---
license: mit
---
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
1 Kuaishou Technology 2 University of Science and Technology of China 3 Fudan University
🔥 For more results, visit our homepage 🔥
## 🔥 Updates
- **`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==3.9.18
conda activate LivePortrait
# install dependencies with pip
pip install -r requirements.txt
```
### 2. Download pretrained weights
Download our pretrained LivePortrait weights and face detection models of InsightFace from [Google Drive](https://drive.google.com/drive/folders/1UtKgzKjFAOmZkhNK-OYT0caJ_w2XAnib) or [Baidu Yun](https://pan.baidu.com/s/1MGctWmNla_vZxDbEp2Dtzw?pwd=z5cn). We have packed all weights in one directory 😊. Unzip and place them in `./pretrained_weights` ensuring the directory structure is as follows:
```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 🚀
```bash
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, and generated result.
Or, you can change the input by specifying the `-s` and `-d` arguments:
```bash
python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4
# or disable pasting back
python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
# more options to see
python inference.py -h
```
**More interesting results can be found in our [Homepage](https://liveportrait.github.io)** 😊
### 4. Gradio interface
We also provide a Gradio interface for a better experience, just run by:
```bash
python app.py
```
### 5. Inference speed evaluation 🚀🚀🚀
We have also provided a script to evaluate the inference speed of each module:
```bash
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 listed values of Stitching and Retargeting Modules represent the combined parameter counts and the total sequential inference time of three MLP networks.*
## 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{guo2024live,
title = {LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control},
author = {Jianzhu Guo and Dingyun Zhang and Xiaoqiang Liu and Zhizhou Zhong and Yuan Zhang and Pengfei Wan and Di Zhang},
year = {2024},
journal = {arXiv preprint:2407.03168},
}
```
