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license: mit |
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library_name: liveportrait |
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--- |
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<h1 align="center">LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control</h1> |
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<div align='center'> |
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<a href='https://github.com/cleardusk' target='_blank'><strong>Jianzhu Guo</strong></a><sup> 1β </sup>  |
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<a href='https://github.com/Mystery099' target='_blank'><strong>Dingyun Zhang</strong></a><sup> 1,2</sup>  |
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<a href='https://github.com/KwaiVGI' target='_blank'><strong>Xiaoqiang Liu</strong></a><sup> 1</sup>  |
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<a href='https://github.com/zzzweakman' target='_blank'><strong>Zhizhou Zhong</strong></a><sup> 1,3</sup>  |
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<a href='https://scholar.google.com.hk/citations?user=_8k1ubAAAAAJ' target='_blank'><strong>Yuan Zhang</strong></a><sup> 1</sup>  |
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</div> |
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<div align='center'> |
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<a href='https://scholar.google.com/citations?user=P6MraaYAAAAJ' target='_blank'><strong>Pengfei Wan</strong></a><sup> 1</sup>  |
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<a href='https://openreview.net/profile?id=~Di_ZHANG3' target='_blank'><strong>Di Zhang</strong></a><sup> 1</sup>  |
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</div> |
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<div align='center'> |
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<sup>1 </sup>Kuaishou Technology  <sup>2 </sup>University of Science and Technology of China  <sup>3 </sup>Fudan University  |
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</div> |
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<div align='center'> |
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<small><sup>β </sup> Corresponding author</small> |
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</div> |
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<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> |
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<!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> --> |
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<a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a> |
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<a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a> |
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<a href='https://huggingface.co/spaces/KwaiVGI/liveportrait'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a> |
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<a href="https://github.com/KwaiVGI/LivePortrait"><img src="https://img.shields.io/github/stars/KwaiVGI/LivePortrait"></a> |
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</div> |
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<br> |
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<p align="center"> |
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<img src="./docs/showcase2.gif" alt="showcase"> |
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π₯ For more results, visit our <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> π₯ |
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</p> |
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## π₯ Updates |
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- **`2024/08/02`**: πΈ We released a version of the **Animals model**, along with several other updates and improvements. Check out the details [**here**](https://github.com/KwaiVGI/LivePortrait/blob/main/assets/docs/changelog/2024-08-02.md)! |
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- **`2024/07/25`**: π¦ Windows users can now download the package from [HuggingFace](https://huggingface.co/cleardusk/LivePortrait-Windows/tree/main) or [BaiduYun](https://pan.baidu.com/s/1FWsWqKe0eNfXrwjEhhCqlw?pwd=86q2). Simply unzip and double-click `run_windows.bat` to enjoy! |
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- **`2024/07/24`**: π¨ We support pose editing for source portraits in the Gradio interface. Weβve also lowered the default detection threshold to increase recall. [Have fun](https://github.com/KwaiVGI/LivePortrait/blob/main/assets/docs/changelog/2024-07-24.md)! |
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- **`2024/07/19`**: β¨ We support ποΈ portrait video editing (aka v2v)! More to see [here](https://github.com/KwaiVGI/LivePortrait/blob/main/assets/docs/changelog/2024-07-19.md). |
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- **`2024/07/17`**: π We support macOS with Apple Silicon, modified from [jeethu](https://github.com/jeethu)'s PR [#143](https://github.com/KwaiVGI/LivePortrait/pull/143). |
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- **`2024/07/10`**: πͺ We support audio and video concatenating, driving video auto-cropping, and template making to protect privacy. More to see [here](https://github.com/KwaiVGI/LivePortrait/blob/main/assets/docs/changelog/2024-07-10.md). |
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- **`2024/07/09`**: π€ We released the [HuggingFace Space](https://huggingface.co/spaces/KwaiVGI/liveportrait), thanks to the HF team and [Gradio](https://github.com/gradio-app/gradio)! |
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- **`2024/07/04`**: π We released the initial version of the inference code and models. Continuous updates, stay tuned! |
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- **`2024/07/04`**: π₯ We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168). |
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## Introduction π |
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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). |
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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) π. |
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## Getting Started π |
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### 1. Clone the code and prepare the environment |
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```bash |
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git clone https://github.com/KwaiVGI/LivePortrait |
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cd LivePortrait |
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# create env using conda |
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conda create -n LivePortrait python==3.9 |
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conda activate LivePortrait |
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# install dependencies with pip |
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# for Linux and Windows users |
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pip install -r requirements.txt |
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# for macOS with Apple Silicon users |
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pip install -r requirements_macOS.txt |
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``` |
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**Note:** make sure your system has [FFmpeg](https://ffmpeg.org/download.html) installed, including both `ffmpeg` and `ffprobe`! |
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### 2. Download pretrained weights |
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The easiest way to download the pretrained weights is from HuggingFace: |
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```bash |
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# first, ensure git-lfs is installed, see: https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage |
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git lfs install |
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# clone and move the weights |
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git clone https://huggingface.co/KwaiVGI/LivePortrait temp_pretrained_weights |
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mv temp_pretrained_weights/* pretrained_weights/ |
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rm -rf temp_pretrained_weights |
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``` |
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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`. |
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Ensuring the directory structure is as follows, or contains: |
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```text |
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pretrained_weights |
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βββ insightface |
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β βββ models |
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β βββ buffalo_l |
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β βββ 2d106det.onnx |
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β βββ det_10g.onnx |
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βββ liveportrait |
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βββ base_models |
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β βββ appearance_feature_extractor.pth |
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β βββ motion_extractor.pth |
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β βββ spade_generator.pth |
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β βββ warping_module.pth |
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βββ landmark.onnx |
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βββ retargeting_models |
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βββ stitching_retargeting_module.pth |
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``` |
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### 3. Inference π |
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#### Fast hands-on |
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```bash |
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# For Linux and Windows |
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python inference.py |
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# For macOS with Apple Silicon, Intel not supported, this maybe 20x slower than RTX 4090 |
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PYTORCH_ENABLE_MPS_FALLBACK=1 python inference.py |
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``` |
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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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<p align="center"> |
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<img src="./docs/inference.gif" alt="image"> |
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</p> |
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Or, you can change the input by specifying the `-s` and `-d` arguments: |
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```bash |
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# source input is an image |
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 |
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# source input is a video β¨ |
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python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d0.mp4 |
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# more options to see |
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python inference.py -h |
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``` |
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#### Driving video auto-cropping π’π’π’ |
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To use your own driving video, we **recommend**: β¬οΈ |
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- Crop it to a **1:1** aspect ratio (e.g., 512x512 or 256x256 pixels), or enable auto-cropping by `--flag_crop_driving_video`. |
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- Focus on the head area, similar to the example videos. |
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- Minimize shoulder movement. |
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- Make sure the first frame of driving video is a frontal face with **neutral expression**. |
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Below is a auto-cropping case by `--flag_crop_driving_video`: |
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```bash |
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d13.mp4 --flag_crop_driving_video |
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``` |
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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. |
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#### Motion template making |
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You can also use the auto-generated motion template files ending with `.pkl` to speed up inference, and **protect privacy**, such as: |
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```bash |
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d5.pkl # portrait animation |
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python inference.py -s assets/examples/source/s13.mp4 -d assets/examples/driving/d5.pkl # portrait video editing |
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``` |
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### 4. Gradio interface π€ |
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We also provide a Gradio <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a> interface for a better experience, just run by: |
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```bash |
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# For Linux and Windows users (and macOS with Intel??) |
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python app.py |
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# For macOS with Apple Silicon users, Intel not supported, this maybe 20x slower than RTX 4090 |
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PYTORCH_ENABLE_MPS_FALLBACK=1 python app.py |
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``` |
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You can specify the `--server_port`, `--share`, `--server_name` arguments to satisfy your needs! |
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π 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. |
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```bash |
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# enable torch.compile for faster inference |
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python app.py --flag_do_torch_compile |
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``` |
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**Note**: This method is not supported on Windows and macOS. |
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**Or, try it out effortlessly on [HuggingFace](https://huggingface.co/spaces/KwaiVGI/LivePortrait) π€** |
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### 5. Inference speed evaluation πππ |
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We have also provided a script to evaluate the inference speed of each module: |
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```bash |
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# For NVIDIA GPU |
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python speed.py |
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``` |
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Below are the results of inferring one frame on an RTX 4090 GPU using the native PyTorch framework with `torch.compile`: |
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| Model | Parameters(M) | Model Size(MB) | Inference(ms) | |
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|-----------------------------------|:-------------:|:--------------:|:-------------:| |
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| Appearance Feature Extractor | 0.84 | 3.3 | 0.82 | |
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| Motion Extractor | 28.12 | 108 | 0.84 | |
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| Spade Generator | 55.37 | 212 | 7.59 | |
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| Warping Module | 45.53 | 174 | 5.21 | |
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| Stitching and Retargeting Modules | 0.23 | 2.3 | 0.31 | |
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*Note: The values for the Stitching and Retargeting Modules represent the combined parameter counts and total inference time of three sequential MLP networks.* |
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## Community Resources π€ |
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Discover the invaluable resources contributed by our community to enhance your LivePortrait experience: |
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- [ComfyUI-LivePortraitKJ](https://github.com/kijai/ComfyUI-LivePortraitKJ) by [@kijai](https://github.com/kijai) |
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- [comfyui-liveportrait](https://github.com/shadowcz007/comfyui-liveportrait) by [@shadowcz007](https://github.com/shadowcz007) |
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- [LivePortrait In ComfyUI](https://www.youtube.com/watch?v=aFcS31OWMjE) by [@Benji](https://www.youtube.com/@TheFutureThinker) |
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- [LivePortrait hands-on tutorial](https://www.youtube.com/watch?v=uyjSTAOY7yI) by [@AI Search](https://www.youtube.com/@theAIsearch) |
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- [ComfyUI tutorial](https://www.youtube.com/watch?v=8-IcDDmiUMM) by [@Sebastian Kamph](https://www.youtube.com/@sebastiankamph) |
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- [Replicate Playground](https://replicate.com/fofr/live-portrait) and [cog-comfyui](https://github.com/fofr/cog-comfyui) by [@fofr](https://github.com/fofr) |
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And many more amazing contributions from our community! |
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## Acknowledgements π |
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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. |
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## Citation π |
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If you find LivePortrait useful for your research, welcome to π this repo and cite our work using the following BibTeX: |
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```bibtex |
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@article{guo2024liveportrait, |
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title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control}, |
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author = {Guo, Jianzhu and Zhang, Dingyun and Liu, Xiaoqiang and Zhong, Zhizhou and Zhang, Yuan and Wan, Pengfei and Zhang, Di}, |
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journal = {arXiv preprint arXiv:2407.03168}, |
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year = {2024} |
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} |
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``` |
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## Contact π§ |
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[**Jianzhu Guo (ιε»Ίη )**](https://guojianzhu.com); **[email protected]** |
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