acvlab/FantasyPortrait

FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers

🔥 Latest News!!

• August 12, 2025: We released the inference code, model weights and datasets.

Demo

For more interesting results, please visit our website.

Quickstart

🛠️Installation

Clone the repo:

git clone https://github.com/Fantasy-AMAP/fantasy-portrait.git
cd fantasy-portrait

Install dependencies:

apt-get install ffmpeg
# Ensure torch >= 2.0.0
pip install -r requirements.txt
# Note: flash attention must be installed
pip install flash_attn

📦Multi-Expr Dataset

We make public the first multi-portrait facial expression video dataset Multi-Expr Dataset, Please download it via the ModelScope or Huggingface.

🧱Model Download

Download models using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./models/Wan2.1-I2V-14B-720P
huggingface-cli download acvlab/FantasyPortrait --local-dir ./models

Download models using modelscope-cli:

pip install modelscope
modelscope download Wan-AI/Wan2.1-I2V-14B-720P --local_dir ./models/Wan2.1-I2V-14B-720P
modelscope download amap_cvlab/FantasyPortrait  --local_dir ./models

🔑 Single-Portrait Inference

bash infer_single.sh

🔑 Multi-Portrait Inference

If you use input image and drive videos with multiple people, you can run as follows:

bash infer_multi.sh

If you use input image with multiple people and different multiple single-human driven videos, you can run as follows:

bash infer_multi_diff.sh

📦Speed and VRAM Usage

We present a detailed table here. The model is tested on a single A100.

🧩 Community Works

We ❤️ contributions from the open-source community! If your work has improved FantasyPortrait, please inform us. Or you can directly e-mail frank.jf@alibaba-inc.com. We are happy to reference your project for everyone's convenience.

🔗Citation

If you find this repository useful, please consider giving a star ⭐ and citation

@article{wang2025fantasyportrait,
  title={FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers},
  author={Wang, Qiang and Wang, Mengchao and Jiang, Fan and Fan, Yaqi and Qi, Yonggang and Xu, Mu},
  journal={arXiv preprint arXiv:2507.12956},
  year={2025}
}

Acknowledgments

Thanks to Wan2.1, PD-FGC and DiffSynth-Studio for open-sourcing their models and code, which provided valuable references and support for this project. Their contributions to the open-source community are truly appreciated.