SuperCam_Models

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⏬ Download weights

Option 1: Download pre-trained weights of base models and other components (sd-image-variations-diffusers and sd-vae-ft-mse). You can run the following command to download weights automatically:

python tools/download_weights.py

Option 2: Download pre-trained weights into the ./pretrained_weights folder from one of the below URLs:

<a href='https://drive.google.com/drive/folders/1GOhDBKIeowkMpBnKhGB8jgEhJt_--vbT?usp=drive_link'><img src='https://img.shields.io/badge/Google%20Drive-5B8DEF?style=for-the-badge&logo=googledrive&logoColor=white'></a> <a href='https://pan.baidu.com/s/1DCv4NvUy_z7Gj2xCGqRMkQ?pwd=gj64'><img src='https://img.shields.io/badge/Baidu%20Netdisk-3E4A89?style=for-the-badge&logo=baidu&logoColor=white'></a> <a href='https://modelscope.cn/models/huaichang/SuperCam'><img src='https://img.shields.io/badge/ModelScope-624AFF?style=for-the-badge&logo=alibabacloud&logoColor=white'></a> <a href='https://huggingface.co/huaichang/SuperCam'><img src='https://img.shields.io/badge/HuggingFace-E67E22?style=for-the-badge&logo=huggingface&logoColor=white'></a>

Finally, these weights should be organized as follows:

pretrained_weights
├── onnx
│   ├── unet_opt
│   │   ├── unet_opt.onnx
│   │   └── unet_opt.onnx.data
│   └── unet
├── SuperCam
│   ├── denoising_unet.pth
│   ├── motion_encoder.pth
│   ├── motion_extractor.pth
│   ├── pose_guider.pth
│   ├── reference_unet.pth
│   └── temporal_module.pth
├── sd-vae-ft-mse
│   ├── diffusion_pytorch_model.bin
│   └── config.json
├── sd-image-variations-diffusers
│   ├── image_encoder
│   │   ├── pytorch_model.bin
│   │   └── config.json
│   ├── unet
│   │   ├── diffusion_pytorch_model.bin
│   │   └── config.json
│   └── model_index.json
└── tensorrt
    └── unet_work.engine

🎞️ Offline Inference

python inference_offline.py

⚠️ Note for RTX 50-Series (Blackwell) Users: xformers is not yet fully compatible with the new architecture. To avoid crashes, please disable it by running:

python inference_offline.py --use_xformers False

📸 Online Inference

📦 Setup Web UI

# install Node.js 18+
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash
nvm install 18

cd webcam
source start.sh

🏎️ Acceleration (Optional)

Converting the model to TensorRT can significantly speed up inference (~ 2x ⚡️). Building the engine may take about 20 minutes depending on your device. Note that TensorRT optimizations may lead to slight variations or a small drop in output quality.

pip install -r requirements_trt.txt

python torch2trt.py

The provided TensorRT model is from an H100. We recommend ALL users (including H100 users) re-run python torch2trt.py locally to ensure best compatibility.

▶️ Start Streaming

python inference_online.py --acceleration none (for RTX 50-Series) or xformers or tensorrt

Then open http://0.0.0.0:7860 in your browser. (*If http://0.0.0.0:7860 does not work well, try http://localhost:7860)

How to use: Upload Image ➡️ Fuse Reference ➡️ Start Animation ➡️ Enjoy! 🎉

Regarding Latency: Latency varies depending on your device's computing power. You can try the following methods to optimize it:

1. Lower the "Driving FPS" setting in the WebUI to reduce the computational workload.
2. You can increase the multiplier (e.g., set to num_frames_needed * 4 or higher) to better match your device's inference speed. https://github.com/GVCLab/SuperCam/blob/6953d1a8b409f360a3ee1d7325093622b29f1e22/webcam/util.py#L73

⭐ Citation

If you find SuperCam useful for your research, welcome to cite our work using the following BibTeX:

@article{li2025SuperCam,
  title={SuperCam! Expressive Portrait Image Animation for Live Streaming},
  author={Li, Zhiyuan and Pun, Chi-Man and Fang, Chen and Wang, Jue and Cun, Xiaodong},
  journal={arXiv preprint arXiv:2512.11253},
  year={2025}
}

❤️ Acknowledgement

This code is mainly built upon Moore-AnimateAnyone, X-NeMo, StreamDiffusion, RAIN and LivePortrait, thanks to their invaluable contributions.