ModelConfig, gradient_checkpoint_forward
+from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
+from ..utils.lora.merge import merge_lora
+
+from ..models.anima_dit import AnimaDiT
+from ..models.z_image_text_encoder import ZImageTextEncoder
+from ..models.wan_video_vae import WanVideoVAE
+
+
+class AnimaImagePipeline(BasePipeline):
+
+    def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
+        super().__init__(
+            device=device, torch_dtype=torch_dtype,
+            height_division_factor=16, width_division_factor=16,
+        )
+        self.scheduler = FlowMatchScheduler("Z-Image")
+        self.text_encoder: ZImageTextEncoder = None
+        self.dit: AnimaDiT = None
+        self.vae: WanVideoVAE = None
+        self.tokenizer: AutoTokenizer = None
+        self.tokenizer_t5xxl: AutoTokenizer = None
+        self.in_iteration_models = ("dit",)
+        self.units = [
+            AnimaUnit_ShapeChecker(),
+            AnimaUnit_NoiseInitializer(),
+            AnimaUnit_InputImageEmbedder(),
+            AnimaUnit_PromptEmbedder(),
+        ]
+        self.model_fn = model_fn_anima
+    
+    
+    @staticmethod
+    def from_pretrained(
+        torch_dtype: torch.dtype = torch.bfloat16,
+        device: Union[str, torch.device] = get_device_type(),
+        model_configs: list[ModelConfig] = [],
+        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+        tokenizer_t5xxl_config: ModelConfig = ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+        vram_limit: float = None,
+    ):
+        # Initialize pipeline
+        pipe = AnimaImagePipeline(device=device, torch_dtype=torch_dtype)
+        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
+        
+        # Fetch models
+        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
+        pipe.dit = model_pool.fetch_model("anima_dit")
+        pipe.vae = model_pool.fetch_model("wan_video_vae")
+        if tokenizer_config is not None:
+            tokenizer_config.download_if_necessary()
+            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
+        if tokenizer_t5xxl_config is not None:
+            tokenizer_t5xxl_config.download_if_necessary()
+            pipe.tokenizer_t5xxl = AutoTokenizer.from_pretrained(tokenizer_t5xxl_config.path)
+        # VRAM Management
+        pipe.vram_management_enabled = pipe.check_vram_management_state()
+        return pipe
+    
+    
+    @torch.no_grad()
+    def __call__(
+        self,
+        # Prompt
+        prompt: str,
+        negative_prompt: str = "",
+        cfg_scale: float = 4.0,
+        # Image
+        input_image: Image.Image = None,
+        denoising_strength: float = 1.0,
+        # Shape
+        height: int = 1024,
+        width: int = 1024,
+        # Randomness
+        seed: int = None,
+        rand_device: str = "cpu",
+        # Steps
+        num_inference_steps: int = 30,
+        sigma_shift: float = None,
+        # Progress bar
+        progress_bar_cmd = tqdm,
+    ):
+        # Scheduler
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
+        
+        # Parameters
+        inputs_posi = {
+            "prompt": prompt,
+        }
+        inputs_nega = {
+            "negative_prompt": negative_prompt,
+        }
+        inputs_shared = {
+            "cfg_scale": cfg_scale,
+            "input_image": input_image, "denoising_strength": denoising_strength,
+            "height": height, "width": width,
+            "seed": seed, "rand_device": rand_device,
+            "num_inference_steps": num_inference_steps,
+        }
+        for unit in self.units:
+            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
+
+        # Denoise
+        self.load_models_to_device(self.in_iteration_models)
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.cfg_guided_model_fn(
+                self.model_fn, cfg_scale,
+                inputs_shared, inputs_posi, inputs_nega,
+                **models, timestep=timestep, progress_id=progress_id
+            )
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
+        
+        # Decode
+        self.load_models_to_device(['vae'])
+        image = self.vae.decode(inputs_shared["latents"].unsqueeze(2), device=self.device).squeeze(2)
+        image = self.vae_output_to_image(image)
+        self.load_models_to_device([])
+
+        return image
+
+
+class AnimaUnit_ShapeChecker(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width"),
+            output_params=("height", "width"),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width):
+        height, width = pipe.check_resize_height_width(height, width)
+        return {"height": height, "width": width}
+
+
+
+class AnimaUnit_NoiseInitializer(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("height", "width", "seed", "rand_device"),
+            output_params=("noise",),
+        )
+
+    def process(self, pipe: AnimaImagePipeline, height, width, seed, rand_device):
+        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
+        return {"noise": noise}
+
+
+
+class AnimaUnit_InputImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("input_image", "noise"),
+            output_params=("latents", "input_latents"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: AnimaImagePipeline, input_image, noise):
+        if input_image is None:
+            return {"latents": noise, "input_latents": None}
+        pipe.load_models_to_device(['vae'])
+        if isinstance(input_image, list):
+            input_latents = []
+            for image in input_image:
+                image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
+                input_latents.append(pipe.vae.encode(image))
+            input_latents = torch.concat(input_latents, dim=0)
+        else:
+            image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+            input_latents = pipe.vae.encode(image.unsqueeze(2), device=pipe.device).squeeze(2)
+        if pipe.scheduler.training:
+            return {"latents": noise, "input_latents": input_latents}
+        else:
+            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
+            return {"latents": latents, "input_latents": input_latents}
+
+
+class AnimaUnit_PromptEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            seperate_cfg=True,
+            input_params_posi={"prompt": "prompt"},
+            input_params_nega={"prompt": "negative_prompt"},
+            output_params=("prompt_emb",),
+            onload_model_names=("text_encoder",)
+        )
+
+    def encode_prompt(
+        self,
+        pipe: AnimaImagePipeline,
+        prompt,
+        device = None,
+        max_sequence_length: int = 512,
+    ):
+        if isinstance(prompt, str):
+            prompt = [prompt]
+
+        text_inputs = pipe.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_masks = text_inputs.attention_mask.to(device).bool()
+
+        prompt_embeds = pipe.text_encoder(
+            input_ids=text_input_ids,
+            attention_mask=prompt_masks,
+            output_hidden_states=True,
+        ).hidden_states[-1]
+        
+        t5xxl_text_inputs = pipe.tokenizer_t5xxl(
+            prompt,
+            max_length=max_sequence_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        t5xxl_ids = t5xxl_text_inputs.input_ids.to(device)
+
+        return prompt_embeds.to(pipe.torch_dtype), t5xxl_ids
+
+    def process(self, pipe: AnimaImagePipeline, prompt):
+        pipe.load_models_to_device(self.onload_model_names)
+        prompt_embeds, t5xxl_ids = self.encode_prompt(pipe, prompt, pipe.device)
+        return {"prompt_emb": prompt_embeds, "t5xxl_ids": t5xxl_ids}
+
+
+def model_fn_anima(
+    dit: AnimaDiT = None,
+    latents=None,
+    timestep=None,
+    prompt_emb=None,
+    t5xxl_ids=None,
+    use_gradient_checkpointing=False,
+    use_gradient_checkpointing_offload=False,
+    **kwargs
+):
+    latents = latents.unsqueeze(2)
+    timestep = timestep / 1000
+    model_output = dit(
+        x=latents,
+        timesteps=timestep,
+        context=prompt_emb,
+        t5xxl_ids=t5xxl_ids,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+    )
+    model_output = model_output.squeeze(2)
+    return model_output

diffsynth/pipelines/flux2_image.py

@@ -348,13 +348,12 @@ class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
         attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
 
         # Forward pass through the model
-        with torch.inference_mode():
+        output = text_encoder(
-            output = text_encoder(
+            input_ids=input_ids,
-                input_ids=input_ids,
+            attention_mask=attention_mask,
-                attention_mask=attention_mask,
+            output_hidden_states=True,
-                output_hidden_states=True,
+            use_cache=False,
-                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)

diffsynth/pipelines/ltx2_audio_video.py

@@ -18,7 +18,7 @@ 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
+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
@@ -50,6 +50,7 @@ class LTX2AudioVideoPipeline(BasePipeline):
 
         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 = [
@@ -57,8 +58,10 @@ class LTX2AudioVideoPipeline(BasePipeline):
             LTX2AudioVideoUnit_ShapeChecker(),
             LTX2AudioVideoUnit_PromptEmbedder(),
             LTX2AudioVideoUnit_NoiseInitializer(),
+            LTX2AudioVideoUnit_InputAudioEmbedder(),
             LTX2AudioVideoUnit_InputVideoEmbedder(),
             LTX2AudioVideoUnit_InputImagesEmbedder(),
+            LTX2AudioVideoUnit_InContextVideoEmbedder(),
         ]
         self.model_fn = model_fn_ltx2
 
@@ -95,7 +98,7 @@ class LTX2AudioVideoPipeline(BasePipeline):
             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")
+        pipe.audio_vae_encoder = model_pool.fetch_model("ltx2_audio_vae_encoder")
 
         # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()
@@ -103,6 +106,8 @@ class LTX2AudioVideoPipeline(BasePipeline):
 
     def stage2_denoise(self, inputs_shared, inputs_posi, inputs_nega, progress_bar_cmd=tqdm):
         if inputs_shared["use_two_stage_pipeline"]:
+            if inputs_shared.get("clear_lora_before_state_two", False):
+                self.clear_lora()
             latent = self.video_vae_encoder.per_channel_statistics.un_normalize(inputs_shared["video_latents"])
             self.load_models_to_device('upsampler',)
             latent = self.upsampler(latent)
@@ -110,11 +115,17 @@ class LTX2AudioVideoPipeline(BasePipeline):
             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
+            # input image
             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})
+            # remove in-context video control in stage 2
+            inputs_shared.pop("in_context_video_latents", None)
+            inputs_shared.pop("in_context_video_positions", None)
+
+            # initialize latents for stage 2
             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"] + (
@@ -143,11 +154,14 @@ class LTX2AudioVideoPipeline(BasePipeline):
         # Prompt
         prompt: str,
         negative_prompt: Optional[str] = "",
-        # Image-to-video
         denoising_strength: float = 1.0,
+        # Image-to-video
         input_images: Optional[list[Image.Image]] = None,
         input_images_indexes: Optional[list[int]] = None,
         input_images_strength: Optional[float] = 1.0,
+        # In-Context Video Control
+        in_context_videos: Optional[list[list[Image.Image]]] = None,
+        in_context_downsample_factor: Optional[int] = 2,
         # Randomness
         seed: Optional[int] = None,
         rand_device: Optional[str] = "cpu",
@@ -155,9 +169,9 @@ class LTX2AudioVideoPipeline(BasePipeline):
         height: Optional[int] = 512,
         width: Optional[int] = 768,
         num_frames=121,
+        frame_rate=24,
         # Classifier-free guidance
         cfg_scale: Optional[float] = 3.0,
-        cfg_merge: Optional[bool] = False,
         # Scheduler
         num_inference_steps: Optional[int] = 40,
         # VAE tiling
@@ -168,6 +182,7 @@ class LTX2AudioVideoPipeline(BasePipeline):
         tile_overlap_in_frames: Optional[int] = 24,
         # Special Pipelines
         use_two_stage_pipeline: Optional[bool] = False,
+        clear_lora_before_state_two: Optional[bool] = False,
         use_distilled_pipeline: Optional[bool] = False,
         # progress_bar
         progress_bar_cmd=tqdm,
@@ -184,12 +199,13 @@ class LTX2AudioVideoPipeline(BasePipeline):
         }
         inputs_shared = {
             "input_images": input_images, "input_images_indexes": input_images_indexes, "input_images_strength": input_images_strength,
+            "in_context_videos": in_context_videos, "in_context_downsample_factor": in_context_downsample_factor,
             "seed": seed, "rand_device": rand_device,
-            "height": height, "width": width, "num_frames": num_frames,
+            "height": height, "width": width, "num_frames": num_frames, "frame_rate": frame_rate,
-            "cfg_scale": cfg_scale, "cfg_merge": cfg_merge,
+            "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,
+            "use_two_stage_pipeline": use_two_stage_pipeline, "use_distilled_pipeline": use_distilled_pipeline, "clear_lora_before_state_two": clear_lora_before_state_two,
             "video_patchifier": self.video_patchifier, "audio_patchifier": self.audio_patchifier,
         }
         for unit in self.units:
@@ -416,13 +432,13 @@ class LTX2AudioVideoUnit_PromptEmbedder(PipelineUnit):
 class LTX2AudioVideoUnit_NoiseInitializer(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("height", "width", "num_frames", "seed", "rand_device", "use_two_stage_pipeline"),
+            input_params=("height", "width", "num_frames", "seed", "rand_device", "frame_rate", "use_two_stage_pipeline"),
-            output_params=("video_noise", "audio_noise",),
+            output_params=("video_noise", "audio_noise", "video_positions", "audio_positions", "video_latent_shape", "audio_latent_shape")
         )
 
     def process_stage(self, pipe: LTX2AudioVideoPipeline, height, width, num_frames, seed, rand_device, frame_rate=24.0):
         video_pixel_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
-        video_latent_shape = VideoLatentShape.from_pixel_shape(shape=video_pixel_shape, latent_channels=pipe.video_vae_encoder.latent_channels)
+        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)
@@ -455,23 +471,51 @@ class LTX2AudioVideoUnit_NoiseInitializer(PipelineUnit):
 class LTX2AudioVideoUnit_InputVideoEmbedder(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("input_video", "video_noise", "audio_noise", "tiled", "tile_size", "tile_stride"),
+            input_params=("input_video", "video_noise", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels"),
-            output_params=("video_latents", "audio_latents"),
+            output_params=("video_latents", "input_latents"),
             onload_model_names=("video_vae_encoder")
         )
 
-    def process(self, pipe: LTX2AudioVideoPipeline, input_video, video_noise, audio_noise, tiled, tile_size, tile_stride):
+    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, "audio_latents": audio_noise}
+            return {"video_latents": video_noise}
         else:
-            # TODO: implement video-to-video
+            pipe.load_models_to_device(self.onload_model_names)
-            raise NotImplementedError("Video-to-video not implemented yet.")
+            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:
+                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:
+                raise NotImplementedError("Audio-to-video not supported.")
 
 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"),
+            output_params=("video_latents", "denoise_mask_video", "input_latents_video", "stage2_input_latents"),
             onload_model_names=("video_vae_encoder")
         )
 
@@ -506,6 +550,54 @@ class LTX2AudioVideoUnit_InputImagesEmbedder(PipelineUnit):
             return output_dicts
 
 
+class LTX2AudioVideoUnit_InContextVideoEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("in_context_videos", "height", "width", "num_frames", "frame_rate", "in_context_downsample_factor", "tiled", "tile_size_in_pixels", "tile_overlap_in_pixels", "use_two_stage_pipeline"),
+            output_params=("in_context_video_latents", "in_context_video_positions"),
+            onload_model_names=("video_vae_encoder")
+        )
+
+    def check_in_context_video(self, pipe, in_context_video, height, width, num_frames, in_context_downsample_factor, use_two_stage_pipeline=True):
+        if in_context_video is None or len(in_context_video) == 0:
+            raise ValueError("In-context video is None or empty.")
+        in_context_video = in_context_video[:num_frames]
+        expected_height = height // in_context_downsample_factor // 2 if use_two_stage_pipeline else height // in_context_downsample_factor
+        expected_width = width // in_context_downsample_factor // 2 if use_two_stage_pipeline else width // in_context_downsample_factor
+        current_h, current_w, current_f = in_context_video[0].size[1], in_context_video[0].size[0], len(in_context_video)
+        h, w, f = pipe.check_resize_height_width(expected_height, expected_width, current_f, verbose=0)
+        if current_h != h or current_w != w:
+            in_context_video = [img.resize((w, h)) for img in in_context_video]
+        if current_f != f:
+            # pad black frames at the end
+            in_context_video = in_context_video + [Image.new("RGB", (w, h), (0, 0, 0))] * (f - current_f)
+        return in_context_video
+
+    def process(self, pipe: LTX2AudioVideoPipeline, in_context_videos, height, width, num_frames, frame_rate, in_context_downsample_factor, tiled, tile_size_in_pixels, tile_overlap_in_pixels, use_two_stage_pipeline=True):
+        if in_context_videos is None or len(in_context_videos) == 0:
+            return {}
+        else:
+            pipe.load_models_to_device(self.onload_model_names)
+            latents, positions = [], []
+            for in_context_video in in_context_videos:
+                in_context_video = self.check_in_context_video(pipe, in_context_video, height, width, num_frames, in_context_downsample_factor, use_two_stage_pipeline)
+                in_context_video = pipe.preprocess_video(in_context_video)
+                in_context_latents = pipe.video_vae_encoder.encode(in_context_video, tiled, tile_size_in_pixels, tile_overlap_in_pixels).to(dtype=pipe.torch_dtype, device=pipe.device)
+
+                latent_coords = pipe.video_patchifier.get_patch_grid_bounds(output_shape=VideoLatentShape.from_torch_shape(in_context_latents.shape), device=pipe.device)
+                video_positions = get_pixel_coords(latent_coords, VIDEO_SCALE_FACTORS, True).float()
+                video_positions[:, 0, ...] = video_positions[:, 0, ...] / frame_rate
+                video_positions[:, 1, ...] *= in_context_downsample_factor  # height axis
+                video_positions[:, 2, ...] *= in_context_downsample_factor  # width axis
+                video_positions = video_positions.to(pipe.torch_dtype)
+
+                latents.append(in_context_latents)
+                positions.append(video_positions)
+            latents = torch.cat(latents, dim=1)
+            positions = torch.cat(positions, dim=1)
+            return {"in_context_video_latents": latents, "in_context_video_positions": positions}
+
+
 def model_fn_ltx2(
     dit: LTXModel,
     video_latents=None,
@@ -518,6 +610,8 @@ def model_fn_ltx2(
     audio_patchifier=None,
     timestep=None,
     denoise_mask_video=None,
+    in_context_video_latents=None,
+    in_context_video_positions=None,
     use_gradient_checkpointing=False,
     use_gradient_checkpointing_offload=False,
     **kwargs,
@@ -527,13 +621,25 @@ def model_fn_ltx2(
     # patchify
     b, c_v, f, h, w = video_latents.shape
     video_latents = video_patchifier.patchify(video_latents)
+    seq_len_video = video_latents.shape[1]
     video_timesteps = timestep.repeat(1, video_latents.shape[1], 1)
     if denoise_mask_video is not None:
         video_timesteps = video_patchifier.patchify(denoise_mask_video) * video_timesteps
-    _, c_a, _, mel_bins  = audio_latents.shape
+
-    audio_latents = audio_patchifier.patchify(audio_latents)
+    if in_context_video_latents is not None:
-    audio_timesteps = timestep.repeat(1, audio_latents.shape[1], 1)
+        in_context_video_latents = video_patchifier.patchify(in_context_video_latents)
-    #TODO: support gradient checkpointing in training
+        in_context_video_timesteps = timestep.repeat(1, in_context_video_latents.shape[1], 1) * 0.
+        video_latents = torch.cat([video_latents, in_context_video_latents], dim=1)
+        video_positions = torch.cat([video_positions, in_context_video_positions], dim=2)
+        video_timesteps = torch.cat([video_timesteps, in_context_video_timesteps], dim=1)
+
+    if audio_latents is not None:
+        _, c_a, _, mel_bins  = audio_latents.shape
+        audio_latents = audio_patchifier.patchify(audio_latents)
+        audio_timesteps = timestep.repeat(1, audio_latents.shape[1], 1)
+    else:
+        audio_timesteps = None
+
     vx, ax = dit(
         video_latents=video_latents,
         video_positions=video_positions,
@@ -543,8 +649,12 @@ def model_fn_ltx2(
         audio_positions=audio_positions,
         audio_context=audio_context,
         audio_timesteps=audio_timesteps,
+        use_gradient_checkpointing=use_gradient_checkpointing,
+        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
     )
+
+    vx = vx[:, :seq_len_video, ...]
     # unpatchify
     vx = video_patchifier.unpatchify_video(vx, f, h, w)
-    ax = audio_patchifier.unpatchify_audio(ax, c_a, mel_bins)
+    ax = audio_patchifier.unpatchify_audio(ax, c_a, mel_bins) if ax is not None else None
     return vx, ax

diffsynth/pipelines/wan_video.py

@@ -1321,11 +1321,6 @@ def model_fn_wan_video(
     if tea_cache_update:
         x = tea_cache.update(x)
     else:
-        def create_custom_forward(module):
-            def custom_forward(*inputs):
-                return module(*inputs)
-            return custom_forward
-        
         def create_custom_forward_vap(block, vap):
             def custom_forward(*inputs):
                 return vap(block, *inputs)
@@ -1339,32 +1334,24 @@ def model_fn_wan_video(
                         x, x_vap = torch.utils.checkpoint.checkpoint(
                             create_custom_forward_vap(block, vap),
                             x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
-                            use_reentrant=False,
+                            use_reentrant=False
                         )
                 elif use_gradient_checkpointing:
                     x, x_vap = torch.utils.checkpoint.checkpoint(
                         create_custom_forward_vap(block, vap),
                         x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
-                        use_reentrant=False,
+                        use_reentrant=False
                     )
                 else:
                     x, x_vap = vap(block, x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id)
             else:
-                if use_gradient_checkpointing_offload:
+                x = gradient_checkpoint_forward(
-                    with torch.autograd.graph.save_on_cpu():
+                    block,
-                        x = torch.utils.checkpoint.checkpoint(
+                    use_gradient_checkpointing,
-                            create_custom_forward(block),
+                    use_gradient_checkpointing_offload,
-                            x, context, t_mod, freqs,
+                    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)
             
             # VACE
             if vace_context is not None and block_id in vace.vace_layers_mapping:
@@ -1487,32 +1474,18 @@ def model_fn_wans2v(
         return custom_forward
 
     for block_id, block in enumerate(dit.blocks):
-        if use_gradient_checkpointing_offload:
+        x = gradient_checkpoint_forward(
-            with torch.autograd.graph.save_on_cpu():
+                block,
-                x = torch.utils.checkpoint.checkpoint(
+                use_gradient_checkpointing,
-                    create_custom_forward(block),
+                use_gradient_checkpointing_offload,
-                    x, context, t_mod, seq_len_x, pre_compute_freqs[0],
+                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 = torch.utils.checkpoint.checkpoint(
+        x = gradient_checkpoint_forward(
-                create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
+            lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
-                x,
+            use_gradient_checkpointing,
-                use_reentrant=False,
+            use_gradient_checkpointing_offload,
-            )
+            x
-        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)
 
     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

@@ -1,4 +1,4 @@
-import torch, math
+import torch, math, warnings
 from PIL import Image
 from typing import Union
 from tqdm import tqdm
@@ -6,7 +6,7 @@ from einops import rearrange
 import numpy as np
 from typing import Union, List, Optional, Tuple, Iterable, Dict
 
-from ..core.device.npu_compatible_device import get_device_type
+from ..core.device.npu_compatible_device import get_device_type, IS_NPU_AVAILABLE
 from ..diffusion import FlowMatchScheduler
 from ..core import ModelConfig, gradient_checkpoint_forward
 from ..core.data.operators import ImageCropAndResize
@@ -63,6 +63,7 @@ class ZImagePipeline(BasePipeline):
         model_configs: list[ModelConfig] = [],
         tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
         vram_limit: float = None,
+        enable_npu_patch: bool = True,
     ):
         # Initialize pipeline
         pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
@@ -84,6 +85,8 @@ class ZImagePipeline(BasePipeline):
         
         # VRAM Management
         pipe.vram_management_enabled = pipe.check_vram_management_state()
+        # NPU patch
+        apply_npu_patch(enable_npu_patch)
         return pipe
     
     
@@ -296,7 +299,7 @@ class ZImageUnit_PromptEmbedder(PipelineUnit):
 
     def process(self, pipe: ZImagePipeline, prompt, edit_image):
         pipe.load_models_to_device(self.onload_model_names)
-        if hasattr(pipe, "dit") and pipe.dit.siglip_embedder is not None:
+        if hasattr(pipe, "dit") and pipe.dit is not None and pipe.dit.siglip_embedder is not None:
             # Z-Image-Turbo and Z-Image-Omni-Base use different prompt encoding methods.
             # We determine which encoding method to use based on the model architecture.
             # If you are using two-stage split training,
@@ -667,3 +670,19 @@ def model_fn_z_image_turbo(
     x = rearrange(x, "C B H W -> B C H W")
     x = -x
     return x
+
+
+def apply_npu_patch(enable_npu_patch: bool=True):
+    if IS_NPU_AVAILABLE and enable_npu_patch:
+        from ..models.general_modules import RMSNorm
+        from transformers.models.qwen3.modeling_qwen3 import Qwen3RMSNorm
+        from ..models.z_image_dit import Attention
+        from ..core.npu_patch.npu_fused_operator import (
+            rms_norm_forward_npu, 
+            rms_norm_forward_transformers_npu,
+            rotary_emb_Zimage_npu
+        )
+        warnings.warn("Replacing RMSNorm and Rope with NPU fusion operators to improve the performance of the model on NPU.Set enable_npu_patch=False to disable this feature.")
+        RMSNorm.forward = rms_norm_forward_npu
+        Qwen3RMSNorm.forward = rms_norm_forward_transformers_npu
+        Attention.apply_rotary_emb = rotary_emb_Zimage_npu

diffsynth/utils/data/__init__.py

@@ -116,7 +116,7 @@ class VideoData:
         if self.height is not None and self.width is not None:
             return self.height, self.width
         else:
-            height, width, _ = self.__getitem__(0).shape
+            width, height = self.__getitem__(0).size
             return height, width
 
     def __getitem__(self, item):

diffsynth/utils/lora/general.py

@@ -1,4 +1,4 @@
-import torch
+import torch, warnings
 
 
 class GeneralLoRALoader:
@@ -26,7 +26,11 @@ class GeneralLoRALoader:
                 keys.pop(0)
             keys.pop(-1)
             target_name = ".".join(keys)
-            lora_name_dict[target_name] = (key, key.replace(lora_B_key, lora_A_key))
+            # Alpha: Deprecated but retained for compatibility.
+            key_alpha = key.replace(lora_B_key + ".weight", "alpha").replace(lora_B_key + ".default.weight", "alpha")
+            if key_alpha == key or key_alpha not in lora_state_dict:
+                key_alpha = None
+            lora_name_dict[target_name] = (key, key.replace(lora_B_key, lora_A_key), key_alpha)
         return lora_name_dict
     
     
@@ -36,6 +40,10 @@ class GeneralLoRALoader:
         for name in name_dict:
             weight_up = state_dict[name_dict[name][0]]
             weight_down = state_dict[name_dict[name][1]]
+            if name_dict[name][2] is not None:
+                warnings.warn("Alpha detected in the LoRA file. This may be a LoRA model not trained by DiffSynth-Studio. To ensure compatibility, the LoRA weights will be converted to weight * alpha / rank.")
+                alpha = state_dict[name_dict[name][2]] / weight_down.shape[0]
+                weight_down = weight_down * alpha
             state_dict_[name + f".lora_B{suffix}"] = weight_up
             state_dict_[name + f".lora_A{suffix}"] = weight_down
         return state_dict_

diffsynth/utils/state_dict_converters/anima_dit.py

@@ -0,0 +1,6 @@
+def AnimaDiTStateDictConverter(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        value = state_dict[key]
+        new_state_dict[key.replace("net.", "")] = value
+    return new_state_dict

diffsynth/utils/xfuser/xdit_context_parallel.py

@@ -9,6 +9,7 @@ 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):
@@ -88,11 +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]
@@ -100,20 +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 self.training:
-            if use_gradient_checkpointing_offload:
+            x = gradient_checkpoint_forward(
-                with torch.autograd.graph.save_on_cpu():
+                block,
-                    x = torch.utils.checkpoint.checkpoint(
+                use_gradient_checkpointing,
-                        create_custom_forward(block),
+                use_gradient_checkpointing_offload,
-                        x, context, t_mod, freqs,
+                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,
-                )
         else:
             x = block(x, context, t_mod, freqs)
 

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/Anima.md

@@ -0,0 +1,139 @@
+# Anima
+
+Anima is an image generation model trained and open-sourced by CircleStone Labs and Comfy Org.
+
+## Installation
+
+Before using this project for model inference and training, please install DiffSynth-Studio first.
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+For more installation information, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
+
+## Quick Start
+
+The following code demonstrates how to quickly load the [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) model for inference. VRAM management is enabled by default, allowing the framework to automatically control model parameter loading based on available VRAM. Minimum 8GB VRAM required.
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+## Model Overview
+
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+Special training scripts:
+
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
+* Two-Stage Split Training: [doc](../Training/Split_Training.md)
+* End-to-End Direct Distillation: [doc](../Training/Direct_Distill.md)
+
+## Model Inference
+
+Models are loaded through `AnimaImagePipeline.from_pretrained`, see [Model Inference](../Pipeline_Usage/Model_Inference.md#loading-models) for details.
+
+Input parameters for `AnimaImagePipeline` inference include:
+
+* `prompt`: Text description of the desired image content.
+* `negative_prompt`: Content to exclude from the generated image (default: `""`).
+* `cfg_scale`: Classifier-free guidance parameter (default: 4.0).
+* `input_image`: Input image for image-to-image generation (default: `None`).
+* `denoising_strength`: Controls similarity to input image (default: 1.0).
+* `height`: Image height (must be multiple of 16, default: 1024).
+* `width`: Image width (must be multiple of 16, default: 1024).
+* `seed`: Random seed (default: `None`).
+* `rand_device`: Device for random noise generation (default: `"cpu"`).
+* `num_inference_steps`: Inference steps (default: 30).
+* `sigma_shift`: Scheduler sigma offset (default: `None`).
+* `progress_bar_cmd`: Progress bar implementation (default: `tqdm.tqdm`).
+
+For VRAM constraints, enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). Recommended low-VRAM configurations are provided in the "Model Overview" table above.
+
+## Model Training
+
+Anima models are trained through [`examples/anima/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/train.py) with parameters including:
+
+* General Training Parameters
+    * Dataset Configuration
+        * `--dataset_base_path`: Dataset root directory.
+        * `--dataset_metadata_path`: Metadata file path.
+        * `--dataset_repeat`: Dataset repetition per epoch.
+        * `--dataset_num_workers`: Dataloader worker count.
+        * `--data_file_keys`: Metadata fields to load (comma-separated).
+    * Model Loading
+        * `--model_paths`: Model paths (JSON format).
+        * `--model_id_with_origin_paths`: Model IDs with origin paths (e.g., `"anima-team/anima-1B:text_encoder/*.safetensors"`).
+        * `--extra_inputs`: Additional pipeline inputs (e.g., `controlnet_inputs` for ControlNet).
+        * `--fp8_models`: FP8-formatted models (same format as `--model_paths`).
+    * Training Configuration
+        * `--learning_rate`: Learning rate.
+        * `--num_epochs`: Training epochs.
+        * `--trainable_models`: Trainable components (e.g., `dit`, `vae`, `text_encoder`).
+        * `--find_unused_parameters`: Handle unused parameters in DDP training.
+        * `--weight_decay`: Weight decay value.
+        * `--task`: Training task (default: `sft`).
+    * Output Configuration
+        * `--output_path`: Model output directory.
+        * `--remove_prefix_in_ckpt`: Remove state dict prefixes.
+        * `--save_steps`: Model saving interval.
+    * LoRA Configuration
+        * `--lora_base_model`: Target model for LoRA.
+        * `--lora_target_modules`: Target modules for LoRA.
+        * `--lora_rank`: LoRA rank.
+        * `--lora_checkpoint`: LoRA checkpoint path.
+        * `--preset_lora_path`: Preloaded LoRA checkpoint path.
+        * `--preset_lora_model`: Model to merge LoRA with (e.g., `dit`).
+    * Gradient Configuration
+        * `--use_gradient_checkpointing`: Enable gradient checkpointing.
+        * `--use_gradient_checkpointing_offload`: Offload checkpointing to CPU.
+        * `--gradient_accumulation_steps`: Gradient accumulation steps.
+    * Image Resolution
+        * `--height`: Image height (empty for dynamic resolution).
+        * `--width`: Image width (empty for dynamic resolution).
+        * `--max_pixels`: Maximum pixel area for dynamic resolution.
+* Anima-Specific Parameters
+    * `--tokenizer_path`: Tokenizer path for text-to-image models.
+    * `--tokenizer_t5xxl_path`: T5-XXL tokenizer path.
+
+We provide a sample image dataset for testing:
+
+```shell
+modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+```
+
+For training script details, refer to [Model Training](../Pipeline_Usage/Model_Training.md). For advanced training techniques, see [Training Framework Documentation](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/).

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

@@ -21,7 +21,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-For more information about installation, please refer to [Install Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Install Dependencies](../Pipeline_Usage/Setup.md).
 
 ## Quick Start
 
@@ -61,22 +61,22 @@ image.save("image.jpg")
 
 | Model ID | Inference | Low VRAM Inference | Full Training | Validation After Full Training | LoRA Training | Validation After LoRA Training |
 | - | - | - | - | - | - | - |
-|[black-forest-labs/FLUX.2-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.2-dev)|[code](/examples/flux2/model_inference/FLUX.2-dev.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-dev.py)|-|-|[code](/examples/flux2/model_training/lora/FLUX.2-dev.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-dev.py)|
+|[black-forest-labs/FLUX.2-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](/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-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](/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-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](/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-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](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|
+|[black-forest-labs/FLUX.2-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)
+* Differential LoRA Training: [doc](../Training/Differential_LoRA.md)
-* FP8 Precision Training: [doc](/docs/en/Training/FP8_Precision.md)
+* FP8 Precision Training: [doc](../Training/FP8_Precision.md)
-* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md)
+* Two-stage Split Training: [doc](../Training/Split_Training.md)
-* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md)
+* End-to-end Direct Distillation: [doc](../Training/Direct_Distill.md)
 
 ## Model Inference
 
-Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `Flux2ImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
 
 Input parameters for `Flux2ImagePipeline` inference include:
 
@@ -95,11 +95,11 @@ Input parameters for `Flux2ImagePipeline` inference include:
 * `tile_stride`: Tile stride during VAE encoding/decoding stages, default is 64, only effective when `tiled=True`, must be less than or equal to `tile_size`.
 * `progress_bar_cmd`: Progress bar, default is `tqdm.tqdm`. Can be disabled by setting to `lambda x:x`.
 
-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
 
 ## Model Training
 
-FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](/examples/flux2/model_training/train.py), and the script parameters include:
+FLUX.2 series models are uniformly trained through [`examples/flux2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/train.py), and the script parameters include:
 
 * General Training Parameters
     * Dataset Basic Configuration
@@ -148,4 +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

@@ -12,7 +12,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-For more information about installation, please refer to [Installation Dependencies](/docs/en/Pipeline_Usage/Setup.md).
+For more information about installation, please refer to [Installation Dependencies](../Pipeline_Usage/Setup.md).
 
 ## Quick Start
 
@@ -33,19 +33,62 @@ vram_config = {
     "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="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.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."
+negative_prompt = (
-height, width, num_frames = 512, 768, 121
+    "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,
@@ -54,11 +97,12 @@ video, audio = pipe(
     width=width,
     num_frames=num_frames,
     tiled=True,
+    use_two_stage_pipeline=True,
 )
 write_video_audio_ltx2(
     video=video,
     audio=audio,
-    output_path='ltx2_onestage.mp4',
+    output_path='ltx2_twostage.mp4',
     fps=24,
     audio_sample_rate=24000,
 )
@@ -67,16 +111,25 @@ write_video_audio_ltx2(
 ## 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](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
+|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/full/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_full/LTX-2-T2AV.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV.py)|
-|[Lightricks/LTX-2: 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-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
-|[Lightricks/LTX-2: 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-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
-|[Lightricks/LTX-2: 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-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: 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-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: 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: 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](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models) for details.
+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:
 
@@ -102,8 +155,59 @@ Input parameters for `LTX2AudioVideoPipeline` inference include:
 * `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](/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 previous "Supported Inference Scripts" section.
+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
 
-The LTX-2 series models currently do not support training functionality. We will add related support as soon as possible.
+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](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](/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](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](/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-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](/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](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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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-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](/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-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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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/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](/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/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](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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-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](/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-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](/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-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](/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-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](/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) |
+| [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](/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) |
+| [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](/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-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](/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) |
+| [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](/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-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](/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-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](/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-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](/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-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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-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](/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-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](/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-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](/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-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](/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) |
+| [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](/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-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](/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-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](/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](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](/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) |
+| [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/)
+* 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](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_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](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
+* 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
 
@@ -80,36 +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](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](/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-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](/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](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](/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-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](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-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)|
-|[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)|-|-|-|-|
+|[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)|
-|[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)|
+|[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)|-|-|-|-|
-|[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)|
+|[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-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-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-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](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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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)|-|-|-|-|
+| [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/)
+* 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](/docs/en/Training/FP8_Precision.md), [code](/examples/qwen_image/model_training/special/fp8_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](/docs/en/Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_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](/docs/en/Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
+* 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:
 
@@ -140,11 +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
@@ -194,4 +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-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](/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-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](/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-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](/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-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](/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) |
+| [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](/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) |
+| [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](/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-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](/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) |
+| [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](/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-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](/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-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](/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-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](/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-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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) |
+| [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](/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-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](/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-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](/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-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](/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-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](/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) |
+| [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](/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-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](/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-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](/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](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](/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) |
+| [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/)
+* 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](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_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](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
+* 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
 
@@ -52,21 +52,21 @@ image.save("image.jpg")
 
 |Model ID|Inference|Low VRAM Inference|Full Training|Validation After Full Training|LoRA Training|Validation After LoRA Training|
 |-|-|-|-|-|-|-|
-|[Tongyi-MAI/Z-Image](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image)|[code](/examples/z_image/model_inference/Z-Image.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image.py)|[code](/examples/z_image/model_training/full/Z-Image.sh)|[code](/examples/z_image/model_training/validate_full/Z-Image.py)|[code](/examples/z_image/model_training/lora/Z-Image.sh)|[code](/examples/z_image/model_training/validate_lora/Z-Image.py)|
+|[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](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.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](/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)|
+|[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](/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](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](/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-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](/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)|
+|[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/)
+* 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](/examples/z_image/model_training/special/trajectory_imitation/)
+* Trajectory Imitation Distillation Training (Experimental Feature): [code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)
 
 ## Model Inference
 
-Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
+Models are loaded via `ZImagePipeline.from_pretrained`, see [Loading Models](../Pipeline_Usage/Model_Inference.md#loading-models).
 
 Input parameters for `ZImagePipeline` inference include:
 
@@ -84,11 +84,11 @@ Input parameters for `ZImagePipeline` inference include:
 * `edit_image`: Edit images for image editing models, supporting multiple images.
 * `positive_only_lora`: LoRA weights used only in positive prompts.
 
-If VRAM is insufficient, please enable [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
+If VRAM is insufficient, please enable [VRAM Management](../Pipeline_Usage/VRAM_management.md). We provide recommended low VRAM configurations for each model in the example code, see the table in the "Model Overview" section above.
 
 ## Model Training
 
-Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](/examples/z_image/model_training/train.py), and the script parameters include:
+Z-Image series models are uniformly trained through [`examples/z_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/train.py), and the script parameters include:
 
 * General Training Parameters
     * Dataset Basic Configuration
@@ -137,13 +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
+    * 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](/examples/z_image/model_training/special/differential_training/)) + 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](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](/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)) + 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
 
@@ -90,3 +90,5 @@ Set 0 or not set: indicates not enabling the binding function
 | 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 |
+| Z-Image series | --enable_npu_patch | Using NPU fusion operator to replace the corresponding operator in Z-image model to improve the performance of the model on NPU |

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

@@ -37,11 +37,11 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
    git clone https://github.com/modelscope/DiffSynth-Studio.git
    cd DiffSynth-Studio
    # aarch64/ARM
-   pip install -e .[npu_aarch64] --extra-index-url "https://download.pytorch.org/whl/cpu"
+   pip install -e .[npu_aarch64] 
    # x86
-   pip install -e .[npu]
+   pip install -e .[npu] --extra-index-url "https://download.pytorch.org/whl/cpu"
 
-When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](/docs/en/Pipeline_Usage/GPU_support.md#ascend-npu).
+When using Ascend NPU, please replace `"cuda"` with `"npu"` in your Python code. For details, see [NPU Support](../Pipeline_Usage/GPU_support.md#ascend-npu).
 
 ## Other Installation Issues
 

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 +26,60 @@ 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)
+* [Installation Dependencies](./Pipeline_Usage/Setup.md)
-* [Model Inference](/docs/en/Pipeline_Usage/Model_Inference.md)
+* [Model Inference](./Pipeline_Usage/Model_Inference.md)
-* [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md)
+* [VRAM Management](./Pipeline_Usage/VRAM_management.md)
-* [Model Training](/docs/en/Pipeline_Usage/Model_Training.md)
+* [Model Training](./Pipeline_Usage/Model_Training.md)
-* [Environment Variables](/docs/en/Pipeline_Usage/Environment_Variables.md)
+* [Environment Variables](./Pipeline_Usage/Environment_Variables.md)
-* [GPU/NPU Support](/docs/en/Pipeline_Usage/GPU_support.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)
+* [FLUX.1](./Model_Details/FLUX.md)
-* [Wan](/docs/en/Model_Details/Wan.md)
+* [Wan](./Model_Details/Wan.md)
-* [Qwen-Image](/docs/en/Model_Details/Qwen-Image.md)
+* [Qwen-Image](./Model_Details/Qwen-Image.md)
-* [FLUX.2](/docs/en/Model_Details/FLUX2.md)
+* [FLUX.2](./Model_Details/FLUX2.md)
-* [Z-Image](/docs/en/Model_Details/Z-Image.md)
+* [Z-Image](./Model_Details/Z-Image.md)
+* [Anima](./Model_Details/Anima.md)
+* [LTX-2](./Model_Details/LTX-2.md)
 
 ## Section 3: Training Framework
 
 This section introduces the design philosophy of the training framework in `DiffSynth-Studio`, helping developers understand the principles of Diffusion model training algorithms.
 
-* [Basic Principles of Diffusion Models](/docs/en/Training/Understanding_Diffusion_models.md)
+* [Basic Principles of Diffusion Models](./Training/Understanding_Diffusion_models.md)
-* [Standard Supervised Training](/docs/en/Training/Supervised_Fine_Tuning.md)
+* [Standard Supervised Training](./Training/Supervised_Fine_Tuning.md)
-* [Enabling FP8 Precision in Training](/docs/en/Training/FP8_Precision.md)
+* [Enabling FP8 Precision in Training](./Training/FP8_Precision.md)
-* [End-to-End Distillation Accelerated Training](/docs/en/Training/Direct_Distill.md)
+* [End-to-End Distillation Accelerated Training](./Training/Direct_Distill.md)
-* [Two-Stage Split Training](/docs/en/Training/Split_Training.md)
+* [Two-Stage Split Training](./Training/Split_Training.md)
-* [Differential LoRA Training](/docs/en/Training/Differential_LoRA.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)
+* [Integrating Model Architecture](./Developer_Guide/Integrating_Your_Model.md)
-* [Building a Pipeline](/docs/en/Developer_Guide/Building_a_Pipeline.md)
+* [Building a Pipeline](./Developer_Guide/Building_a_Pipeline.md)
-* [Enabling Fine-Grained VRAM Management](/docs/en/Developer_Guide/Enabling_VRAM_management.md)
+* [Enabling Fine-Grained VRAM Management](./Developer_Guide/Enabling_VRAM_management.md)
-* [Model Training Integration](/docs/en/Developer_Guide/Training_Diffusion_Models.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.attention`](./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.data`](./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.gradient`](./API_Reference/core/gradient.md): Gradient checkpointing
-* [`diffsynth.core.loader`](/docs/en/API_Reference/core/loader.md): Model download and loading
+* [`diffsynth.core.loader`](./API_Reference/core/loader.md): Model download and loading
-* [`diffsynth.core.vram`](/docs/en/API_Reference/core/vram.md): VRAM management
+* [`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](/docs/en/Research_Tutorial/train_from_scratch.md)
+* [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 +88,4 @@ This section introduces how to use `DiffSynth-Studio` to train new models, helpi
 
 This section summarizes common developer questions. If you encounter issues during usage or development, please refer to this section. If you still cannot resolve the problem, please submit an issue on GitHub.
 
-* [Frequently Asked Questions](/docs/en/QA.md)
+* [Frequently Asked Questions](./QA.md)

docs/en/Research_Tutorial/train_from_scratch.md

@@ -12,7 +12,7 @@ From UNet [[1]](https://arxiv.org/abs/1505.04597) [[2]](https://arxiv.org/abs/21
 * 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](/docs/en/Training/Understanding_Diffusion_models.md). In this article, we build a DiT model with only 0.1B parameters: `AAADiT`.
+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>
@@ -137,11 +137,11 @@ 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](/diffsynth/models/z_image_text_encoder.py) and [/diffsynth/models/flux2_vae.py](/diffsynth/models/flux2_vae.py), so we don't need to modify any code.
+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](/docs/en/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.
+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>
@@ -328,7 +328,7 @@ def model_fn_aaa(
 
 ## 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](/docs/en/Pipeline_Usage/Model_Training.md#preparing-datasets) and [`diffsynth.core.data`](/docs/en/API_Reference/core/data.md).
+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
@@ -336,7 +336,7 @@ 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 [/docs/en/Research_Tutorial/train_from_scratch.py](/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.
+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.
 

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 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](/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](../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

@@ -138,4 +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 +1,79 @@
+Welcome to DiffSynth-Studio's Documentation
+==========================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Documentation Introduction
+
+   README
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Getting Started
+
+   Pipeline_Usage/Setup
+   Pipeline_Usage/Model_Inference
+   Pipeline_Usage/VRAM_management
+   Pipeline_Usage/Model_Training
+   Pipeline_Usage/Environment_Variables
+   Pipeline_Usage/GPU_support
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Model Details
+
+   Model_Details/FLUX
+   Model_Details/Wan
+   Model_Details/Qwen-Image
+   Model_Details/FLUX2
+   Model_Details/Z-Image
+   Model_Details/Anima
+   Model_Details/LTX-2
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Training Framework
+
+   Training/Understanding_Diffusion_models
+   Training/Supervised_Fine_Tuning
+   Training/FP8_Precision
+   Training/Direct_Distill
+   Training/Split_Training
+   Training/Differential_LoRA
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Model Integration
+
+   Developer_Guide/Integrating_Your_Model
+   Developer_Guide/Building_a_Pipeline
+   Developer_Guide/Enabling_VRAM_management
+   Developer_Guide/Training_Diffusion_Models
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API Reference
+
+   API_Reference/core/attention
+   API_Reference/core/data
+   API_Reference/core/gradient
+   API_Reference/core/loader
+   API_Reference/core/vram
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Research Guide
+
+   Research_Tutorial/train_from_scratch
+
+.. 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：
 

docs/zh/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)
 ```
 
-在 `DiffSynth-Studio` 中，所有预训练模型都遵循这一加载逻辑。开发者在[接入模型](/docs/zh/Developer_Guide/Integrating_Your_Model.md)完毕后即可直接以这种方式快速加载模型。
+在 `DiffSynth-Studio` 中，所有预训练模型都遵循这一加载逻辑。开发者在[接入模型](../../Developer_Guide/Integrating_Your_Model.md)完毕后即可直接以这种方式快速加载模型。
 
 ## State Dict 硬盘映射
 
@@ -57,10 +57,10 @@ state_dict = DiskMap(path, device="cpu") # Fast
 print(state_dict["img_in.weight"])
 ```
 
-`DiskMap` 是 `DiffSynth-Studio` 中 Disk Offload 的基本组件，开发者在[配置细粒度显存管理方案](/docs/zh/Developer_Guide/Enabling_VRAM_management.md)后即可直接启用 Disk Offload。
+`DiskMap` 是 `DiffSynth-Studio` 中 Disk Offload 的基本组件，开发者在[配置细粒度显存管理方案](../../Developer_Guide/Enabling_VRAM_management.md)后即可直接启用 Disk Offload。
 
 `DiskMap` 是利用 `.safetensors` 文件的特性实现的功能，因此在使用 `.bin`、`.pth`、`.ckpt` 等二进制文件时，模型的参数是全量加载的，这也导致 Disk Offload 不支持这些格式的文件。**我们不建议开发者继续使用这些格式的文件。**
 
 ## 显存管理可替换模块
 
-在启用 `DiffSynth-Studio` 的显存管理后，模型内部的模块会被替换为 `diffsynth.core.vram.layers` 中的可替换模块，其使用方式详见[细粒度显存管理方案](/docs/zh/Developer_Guide/Enabling_VRAM_management.md#编写细粒度显存管理方案)。
+在启用 `DiffSynth-Studio` 的显存管理后，模型内部的模块会被替换为 `diffsynth.core.vram.layers` 中的可替换模块，其使用方式详见[细粒度显存管理方案](../../Developer_Guide/Enabling_VRAM_management.md#编写细粒度显存管理方案)。

docs/zh/Developer_Guide/Building_a_Pipeline.md

@@ -1,6 +1,6 @@
 # 接入 Pipeline
 
-在[将 Pipeline 所需的模型接入](/docs/zh/Developer_Guide/Integrating_Your_Model.md)之后，还需构建 `Pipeline` 用于模型推理，本文档提供 `Pipeline` 构建的标准化流程，开发者也可参考现有的 `Pipeline` 进行构建。
+在[将 Pipeline 所需的模型接入](../Developer_Guide/Integrating_Your_Model.md)之后，还需构建 `Pipeline` 用于模型推理，本文档提供 `Pipeline` 构建的标准化流程，开发者也可参考现有的 `Pipeline` 进行构建。
 
 `Pipeline` 的实现位于 `diffsynth/pipelines`，每个 `Pipeline` 包含以下必要的关键组件：
 
@@ -79,7 +79,7 @@ class NewDiffSynthPipeline(BasePipeline):
         return pipe
 ```
 
-开发者需要实现其中获取模型的逻辑，对应的模型名称即为[模型接入时填写的模型 Config](/docs/zh/Developer_Guide/Integrating_Your_Model.md#step-3-编写模型-config) 中的 `"model_name"`。
+开发者需要实现其中获取模型的逻辑，对应的模型名称即为[模型接入时填写的模型 Config](../Developer_Guide/Integrating_Your_Model.md#step-3-编写模型-config) 中的 `"model_name"`。
 
 部分模型还需要加载 `tokenizer`，可根据需要在 `from_pretrained` 上添加额外的 `tokenizer_config` 参数并在获取模型后实现这部分。
 

docs/zh/Developer_Guide/Enabling_VRAM_management.md

@@ -1,6 +1,6 @@
 # 细粒度显存管理方案
 
-本文档介绍如何为模型编写合理的细粒度显存管理方案，以及如何将 `DiffSynth-Studio` 中的显存管理功能用于外部的其他代码库，在阅读本文档前，请先阅读文档[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)。
+本文档介绍如何为模型编写合理的细粒度显存管理方案，以及如何将 `DiffSynth-Studio` 中的显存管理功能用于外部的其他代码库，在阅读本文档前，请先阅读文档[显存管理](../Pipeline_Usage/VRAM_management.md)。
 
 ## 20B 模型需要多少显存？
 
@@ -124,7 +124,7 @@ module_map={
 }
 ```
 
-此外，还需要提供 `vram_config` 与 `vram_limit`，这两个参数在[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md#更多使用方式)中已有介绍。
+此外，还需要提供 `vram_config` 与 `vram_limit`，这两个参数在[显存管理](../Pipeline_Usage/VRAM_management.md#更多使用方式)中已有介绍。
 
 调用 `enable_vram_management` 即可启用显存管理，注意此时模型加载时的 `device` 为 `cpu`，与 `offload_device` 一致：
 
@@ -171,7 +171,7 @@ with torch.no_grad():
 
 ## Disk Offload
 
-[Disk Offload](/docs/zh/Pipeline_Usage/VRAM_management.md#disk-offload) 是特殊的显存管理方案，需在模型加载过程中启用，而非模型加载完毕后。通常，在以上代码能够顺利运行的前提下，Disk Offload 可以直接启用：
+[Disk Offload](../Pipeline_Usage/VRAM_management.md#disk-offload) 是特殊的显存管理方案，需在模型加载过程中启用，而非模型加载完毕后。通常，在以上代码能够顺利运行的前提下，Disk Offload 可以直接启用：
 
 ```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 是极为特殊的显存管理方案，只支持 `.safetensors` 格式文件，不支持 `.bin`、`.pth`、`.ckpt` 等二进制文件，不支持带 Tensor reshape 的 [state dict converter](/docs/zh/Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换)。
+Disk Offload 是极为特殊的显存管理方案，只支持 `.safetensors` 格式文件，不支持 `.bin`、`.pth`、`.ckpt` 等二进制文件，不支持带 Tensor reshape 的 [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换)。
 
 如果出现非 Disk Offload 能正常运行但 Disk Offload 不能正常运行的情况，请在 GitHub 上给我们提 issue。
 

docs/zh/Developer_Guide/Integrating_Your_Model.md

@@ -183,4 +183,4 @@ Loaded model: {
 
 ## Step 5: 编写模型显存管理方案
 
-`DiffSynth-Studio` 支持复杂的显存管理，详见[启用显存管理](/docs/zh/Developer_Guide/Enabling_VRAM_management.md)。
+`DiffSynth-Studio` 支持复杂的显存管理，详见[启用显存管理](../Developer_Guide/Enabling_VRAM_management.md)。

docs/zh/Developer_Guide/Training_Diffusion_Models.md

@@ -1,6 +1,6 @@
 # 接入模型训练
 
-在[接入模型](/docs/zh/Developer_Guide/Integrating_Your_Model.md)并[实现 Pipeline](/docs/zh/Developer_Guide/Building_a_Pipeline.md)后，接下来接入模型训练功能。
+在[接入模型](../Developer_Guide/Integrating_Your_Model.md)并[实现 Pipeline](../Developer_Guide/Building_a_Pipeline.md)后，接下来接入模型训练功能。
 
 ## 训推一致的 Pipeline 改造
 

docs/zh/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/zh/Model_Details/Anima.md

@@ -0,0 +1,139 @@
+# Anima
+
+Anima 是由 CircleStone Labs 与 Comfy Org 训练并开源的图像生成模型。
+
+## 安装
+
+在使用本项目进行模型推理和训练前，请先安装 DiffSynth-Studio。
+
+```shell
+git clone https://github.com/modelscope/DiffSynth-Studio.git
+cd DiffSynth-Studio
+pip install -e .
+```
+
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
+
+## 快速开始
+
+运行以下代码可以快速加载 [circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima) 模型并进行推理。显存管理已启动，框架会自动根据剩余显存控制模型参数的加载，最低 8G 显存即可运行。
+
+```python
+from diffsynth.pipelines.anima_image import AnimaImagePipeline, ModelConfig
+import torch
+
+vram_config = {
+    "offload_dtype": "disk",
+    "offload_device": "disk",
+    "onload_dtype": "disk",
+    "onload_device": "disk",
+    "preparing_dtype": torch.bfloat16,
+    "preparing_device": "cuda",
+    "computation_dtype": torch.bfloat16,
+    "computation_device": "cuda",
+}
+pipe = AnimaImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/diffusion_models/anima-preview.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/text_encoders/qwen_3_06b_base.safetensors", **vram_config),
+        ModelConfig(model_id="circlestone-labs/Anima", origin_file_pattern="split_files/vae/qwen_image_vae.safetensors", **vram_config),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
+    tokenizer_t5xxl_config=ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
+    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
+)
+prompt = "Masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait."
+negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
+image = pipe(prompt, seed=0, num_inference_steps=50)
+image.save("image.jpg")
+```
+
+## 模型总览
+
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[circlestone-labs/Anima](https://www.modelscope.cn/models/circlestone-labs/Anima)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_inference_low_vram/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/full/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_full/anima-preview.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/lora/anima-preview.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/validate_lora/anima-preview.py)|
+
+特殊训练脚本：
+
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)
+* 端到端直接蒸馏：[doc](../Training/Direct_Distill.md)
+
+## 模型推理
+
+模型通过 `AnimaImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
+
+`AnimaImagePipeline` 推理的输入参数包括：
+
+* `prompt`: 提示词，描述画面中出现的内容。
+* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。
+* `cfg_scale`: Classifier-free guidance 的参数，默认值为 4.0。
+* `input_image`: 输入图像，用于图像到图像的生成。默认为 `None`。
+* `denoising_strength`: 去噪强度，控制生成图像与输入图像的相似度，默认值为 1.0。
+* `height`: 图像高度，需保证高度为 16 的倍数，默认值为 1024。
+* `width`: 图像宽度，需保证宽度为 16 的倍数，默认值为 1024。
+* `seed`: 随机种子。默认为 `None`，即完全随机。
+* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。
+* `num_inference_steps`: 推理次数，默认值为 30。
+* `sigma_shift`: 调度器的 sigma 偏移量，默认为 `None`。
+* `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
+
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+
+## 模型训练
+
+Anima 系列模型统一通过 [`examples/anima/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/anima/model_training/train.py) 进行训练，脚本的参数包括：
+
+* 通用训练参数
+    * 数据集基础配置
+        * `--dataset_base_path`: 数据集的根目录。
+        * `--dataset_metadata_path`: 数据集的元数据文件路径。
+        * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+        * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
+        * `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
+    * 模型加载配置
+        * `--model_paths`: 要加载的模型路径。JSON 格式。
+        * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 `"anima-team/anima-1B:text_encoder/*.safetensors"`。用逗号分隔。
+        * `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，例如训练 ControlNet 模型时需要额外参数 `controlnet_inputs`，以 `,` 分隔。
+        * `--fp8_models`：以 FP8 格式加载的模型，格式与 `--model_paths` 或 `--model_id_with_origin_paths` 一致，目前仅支持参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）。
+    * 训练基础配置
+        * `--learning_rate`: 学习率。
+        * `--num_epochs`: 轮数（Epoch）。
+        * `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
+        * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数，少数模型包含不参与梯度计算的冗余参数，需开启这一设置避免在多 GPU 训练中报错。
+        * `--weight_decay`：权重衰减大小，详见 [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html)。
+        * `--task`: 训练任务，默认为 `sft`，部分模型支持更多训练模式，请参考每个特定模型的文档。
+    * 输出配置
+        * `--output_path`: 模型保存路径。
+        * `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
+        * `--save_steps`: 保存模型的训练步数间隔，若此参数留空，则每个 epoch 保存一次。
+    * LoRA 配置
+        * `--lora_base_model`: LoRA 添加到哪个模型上。
+        * `--lora_target_modules`: LoRA 添加到哪些层上。
+        * `--lora_rank`: LoRA 的秩（Rank）。
+        * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。
+        * `--preset_lora_path`: 预置 LoRA 检查点路径，如果提供此路径，这一 LoRA 将会以融入基础模型的形式加载。此参数用于 LoRA 差分训练。
+        * `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
+    * 梯度配置
+        * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
+        * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
+        * `--gradient_accumulation_steps`: 梯度累积步数。
+    * 图像宽高配置（适用于图像生成模型和视频生成模型）
+        * `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--max_pixels`: 图像或视频帧的最大像素面积，当启用动态分辨率时，分辨率大于这个数值的图片都会被缩小，分辨率小于这个数值的图片保持不变。
+* Anima 专有参数
+    * `--tokenizer_path`: tokenizer 的路径，适用于文生图模型，留空则自动从远程下载。
+    * `--tokenizer_t5xxl_path`: T5-XXL tokenizer 的路径，适用于文生图模型，留空则自动从远程下载。
+
+我们构建了一个样例图像数据集，以方便您进行测试，通过以下命令可以下载这个数据集：
+
+```shell
+modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
+```
+
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/FLUX.md

@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -81,31 +81,31 @@ graph LR;
 
 |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|
 |-|-|-|-|-|-|-|-|
-|[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-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](/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-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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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/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](/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/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](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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)|
 
 特殊训练脚本：
 
-* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)、[code](/examples/flux/model_training/special/differential_training/)
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)
-* FP8 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)、[code](/examples/flux/model_training/special/fp8_training/)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)
-* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/flux/model_training/special/split_training/)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)
-* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/flux/model_training/lora/FLUX.1-dev-Distill-LoRA.sh)
+* 端到端直接蒸馏：[doc](../Training/Direct_Distill.md)
 
 ## 模型推理
 
-模型通过 `FluxImagePipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `FluxImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `FluxImagePipeline` 推理的输入参数包括：
 
@@ -143,11 +143,11 @@ graph LR;
 * `flex_control_stop`: Flex 模型的控制停止时间步。
 * `nexus_gen_reference_image`: Nexus-Gen 模型的参考图像。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
 
 ## 模型训练
 
-FLUX 系列模型统一通过 [`examples/flux/model_training/train.py`](/examples/flux/model_training/train.py) 进行训练，脚本的参数包括：
+FLUX 系列模型统一通过 [`examples/flux/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
@@ -198,4 +198,4 @@ FLUX 系列模型统一通过 [`examples/flux/model_training/train.py`](/example
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
 ```
 
-我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/FLUX2.md

@@ -21,7 +21,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -61,22 +61,22 @@ image.save("image.jpg")
 
 |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-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](/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-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](/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-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](/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-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](/examples/flux2/model_inference/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_inference_low_vram/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/full/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_full/FLUX.2-klein-base-9B.py)|[code](/examples/flux2/model_training/lora/FLUX.2-klein-base-9B.sh)|[code](/examples/flux2/model_training/validate_lora/FLUX.2-klein-base-9B.py)|
+|[black-forest-labs/FLUX.2-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)|
 
 特殊训练脚本：
 
-* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)
-* FP8 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)
-* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)
-* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)
+* 端到端直接蒸馏：[doc](../Training/Direct_Distill.md)
 
 ## 模型推理
 
-模型通过 `Flux2ImagePipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `Flux2ImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `Flux2ImagePipeline` 推理的输入参数包括：
 
@@ -95,11 +95,11 @@ image.save("image.jpg")
 * `tile_stride`: VAE 编解码阶段的分块步长，默认为 64，仅在 `tiled=True` 时生效，需保证其数值小于或等于 `tile_size`。
 * `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
 
 ## 模型训练
 
-FLUX.2 系列模型统一通过 [`examples/flux2/model_training/train.py`](/examples/flux2/model_training/train.py) 进行训练，脚本的参数包括：
+FLUX.2 系列模型统一通过 [`examples/flux2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/flux2/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
@@ -148,4 +148,4 @@ FLUX.2 系列模型统一通过 [`examples/flux2/model_training/train.py`](/exam
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
 ```
 
-我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/LTX-2.md

@@ -12,7 +12,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -33,19 +33,62 @@ vram_config = {
     "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="Lightricks/LTX-2", origin_file_pattern="ltx-2-19b-dev.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,
 )
-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."
+# use the following modelconfig if you want to initialize model from offical checkpoints from "Lightricks/LTX-2"
-height, width, num_frames = 512, 768, 121
+# 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,
@@ -54,11 +97,12 @@ video, audio = pipe(
     width=width,
     num_frames=num_frames,
     tiled=True,
+    use_two_stage_pipeline=True,
 )
 write_video_audio_ltx2(
     video=video,
     audio=audio,
-    output_path='ltx2_onestage.mp4',
+    output_path='ltx2_twostage.mp4',
     fps=24,
     audio_sample_rate=24000,
 )
@@ -67,16 +111,25 @@ write_video_audio_ltx2(
 ## 模型总览
 |模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|-|
-|[Lightricks/LTX-2: OneStagePipeline-T2AV](https://www.modelscope.cn/models/Lightricks/LTX-2)||[code](/examples/ltx2/model_inference/LTX-2-T2AV-OneStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-OneStage.py)|-|-|-|-|
+|[Lightricks/LTX-2: 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](/examples/ltx2/model_inference/LTX-2-T2AV-TwoStage.py)|[code](/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-TwoStage.py)|-|-|-|-|
+|[Lightricks/LTX-2-19b-IC-LoRA-Union-Control](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Union-Control)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Union-Control.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Union-Control.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
-|[Lightricks/LTX-2: 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-2-19b-IC-LoRA-Detailer](https://www.modelscope.cn/models/Lightricks/LTX-2-19b-IC-LoRA-Detailer)|`in_context_videos`,`in_context_downsample_factor`|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference/LTX-2-T2AV-IC-LoRA-Detailer.py)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_inference_low_vram/LTX-2-T2AV-IC-LoRA-Detailer.py)|-|-|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/lora/LTX-2-T2AV-IC-LoRA-splited.sh)|[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/validate_lora/LTX-2-T2AV-IC-LoRA.py)|
-|[Lightricks/LTX-2: 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-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: 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-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: 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: 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)|-|-|-|-|
 
 ## 模型推理
 
-模型通过 `LTX2AudioVideoPipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `LTX2AudioVideoPipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `LTX2AudioVideoPipeline` 推理的输入参数包括：
 
@@ -102,8 +155,59 @@ write_video_audio_ltx2(
 * `use_distilled_pipeline`: 是否使用蒸馏管道，默认为 `False`。
 * `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"支持的推理脚本"中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"支持的推理脚本"中的表格。
 
 ## 模型训练
 
-LTX-2 系列模型目前暂不支持训练功能。我们将尽快添加相关支持。
+LTX-2 系列模型统一通过 [`examples/ltx2/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/ltx2/model_training/train.py) 进行训练，脚本的参数包括：
+
+* 通用训练参数
+    * 数据集基础配置
+        * `--dataset_base_path`: 数据集的根目录。
+        * `--dataset_metadata_path`: 数据集的元数据文件路径。
+        * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。
+        * `--dataset_num_workers`: 每个 Dataloder 的进程数量。
+        * `--data_file_keys`: 元数据中需要加载的字段名称，通常是图像或视频文件的路径，以 `,` 分隔。
+    * 模型加载配置
+        * `--model_paths`: 要加载的模型路径。JSON 格式。
+        * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 `"Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors"`。用逗号分隔。
+        * `--extra_inputs`: 模型 Pipeline 所需的额外输入参数，例如训练图像编辑模型时需要额外参数，以 `,` 分隔。
+        * `--fp8_models`：以 FP8 格式加载的模型，格式与 `--model_paths` 或 `--model_id_with_origin_paths` 一致，目前仅支持参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）。
+    * 训练基础配置
+        * `--learning_rate`: 学习率。
+        * `--num_epochs`: 轮数（Epoch）。
+        * `--trainable_models`: 可训练的模型，例如 `dit`、`vae`、`text_encoder`。
+        * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数，少数模型包含不参与梯度计算的冗余参数，需开启这一设置避免在多 GPU 训练中报错。
+        * `--weight_decay`：权重衰减大小，详见 [torch.optim.AdamW](https://docs.pytorch.org/docs/stable/generated/torch.optim.AdamW.html)。
+        * `--task`: 训练任务，默认为 `sft`，部分模型支持更多训练模式，请参考每个特定模型的文档。
+    * 输出配置
+        * `--output_path`: 模型保存路径。
+        * `--remove_prefix_in_ckpt`: 在模型文件的 state dict 中移除前缀。
+        * `--save_steps`: 保存模型的训练步数间隔，若此参数留空，则每个 epoch 保存一次。
+    * LoRA 配置
+        * `--lora_base_model`: LoRA 添加到哪个模型上。
+        * `--lora_target_modules`: LoRA 添加到哪些层上。
+        * `--lora_rank`: LoRA 的秩（Rank）。
+        * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。
+        * `--preset_lora_path`: 预置 LoRA 检查点路径，如果提供此路径，这一 LoRA 将会以融入基础模型的形式加载。此参数用于 LoRA 差分训练。
+        * `--preset_lora_model`: 预置 LoRA 融入的模型，例如 `dit`。
+    * 梯度配置
+        * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。
+        * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。
+        * `--gradient_accumulation_steps`: 梯度累积步数。
+    * 视频宽高配置
+        * `--height`: 视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--width`: 视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。
+        * `--max_pixels`: 视频帧的最大像素面积，当启用动态分辨率时，分辨率大于这个数值的视频帧都会被缩小，分辨率小于这个数值的视频帧保持不变。
+        * `--num_frames`: 视频的帧数。
+* LTX-2 系列特定参数
+    * `--tokenizer_path`: 分词器路径，适用于文生视频模型，留空则从远程自动下载。
+    * `--frame_rate`: 训练视频的帧率。
+
+我们构建了一个样例视频数据集，以方便您进行测试，通过以下命令可以下载这个数据集：
+
+```shell
+modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
+```
+
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/Overview.md

@@ -2,7 +2,7 @@
 
 ## Qwen-Image
 
-文档：[./Qwen-Image.md](/docs/zh/Model_Details/Qwen-Image.md)
+文档：[./Qwen-Image.md](../Model_Details/Qwen-Image.md)
 
 <details>
 
@@ -69,23 +69,23 @@ graph LR;
 
 |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](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](/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](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](/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-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](/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](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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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](/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-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 系列
 
-文档：[./FLUX.md](/docs/zh/Model_Details/FLUX.md)
+文档：[./FLUX.md](../Model_Details/FLUX.md)
 
 <details>
 
@@ -149,24 +149,24 @@ graph LR;
 
 |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|
 |-|-|-|-|-|-|-|-|
-|[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-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](/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-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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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/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](/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/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](/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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 系列
 
-文档：[./Wan.md](/docs/zh/Model_Details/Wan.md)
+文档：[./Wan.md](../Model_Details/Wan.md)
 
 <details>
 
@@ -255,34 +255,34 @@ graph LR;
 
 |模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
-|[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-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](/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-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](/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-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](/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-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](/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)|
+|[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](/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)|
+|[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](/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-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](/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)|
+|[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](/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-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](/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-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](/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-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](/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-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](/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-V1.1-1.3B-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](/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/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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-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](/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-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](/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-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](/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-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](/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)|
+|[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](/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-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](/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-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](/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](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](/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)|
+|[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)|

docs/zh/Model_Details/Qwen-Image.md

@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -80,36 +80,42 @@ graph LR;
 
 |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](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](/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-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](/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](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](/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-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](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/full/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2511.py)|[code](/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2511.sh)|[code](/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2511.py)|
+|[Qwen/Qwen-Image-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)|
-|[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)|-|-|-|-|
+|[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)|
-|[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)|
+|[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)|-|-|-|-|
-|[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)|
+|[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-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-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-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](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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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)|-|-|-|-|
+|[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)|-|-|-|-|
 
 特殊训练脚本：
 
-* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)、[code](/examples/qwen_image/model_training/special/differential_training/)
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/differential_training/)
-* FP8 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)、[code](/examples/qwen_image/model_training/special/fp8_training/)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/fp8_training/)
-* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/qwen_image/model_training/special/split_training/)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/qwen_image/model_training/special/split_training/)
-* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
+* 端到端直接蒸馏：[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 3 训练：Qwen-Image 系列模型支持 DeepSpeed ZeRO 3 训练，将模型拆分到多个 GPU 上，以 Qwen-Image 模型的全量训练为例，需修改：
+
+* `--config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml`
+* `--initialize_model_on_cpu`
 
 ## 模型推理
 
-模型通过 `QwenImagePipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `QwenImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `QwenImagePipeline` 推理的输入参数包括：
 
@@ -140,11 +146,11 @@ graph LR;
 * `tile_stride`: VAE 编解码阶段的分块步长，默认为 64，仅在 `tiled=True` 时生效，需保证其数值小于或等于 `tile_size`。
 * `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文“模型总览”中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文“模型总览”中的表格。
 
 ## 模型训练
 
-Qwen-Image 系列模型统一通过 [`examples/qwen_image/model_training/train.py`](/examples/qwen_image/model_training/train.py) 进行训练，脚本的参数包括：
+Qwen-Image 系列模型统一通过 [`examples/qwen_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/qwen_image/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
@@ -194,4 +200,4 @@ Qwen-Image 系列模型统一通过 [`examples/qwen_image/model_training/train.p
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
 ```
 
-我们为每个模型编写了推荐的训练脚本，请参考前文“模型总览”中的表格。关于如何编写模型训练脚本，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+我们为每个模型编写了推荐的训练脚本，请参考前文“模型总览”中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/Wan.md

@@ -14,7 +14,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -107,45 +107,50 @@ graph LR;
 
 |模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
 |-|-|-|-|-|-|-|
-|[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-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](/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-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](/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-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](/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-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](/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)|
+|[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](/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)|
+|[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](/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-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](/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)|
+|[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](/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-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](/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-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](/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-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](/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-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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-V1.1-14B-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](/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-V1.1-1.3B-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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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)|
+|[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](/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-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](/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-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](/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-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](/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-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](/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)|
+|[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](/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-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](/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-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](/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](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](/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)|
+|[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 精度训练：[doc](/docs/zh/Training/FP8_Precision.md)、[code](/examples/wanvideo/model_training/special/fp8_training/)
+* FP8 精度训练：[doc](../Training/FP8_Precision.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/fp8_training/)
-* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/wanvideo/model_training/special/split_training/)
+* 两阶段拆分训练：[doc](../Training/Split_Training.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/split_training/)
-* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/wanvideo/model_training/special/direct_distill/)
+* 端到端直接蒸馏：[doc](../Training/Direct_Distill.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/wanvideo/model_training/special/direct_distill/)
+
+DeepSpeed ZeRO 3 训练：Wan 系列模型支持 DeepSpeed ZeRO 3 训练，将模型拆分到多个 GPU 上，以 Wan2.1-T2V-14B 模型的全量训练为例，需修改：
+
+* `--config_file examples/wanvideo/model_training/full/accelerate_config_zero3.yaml`
+* `--initialize_model_on_cpu`
 
 ## 模型推理
 
-模型通过 `WanVideoPipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `WanVideoPipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `WanVideoPipeline` 推理的输入参数包括：
 
@@ -195,11 +200,11 @@ graph LR;
 * `tea_cache_model_id`: TeaCache 使用的模型 ID。
 * `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
 
 ## 模型训练
 
-Wan 系列模型统一通过 [`examples/wanvideo/model_training/train.py`](/examples/wanvideo/model_training/train.py) 进行训练，脚本的参数包括：
+Wan 系列模型统一通过 [`examples/wanvideo/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/wanvideo/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
@@ -250,4 +255,4 @@ Wan 系列模型统一通过 [`examples/wanvideo/model_training/train.py`](/exam
 modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset
 ```
 
-我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。

docs/zh/Model_Details/Z-Image.md

@@ -12,7 +12,7 @@ cd DiffSynth-Studio
 pip install -e .
 ```
 
-更多关于安装的信息，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)。
+更多关于安装的信息，请参考[安装依赖](../Pipeline_Usage/Setup.md)。
 
 ## 快速开始
 
@@ -52,21 +52,21 @@ image.save("image.jpg")
 
 |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)|
+|[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](/examples/z_image/model_inference/Z-Image-i2L.py)|[code](/examples/z_image/model_inference_low_vram/Z-Image-i2L.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](/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)|
+|[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](/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](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](/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-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](/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)|
+|[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)|
 
 特殊训练脚本：
 
-* 差分 LoRA 训练：[doc](/docs/zh/Training/Differential_LoRA.md)、[code](/examples/z_image/model_training/special/differential_training/)
+* 差分 LoRA 训练：[doc](../Training/Differential_LoRA.md)、[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)
-* 轨迹模仿蒸馏训练（实验性功能）：[code](/examples/z_image/model_training/special/trajectory_imitation/)
+* 轨迹模仿蒸馏训练（实验性功能）：[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)
 
 ## 模型推理
 
-模型通过 `ZImagePipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
+模型通过 `ZImagePipeline.from_pretrained` 加载，详见[加载模型](../Pipeline_Usage/Model_Inference.md#加载模型)。
 
 `ZImagePipeline` 推理的输入参数包括：
 
@@ -84,11 +84,11 @@ image.save("image.jpg")
 * `edit_image`: 编辑模型的待编辑图像，支持多张图像。
 * `positive_only_lora`: 仅在正向提示词中使用的 LoRA 权重。
 
-如果显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
+如果显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)，我们在示例代码中提供了每个模型推荐的低显存配置，详见前文"模型总览"中的表格。
 
 ## 模型训练
 
-Z-Image 系列模型统一通过 [`examples/z_image/model_training/train.py`](/examples/z_image/model_training/train.py) 进行训练，脚本的参数包括：
+Z-Image 系列模型统一通过 [`examples/z_image/model_training/train.py`](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/train.py) 进行训练，脚本的参数包括：
 
 * 通用训练参数
     * 数据集基础配置
@@ -137,13 +137,13 @@ Z-Image 系列模型统一通过 [`examples/z_image/model_training/train.py`](/e
 modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset
 ```
 
-我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](/docs/Training/)。
+我们为每个模型编写了推荐的训练脚本，请参考前文"模型总览"中的表格。关于如何编写模型训练脚本，请参考[模型训练](../Pipeline_Usage/Model_Training.md)；更多高阶训练算法，请参考[训练框架详解](https://github.com/modelscope/DiffSynth-Studio/tree/main/docs/zh/Training/)。
 
 训练技巧：
 
 * [Tongyi-MAI/Z-Image-Turbo](https://www.modelscope.cn/models/Tongyi-MAI/Z-Image-Turbo) 是一个蒸馏加速的模型，因此直接训练将会迅速让模型失去加速能力，以“加速配置”（`num_inference_steps=8`，`cfg_scale=1`）推理的效果变差，以“无加速配置”（`num_inference_steps=30`，`cfg_scale=2`）推理的效果变好。可采用以下方案训练和推理：
-    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)） + 无加速配置推理
+    * 标准 SFT 训练（[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)） + 无加速配置推理
-    * 差分 LoRA 训练（[code](/examples/z_image/model_training/special/differential_training/)） + 加速配置推理
+    * 差分 LoRA 训练（[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/differential_training/)） + 加速配置推理
         * 差分 LoRA 训练中需加载一个额外的 LoRA，例如 [ostris/zimage_turbo_training_adapter](https://www.modelscope.cn/models/ostris/zimage_turbo_training_adapter)
-    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 轨迹模仿蒸馏训练（[code](/examples/z_image/model_training/special/trajectory_imitation/)）+ 加速配置推理
+    * 标准 SFT 训练（[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 轨迹模仿蒸馏训练（[code](https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/z_image/model_training/special/trajectory_imitation/)）+ 加速配置推理
-    * 标准 SFT 训练（[code](/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 推理时加载蒸馏加速 LoRA（[model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)） + 加速配置推理
+    * 标准 SFT 训练（[code](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/z_image/model_training/lora/Z-Image-Turbo.sh)）+ 推理时加载蒸馏加速 LoRA（[model](https://www.modelscope.cn/models/DiffSynth-Studio/Z-Image-Turbo-DistillPatch)） + 加速配置推理

docs/zh/Pipeline_Usage/Environment_Variables.md

@@ -28,7 +28,7 @@ DIFFSYNTH_MODEL_BASE_PATH="./path_to_my_models" python xxx.py
 
 ## `DIFFSYNTH_ATTENTION_IMPLEMENTATION`
 
-注意力机制实现的方式，可以设置为 `flash_attention_3`、`flash_attention_2`、`sage_attention`、`xformers`、`torch`。详见 [`./core/attention.md`](/docs/zh/API_Reference/core/attention.md).
+注意力机制实现的方式，可以设置为 `flash_attention_3`、`flash_attention_2`、`sage_attention`、`xformers`、`torch`。详见 [`./core/attention.md`](../API_Reference/core/attention.md).
 
 ## `DIFFSYNTH_DISK_MAP_BUFFER_SIZE`
 

docs/zh/Pipeline_Usage/GPU_support.md

@@ -2,7 +2,7 @@
 
 `DiffSynth-Studio` 支持多种 GPU/NPU，本文介绍如何在这些设备上运行模型推理和训练。
 
-在开始前，请参考[安装依赖](/docs/zh/Pipeline_Usage/Setup.md)安装好 GPU/NPU 相关的依赖包。
+在开始前，请参考[安装依赖](../Pipeline_Usage/Setup.md)安装好 GPU/NPU 相关的依赖包。
 
 ## NVIDIA GPU
 
@@ -89,3 +89,5 @@ export CPU_AFFINITY_CONF=1
 | 模型        | 参数 | 备注                |
 |-----------|------|-------------------|
 | Wan 14B系列 | --initialize_model_on_cpu | 14B模型需要在cpu上进行初始化 |
+| Qwen-Image系列 | --initialize_model_on_cpu | 模型需要在cpu上进行初始化 |
+| Z-Image 系列 | --enable_npu_patch | 使用NPU融合算子来替换Z-image模型中的对应算子以提升模型在NPU上的性能 |

docs/zh/Pipeline_Usage/Model_Inference.md

@@ -22,7 +22,7 @@ pipe = QwenImagePipeline.from_pretrained(
 )
 ```
 
-其中 `torch_dtype` 和 `device` 是计算精度和计算设备（不是模型的精度和设备）。`model_configs` 可通过多种方式配置模型路径，关于本项目内部是如何加载模型的，请参考 [`diffsynth.core.loader`](/docs/zh/API_Reference/core/loader.md)。
+其中 `torch_dtype` 和 `device` 是计算精度和计算设备（不是模型的精度和设备）。`model_configs` 可通过多种方式配置模型路径，关于本项目内部是如何加载模型的，请参考 [`diffsynth.core.loader`](../API_Reference/core/loader.md)。
 
 <details>
 
@@ -34,7 +34,7 @@ pipe = QwenImagePipeline.from_pretrained(
 > ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
 > ```
 > 
-> 模型文件默认下载到 `./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。
+> 模型文件默认下载到 `./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。
 
 </details>
 
@@ -61,7 +61,7 @@ pipe = QwenImagePipeline.from_pretrained(
 
 </details>
 
-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`。
 
 ```shell
 import os
@@ -69,7 +69,7 @@ os.environ["DIFFSYNTH_SKIP_DOWNLOAD"] = "True"
 import diffsynth
 ```
 
-如需从 [HuggingFace](https://huggingface.co/) 下载模型，请将[环境变量 DIFFSYNTH_DOWNLOAD_SOURCE](/docs/zh/Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) 设置为 `huggingface`。
+如需从 [HuggingFace](https://huggingface.co/) 下载模型，请将[环境变量 DIFFSYNTH_DOWNLOAD_SOURCE](../Pipeline_Usage/Environment_Variables.md#diffsynth_download_source) 设置为 `huggingface`。
 
 ```shell
 import os
@@ -102,4 +102,65 @@ image.save("image.jpg")
 
 每个模型 `Pipeline` 的输入参数不同，请参考各模型的文档。
 
-如果模型参数量太大，导致显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)。
+如果模型参数量太大，导致显存不足，请开启[显存管理](../Pipeline_Usage/VRAM_management.md)。
+
+## 加载 LoRA
+
+LoRA 是一种轻量化的模型训练方式，产生少量参数，扩展模型的能力。DiffSynth-Studio 的 LoRA 加载有两种方式：冷加载和热加载。
+
+* 冷加载：当基础模型未开启[显存管理](../Pipeline_Usage/VRAM_management.md)时，LoRA 会融合进基础模型权重，此时推理速度没有变化，LoRA 加载后无法卸载。
+
+```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 = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+```
+
+* 热加载：当基础模型开启[显存管理](../Pipeline_Usage/VRAM_management.md)时，LoRA 不会融合进基础模型权重，此时推理速度会变慢，LoRA 加载后可通过 `pipe.clear_lora()` 卸载。
+
+```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 = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
+image = pipe(prompt, seed=0, num_inference_steps=40)
+image.save("image.jpg")
+pipe.clear_lora()
+```

docs/zh/Pipeline_Usage/Model_Training.md

@@ -65,7 +65,7 @@ image_1.jpg,"a dog"
 image_2.jpg,"a cat"
 ```
 
-我们构建了样例数据集，以方便您进行测试。了解通用数据集架构是如何实现的，请参考 [`diffsynth.core.data`](/docs/zh/API_Reference/core/data.md)。
+我们构建了样例数据集，以方便您进行测试。了解通用数据集架构是如何实现的，请参考 [`diffsynth.core.data`](../API_Reference/core/data.md)。
 
 <details>
 
@@ -93,7 +93,7 @@ image_2.jpg,"a cat"
 
 ## 加载模型
 
-类似于[推理时的模型加载](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)，我们支持多种方式配置模型路径，两种方式是可以混用的。
+类似于[推理时的模型加载](../Pipeline_Usage/Model_Inference.md#加载模型)，我们支持多种方式配置模型路径，两种方式是可以混用的。
 
 <details>
 
@@ -115,9 +115,9 @@ image_2.jpg,"a cat"
 > --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"
 > ```
 > 
-> 模型文件默认下载到 `./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。
+> 模型文件默认下载到 `./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`。
 
 </details>
 
@@ -235,11 +235,11 @@ accelerate launch --config_file examples/qwen_image/model_training/full/accelera
 
 ## 训练注意事项
 
-* 数据集的元数据除 `csv` 格式外，还支持 `json`、`jsonl` 格式，关于如何选择最佳的元数据格式，请参考[](/docs/zh/API_Reference/core/data.md#元数据)
+* 数据集的元数据除 `csv` 格式外，还支持 `json`、`jsonl` 格式，关于如何选择最佳的元数据格式，请参考[](../API_Reference/core/data.md#元数据)
 * 通常训练效果与训练步数强相关，与 epoch 数量弱相关，因此我们更推荐使用参数 `--save_steps` 按训练步数间隔来保存模型文件。
 * 当数据量 * `dataset_repeat` 超过 $10^9$ 时，我们观测到数据集的速度明显变慢，这似乎是 `PyTorch` 的 bug，我们尚不确定新版本的 `PyTorch` 是否已经修复了这一问题。
 * 学习率 `--learning_rate` 在 LoRA 训练中建议设置为 `1e-4`，在全量训练中建议设置为 `1e-5`。
-* 训练框架不支持 batch size > 1，原因是复杂的，详见 [Q&A: 为什么训练框架不支持 batch size > 1？](/docs/zh/QA.md#为什么训练框架不支持-batch-size--1)
+* 训练框架不支持 batch size > 1，原因是复杂的，详见 [Q&A: 为什么训练框架不支持 batch size > 1？](../QA.md#为什么训练框架不支持-batch-size--1)
 * 少数模型包含冗余参数，例如 Qwen-Image 的 DiT 部分最后一层的文本编码部分，在训练这些模型时，需设置 `--find_unused_parameters` 避免在多 GPU 训练中报错。出于对开源社区模型兼容性的考虑，我们不打算删除这些冗余参数。
 * Diffusion 模型的损失函数值与实际效果的关系不大，因此我们在训练过程中不会记录损失函数值。我们建议把 `--num_epochs` 设置为足够大的数值，边训边测，直至效果收敛后手动关闭训练程序。
-* `--use_gradient_checkpointing` 通常是开启的，除非 GPU 显存足够；`--use_gradient_checkpointing_offload` 则按需开启，详见 [`diffsynth.core.gradient`](/docs/zh/API_Reference/core/gradient.md)。
+* `--use_gradient_checkpointing` 通常是开启的，除非 GPU 显存足够；`--use_gradient_checkpointing_offload` 则按需开启，详见 [`diffsynth.core.gradient`](../API_Reference/core/gradient.md)。

docs/zh/Pipeline_Usage/Setup.md

@@ -37,11 +37,11 @@ pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6
    git clone https://github.com/modelscope/DiffSynth-Studio.git
    cd DiffSynth-Studio
    # aarch64/ARM
-   pip install -e .[npu_aarch64] --extra-index-url "https://download.pytorch.org/whl/cpu"
+   pip install -e .[npu_aarch64] 
    # x86
-   pip install -e .[npu]
+   pip install -e .[npu] --extra-index-url "https://download.pytorch.org/whl/cpu"
 
-使用 Ascend NPU 时，请将 Python 代码中的 `"cuda"` 改为 `"npu"`，详见[NPU 支持](/docs/zh/Pipeline_Usage/GPU_support.md#ascend-npu)。
+使用 Ascend NPU 时，请将 Python 代码中的 `"cuda"` 改为 `"npu"`，详见[NPU 支持](../Pipeline_Usage/GPU_support.md#ascend-npu)。
 
 ## 其他安装问题
 

docs/zh/Pipeline_Usage/VRAM_management.md

@@ -140,7 +140,7 @@ image.save("image.jpg")
 
 在更为极端的情况下，当内存也不足以存储整个模型时，Disk Offload 功能可以让模型参数惰性加载，即，模型中的每个 Layer 仅在调用 forward 时才会从硬盘中读取相应的参数。启用这一功能时，我们建议使用高速的 SSD 硬盘。
 
-Disk Offload 是极为特殊的显存管理方案，只支持 `.safetensors` 格式文件，不支持 `.bin`、`.pth`、`.ckpt` 等二进制文件，不支持带 Tensor reshape 的 [state dict converter](/docs/zh/Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换)。
+Disk Offload 是极为特殊的显存管理方案，只支持 `.safetensors` 格式文件，不支持 `.bin`、`.pth`、`.ckpt` 等二进制文件，不支持带 Tensor reshape 的 [state dict converter](../Developer_Guide/Integrating_Your_Model.md#step-2-模型文件格式转换)。
 
 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
@@ -196,7 +196,7 @@ vram_config = {
 * Preparing：Onload 和 Computation 的中间状态，在显存允许的前提下的暂存状态，这个状态由显存管理机制控制切换，当且仅当【vram_limit 设置为无限制】或【vram_limit 已设置且有空余显存】时会进入这一状态
 * Computation：模型正在计算过程中，这个状态由显存管理机制控制切换，仅在 `forward` 中临时进入
 
-如果你是模型开发者，希望自行控制某个模型的显存管理粒度，请参考[../Developer_Guide/Enabling_VRAM_management.md](/docs/zh/Developer_Guide/Enabling_VRAM_management.md)。
+如果你是模型开发者，希望自行控制某个模型的显存管理粒度，请参考[../Developer_Guide/Enabling_VRAM_management.md](../Developer_Guide/Enabling_VRAM_management.md)。
 
 ## 最佳实践
 

docs/zh/QA.md

@@ -26,3 +26,10 @@
 * 此外，使用原生 FP8 精度训练的模型，在推理时若没有 Hopper 架构 GPU，则只能以 BF16 精度进行计算，理论上其生成效果反而不如 FP8。
 
 因此，原生 FP8 精度训练技术是极不成熟的，我们静观开源社区的技术发展。
+
+## 如何在推理时动态加载 LoRA 模型？
+
+我们支持 LoRA 模型的两种加载方式，详见[LoRA 加载](./Pipeline_Usage/Model_Inference.md#加载-lora)：
+
+* 冷加载：当基础模型未开启[显存管理](./Pipeline_Usage/VRAM_management.md)时，LoRA 会融合进基础模型权重，此时推理速度没有变化，LoRA 加载后无法卸载。
+* 热加载：当基础模型开启[显存管理](./Pipeline_Usage/VRAM_management.md)时，LoRA 不会融合进基础模型权重，此时推理速度会变慢，LoRA 加载后可通过 `pipe.clear_lora()` 卸载。

docs/zh/README.md

@@ -26,58 +26,60 @@ graph LR;
 
 本节介绍 `DiffSynth-Studio` 的基本使用方式，包括如何启用显存管理从而在极低显存的 GPU 上进行推理，以及如何训练任意基础模型、LoRA、ControlNet 等模型。
 
-* [安装依赖](/docs/zh/Pipeline_Usage/Setup.md)
+* [安装依赖](./Pipeline_Usage/Setup.md)
-* [模型推理](/docs/zh/Pipeline_Usage/Model_Inference.md)
+* [模型推理](./Pipeline_Usage/Model_Inference.md)
-* [显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)
+* [显存管理](./Pipeline_Usage/VRAM_management.md)
-* [模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)
+* [模型训练](./Pipeline_Usage/Model_Training.md)
-* [环境变量](/docs/zh/Pipeline_Usage/Environment_Variables.md)
+* [环境变量](./Pipeline_Usage/Environment_Variables.md)
-* [GPU/NPU 支持](/docs/zh/Pipeline_Usage/GPU_support.md)
+* [GPU/NPU 支持](./Pipeline_Usage/GPU_support.md)
 
 ## Section 2: 模型详解
 
 本节介绍 `DiffSynth-Studio` 所支持的 Diffusion 模型，部分模型 Pipeline 具备可控生成、并行加速等特色功能。
 
-* [FLUX.1](/docs/zh/Model_Details/FLUX.md)
+* [FLUX.1](./Model_Details/FLUX.md)
-* [Wan](/docs/zh/Model_Details/Wan.md)
+* [Wan](./Model_Details/Wan.md)
-* [Qwen-Image](/docs/zh/Model_Details/Qwen-Image.md)
+* [Qwen-Image](./Model_Details/Qwen-Image.md)
-* [FLUX.2](/docs/zh/Model_Details/FLUX2.md)
+* [FLUX.2](./Model_Details/FLUX2.md)
-* [Z-Image](/docs/zh/Model_Details/Z-Image.md)
+* [Z-Image](./Model_Details/Z-Image.md)
+* [Anima](./Model_Details/Anima.md)
+* [LTX-2](./Model_Details/LTX-2.md)
 
 ## Section 3: 训练框架
 
 本节介绍 `DiffSynth-Studio` 中训练框架的设计思路，帮助开发者理解 Diffusion 模型训练算法的原理。
 
-* [Diffusion 模型基本原理](/docs/zh/Training/Understanding_Diffusion_models.md)
+* [Diffusion 模型基本原理](./Training/Understanding_Diffusion_models.md)
-* [标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)
+* [标准监督训练](./Training/Supervised_Fine_Tuning.md)
-* [在训练中启用 FP8 精度](/docs/zh/Training/FP8_Precision.md)
+* [在训练中启用 FP8 精度](./Training/FP8_Precision.md)
-* [端到端的蒸馏加速训练](/docs/zh/Training/Direct_Distill.md)
+* [端到端的蒸馏加速训练](./Training/Direct_Distill.md)
-* [两阶段拆分训练](/docs/zh/Training/Split_Training.md)
+* [两阶段拆分训练](./Training/Split_Training.md)
-* [差分 LoRA 训练](/docs/zh/Training/Differential_LoRA.md)
+* [差分 LoRA 训练](./Training/Differential_LoRA.md)
 
 ## Section 4: 模型接入
 
 本节介绍如何将模型接入 `DiffSynth-Studio` 从而使用框架基础功能，帮助开发者为本项目提供新模型的支持，或进行私有化模型的推理和训练。
 
-* [接入模型结构](/docs/zh/Developer_Guide/Integrating_Your_Model.md)
+* [接入模型结构](./Developer_Guide/Integrating_Your_Model.md)
-* [接入 Pipeline](/docs/zh/Developer_Guide/Building_a_Pipeline.md)
+* [接入 Pipeline](./Developer_Guide/Building_a_Pipeline.md)
-* [接入细粒度显存管理](/docs/zh/Developer_Guide/Enabling_VRAM_management.md)
+* [接入细粒度显存管理](./Developer_Guide/Enabling_VRAM_management.md)
-* [接入模型训练](/docs/zh/Developer_Guide/Training_Diffusion_Models.md)
+* [接入模型训练](./Developer_Guide/Training_Diffusion_Models.md)
 
 ## Section 5: API 参考
 
 本节介绍 `DiffSynth-Studio` 中的独立核心模块 `diffsynth.core`，介绍内部的功能是如何设计和运作的，开发者如有需要，可将其中的功能模块用于其他代码库的开发中。
 
-* [`diffsynth.core.attention`](/docs/zh/API_Reference/core/attention.md): 注意力机制实现
+* [`diffsynth.core.attention`](./API_Reference/core/attention.md): 注意力机制实现
-* [`diffsynth.core.data`](/docs/zh/API_Reference/core/data.md): 数据处理算子与通用数据集
+* [`diffsynth.core.data`](./API_Reference/core/data.md): 数据处理算子与通用数据集
-* [`diffsynth.core.gradient`](/docs/zh/API_Reference/core/gradient.md): 梯度检查点
+* [`diffsynth.core.gradient`](./API_Reference/core/gradient.md): 梯度检查点
-* [`diffsynth.core.loader`](/docs/zh/API_Reference/core/loader.md): 模型下载与加载
+* [`diffsynth.core.loader`](./API_Reference/core/loader.md): 模型下载与加载
-* [`diffsynth.core.vram`](/docs/zh/API_Reference/core/vram.md): 显存管理
+* [`diffsynth.core.vram`](./API_Reference/core/vram.md): 显存管理
 
 ## Section 6: 学术导引
 
 本节介绍如何利用 `DiffSynth-Studio` 训练新的模型，帮助科研工作者探索新的模型技术。
 
-* [从零开始训练模型](/docs/zh/Research_Tutorial/train_from_scratch.md)
+* [从零开始训练模型](./Research_Tutorial/train_from_scratch.md)
 * 推理改进优化技术【coming soon】
 * 设计可控生成模型【coming soon】
 * 创建新的训练范式【coming soon】
@@ -86,4 +88,4 @@ graph LR;
 
 本节总结了开发者常见的问题，如果你在使用和开发中遇到了问题，请参考本节内容，如果仍无法解决，请到 GitHub 上给我们提 issue。
 
-* [常见问题](/docs/zh/QA.md)
+* [常见问题](./QA.md)

docs/zh/Research_Tutorial/train_from_scratch.md

@@ -12,7 +12,7 @@ DiffSynth-Studio 的训练引擎支持从零开始训练基础模型，本文介
 * 文本张量（`prompt_embeds`）：文本的编码，由文本编码器产生
 * 时间步（`timestep`）：标量，用于标记当前处于 Diffusion 过程的哪个阶段
 
-模型的输出是与图像张量形状相同的张量，表示模型预测的去噪方向，关于 Diffusion 模型理论的细节，请参考 [Diffusion 模型基本原理](/docs/zh/Training/Understanding_Diffusion_models.md)。在本文中，我们构建一个仅含 0.1B 参数的 DiT 模型：`AAADiT`。
+模型的输出是与图像张量形状相同的张量，表示模型预测的去噪方向，关于 Diffusion 模型理论的细节，请参考 [Diffusion 模型基本原理](../Training/Understanding_Diffusion_models.md)。在本文中，我们构建一个仅含 0.1B 参数的 DiT 模型：`AAADiT`。
 
 <details>
 <summary>模型结构代码</summary>
@@ -137,11 +137,11 @@ class AAADiT(torch.nn.Module):
 * 文本编码器：用于将文本编码为张量。我们采用 [Qwen/Qwen3-0.6B](https://modelscope.cn/models/Qwen/Qwen3-0.6B) 模型。
 * VAE 编解码器：编码器部分用于将图像编码为张量，解码器部分用于将图像张量解码为图像。我们采用 [black-forest-labs/FLUX.2-klein-4B](https://modelscope.cn/models/black-forest-labs/FLUX.2-klein-4B) 中的 VAE 模型。
 
-这两个模型的结构都已集成在 DiffSynth-Studio 中，分别位于 [/diffsynth/models/z_image_text_encoder.py](/diffsynth/models/z_image_text_encoder.py) 和 [/diffsynth/models/flux2_vae.py](/diffsynth/models/flux2_vae.py)，因此我们不需要修改任何代码。
+这两个模型的结构都已集成在 DiffSynth-Studio 中，分别位于 [/diffsynth/models/z_image_text_encoder.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/z_image_text_encoder.py) 和 [/diffsynth/models/flux2_vae.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/diffsynth/models/flux2_vae.py)，因此我们不需要修改任何代码。
 
 ## 2. 构建 Pipeline
 
-我们在文档 [接入 Pipeline](/docs/zh/Developer_Guide/Building_a_Pipeline.md) 中介绍了如何构建一个模型 Pipeline，对于本文中的模型，我们也需要构建一个 Pipeline，连接文本编码器、Diffusion 模型、VAE 编解码器。
+我们在文档 [接入 Pipeline](../Developer_Guide/Building_a_Pipeline.md) 中介绍了如何构建一个模型 Pipeline，对于本文中的模型，我们也需要构建一个 Pipeline，连接文本编码器、Diffusion 模型、VAE 编解码器。
 
 <details>
 <summary>Pipeline 代码</summary>
@@ -328,7 +328,7 @@ def model_fn_aaa(
 
 ## 3. 准备数据集
 
-为了快速验证训练效果，我们使用数据集 [宝可梦-第一世代](https://modelscope.cn/datasets/DiffSynth-Studio/pokemon-gen1)，这个数据集转载自开源项目 [pokemon-dataset-zh](https://github.com/42arch/pokemon-dataset-zh)，包含从妙蛙种子到梦幻的 151 个第一世代宝可梦。如果你想使用其他数据集，请参考文档 [准备数据集](/docs/zh/Pipeline_Usage/Model_Training.md#准备数据集) 和 [`diffsynth.core.data`](/docs/zh/API_Reference/core/data.md)。
+为了快速验证训练效果，我们使用数据集 [宝可梦-第一世代](https://modelscope.cn/datasets/DiffSynth-Studio/pokemon-gen1)，这个数据集转载自开源项目 [pokemon-dataset-zh](https://github.com/42arch/pokemon-dataset-zh)，包含从妙蛙种子到梦幻的 151 个第一世代宝可梦。如果你想使用其他数据集，请参考文档 [准备数据集](../Pipeline_Usage/Model_Training.md#准备数据集) 和 [`diffsynth.core.data`](../API_Reference/core/data.md)。
 
 ```shell
 modelscope download --dataset DiffSynth-Studio/pokemon-gen1 --local_dir ./data
@@ -336,7 +336,7 @@ modelscope download --dataset DiffSynth-Studio/pokemon-gen1 --local_dir ./data
 
 ### 4. 开始训练
 
-训练过程可使用 Pipeline 快速实现，我们已将完整的代码放在 [/docs/zh/Research_Tutorial/train_from_scratch.py](/docs/zh/Research_Tutorial/train_from_scratch.py)，可直接通过 `python docs/zh/Research_Tutorial/train_from_scratch.py` 开始单 GPU 训练。
+训练过程可使用 Pipeline 快速实现，我们已将完整的代码放在 [../Research_Tutorial/train_from_scratch.py](https://github.com/modelscope/DiffSynth-Studio/blob/main/docs/zh/Research_Tutorial/train_from_scratch.py)，可直接通过 `python docs/zh/Research_Tutorial/train_from_scratch.py` 开始单 GPU 训练。
 
 如需开启多 GPU 并行训练，请运行 `accelerate config` 设置相关参数，然后使用命令 `accelerate launch docs/zh/Research_Tutorial/train_from_scratch.py` 开始训练。
 

docs/zh/Training/Differential_LoRA.md

@@ -8,8 +8,8 @@
 
 假设我们有两张内容相似的图像：图 1 和图 2。例如两张图中分别有一辆车，但图 1 中画面细节更少，图 2 中画面细节更多。在差分 LoRA 训练中，我们进行两步训练：
 
-* 以图 1 为训练数据，以[标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)的方式，训练 LoRA 1
+* 以图 1 为训练数据，以[标准监督训练](../Training/Supervised_Fine_Tuning.md)的方式，训练 LoRA 1
-* 以图 2 为训练数据，将 LoRA 1 融入基础模型后，以[标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)的方式，训练 LoRA 2
+* 以图 2 为训练数据，将 LoRA 1 融入基础模型后，以[标准监督训练](../Training/Supervised_Fine_Tuning.md)的方式，训练 LoRA 2
 
 在第一步训练中，由于训练数据仅有一张图，LoRA 模型很容易过拟合，因此训练完成后，LoRA 1 会让模型毫不犹豫地生成图 1，无论随机种子是什么。在第二步训练中，LoRA 模型再次过拟合，因此训练完成后，在 LoRA 1 和 LoRA 2 的共同作用下，模型会毫不犹豫地生成图 2。简言之：
 

docs/zh/Training/Direct_Distill.md

@@ -44,7 +44,7 @@ loss = torch.nn.functional.mse_loss(image_1, image_2)
 
 ## 在训练框架中使用蒸馏加速训练
 
-首先，需要生成训练数据，请参考[模型推理](/docs/zh/Pipeline_Usage/Model_Inference.md)部分编写推理代码，以足够多的推理步数生成训练数据。
+首先，需要生成训练数据，请参考[模型推理](../Pipeline_Usage/Model_Inference.md)部分编写推理代码，以足够多的推理步数生成训练数据。
 
 以 Qwen-Image 为例，以下代码可以生成一张图片：
 
@@ -67,7 +67,7 @@ image = pipe(prompt, seed=0, num_inference_steps=40)
 image.save("image.jpg")
 ```
 
-然后，我们把必要的信息编写成[元数据文件](/docs/zh/API_Reference/core/data.md#元数据)：
+然后，我们把必要的信息编写成[元数据文件](../API_Reference/core/data.md#元数据)：
 
 ```csv
 image,prompt,seed,rand_device,num_inference_steps,cfg_scale
@@ -86,11 +86,11 @@ modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir
 bash examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh
 ```
 
-请注意，在[训练脚本参数](/docs/zh/Pipeline_Usage/Model_Training.md#脚本参数)中，数据集的图像分辨率设置要避免触发缩放处理。当设定 `--height` 和 `--width` 以启用固定分辨率时，所有训练数据必须是以完全一致的宽高生成的；当设定 `--max_pixels` 以启用动态分辨率时，`--max_pixels` 的数值必须大于或等于任一训练图像的像素面积。
+请注意，在[训练脚本参数](../Pipeline_Usage/Model_Training.md#脚本参数)中，数据集的图像分辨率设置要避免触发缩放处理。当设定 `--height` 和 `--width` 以启用固定分辨率时，所有训练数据必须是以完全一致的宽高生成的；当设定 `--max_pixels` 以启用动态分辨率时，`--max_pixels` 的数值必须大于或等于任一训练图像的像素面积。
 
 ## 训练框架设计思路
 
-直接蒸馏与[标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)相比，仅训练的损失函数不同，直接蒸馏的损失函数是 `diffsynth.diffusion.loss` 中的 `DirectDistillLoss`。
+直接蒸馏与[标准监督训练](../Training/Supervised_Fine_Tuning.md)相比，仅训练的损失函数不同，直接蒸馏的损失函数是 `diffsynth.diffusion.loss` 中的 `DirectDistillLoss`。
 
 ## 未来工作
 

docs/zh/Training/FP8_Precision.md

@@ -1,12 +1,12 @@
 # 在训练中启用 FP8 精度
 
-尽管 `DiffSynth-Studio` 在模型推理中支持[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)，但其中的大部分减少显存占用的技术不适合用于训练中，Offload 会导致极为缓慢的训练过程。
+尽管 `DiffSynth-Studio` 在模型推理中支持[显存管理](../Pipeline_Usage/VRAM_management.md)，但其中的大部分减少显存占用的技术不适合用于训练中，Offload 会导致极为缓慢的训练过程。
 
-FP8 精度是唯一可在训练过程中启用的显存管理策略，但本框架目前不支持原生 FP8 精度训练，原因详见 [Q&A: 为什么训练框架不支持原生 FP8 精度训练？](/docs/zh/QA.md#为什么训练框架不支持原生-fp8-精度训练)，仅支持将参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）以 FP8 精度进行存储。
+FP8 精度是唯一可在训练过程中启用的显存管理策略，但本框架目前不支持原生 FP8 精度训练，原因详见 [Q&A: 为什么训练框架不支持原生 FP8 精度训练？](../QA.md#为什么训练框架不支持原生-fp8-精度训练)，仅支持将参数不被梯度更新的模型（不需要梯度回传，或梯度仅更新其 LoRA）以 FP8 精度进行存储。
 
 ## 启用 FP8
 
-在我们提供的训练脚本中，通过参数 `--fp8_models` 即可快速设置以 FP8 精度存储的模型。以 Qwen-Image 的 LoRA 训练为例，我们提供了启用 FP8 训练的脚本，位于 [`/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh`](/examples/qwen_image/model_training/special/fp8_training/Qwen-Image-LoRA.sh)。训练完成后，可通过脚本 [`/examples/qwen_image/model_training/special/fp8_training/validate.py`](/examples/qwen_image/model_training/special/fp8_training/validate.py) 验证训练效果。
+在我们提供的训练脚本中，通过参数 `--fp8_models` 即可快速设置以 FP8 精度存储的模型。以 Qwen-Image 的 LoRA 训练为例，我们提供了启用 FP8 训练的脚本，位于 [`/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)。训练完成后，可通过脚本 [`/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) 验证训练效果。
 
 请注意，这种 FP8 显存管理策略不支持梯度更新，当某个模型被设置为可训练时，不能为这个模型开启 FP8 精度，支持开启 FP8 的模型包括两类：
 

docs/zh/Training/Split_Training.md

@@ -8,7 +8,7 @@
 
 在大部分模型的训练过程中，大量计算发生在“前处理”中，即“与去噪模型无关的计算”，包括 VAE 编码、文本编码等。当对应的模型参数固定时，这部分计算的结果是重复的，在多个 epoch 中每个数据样本的计算结果完全相同，因此我们提供了“拆分训练”功能，该功能可以自动分析并拆分训练过程。
 
-对于普通文生图模型的标准监督训练，拆分过程是非常简单的，只需要把所有 [`Pipeline Units`](/docs/zh/Developer_Guide/Building_a_Pipeline.md#units) 的计算拆分到第一阶段，将计算结果存储到硬盘中，然后在第二阶段从硬盘中读取这些结果并进行后续计算即可。但如果前处理过程中需要梯度回传，情况就变得极其复杂，为此，我们引入了一个计算图拆分算法用于分析如何拆分计算。
+对于普通文生图模型的标准监督训练，拆分过程是非常简单的，只需要把所有 [`Pipeline Units`](../Developer_Guide/Building_a_Pipeline.md#units) 的计算拆分到第一阶段，将计算结果存储到硬盘中，然后在第二阶段从硬盘中读取这些结果并进行后续计算即可。但如果前处理过程中需要梯度回传，情况就变得极其复杂，为此，我们引入了一个计算图拆分算法用于分析如何拆分计算。
 
 ## 计算图拆分算法
 
@@ -16,7 +16,7 @@
 
 ## 使用拆分训练
 
-拆分训练已支持[标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)和[直接蒸馏训练](/docs/zh/Training/Direct_Distill.md)，在训练命令中通过 `--task` 参数控制，以 Qwen-Image 模型的 LoRA 训练为例，拆分前的训练命令为：
+拆分训练已支持[标准监督训练](../Training/Supervised_Fine_Tuning.md)和[直接蒸馏训练](../Training/Direct_Distill.md)，在训练命令中通过 `--task` 参数控制，以 Qwen-Image 模型的 LoRA 训练为例，拆分前的训练命令为：
 
 ```shell
 accelerate launch examples/qwen_image/model_training/train.py \

docs/zh/Training/Supervised_Fine_Tuning.md

@@ -1,10 +1,10 @@
 # 标准监督训练
 
-在理解 [Diffusion 模型基本原理](/docs/zh/Training/Understanding_Diffusion_models.md)之后，本文档介绍框架如何实现 Diffusion 模型的训练。本文档介绍框架的原理，帮助开发者编写新的训练代码，如需使用我们提供的默认训练功能，请参考[模型训练](/docs/zh/Pipeline_Usage/Model_Training.md)。
+在理解 [Diffusion 模型基本原理](../Training/Understanding_Diffusion_models.md)之后，本文档介绍框架如何实现 Diffusion 模型的训练。本文档介绍框架的原理，帮助开发者编写新的训练代码，如需使用我们提供的默认训练功能，请参考[模型训练](../Pipeline_Usage/Model_Training.md)。
 
 回顾前文中的模型训练伪代码，当我们实际编写代码时，情况会变得极为复杂。部分模型需要输入额外的引导条件并进行预处理，例如 ControlNet；部分模型需要与去噪模型进行交叉式的计算，例如 VACE；部分模型因显存需求过大，需要开启 Gradient Checkpointing，例如 Qwen-Image 的 DiT。
 
-为了实现严格的推理和训练一致性，我们对 `Pipeline` 等组件进行了抽象封装，在训练过程中大量复用推理代码。请参考[接入 Pipeline](/docs/zh/Developer_Guide/Building_a_Pipeline.md) 了解 `Pipeline` 组件的设计。接下来我们介绍训练框架如何利用 `Pipeline` 组件构建训练算法。
+为了实现严格的推理和训练一致性，我们对 `Pipeline` 等组件进行了抽象封装，在训练过程中大量复用推理代码。请参考[接入 Pipeline](../Developer_Guide/Building_a_Pipeline.md) 了解 `Pipeline` 组件的设计。接下来我们介绍训练框架如何利用 `Pipeline` 组件构建训练算法。
 
 ## 框架设计思路
 
@@ -48,13 +48,13 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
         )
 ```
 
-加载模型的逻辑与推理时基本一致，支持从远程和本地路径加载模型，详见[模型推理](/docs/zh/Pipeline_Usage/Model_Inference.md)，但请注意不要启用[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)。
+加载模型的逻辑与推理时基本一致，支持从远程和本地路径加载模型，详见[模型推理](../Pipeline_Usage/Model_Inference.md)，但请注意不要启用[显存管理](../Pipeline_Usage/VRAM_management.md)。
 
 `switch_pipe_to_training_mode` 可以将模型切换到训练模式，详见 `switch_pipe_to_training_mode`。
 
 ### `forward`
 
-在 `forward` 中需计算损失函数值，先进行前处理，然后经过 `Pipeline` 的 [`model_fn`](/docs/zh/Developer_Guide/Building_a_Pipeline.md#model_fn) 计算损失函数。
+在 `forward` 中需计算损失函数值，先进行前处理，然后经过 `Pipeline` 的 [`model_fn`](../Developer_Guide/Building_a_Pipeline.md#model_fn) 计算损失函数。
 
 ```python
     def forward(self, data):
@@ -90,7 +90,7 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
 训练框架还需其他模块，包括：
 
 * accelerator: `accelerate` 提供的训练启动器，详见 [`accelerate`](https://huggingface.co/docs/accelerate/index)
-* dataset: 通用数据集，详见 [`diffsynth.core.data`](/docs/zh/API_Reference/core/data.md)
+* dataset: 通用数据集，详见 [`diffsynth.core.data`](../API_Reference/core/data.md)
 * model_logger: 模型记录器，详见 `diffsynth.diffusion.logger`
 
 ```python

docs/zh/Training/Understanding_Diffusion_models.md

@@ -43,6 +43,7 @@ Diffusion 模型通过多步迭代式地去噪（denoise）生成清晰的图像
 而模型的输出 $\hat \epsilon(x_t,c,t)$，则近似地等于 $x_T-x_0$，也就是整个扩散过程（去噪过程的反向过程）的方向。
 
 接下来我们分析一步迭代中发生的计算，在时间步 $t$，模型通过计算得到近似的 $x_T-x_0$ 后，我们计算下一步的 $x_{t-1}$：
+
 $$
 \begin{aligned}
 x_{t-1}&=x_t + (\sigma_{t-1} - \sigma_t) \cdot \hat \epsilon(x_t,c,t)\\
@@ -51,6 +52,7 @@ x_{t-1}&=x_t + (\sigma_{t-1} - \sigma_t) \cdot \hat \epsilon(x_t,c,t)\\
 &=(1-\sigma_{t-1})x_0+\sigma_{t-1}x_T
 \end{aligned}
 $$
+
 完美！与时间步 $t-1$ 时的噪声含量定义完美契合。
 
 > （这部分可能有点难懂，请不必担心，首次阅读本文时建议跳过这部分，不影响后文的阅读。）
@@ -136,4 +138,4 @@ $$
 
 ## 本项目如何封装和实现模型训练？
 
-请阅读下一文档：[标准监督训练](/docs/zh/Training/Supervised_Fine_Tuning.md)
+请阅读下一文档：[标准监督训练](../Training/Supervised_Fine_Tuning.md)

docs/zh/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 = 'zh_CN'
+
+
+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',
+]