LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control

Jianzhu Guo 1†  Dingyun Zhang 1,2  Xiaoqiang Liu 1  Zhizhou Zhong 1,3  Yuan Zhang 1 
Pengfei Wan 1  Di Zhang 1 
1 Kuaishou Technology  2 University of Science and Technology of China  3 Fudan University 
† Corresponding author


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## 🔥 Updates - **`2024/07/19`**: ✨ We support 🎞️ portrait video editing (aka v2v)! More to see [here](assets/docs/changelog/2024-07-19.md). - **`2024/07/17`**: 🍎 We support macOS with Apple Silicon, modified from [jeethu](https://github.com/jeethu)'s PR [#143](https://github.com/KwaiVGI/LivePortrait/pull/143). - **`2024/07/10`**: 💪 We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here](assets/docs/changelog/2024-07-10.md). - **`2024/07/09`**: 🤗 We released the [HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait), thanks to the HF team and [Gradio](https://github.com/gradio-app/gradio)! - **`2024/07/04`**: 😊 We released the initial version of the inference code and models. Continuous updates, stay tuned! - **`2024/07/04`**: 🔥 We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168). ## Introduction 📖 This repo, named **LivePortrait**, contains the official PyTorch implementation of our paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168). We are actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) 💖. ## Getting Started 🏁 ### 1. Clone the code and prepare the environment ```bash git clone https://github.com/KwaiVGI/LivePortrait cd LivePortrait # create env using conda conda create -n LivePortrait python=3.9 conda activate LivePortrait # install dependencies with pip # for Linux and Windows users pip install -r requirements.txt # for macOS with Apple Silicon users pip install -r requirements_macOS.txt ``` **Note:** make sure your system has [FFmpeg](https://ffmpeg.org/download.html) installed, including both `ffmpeg` and `ffprobe`! ### 2. Download pretrained weights The easiest way to download the pretrained weights is from HuggingFace: ```bash # first, ensure git-lfs is installed, see: https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage git lfs install # clone and move the weights git clone https://huggingface.co/KwaiVGI/LivePortrait temp_pretrained_weights mv temp_pretrained_weights/* pretrained_weights/ rm -rf temp_pretrained_weights ``` Alternatively, you can download all pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). Unzip and place them in `./pretrained_weights`. Ensuring the directory structure is as follows, or contains: ```text pretrained_weights ├── insightface │ └── models │ └── buffalo_l │ ├── 2d106det.onnx │ └── det_10g.onnx └── liveportrait ├── base_models │ ├── appearance_feature_extractor.pth │ ├── motion_extractor.pth │ ├── spade_generator.pth │ └── warping_module.pth ├── landmark.onnx └── retargeting_models └── stitching_retargeting_module.pth ``` ### 3. Inference 🚀 #### Fast hands-on ```bash # For Linux and Windows python inference.py # For macOS with Apple Silicon, Intel not supported, this maybe 20x slower than RTX 4090 PYTORCH_ENABLE_MPS_FALLBACK=1 python inference.py ``` If the script runs successfully, you will get an output mp4 file named `animations/s6--d0_concat.mp4`. This file includes the following results: driving video, input image or video, and generated result.

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Or, you can change the input by specifying the `-s` and `-d` arguments: ```bash # source input is an image python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 # source input is a video ✨ python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d0.mp4 # more options to see python inference.py -h ``` #### Driving video auto-cropping 📢📢📢 To use your own driving video, we **recommend**: ⬇️ - Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`. - Focus on the head area, similar to the example videos. - Minimize shoulder movement. - Make sure the first frame of driving video is a frontal face with **neutral expression**. Below is a auto-cropping case by `--flag_crop_driving_video`: ```bash python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video ``` If you find the results of auto-cropping is not well, you can modify the `--scale_crop_driving_video`, `--vy_ratio_crop_driving_video` options to adjust the scale and offset, or do it manually. #### Motion template making You can also use the auto-generated motion template files ending with `.pkl` to speed up inference, and **protect privacy**, such as: ```bash python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl # portrait animation python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d5.pkl # portrait video editing ``` ### 4. Gradio interface 🤗 We also provide a Gradio interface for a better experience, just run by: ```bash # For Linux and Windows users (and macOS with Intel??) python app.py # For macOS with Apple Silicon users, Intel not supported, this maybe 20x slower than RTX 4090 PYTORCH_ENABLE_MPS_FALLBACK=1 python app.py ``` You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs! 🚀 We also provide an acceleration option `--flag_do_torch_compile`. The first-time inference triggers an optimization process (about one minute), making subsequent inferences 20-30% faster. Performance gains may vary with different CUDA versions. ```bash # enable torch.compile for faster inference python app.py --flag_do_torch_compile ``` **Note**: This method is not supported on Windows and macOS. **Or, try it out effortlessly on [HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait) 🤗** ### 5. Inference speed evaluation 🚀🚀🚀 We have also provided a script to evaluate the inference speed of each module: ```bash # For NVIDIA GPU python speed.py ``` Below are the results of inferring one frame on an RTX 4090 GPU using the native PyTorch framework with `torch.compile`: | Model | Parameters(M) | Model Size(MB) | Inference(ms) | |-----------------------------------|:-------------:|:--------------:|:-------------:| | Appearance Feature Extractor | 0.84 | 3.3 | 0.82 | | Motion Extractor | 28.12 | 108 | 0.84 | | Spade Generator | 55.37 | 212 | 7.59 | | Warping Module | 45.53 | 174 | 5.21 | | Stitching and Retargeting Modules | 0.23 | 2.3 | 0.31 | *Note: The values for the Stitching and Retargeting Modules represent the combined parameter counts and total inference time of three sequential MLP networks.* ## Community Resources 🤗 Discover the invaluable resources contributed by our community to enhance your LivePortrait experience: - [ComfyUI-LivePortraitKJ](https://github.com/kijai/ComfyUI-LivePortraitKJ) by [@kijai](https://github.com/kijai) - [comfyui-liveportrait](https://github.com/shadowcz007/comfyui-liveportrait) by [@shadowcz007](https://github.com/shadowcz007) - [LivePortrait In ComfyUI](https://www.youtube.com/watch?v=aFcS31OWMjE) by [@Benji](https://www.youtube.com/@TheFutureThinker) - [LivePortrait hands-on tutorial](https://www.youtube.com/watch?v=uyjSTAOY7yI) by [@AI Search](https://www.youtube.com/@theAIsearch) - [ComfyUI tutorial](https://www.youtube.com/watch?v=8-IcDDmiUMM) by [@Sebastian Kamph](https://www.youtube.com/@sebastiankamph) - [Replicate Playground](https://replicate.com/fofr/live-portrait) and [cog-comfyui](https://github.com/fofr/cog-comfyui) by [@fofr](https://github.com/fofr) And many more amazing contributions from our community! ## Acknowledgements 💐 We would like to thank the contributors of [FOMM](https://github.com/AliaksandrSiarohin/first-order-model), [Open Facevid2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis), [SPADE](https://github.com/NVlabs/SPADE), [InsightFace](https://github.com/deepinsight/insightface) repositories, for their open research and contributions. ## Citation 💖 If you find LivePortrait useful for your research, welcome to 🌟 this repo and cite our work using the following BibTeX: ```bibtex @article{guo2024liveportrait, title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control}, author = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di}, journal = {arXiv preprint arXiv:2407.03168}, year = {2024} } ```