--- license: apache-2.0 language: - en - zh pipeline_tag: image-to-video library_name: diffusers tags: - video - video genration - music-to-dance --- # Wan-Dancer-14B

πŸ’œ Wan-Dancer    |    πŸ–₯️ GitHub    |   πŸ€— Hugging Face   |   πŸ€– ModelScope   |    πŸ“‘ Paper   
[**Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation**](https://arxiv.org/abs/2607.09581) ## πŸ”₯ Latest News!! * July 13, 2026: πŸ’ƒ We introduce **[Wan-Dancer](https://humanaigc.github.io/wan-dancer/)**, a method can generate long-duration, high-quality, rhythmic dance videos from music with global structure and temporal continuity. We released the [model weights](#model-download) and [inference code](https://github.com/Wan-Video/Wan-Dancer). And now you can try it on [ModelScope Studio](https://www.modelscope.cn/studios/Wan-AI/Wan-Dancer) or [HuggingFace Space](https://huggingface.co/spaces/Wan-AI/Wan-Dancer)! ## πŸ“‘ Todo List - Wan-Dancer Music-to-Dance - [x] Inference code of Wan-Dancer - [x] Checkpoints of Wan-Dancer - [x] ComfyUI integration ## Run Wan-Dancer #### Installation Clone the repo: ```sh git clone https://github.com/Wan-Video/Wan-Dancer.git cd Wan-Dancer ``` Install dependencies: ```sh python -m venv venv_wan_dancer source venv_wan_dancer/bin/activate # Install package in editable mode pip install -e . # Install additional and specific versions dependencies pip install moviepy loguru librosa pip install https://mirrors.aliyun.com/pytorch-wheels/cu124/torch-2.6.0+cu124-cp310-cp310-linux_x86_64.whl pip install torchvision==0.21.0 pip install diffusers==0.34.0 pip install yunchang==0.5.0 pip install flash_attn==2.6.3 pip install xfuser==0.4.0 pip install transformers==4.46.2 ``` #### Model Download | Models | Download Links | Description | |--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------| | Wan-Dancer-14B | πŸ€— [Huggingface](https://huggingface.co/Wan-AI/Wan-Dancer-14B) πŸ€– [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan-Dancer-14B) | Music-to-Dance | | Download models using huggingface-cli: ``` sh pip install "huggingface_hub[cli]" huggingface-cli download Wan-AI/Wan-Dancer-14B --local-dir ./Wan-Dancer-14B ``` Download models using modelscope-cli: ``` sh pip install modelscope modelscope download Wan-AI/Wan-Dancer-14B --local_dir ./Wan-Dancer-14B ``` #### Run Wan-Dancer Wan-Dancer can generate long-duration, high-quality, rhythmic dance videos from music with global structure and temporal continuity. Our method decouples the process into global keyframe planning and local temporal refinement, leveraging full-track musical context to ensure long-range coherence. ##### 1. 🎬 Generate Global Keyframe Video Run the global stage script: ```bash cd Wan-Dancer ./gen_video_global.sh ``` ###### πŸ”§ Important Parameters | Parameter | Description | |------------------------|-------------| | `seed` | Random seed for reproducibility. | | `image_path` | Path to reference image. Example: `gen_video/ref_image/1001.jpg` | | `prompt_path` | Path to prompt file (defines dance style).
Available styles:

| | `music_path` | Path to input music file. Example: `gen_video/music/ChineseClassicDance.WAV` | | `output_folder` | Output directory for generated video. | | `timestamp` | Timestamp identifier for output files. | | `num_inference_steps` | Number of diffusion inference steps (e.g., 48). | ###### 🌰 Examples | Dance Genres | Parameter | Generated Global Video | | ------------ |-----------------------|-----------------| | Chinese Classical Dance | seed=0
image_path='gen_video/ref_image/1001.jpg'
prompt_path='gen_video/prompt/ε€ε…Έθˆž_global.txt'
music_path='gen_video/music/ChineseClassicDance.WAV'
num_inference_steps=48
cfg_scale=5 | [![Chinese Classical Dance](assets/1001.jpg)](https://cloud.video.taobao.com/vod/mV2fwDpfJ-pODxx6qn-ifq3_UMgbze7P_cI4cLO_vOo.mp4) | | Street Dance | seed=0
image_path='gen_video/ref_image/2001.jpg'
prompt_path='gen_video/prompt/θ‘—θˆž_global.txt'
music_path='gen_video/music/StreetDance.WAV'
num_inference_steps=48
cfg_scale=5 | [![Street Dance](assets/2001.jpg)](https://cloud.video.taobao.com/vod/MQiVGjY_ngH3imgfIl37xaQoJfbWadYldlZoMWJFMKQ.mp4) | | K-Pop Dance | seed=0
image_path='gen_video/ref_image/3001.jpg'
prompt_path='gen_video/prompt/kpop_global.txt'
music_path='gen_video/music_suno/3001.WAV'
num_inference_steps=48
cfg_scale=5 | [![K-Pop Dance](assets/3001.jpg)](https://cloud.video.taobao.com/vod/WGS6Z3VWpgGh8jnt2lrW99XeTB6uu9-H6lCGk1HBLZg.mp4) | | Latin Dance | seed=0
image_path='gen_video/ref_image/4001.jpg'
prompt_path='gen_video/prompt/ζ‹‰δΈθˆž_global.txt'
music_path='gen_video/music/LatinDance.WAV'
num_inference_steps=48
cfg_scale=5 | [![Latin Dance](assets/4001.jpg)](https://cloud.video.taobao.com/vod/jnwCUj3WvuErBAxF78b-kttEJoegA6-8VmLMZsayBGI.mp4) | | Tap Dance | seed=0
image_path='gen_video/ref_image/5001.jpg'
prompt_path='gen_video/prompt/踒踏舞_global.txt'
music_path='gen_video/music/TapDance.wav'
num_inference_steps=48
cfg_scale=5 | [![Tap Dance](assets/5001.jpg)](https://cloud.video.taobao.com/vod/lfrYGNMKzYaLvU3IsMyVJM003T5WZL6QKR7xiifEVAg.mp4)| ##### 2. πŸŽ₯ Generate Final High-Resolution Video Run the local refinement stage: ```bash cd Wan-Dancer ./gen_video_local.sh ``` ###### πŸ”§ Additional Required Parameters | Parameter | Description | |-----------------------|-------------| | `global_video_path` | Path to the global video generated in Step 1. **Required** for local refinement. | | `prompt_path` | Path to prompt file (defines dance style).
Available styles: | > βœ… All other parameters (`seed`, `image_path`, etc.) are identical to Step 1. ###### 🌰 Examples | Dance Genres | Parameter | Generated Final Video | | ------------ |-----------------------|-----------------| | Chinese Classical Dance | seed=0
image_path='gen_video/ref_image/1001.jpg'
prompt_path='gen_video/prompt/ε€ε…Έθˆž_local.txt'
music_path='gen_video/music/ChineseClassicDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/1001_ChineseClassicDance_seed0.mp4' | [![Chinese Classical Dance](assets/1001.jpg)](https://cloud.video.taobao.com/vod/UycK9FTbYM6imr_6jF9aYbNYTiBggyE0EYptc2TRIAw.mp4) | | Street Dance | seed=0
image_path='gen_video/ref_image/2001.jpg'
prompt_path='gen_video/prompt/θ‘—θˆž_local.txt'
music_path='gen_video/music/StreetDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/2001_StreetDance_seed0.mp4' | [![Street Dance](assets/2001.jpg)](https://cloud.video.taobao.com/vod/JZtIncJf7zPptZAYsQsoSxA_tyW_r62JfBBikBiTPcY.mp4) | | K-Pop Dance | seed=100
image_path='gen_video/ref_image/3001.jpg'
prompt_path='gen_video/prompt/kpop_local.txt'
music_path='gen_video/music_suno/3001.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/3001_KPopDance_seed0.mp4' | [![K-Pop Dance](assets/3001.jpg)](https://cloud.video.taobao.com/vod/Si5ze8sR0Rm-aPUGSKsTJ2PXJAu3HtnVAzEPM85bkrc.mp4) | | Latin Dance | seed=0
image_path='gen_video/ref_image/4001.jpg'
prompt_path='gen_video/prompt/ζ‹‰δΈθˆž_local.txt'
music_path='gen_video/music/LatinDance.WAV'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/4001_LatinDance_seed0.mp4' | [![Latin Dance](assets/4001.jpg)](https://cloud.video.taobao.com/vod/kL-0AAqQtigvaidF8Xa8YeTIs4pDLOa_4n5nqXmYiRk.mp4) | | Tap Dance | seed=0
image_path='gen_video/ref_image/5001.jpg'
prompt_path='gen_video/prompt/踒踏舞_local.txt'
music_path='gen_video/music/TapDance.wav'
num_inference_steps=24
cfg_scale=5
global_video_path='outputs/global_video/5001_TapDance_seed0.mp4' | [![Tap Dance](assets/5001.jpg)](https://cloud.video.taobao.com/vod/GbnX-XzekrvNulbbDMw_2kEotadZmUT6KFY5smTkNZ0.mp4) | Note: The `num_inference_steps` should be set to a larger value (e.g., 48) for longer time videos. ------- ## Citation If you use this code or framework in your research, please cite: ```bibtex @article{wan-dancer-2026, title={Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation}, author={Mingyang Huang, Peng Zhang, Li Hu, Guangyuan Wang, Bang Zhang}, website={https://humanaigc.github.io/wan-dancer/}, url={https://arxiv.org/abs/2607.09581}, year={2026} } ``` ## License Agreement This project is licensed under the Apache 2.0 License β€” see the [LICENSE](LICENSE) file for details. ## Acknowledgements This work builds upon and integrates components from the following open-source projects: 1. [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 2. [Wan2.1](https://github.com/Wan-Video/Wan2.1)