Wan-Dancer-14B

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Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation

🔥 Latest News!!

• July 13, 2026: 💃 We introduce 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 and inference code. And now you can try it on ModelScope Studio or HuggingFace Space!

📑 Todo List

• Wan-Dancer Music-to-Dance
  • Inference code of Wan-Dancer
  • Checkpoints of Wan-Dancer
  • ComfyUI integration

Run Wan-Dancer

Installation

Clone the repo:

git clone https://github.com/Wan-Video/Wan-Dancer.git
cd Wan-Dancer

Install dependencies:

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

Download models using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan-Dancer-14B --local-dir ./Wan-Dancer-14B

Download models using modelscope-cli:

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:

cd Wan-Dancer
./gen_video_global.sh

🔧 Important Parameters

🌰 Examples

2. 🎥 Generate Final High-Resolution Video

Run the local refinement stage:

cd Wan-Dancer
./gen_video_local.sh

🔧 Additional Required Parameters

✅ All other parameters (seed, image_path, etc.) are identical to Step 1.

🌰 Examples

Note: The num_inference_steps should be set to a larger value (e.g., 48) for longer time videos.

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Citation

If you use this code or framework in your research, please cite:

@article{wan-dancer-2026,
  title         = {Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation},
  author        = {Huang, Mingyang and Zhang, Peng and Hu, Li and Wang, Guangyuan and Zhang, Ruoshi and Lu, Yi and Cheng, Gang and Zhang, Bang},
  year          = {2026},
  eprint        = {2607.09581},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2607.09581},
  note          = {Project page: \url{https://humanaigc.github.io/wan-dancer-project/}}
}

License Agreement

This project is licensed under the Apache 2.0 License — see the LICENSE file for details.

Acknowledgements

This work builds upon and integrates components from the following open-source projects:

1. DiffSynth-Studio
2. Wan2.1