Merge pull request #899 from modelscope/version_update_1.1.8

Update setup.py
2026-03-19 06:48:12 +00:00 · 2025-09-09 18:43:03 +08:00 · 2025-09-09 18:42:39 +08:00 · 2025-09-09 16:16:32 +08:00 · 2025-09-09 16:12:31 +08:00 · 2025-09-04 15:53:52 +08:00
98 changed files with 4710 additions and 280 deletions
--- a/README.md
+++ b/README.md
@@ -64,6 +64,7 @@ Details: [./examples/qwen_image/](./examples/qwen_image/)

 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from PIL import Image
 import torch

 pipe = QwenImagePipeline.from_pretrained(
@@ -77,7 +78,10 @@ pipe = QwenImagePipeline.from_pretrained(
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
 )
 prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful."
-image = pipe(prompt, seed=0, num_inference_steps=40)
+image = pipe(
+    prompt, seed=0, num_inference_steps=40,
+    # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit
+)
 image.save("image.jpg")
 ```

@@ -87,11 +91,20 @@ image.save("image.jpg")

 <summary>Model Overview</summary>

-|Model ID|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_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)|
-|[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_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](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_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training|
+|-|-|-|-|-|-|-|
+|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
+|[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-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py)|
+|[DiffSynth-Studio/Qwen-Image-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-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
+|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
+|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|
+
 </details>

 ### FLUX Series
@@ -192,9 +205,13 @@ save_video(video, "video1.mp4", fps=15, quality=5)

 | Model ID | Extra Parameters | Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
 |-|-|-|-|-|-|-|
+|[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)|-|-|-|-|
 |[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-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-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)|
+|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)|
+|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)|
+|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
 |[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
 |[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
 |[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
@@ -363,6 +380,29 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-44


 ## Update History
+
+- **September 9, 2025**: Our training framework now supports multiple training modes and has been adapted for Qwen-Image. In addition to the standard SFT training mode, Direct Distill is now also supported; please refer to [our example code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh). This feature is experimental, and we will continue to improve it to support comprehensive model training capabilities.
+
+- **August 28, 2025** We support Wan2.2-S2V, an audio-driven cinematic video generation model open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/).
+
+- **August 21, 2025**: [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) is released! Compared to the V1 version, the training dataset has been updated to the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset), enabling generated images to better align with the inherent image distribution and style of Qwen-Image. Please refer to [our sample code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py).
+
+- **August 21, 2025**: We open-sourced the [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) structure control LoRA model. Following "In Context" routine, it supports various types of structural control conditions, including canny, depth, lineart, softedge, normal, and openpose. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py).
+
+- **August 20, 2025** We open-sourced [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix), which improves the editing performance of Qwen-Image-Edit on low-resolution image inputs. Please refer to [our example code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py).
+
+- **August 19, 2025** 🔥 Qwen-Image-Edit is now open source. Welcome the new member to the image editing model family!
+
+- **August 18, 2025** We trained and open-sourced the Inpaint ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py).
+
+- **August 15, 2025** We open-sourced the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset). This is an image dataset generated using the Qwen-Image model, with a total of 160,000 `1024 x 1024` images. It includes the general, English text rendering, and Chinese text rendering subsets. We provide caption, entity and control images annotations for each image. Developers can use this dataset to train models such as ControlNet and EliGen for the Qwen-Image model. We aim to promote technological development through open-source contributions!
+
+- **August 13, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py).
+
+- **August 12, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py).
+
+- **August 11, 2025** We released another distilled acceleration model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA). It uses the same training process as [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), but the model structure is changed to LoRA. This makes it work better with other open-source models.
+
 - **August 7, 2025** We open-sourced the entity control LoRA of Qwen-Image, [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen). Qwen-Image-EliGen is able to achieve entity-level controlled text-to-image generation. See the [paper](https://arxiv.org/abs/2501.01097) for technical details. Training dataset: [EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet).

 - **August 5, 2025** We open-sourced the distilled acceleration model of Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), achieving approximately 5x speedup.
--- a/README_zh.md
+++ b/README_zh.md
@@ -66,6 +66,7 @@ DiffSynth-Studio 为主流 Diffusion 模型（包括 FLUX、Wan 等）重新设

 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from PIL import Image
 import torch

 pipe = QwenImagePipeline.from_pretrained(
@@ -79,7 +80,10 @@ pipe = QwenImagePipeline.from_pretrained(
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
 )
 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 = pipe(
+    prompt, seed=0, num_inference_steps=40,
+    # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit
+)
 image.save("image.jpg")
 ```

@@ -89,11 +93,19 @@ image.save("image.jpg")

 <summary>模型总览</summary>

-|模型 ID|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_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)|
-|[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_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](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_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)|
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)|
+|[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-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py)|
+|[DiffSynth-Studio/Qwen-Image-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-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|
+|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)|
+|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|

 </details>

@@ -193,9 +205,13 @@ save_video(video, "video1.mp4", fps=15, quality=5)

 |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/Wan2.2-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)|-|-|-|-|
 |[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-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-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)|
+|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)|
+|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)|
+|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
 |[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)|
 |[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)|
 |[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
@@ -380,6 +396,29 @@ https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-44


 ## 更新历史
+
+- **2025年9月9日** 我们的训练框架支持了多种训练模式，目前已适配 Qwen-Image，除标准 SFT 训练模式外，已支持 Direct Distill，请参考[我们的示例代码](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)。这项功能是实验性的，我们将会继续完善已支持更全面的模型训练功能。
+
+- **2025年8月28日** 我们支持了Wan2.2-S2V，一个音频驱动的电影级视频生成模型。请参见[./examples/wanvideo/](./examples/wanvideo/)。
+
+- **2025年8月21日** [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) 发布！相比于 V1 版本，训练数据集变为 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset)，因此，生成的图像更符合 Qwen-Image 本身的图像分布和风格。 请参考[我们的示例代码](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)。
+
+- **2025年8月21日** 我们开源了 [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) 结构控制 LoRA 模型，采用 In Context 的技术路线，支持多种类别的结构控制条件，包括 canny, depth, lineart, softedge, normal, openpose。 请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)。
+
+- **2025年8月20日** 我们开源了 [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) 模型，提升了 Qwen-Image-Edit 对低分辨率图像输入的编辑效果。请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)
+
+- **2025年8月19日** 🔥 Qwen-Image-Edit 开源，欢迎图像编辑模型新成员！
+
+- **2025年8月18日** 我们训练并开源了 Qwen-Image 的图像重绘 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)。
+
+- **2025年8月15日** 我们开源了 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset) 数据集。这是一个使用 Qwen-Image 模型生成的图像数据集，共包含 160,000 张`1024 x 1024`图像。它包括通用、英文文本渲染和中文文本渲染子集。我们为每张图像提供了图像描述、实体和结构控制图像的标注。开发者可以使用这个数据集来训练 Qwen-Image 模型的 ControlNet 和 EliGen 等模型，我们旨在通过开源推动技术发展！
+
+- **2025年8月13日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)。
+
+- **2025年8月12日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)。
+
+- **2025年8月11日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)，沿用了与 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) 相同的训练流程，但模型结构修改为了 LoRA，因此能够更好地与其他开源生态模型兼容。
+
 - **2025年8月7日** 我们开源了 Qwen-Image 的实体控制 LoRA 模型 [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)。Qwen-Image-EliGen 能够实现实体级可控的文生图。技术细节请参见[论文](https://arxiv.org/abs/2501.01097)。训练数据集：[EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)。

 - **2025年8月5日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)，实现了约 5 倍加速。
--- a/diffsynth/configs/model_config.py
+++ b/diffsynth/configs/model_config.py
@@ -56,11 +56,13 @@ from ..models.stepvideo_vae import StepVideoVAE
 from ..models.stepvideo_dit import StepVideoModel

 from ..models.wan_video_dit import WanModel
+from ..models.wan_video_dit_s2v import WanS2VModel
 from ..models.wan_video_text_encoder import WanTextEncoder
 from ..models.wan_video_image_encoder import WanImageEncoder
 from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38
 from ..models.wan_video_motion_controller import WanMotionControllerModel
 from ..models.wan_video_vace import VaceWanModel
+from ..models.wav2vec import WanS2VAudioEncoder

 from ..models.step1x_connector import Qwen2Connector

@@ -75,6 +77,7 @@ from ..models.nexus_gen import NexusGenAutoregressiveModel
 from ..models.qwen_image_dit import QwenImageDiT
 from ..models.qwen_image_text_encoder import QwenImageTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
+from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet

 model_loader_configs = [
    # These configs are provided for detecting model type automatically.
@@ -149,9 +152,12 @@ model_loader_configs = [
    (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), 
    (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
    (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "2267d489f0ceb9f21836532952852ee5", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"),
    (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
    (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
    (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),
+    (None, "966cffdcc52f9c46c391768b27637614", ["wan_video_dit"], [WanS2VModel], "civitai"),
    (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"),
    (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"),
    (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"),
@@ -167,6 +173,9 @@ model_loader_configs = [
    (None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"),
    (None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"),
    (None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"),
+    (None, "073bce9cf969e317e5662cd570c3e79c", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
+    (None, "a9e54e480a628f0b956a688a81c33bab", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"),
+    (None, "06be60f3a4526586d8431cd038a71486", ["wans2v_audio_encoder"], [WanS2VAudioEncoder], "civitai"),
 ]
 huggingface_model_loader_configs = [
    # These configs are provided for detecting model type automatically.
--- a/diffsynth/data/init.py
+++ b/diffsynth/data/init.py
@@ -1 +1 @@
-from .video import VideoData, save_video, save_frames
+from .video import VideoData, save_video, save_frames, merge_video_audio, save_video_with_audio
--- a/diffsynth/data/video.py
+++ b/diffsynth/data/video.py
@@ -2,6 +2,8 @@ import imageio, os
 import numpy as np
 from PIL import Image
 from tqdm import tqdm
+import subprocess
+import shutil


 class LowMemoryVideo:
@@ -146,3 +148,70 @@ def save_frames(frames, save_path):
    os.makedirs(save_path, exist_ok=True)
    for i, frame in enumerate(tqdm(frames, desc="Saving images")):
        frame.save(os.path.join(save_path, f"{i}.png"))
+
+
+def merge_video_audio(video_path: str, audio_path: str):
+    # TODO: may need a in-python implementation to avoid subprocess dependency
+    """
+    Merge the video and audio into a new video, with the duration set to the shorter of the two,
+    and overwrite the original video file.
+
+    Parameters:
+    video_path (str): Path to the original video file
+    audio_path (str): Path to the audio file
+    """
+
+    # check
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"video file {video_path} does not exist")
+    if not os.path.exists(audio_path):
+        raise FileNotFoundError(f"audio file {audio_path} does not exist")
+
+    base, ext = os.path.splitext(video_path)
+    temp_output = f"{base}_temp{ext}"
+
+    try:
+        # create ffmpeg command
+        command = [
+            'ffmpeg',
+            '-y',  # overwrite
+            '-i',
+            video_path,
+            '-i',
+            audio_path,
+            '-c:v',
+            'copy',  # copy video stream
+            '-c:a',
+            'aac',  # use AAC audio encoder
+            '-b:a',
+            '192k',  # set audio bitrate (optional)
+            '-map',
+            '0:v:0',  # select the first video stream
+            '-map',
+            '1:a:0',  # select the first audio stream
+            '-shortest',  # choose the shortest duration
+            temp_output
+        ]
+
+        # execute the command
+        result = subprocess.run(
+            command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+
+        # check result
+        if result.returncode != 0:
+            error_msg = f"FFmpeg execute failed: {result.stderr}"
+            print(error_msg)
+            raise RuntimeError(error_msg)
+
+        shutil.move(temp_output, video_path)
+        print(f"Merge completed, saved to {video_path}")
+
+    except Exception as e:
+        if os.path.exists(temp_output):
+            os.remove(temp_output)
+        print(f"merge_video_audio failed with error: {e}")
+
+
+def save_video_with_audio(frames, save_path, audio_path, fps=16, quality=9, ffmpeg_params=None):
+    save_video(frames, save_path, fps, quality, ffmpeg_params)
+    merge_video_audio(save_path, audio_path)
--- a/diffsynth/models/qwen_image_controlnet.py
+++ b/diffsynth/models/qwen_image_controlnet.py
@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+from .sd3_dit import RMSNorm
+from .utils import hash_state_dict_keys
+
+
+class BlockWiseControlBlock(torch.nn.Module):
+    # [linear, gelu, linear]
+    def __init__(self, dim: int = 3072):
+        super().__init__()
+        self.x_rms = RMSNorm(dim, eps=1e-6)
+        self.y_rms = RMSNorm(dim, eps=1e-6)
+        self.input_proj = nn.Linear(dim, dim)
+        self.act = nn.GELU()
+        self.output_proj = nn.Linear(dim, dim)
+
+    def forward(self, x, y):
+        x, y = self.x_rms(x), self.y_rms(y)
+        x = self.input_proj(x + y)
+        x = self.act(x)
+        x = self.output_proj(x)
+        return x
+
+    def init_weights(self):
+        # zero initialize output_proj
+        nn.init.zeros_(self.output_proj.weight)
+        nn.init.zeros_(self.output_proj.bias)
+
+
+class QwenImageBlockWiseControlNet(torch.nn.Module):
+    def __init__(
+        self,
+        num_layers: int = 60,
+        in_dim: int = 64,
+        additional_in_dim: int = 0,
+        dim: int = 3072,
+    ):
+        super().__init__()
+        self.img_in = nn.Linear(in_dim + additional_in_dim, dim)
+        self.controlnet_blocks = nn.ModuleList(
+            [
+                BlockWiseControlBlock(dim)
+                for _ in range(num_layers)
+            ]
+        )
+
+    def init_weight(self):
+        nn.init.zeros_(self.img_in.weight)
+        nn.init.zeros_(self.img_in.bias)
+        for block in self.controlnet_blocks:
+            block.init_weights()
+
+    def process_controlnet_conditioning(self, controlnet_conditioning):
+        return self.img_in(controlnet_conditioning)
+
+    def blockwise_forward(self, img, controlnet_conditioning, block_id):
+        return self.controlnet_blocks[block_id](img, controlnet_conditioning)
+
+    @staticmethod
+    def state_dict_converter():
+        return QwenImageBlockWiseControlNetStateDictConverter()
+
+
+class QwenImageBlockWiseControlNetStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        extra_kwargs = {}
+        if hash_value == "a9e54e480a628f0b956a688a81c33bab":
+            # inpaint controlnet
+            extra_kwargs = {"additional_in_dim": 4}
+        return state_dict, extra_kwargs
--- a/diffsynth/models/qwen_image_dit.py
+++ b/diffsynth/models/qwen_image_dit.py
@@ -63,8 +63,8 @@ class QwenEmbedRope(nn.Module):
        super().__init__()
        self.theta = theta
        self.axes_dim = axes_dim
-        pos_index = torch.arange(1024)
-        neg_index = torch.arange(1024).flip(0) * -1 - 1
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
        self.pos_freqs = torch.cat([
            self.rope_params(pos_index, self.axes_dim[0], self.theta),
            self.rope_params(pos_index, self.axes_dim[1], self.theta),
@@ -90,55 +90,139 @@ class QwenEmbedRope(nn.Module):
        )
        freqs = torch.polar(torch.ones_like(freqs), freqs)
        return freqs
-    
-    def forward(self, video_fhw, txt_seq_lens, device):
-        if self.pos_freqs.device != device:
-            self.pos_freqs = self.pos_freqs.to(device)
-            self.neg_freqs = self.neg_freqs.to(device)

+
+    def _expand_pos_freqs_if_needed(self, video_fhw, txt_seq_lens):
        if isinstance(video_fhw, list):
-            video_fhw = video_fhw[0]
-        frame, height, width = video_fhw
-        rope_key = f"{frame}_{height}_{width}"
-
-        if rope_key not in self.rope_cache:
-            seq_lens = frame * height * width
-            freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
-            freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
-            freqs_frame = freqs_pos[0][:frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
-            if self.scale_rope:
-                freqs_height = torch.cat(
-                    [
-                        freqs_neg[1][-(height - height//2):],
-                        freqs_pos[1][:height//2]
-                    ], 
-                    dim=0
-                )
-                freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
-                freqs_width = torch.cat(
-                    [
-                        freqs_neg[2][-(width - width//2):],
-                        freqs_pos[2][:width//2]
-                    ], 
-                    dim=0
-                )
-                freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
-                
-            else:
-                freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
-                freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
-            
-            freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
-            self.rope_cache[rope_key] = freqs.clone().contiguous()
-        vid_freqs = self.rope_cache[rope_key]
-
+            video_fhw = tuple(max([i[j] for i in video_fhw]) for j in range(3))
+        _, height, width = video_fhw
        if self.scale_rope:
            max_vid_index = max(height // 2, width // 2)
        else:
            max_vid_index = max(height, width)
+        required_len = max_vid_index + max(txt_seq_lens)
+        cur_max_len = self.pos_freqs.shape[0]
+        if required_len <= cur_max_len:
+            return
+
+        new_max_len = math.ceil(required_len / 512) * 512
+        pos_index = torch.arange(new_max_len)
+        neg_index = torch.arange(new_max_len).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat([
+            self.rope_params(pos_index, self.axes_dim[0], self.theta),
+            self.rope_params(pos_index, self.axes_dim[1], self.theta),
+            self.rope_params(pos_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        self.neg_freqs = torch.cat([
+            self.rope_params(neg_index, self.axes_dim[0], self.theta),
+            self.rope_params(neg_index, self.axes_dim[1], self.theta),
+            self.rope_params(neg_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        return
+
+
+    def forward(self, video_fhw, txt_seq_lens, device):
+        self._expand_pos_freqs_if_needed(video_fhw, txt_seq_lens)
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+
+            if rope_key not in self.rope_cache:
+                seq_lens = frame * height * width
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                if self.scale_rope:
+                    freqs_height = torch.cat(
+                        [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0
+                    )
+                    freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+                    freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                else:
+                    freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+                self.rope_cache[rope_key] = freqs.clone().contiguous()
+            vid_freqs.append(self.rope_cache[rope_key])
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)

        max_len = max(txt_seq_lens)
-        txt_freqs = self.pos_freqs[max_vid_index: max_vid_index + max_len, ...]
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+
+    def forward_sampling(self, video_fhw, txt_seq_lens, device):
+        self._expand_pos_freqs_if_needed(video_fhw, txt_seq_lens)
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+            if idx > 0 and f"{0}_{height}_{width}" not in self.rope_cache:
+                frame_0, height_0, width_0 = video_fhw[0]
+
+                rope_key_0 = f"0_{height_0}_{width_0}"
+                spatial_freqs_0 = self.rope_cache[rope_key_0].reshape(frame_0, height_0, width_0, -1)
+                h_indices = torch.linspace(0, height_0 - 1, height).long()
+                w_indices = torch.linspace(0, width_0 - 1, width).long()
+                h_grid, w_grid = torch.meshgrid(h_indices, w_indices, indexing='ij')
+                sampled_rope = spatial_freqs_0[:, h_grid, w_grid, :]
+
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                sampled_rope[:, :, :, :freqs_frame.shape[-1]] = freqs_frame
+
+                seq_lens = frame * height * width
+                self.rope_cache[rope_key] = sampled_rope.reshape(seq_lens, -1).clone()
+            if rope_key not in self.rope_cache:
+                seq_lens = frame * height * width
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                if self.scale_rope:
+                    freqs_height = torch.cat(
+                        [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0
+                    )
+                    freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+                    freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                else:
+                    freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+                self.rope_cache[rope_key] = freqs.clone()
+            vid_freqs.append(self.rope_cache[rope_key].contiguous())
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
        return vid_freqs, txt_freqs


@@ -383,6 +467,7 @@ class QwenImageDiT(torch.nn.Module):
        image_start = sum(seq_lens)
        image_end = total_seq_len
        cumsum = [0]
+        single_image_seq = image_end - image_start
        for length in seq_lens:
            cumsum.append(cumsum[-1] + length)
        for i in range(N):
@@ -390,6 +475,9 @@ class QwenImageDiT(torch.nn.Module):
            prompt_end = cumsum[i+1]
            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
            image_mask = image_mask.unsqueeze(1).repeat(1, seq_lens[i], 1)
+            # repeat image mask to match the single image sequence length
+            repeat_time = single_image_seq // image_mask.shape[-1]
+            image_mask = image_mask.repeat(1, 1, repeat_time)
            # prompt update with image
            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
            # image update with prompt
@@ -409,7 +497,8 @@ class QwenImageDiT(torch.nn.Module):
        attention_mask = attention_mask.to(device=latents.device, dtype=latents.dtype).unsqueeze(1)

        return all_prompt_emb, image_rotary_emb, attention_mask
-        
+
+
    def forward(
        self,
        latents=None,
@@ -422,7 +511,7 @@ class QwenImageDiT(torch.nn.Module):
        img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
        txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
        
-        image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (P Q C)", H=height//16, W=width//16, P=2, Q=2)
+        image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
        image = self.img_in(image)
        text = self.txt_in(self.txt_norm(prompt_emb))

@@ -441,7 +530,7 @@ class QwenImageDiT(torch.nn.Module):
        image = self.norm_out(image, conditioning)
        image = self.proj_out(image)
        
-        latents = rearrange(image, "B (H W) (P Q C) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
+        latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
        return image
    
    @staticmethod
--- a/diffsynth/models/wan_video_camera_controller.py
+++ b/diffsynth/models/wan_video_camera_controller.py
@@ -182,7 +182,7 @@ def process_pose_file(cam_params, width=672, height=384, original_pose_width=128


 def generate_camera_coordinates(
-    direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"],
+    direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown", "In", "Out"],
    length: int,
    speed: float = 1/54,
    origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
@@ -198,5 +198,9 @@ def generate_camera_coordinates(
            coor[13] += speed
        if "Down" in direction:
            coor[13] -= speed
+        if "In" in direction:
+            coor[18] -= speed
+        if "Out" in direction:
+            coor[18] += speed
        coordinates.append(coor)
    return coordinates
--- a/diffsynth/models/wan_video_dit.py
+++ b/diffsynth/models/wan_video_dit.py
@@ -294,6 +294,7 @@ class WanModel(torch.nn.Module):
    ):
        super().__init__()
        self.dim = dim
+        self.in_dim = in_dim
        self.freq_dim = freq_dim
        self.has_image_input = has_image_input
        self.patch_size = patch_size
@@ -713,6 +714,42 @@ class WanModelStateDictConverter:
                "eps": 1e-6,
                "require_clip_embedding": False,
            }
+        elif hash_state_dict_keys(state_dict) == "2267d489f0ceb9f21836532952852ee5":
+            # Wan2.2-Fun-A14B-Control
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 52,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": True,
+                "require_clip_embedding": False,
+            }
+        elif hash_state_dict_keys(state_dict) == "47dbeab5e560db3180adf51dc0232fb1":
+            # Wan2.2-Fun-A14B-Control-Camera
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": False,
+                "add_control_adapter": True,
+                "in_dim_control_adapter": 24,
+                "require_clip_embedding": False,
+            }
        else:
            config = {}
        return state_dict, config
--- a/diffsynth/models/wan_video_dit_s2v.py
+++ b/diffsynth/models/wan_video_dit_s2v.py
@@ -0,0 +1,625 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Tuple
+from .utils import hash_state_dict_keys
+from .wan_video_dit import rearrange, precompute_freqs_cis_3d, DiTBlock, Head, CrossAttention, modulate, sinusoidal_embedding_1d
+
+
+def torch_dfs(model: nn.Module, parent_name='root'):
+    module_names, modules = [], []
+    current_name = parent_name if parent_name else 'root'
+    module_names.append(current_name)
+    modules.append(model)
+
+    for name, child in model.named_children():
+        if parent_name:
+            child_name = f'{parent_name}.{name}'
+        else:
+            child_name = name
+        child_modules, child_names = torch_dfs(child, child_name)
+        module_names += child_names
+        modules += child_modules
+    return modules, module_names
+
+
+def rope_precompute(x, grid_sizes, freqs, start=None):
+    b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2
+
+    # split freqs
+    if type(freqs) is list:
+        trainable_freqs = freqs[1]
+        freqs = freqs[0]
+    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
+
+    # loop over samples
+    output = torch.view_as_complex(x.detach().reshape(b, s, n, -1, 2).to(torch.float64))
+    seq_bucket = [0]
+    if not type(grid_sizes) is list:
+        grid_sizes = [grid_sizes]
+    for g in grid_sizes:
+        if not type(g) is list:
+            g = [torch.zeros_like(g), g]
+        batch_size = g[0].shape[0]
+        for i in range(batch_size):
+            if start is None:
+                f_o, h_o, w_o = g[0][i]
+            else:
+                f_o, h_o, w_o = start[i]
+
+            f, h, w = g[1][i]
+            t_f, t_h, t_w = g[2][i]
+            seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o
+            seq_len = int(seq_f * seq_h * seq_w)
+            if seq_len > 0:
+                if t_f > 0:
+                    factor_f, factor_h, factor_w = (t_f / seq_f).item(), (t_h / seq_h).item(), (t_w / seq_w).item()
+                    # Generate a list of seq_f integers starting from f_o and ending at math.ceil(factor_f * seq_f.item() + f_o.item())
+                    if f_o >= 0:
+                        f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1, seq_f).astype(int).tolist()
+                    else:
+                        f_sam = np.linspace(-f_o.item(), (-t_f - f_o).item() + 1, seq_f).astype(int).tolist()
+                    h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1, seq_h).astype(int).tolist()
+                    w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1, seq_w).astype(int).tolist()
+
+                    assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0
+                    freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][f_sam].conj()
+                    freqs_0 = freqs_0.view(seq_f, 1, 1, -1)
+
+                    freqs_i = torch.cat(
+                        [
+                            freqs_0.expand(seq_f, seq_h, seq_w, -1),
+                            freqs[1][h_sam].view(1, seq_h, 1, -1).expand(seq_f, seq_h, seq_w, -1),
+                            freqs[2][w_sam].view(1, 1, seq_w, -1).expand(seq_f, seq_h, seq_w, -1),
+                        ],
+                        dim=-1
+                    ).reshape(seq_len, 1, -1)
+                elif t_f < 0:
+                    freqs_i = trainable_freqs.unsqueeze(1)
+                # apply rotary embedding
+                output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = freqs_i
+        seq_bucket.append(seq_bucket[-1] + seq_len)
+    return output
+
+
+class CausalConv1d(nn.Module):
+
+    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode='replicate', **kwargs):
+        super().__init__()
+
+        self.pad_mode = pad_mode
+        padding = (kernel_size - 1, 0)  # T
+        self.time_causal_padding = padding
+
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class MotionEncoder_tc(nn.Module):
+
+    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, need_global=True, dtype=None, device=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.need_global = need_global
+        self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1)
+        if need_global:
+            self.conv1_global = CausalConv1d(in_dim, hidden_dim // 4, 3, stride=1)
+        self.norm1 = nn.LayerNorm(hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.act = nn.SiLU()
+        self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2)
+        self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2)
+
+        if need_global:
+            self.final_linear = nn.Linear(hidden_dim, hidden_dim, **factory_kwargs)
+
+        self.norm1 = nn.LayerNorm(hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_dim // 2, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.norm3 = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim))
+
+    def forward(self, x):
+        x = rearrange(x, 'b t c -> b c t')
+        x_ori = x.clone()
+        b, c, t = x.shape
+        x = self.conv1_local(x)
+        x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads)
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv2(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv3(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm3(x)
+        x = self.act(x)
+        x = rearrange(x, '(b n) t c -> b t n c', b=b)
+        padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1).to(device=x.device, dtype=x.dtype)
+        x = torch.cat([x, padding], dim=-2)
+        x_local = x.clone()
+
+        if not self.need_global:
+            return x_local
+
+        x = self.conv1_global(x_ori)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv2(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv3(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm3(x)
+        x = self.act(x)
+        x = self.final_linear(x)
+        x = rearrange(x, '(b n) t c -> b t n c', b=b)
+
+        return x, x_local
+
+
+class FramePackMotioner(nn.Module):
+
+    def __init__(self, inner_dim=1024, num_heads=16, zip_frame_buckets=[1, 2, 16], drop_mode="drop", *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.proj = nn.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+        self.proj_2x = nn.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4))
+        self.proj_4x = nn.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8))
+        self.zip_frame_buckets = torch.tensor(zip_frame_buckets, dtype=torch.long)
+
+        self.inner_dim = inner_dim
+        self.num_heads = num_heads
+        self.freqs = torch.cat(precompute_freqs_cis_3d(inner_dim // num_heads), dim=1)
+        self.drop_mode = drop_mode
+
+    def forward(self, motion_latents, add_last_motion=2):
+        motion_frames = motion_latents[0].shape[1]
+        mot = []
+        mot_remb = []
+        for m in motion_latents:
+            lat_height, lat_width = m.shape[2], m.shape[3]
+            padd_lat = torch.zeros(16, self.zip_frame_buckets.sum(), lat_height, lat_width).to(device=m.device, dtype=m.dtype)
+            overlap_frame = min(padd_lat.shape[1], m.shape[1])
+            if overlap_frame > 0:
+                padd_lat[:, -overlap_frame:] = m[:, -overlap_frame:]
+
+            if add_last_motion < 2 and self.drop_mode != "drop":
+                zero_end_frame = self.zip_frame_buckets[:self.zip_frame_buckets.__len__() - add_last_motion - 1].sum()
+                padd_lat[:, -zero_end_frame:] = 0
+
+            padd_lat = padd_lat.unsqueeze(0)
+            clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -self.zip_frame_buckets.sum():, :, :].split(
+                list(self.zip_frame_buckets)[::-1], dim=2
+            )  # 16, 2 ,1
+
+            # patchfy
+            clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2)
+            clean_latents_2x = self.proj_2x(clean_latents_2x).flatten(2).transpose(1, 2)
+            clean_latents_4x = self.proj_4x(clean_latents_4x).flatten(2).transpose(1, 2)
+
+            if add_last_motion < 2 and self.drop_mode == "drop":
+                clean_latents_post = clean_latents_post[:, :0] if add_last_motion < 2 else clean_latents_post
+                clean_latents_2x = clean_latents_2x[:, :0] if add_last_motion < 1 else clean_latents_2x
+
+            motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1)
+
+            # rope
+            start_time_id = -(self.zip_frame_buckets[:1].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[0]
+            grid_sizes = [] if add_last_motion < 2 and self.drop_mode == "drop" else \
+                        [
+                            [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                            torch.tensor([end_time_id, lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1),
+                            torch.tensor([self.zip_frame_buckets[0], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ]
+                        ]
+
+            start_time_id = -(self.zip_frame_buckets[:2].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[1] // 2
+            grid_sizes_2x = [] if add_last_motion < 1 and self.drop_mode == "drop" else \
+            [
+                [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                torch.tensor([end_time_id, lat_height // 4, lat_width // 4]).unsqueeze(0).repeat(1, 1),
+                torch.tensor([self.zip_frame_buckets[1], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ]
+            ]
+
+            start_time_id = -(self.zip_frame_buckets[:3].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[2] // 4
+            grid_sizes_4x = [
+                [
+                    torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                    torch.tensor([end_time_id, lat_height // 8, lat_width // 8]).unsqueeze(0).repeat(1, 1),
+                    torch.tensor([self.zip_frame_buckets[2], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1),
+                ]
+            ]
+
+            grid_sizes = grid_sizes + grid_sizes_2x + grid_sizes_4x
+
+            motion_rope_emb = rope_precompute(
+                motion_lat.detach().view(1, motion_lat.shape[1], self.num_heads, self.inner_dim // self.num_heads),
+                grid_sizes,
+                self.freqs,
+                start=None
+            )
+
+            mot.append(motion_lat)
+            mot_remb.append(motion_rope_emb)
+        return mot, mot_remb
+
+
+class AdaLayerNorm(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        output_dim: int,
+        norm_eps: float = 1e-5,
+    ):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, elementwise_affine=False)
+
+    def forward(self, x, temb):
+        temb = self.linear(F.silu(temb))
+        shift, scale = temb.chunk(2, dim=1)
+        shift = shift[:, None, :]
+        scale = scale[:, None, :]
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class AudioInjector_WAN(nn.Module):
+
+    def __init__(
+        self,
+        all_modules,
+        all_modules_names,
+        dim=2048,
+        num_heads=32,
+        inject_layer=[0, 27],
+        enable_adain=False,
+        adain_dim=2048,
+    ):
+        super().__init__()
+        self.injected_block_id = {}
+        audio_injector_id = 0
+        for mod_name, mod in zip(all_modules_names, all_modules):
+            if isinstance(mod, DiTBlock):
+                for inject_id in inject_layer:
+                    if f'transformer_blocks.{inject_id}' in mod_name:
+                        self.injected_block_id[inject_id] = audio_injector_id
+                        audio_injector_id += 1
+
+        self.injector = nn.ModuleList([CrossAttention(
+            dim=dim,
+            num_heads=num_heads,
+        ) for _ in range(audio_injector_id)])
+        self.injector_pre_norm_feat = nn.ModuleList([nn.LayerNorm(
+            dim,
+            elementwise_affine=False,
+            eps=1e-6,
+        ) for _ in range(audio_injector_id)])
+        self.injector_pre_norm_vec = nn.ModuleList([nn.LayerNorm(
+            dim,
+            elementwise_affine=False,
+            eps=1e-6,
+        ) for _ in range(audio_injector_id)])
+        if enable_adain:
+            self.injector_adain_layers = nn.ModuleList([AdaLayerNorm(output_dim=dim * 2, embedding_dim=adain_dim) for _ in range(audio_injector_id)])
+
+
+class CausalAudioEncoder(nn.Module):
+
+    def __init__(self, dim=5120, num_layers=25, out_dim=2048, num_token=4, need_global=False):
+        super().__init__()
+        self.encoder = MotionEncoder_tc(in_dim=dim, hidden_dim=out_dim, num_heads=num_token, need_global=need_global)
+        weight = torch.ones((1, num_layers, 1, 1)) * 0.01
+
+        self.weights = torch.nn.Parameter(weight)
+        self.act = torch.nn.SiLU()
+
+    def forward(self, features):
+        # features B * num_layers * dim * video_length
+        weights = self.act(self.weights.to(device=features.device, dtype=features.dtype))
+        weights_sum = weights.sum(dim=1, keepdims=True)
+        weighted_feat = ((features * weights) / weights_sum).sum(dim=1)  # b dim f
+        weighted_feat = weighted_feat.permute(0, 2, 1)  # b f dim
+        res = self.encoder(weighted_feat)  # b f n dim
+        return res  # b f n dim
+
+
+class WanS2VDiTBlock(DiTBlock):
+
+    def forward(self, x, context, t_mod, seq_len_x, freqs):
+        t_mod = (self.modulation.unsqueeze(2).to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1)
+        # t_mod[:, :, 0] for x, t_mod[:, :, 1] for other like ref, motion, etc.
+        t_mod = [
+            torch.cat([element[:, :, 0].expand(1, seq_len_x, x.shape[-1]), element[:, :, 1].expand(1, x.shape[1] - seq_len_x, x.shape[-1])], dim=1)
+            for element in t_mod
+        ]
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = t_mod
+        input_x = modulate(self.norm1(x), shift_msa, scale_msa)
+        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
+        x = x + self.cross_attn(self.norm3(x), context)
+        input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
+        x = self.gate(x, gate_mlp, self.ffn(input_x))
+        return x
+
+
+class WanS2VModel(torch.nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        in_dim: int,
+        ffn_dim: int,
+        out_dim: int,
+        text_dim: int,
+        freq_dim: int,
+        eps: float,
+        patch_size: Tuple[int, int, int],
+        num_heads: int,
+        num_layers: int,
+        cond_dim: int,
+        audio_dim: int,
+        num_audio_token: int,
+        enable_adain: bool = True,
+        audio_inject_layers: list = [0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39],
+        zero_timestep: bool = True,
+        add_last_motion: bool = True,
+        framepack_drop_mode: str = "padd",
+        fuse_vae_embedding_in_latents: bool = True,
+        require_vae_embedding: bool = False,
+        seperated_timestep: bool = False,
+        require_clip_embedding: bool = False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.in_dim = in_dim
+        self.freq_dim = freq_dim
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.enbale_adain = enable_adain
+        self.add_last_motion = add_last_motion
+        self.zero_timestep = zero_timestep
+        self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
+        self.require_vae_embedding = require_vae_embedding
+        self.seperated_timestep = seperated_timestep
+        self.require_clip_embedding = require_clip_embedding
+
+        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.text_embedding = nn.Sequential(nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim))
+        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
+
+        self.blocks = nn.ModuleList([WanS2VDiTBlock(False, dim, num_heads, ffn_dim, eps) for _ in range(num_layers)])
+        self.head = Head(dim, out_dim, patch_size, eps)
+        self.freqs = torch.cat(precompute_freqs_cis_3d(dim // num_heads), dim=1)
+
+        self.cond_encoder = nn.Conv3d(cond_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.casual_audio_encoder = CausalAudioEncoder(dim=audio_dim, out_dim=dim, num_token=num_audio_token, need_global=enable_adain)
+        all_modules, all_modules_names = torch_dfs(self.blocks, parent_name="root.transformer_blocks")
+        self.audio_injector = AudioInjector_WAN(
+            all_modules,
+            all_modules_names,
+            dim=dim,
+            num_heads=num_heads,
+            inject_layer=audio_inject_layers,
+            enable_adain=enable_adain,
+            adain_dim=dim,
+        )
+        self.trainable_cond_mask = nn.Embedding(3, dim)
+        self.frame_packer = FramePackMotioner(inner_dim=dim, num_heads=num_heads, zip_frame_buckets=[1, 2, 16], drop_mode=framepack_drop_mode)
+
+    def patchify(self, x: torch.Tensor):
+        grid_size = x.shape[2:]
+        x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
+        return x, grid_size  # x, grid_size: (f, h, w)
+
+    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
+        return rearrange(
+            x,
+            'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
+            f=grid_size[0],
+            h=grid_size[1],
+            w=grid_size[2],
+            x=self.patch_size[0],
+            y=self.patch_size[1],
+            z=self.patch_size[2]
+        )
+
+    def process_motion_frame_pack(self, motion_latents, drop_motion_frames=False, add_last_motion=2):
+        flattern_mot, mot_remb = self.frame_packer(motion_latents, add_last_motion)
+        if drop_motion_frames:
+            return [m[:, :0] for m in flattern_mot], [m[:, :0] for m in mot_remb]
+        else:
+            return flattern_mot, mot_remb
+
+    def inject_motion(self, x, rope_embs, mask_input, motion_latents, drop_motion_frames=True, add_last_motion=2):
+        # inject the motion frames token to the hidden states
+        mot, mot_remb = self.process_motion_frame_pack(motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=add_last_motion)
+        if len(mot) > 0:
+            x = torch.cat([x, mot[0]], dim=1)
+            rope_embs = torch.cat([rope_embs, mot_remb[0]], dim=1)
+            mask_input = torch.cat(
+                [mask_input, 2 * torch.ones([1, x.shape[1] - mask_input.shape[1]], device=mask_input.device, dtype=mask_input.dtype)], dim=1
+            )
+        return x, rope_embs, mask_input
+
+    def after_transformer_block(self, block_idx, hidden_states, audio_emb_global, audio_emb, original_seq_len, use_unified_sequence_parallel=False):
+        if block_idx in self.audio_injector.injected_block_id.keys():
+            audio_attn_id = self.audio_injector.injected_block_id[block_idx]
+            num_frames = audio_emb.shape[1]
+            if use_unified_sequence_parallel:
+                from xfuser.core.distributed import get_sp_group
+                hidden_states = get_sp_group().all_gather(hidden_states, dim=1)
+
+            input_hidden_states = hidden_states[:, :original_seq_len].clone()  # b (f h w) c
+            input_hidden_states = rearrange(input_hidden_states, "b (t n) c -> (b t) n c", t=num_frames)
+
+            audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c")
+            adain_hidden_states = self.audio_injector.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0])
+            attn_hidden_states = adain_hidden_states
+
+            audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
+            attn_audio_emb = audio_emb
+            residual_out = self.audio_injector.injector[audio_attn_id](attn_hidden_states, attn_audio_emb)
+            residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames)
+            hidden_states[:, :original_seq_len] = hidden_states[:, :original_seq_len] + residual_out
+            if use_unified_sequence_parallel:
+                from xfuser.core.distributed import get_sequence_parallel_world_size, get_sequence_parallel_rank
+                hidden_states = torch.chunk(hidden_states, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
+        return hidden_states
+
+    def cal_audio_emb(self, audio_input, motion_frames=[73, 19]):
+        audio_input = torch.cat([audio_input[..., 0:1].repeat(1, 1, 1, motion_frames[0]), audio_input], dim=-1)
+        audio_emb_global, audio_emb = self.casual_audio_encoder(audio_input)
+        audio_emb_global = audio_emb_global[:, motion_frames[1]:].clone()
+        merged_audio_emb = audio_emb[:, motion_frames[1]:, :]
+        return audio_emb_global, merged_audio_emb
+
+    def get_grid_sizes(self, grid_size_x, grid_size_ref):
+        f, h, w = grid_size_x
+        rf, rh, rw = grid_size_ref
+        grid_sizes_x = torch.tensor([f, h, w], dtype=torch.long).unsqueeze(0)
+        grid_sizes_x = [[torch.zeros_like(grid_sizes_x), grid_sizes_x, grid_sizes_x]]
+        grid_sizes_ref = [[
+            torch.tensor([30, 0, 0]).unsqueeze(0),
+            torch.tensor([31, rh, rw]).unsqueeze(0),
+            torch.tensor([1, rh, rw]).unsqueeze(0),
+        ]]
+        return grid_sizes_x + grid_sizes_ref
+
+    def forward(
+        self,
+        latents,
+        timestep,
+        context,
+        audio_input,
+        motion_latents,
+        pose_cond,
+        use_gradient_checkpointing_offload=False,
+        use_gradient_checkpointing=False
+    ):
+        origin_ref_latents = latents[:, :, 0:1]
+        x = latents[:, :, 1:]
+
+        # context embedding
+        context = self.text_embedding(context)
+
+        # audio encode
+        audio_emb_global, merged_audio_emb = self.cal_audio_emb(audio_input)
+
+        # x and pose_cond
+        pose_cond = torch.zeros_like(x) if pose_cond is None else pose_cond
+        x, (f, h, w) = self.patchify(self.patch_embedding(x) + self.cond_encoder(pose_cond))  # torch.Size([1, 29120, 5120])
+        seq_len_x = x.shape[1]
+
+        # reference image
+        ref_latents, (rf, rh, rw) = self.patchify(self.patch_embedding(origin_ref_latents))  # torch.Size([1, 1456, 5120])
+        grid_sizes = self.get_grid_sizes((f, h, w), (rf, rh, rw))
+        x = torch.cat([x, ref_latents], dim=1)
+        # mask
+        mask = torch.cat([torch.zeros([1, seq_len_x]), torch.ones([1, ref_latents.shape[1]])], dim=1).to(torch.long).to(x.device)
+        # freqs
+        pre_compute_freqs = rope_precompute(
+            x.detach().view(1, x.size(1), self.num_heads, self.dim // self.num_heads), grid_sizes, self.freqs, start=None
+        )
+        # motion
+        x, pre_compute_freqs, mask = self.inject_motion(x, pre_compute_freqs, mask, motion_latents, add_last_motion=2)
+
+        x = x + self.trainable_cond_mask(mask).to(x.dtype)
+
+        # t_mod
+        timestep = torch.cat([timestep, torch.zeros([1], dtype=timestep.dtype, device=timestep.device)])
+        t = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, timestep))
+        t_mod = self.time_projection(t).unflatten(1, (6, self.dim)).unsqueeze(2).transpose(0, 2)
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+
+        for block_id, block in enumerate(self.blocks):
+            if use_gradient_checkpointing_offload:
+                with torch.autograd.graph.save_on_cpu():
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        x,
+                        context,
+                        t_mod,
+                        seq_len_x,
+                        pre_compute_freqs[0],
+                        use_reentrant=False,
+                    )
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
+                        x,
+                        use_reentrant=False,
+                    )
+            elif use_gradient_checkpointing:
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    x,
+                    context,
+                    t_mod,
+                    seq_len_x,
+                    pre_compute_freqs[0],
+                    use_reentrant=False,
+                )
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
+                    x,
+                    use_reentrant=False,
+                )
+            else:
+                x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
+                x = self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)
+
+        x = x[:, :seq_len_x]
+        x = self.head(x, t[:-1])
+        x = self.unpatchify(x, (f, h, w))
+        # make compatible with wan video
+        x = torch.cat([origin_ref_latents, x], dim=2)
+        return x
+
+    @staticmethod
+    def state_dict_converter():
+        return WanS2VModelStateDictConverter()
+
+
+class WanS2VModelStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        config = {}
+        if hash_state_dict_keys(state_dict) == "966cffdcc52f9c46c391768b27637614":
+            config = {
+                "dim": 5120,
+                "in_dim": 16,
+                "ffn_dim": 13824,
+                "out_dim": 16,
+                "text_dim": 4096,
+                "freq_dim": 256,
+                "eps": 1e-06,
+                "patch_size": (1, 2, 2),
+                "num_heads": 40,
+                "num_layers": 40,
+                "cond_dim": 16,
+                "audio_dim": 1024,
+                "num_audio_token": 4,
+            }
+        return state_dict, config
--- a/diffsynth/models/wan_video_vae.py
+++ b/diffsynth/models/wan_video_vae.py
@@ -1216,7 +1216,6 @@ class WanVideoVAE(nn.Module):


    def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
-
        videos = [video.to("cpu") for video in videos]
        hidden_states = []
        for video in videos:
@@ -1234,11 +1233,18 @@ class WanVideoVAE(nn.Module):


    def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
-        if tiled:
-            video = self.tiled_decode(hidden_states, device, tile_size, tile_stride)
-        else:
-            video = self.single_decode(hidden_states, device)
-        return video
+        hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states]
+        videos = []
+        for hidden_state in hidden_states:
+            hidden_state = hidden_state.unsqueeze(0)
+            if tiled:
+                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride)
+            else:
+                video = self.single_decode(hidden_state, device)
+            video = video.squeeze(0)
+            videos.append(video)
+        videos = torch.stack(videos)
+        return videos


    @staticmethod
--- a/diffsynth/models/wav2vec.py
+++ b/diffsynth/models/wav2vec.py
@@ -0,0 +1,204 @@
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def get_sample_indices(original_fps, total_frames, target_fps, num_sample, fixed_start=None):
+    required_duration = num_sample / target_fps
+    required_origin_frames = int(np.ceil(required_duration * original_fps))
+    if required_duration > total_frames / original_fps:
+        raise ValueError("required_duration must be less than video length")
+
+    if not fixed_start is None and fixed_start >= 0:
+        start_frame = fixed_start
+    else:
+        max_start = total_frames - required_origin_frames
+        if max_start < 0:
+            raise ValueError("video length is too short")
+        start_frame = np.random.randint(0, max_start + 1)
+    start_time = start_frame / original_fps
+
+    end_time = start_time + required_duration
+    time_points = np.linspace(start_time, end_time, num_sample, endpoint=False)
+
+    frame_indices = np.round(np.array(time_points) * original_fps).astype(int)
+    frame_indices = np.clip(frame_indices, 0, total_frames - 1)
+    return frame_indices
+
+
+def linear_interpolation(features, input_fps, output_fps, output_len=None):
+    """
+    features: shape=[1, T, 512]
+    input_fps: fps for audio, f_a
+    output_fps: fps for video, f_m
+    output_len: video length
+    """
+    features = features.transpose(1, 2)
+    seq_len = features.shape[2] / float(input_fps)
+    if output_len is None:
+        output_len = int(seq_len * output_fps)
+    output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear')  # [1, 512, output_len]
+    return output_features.transpose(1, 2)
+
+
+class WanS2VAudioEncoder(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        from transformers import Wav2Vec2ForCTC, Wav2Vec2Config
+        config = {
+            "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
+            "activation_dropout": 0.05,
+            "apply_spec_augment": True,
+            "architectures": ["Wav2Vec2ForCTC"],
+            "attention_dropout": 0.1,
+            "bos_token_id": 1,
+            "conv_bias": True,
+            "conv_dim": [512, 512, 512, 512, 512, 512, 512],
+            "conv_kernel": [10, 3, 3, 3, 3, 2, 2],
+            "conv_stride": [5, 2, 2, 2, 2, 2, 2],
+            "ctc_loss_reduction": "mean",
+            "ctc_zero_infinity": True,
+            "do_stable_layer_norm": True,
+            "eos_token_id": 2,
+            "feat_extract_activation": "gelu",
+            "feat_extract_dropout": 0.0,
+            "feat_extract_norm": "layer",
+            "feat_proj_dropout": 0.05,
+            "final_dropout": 0.0,
+            "hidden_act": "gelu",
+            "hidden_dropout": 0.05,
+            "hidden_size": 1024,
+            "initializer_range": 0.02,
+            "intermediate_size": 4096,
+            "layer_norm_eps": 1e-05,
+            "layerdrop": 0.05,
+            "mask_channel_length": 10,
+            "mask_channel_min_space": 1,
+            "mask_channel_other": 0.0,
+            "mask_channel_prob": 0.0,
+            "mask_channel_selection": "static",
+            "mask_feature_length": 10,
+            "mask_feature_prob": 0.0,
+            "mask_time_length": 10,
+            "mask_time_min_space": 1,
+            "mask_time_other": 0.0,
+            "mask_time_prob": 0.05,
+            "mask_time_selection": "static",
+            "model_type": "wav2vec2",
+            "num_attention_heads": 16,
+            "num_conv_pos_embedding_groups": 16,
+            "num_conv_pos_embeddings": 128,
+            "num_feat_extract_layers": 7,
+            "num_hidden_layers": 24,
+            "pad_token_id": 0,
+            "transformers_version": "4.7.0.dev0",
+            "vocab_size": 33
+        }
+        self.model = Wav2Vec2ForCTC(Wav2Vec2Config(**config))
+        self.video_rate = 30
+
+    def extract_audio_feat(self, input_audio, sample_rate, processor, return_all_layers=False, dtype=torch.float32, device='cpu'):
+        input_values = processor(input_audio, sampling_rate=sample_rate, return_tensors="pt").input_values.to(dtype=dtype, device=device)
+
+        # retrieve logits & take argmax
+        res = self.model(input_values, output_hidden_states=True)
+        if return_all_layers:
+            feat = torch.cat(res.hidden_states)
+        else:
+            feat = res.hidden_states[-1]
+        feat = linear_interpolation(feat, input_fps=50, output_fps=self.video_rate)
+        return feat
+
+    def get_audio_embed_bucket(self, audio_embed, stride=2, batch_frames=12, m=2):
+        num_layers, audio_frame_num, audio_dim = audio_embed.shape
+
+        if num_layers > 1:
+            return_all_layers = True
+        else:
+            return_all_layers = False
+
+        min_batch_num = int(audio_frame_num / (batch_frames * stride)) + 1
+
+        bucket_num = min_batch_num * batch_frames
+        batch_idx = [stride * i for i in range(bucket_num)]
+        batch_audio_eb = []
+        for bi in batch_idx:
+            if bi < audio_frame_num:
+                audio_sample_stride = 2
+                chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
+                chosen_idx = [0 if c < 0 else c for c in chosen_idx]
+                chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
+
+                if return_all_layers:
+                    frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
+                else:
+                    frame_audio_embed = audio_embed[0][chosen_idx].flatten()
+            else:
+                frame_audio_embed = \
+                torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
+                    else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
+            batch_audio_eb.append(frame_audio_embed)
+        batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
+
+        return batch_audio_eb, min_batch_num
+
+    def get_audio_embed_bucket_fps(self, audio_embed, fps=16, batch_frames=81, m=0):
+        num_layers, audio_frame_num, audio_dim = audio_embed.shape
+
+        if num_layers > 1:
+            return_all_layers = True
+        else:
+            return_all_layers = False
+
+        scale = self.video_rate / fps
+
+        min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1
+
+        bucket_num = min_batch_num * batch_frames
+        padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * self.video_rate) - audio_frame_num
+        batch_idx = get_sample_indices(
+            original_fps=self.video_rate, total_frames=audio_frame_num + padd_audio_num, target_fps=fps, num_sample=bucket_num, fixed_start=0
+        )
+        batch_audio_eb = []
+        audio_sample_stride = int(self.video_rate / fps)
+        for bi in batch_idx:
+            if bi < audio_frame_num:
+
+                chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
+                chosen_idx = [0 if c < 0 else c for c in chosen_idx]
+                chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
+
+                if return_all_layers:
+                    frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
+                else:
+                    frame_audio_embed = audio_embed[0][chosen_idx].flatten()
+            else:
+                frame_audio_embed = \
+                torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
+                    else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
+            batch_audio_eb.append(frame_audio_embed)
+        batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
+
+        return batch_audio_eb, min_batch_num
+
+    def get_audio_feats_per_inference(self, input_audio, sample_rate, processor, fps=16, batch_frames=80, m=0, dtype=torch.float32, device='cpu'):
+        audio_feat = self.extract_audio_feat(input_audio, sample_rate, processor, return_all_layers=True, dtype=dtype, device=device)
+        audio_embed_bucket, min_batch_num = self.get_audio_embed_bucket_fps(audio_feat, fps=fps, batch_frames=batch_frames, m=m)
+        audio_embed_bucket = audio_embed_bucket.unsqueeze(0).permute(0, 2, 3, 1).to(device, dtype)
+        audio_embeds = [audio_embed_bucket[..., i * batch_frames:(i + 1) * batch_frames] for i in range(min_batch_num)]
+        return audio_embeds
+
+    @staticmethod
+    def state_dict_converter():
+        return WanS2VAudioEncoderStateDictConverter()
+
+
+class WanS2VAudioEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict = {'model.' + k: v for k, v in state_dict.items()}
+        return state_dict
--- a/diffsynth/pipelines/qwen_image.py
+++ b/diffsynth/pipelines/qwen_image.py
@@ -4,18 +4,46 @@ from typing import Union
 from PIL import Image
 from tqdm import tqdm
 from einops import rearrange
+import numpy as np

 from ..models import ModelManager, load_state_dict
 from ..models.qwen_image_dit import QwenImageDiT
 from ..models.qwen_image_text_encoder import QwenImageTextEncoder
 from ..models.qwen_image_vae import QwenImageVAE
+from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet
 from ..schedulers import FlowMatchScheduler
 from ..utils import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit
 from ..lora import GeneralLoRALoader
+from .flux_image_new import ControlNetInput

 from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear


+class QwenImageBlockwiseMultiControlNet(torch.nn.Module):
+    def __init__(self, models: list[QwenImageBlockWiseControlNet]):
+        super().__init__()
+        if not isinstance(models, list):
+            models = [models]
+        self.models = torch.nn.ModuleList(models)
+
+    def preprocess(self, controlnet_inputs: list[ControlNetInput], conditionings: list[torch.Tensor], **kwargs):
+        processed_conditionings = []
+        for controlnet_input, conditioning in zip(controlnet_inputs, conditionings):
+            conditioning = rearrange(conditioning, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+            model_output = self.models[controlnet_input.controlnet_id].process_controlnet_conditioning(conditioning)
+            processed_conditionings.append(model_output)
+        return processed_conditionings
+
+    def blockwise_forward(self, image, conditionings: list[torch.Tensor], controlnet_inputs: list[ControlNetInput], progress_id, num_inference_steps, block_id, **kwargs):
+        res = 0
+        for controlnet_input, conditioning in zip(controlnet_inputs, conditionings):
+            progress = (num_inference_steps - 1 - progress_id) / max(num_inference_steps - 1, 1)
+            if progress > controlnet_input.start + (1e-4) or progress < controlnet_input.end - (1e-4):
+                continue
+            model_output = self.models[controlnet_input.controlnet_id].blockwise_forward(image, conditioning, block_id)
+            res = res + model_output * controlnet_input.scale
+        return res
+

 class QwenImagePipeline(BasePipeline):

@@ -24,29 +52,88 @@ class QwenImagePipeline(BasePipeline):
            device=device, torch_dtype=torch_dtype,
            height_division_factor=16, width_division_factor=16,
        )
-        from transformers import Qwen2Tokenizer
+        from transformers import Qwen2Tokenizer, Qwen2VLProcessor
        
        self.scheduler = FlowMatchScheduler(sigma_min=0, sigma_max=1, extra_one_step=True, exponential_shift=True, exponential_shift_mu=0.8, shift_terminal=0.02)
        self.text_encoder: QwenImageTextEncoder = None
        self.dit: QwenImageDiT = None
        self.vae: QwenImageVAE = None
+        self.blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None
        self.tokenizer: Qwen2Tokenizer = None
+        self.processor: Qwen2VLProcessor = None
        self.unit_runner = PipelineUnitRunner()
-        self.in_iteration_models = ("dit",)
+        self.in_iteration_models = ("dit", "blockwise_controlnet")
        self.units = [
            QwenImageUnit_ShapeChecker(),
            QwenImageUnit_NoiseInitializer(),
            QwenImageUnit_InputImageEmbedder(),
+            QwenImageUnit_Inpaint(),
+            QwenImageUnit_EditImageEmbedder(),
+            QwenImageUnit_ContextImageEmbedder(),
            QwenImageUnit_PromptEmbedder(),
            QwenImageUnit_EntityControl(),
+            QwenImageUnit_BlockwiseControlNet(),
        ]
        self.model_fn = model_fn_qwen_image
        
        
-    def load_lora(self, module, path, alpha=1):
-        loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device)
-        lora = load_state_dict(path, torch_dtype=self.torch_dtype, device=self.device)
-        loader.load(module, lora, alpha=alpha)
+    def load_lora(
+        self,
+        module: torch.nn.Module,
+        lora_config: Union[ModelConfig, str] = None,
+        alpha=1,
+        hotload=False,
+        state_dict=None,
+    ):
+        if state_dict is None:
+            if isinstance(lora_config, str):
+                lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device)
+            else:
+                lora_config.download_if_necessary()
+                lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device)
+        else:
+            lora = state_dict
+        if hotload:
+            for name, module in module.named_modules():
+                if isinstance(module, AutoWrappedLinear):
+                    lora_a_name = f'{name}.lora_A.default.weight'
+                    lora_b_name = f'{name}.lora_B.default.weight'
+                    if lora_a_name in lora and lora_b_name in lora:
+                        module.lora_A_weights.append(lora[lora_a_name] * alpha)
+                        module.lora_B_weights.append(lora[lora_b_name])
+        else:
+            loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device)
+            loader.load(module, lora, alpha=alpha)
+            
+            
+    def clear_lora(self):
+        for name, module in self.named_modules():
+            if isinstance(module, AutoWrappedLinear): 
+                if hasattr(module, "lora_A_weights"):
+                    module.lora_A_weights.clear()
+                if hasattr(module, "lora_B_weights"):
+                    module.lora_B_weights.clear()
+                    
+    
+    def enable_lora_magic(self):
+        if self.dit is not None:
+            if not (hasattr(self.dit, "vram_management_enabled") and self.dit.vram_management_enabled):
+                dtype = next(iter(self.dit.parameters())).dtype
+                enable_vram_management(
+                    self.dit,
+                    module_map = {
+                        torch.nn.Linear: AutoWrappedLinear,
+                    },
+                    module_config = dict(
+                        offload_dtype=dtype,
+                        offload_device=self.device,
+                        onload_dtype=dtype,
+                        onload_device=self.device,
+                        computation_dtype=self.torch_dtype,
+                        computation_device=self.device,
+                    ),
+                    vram_limit=None,
+                )
    
    
    def training_loss(self, **inputs):
@@ -63,6 +150,49 @@ class QwenImagePipeline(BasePipeline):
        return loss
    
    
+    def direct_distill_loss(self, **inputs):
+        self.scheduler.set_timesteps(inputs["num_inference_steps"])
+        models = {name: getattr(self, name) for name in self.in_iteration_models}
+        for progress_id, timestep in enumerate(self.scheduler.timesteps):
+            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
+            noise_pred = self.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id)
+            inputs["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs)
+        loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float())
+        return loss
+    
+    
+    def _enable_fp8_lora_training(self, dtype):
+        from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding
+        from ..models.qwen_image_dit import RMSNorm
+        from ..models.qwen_image_vae import QwenImageRMS_norm
+        module_map = {
+            RMSNorm: AutoWrappedModule,
+            torch.nn.Linear: AutoWrappedLinear,
+            torch.nn.Conv3d: AutoWrappedModule,
+            torch.nn.Conv2d: AutoWrappedModule,
+            torch.nn.Embedding: AutoWrappedModule,
+            Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
+            Qwen2RMSNorm: AutoWrappedModule,
+            Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
+            Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
+            QwenImageRMS_norm: AutoWrappedModule,
+        }
+        model_config = dict(
+            offload_dtype=dtype,
+            offload_device="cuda",
+            onload_dtype=dtype,
+            onload_device="cuda",
+            computation_dtype=self.torch_dtype,
+            computation_device="cuda",
+        )
+        if self.text_encoder is not None:
+            enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
+        if self.dit is not None:
+            enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
+        if self.vae is not None:
+            enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
+    
+    
    def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, enable_dit_fp8_computation=False):
        self.vram_management_enabled = True
        if vram_limit is None:
@@ -70,7 +200,7 @@ class QwenImagePipeline(BasePipeline):
        vram_limit = vram_limit - vram_buffer
        
        if self.text_encoder is not None:
-            from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm
+            from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding
            dtype = next(iter(self.text_encoder.parameters())).dtype
            enable_vram_management(
                self.text_encoder,
@@ -79,6 +209,8 @@ class QwenImagePipeline(BasePipeline):
                    torch.nn.Embedding: AutoWrappedModule,
                    Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
                    Qwen2RMSNorm: AutoWrappedModule,
+                    Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
+                    Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
@@ -163,6 +295,23 @@ class QwenImagePipeline(BasePipeline):
                ),
                vram_limit=vram_limit,
            )
+        if self.blockwise_controlnet is not None:
+            enable_vram_management(
+                self.blockwise_controlnet,
+                module_map = {
+                    RMSNorm: AutoWrappedModule,
+                    torch.nn.Linear: AutoWrappedLinear,
+                },
+                module_config = dict(
+                    offload_dtype=dtype,
+                    offload_device="cpu",
+                    onload_dtype=dtype,
+                    onload_device=device,
+                    computation_dtype=self.torch_dtype,
+                    computation_device=self.device,
+                ),
+                vram_limit=vram_limit,
+            )
    
    
    @staticmethod
@@ -171,6 +320,7 @@ class QwenImagePipeline(BasePipeline):
        device: Union[str, torch.device] = "cuda",
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+        processor_config: ModelConfig = None,
    ):
        # Download and load models
        model_manager = ModelManager()
@@ -187,10 +337,15 @@ class QwenImagePipeline(BasePipeline):
        pipe.text_encoder = model_manager.fetch_model("qwen_image_text_encoder")
        pipe.dit = model_manager.fetch_model("qwen_image_dit")
        pipe.vae = model_manager.fetch_model("qwen_image_vae")
+        pipe.blockwise_controlnet = QwenImageBlockwiseMultiControlNet(model_manager.fetch_model("qwen_image_blockwise_controlnet", index="all"))
        if tokenizer_config is not None and pipe.text_encoder is not None:
            tokenizer_config.download_if_necessary()
            from transformers import Qwen2Tokenizer
            pipe.tokenizer = Qwen2Tokenizer.from_pretrained(tokenizer_config.path)
+        if processor_config is not None:
+            processor_config.download_if_necessary()
+            from transformers import Qwen2VLProcessor
+            pipe.processor = Qwen2VLProcessor.from_pretrained(processor_config.path)
        return pipe
    
    
@@ -204,6 +359,10 @@ class QwenImagePipeline(BasePipeline):
        # Image
        input_image: Image.Image = None,
        denoising_strength: float = 1.0,
+        # Inpaint
+        inpaint_mask: Image.Image = None,
+        inpaint_blur_size: int = None,
+        inpaint_blur_sigma: float = None,
        # Shape
        height: int = 1328,
        width: int = 1328,
@@ -212,10 +371,18 @@ class QwenImagePipeline(BasePipeline):
        rand_device: str = "cpu",
        # Steps
        num_inference_steps: int = 30,
+        # Blockwise ControlNet
+        blockwise_controlnet_inputs: list[ControlNetInput] = None,
        # EliGen
        eligen_entity_prompts: list[str] = None,
        eligen_entity_masks: list[Image.Image] = None,
        eligen_enable_on_negative: bool = False,
+        # Qwen-Image-Edit
+        edit_image: Image.Image = None,
+        edit_image_auto_resize: bool = True,
+        edit_rope_interpolation: bool = False,
+        # In-context control
+        context_image: Image.Image = None,
        # FP8
        enable_fp8_attention: bool = False,
        # Tile
@@ -238,11 +405,16 @@ class QwenImagePipeline(BasePipeline):
        inputs_shared = {
            "cfg_scale": cfg_scale,
            "input_image": input_image, "denoising_strength": denoising_strength,
+            "inpaint_mask": inpaint_mask, "inpaint_blur_size": inpaint_blur_size, "inpaint_blur_sigma": inpaint_blur_sigma,
            "height": height, "width": width,
            "seed": seed, "rand_device": rand_device,
            "enable_fp8_attention": enable_fp8_attention,
+            "num_inference_steps": num_inference_steps,
+            "blockwise_controlnet_inputs": blockwise_controlnet_inputs,
            "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
            "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative,
+            "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, 
+            "context_image": context_image,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -262,7 +434,7 @@ class QwenImagePipeline(BasePipeline):
                noise_pred = noise_pred_posi

            # Scheduler
-            inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"])
+            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
        
        # Decode
        self.load_models_to_device(['vae'])
@@ -311,16 +483,35 @@ class QwenImageUnit_InputImageEmbedder(PipelineUnit):
            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": None}
+            return {"latents": latents, "input_latents": input_latents}



+class QwenImageUnit_Inpaint(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("inpaint_mask", "height", "width", "inpaint_blur_size", "inpaint_blur_sigma"),
+        )
+
+    def process(self, pipe: QwenImagePipeline, inpaint_mask, height, width, inpaint_blur_size, inpaint_blur_sigma):
+        if inpaint_mask is None:
+            return {}
+        inpaint_mask = pipe.preprocess_image(inpaint_mask.convert("RGB").resize((width // 8, height // 8)), min_value=0, max_value=1)
+        inpaint_mask = inpaint_mask.mean(dim=1, keepdim=True)
+        if inpaint_blur_size is not None and inpaint_blur_sigma is not None:
+            from torchvision.transforms import GaussianBlur
+            blur = GaussianBlur(kernel_size=inpaint_blur_size * 2 + 1, sigma=inpaint_blur_sigma)
+            inpaint_mask = blur(inpaint_mask)
+        return {"inpaint_mask": inpaint_mask}
+
+
 class QwenImageUnit_PromptEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
            input_params_posi={"prompt": "prompt"},
            input_params_nega={"prompt": "negative_prompt"},
+            input_params=("edit_image",),
            onload_model_names=("text_encoder",)
        )
        
@@ -331,16 +522,35 @@ class QwenImageUnit_PromptEmbedder(PipelineUnit):
        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
        return split_result

-    def process(self, pipe: QwenImagePipeline, prompt) -> dict:
+    def process(self, pipe: QwenImagePipeline, prompt, edit_image=None) -> dict:
        if pipe.text_encoder is not None:
            prompt = [prompt]
-            template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
-            drop_idx = 34
+            # If edit_image is None, use the default template for Qwen-Image, otherwise use the template for Qwen-Image-Edit
+            if edit_image is None:
+                template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+                drop_idx = 34
+            else:
+                template =  "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+                drop_idx = 64
            txt = [template.format(e) for e in prompt]
-            txt_tokens = pipe.tokenizer(txt, max_length=1024+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
-            hidden_states = pipe.text_encoder(input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True,)[-1]
-            
-            split_hidden_states = self.extract_masked_hidden(hidden_states, txt_tokens.attention_mask)
+
+            # Qwen-Image-Edit model
+            if pipe.processor is not None:
+                model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device)
+            # Qwen-Image model
+            elif pipe.tokenizer is not None:
+                model_inputs = pipe.tokenizer(txt, max_length=4096+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device)
+                if model_inputs.input_ids.shape[1] >= 1024:
+                    print(f"Warning!!! QwenImage model was trained on prompts up to 512 tokens. Current prompt requires {model_inputs['input_ids'].shape[1] - drop_idx} tokens, which may lead to unpredictable behavior.")
+            else:
+                assert False, "QwenImagePipeline requires either tokenizer or processor to be loaded."
+
+            if 'pixel_values' in model_inputs:
+                hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, pixel_values=model_inputs.pixel_values, image_grid_thw=model_inputs.image_grid_thw, output_hidden_states=True,)[-1]
+            else:
+                hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, output_hidden_states=True,)[-1]
+
+            split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask)
            split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
            attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
            max_seq_len = max([e.size(0) for e in split_hidden_states])
@@ -356,7 +566,7 @@ class QwenImageUnit_EntityControl(PipelineUnit):
    def __init__(self):
        super().__init__(
            take_over=True,
-            onload_model_names=("text_encoder")
+            onload_model_names=("text_encoder",)
        )

    def extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
@@ -431,20 +641,118 @@ class QwenImageUnit_EntityControl(PipelineUnit):
        return inputs_shared, inputs_posi, inputs_nega


+
+class QwenImageUnit_BlockwiseControlNet(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("blockwise_controlnet_inputs", "tiled", "tile_size", "tile_stride"),
+            onload_model_names=("vae",)
+        )
+
+    def apply_controlnet_mask_on_latents(self, pipe, latents, mask):
+        mask = (pipe.preprocess_image(mask) + 1) / 2
+        mask = mask.mean(dim=1, keepdim=True)
+        mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:])
+        latents = torch.concat([latents, mask], dim=1)
+        return latents
+
+    def apply_controlnet_mask_on_image(self, pipe, image, mask):
+        mask = mask.resize(image.size)
+        mask = pipe.preprocess_image(mask).mean(dim=[0, 1]).cpu()
+        image = np.array(image)
+        image[mask > 0] = 0
+        image = Image.fromarray(image)
+        return image
+
+    def process(self, pipe: QwenImagePipeline, blockwise_controlnet_inputs: list[ControlNetInput], tiled, tile_size, tile_stride):
+        if blockwise_controlnet_inputs is None:
+            return {}
+        pipe.load_models_to_device(self.onload_model_names)
+        conditionings = []
+        for controlnet_input in blockwise_controlnet_inputs:
+            image = controlnet_input.image
+            if controlnet_input.inpaint_mask is not None:
+                image = self.apply_controlnet_mask_on_image(pipe, image, controlnet_input.inpaint_mask)
+
+            image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
+            image = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+
+            if controlnet_input.inpaint_mask is not None:
+                image = self.apply_controlnet_mask_on_latents(pipe, image, controlnet_input.inpaint_mask)
+            conditionings.append(image)
+            
+        return {"blockwise_controlnet_conditioning": conditionings}
+
+
+class QwenImageUnit_EditImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("edit_image", "tiled", "tile_size", "tile_stride", "edit_image_auto_resize"),
+            onload_model_names=("vae",)
+        )
+
+
+    def calculate_dimensions(self, target_area, ratio):
+        import math
+        width = math.sqrt(target_area * ratio)
+        height = width / ratio
+        width = round(width / 32) * 32
+        height = round(height / 32) * 32
+        return width, height
+
+
+    def edit_image_auto_resize(self, edit_image):
+        calculated_width, calculated_height = self.calculate_dimensions(1024 * 1024, edit_image.size[0] / edit_image.size[1])
+        return edit_image.resize((calculated_width, calculated_height))
+
+
+    def process(self, pipe: QwenImagePipeline, edit_image, tiled, tile_size, tile_stride, edit_image_auto_resize=False):
+        if edit_image is None:
+            return {}
+        resized_edit_image = self.edit_image_auto_resize(edit_image) if edit_image_auto_resize else edit_image
+        pipe.load_models_to_device(['vae'])
+        edit_image = pipe.preprocess_image(resized_edit_image).to(device=pipe.device, dtype=pipe.torch_dtype)
+        edit_latents = pipe.vae.encode(edit_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return {"edit_latents": edit_latents, "edit_image": resized_edit_image}
+
+class QwenImageUnit_ContextImageEmbedder(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            input_params=("context_image", "height", "width", "tiled", "tile_size", "tile_stride"),
+            onload_model_names=("vae",)
+        )
+
+    def process(self, pipe: QwenImagePipeline, context_image, height, width, tiled, tile_size, tile_stride):
+        if context_image is None:
+            return {}
+        pipe.load_models_to_device(['vae'])
+        context_image = pipe.preprocess_image(context_image.resize((width, height))).to(device=pipe.device, dtype=pipe.torch_dtype)
+        context_latents = pipe.vae.encode(context_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        return {"context_latents": context_latents}
+
+
 def model_fn_qwen_image(
    dit: QwenImageDiT = None,
+    blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None,
    latents=None,
    timestep=None,
    prompt_emb=None,
    prompt_emb_mask=None,
    height=None,
    width=None,
+    blockwise_controlnet_conditioning=None,
+    blockwise_controlnet_inputs=None,
+    progress_id=0,
+    num_inference_steps=1,
    entity_prompt_emb=None,
    entity_prompt_emb_mask=None,
    entity_masks=None,
+    edit_latents=None,
+    context_latents=None,
    enable_fp8_attention=False,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
+    edit_rope_interpolation=False,
    **kwargs
 ):
    img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
@@ -452,7 +760,17 @@ def model_fn_qwen_image(
    timestep = timestep / 1000
    
    image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
-    
+    image_seq_len = image.shape[1]
+
+    if context_latents is not None:
+        img_shapes += [(context_latents.shape[0], context_latents.shape[2]//2, context_latents.shape[3]//2)]
+        context_image = rearrange(context_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=context_latents.shape[2]//2, W=context_latents.shape[3]//2, P=2, Q=2)
+        image = torch.cat([image, context_image], dim=1)
+    if edit_latents is not None:
+        img_shapes += [(edit_latents.shape[0], edit_latents.shape[2]//2, edit_latents.shape[3]//2)]
+        edit_image = rearrange(edit_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=edit_latents.shape[2]//2, W=edit_latents.shape[3]//2, P=2, Q=2)
+        image = torch.cat([image, edit_image], dim=1)
+
    image = dit.img_in(image)
    conditioning = dit.time_text_embed(timestep, image.dtype)

@@ -463,10 +781,17 @@ def model_fn_qwen_image(
        )
    else:
        text = dit.txt_in(dit.txt_norm(prompt_emb))
-        image_rotary_emb = dit.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+        if edit_rope_interpolation:
+            image_rotary_emb = dit.pos_embed.forward_sampling(img_shapes, txt_seq_lens, device=latents.device)
+        else:
+            image_rotary_emb = dit.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
        attention_mask = None
+        
+    if blockwise_controlnet_conditioning is not None:
+        blockwise_controlnet_conditioning = blockwise_controlnet.preprocess(
+            blockwise_controlnet_inputs, blockwise_controlnet_conditioning)

-    for block in dit.transformer_blocks:
+    for block_id, block in enumerate(dit.transformer_blocks):
        text, image = gradient_checkpoint_forward(
            block,
            use_gradient_checkpointing,
@@ -478,9 +803,18 @@ def model_fn_qwen_image(
            attention_mask=attention_mask,
            enable_fp8_attention=enable_fp8_attention,
        )
+        if blockwise_controlnet_conditioning is not None:
+            image_slice = image[:, :image_seq_len].clone()
+            controlnet_output = blockwise_controlnet.blockwise_forward(
+                image=image_slice, conditionings=blockwise_controlnet_conditioning,
+                controlnet_inputs=blockwise_controlnet_inputs, block_id=block_id,
+                progress_id=progress_id, num_inference_steps=num_inference_steps,
+            )
+            image[:, :image_seq_len] = image_slice + controlnet_output
    
    image = dit.norm_out(image, conditioning)
    image = dit.proj_out(image)
+    image = image[:, :image_seq_len]
    
    latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2)
    return latents
--- a/diffsynth/pipelines/wan_video_new.py
+++ b/diffsynth/pipelines/wan_video_new.py
@@ -15,6 +15,7 @@ from typing_extensions import Literal
 from ..utils import BasePipeline, ModelConfig, PipelineUnit, PipelineUnitRunner
 from ..models import ModelManager, load_state_dict
 from ..models.wan_video_dit import WanModel, RMSNorm, sinusoidal_embedding_1d
+from ..models.wan_video_dit_s2v import rope_precompute
 from ..models.wan_video_text_encoder import WanTextEncoder, T5RelativeEmbedding, T5LayerNorm
 from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample
 from ..models.wan_video_image_encoder import WanImageEncoder
@@ -49,8 +50,9 @@ class WanVideoPipeline(BasePipeline):
        self.units = [
            WanVideoUnit_ShapeChecker(),
            WanVideoUnit_NoiseInitializer(),
-            WanVideoUnit_InputVideoEmbedder(),
            WanVideoUnit_PromptEmbedder(),
+            WanVideoUnit_S2V(),
+            WanVideoUnit_InputVideoEmbedder(),
            WanVideoUnit_ImageEmbedderVAE(),
            WanVideoUnit_ImageEmbedderCLIP(),
            WanVideoUnit_ImageEmbedderFused(),
@@ -63,6 +65,9 @@ class WanVideoPipeline(BasePipeline):
            WanVideoUnit_TeaCache(),
            WanVideoUnit_CfgMerger(),
        ]
+        self.post_units = [
+            WanVideoPostUnit_S2V(),
+        ]
        self.model_fn = model_fn_wan_video
        
    
@@ -127,6 +132,8 @@ class WanVideoPipeline(BasePipeline):
                    torch.nn.LayerNorm: WanAutoCastLayerNorm,
                    RMSNorm: AutoWrappedModule,
                    torch.nn.Conv2d: AutoWrappedModule,
+                    torch.nn.Conv1d: AutoWrappedModule,
+                    torch.nn.Embedding: AutoWrappedModule,
                },
                module_config = dict(
                    offload_dtype=dtype,
@@ -254,6 +261,25 @@ class WanVideoPipeline(BasePipeline):
                ),
                vram_limit=vram_limit,
            )
+        if self.audio_encoder is not None:
+            # TODO: need check
+            dtype = next(iter(self.audio_encoder.parameters())).dtype
+            enable_vram_management(
+                self.audio_encoder,
+                module_map = {
+                    torch.nn.Linear: AutoWrappedLinear,
+                    torch.nn.LayerNorm: AutoWrappedModule,
+                    torch.nn.Conv1d: AutoWrappedModule,
+                },
+                module_config = dict(
+                    offload_dtype=dtype,
+                    offload_device="cpu",
+                    onload_dtype=dtype,
+                    onload_device="cpu",
+                    computation_dtype=self.torch_dtype,
+                    computation_device=self.device,
+                ),
+            )
            
            
    def initialize_usp(self):
@@ -290,6 +316,7 @@ class WanVideoPipeline(BasePipeline):
        device: Union[str, torch.device] = "cuda",
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*"),
+        audio_processor_config: ModelConfig = None,
        redirect_common_files: bool = True,
        use_usp=False,
    ):
@@ -332,7 +359,8 @@ class WanVideoPipeline(BasePipeline):
        pipe.image_encoder = model_manager.fetch_model("wan_video_image_encoder")
        pipe.motion_controller = model_manager.fetch_model("wan_video_motion_controller")
        pipe.vace = model_manager.fetch_model("wan_video_vace")
-        
+        pipe.audio_encoder = model_manager.fetch_model("wans2v_audio_encoder")
+
        # Size division factor
        if pipe.vae is not None:
            pipe.height_division_factor = pipe.vae.upsampling_factor * 2
@@ -342,7 +370,11 @@ class WanVideoPipeline(BasePipeline):
        tokenizer_config.download_if_necessary(use_usp=use_usp)
        pipe.prompter.fetch_models(pipe.text_encoder)
        pipe.prompter.fetch_tokenizer(tokenizer_config.path)
-        
+
+        if audio_processor_config is not None:
+            audio_processor_config.download_if_necessary(use_usp=use_usp)
+            from transformers import Wav2Vec2Processor
+            pipe.audio_processor = Wav2Vec2Processor.from_pretrained(audio_processor_config.path)
        # Unified Sequence Parallel
        if use_usp: pipe.enable_usp()
        return pipe
@@ -361,6 +393,13 @@ class WanVideoPipeline(BasePipeline):
        # Video-to-video
        input_video: Optional[list[Image.Image]] = None,
        denoising_strength: Optional[float] = 1.0,
+        # Speech-to-video
+        input_audio: Optional[np.array] = None,
+        audio_embeds: Optional[torch.Tensor] = None,
+        audio_sample_rate: Optional[int] = 16000,
+        s2v_pose_video: Optional[list[Image.Image]] = None,
+        s2v_pose_latents: Optional[torch.Tensor] = None,
+        motion_video: Optional[list[Image.Image]] = None,
        # ControlNet
        control_video: Optional[list[Image.Image]] = None,
        reference_image: Optional[Image.Image] = None,
@@ -429,6 +468,7 @@ class WanVideoPipeline(BasePipeline):
            "motion_bucket_id": motion_bucket_id,
            "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride,
            "sliding_window_size": sliding_window_size, "sliding_window_stride": sliding_window_stride,
+            "input_audio": input_audio, "audio_sample_rate": audio_sample_rate, "s2v_pose_video": s2v_pose_video, "audio_embeds": audio_embeds, "s2v_pose_latents": s2v_pose_latents, "motion_video": motion_video,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -464,7 +504,9 @@ class WanVideoPipeline(BasePipeline):
        # VACE (TODO: remove it)
        if vace_reference_image is not None:
            inputs_shared["latents"] = inputs_shared["latents"][:, :, 1:]
-
+        # post-denoising, pre-decoding processing logic
+        for unit in self.post_units:
+            inputs_shared, _, _ = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
        # Decode
        self.load_models_to_device(['vae'])
        video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
@@ -663,22 +705,23 @@ class WanVideoUnit_ImageEmbedderFused(PipelineUnit):
 class WanVideoUnit_FunControl(PipelineUnit):
    def __init__(self):
        super().__init__(
-            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y"),
+            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y", "latents"),
            onload_model_names=("vae",)
        )

-    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y):
+    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y, latents):
        if control_video is None:
            return {}
        pipe.load_models_to_device(self.onload_model_names)
        control_video = pipe.preprocess_video(control_video)
        control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
        control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device)
+        y_dim = pipe.dit.in_dim-control_latents.shape[1]-latents.shape[1]
        if clip_feature is None or y is None:
            clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device)
-            y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
+            y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
        else:
-            y = y[:, -16:]
+            y = y[:, -y_dim:]
        y = torch.concat([control_latents, y], dim=1)
        return {"clip_feature": clip_feature, "y": y}
    
@@ -698,6 +741,8 @@ class WanVideoUnit_FunReference(PipelineUnit):
        reference_image = reference_image.resize((width, height))
        reference_latents = pipe.preprocess_video([reference_image])
        reference_latents = pipe.vae.encode(reference_latents, device=pipe.device)
+        if pipe.image_encoder is None:
+            return {"reference_latents": reference_latents}
        clip_feature = pipe.preprocess_image(reference_image)
        clip_feature = pipe.image_encoder.encode_image([clip_feature])
        return {"reference_latents": reference_latents, "clip_feature": clip_feature}
@@ -707,13 +752,14 @@ class WanVideoUnit_FunReference(PipelineUnit):
 class WanVideoUnit_FunCameraControl(PipelineUnit):
    def __init__(self):
        super().__init__(
-            input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image"),
+            input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image", "tiled", "tile_size", "tile_stride"),
            onload_model_names=("vae",)
        )

-    def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image):
+    def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image, tiled, tile_size, tile_stride):
        if camera_control_direction is None:
            return {}
+        pipe.load_models_to_device(self.onload_model_names)
        camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates(
            camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin)
        
@@ -728,14 +774,27 @@ class WanVideoUnit_FunCameraControl(PipelineUnit):
        control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
        control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
        control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype)
-
+        
        input_image = input_image.resize((width, height))
        input_latents = pipe.preprocess_video([input_image])
-        pipe.load_models_to_device(self.onload_model_names)
        input_latents = pipe.vae.encode(input_latents, device=pipe.device)
        y = torch.zeros_like(latents).to(pipe.device)
        y[:, :, :1] = input_latents
        y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+
+        if y.shape[1] != pipe.dit.in_dim - latents.shape[1]:
+            image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
+            vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
+            y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+            msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+            msk[:, 1:] = 0
+            msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
+            msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+            msk = msk.transpose(1, 2)[0]
+            y = torch.cat([msk,y])
+            y = y.unsqueeze(0)
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
        return {"control_camera_latents_input": control_camera_latents_input, "y": y}


@@ -851,6 +910,98 @@ class WanVideoUnit_CfgMerger(PipelineUnit):
        return inputs_shared, inputs_posi, inputs_nega


+class WanVideoUnit_S2V(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            take_over=True,
+            onload_model_names=("audio_encoder", "vae",)
+        )
+
+    def process_audio(self, pipe: WanVideoPipeline, input_audio, audio_sample_rate, num_frames, fps=16, audio_embeds=None, return_all=False):
+        if audio_embeds is not None:
+            return {"audio_embeds": audio_embeds}
+        pipe.load_models_to_device(["audio_encoder"])
+        audio_embeds = pipe.audio_encoder.get_audio_feats_per_inference(input_audio, audio_sample_rate, pipe.audio_processor, fps=fps, batch_frames=num_frames-1, dtype=pipe.torch_dtype, device=pipe.device)
+        if return_all:
+            return audio_embeds
+        else:
+            return {"audio_embeds": audio_embeds[0]}
+
+    def process_motion_latents(self, pipe: WanVideoPipeline, height, width, tiled, tile_size, tile_stride, motion_video=None):
+        pipe.load_models_to_device(["vae"])
+        motion_frames = 73
+        kwargs = {}
+        if motion_video is not None and len(motion_video) > 0:
+            assert len(motion_video) == motion_frames, f"motion video must have {motion_frames} frames, but got {len(motion_video)}"
+            motion_latents = pipe.preprocess_video(motion_video)
+            kwargs["drop_motion_frames"] = False
+        else:
+            motion_latents = torch.zeros([1, 3, motion_frames, height, width], dtype=pipe.torch_dtype, device=pipe.device)
+            kwargs["drop_motion_frames"] = True
+        motion_latents = pipe.vae.encode(motion_latents, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
+        kwargs.update({"motion_latents": motion_latents})
+        return kwargs
+
+    def process_pose_cond(self, pipe: WanVideoPipeline, s2v_pose_video, num_frames, height, width, tiled, tile_size, tile_stride, s2v_pose_latents=None, num_repeats=1, return_all=False):
+        if s2v_pose_latents is not None:
+            return {"s2v_pose_latents": s2v_pose_latents}
+        if s2v_pose_video is None:
+            return {"s2v_pose_latents": None}
+        pipe.load_models_to_device(["vae"])
+        infer_frames = num_frames - 1
+        input_video = pipe.preprocess_video(s2v_pose_video)[:, :, :infer_frames * num_repeats]
+        # pad if not enough frames
+        padding_frames = infer_frames * num_repeats - input_video.shape[2]
+        input_video = torch.cat([input_video, -torch.ones(1, 3, padding_frames, height, width, device=input_video.device, dtype=input_video.dtype)], dim=2)
+        input_videos = input_video.chunk(num_repeats, dim=2)
+        pose_conds = []
+        for r in range(num_repeats):
+            cond = input_videos[r]
+            cond = torch.cat([cond[:, :, 0:1].repeat(1, 1, 1, 1, 1), cond], dim=2)
+            cond_latents = pipe.vae.encode(cond, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
+            pose_conds.append(cond_latents[:,:,1:])
+        if return_all:
+            return pose_conds
+        else:
+            return {"s2v_pose_latents": pose_conds[0]}
+
+    def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega):
+        if (inputs_shared.get("input_audio") is None and inputs_shared.get("audio_embeds") is None) or pipe.audio_encoder is None or pipe.audio_processor is None:
+            return inputs_shared, inputs_posi, inputs_nega
+        num_frames, height, width, tiled, tile_size, tile_stride = inputs_shared.get("num_frames"), inputs_shared.get("height"), inputs_shared.get("width"), inputs_shared.get("tiled"), inputs_shared.get("tile_size"), inputs_shared.get("tile_stride")
+        input_audio, audio_embeds, audio_sample_rate = inputs_shared.pop("input_audio"), inputs_shared.pop("audio_embeds"), inputs_shared.get("audio_sample_rate")
+        s2v_pose_video, s2v_pose_latents, motion_video = inputs_shared.pop("s2v_pose_video"), inputs_shared.pop("s2v_pose_latents"), inputs_shared.pop("motion_video")
+
+        audio_input_positive = self.process_audio(pipe, input_audio, audio_sample_rate, num_frames, audio_embeds=audio_embeds)
+        inputs_posi.update(audio_input_positive)
+        inputs_nega.update({"audio_embeds": 0.0 * audio_input_positive["audio_embeds"]})
+
+        inputs_shared.update(self.process_motion_latents(pipe, height, width, tiled, tile_size, tile_stride, motion_video))
+        inputs_shared.update(self.process_pose_cond(pipe, s2v_pose_video, num_frames, height, width, tiled, tile_size, tile_stride, s2v_pose_latents=s2v_pose_latents))
+        return inputs_shared, inputs_posi, inputs_nega
+
+    @staticmethod
+    def pre_calculate_audio_pose(pipe: WanVideoPipeline, input_audio=None, audio_sample_rate=16000, s2v_pose_video=None, num_frames=81, height=448, width=832, fps=16, tiled=True, tile_size=(30, 52), tile_stride=(15, 26)):
+        assert pipe.audio_encoder is not None and pipe.audio_processor is not None, "Please load audio encoder and audio processor first."
+        shapes = WanVideoUnit_ShapeChecker().process(pipe, height, width, num_frames)
+        height, width, num_frames = shapes["height"], shapes["width"], shapes["num_frames"]
+        unit = WanVideoUnit_S2V()
+        audio_embeds = unit.process_audio(pipe, input_audio, audio_sample_rate, num_frames, fps, return_all=True)
+        pose_latents = unit.process_pose_cond(pipe, s2v_pose_video, num_frames, height, width, num_repeats=len(audio_embeds), return_all=True, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+        pose_latents = None if s2v_pose_video is None else pose_latents
+        return audio_embeds, pose_latents, len(audio_embeds)
+
+
+class WanVideoPostUnit_S2V(PipelineUnit):
+    def __init__(self):
+        super().__init__(input_params=("latents", "motion_latents", "drop_motion_frames"))
+
+    def process(self, pipe: WanVideoPipeline, latents, motion_latents, drop_motion_frames):
+        if pipe.audio_encoder is None or motion_latents is None or drop_motion_frames:
+            return {}
+        latents = torch.cat([motion_latents, latents[:,:,1:]], dim=2)
+        return {"latents": latents}
+

 class TeaCache:
    def __init__(self, num_inference_steps, rel_l1_thresh, model_id):
@@ -970,6 +1121,10 @@ def model_fn_wan_video(
    reference_latents = None,
    vace_context = None,
    vace_scale = 1.0,
+    audio_embeds: Optional[torch.Tensor] = None,
+    motion_latents: Optional[torch.Tensor] = None,
+    s2v_pose_latents: Optional[torch.Tensor] = None,
+    drop_motion_frames: bool = True,
    tea_cache: TeaCache = None,
    use_unified_sequence_parallel: bool = False,
    motion_bucket_id: Optional[torch.Tensor] = None,
@@ -1007,7 +1162,22 @@ def model_fn_wan_video(
            tensor_names=["latents", "y"],
            batch_size=2 if cfg_merge else 1
        )
-    
+    # wan2.2 s2v
+    if audio_embeds is not None:
+        return model_fn_wans2v(
+            dit=dit,
+            latents=latents,
+            timestep=timestep,
+            context=context,
+            audio_embeds=audio_embeds,
+            motion_latents=motion_latents,
+            s2v_pose_latents=s2v_pose_latents,
+            drop_motion_frames=drop_motion_frames,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_unified_sequence_parallel=use_unified_sequence_parallel,
+        )
+
    if use_unified_sequence_parallel:
        import torch.distributed as dist
        from xfuser.core.distributed import (get_sequence_parallel_rank,
@@ -1021,6 +1191,10 @@ def model_fn_wan_video(
            torch.ones((latents.shape[2] - 1, latents.shape[3] * latents.shape[4] // 4), dtype=latents.dtype, device=latents.device) * timestep
        ]).flatten()
        t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep).unsqueeze(0))
+        if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
+            t_chunks = torch.chunk(t, get_sequence_parallel_world_size(), dim=1)
+            t_chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, t_chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in t_chunks]
+            t = t_chunks[get_sequence_parallel_rank()]
        t_mod = dit.time_projection(t).unflatten(2, (6, dit.dim))
    else:
        t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
@@ -1044,7 +1218,7 @@ def model_fn_wan_video(
    if clip_feature is not None and dit.require_clip_embedding:
        clip_embdding = dit.img_emb(clip_feature)
        context = torch.cat([clip_embdding, context], dim=1)
-    
+
    # Add camera control
    x, (f, h, w) = dit.patchify(x, control_camera_latents_input)
    
@@ -1122,3 +1296,105 @@ def model_fn_wan_video(
        f -= 1
    x = dit.unpatchify(x, (f, h, w))
    return x
+
+
+def model_fn_wans2v(
+    dit,
+    latents,
+    timestep,
+    context,
+    audio_embeds,
+    motion_latents,
+    s2v_pose_latents,
+    drop_motion_frames=True,
+    use_gradient_checkpointing_offload=False,
+    use_gradient_checkpointing=False,
+    use_unified_sequence_parallel=False,
+):
+    if use_unified_sequence_parallel:
+        import torch.distributed as dist
+        from xfuser.core.distributed import (get_sequence_parallel_rank,
+                                            get_sequence_parallel_world_size,
+                                            get_sp_group)
+    origin_ref_latents = latents[:, :, 0:1]
+    x = latents[:, :, 1:]
+
+    # context embedding
+    context = dit.text_embedding(context)
+
+    # audio encode
+    audio_emb_global, merged_audio_emb = dit.cal_audio_emb(audio_embeds)
+
+    # x and s2v_pose_latents
+    s2v_pose_latents = torch.zeros_like(x) if s2v_pose_latents is None else s2v_pose_latents
+    x, (f, h, w) = dit.patchify(dit.patch_embedding(x) + dit.cond_encoder(s2v_pose_latents))
+    seq_len_x = seq_len_x_global = x.shape[1] # global used for unified sequence parallel
+
+    # reference image
+    ref_latents, (rf, rh, rw) = dit.patchify(dit.patch_embedding(origin_ref_latents))
+    grid_sizes = dit.get_grid_sizes((f, h, w), (rf, rh, rw))
+    x = torch.cat([x, ref_latents], dim=1)
+    # mask
+    mask = torch.cat([torch.zeros([1, seq_len_x]), torch.ones([1, ref_latents.shape[1]])], dim=1).to(torch.long).to(x.device)
+    # freqs
+    pre_compute_freqs = rope_precompute(x.detach().view(1, x.size(1), dit.num_heads, dit.dim // dit.num_heads), grid_sizes, dit.freqs, start=None)
+    # motion
+    x, pre_compute_freqs, mask = dit.inject_motion(x, pre_compute_freqs, mask, motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=2)
+
+    x = x + dit.trainable_cond_mask(mask).to(x.dtype)
+
+    # tmod
+    timestep = torch.cat([timestep, torch.zeros([1], dtype=timestep.dtype, device=timestep.device)])
+    t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
+    t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)).unsqueeze(2).transpose(0, 2)
+
+    if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
+        world_size, sp_rank = get_sequence_parallel_world_size(), get_sequence_parallel_rank()
+        assert x.shape[1] % world_size == 0, f"the dimension after chunk must be divisible by world size, but got {x.shape[1]} and {get_sequence_parallel_world_size()}"
+        x = torch.chunk(x, world_size, dim=1)[sp_rank]
+        seg_idxs = [0] + list(torch.cumsum(torch.tensor([x.shape[1]] * world_size), dim=0).cpu().numpy())
+        seq_len_x_list = [min(max(0, seq_len_x - seg_idxs[i]), x.shape[1]) for i in range(len(seg_idxs)-1)]
+        seq_len_x = seq_len_x_list[sp_rank]
+
+    def create_custom_forward(module):
+        def custom_forward(*inputs):
+            return module(*inputs)
+        return custom_forward
+
+    for block_id, block in enumerate(dit.blocks):
+        if use_gradient_checkpointing_offload:
+            with torch.autograd.graph.save_on_cpu():
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    x, context, t_mod, seq_len_x, pre_compute_freqs[0],
+                    use_reentrant=False,
+                )
+                x = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
+                    x,
+                    use_reentrant=False,
+                )
+        elif use_gradient_checkpointing:
+            x = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(block),
+                x, context, t_mod, seq_len_x, pre_compute_freqs[0],
+                use_reentrant=False,
+            )
+            x = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
+                x,
+                use_reentrant=False,
+            )
+        else:
+            x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
+            x = dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x_global, use_unified_sequence_parallel)
+
+    if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
+        x = get_sp_group().all_gather(x, dim=1)
+
+    x = x[:, :seq_len_x_global]
+    x = dit.head(x, t[:-1])
+    x = dit.unpatchify(x, (f, h, w))
+    # make compatible with wan video
+    x = torch.cat([origin_ref_latents, x], dim=2)
+    return x
--- a/diffsynth/trainers/unified_dataset.py
+++ b/diffsynth/trainers/unified_dataset.py
@@ -0,0 +1,334 @@
+import torch, torchvision, imageio, os, json, pandas
+import imageio.v3 as iio
+from PIL import Image
+
+
+
+class DataProcessingPipeline:
+    def __init__(self, operators=None):
+        self.operators: list[DataProcessingOperator] = [] if operators is None else operators
+        
+    def __call__(self, data):
+        for operator in self.operators:
+            data = operator(data)
+        return data
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        return DataProcessingPipeline(self.operators + pipe.operators)
+
+
+
+class DataProcessingOperator:
+    def __call__(self, data):
+        raise NotImplementedError("DataProcessingOperator cannot be called directly.")
+    
+    def __rshift__(self, pipe):
+        if isinstance(pipe, DataProcessingOperator):
+            pipe = DataProcessingPipeline([pipe])
+        return DataProcessingPipeline([self]).__rshift__(pipe)
+
+
+
+class DataProcessingOperatorRaw(DataProcessingOperator):
+    def __call__(self, data):
+        return data
+
+
+
+class ToInt(DataProcessingOperator):
+    def __call__(self, data):
+        return int(data)
+
+
+
+class ToFloat(DataProcessingOperator):
+    def __call__(self, data):
+        return float(data)
+
+
+
+class ToStr(DataProcessingOperator):
+    def __init__(self, none_value=""):
+        self.none_value = none_value
+    
+    def __call__(self, data):
+        if data is None: data = self.none_value
+        return str(data)
+
+
+
+class LoadImage(DataProcessingOperator):
+    def __init__(self, convert_RGB=True):
+        self.convert_RGB = convert_RGB
+    
+    def __call__(self, data: str):
+        image = Image.open(data)
+        if self.convert_RGB: image = image.convert("RGB")
+        return image
+
+
+
+class ImageCropAndResize(DataProcessingOperator):
+    def __init__(self, height, width, max_pixels, height_division_factor, width_division_factor):
+        self.height = height
+        self.width = width
+        self.max_pixels = max_pixels
+        self.height_division_factor = height_division_factor
+        self.width_division_factor = width_division_factor
+
+    def crop_and_resize(self, image, target_height, target_width):
+        width, height = image.size
+        scale = max(target_width / width, target_height / height)
+        image = torchvision.transforms.functional.resize(
+            image,
+            (round(height*scale), round(width*scale)),
+            interpolation=torchvision.transforms.InterpolationMode.BILINEAR
+        )
+        image = torchvision.transforms.functional.center_crop(image, (target_height, target_width))
+        return image
+    
+    def get_height_width(self, image):
+        if self.height is None or self.width is None:
+            width, height = image.size
+            if width * height > self.max_pixels:
+                scale = (width * height / self.max_pixels) ** 0.5
+                height, width = int(height / scale), int(width / scale)
+            height = height // self.height_division_factor * self.height_division_factor
+            width = width // self.width_division_factor * self.width_division_factor
+        else:
+            height, width = self.height, self.width
+        return height, width
+    
+    
+    def __call__(self, data: Image.Image):
+        image = self.crop_and_resize(data, *self.get_height_width(data))
+        return image
+
+
+
+class ToList(DataProcessingOperator):
+    def __call__(self, data):
+        return [data]
+    
+
+
+class LoadVideo(DataProcessingOperator):
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
+        self.num_frames = num_frames
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, reader):
+        num_frames = self.num_frames
+        if int(reader.count_frames()) < num_frames:
+            num_frames = int(reader.count_frames())
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        reader = imageio.get_reader(data)
+        num_frames = self.get_num_frames(reader)
+        frames = []
+        for frame_id in range(num_frames):
+            frame = reader.get_data(frame_id)
+            frame = Image.fromarray(frame)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+        reader.close()
+        return frames
+
+
+
+class SequencialProcess(DataProcessingOperator):
+    def __init__(self, operator=lambda x: x):
+        self.operator = operator
+        
+    def __call__(self, data):
+        return [self.operator(i) for i in data]
+
+
+
+class LoadGIF(DataProcessingOperator):
+    def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x):
+        self.num_frames = num_frames
+        self.time_division_factor = time_division_factor
+        self.time_division_remainder = time_division_remainder
+        # frame_processor is build in the video loader for high efficiency.
+        self.frame_processor = frame_processor
+        
+    def get_num_frames(self, path):
+        num_frames = self.num_frames
+        images = iio.imread(path, mode="RGB")
+        if len(images) < num_frames:
+            num_frames = len(images)
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        return num_frames
+        
+    def __call__(self, data: str):
+        num_frames = self.get_num_frames(data)
+        frames = []
+        images = iio.imread(data, mode="RGB")
+        for img in images:
+            frame = Image.fromarray(img)
+            frame = self.frame_processor(frame)
+            frames.append(frame)
+            if len(frames) >= num_frames:
+                break
+        return frames
+    
+
+
+class RouteByExtensionName(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data: str):
+        file_ext_name = data.split(".")[-1].lower()
+        for ext_names, operator in self.operator_map:
+            if ext_names is None or file_ext_name in ext_names:
+                return operator(data)
+        raise ValueError(f"Unsupported file: {data}")
+
+
+
+class RouteByType(DataProcessingOperator):
+    def __init__(self, operator_map):
+        self.operator_map = operator_map
+        
+    def __call__(self, data):
+        for dtype, operator in self.operator_map:
+            if dtype is None or isinstance(data, dtype):
+                return operator(data)
+        raise ValueError(f"Unsupported data: {data}")
+
+
+
+class LoadTorchPickle(DataProcessingOperator):
+    def __init__(self, map_location="cpu"):
+        self.map_location = map_location
+        
+    def __call__(self, data):
+        return torch.load(data, map_location=self.map_location, weights_only=False)
+
+
+
+class ToAbsolutePath(DataProcessingOperator):
+    def __init__(self, base_path=""):
+        self.base_path = base_path
+        
+    def __call__(self, data):
+        return os.path.join(self.base_path, data)
+
+
+
+class UnifiedDataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        base_path=None, metadata_path=None,
+        repeat=1,
+        data_file_keys=tuple(),
+        main_data_operator=lambda x: x,
+        special_operator_map=None,
+    ):
+        self.base_path = base_path
+        self.metadata_path = metadata_path
+        self.repeat = repeat
+        self.data_file_keys = data_file_keys
+        self.main_data_operator = main_data_operator
+        self.cached_data_operator = LoadTorchPickle()
+        self.special_operator_map = {} if special_operator_map is None else special_operator_map
+        self.data = []
+        self.cached_data = []
+        self.load_from_cache = metadata_path is None
+        self.load_metadata(metadata_path)
+    
+    @staticmethod
+    def default_image_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
+            (list, SequencialProcess(ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor))),
+        ])
+    
+    @staticmethod
+    def default_video_operator(
+        base_path="",
+        max_pixels=1920*1080, height=None, width=None,
+        height_division_factor=16, width_division_factor=16,
+        num_frames=81, time_division_factor=4, time_division_remainder=1,
+    ):
+        return RouteByType(operator_map=[
+            (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[
+                (("jpg", "jpeg", "png", "webp"), LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor) >> ToList()),
+                (("gif",), LoadGIF(num_frames, time_division_factor, time_division_remainder) >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)),
+                (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo(
+                    num_frames, time_division_factor, time_division_remainder,
+                    frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor),
+                )),
+            ])),
+        ])
+        
+    def search_for_cached_data_files(self, path):
+        for file_name in os.listdir(path):
+            subpath = os.path.join(path, file_name)
+            if os.path.isdir(subpath):
+                self.search_for_cached_data_files(subpath)
+            elif subpath.endswith(".pth"):
+                self.cached_data.append(subpath)
+    
+    def load_metadata(self, metadata_path):
+        if metadata_path is None:
+            print("No metadata_path. Searching for cached data files.")
+            self.search_for_cached_data_files(self.base_path)
+            print(f"{len(self.cached_data)} cached data files found.")
+        elif metadata_path.endswith(".json"):
+            with open(metadata_path, "r") as f:
+                metadata = json.load(f)
+            self.data = metadata
+        elif metadata_path.endswith(".jsonl"):
+            metadata = []
+            with open(metadata_path, 'r') as f:
+                for line in f:
+                    metadata.append(json.loads(line.strip()))
+            self.data = metadata
+        else:
+            metadata = pandas.read_csv(metadata_path)
+            self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))]
+
+    def __getitem__(self, data_id):
+        if self.load_from_cache:
+            data = self.cached_data[data_id % len(self.cached_data)]
+            data = self.cached_data_operator(data)
+        else:
+            data = self.data[data_id % len(self.data)].copy()
+            for key in self.data_file_keys:
+                if key in data:
+                    if key in self.special_operator_map:
+                        data[key] = self.special_operator_map[key]
+                    elif key in self.data_file_keys:
+                        data[key] = self.main_data_operator(data[key])
+        return data
+
+    def __len__(self):
+        if self.load_from_cache:
+            return len(self.cached_data) * self.repeat
+        else:
+            return len(self.data) * self.repeat
+        
+    def check_data_equal(self, data1, data2):
+        # Debug only
+        if len(data1) != len(data2):
+            return False
+        for k in data1:
+            if data1[k] != data2[k]:
+                return False
+        return True
--- a/diffsynth/trainers/utils.py
+++ b/diffsynth/trainers/utils.py
@@ -1,4 +1,6 @@
 import imageio, os, torch, warnings, torchvision, argparse, json
+from ..utils import ModelConfig
+from ..models.utils import load_state_dict
 from peft import LoraConfig, inject_adapter_in_model
 from PIL import Image
 import pandas as pd
@@ -154,7 +156,7 @@ class VideoDataset(torch.utils.data.Dataset):
        height_division_factor=16, width_division_factor=16,
        data_file_keys=("video",),
        image_file_extension=("jpg", "jpeg", "png", "webp"),
-        video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"),
+        video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm", "gif"),
        repeat=1,
        args=None,
    ):
@@ -259,8 +261,53 @@ class VideoDataset(torch.utils.data.Dataset):
                num_frames -= 1
        return num_frames
    
-
+    def _load_gif(self, file_path):
+        gif_img = Image.open(file_path)
+        frame_count = 0
+        delays, frames = [], []
+        while True:
+            delay = gif_img.info.get('duration', 100) # ms
+            delays.append(delay)
+            rgb_frame = gif_img.convert("RGB")   
+            croped_frame = self.crop_and_resize(rgb_frame, *self.get_height_width(rgb_frame))
+            frames.append(croped_frame)             
+            frame_count += 1
+            try:
+                gif_img.seek(frame_count)
+            except:
+                break
+        # delays canbe used to calculate framerates
+        # i guess it is better to sample images with stable interval,
+        # and using minimal_interval as the interval, 
+        # and framerate = 1000 / minimal_interval
+        if any((delays[0] != i) for i in delays):
+            minimal_interval = min([i for i in delays if i > 0])
+            # make a ((start,end),frameid) struct
+            start_end_idx_map = [((sum(delays[:i]), sum(delays[:i+1])), i) for i in range(len(delays))]
+            _frames = []
+            # according gemini-code-assist, make it more efficient to locate
+            # where to sample the frame
+            last_match = 0
+            for i in range(sum(delays) // minimal_interval):
+                current_time = minimal_interval * i
+                for idx, ((start, end), frame_idx) in enumerate(start_end_idx_map[last_match:]):
+                    if start <= current_time < end:
+                        _frames.append(frames[frame_idx])
+                        last_match = idx + last_match
+                        break
+            frames = _frames
+        num_frames = len(frames)
+        if num_frames > self.num_frames:
+            num_frames = self.num_frames
+        else:
+            while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder:
+                num_frames -= 1
+        frames = frames[:num_frames]
+        return frames
+    
    def load_video(self, file_path):
+        if file_path.lower().endswith(".gif"):
+            return self._load_gif(file_path)
        reader = imageio.get_reader(file_path)
        num_frames = self.get_num_frames(reader)
        frames = []
@@ -338,14 +385,29 @@ class DiffusionTrainingModule(torch.nn.Module):
        return trainable_param_names
    
    
-    def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None):
+    def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None):
        if lora_alpha is None:
            lora_alpha = lora_rank
        lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
        model = inject_adapter_in_model(lora_config, model)
+        if upcast_dtype is not None:
+            for param in model.parameters():
+                if param.requires_grad:
+                    param.data = param.to(upcast_dtype)
        return model
-    
-    
+
+
+    def mapping_lora_state_dict(self, state_dict):
+        new_state_dict = {}
+        for key, value in state_dict.items():
+            if "lora_A.weight" in key or "lora_B.weight" in key:
+                new_key = key.replace("lora_A.weight", "lora_A.default.weight").replace("lora_B.weight", "lora_B.default.weight")
+                new_state_dict[new_key] = value
+            elif "lora_A.default.weight" in key or "lora_B.default.weight" in key:
+                new_state_dict[key] = value
+        return new_state_dict
+
+
    def export_trainable_state_dict(self, state_dict, remove_prefix=None):
        trainable_param_names = self.trainable_param_names()
        state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names}
@@ -357,7 +419,60 @@ class DiffusionTrainingModule(torch.nn.Module):
                state_dict_[name] = param
            state_dict = state_dict_
        return state_dict
-
+    
+    
+    def transfer_data_to_device(self, data, device):
+        for key in data:
+            if isinstance(data[key], torch.Tensor):
+                data[key] = data[key].to(device)
+        return data
+    
+    
+    def parse_model_configs(self, model_paths, model_id_with_origin_paths, enable_fp8_training=False):
+        offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None
+        model_configs = []
+        if model_paths is not None:
+            model_paths = json.loads(model_paths)
+            model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths]
+        if model_id_with_origin_paths is not None:
+            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
+            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths]
+        return model_configs
+    
+    
+    def switch_pipe_to_training_mode(
+        self,
+        pipe,
+        trainable_models,
+        lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=None,
+        enable_fp8_training=False,
+    ):
+        # Scheduler
+        pipe.scheduler.set_timesteps(1000, training=True)
+        
+        # Freeze untrainable models
+        pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
+        
+        # Enable FP8 if pipeline supports
+        if enable_fp8_training and hasattr(pipe, "_enable_fp8_lora_training"):
+            pipe._enable_fp8_lora_training(torch.float8_e4m3fn)
+        
+        # Add LoRA to the base models
+        if lora_base_model is not None:
+            model = self.add_lora_to_model(
+                getattr(pipe, lora_base_model),
+                target_modules=lora_target_modules.split(","),
+                lora_rank=lora_rank,
+                upcast_dtype=pipe.torch_dtype,
+            )
+            if lora_checkpoint is not None:
+                state_dict = load_state_dict(lora_checkpoint)
+                state_dict = self.mapping_lora_state_dict(state_dict)
+                load_result = model.load_state_dict(state_dict, strict=False)
+                print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys")
+                if len(load_result[1]) > 0:
+                    print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}")
+            setattr(pipe, lora_base_model, model)


 class ModelLogger:
@@ -405,14 +520,26 @@ def launch_training_task(
    dataset: torch.utils.data.Dataset,
    model: DiffusionTrainingModule,
    model_logger: ModelLogger,
-    optimizer: torch.optim.Optimizer,
-    scheduler: torch.optim.lr_scheduler.LRScheduler,
+    learning_rate: float = 1e-5,
+    weight_decay: float = 1e-2,
    num_workers: int = 8,
    save_steps: int = None,
    num_epochs: int = 1,
    gradient_accumulation_steps: int = 1,
    find_unused_parameters: bool = False,
+    args = None,
 ):
+    if args is not None:
+        learning_rate = args.learning_rate
+        weight_decay = args.weight_decay
+        num_workers = args.dataset_num_workers
+        save_steps = args.save_steps
+        num_epochs = args.num_epochs
+        gradient_accumulation_steps = args.gradient_accumulation_steps
+        find_unused_parameters = args.find_unused_parameters
+    
+    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)
    accelerator = Accelerator(
        gradient_accumulation_steps=gradient_accumulation_steps,
@@ -424,7 +551,10 @@ def launch_training_task(
        for data in tqdm(dataloader):
            with accelerator.accumulate(model):
                optimizer.zero_grad()
-                loss = model(data)
+                if dataset.load_from_cache:
+                    loss = model({}, inputs=data)
+                else:
+                    loss = model(data)
                accelerator.backward(loss)
                optimizer.step()
                model_logger.on_step_end(accelerator, model, save_steps)
@@ -434,16 +564,28 @@ def launch_training_task(
    model_logger.on_training_end(accelerator, model, save_steps)


-def launch_data_process_task(model: DiffusionTrainingModule, dataset, output_path="./models"):
-    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0])
+def launch_data_process_task(
+    dataset: torch.utils.data.Dataset,
+    model: DiffusionTrainingModule,
+    model_logger: ModelLogger,
+    num_workers: int = 8,
+    args = None,
+):
+    if args is not None:
+        num_workers = args.dataset_num_workers
+        
+    dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
    accelerator = Accelerator()
    model, dataloader = accelerator.prepare(model, dataloader)
-    os.makedirs(os.path.join(output_path, "data_cache"), exist_ok=True)
-    for data_id, data in enumerate(tqdm(dataloader)):
-        with torch.no_grad():
-            inputs = model.forward_preprocess(data)
-            inputs = {key: inputs[key] for key in model.model_input_keys if key in inputs}
-            torch.save(inputs, os.path.join(output_path, "data_cache", f"{data_id}.pth"))
+    
+    for data_id, data in tqdm(enumerate(dataloader)):
+        with accelerator.accumulate(model):
+            with torch.no_grad():
+                folder = os.path.join(model_logger.output_path, str(accelerator.process_index))
+                os.makedirs(folder, exist_ok=True)
+                save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth")
+                data = model(data, return_inputs=True)
+                torch.save(data, save_path)



@@ -467,6 +609,7 @@ def wan_parser():
    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
+    parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
@@ -475,6 +618,7 @@ def wan_parser():
    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
+    parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
    return parser


@@ -498,6 +642,7 @@ def flux_parser():
    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
+    parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
    parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
    parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
@@ -506,6 +651,7 @@ def flux_parser():
    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
+    parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
    return parser


@@ -530,12 +676,16 @@ def qwen_image_parser():
    parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.")
    parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.")
    parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.")
+    parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.")
    parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.")
-    parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.")
    parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.")
    parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.")
    parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
    parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.")
    parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.")
    parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
+    parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
+    parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.")
+    parser.add_argument("--enable_fp8_training", default=False, action="store_true", help="Whether to enable FP8 training. Only available for LoRA training on a single GPU.")
+    parser.add_argument("--task", type=str, default="sft", required=False, help="Task type.")
    return parser
--- a/diffsynth/utils/init.py
+++ b/diffsynth/utils/init.py
@@ -139,6 +139,20 @@ class BasePipeline(torch.nn.Module):
            else:
                model.eval()
                model.requires_grad_(False)
+                
+    
+    def blend_with_mask(self, base, addition, mask):
+        return base * (1 - mask) + addition * mask
+    
+    
+    def step(self, scheduler, latents, progress_id, noise_pred, input_latents=None, inpaint_mask=None, **kwargs):
+        timestep = scheduler.timesteps[progress_id]
+        if inpaint_mask is not None:
+            noise_pred_expected = scheduler.return_to_timestep(scheduler.timesteps[progress_id], latents, input_latents)
+            noise_pred = self.blend_with_mask(noise_pred_expected, noise_pred, inpaint_mask)
+        latents_next = scheduler.step(noise_pred, timestep, latents)
+        return latents_next
+


@dataclass
--- a/examples/flux/README.md
+++ b/examples/flux/README.md
@@ -255,6 +255,7 @@ The script includes the following parameters:
  * `--model_id_with_origin_paths`: Model ID with original paths, e.g., black-forest-labs/FLUX.1-dev:flux1-dev.safetensors. Separate with commas.
 * Training
  * `--learning_rate`: Learning rate.
+  * `--weight_decay`: Weight decay.
  * `--num_epochs`: Number of epochs.
  * `--output_path`: Save path.
  * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint.
@@ -265,6 +266,7 @@ The script includes the following parameters:
  * `--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 to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.
 * Extra Model Inputs
  * `--extra_inputs`: Extra model inputs, separated by commas.
 * VRAM Management
--- a/examples/flux/README_zh.md
+++ b/examples/flux/README_zh.md
@@ -255,6 +255,7 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
  * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 black-forest-labs/FLUX.1-dev:flux1-dev.safetensors。用逗号分隔。
 * 训练
  * `--learning_rate`: 学习率。
+  * `--weight_decay`：权重衰减大小。
  * `--num_epochs`: 轮数（Epoch）。
  * `--output_path`: 保存路径。
  * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
@@ -265,6 +266,7 @@ FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_tra
  * `--lora_base_model`: LoRA 添加到哪个模型上。
  * `--lora_target_modules`: LoRA 添加到哪一层上。
  * `--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。
 * 额外模型输入
  * `--extra_inputs`: 额外的模型输入，以逗号分隔。
 * 显存管理
--- a/examples/flux/model_training/train.py
+++ b/examples/flux/model_training/train.py
@@ -1,7 +1,9 @@
 import torch, os, json
+from diffsynth import load_state_dict
 from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput
-from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, flux_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, flux_parser
 from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"


@@ -11,37 +13,23 @@ class FluxTrainingModule(DiffusionTrainingModule):
        self,
        model_paths=None, model_id_with_origin_paths=None,
        trainable_models=None,
-        lora_base_model=None, lora_target_modules="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", lora_rank=32,
+        lora_base_model=None, lora_target_modules="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", lora_rank=32, lora_checkpoint=None,
        use_gradient_checkpointing=True,
        use_gradient_checkpointing_offload=False,
        extra_inputs=None,
    ):
        super().__init__()
        # Load models
-        model_configs = []
-        if model_paths is not None:
-            model_paths = json.loads(model_paths)
-            model_configs += [ModelConfig(path=path) for path in model_paths]
-        if model_id_with_origin_paths is not None:
-            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
-            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths]
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False)
        self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
        
-        # Reset training scheduler
-        self.pipe.scheduler.set_timesteps(1000, training=True)
+        # Training mode
+        self.switch_pipe_to_training_mode(
+            self.pipe, trainable_models,
+            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
+            enable_fp8_training=False,
+        )
        
-        # Freeze untrainable models
-        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
-        
-        # Add LoRA to the base models
-        if lora_base_model is not None:
-            model = self.add_lora_to_model(
-                getattr(self.pipe, lora_base_model),
-                target_modules=lora_target_modules.split(","),
-                lora_rank=lora_rank
-            )
-            setattr(self.pipe, lora_base_model, model)
-            
        # Store other configs
        self.use_gradient_checkpointing = use_gradient_checkpointing
        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
@@ -98,7 +86,20 @@ class FluxTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
    parser = flux_parser()
    args = parser.parse_args()
-    dataset = ImageDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
    model = FluxTrainingModule(
        model_paths=args.model_paths,
        model_id_with_origin_paths=args.model_id_with_origin_paths,
@@ -106,6 +107,7 @@ if __name__ == "__main__":
        lora_base_model=args.lora_base_model,
        lora_target_modules=args.lora_target_modules,
        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
        use_gradient_checkpointing=args.use_gradient_checkpointing,
        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
        extra_inputs=args.extra_inputs,
@@ -115,13 +117,4 @@ if __name__ == "__main__":
        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
        state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x,
    )
-    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate)
-    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
-    launch_training_task(
-        dataset, model, model_logger, optimizer, scheduler,
-        num_epochs=args.num_epochs,
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        save_steps=args.save_steps,
-        find_unused_parameters=args.find_unused_parameters,
-        num_workers=args.dataset_num_workers,
-    )
+    launch_training_task(dataset, model, model_logger, args=args)
--- a/examples/qwen_image/README.md
+++ b/examples/qwen_image/README.md
@@ -20,9 +20,9 @@ Run the following code to quickly load the [Qwen/Qwen-Image](https://www.modelsc

 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from PIL import Image
 import torch

-
 pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
@@ -34,17 +34,28 @@ pipe = QwenImagePipeline.from_pretrained(
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
 )
 prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful."
-image = pipe(prompt, seed=0, num_inference_steps=40)
+image = pipe(
+    prompt, seed=0, num_inference_steps=40,
+    # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit
+)
 image.save("image.jpg")
 ```

 ## Model Overview

-|Model ID|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](./model_inference/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
-|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-Full.py)|
-|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
+|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](./model_inference/Qwen-Image.py)|[code](./model_inference_low_vram/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./model_inference/Qwen-Image-Edit.py)|[code](./model_inference_low_vram/Qwen-Image-Edit.py)|[code](./model_training/full/Qwen-Image-Edit.sh)|[code](./model_training/validate_full/Qwen-Image-Edit.py)|[code](./model_training/lora/Qwen-Image-Edit.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./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](./model_inference/Qwen-Image-Distill-LoRA.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-LoRA.py)|
+|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./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](./model_inference/Qwen-Image-EliGen-V2.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./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](./model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./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](./model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./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](./model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./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](./model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|

 ## Model Inference

@@ -174,10 +185,13 @@ After enabling VRAM management, the framework will automatically choose a memory
 <summary>Inference Acceleration</summary>

 * FP8 Quantization: Choose the appropriate quantization method based on your hardware and requirements.
-    * GPUs that do not support FP8 computation (e.g., A100, 4090, etc.): FP8 quantization will only reduce VRAM usage without speeding up inference. Code: [./model_inference_lor_vram/Qwen-Image.py](./model_inference_lor_vram/Qwen-Image.py)
+    * GPUs that do not support FP8 computation (e.g., A100, 4090, etc.): FP8 quantization will only reduce VRAM usage without speeding up inference. Code: [./model_inference_low_vram/Qwen-Image.py](./model_inference_low_vram/Qwen-Image.py)
    * GPUs that support FP8 operations (e.g., H200, etc.): Please install [Flash Attention 3](https://github.com/Dao-AILab/flash-attention). Otherwise, FP8 acceleration will only apply to Linear layers.
        * Faster inference but higher VRAM usage: Use [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py)
        * Slightly slower inference but lower VRAM usage: Use [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py)
+* Distillation acceleration: We trained two distillation models for fast inference at `cfg_scale=1` and `num_inference_steps=15`.
+    * [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full): Full distillation version. Better image quality but lower LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py).
+    * [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA): LoRA distillation version. Slightly lower image quality but better LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py).

 </details>

@@ -229,8 +243,10 @@ The script includes the following parameters:
  * `--model_paths`: Model paths to load. In JSON format.
  * `--model_id_with_origin_paths`: Model ID with original paths, e.g., Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors. Separate with commas.
  * `--tokenizer_path`: Tokenizer path. Leave empty to auto-download.
+  * `--processor_path`: Path to the processor of Qwen-Image-Edit. Leave empty to auto-download.
 * Training
  * `--learning_rate`: Learning rate.
+  * `--weight_decay`: Weight decay.
  * `--num_epochs`: Number of epochs.
  * `--output_path`: Save path.
  * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint.
@@ -241,14 +257,13 @@ The script includes the following parameters:
  * `--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 to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.
 * Extra Model Inputs
  * `--extra_inputs`: Extra model inputs, separated by commas.
 * VRAM Management
  * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing.
  * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to CPU memory.
  * `--gradient_accumulation_steps`: Number of gradient accumulation steps.
-* Others
-  * `--align_to_opensource_format`: Whether to align DiT LoRA format with open-source version. Only works for LoRA training.

 In addition, the training framework is built on [`accelerate`](https://huggingface.co/docs/accelerate/index). Run `accelerate config` before training to set GPU-related settings. For some training tasks (e.g., full training of 20B model), we provide suggested `accelerate` config files. Check the corresponding training script for details.

--- a/examples/qwen_image/README_zh.md
+++ b/examples/qwen_image/README_zh.md
@@ -20,9 +20,9 @@ pip install -e .

 ```python
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from PIL import Image
 import torch

-
 pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
@@ -34,17 +34,28 @@ pipe = QwenImagePipeline.from_pretrained(
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
 )
 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 = pipe(
+    prompt, seed=0, num_inference_steps=40,
+    # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit
+)
 image.save("image.jpg")
 ```

 ## 模型总览

-|模型 ID|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](./model_inference/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
-|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-Full.py)|
-|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
+|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证|
+|-|-|-|-|-|-|-|
+|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./model_inference/Qwen-Image.py)|[code](./model_inference_low_vram/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)|
+|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./model_inference/Qwen-Image-Edit.py)|[code](./model_inference_low_vram/Qwen-Image-Edit.py)|[code](./model_training/full/Qwen-Image-Edit.sh)|[code](./model_training/validate_full/Qwen-Image-Edit.py)|[code](./model_training/lora/Qwen-Image-Edit.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit.py)|
+|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./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](./model_inference/Qwen-Image-Distill-LoRA.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-LoRA.py)|
+|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./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](./model_inference/Qwen-Image-EliGen-V2.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)|
+|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./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](./model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./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](./model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./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](./model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./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](./model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-|

 ## 模型推理

@@ -174,10 +185,13 @@ FP8 量化能够大幅度减少显存占用，但不会加速，部分模型在
 <summary>推理加速</summary>

 * FP8 量化：根据您的硬件与需求，请选择合适的量化方式
-    * GPU 不支持 FP8 计算（例如 A100、4090 等）：FP8 量化仅能降低显存占用，无法加速，代码：[./model_inference_lor_vram/Qwen-Image.py](./model_inference_lor_vram/Qwen-Image.py)
+    * GPU 不支持 FP8 计算（例如 A100、4090 等）：FP8 量化仅能降低显存占用，无法加速，代码：[./model_inference_low_vram/Qwen-Image.py](./model_inference_low_vram/Qwen-Image.py)
    * GPU 支持 FP8 运算（例如 H200 等）：请安装 [Flash Attention 3](https://github.com/Dao-AILab/flash-attention)，否则 FP8 加速仅对 Linear 层生效
        * 更快的速度，但更大的显存：请使用 [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py)
        * 稍慢的速度，但更小的显存：请使用 [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py)
+* 蒸馏加速：我们训练了两个蒸馏加速模型，可以在 `cfg_scale=1` 和 `num_inference_steps=15` 设置下进行快速推理
+    * [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)：全量蒸馏训练版本，更好的生成效果，稍差的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py)
+    * [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)：LoRA 蒸馏训练版本，稍差的生成效果，更好的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py)

 </details>

@@ -229,8 +243,10 @@ Qwen-Image 系列模型训练通过统一的 [`./model_training/train.py`](./mod
  * `--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 Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors。用逗号分隔。
  * `--tokenizer_path`: tokenizer 路径，留空将会自动下载。
+  * `--processor_path`：Qwen-Image-Edit 的 processor 路径。留空则自动下载。
 * 训练
  * `--learning_rate`: 学习率。
+  * `--weight_decay`：权重衰减大小。
  * `--num_epochs`: 轮数（Epoch）。
  * `--output_path`: 保存路径。
  * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
@@ -241,14 +257,13 @@ Qwen-Image 系列模型训练通过统一的 [`./model_training/train.py`](./mod
  * `--lora_base_model`: LoRA 添加到哪个模型上。
  * `--lora_target_modules`: LoRA 添加到哪一层上。
  * `--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。
 * 额外模型输入
  * `--extra_inputs`: 额外的模型输入，以逗号分隔。
 * 显存管理
  * `--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。
-* 其他
-  * `--align_to_opensource_format`: 是否将 DiT LoRA 的格式与开源版本对齐，仅对 LoRA 训练生效。

 此外，训练框架基于 [`accelerate`](https://huggingface.co/docs/accelerate/index) 构建，在开始训练前运行 `accelerate config` 可配置 GPU 的相关参数。对于部分模型训练（例如 20B 模型的全量训练）脚本，我们提供了建议的 `accelerate` 配置文件，可在对应的训练脚本中查看。

--- a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py
@@ -0,0 +1,32 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="depth/image_1.jpg"
+)
+
+controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
+
+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,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py
@@ -0,0 +1,33 @@
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "a cat with sunglasses"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    num_inference_steps=40,
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py
@@ -0,0 +1,20 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import snapshot_download
+import torch
+
+snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA")
+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/"),
+)
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors")
+
+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=15, cfg_scale=1)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py
@@ -0,0 +1,26 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+from modelscope import snapshot_download
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit", 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=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors")
+
+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=prompt, seed=0, num_inference_steps=40, height=1024, width=768)
+image.save("image.jpg")
+
+prompt = "将裙子变成粉色"
+image = image.resize((512, 384))
+image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False)
+image.save(f"image2.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-Edit.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-Edit.py
@@ -0,0 +1,26 @@
+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-Edit", 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=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+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。"
+input_image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1328, width=1024)
+input_image.save("image1.jpg")
+
+prompt = "将裙子改为粉色"
+# edit_image_auto_resize=True: auto resize input image to match the area of 1024*1024 with the original aspect ratio
+image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True)
+image.save(f"image2.jpg")
+
+# edit_image_auto_resize=False: do not resize input image
+image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=False)
+image.save(f"image3.jpg")
--- a/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py
@@ -0,0 +1,106 @@
+import torch
+import random
+from PIL import Image, ImageDraw, ImageFont
+from modelscope import dataset_snapshot_download, snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+
+def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
+    # Create a blank image for overlays
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+
+    colors = [
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+    ]
+    # Generate random colors for each mask
+    if use_random_colors:
+        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
+
+    # Font settings
+    try:
+        font = ImageFont.truetype("wqy-zenhei.ttc", font_size)  # Adjust as needed
+    except IOError:
+        font = ImageFont.load_default(font_size)
+
+    # Overlay each mask onto the overlay image
+    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
+        # Convert mask to RGBA mode
+        mask_rgba = mask.convert('RGBA')
+        mask_data = mask_rgba.getdata()
+        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
+        mask_rgba.putdata(new_data)
+
+        # Draw the mask prompt text on the mask
+        draw = ImageDraw.Draw(mask_rgba)
+        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
+        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
+        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
+
+        # Alpha composite the overlay with this mask
+        overlay = Image.alpha_composite(overlay, mask_rgba)
+
+    # Composite the overlay onto the original image
+    result = Image.alpha_composite(image.convert('RGBA'), overlay)
+
+    # Save or display the resulting image
+    result.save(output_path)
+
+    return result
+
+def example(pipe, seeds, example_id, global_prompt, entity_prompts):
+    dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
+    masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))]
+    negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
+    for seed in seeds:
+        # generate image
+        image = pipe(
+            prompt=global_prompt,
+            cfg_scale=4.0,
+            negative_prompt=negative_prompt,
+            num_inference_steps=40,
+            seed=seed,
+            height=1024,
+            width=1024,
+            eligen_entity_prompts=entity_prompts,
+            eligen_entity_masks=masks,
+        )
+        image.save(f"eligen_example_{example_id}_{seed}.png")
+        visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
+
+
+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/"),
+)
+snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors")
+
+seeds = [0]
+
+global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background."
+entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
+example(pipe, seeds, 7, global_prompt, entity_prompts)
+
+global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。"
+entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"]
+example(pipe, seeds, 4, global_prompt, entity_prompts)
--- a/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py
+++ b/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py
@@ -0,0 +1,35 @@
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download, snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth.controlnets.processors import Annotator
+
+allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"]
+snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern)
+
+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/"),
+)
+snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors")
+
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg")
+origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024))
+annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal']
+for annotator_id in annotator_ids:
+    annotator = Annotator(processor_id=annotator_id, device="cuda")
+    control_image = annotator(origin_image)
+    control_image.save(f"{annotator.processor_id}.png")
+
+    control_prompt = "Context_Control. "
+    prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。"
+    negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
+    image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024)
+    image.save(f"image_{annotator.processor_id}.png")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py
@@ -0,0 +1,32 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py
@@ -0,0 +1,33 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="depth/image_1.jpg"
+)
+
+controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
+
+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,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py
@@ -0,0 +1,34 @@
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+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", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "a cat with sunglasses"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
+image = pipe(
+    prompt, seed=0,
+    input_image=controlnet_image, inpaint_mask=inpaint_mask,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    num_inference_steps=40,
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py
@@ -0,0 +1,22 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from modelscope import snapshot_download
+import torch
+
+# Please do not use float8 on this model
+snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA")
+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", offload_device="cpu"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors")
+
+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=15, cfg_scale=1)
+image.save("image.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py
@@ -0,0 +1,28 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+from modelscope import snapshot_download
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+pipe.enable_vram_management()
+
+snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors")
+
+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=prompt, seed=0, num_inference_steps=40, height=1024, width=768)
+image.save("image.jpg")
+
+prompt = "将裙子变成粉色"
+image = image.resize((512, 384))
+image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False)
+image.save(f"image2.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py
@@ -0,0 +1,23 @@
+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-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+pipe.enable_vram_management()
+
+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=prompt, seed=0, num_inference_steps=40, height=1024, width=1024)
+image.save("image1.jpg")
+
+prompt = "将裙子改为粉色"
+image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=1024)
+image.save(f"image2.jpg")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py
@@ -0,0 +1,108 @@
+import torch
+import random
+from PIL import Image, ImageDraw, ImageFont
+from modelscope import dataset_snapshot_download, snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+
+
+def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
+    # Create a blank image for overlays
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+
+    colors = [
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+    ]
+    # Generate random colors for each mask
+    if use_random_colors:
+        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
+
+    # Font settings
+    try:
+        font = ImageFont.truetype("wqy-zenhei.ttc", font_size)  # Adjust as needed
+    except IOError:
+        font = ImageFont.load_default(font_size)
+
+    # Overlay each mask onto the overlay image
+    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
+        # Convert mask to RGBA mode
+        mask_rgba = mask.convert('RGBA')
+        mask_data = mask_rgba.getdata()
+        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
+        mask_rgba.putdata(new_data)
+
+        # Draw the mask prompt text on the mask
+        draw = ImageDraw.Draw(mask_rgba)
+        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
+        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
+        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
+
+        # Alpha composite the overlay with this mask
+        overlay = Image.alpha_composite(overlay, mask_rgba)
+
+    # Composite the overlay onto the original image
+    result = Image.alpha_composite(image.convert('RGBA'), overlay)
+
+    # Save or display the resulting image
+    result.save(output_path)
+
+    return result
+
+def example(pipe, seeds, example_id, global_prompt, entity_prompts):
+    dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
+    masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))]
+    negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
+    for seed in seeds:
+        # generate image
+        image = pipe(
+            prompt=global_prompt,
+            cfg_scale=4.0,
+            negative_prompt=negative_prompt,
+            num_inference_steps=40,
+            seed=seed,
+            height=1024,
+            width=1024,
+            eligen_entity_prompts=entity_prompts,
+            eligen_entity_masks=masks,
+        )
+        image.save(f"eligen_example_{example_id}_{seed}.png")
+        visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
+
+
+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", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors")
+
+seeds = [0]
+
+global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background."
+entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
+example(pipe, seeds, 7, global_prompt, entity_prompts)
+
+global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。"
+entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"]
+example(pipe, seeds, 4, global_prompt, entity_prompts)
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py
@@ -0,0 +1,129 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+import torch
+from PIL import Image, ImageDraw, ImageFont
+from modelscope import dataset_snapshot_download, snapshot_download
+import random
+
+
+def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False):
+    # Create a blank image for overlays
+    overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
+
+    colors = [
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+        (165, 238, 173, 80),
+        (76, 102, 221, 80),
+        (221, 160, 77, 80),
+        (204, 93, 71, 80),
+        (145, 187, 149, 80),
+        (134, 141, 172, 80),
+        (157, 137, 109, 80),
+        (153, 104, 95, 80),
+    ]
+    # Generate random colors for each mask
+    if use_random_colors:
+        colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
+
+    # Font settings
+    try:
+        font = ImageFont.truetype("wqy-zenhei.ttc", font_size)  # Adjust as needed
+    except IOError:
+        font = ImageFont.load_default(font_size)
+
+    # Overlay each mask onto the overlay image
+    for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
+        # Convert mask to RGBA mode
+        mask_rgba = mask.convert('RGBA')
+        mask_data = mask_rgba.getdata()
+        new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
+        mask_rgba.putdata(new_data)
+
+        # Draw the mask prompt text on the mask
+        draw = ImageDraw.Draw(mask_rgba)
+        mask_bbox = mask.getbbox()  # Get the bounding box of the mask
+        text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10)  # Adjust text position based on mask position
+        draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
+
+        # Alpha composite the overlay with this mask
+        overlay = Image.alpha_composite(overlay, mask_rgba)
+
+    # Composite the overlay onto the original image
+    result = Image.alpha_composite(image.convert('RGBA'), overlay)
+
+    # Save or display the resulting image
+    result.save(output_path)
+
+    return result
+
+def example(pipe, seeds, example_id, global_prompt, entity_prompts):
+    dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png")
+    masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
+    negative_prompt = ""
+    for seed in seeds:
+        # generate image
+        image = pipe(
+            prompt=global_prompt,
+            cfg_scale=4.0,
+            negative_prompt=negative_prompt,
+            num_inference_steps=30,
+            seed=seed,
+            height=1024,
+            width=1024,
+            eligen_entity_prompts=entity_prompts,
+            eligen_entity_masks=masks,
+        )
+        image.save(f"eligen_example_{example_id}_{seed}.png")
+        visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png")
+
+
+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", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors")
+
+# example 1
+global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n"
+entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"]
+example(pipe, [0], 1, global_prompt, entity_prompts)
+
+# example 2
+global_prompt = "samurai girl wearing a kimono, she's holding a sword  glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render."
+entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"]
+example(pipe, [0], 2, global_prompt, entity_prompts)
+
+# example 3
+global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning,"
+entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"]
+example(pipe, [27], 3, global_prompt, entity_prompts)
+
+# example 4
+global_prompt = "A beautiful girl wearing shirt and shorts in the street,  holding a sign 'Entity Control'"
+entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"]
+example(pipe, [21], 4, global_prompt, entity_prompts)
+
+# example 5
+global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere."
+entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"]
+example(pipe, [0], 5, global_prompt, entity_prompts)
+
+# example 7, same prompt with different seeds
+seeds = range(5, 9)
+global_prompt = "A beautiful asia woman wearing white dress, holding a mirror, with a forest background."
+entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"]
+example(pipe, seeds, 7, global_prompt, entity_prompts)
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py
@@ -0,0 +1,36 @@
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download, snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth.controlnets.processors import Annotator
+
+allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"]
+snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern)
+
+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", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn),
+        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.enable_vram_management()
+snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors")
+pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors")
+
+dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg")
+origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024))
+annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal']
+for annotator_id in annotator_ids:
+    annotator = Annotator(processor_id=annotator_id, device="cuda")
+    control_image = annotator(origin_image)
+    control_image.save(f"{annotator.processor_id}.png")
+
+    control_prompt = "Context_Control. "
+    prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。"
+    negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴"
+    image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024)
+    image.save(f"image_{annotator.processor_id}.png")
--- a/examples/qwen_image/model_inference_low_vram/Qwen-Image.py
+++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image.py
--- a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh
@@ -0,0 +1,38 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \
+  --data_file_keys "image,blockwise_controlnet_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \
+  --trainable_models "blockwise_controlnet" \
+  --extra_inputs "blockwise_controlnet_image" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
+
+# If you want to pre-train a Blockwise ControlNet from scratch,
+# please run the following script to first generate the initialized model weights file,
+# and then start training with a high learning rate (1e-3).
+
+# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py
+
+# accelerate launch examples/qwen_image/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \
+#   --data_file_keys "image,blockwise_controlnet_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --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_paths '["models/blockwise_controlnet.safetensors"]' \
+#   --learning_rate 1e-3 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+#   --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \
+#   --trainable_models "blockwise_controlnet" \
+#   --extra_inputs "blockwise_controlnet_image" \
+#   --use_gradient_checkpointing \
+#   --find_unused_parameters
--- a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh
@@ -0,0 +1,38 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \
+  --data_file_keys "image,blockwise_controlnet_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \
+  --trainable_models "blockwise_controlnet" \
+  --extra_inputs "blockwise_controlnet_image" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
+
+# If you want to pre-train a Blockwise ControlNet from scratch,
+# please run the following script to first generate the initialized model weights file,
+# and then start training with a high learning rate (1e-3).
+
+# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py
+
+# accelerate launch examples/qwen_image/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \
+#   --data_file_keys "image,blockwise_controlnet_image" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --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_paths '["models/blockwise_controlnet.safetensors"]' \
+#   --learning_rate 1e-3 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+#   --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \
+#   --trainable_models "blockwise_controlnet" \
+#   --extra_inputs "blockwise_controlnet_image" \
+#   --use_gradient_checkpointing \
+#   --find_unused_parameters
--- a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh
@@ -0,0 +1,38 @@
+accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \
+  --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \
+  --trainable_models "blockwise_controlnet" \
+  --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
+
+# If you want to pre-train a Inpaint Blockwise ControlNet from scratch,
+# please run the following script to first generate the initialized model weights file,
+# and then start training with a high learning rate (1e-3).
+
+# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py
+
+# accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \
+#   --dataset_base_path data/example_image_dataset \
+#   --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \
+#   --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+#   --max_pixels 1048576 \
+#   --dataset_repeat 50 \
+#   --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_paths '["models/blockwise_controlnet_inpaint.safetensors"]' \
+#   --learning_rate 1e-3 \
+#   --num_epochs 2 \
+#   --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \
+#   --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \
+#   --trainable_models "blockwise_controlnet" \
+#   --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+#   --use_gradient_checkpointing \
+#   --find_unused_parameters
--- a/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh
@@ -9,4 +9,5 @@ accelerate launch --config_file examples/qwen_image/model_training/full/accelera
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Qwen-Image-Distill-Full_full" \
  --trainable_models "dit" \
-  --use_gradient_checkpointing
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh
@@ -0,0 +1,15 @@
+accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_edit.csv \
+  --data_file_keys "image,edit_image" \
+  --extra_inputs "edit_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-5 \
+  --num_epochs 2 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit_full" \
+  --trainable_models "dit" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/full/Qwen-Image.sh
+++ b/examples/qwen_image/model_training/full/Qwen-Image.sh
@@ -9,4 +9,5 @@ accelerate launch --config_file examples/qwen_image/model_training/full/accelera
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Qwen-Image_full" \
  --trainable_models "dit" \
-  --use_gradient_checkpointing
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/full/accelerate_config.yaml
+++ b/examples/qwen_image/model_training/full/accelerate_config.yaml
@@ -0,0 +1,22 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh
@@ -0,0 +1,17 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \
+  --data_file_keys "image,blockwise_controlnet_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --extra_inputs "blockwise_controlnet_image" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh
@@ -0,0 +1,17 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \
+  --data_file_keys "image,blockwise_controlnet_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --extra_inputs "blockwise_controlnet_image" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh
@@ -0,0 +1,17 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \
+  --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh
@@ -11,5 +11,5 @@ accelerate launch examples/qwen_image/model_training/train.py \
  --lora_base_model "dit" \
  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
  --lora_rank 32 \
-  --align_to_opensource_format \
-  --use_gradient_checkpointing
+  --use_gradient_checkpointing \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh
@@ -0,0 +1,24 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_distill_qwen_image.csv \
+  --data_file_keys "image" \
+  --extra_inputs "seed,rand_device,num_inference_steps,cfg_scale" \
+  --height 1328 \
+  --width 1328 \
+  --dataset_repeat 50 \
+  --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" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Distill-LoRA_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --task direct_distill
+
+# This is an experimental training feature designed to directly distill the model, enabling generation results with fewer steps to approximate those achieved with more steps.
+# The model (https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) is trained using this script.
+# The sample dataset is provided solely to demonstrate the dataset format. For actual usage, please construct a larger dataset using the base model.
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh
@@ -0,0 +1,18 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata_edit.csv \
+  --data_file_keys "image,edit_image" \
+  --extra_inputs "edit_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-Edit_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh
@@ -12,7 +12,6 @@ accelerate launch examples/qwen_image/model_training/train.py \
  --lora_base_model "dit" \
  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
  --lora_rank 32 \
-  --align_to_opensource_format \
  --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \
  --use_gradient_checkpointing \
  --find_unused_parameters
--- a/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh
@@ -0,0 +1,20 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path "data/example_image_dataset" \
+  --dataset_metadata_path data/example_image_dataset/metadata_qwenimage_context.csv \
+  --data_file_keys "image,context_image" \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --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" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image-In-Context-Control-Union_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 64 \
+  --lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors" \
+  --extra_inputs "context_image" \
+  --use_gradient_checkpointing \
+  --find_unused_parameters
+
+# if you want to train from scratch, you can remove the --lora_checkpoint argument
--- a/examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh
@@ -0,0 +1,26 @@
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path data/example_image_dataset \
+  --dataset_metadata_path data/example_image_dataset/metadata.csv \
+  --max_pixels 1048576 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
+  --output_path "./models/train/Qwen-Image_lora_cache" \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --task data_process
+
+accelerate launch examples/qwen_image/model_training/train.py \
+  --dataset_base_path models/train/Qwen-Image_lora_cache \
+  --max_pixels 1048576 \
+  --dataset_repeat 50 \
+  --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Qwen-Image_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
+  --lora_rank 32 \
+  --use_gradient_checkpointing \
+  --dataset_num_workers 8 \
+  --find_unused_parameters \
+  --enable_fp8_training
--- a/examples/qwen_image/model_training/lora/Qwen-Image.sh
+++ b/examples/qwen_image/model_training/lora/Qwen-Image.sh
@@ -11,7 +11,6 @@ accelerate launch examples/qwen_image/model_training/train.py \
  --lora_base_model "dit" \
  --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \
  --lora_rank 32 \
-  --align_to_opensource_format \
  --use_gradient_checkpointing \
  --dataset_num_workers 8 \
  --find_unused_parameters
--- a/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py
+++ b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py
@@ -0,0 +1,13 @@
+# This script is for initializing a Qwen-Image-Blockwise-ControlNet
+from diffsynth import hash_state_dict_keys
+from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet
+import torch
+from safetensors.torch import save_file
+
+
+controlnet = QwenImageBlockWiseControlNet().to(dtype=torch.bfloat16, device="cuda")
+controlnet.init_weight()
+state_dict_controlnet = controlnet.state_dict()
+
+print(hash_state_dict_keys(state_dict_controlnet))
+save_file(state_dict_controlnet, "models/blockwise_controlnet.safetensors")
--- a/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py
+++ b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py
@@ -0,0 +1,12 @@
+# This script is for initializing a Inpaint Qwen-Image-ControlNet
+import torch
+from diffsynth import hash_state_dict_keys
+from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet
+from safetensors.torch import save_file
+
+controlnet = QwenImageBlockWiseControlNet(additional_in_dim=4).to(dtype=torch.bfloat16, device="cuda")
+controlnet.init_weight()
+state_dict_controlnet = controlnet.state_dict()
+
+print(hash_state_dict_keys(state_dict_controlnet))
+save_file(state_dict_controlnet, "models/blockwise_controlnet_inpaint.safetensors")
--- a/examples/qwen_image/model_training/train.py
+++ b/examples/qwen_image/model_training/train.py
@@ -1,7 +1,9 @@
 import torch, os, json
+from diffsynth import load_state_dict
 from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
-from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, qwen_image_parser
-from diffsynth.models.lora import QwenImageLoRAConverter
+from diffsynth.pipelines.flux_image_new import ControlNetInput
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, qwen_image_parser, launch_training_task, launch_data_process_task
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"


@@ -10,46 +12,34 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
    def __init__(
        self,
        model_paths=None, model_id_with_origin_paths=None,
-        tokenizer_path=None,
+        tokenizer_path=None, processor_path=None,
        trainable_models=None,
-        lora_base_model=None, lora_target_modules="", lora_rank=32,
+        lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None,
        use_gradient_checkpointing=True,
        use_gradient_checkpointing_offload=False,
        extra_inputs=None,
+        enable_fp8_training=False,
+        task="sft",
    ):
        super().__init__()
        # Load models
-        model_configs = []
-        if model_paths is not None:
-            model_paths = json.loads(model_paths)
-            model_configs += [ModelConfig(path=path) for path in model_paths]
-        if model_id_with_origin_paths is not None:
-            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
-            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths]
-        if tokenizer_path is not None:
-            self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=ModelConfig(tokenizer_path))
-        else:
-            self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=enable_fp8_training)
+        tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
+        processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path)
+        self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config)

-        # Reset training scheduler (do it in each training step)
-        self.pipe.scheduler.set_timesteps(1000, training=True)
+        # Training mode
+        self.switch_pipe_to_training_mode(
+            self.pipe, trainable_models,
+            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
+            enable_fp8_training=enable_fp8_training,
+        )
        
-        # Freeze untrainable models
-        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
-        
-        # Add LoRA to the base models
-        if lora_base_model is not None:
-            model = self.add_lora_to_model(
-                getattr(self.pipe, lora_base_model),
-                target_modules=lora_target_modules.split(","),
-                lora_rank=lora_rank
-            )
-            setattr(self.pipe, lora_base_model, model)
-            
        # Store other configs
        self.use_gradient_checkpointing = use_gradient_checkpointing
        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
        self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else []
+        self.task = task

    
    def forward_preprocess(self, data):
@@ -70,11 +60,22 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
            "rand_device": self.pipe.device,
            "use_gradient_checkpointing": self.use_gradient_checkpointing,
            "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload,
+            "edit_image_auto_resize": True,
        }
        
        # Extra inputs
+        controlnet_input, blockwise_controlnet_input = {}, {}
        for extra_input in self.extra_inputs:
-            inputs_shared[extra_input] = data[extra_input]
+            if extra_input.startswith("blockwise_controlnet_"):
+                blockwise_controlnet_input[extra_input.replace("blockwise_controlnet_", "")] = data[extra_input]
+            elif extra_input.startswith("controlnet_"):
+                controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input]
+            else:
+                inputs_shared[extra_input] = data[extra_input]
+        if len(controlnet_input) > 0:
+            inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)]
+        if len(blockwise_controlnet_input) > 0:
+            inputs_shared["blockwise_controlnet_inputs"] = [ControlNetInput(**blockwise_controlnet_input)]
        
        # Pipeline units will automatically process the input parameters.
        for unit in self.pipe.units:
@@ -82,10 +83,22 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
        return {**inputs_shared, **inputs_posi}
    
    
-    def forward(self, data, inputs=None):
+    def forward(self, data, inputs=None, return_inputs=False):
+        # Inputs
        if inputs is None: inputs = self.forward_preprocess(data)
-        models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
-        loss = self.pipe.training_loss(**models, **inputs)
+        else: inputs = self.transfer_data_to_device(inputs, self.pipe.device)
+        if return_inputs: return inputs
+        
+        # Loss
+        if self.task == "sft":
+            models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models}
+            loss = self.pipe.training_loss(**models, **inputs)
+        elif self.task == "data_process":
+            loss = inputs
+        elif self.task == "direct_distill":
+            loss = self.pipe.direct_distill_loss(**inputs)
+        else:
+            raise NotImplementedError(f"Unsupported task: {self.task}.")
        return loss


@@ -93,31 +106,40 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
    parser = qwen_image_parser()
    args = parser.parse_args()
-    dataset = ImageDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_image_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+        )
+    )
    model = QwenImageTrainingModule(
        model_paths=args.model_paths,
        model_id_with_origin_paths=args.model_id_with_origin_paths,
        tokenizer_path=args.tokenizer_path,
+        processor_path=args.processor_path,
        trainable_models=args.trainable_models,
        lora_base_model=args.lora_base_model,
        lora_target_modules=args.lora_target_modules,
        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
        use_gradient_checkpointing=args.use_gradient_checkpointing,
        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
        extra_inputs=args.extra_inputs,
+        enable_fp8_training=args.enable_fp8_training,
+        task=args.task,
    )
-    model_logger = ModelLogger(
-        args.output_path,
-        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt,
-        state_dict_converter=QwenImageLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x,
-    )
-    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate)
-    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
-    launch_training_task(
-        dataset, model, model_logger, optimizer, scheduler,
-        num_epochs=args.num_epochs,
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        save_steps=args.save_steps,
-        find_unused_parameters=args.find_unused_parameters,
-        num_workers=args.dataset_num_workers,
-    )
+    model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
+    launcher_map = {
+        "sft": launch_training_task,
+        "data_process": launch_data_process_task,
+        "direct_distill": launch_training_task,
+    }
+    launcher_map[args.task](dataset, model, model_logger, args=args)
--- a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py
+++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Canny_full/epoch-1.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py
+++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py
@@ -0,0 +1,31 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Depth_full/epoch-1.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="depth/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
+
+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,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py
+++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py
@@ -0,0 +1,32 @@
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+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"),
+        ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full/epoch-1.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "a cat with sunglasses"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024))
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    height=1024, width=1024,
+    num_inference_steps=40,
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py
+++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py
@@ -0,0 +1,23 @@
+import torch
+from PIL import Image
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+from diffsynth import load_state_dict
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit", 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=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+state_dict = load_state_dict("models/train/Qwen-Image-Edit_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+
+prompt = "将裙子改为粉色"
+image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024))
+image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024)
+image.save(f"image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py
@@ -0,0 +1,32 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora/epoch-4.safetensors")
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="canny/image_1.jpg"
+)
+controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
+
+prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py
@@ -0,0 +1,33 @@
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+from PIL import Image
+import torch
+from modelscope import dataset_snapshot_download
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora/epoch-4.safetensors")
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="depth/image_1.jpg"
+)
+
+controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
+
+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,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py
@@ -0,0 +1,34 @@
+import torch
+from PIL import Image
+from modelscope import dataset_snapshot_download
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
+
+
+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"),
+        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
+    ],
+    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora/epoch-4.safetensors")
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_image_dataset",
+    local_dir="./data/example_image_dataset",
+    allow_file_pattern="inpaint/*.jpg"
+)
+prompt = "a cat with sunglasses"
+controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024))
+inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024))
+image = pipe(
+    prompt, seed=0,
+    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
+    height=1024, width=1024,
+    num_inference_steps=40,
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py
@@ -0,0 +1,23 @@
+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/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Distill-LoRA_lora/epoch-4.safetensors")
+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=4,
+    cfg_scale=1,
+)
+image.save("image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py
@@ -0,0 +1,21 @@
+import torch
+from PIL import Image
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+
+pipe = QwenImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Qwen/Qwen-Image-Edit", 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=None,
+    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit_lora/epoch-4.safetensors")
+
+prompt = "将裙子改为粉色"
+image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024))
+image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024)
+image.save(f"image.jpg")
--- a/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py
+++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py
@@ -0,0 +1,19 @@
+from PIL import Image
+import torch
+from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
+
+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/"),
+)
+pipe.load_lora(pipe.dit, "models/train/Qwen-Image-In-Context-Control-Union_lora/epoch-4.safetensors")
+image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1024, 1024))
+prompt = "Context_Control. a dog"
+image = pipe(prompt=prompt, seed=0, context_image=image, height=1024, width=1024)
+image.save("image_context.jpg")
--- a/examples/wanvideo/README.md
+++ b/examples/wanvideo/README.md
@@ -48,9 +48,13 @@ save_video(video, "video1.mp4", fps=15, quality=5)

 | Model ID | Extra Parameters | Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation |
 |-|-|-|-|-|-|-|
+|[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](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|-|-|-|-|
 |[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)|
 |[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)|
 |[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)|
+|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./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](./model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./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](./model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
 |[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./model_inference/Wan2.1-T2V-1.3B.py)|[code](./model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./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](./model_inference/Wan2.1-T2V-14B.py)|[code](./model_training/full/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./model_training/lora/Wan2.1-T2V-14B.sh)|[code](./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](./model_inference/Wan2.1-I2V-14B-480P.py)|[code](./model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
@@ -288,6 +292,7 @@ The script includes the following parameters:
  * `--min_timestep_boundary`: Minimum value of the timestep interval, ranging from 0 to 1. Default is 1. This needs to be manually set only when training mixed models with multiple DiTs, for example, [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B).
 * Training
  * `--learning_rate`: Learning rate.
+  * `--weight_decay`: Weight decay.
  * `--num_epochs`: Number of epochs.
  * `--output_path`: Output save path.
  * `--remove_prefix_in_ckpt`: Remove prefix in ckpt.
@@ -298,6 +303,7 @@ The script includes the following parameters:
  * `--lora_base_model`: Which model LoRA is added to.
  * `--lora_target_modules`: Which layers LoRA is added to.
  * `--lora_rank`: Rank of LoRA.
+  * `--lora_checkpoint`: Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.
 * Extra Inputs
  * `--extra_inputs`: Additional model inputs, comma-separated.
 * VRAM Management
--- a/examples/wanvideo/README_zh.md
+++ b/examples/wanvideo/README_zh.md
@@ -48,9 +48,13 @@ save_video(video, "video1.mp4", fps=15, quality=5)

 |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/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|-|-|-|-|
 |[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)|
 |[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)|
 |[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)|
+|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./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](./model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./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](./model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)|
 |[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./model_inference/Wan2.1-T2V-1.3B.py)|[code](./model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./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](./model_inference/Wan2.1-T2V-14B.py)|[code](./model_training/full/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./model_training/lora/Wan2.1-T2V-14B.sh)|[code](./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](./model_inference/Wan2.1-I2V-14B-480P.py)|[code](./model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-480P.py)|
@@ -290,6 +294,7 @@ Wan 系列模型训练通过统一的 [`./model_training/train.py`](./model_trai
  * `--min_timestep_boundary`: Timestep 区间最小值，范围为 0～1，默认为 1，仅在多 DiT 的混合模型训练中需要手动设置，例如 [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)。
 * 训练
  * `--learning_rate`: 学习率。
+  * `--weight_decay`：权重衰减大小。
  * `--num_epochs`: 轮数（Epoch）。
  * `--output_path`: 保存路径。
  * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。
@@ -300,6 +305,7 @@ Wan 系列模型训练通过统一的 [`./model_training/train.py`](./model_trai
  * `--lora_base_model`: LoRA 添加到哪个模型上。
  * `--lora_target_modules`: LoRA 添加到哪一层上。
  * `--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。
 * 额外模型输入
  * `--extra_inputs`: 额外的模型输入，以逗号分隔。
 * 显存管理
--- a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py
+++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py
@@ -0,0 +1,43 @@
+import torch
+from diffsynth import save_video,VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/examples_in_diffsynth",
+    local_dir="./",
+    allow_file_pattern=f"data/examples/wan/input_image.jpg"
+)
+input_image = Image.open("data/examples/wan/input_image.jpg")
+
+video = pipe(
+    prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+    input_image=input_image,
+    camera_control_direction="Left", camera_control_speed=0.01,
+)
+save_video(video, "video_left.mp4", fps=15, quality=5)
+
+video = pipe(
+    prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+    input_image=input_image,
+    camera_control_direction="Up", camera_control_speed=0.01,
+)
+save_video(video, "video_up.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py
+++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py
@@ -0,0 +1,35 @@
+import torch
+from diffsynth import save_video,VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/examples_in_diffsynth",
+    local_dir="./",
+    allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
+)
+
+# Control video
+control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
+reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832))
+video = pipe(
+    prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    control_video=control_video, reference_image=reference_image,
+    height=832, width=576, num_frames=49,
+    seed=1, tiled=True
+)
+save_video(video, "video.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py
+++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py
@@ -0,0 +1,35 @@
+import torch
+from diffsynth import save_video
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/examples_in_diffsynth",
+    local_dir="./",
+    allow_file_pattern=f"data/examples/wan/input_image.jpg"
+)
+image = Image.open("data/examples/wan/input_image.jpg")
+
+# First and last frame to video
+video = pipe(
+    prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    input_image=image,
+    seed=0, tiled=True,
+    # You can input `end_image=xxx` to control the last frame of the video.
+    # The model will automatically generate the dynamic content between `input_image` and `end_image`.
+)
+save_video(video, "video.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py
+++ b/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py
@@ -28,5 +28,6 @@ video = pipe(
    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
    seed=0, tiled=True,
    input_image=input_image,
+    switch_DiT_boundary=0.9,
 )
 save_video(video, "video1.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py
+++ b/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py
@@ -0,0 +1,69 @@
+import torch
+from PIL import Image
+import librosa
+from diffsynth import VideoData, save_video_with_audio
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
+)
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_video_dataset",
+    local_dir="./data/example_video_dataset",
+    allow_file_pattern=f"wans2v/*"
+)
+
+num_frames = 81 # 4n+1
+height = 448
+width = 832
+
+prompt = "a person is singing"
+negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height))
+# s2v audio input, recommend 16kHz sampling rate
+audio_path = 'data/example_video_dataset/wans2v/sing.MP3'
+input_audio, sample_rate = librosa.load(audio_path, sr=16000)
+
+# Speech-to-video
+video = pipe(
+    prompt=prompt,
+    input_image=input_image,
+    negative_prompt=negative_prompt,
+    seed=0,
+    num_frames=num_frames,
+    height=height,
+    width=width,
+    audio_sample_rate=sample_rate,
+    input_audio=input_audio,
+    num_inference_steps=40,
+)
+save_video_with_audio(video[1:], "video_with_audio.mp4", audio_path, fps=16, quality=5)
+
+# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps.
+pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4'
+pose_video = VideoData(pose_video_path, height=height, width=width)
+
+# Speech-to-video with pose
+video = pipe(
+    prompt=prompt,
+    input_image=input_image,
+    negative_prompt=negative_prompt,
+    seed=0,
+    num_frames=num_frames,
+    height=height,
+    width=width,
+    audio_sample_rate=sample_rate,
+    input_audio=input_audio,
+    s2v_pose_video=pose_video,
+    num_inference_steps=40,
+)
+save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5)
--- a/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py
+++ b/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py
@@ -0,0 +1,116 @@
+import torch
+from PIL import Image
+import librosa
+from diffsynth import VideoData, save_video_with_audio
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig, WanVideoUnit_S2V
+from modelscope import dataset_snapshot_download
+
+
+def speech_to_video(
+    prompt,
+    input_image,
+    audio_path,
+    negative_prompt="",
+    num_clip=None,
+    audio_sample_rate=16000,
+    pose_video_path=None,
+    infer_frames=80,
+    height=448,
+    width=832,
+    num_inference_steps=40,
+    fps=16, # recommend fixing fps as 16 for s2v
+    motion_frames=73, # hyperparameter of wan2.2-s2v
+    save_path=None,
+):
+    # s2v audio input, recommend 16kHz sampling rate
+    input_audio, sample_rate = librosa.load(audio_path, sr=audio_sample_rate)
+    # s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps.
+    pose_video = VideoData(pose_video_path, height=height, width=width) if pose_video_path is not None else None
+
+    audio_embeds, pose_latents, num_repeat = WanVideoUnit_S2V.pre_calculate_audio_pose(
+        pipe=pipe,
+        input_audio=input_audio,
+        audio_sample_rate=sample_rate,
+        s2v_pose_video=pose_video,
+        num_frames=infer_frames + 1,
+        height=height,
+        width=width,
+        fps=fps,
+    )
+    num_repeat = min(num_repeat, num_clip) if num_clip is not None else num_repeat
+    print(f"Generating {num_repeat} video clips...")
+    motion_videos = []
+    video = []
+    for r in range(num_repeat):
+        s2v_pose_latents = pose_latents[r] if pose_latents is not None else None
+        current_clip = pipe(
+            prompt=prompt,
+            input_image=input_image,
+            negative_prompt=negative_prompt,
+            seed=0,
+            num_frames=infer_frames + 1,
+            height=height,
+            width=width,
+            audio_embeds=audio_embeds[r],
+            s2v_pose_latents=s2v_pose_latents,
+            motion_video=motion_videos,
+            num_inference_steps=num_inference_steps,
+        )
+        current_clip = current_clip[-infer_frames:]
+        if r == 0:
+            current_clip = current_clip[3:]
+        overlap_frames_num = min(motion_frames, len(current_clip))
+        motion_videos = motion_videos[overlap_frames_num:] + current_clip[-overlap_frames_num:]
+        video.extend(current_clip)
+        save_video_with_audio(video, save_path, audio_path, fps=16, quality=5)
+        print(f"processed the {r+1}th clip of total {num_repeat} clips.")
+    return video
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"),
+        ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"),
+    ],
+    audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
+)
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/example_video_dataset",
+    local_dir="./data/example_video_dataset",
+    allow_file_pattern=f"wans2v/*",
+)
+
+infer_frames = 80  # 4n
+height = 448
+width = 832
+
+prompt = "a person is singing"
+negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height))
+
+video_with_audio = speech_to_video(
+    prompt=prompt,
+    input_image=input_image,
+    audio_path='data/example_video_dataset/wans2v/sing.MP3',
+    negative_prompt=negative_prompt,
+    pose_video_path='data/example_video_dataset/wans2v/pose.mp4',
+    save_path="video_with_audio_full.mp4",
+    infer_frames=infer_frames,
+    height=height,
+    width=width,
+)
+# num_clip means generating only the first n clips with n * infer_frames frames.
+video_with_audio_pose = speech_to_video(
+    prompt=prompt,
+    input_image=input_image,
+    audio_path='data/example_video_dataset/wans2v/sing.MP3',
+    negative_prompt=negative_prompt,
+    pose_video_path='data/example_video_dataset/wans2v/pose.mp4',
+    save_path="video_with_audio_pose_clip_2.mp4",
+    num_clip=2
+)
--- a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh
+++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh
@@ -0,0 +1,35 @@
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_full" \
+  --trainable_models "dit" \
+  --extra_inputs "input_image,camera_control_direction,camera_control_speed" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full" \
+  --trainable_models "dit" \
+  --extra_inputs "input_image,camera_control_direction,camera_control_speed" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh
+++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh
@@ -0,0 +1,35 @@
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_full" \
+  --trainable_models "dit" \
+  --extra_inputs "control_video,reference_image" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_full" \
+  --trainable_models "dit" \
+  --extra_inputs "control_video,reference_image" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh
+++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh
@@ -0,0 +1,33 @@
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_full" \
+  --trainable_models "dit" \
+  --extra_inputs "input_image,end_image" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_full" \
+  --trainable_models "dit" \
+  --extra_inputs "input_image,end_image" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh
+++ b/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh
@@ -13,8 +13,9 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
  --trainable_models "dit" \
  --extra_inputs "input_image" \
  --use_gradient_checkpointing_offload \
-  --max_timestep_boundary 1 \
-  --min_timestep_boundary 0.875
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]

 accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/example_video_dataset \
@@ -31,5 +32,6 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
  --trainable_models "dit" \
  --extra_inputs "input_image" \
  --use_gradient_checkpointing_offload \
-  --max_timestep_boundary 0.875 \
-  --min_timestep_boundary 0
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900)
--- a/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh
+++ b/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh
@@ -11,8 +11,9 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_full" \
  --trainable_models "dit" \
-  --max_timestep_boundary 1 \
-  --min_timestep_boundary 0.875
+  --max_timestep_boundary 0.417 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [875, 1000]

 accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/example_video_dataset \
@@ -27,5 +28,6 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
  --remove_prefix_in_ckpt "pipe.dit." \
  --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_full" \
  --trainable_models "dit" \
-  --max_timestep_boundary 0.875 \
-  --min_timestep_boundary 0
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.417
+# boundary corresponds to timesteps [0, 875)
--- a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh
+++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh
@@ -0,0 +1,39 @@
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "input_image,camera_control_direction,camera_control_speed" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "input_image,camera_control_direction,camera_control_speed" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh
+++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh
@@ -0,0 +1,39 @@
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "control_video,reference_image" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \
+  --data_file_keys "video,control_video,reference_image" \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "control_video,reference_image" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh
+++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh
@@ -0,0 +1,37 @@
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "input_image,end_image" \
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]
+
+accelerate launch examples/wanvideo/model_training/train.py \
+  --dataset_base_path data/example_video_dataset \
+  --dataset_metadata_path data/example_video_dataset/metadata.csv \
+  --height 480 \
+  --width 832 \
+  --dataset_repeat 100 \
+  --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \
+  --learning_rate 1e-4 \
+  --num_epochs 5 \
+  --remove_prefix_in_ckpt "pipe.dit." \
+  --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_lora" \
+  --lora_base_model "dit" \
+  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
+  --lora_rank 32 \
+  --extra_inputs "input_image,end_image" \
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900]
--- a/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh
+++ b/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh
@@ -14,8 +14,9 @@ accelerate launch examples/wanvideo/model_training/train.py \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
  --extra_inputs "input_image" \
-  --max_timestep_boundary 1 \
-  --min_timestep_boundary 0.875
+  --max_timestep_boundary 0.358 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [900, 1000]

 accelerate launch examples/wanvideo/model_training/train.py \
  --dataset_base_path data/example_video_dataset \
@@ -33,5 +34,6 @@ accelerate launch examples/wanvideo/model_training/train.py \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
  --extra_inputs "input_image" \
-  --max_timestep_boundary 0.875 \
-  --min_timestep_boundary 0
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.358
+# boundary corresponds to timesteps [0, 900)
--- a/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh
+++ b/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh
@@ -13,8 +13,9 @@ accelerate launch examples/wanvideo/model_training/train.py \
  --lora_base_model "dit" \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
-  --max_timestep_boundary 1 \
-  --min_timestep_boundary 0.875
+  --max_timestep_boundary 0.417 \
+  --min_timestep_boundary 0
+# boundary corresponds to timesteps [875, 1000]


 accelerate launch examples/wanvideo/model_training/train.py \
@@ -32,5 +33,6 @@ accelerate launch examples/wanvideo/model_training/train.py \
  --lora_base_model "dit" \
  --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \
  --lora_rank 32 \
-  --max_timestep_boundary 0.875 \
-  --min_timestep_boundary 0
+  --max_timestep_boundary 1 \
+  --min_timestep_boundary 0.417
+# boundary corresponds to timesteps [0, 875)
--- a/examples/wanvideo/model_training/train.py
+++ b/examples/wanvideo/model_training/train.py
@@ -1,6 +1,8 @@
 import torch, os, json
+from diffsynth import load_state_dict
 from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
-from diffsynth.trainers.utils import DiffusionTrainingModule, VideoDataset, ModelLogger, launch_training_task, wan_parser
+from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser
+from diffsynth.trainers.unified_dataset import UnifiedDataset
 os.environ["TOKENIZERS_PARALLELISM"] = "false"


@@ -10,7 +12,7 @@ class WanTrainingModule(DiffusionTrainingModule):
        self,
        model_paths=None, model_id_with_origin_paths=None,
        trainable_models=None,
-        lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32,
+        lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32, lora_checkpoint=None,
        use_gradient_checkpointing=True,
        use_gradient_checkpointing_offload=False,
        extra_inputs=None,
@@ -19,30 +21,16 @@ class WanTrainingModule(DiffusionTrainingModule):
    ):
        super().__init__()
        # Load models
-        model_configs = []
-        if model_paths is not None:
-            model_paths = json.loads(model_paths)
-            model_configs += [ModelConfig(path=path) for path in model_paths]
-        if model_id_with_origin_paths is not None:
-            model_id_with_origin_paths = model_id_with_origin_paths.split(",")
-            model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths]
+        model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False)
        self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs)
        
-        # Reset training scheduler
-        self.pipe.scheduler.set_timesteps(1000, training=True)
+        # Training mode
+        self.switch_pipe_to_training_mode(
+            self.pipe, trainable_models,
+            lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint,
+            enable_fp8_training=False,
+        )
        
-        # Freeze untrainable models
-        self.pipe.freeze_except([] if trainable_models is None else trainable_models.split(","))
-        
-        # Add LoRA to the base models
-        if lora_base_model is not None:
-            model = self.add_lora_to_model(
-                getattr(self.pipe, lora_base_model),
-                target_modules=lora_target_modules.split(","),
-                lora_rank=lora_rank
-            )
-            setattr(self.pipe, lora_base_model, model)
-            
        # Store other configs
        self.use_gradient_checkpointing = use_gradient_checkpointing
        self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload
@@ -104,7 +92,23 @@ class WanTrainingModule(DiffusionTrainingModule):
 if __name__ == "__main__":
    parser = wan_parser()
    args = parser.parse_args()
-    dataset = VideoDataset(args=args)
+    dataset = UnifiedDataset(
+        base_path=args.dataset_base_path,
+        metadata_path=args.dataset_metadata_path,
+        repeat=args.dataset_repeat,
+        data_file_keys=args.data_file_keys.split(","),
+        main_data_operator=UnifiedDataset.default_video_operator(
+            base_path=args.dataset_base_path,
+            max_pixels=args.max_pixels,
+            height=args.height,
+            width=args.width,
+            height_division_factor=16,
+            width_division_factor=16,
+            num_frames=args.num_frames,
+            time_division_factor=4,
+            time_division_remainder=1,
+        ),
+    )
    model = WanTrainingModule(
        model_paths=args.model_paths,
        model_id_with_origin_paths=args.model_id_with_origin_paths,
@@ -112,6 +116,7 @@ if __name__ == "__main__":
        lora_base_model=args.lora_base_model,
        lora_target_modules=args.lora_target_modules,
        lora_rank=args.lora_rank,
+        lora_checkpoint=args.lora_checkpoint,
        use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
        extra_inputs=args.extra_inputs,
        max_timestep_boundary=args.max_timestep_boundary,
@@ -121,13 +126,4 @@ if __name__ == "__main__":
        args.output_path,
        remove_prefix_in_ckpt=args.remove_prefix_in_ckpt
    )
-    optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate)
-    scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
-    launch_training_task(
-        dataset, model, model_logger, optimizer, scheduler,
-        num_epochs=args.num_epochs,
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        save_steps=args.save_steps,
-        find_unused_parameters=args.find_unused_parameters,
-        num_workers=args.dataset_num_workers,
-    )
+    launch_training_task(dataset, model, model_logger, args=args)
--- a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py
+++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py
@@ -0,0 +1,34 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full/epoch-1.safetensors")
+pipe.dit2.load_state_dict(state_dict)
+pipe.enable_vram_management()
+
+video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
+
+# First and last frame to video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    input_image=video[0],
+    camera_control_direction="Left", camera_control_speed=0.0,
+    seed=0, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py
+++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py
@@ -0,0 +1,35 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_high_noise_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_low_noise_full/epoch-1.safetensors")
+pipe.dit2.load_state_dict(state_dict)
+pipe.enable_vram_management()
+
+video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832)
+video = [video[i] for i in range(81)]
+reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0]
+
+# Control video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    control_video=video, reference_image=reference_image,
+    seed=1, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py
+++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py
@@ -0,0 +1,32 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_high_noise_full/epoch-1.safetensors")
+pipe.dit.load_state_dict(state_dict)
+state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_low_noise_full/epoch-1.safetensors")
+pipe.dit2.load_state_dict(state_dict)
+pipe.enable_vram_management()
+video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
+
+# First and last frame to video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    input_image=video[0], end_image=video[80],
+    seed=0, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py
+++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py
@@ -0,0 +1,32 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData, load_state_dict
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.enable_vram_management()
+
+video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
+
+# First and last frame to video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    input_image=video[0],
+    camera_control_direction="Left", camera_control_speed=0.0,
+    seed=0, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py
+++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py
@@ -0,0 +1,33 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-Control_high_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-Control_low_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.enable_vram_management()
+
+video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832)
+video = [video[i] for i in range(81)]
+reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0]
+
+# Control video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    control_video=video, reference_image=reference_image,
+    seed=1, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5)
--- a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py
+++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py
@@ -0,0 +1,31 @@
+import torch
+from PIL import Image
+from diffsynth import save_video, VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from modelscope import dataset_snapshot_download
+
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-InP_high_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-InP_low_noise_lora/epoch-4.safetensors", alpha=1)
+pipe.enable_vram_management()
+
+video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)
+
+# First and last frame to video
+video = pipe(
+    prompt="from sunset to night, a small town, light, house, river",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    input_image=video[0], end_image=video[80],
+    seed=0, tiled=True
+)
+save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5)
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,6 @@
 torch>=2.0.0
 torchvision
-cupy-cuda12x
 transformers
-controlnet-aux==0.0.7
 imageio
 imageio[ffmpeg]
 safetensors
@@ -14,3 +12,4 @@ ftfy
 pynvml
 pandas
 accelerate
+peft
--- a/setup.py
+++ b/setup.py
@@ -14,7 +14,7 @@ else:

 setup(
    name="diffsynth",
-    version="1.1.7",
+    version="1.1.8",
    description="Enjoy the magic of Diffusion models!",
    author="Artiprocher",
    packages=find_packages(),
Author	SHA1	Message	Date
Zhongjie Duan	090074e395	Merge pull request #899 from modelscope/version_update_1.1.8 Update setup.py	2025-09-09 18:43:03 +08:00
Zhongjie Duan	2dcdeefca8	Update setup.py	2025-09-09 18:42:39 +08:00
Zhongjie Duan	452a6ca5cf	Merge pull request #898 from modelscope/direct_distill support direct distill	2025-09-09 16:16:32 +08:00
Artiprocher	d6cf20ef33	support direct distill	2025-09-09 16:12:31 +08:00
Zhongjie Duan	efdd6a59b6	Merge pull request #892 from modelscope/dev2-dzj refine training framework	2025-09-04 15:53:52 +08:00
Artiprocher	42ec7b08eb	bugfix	2025-09-04 15:45:39 +08:00
Artiprocher	d049fb6d1d	bugfix	2025-09-04 15:44:37 +08:00
Artiprocher	144365b07d	merge data process to training script	2025-09-04 15:18:56 +08:00
Artiprocher	cb8de6be1b	move training code to base trainer	2025-09-03 12:03:49 +08:00
Zhongjie Duan	8c13362dcf	Merge pull request #884 from modelscope/dev2-dzj Unified Dataset & Splited Training	2025-09-03 09:50:23 +08:00
Zhongjie Duan	c13fd7e0ee	Merge pull request #877 from mi804/wans2v_framepack support s2v framepack	2025-09-02 16:54:37 +08:00
Artiprocher	958ebf1352	remove testing script	2025-09-02 16:44:36 +08:00
Artiprocher	b6da77e468	qwen-image splited training	2025-09-02 16:44:14 +08:00
Artiprocher	260e32217f	unified dataset	2025-09-02 13:14:08 +08:00
mi804	5cee326f92	support s2v framepack	2025-09-01 16:48:46 +08:00
Zhongjie Duan	1d240994e7	Merge pull request #874 from mi804/wans2v_example Wans2v example	2025-08-29 15:13:28 +08:00
mi804	a0bae07825	add wans2v example	2025-08-29 15:11:30 +08:00
ShunqiangBian	ff71720297	Create Wan2.2-S2V-14B.py This commit introduces the core inference functionality for the Wan2.2-S2V-14B model.	2025-08-29 14:54:41 +08:00
Zhongjie Duan	dea85643e6	Merge pull request #872 from modelscope/dev2-dzj remove some requirements & update Qwen-Image Quickstart	2025-08-29 14:22:35 +08:00
Artiprocher	6a46f32afe	update Qwen-Image Quickstart	2025-08-29 14:09:49 +08:00
Artiprocher	4641d0f360	remove some requirements	2025-08-29 14:04:58 +08:00
Zhongjie Duan	826bab5962	Merge pull request #859 from krahets/main Fix batch decoding for Wan-Video-VAE	2025-08-29 12:45:49 +08:00
Zhongjie Duan	5b6d112c15	Merge pull request #843 from wuutiing/main add read gifs as video support	2025-08-29 12:36:24 +08:00
Zhongjie Duan	febdaf6067	Merge pull request #856 from lzws/main add wan2.2-fun training scripts	2025-08-29 12:34:55 +08:00
Zhongjie Duan	0a78bb9d38	Merge pull request #864 from modelscope/wans2v Support Wan-S2V	2025-08-28 10:21:12 +08:00
mi804	9cea10cc69	minor fix	2025-08-28 10:13:52 +08:00
mi804	caa17da5b9	wans2v readme	2025-08-27 20:05:44 +08:00
mi804	fdeb363fa2	wans2v usp	2025-08-27 19:50:33 +08:00
mi804	4147473c81	wans2v refactor	2025-08-27 16:18:22 +08:00
mi804	8a0bd7c377	wans2v lowvram	2025-08-27 13:05:53 +08:00
mi804	b541b9bed2	wans2v inference	2025-08-27 11:51:56 +08:00
Yudong Jin	419d47c195	Remove unnecessary newline in encode method	2025-08-27 02:24:29 +08:00
Yudong Jin	ac2e859960	Fix batch decoding for Wan VAE.	2025-08-27 02:24:00 +08:00
Zhongjie Duan	6663dca015	Merge pull request #857 from modelscope/Artiprocher-patch-1 bugfix	2025-08-26 17:23:32 +08:00
lzws	86e509ad31	update wan2.2-fun training scripts	2025-08-26 17:22:41 +08:00
Zhongjie Duan	8fcfa1dd2d	bugfix	2025-08-26 17:22:25 +08:00
lzws	2b7a2548b4	update wan2.2-fun model overview in readme	2025-08-26 17:11:48 +08:00
lzws	f0916e6bae	update wan2.2-fun training scripts	2025-08-26 16:37:47 +08:00
lzws	822e80ec2f	Merge branch 'modelscope:main' into main	2025-08-26 15:08:43 +08:00
Zhongjie Duan	04e39f7de5	Merge pull request #853 from modelscope/qwen-image-fp8-lora support qwen-image fp8 lora training	2025-08-25 20:33:36 +08:00
Artiprocher	ce0b948655	support qwen-image fp8 lora training	2025-08-25 20:32:36 +08:00
lzws	c795e35142	add wan2.2-fun-A14B inp, control and control-camera (#839 ) * update wan2.2-fun * update wan2.2-fun * update wan2.2-fun * add examples * update wan2.2-fun * update wan2.2-fun * Rename Wan2.2-Fun-A14B-Inp.py to Wan2.2-Fun-A14B-InP.py --------- Co-authored-by: lzw478614@alibaba-inc.com <lzw478614@alibaba-inc.com>	2025-08-22 14:20:31 +08:00
lzws	f7c01f1367	Merge branch 'modelscope:main' into main	2025-08-22 14:18:36 +08:00
lzws	cb49f0283f	Rename Wan2.2-Fun-A14B-Inp.py to Wan2.2-Fun-A14B-InP.py	2025-08-22 14:18:16 +08:00
Zhongjie Duan	6a45815b23	Merge pull request #844 from mi804/blockwisecontrolnet_fix fix blockwise controlnet training by avoid inplace	2025-08-22 11:47:21 +08:00
mi804	8dae8d7bc8	fix blockwise controlnet training by avoid inplace	2025-08-22 11:28:57 +08:00
twu	f6418004bb	as numframe limit is impled in reader, add that	2025-08-22 03:00:35 +00:00
lzw478614@alibaba-inc.com	c4b97cd591	update wan2.2-fun	2025-08-22 09:38:19 +08:00
lzws	b6d1ff01e0	Merge branch 'modelscope:main' into main	2025-08-21 20:53:19 +08:00
lzw478614@alibaba-inc.com	0d81626fe7	update wan2.2-fun	2025-08-21 20:08:49 +08:00
twu	e3f47a799b	make it more efficient to locate where to sample the frame	2025-08-21 09:13:45 +00:00
twu	e014cad820	add read gifs as video support	2025-08-21 09:01:48 +00:00
Zhongjie Duan	89bf3ce5cf	Merge pull request #841 from modelscope/qwen-image-lora-hotload support qwen-image lora hotload	2025-08-21 15:14:46 +08:00
Zhongjie Duan	3ebe118f23	Merge pull request #840 from modelscope/qwen-image-incontext Qwen image incontext	2025-08-21 15:11:42 +08:00
Artiprocher	7f719cefe6	refine code	2025-08-21 14:25:17 +08:00
lzw478614@alibaba-inc.com	46bd05b54d	add examples	2025-08-21 13:41:07 +08:00
Artiprocher	613dafbd09	rename model	2025-08-21 13:35:47 +08:00
lzw478614@alibaba-inc.com	952933eeb1	update wan2.2-fun	2025-08-21 13:34:09 +08:00
lzw478614@alibaba-inc.com	c0172e70b1	update wan2.2-fun	2025-08-21 12:59:41 +08:00
Artiprocher	6ab426e641	support qwen-image lora hotload	2025-08-21 10:12:52 +08:00
mi804	d0467a7e8d	fix controlnet annotator	2025-08-20 23:28:40 +08:00
mi804	36838a05ee	minor fix	2025-08-20 22:50:18 +08:00
mi804	5e6f9f89f1	support eligenv2 and context_control	2025-08-20 22:48:34 +08:00
lzw478614@alibaba-inc.com	2dad9a319c	update wan2.2-fun	2025-08-20 20:17:41 +08:00
Zhongjie Duan	9ec0652339	Merge pull request #829 from mi804/qwen-image-edit-autoresize support edit_image_auto_resize	2025-08-20 13:40:02 +08:00
mi804	7e348083ae	minor fix	2025-08-20 12:42:11 +08:00
mi804	29b12b2f4e	support edit_image_auto_resize	2025-08-20 12:36:26 +08:00
Zhongjie Duan	b3f57ed920	Merge pull request #826 from mi804/qwen-image-edit-lowvram fix qwen-image-edit-lowvram	2025-08-20 11:39:56 +08:00
mi804	c9fea729d8	fix qwen-image-edit-lowvram	2025-08-20 10:31:43 +08:00
Hong Zhang	9d0683df25	Merge pull request #824 from mi804/low_res_fix support qwen-image-edit lowres fix	2025-08-20 10:24:11 +08:00
mi804	838b8109b1	support qwen-image-edit lowres fix	2025-08-19 20:15:36 +08:00
Zhongjie Duan	3a9621f6da	Merge pull request #815 from mi804/lora_checkpoint fix bug	2025-08-19 12:43:04 +08:00
mi804	fff2c89360	fix bug	2025-08-19 12:38:33 +08:00
Zhongjie Duan	ce61bef2b0	Merge pull request #814 from mi804/qwen-image-edit Qwen image edit	2025-08-19 09:33:39 +08:00
mi804	123f6dbadb	update lora and full train	2025-08-18 19:09:19 +08:00
Hong Zhang	f9ce261a0e	Merge branch 'main' into qwen-image-edit	2025-08-18 18:56:26 +08:00
mi804	d93de98a21	fix qwen_rope	2025-08-18 17:31:18 +08:00
mi804	ad1da43476	fix validate full	2025-08-18 16:17:40 +08:00
mi804	398b1dbd7a	fix inference	2025-08-18 16:10:01 +08:00
mi804	9f6922bba9	support qwen-image-edit	2025-08-18 16:07:45 +08:00
Zhongjie Duan	f11a91e610	Merge pull request #813 from modelscope/qwen-image-inpaint Qwen image inpaint	2025-08-18 15:26:06 +08:00
Artiprocher	7ed09bb78d	add inpaint mask in qwen-image	2025-08-18 15:16:38 +08:00
mi804	ac931856d5	minor fix	2025-08-16 17:24:37 +08:00
mi804	2d09318236	support qwen-image inpaint controlnet	2025-08-16 17:12:29 +08:00
Zhongjie Duan	7dc49bd036	Merge pull request #806 from mi804/wan2.2_boundary fix training boundary for wan2.2 A14B	2025-08-15 18:43:37 +08:00
Zhongjie Duan	4d16bdf853	Merge pull request #807 from modelscope/qwen-image-blockwise-controlnet-train support qwen-image blockwise controlnet training	2025-08-15 18:42:29 +08:00
Artiprocher	01a1f48f70	support qwen-image blockwise controlnet training	2025-08-15 18:41:01 +08:00
mi804	6a9d875d65	fix training boundary for wan2.2 A14B	2025-08-15 17:54:52 +08:00
Zhongjie Duan	f1c96d31b4	Merge pull request #804 from mi804/qwen-image-dataset qwen-image-dataset	2025-08-15 14:39:44 +08:00
mi804	aafcca8d77	add announcements	2025-08-15 14:38:03 +08:00
mi804	bf369cad4d	qwen-image-dataset	2025-08-15 14:28:55 +08:00
Zhongjie Duan	024fdad76d	Merge pull request #801 from modelscope/qwen-image-lowvram add low vram examples	2025-08-15 11:34:24 +08:00
Artiprocher	e1c2eda5f5	add low vram examples	2025-08-15 11:31:57 +08:00
Zhongjie Duan	0b574cc0c2	Merge pull request #794 from mi804/training_optimize lora_checkpoint & weight_decay	2025-08-14 14:20:03 +08:00
mi804	3212c83398	minor fix	2025-08-14 13:59:04 +08:00
mi804	49f9a11eb3	lora_checkpoint & weight_decay & qwen_image_controlnet_train	2025-08-14 13:50:04 +08:00
Zhongjie Duan	fa36739f01	Merge pull request #791 from mi804/qwen-image-longprompt fix long prompt for qwen-image	2025-08-14 09:59:42 +08:00
Zhongjie Duan	42e9764b60	Merge pull request #790 from mi804/qwen-image-blockwise-controlnet support qwen-image blockwise-controlnet depth	2025-08-13 20:35:10 +08:00
mi804	f7f5c07570	fix long prompt for qwen-image	2025-08-13 17:23:00 +08:00
mi804	ec1a936624	update date	2025-08-13 13:38:19 +08:00
mi804	6e6136586c	support controlnet depth	2025-08-13 13:36:26 +08:00
Zhongjie Duan	34766863f8	Merge pull request #787 from modelscope/qwen-image-controlnet-update-1 support qwen-image controlnet	2025-08-12 20:37:05 +08:00
Artiprocher	1d76d5e828	support qwen-image controlnet	2025-08-12 17:17:08 +08:00
Zhongjie Duan	250540a398	Merge pull request #780 from modelscope/qwen-image-distill-lora Qwen image distill lora	2025-08-11 15:05:19 +08:00
Artiprocher	46f3c38c37	Qwen-Image-Distill-LoRA	2025-08-11 15:04:21 +08:00
Artiprocher	9a8982efb1	Qwen-Image-Distill-LoRA	2025-08-11 15:01:21 +08:00
Zhongjie Duan	3c815cce4b	Merge pull request #779 from modelscope/qwen-image-forward-fix qwen-image dit original forward fix	2025-08-11 14:42:02 +08:00
Artiprocher	39d199c8bb	qwen-image dit original forward fix	2025-08-11 14:41:32 +08:00
Zhongjie Duan	f5506d1e13	Merge pull request #769 from modelscope/qwen-image-lora-format remove lora format alignment	2025-08-08 19:06:03 +08:00
Artiprocher	166a8734fe	remove lora format alignment	2025-08-08 19:05:06 +08:00
Zhongjie Duan	b2273ec568	Merge pull request #768 from modelscope/lora-fix lora-fix	2025-08-08 18:55:57 +08:00
Artiprocher	89c4e3bdb6	lora-fix	2025-08-08 18:55:13 +08:00
Zhongjie Duan	051ebf3439	fix wan2.2 5B usp (#763 )	2025-08-08 16:26:04 +08:00
mi804	7cfadc2ca8	fix wan2.2 5B usp	2025-08-07 23:06:52 +08:00