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	---
	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

	<p align="center">
	<img src="assets/logo.png" width="400"/>
	<p>

	<p align="center">
	💜 <a href="https://humanaigc.github.io/wan-dancer-project/"><b>Wan-Dancer</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan-Dancer">GitHub</a> &nbsp&nbsp \| &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/Wan-Dancer-14B">Hugging Face</a>&nbsp&nbsp \| &nbsp&nbsp🤖 <a href="https://www.modelscope.cn/models/Wan-AI/Wan-Dancer-14B">ModelScope</a>&nbsp&nbsp \| &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2607.09581">Paper</a> &nbsp&nbsp
	<br>


	[Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation](https://arxiv.org/abs/2607.09581) <be>


	## 🔥 Latest News!!
	* July 13, 2026: 💃 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)!


	## 📑 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 \| 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan-Dancer-14B) 🤖 [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
	```

	###### 🔧 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).<br>Available styles:<ul><li>Chinese Classic Dance: `gen_video/prompt/古典舞_global.txt`</li><li>K-Pop Dance: `gen_video/prompt/kpop_global.txt`</li><li>Street Dance: `gen_video/prompt/街舞_global.txt`</li><li>Tap Dance: `gen_video/prompt/踢踏舞_global.txt`</li><li>Latin Dance: `gen_video/prompt/拉丁舞_global.txt`</li></ul> \|
	\| `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<br>image_path='gen_video/ref_image/1001.jpg'<br>prompt_path='gen_video/prompt/古典舞_global.txt'<br>music_path='gen_video/music/ChineseClassicDance.WAV'<br>num_inference_steps=48<br>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<br>image_path='gen_video/ref_image/2001.jpg'<br>prompt_path='gen_video/prompt/街舞_global.txt'<br>music_path='gen_video/music/StreetDance.WAV'<br>num_inference_steps=48<br>cfg_scale=5 \| [![Street Dance](assets/2001.jpg)](https://cloud.video.taobao.com/vod/MQiVGjY_ngH3imgfIl37xaQoJfbWadYldlZoMWJFMKQ.mp4) \|
	\| K-Pop Dance \| seed=0<br>image_path='gen_video/ref_image/3001.jpg'<br>prompt_path='gen_video/prompt/kpop_global.txt'<br>music_path='gen_video/music_suno/3001.WAV'<br>num_inference_steps=48<br>cfg_scale=5 \| [![K-Pop Dance](assets/3001.jpg)](https://cloud.video.taobao.com/vod/WGS6Z3VWpgGh8jnt2lrW99XeTB6uu9-H6lCGk1HBLZg.mp4) \|
	\| Latin Dance \| seed=0<br>image_path='gen_video/ref_image/4001.jpg'<br>prompt_path='gen_video/prompt/拉丁舞_global.txt'<br>music_path='gen_video/music/LatinDance.WAV'<br>num_inference_steps=48<br>cfg_scale=5 \| [![Latin Dance](assets/4001.jpg)](https://cloud.video.taobao.com/vod/jnwCUj3WvuErBAxF78b-kttEJoegA6-8VmLMZsayBGI.mp4) \|
	\| Tap Dance \| seed=0<br>image_path='gen_video/ref_image/5001.jpg'<br>prompt_path='gen_video/prompt/踢踏舞_global.txt'<br>music_path='gen_video/music/TapDance.wav'<br>num_inference_steps=48<br>cfg_scale=5 \| [![Tap Dance](assets/5001.jpg)](https://cloud.video.taobao.com/vod/lfrYGNMKzYaLvU3IsMyVJM003T5WZL6QKR7xiifEVAg.mp4)\|

	##### 2. 🎥 Generate Final High-Resolution Video

	Run the local refinement stage:

	```bash
	cd Wan-Dancer
	./gen_video_local.sh
	```

	###### 🔧 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).<br>Available styles:<ul><li>Chinese Classic Dance: `gen_video/prompt/古典舞_local.txt`</li><li>K-Pop Dance: `gen_video/prompt/kpop_local.txt`</li><li>Street Dance: `gen_video/prompt/街舞_local.txt`</li><li>Tap Dance: `gen_video/prompt/踢踏舞_local.txt`</li><li>Latin Dance: `gen_video/prompt/拉丁舞_local.txt`</li></ul> \|

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

	###### 🌰 Examples
	\| Dance Genres \| Parameter \| Generated Final Video \|
	\| ------------ \|-----------------------\|-----------------\|
	\| Chinese Classical Dance \| seed=0<br>image_path='gen_video/ref_image/1001.jpg'<br>prompt_path='gen_video/prompt/古典舞_local.txt'<br>music_path='gen_video/music/ChineseClassicDance.WAV'<br>num_inference_steps=24<br>cfg_scale=5<br>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<br>image_path='gen_video/ref_image/2001.jpg'<br>prompt_path='gen_video/prompt/街舞_local.txt'<br>music_path='gen_video/music/StreetDance.WAV'<br>num_inference_steps=24<br>cfg_scale=5<br>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<br>image_path='gen_video/ref_image/3001.jpg'<br>prompt_path='gen_video/prompt/kpop_local.txt'<br>music_path='gen_video/music_suno/3001.WAV'<br>num_inference_steps=24<br>cfg_scale=5<br>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<br>image_path='gen_video/ref_image/4001.jpg'<br>prompt_path='gen_video/prompt/拉丁舞_local.txt'<br>music_path='gen_video/music/LatinDance.WAV'<br>num_inference_steps=24<br>cfg_scale=5<br>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<br>image_path='gen_video/ref_image/5001.jpg'<br>prompt_path='gen_video/prompt/踢踏舞_local.txt'<br>music_path='gen_video/music/TapDance.wav'<br>num_inference_steps=24<br>cfg_scale=5<br>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) \|

	<strong>Note:</strong> 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 — 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)