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@@ -27,10 +27,10 @@ base_model:
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  <b>Tongyi Lab - <a href="https://github.com/Wan-Video/Wan2.1"><img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 20px;'></a> </b>
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  <br>
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  <br>
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- <a href="https://arxiv.org/abs/2503.07598"><img src='https://img.shields.io/badge/arXiv-VACE-red' alt='Paper PDF'></a>
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- <a href="https://ali-vilab.github.io/VACE-Page/"><img src='https://img.shields.io/badge/Project_Page-VACE-green' alt='Project Page'></a>
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- <a href="https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview"><img src='https://img.shields.io/badge/Model-VACE-yellow'></a>
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- <a href="https://modelscope.cn/collections/VACE-8fa5fcfd386e43"><img src='https://img.shields.io/badge/VACE-ModelScope-purple'></a>
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  <br>
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  </p>
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@@ -42,7 +42,7 @@ base_model:
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  ## 🎉 News
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- - [x] Mar 31, 2025: 🔥[VACE-Wan2.1-1.3B-Preview](https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview) and [VACE-LTX-Video-0.9](https://huggingface.co/ali-vilab/VACE-LTX-Video-0.9) models are now available at HuggingFace and [ModelScope](https://modelscope.cn/collections/VACE-8fa5fcfd386e43)!
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  - [x] Mar 31, 2025: 🔥Release code of model inference, preprocessing, and gradio demos.
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  - [x] Mar 11, 2025: We propose [VACE](https://ali-vilab.github.io/VACE-Page/), an all-in-one model for video creation and editing.
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@@ -81,7 +81,7 @@ pip install -r requirements/annotator.txt
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  Please download [VACE-Annotators](https://huggingface.co/ali-vilab/VACE-Annotators) to `<repo-root>/models/`.
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  ### Local Directories Setup
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- It is recommended to download [VACE-Benchmark](https://huggingface.co/ali-vilab) to `<repo-root>/benchmarks/` as examples in `run_vace_xxx.sh`.
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  We recommend to organize local directories as:
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  ```angular2html
@@ -128,7 +128,7 @@ The output video together with intermediate video, mask and images will be saved
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  #### 2) Preprocessing
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  To have more flexible control over the input, before VACE model inference, user inputs need to be preprocessed into `src_video`, `src_mask`, and `src_ref_images` first.
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- We assign each [preprocessor](https://github.com/ali-vilab/VACE/blob/main/vace/configs/__init__.py) a task name, so simply call [`vace_preprocess.py`](https://github.com/ali-vilab/VACE/blob/main/vace/vace_preproccess.py) and specify the task name and task params. For example:
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  ```angular2html
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  # process video depth
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  python vace/vace_preproccess.py --task depth --video assets/videos/test.mp4
@@ -139,7 +139,7 @@ python vace/vace_preproccess.py --task inpainting --mode bbox --bbox 50,50,550,7
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  The outputs will be saved to `./proccessed/` by default.
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  > 💡**Note**:
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- > Please refer to [run_vace_pipeline.sh](https://github.com/ali-vilab/VACE/blob/main//run_vace_pipeline.sh) preprocessing methods for different tasks.
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  Moreover, refer to [vace/configs/](https://github.com/ali-vilab/VACE/blob/main/vace/configs/) for all the pre-defined tasks and required params.
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  You can also customize preprocessors by implementing at [`annotators`](https://github.com/ali-vilab/VACE/blob/main/vace/annotators/__init__.py) and register them at [`configs`](https://github.com/ali-vilab/VACE/blob/main/vace/configs).
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@@ -160,7 +160,7 @@ python vace/vace_ltx_inference.py --ckpt_path <path-to-model> --text_encoder_pat
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  The output video together with intermediate video, mask and images will be saved into `./results/` by default.
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  > 💡**Note**:
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- > (1) Please refer to [vace/vace_wan_inference.pyhttps://github.com/ali-vilab/VACE/blob/main/vace/vace_wan_inference.py) and [vace/vace_ltx_inference.py](https://github.com/ali-vilab/VACE/blob/main/vace/vace_ltx_inference.py) for the inference args.
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  > (2) For LTX-Video and English language Wan2.1 users, you need prompt extension to unlock the full model performance.
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  Please follow the [instruction of Wan2.1](https://github.com/Wan-Video/Wan2.1?tab=readme-ov-file#2-using-prompt-extension) and set `--use_prompt_extend` while running inference.
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  <b>Tongyi Lab - <a href="https://github.com/Wan-Video/Wan2.1"><img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 20px;'></a> </b>
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  <br>
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  <br>
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+ <a href="https://arxiv.org/abs/2503.07598"><img src='https://img.shields.io/badge/VACE-arXiv-red' alt='Paper PDF'></a>
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+ <a href="https://ali-vilab.github.io/VACE-Page/"><img src='https://img.shields.io/badge/VACE-Project_Page-green' alt='Project Page'></a>
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+ <a href="https://huggingface.co/collections/ali-vilab/vace-67eca186ff3e3564726aff38"><img src='https://img.shields.io/badge/VACE-HuggingFace_Model-yellow'></a>
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+ <a href="https://modelscope.cn/collections/VACE-8fa5fcfd386e43"><img src='https://img.shields.io/badge/VACE-ModelScope_Model-purple'></a>
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  <br>
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  </p>
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  ## 🎉 News
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+ - [x] Mar 31, 2025: 🔥VACE-Wan2.1-1.3B-Preview and VACE-LTX-Video-0.9 models are now available at [HuggingFace](https://huggingface.co/collections/ali-vilab/vace-67eca186ff3e3564726aff38) and [ModelScope](https://modelscope.cn/collections/VACE-8fa5fcfd386e43)!
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  - [x] Mar 31, 2025: 🔥Release code of model inference, preprocessing, and gradio demos.
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  - [x] Mar 11, 2025: We propose [VACE](https://ali-vilab.github.io/VACE-Page/), an all-in-one model for video creation and editing.
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  Please download [VACE-Annotators](https://huggingface.co/ali-vilab/VACE-Annotators) to `<repo-root>/models/`.
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83
  ### Local Directories Setup
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+ It is recommended to download [VACE-Benchmark](https://huggingface.co/datasets/ali-vilab/VACE-Benchmark) to `<repo-root>/benchmarks/` as examples in `run_vace_xxx.sh`.
85
 
86
  We recommend to organize local directories as:
87
  ```angular2html
 
128
 
129
  #### 2) Preprocessing
130
  To have more flexible control over the input, before VACE model inference, user inputs need to be preprocessed into `src_video`, `src_mask`, and `src_ref_images` first.
131
+ We assign each [preprocessor](https://raw.githubusercontent.com/ali-vilab/VACE/refs/heads/main/vace/configs/__init__.py) a task name, so simply call [`vace_preprocess.py`](https://raw.githubusercontent.com/ali-vilab/VACE/refs/heads/main/vace/vace_preproccess.py) and specify the task name and task params. For example:
132
  ```angular2html
133
  # process video depth
134
  python vace/vace_preproccess.py --task depth --video assets/videos/test.mp4
 
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  The outputs will be saved to `./proccessed/` by default.
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141
  > 💡**Note**:
142
+ > Please refer to [run_vace_pipeline.sh](https://github.com/ali-vilab/VACE/blob/main/run_vace_pipeline.sh) preprocessing methods for different tasks.
143
  Moreover, refer to [vace/configs/](https://github.com/ali-vilab/VACE/blob/main/vace/configs/) for all the pre-defined tasks and required params.
144
  You can also customize preprocessors by implementing at [`annotators`](https://github.com/ali-vilab/VACE/blob/main/vace/annotators/__init__.py) and register them at [`configs`](https://github.com/ali-vilab/VACE/blob/main/vace/configs).
145
 
 
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  The output video together with intermediate video, mask and images will be saved into `./results/` by default.
161
 
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  > 💡**Note**:
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+ > (1) Please refer to [vace/vace_wan_inference.py](https://github.com/ali-vilab/VACE/blob/main/vace/vace_wan_inference.py) and [vace/vace_ltx_inference.py](https://github.com/ali-vilab/VACE/blob/main/vace/vace_ltx_inference.py) for the inference args.
164
  > (2) For LTX-Video and English language Wan2.1 users, you need prompt extension to unlock the full model performance.
165
  Please follow the [instruction of Wan2.1](https://github.com/Wan-Video/Wan2.1?tab=readme-ov-file#2-using-prompt-extension) and set `--use_prompt_extend` while running inference.
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