test

README.md

@@ -1,17 +1,10 @@
 ---
 license: apache-2.0
-language:
-- en
-- zh
-tags:
-- art
-size_categories:
-- n<1K
 ---
+
 <p align="center">
 
 <h1 align="center">VACE: All-in-One Video Creation and Editing</h1>
-<h3 align="center">(ICCV 2025)</h3>
 <p align="center">
     <strong>Zeyinzi Jiang<sup>*</sup></strong>
     ·
@@ -43,21 +36,18 @@ size_categories:
 
 
 ## 🎉 News
-- [x] Oct 17, 2025: [VACE-Benchmark](https://huggingface.co/datasets/ali-vilab/VACE-Benchmark) has been updated to incorporate the evaluation data. [VACE-Page](https://ali-vilab.github.io/VACE-Page/) also features creative community cases, offering researchers and community members better project insight and tracking.
-- [x] Jun 26, 2025: [VACE](https://openaccess.thecvf.com/content/ICCV2025/html/Jiang_VACE_All-in-One_Video_Creation_and_Editing_ICCV_2025_paper.html) is accepted by ICCV 2025.
-- [x] May 14, 2025: 🔥Wan2.1-VACE-1.3B and Wan2.1-VACE-14B models are now available at [HuggingFace](https://huggingface.co/collections/Wan-AI/wan21-68ac4ba85372ae5a8e282a1b) and [ModelScope](https://modelscope.cn/collections/tongyiwanxiang-Wan21-shipinshengcheng-67ec9b23fd8d4f)!
 - [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)!
 - [x] Mar 31, 2025: 🔥Release code of model inference, preprocessing, and gradio demos. 
 - [x] Mar 11, 2025: We propose [VACE](https://ali-vilab.github.io/VACE-Page/), an all-in-one model for video creation and editing.
 
 
 ## 🪄 Models
-| Models                   | Download Link                                                                                                                                           | Video Size        | License                                                                                       |
-|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|-----------------------------------------------------------------------------------------------|
-| VACE-Wan2.1-1.3B-Preview | [Huggingface](https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview) 🤗  [ModelScope](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) 🤖 | ~ 81 x 480 x 832  | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt)             |
-| VACE-LTX-Video-0.9       | [Huggingface](https://huggingface.co/ali-vilab/VACE-LTX-Video-0.9) 🤗     [ModelScope](https://modelscope.cn/models/iic/VACE-LTX-Video-0.9) 🤖          | ~ 97 x 512 x 768  | [RAIL-M](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.license.txt) |
-| Wan2.1-VACE-1.3B         | [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B) 🤗     [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B) 🤖          | ~ 81 x 480 x 832  | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt)             |
-| Wan2.1-VACE-14B          | [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) 🤗     [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B) 🤖            | ~ 81 x 720 x 1280 | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/blob/main/LICENSE.txt)             |
+| Models                   | Download Link                                                                                                                                                                           | Video Size          | License                                                                                       |
+|--------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------|-----------------------------------------------------------------------------------------------|
+| VACE-Wan2.1-1.3B-Preview | [Huggingface](https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview) 🤗  [ModelScope](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview) 🤖                                                                                                                                                                   | ~ 81 x 480 x 832    | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt)             |
+| VACE-Wan2.1-1.3B         | [To be released](https://github.com/Wan-Video) <img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 15px;'>  | ~ 81 x 480 x 832    | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B/blob/main/LICENSE.txt)             |
+| VACE-Wan2.1-14B          | [To be released](https://github.com/Wan-Video) <img src='https://ali-vilab.github.io/VACE-Page/assets/logos/wan_logo.png' alt='wan_logo' style='margin-bottom: -4px; height: 15px;'>  | ~ 81 x 720 x 1080   | [Apache-2.0](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B/blob/main/LICENSE.txt)             |
+| VACE-LTX-Video-0.9       | [Huggingface](https://huggingface.co/ali-vilab/VACE-LTX-Video-0.9) 🤗     [ModelScope](https://modelscope.cn/models/iic/VACE-LTX-Video-0.9) 🤖                                                                                                                                                                   | ~ 97 x 512 x 768    | [RAIL-M](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.license.txt) |
 
 - The input supports any resolution, but to achieve optimal results, the video size should fall within a specific range.
 - All models inherit the license of the original model.
@@ -110,7 +100,7 @@ VACE
 
 ## 🚀 Usage
 In VACE, users can input **text prompt** and optional **video**, **mask**, and **image** for video generation or editing.
-Detailed instructions for using VACE can be found in the [User Guide](./UserGuide.md).
+Detailed instructions for using VACE can be found in the [User Guide](https://github.com/ali-vilab/VACE/blob/main/UserGuide.md).
 
 ### Inference CIL
 #### 1) End-to-End Running
@@ -140,7 +130,7 @@ python vace/vace_preproccess.py --task depth --video assets/videos/test.mp4
 # process video inpainting by providing bbox
 python vace/vace_preproccess.py --task inpainting --mode bbox --bbox 50,50,550,700 --video assets/videos/test.mp4
 ```
-The outputs will be saved to `./processed/` by default.
+The outputs will be saved to `./proccessed/` by default.
 
 > 💡**Note**:
 > Please refer to [run_vace_pipeline.sh](https://github.com/ali-vilab/VACE/blob/main/run_vace_pipeline.sh) preprocessing methods for different tasks.
@@ -151,16 +141,13 @@ You can also customize preprocessors by implementing at [`annotators`](https://g
 #### 3) Model inference
 Using the input data obtained from **Preprocessing**, the model inference process can be performed as follows:
 ```bash
-# For Wan2.1 single GPU inference (1.3B-480P)
+# For Wan2.1 single GPU inference 
 python vace/vace_wan_inference.py --ckpt_dir <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx"
 
-# For Wan2.1 Multi GPU Acceleration inference (1.3B-480P)
+# For Wan2.1 Multi GPU Acceleration inference
 pip install "xfuser>=0.4.1"
 torchrun --nproc_per_node=8 vace/vace_wan_inference.py --dit_fsdp --t5_fsdp --ulysses_size 1 --ring_size 8 --ckpt_dir <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx"
 
-# For Wan2.1 Multi GPU Acceleration inference (14B-720P)
-torchrun --nproc_per_node=8 vace/vace_wan_inference.py --dit_fsdp --t5_fsdp --ulysses_size 8 --ring_size 1 --size 720p --model_name 'vace-14B' --ckpt_dir <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx"
-
 # For LTX inference, run
 python vace/vace_ltx_inference.py --ckpt_path <path-to-model> --text_encoder_path <path-to-model> --src_video <path-to-src-video> --src_mask <path-to-src-mask> --src_ref_images <paths-to-src-ref-images> --prompt "xxx"
 ```
@@ -170,12 +157,12 @@ The output video together with intermediate video, mask and images will be saved
 > (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.
 > (2) For LTX-Video and English language Wan2.1 users, you need prompt extension to unlock the full model performance. 
 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.
-> (3) When performing prompt extension in editing tasks, it's important to pay attention to the results of expanding plain text. Since the visual information being input is unknown, this may lead to the extended output not matching the video being edited, which can affect the final outcome.
+
 
 ### Inference Gradio
 For preprocessors, run 
 ```bash
-python vace/gradios/vace_preprocess_demo.py
+python vace/gradios/preprocess_demo.py
 ```
 For model inference, run
 ```bash
@@ -188,16 +175,15 @@ python vace/gradios/vace_ltx_demo.py
 
 ## Acknowledgement
 
-We are grateful for the following awesome projects, including [Scepter](https://github.com/modelscope/scepter), [Wan](https://github.com/Wan-Video/Wan2.1), and [LTX-Video](https://github.com/Lightricks/LTX-Video). Additionally, we extend our deepest gratitude to all community creators. It is their proactive exploration, experimentation, and boundless creativity that have brought immense inspiration to the project, fostering the emergence of even more refined workflows and stunning video generation content based on it. This includes, but is not limited to: [Kijai's Workflow](https://github.com/kijai/ComfyUI-WanVideoWrapper), native code support for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [Diffusers](https://github.com/huggingface/diffusers), crucial model quantization support, a diverse ecosystem of LoRA adapters, and the ever-evolving innovative workflows from our community members.
+We are grateful for the following awesome projects, including [Scepter](https://github.com/modelscope/scepter), [Wan](https://github.com/Wan-Video/Wan2.1), and [LTX-Video](https://github.com/Lightricks/LTX-Video).
 
 
 ## BibTeX
 
 ```bibtex
-@inproceedings{vace,
+@article{vace,
     title = {VACE: All-in-One Video Creation and Editing},
     author = {Jiang, Zeyinzi and Han, Zhen and Mao, Chaojie and Zhang, Jingfeng and Pan, Yulin and Liu, Yu},
-    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
-    pages = {17191-17202},
+    journal = {arXiv preprint arXiv:2503.07598},
     year = {2025}
 }

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