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---

license: mit
---


<h1 align="center">LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control</h1>

<div align='center'>
    <a href='https://github.com/cleardusk' target='_blank'><strong>Jianzhu Guo</strong></a><sup> 1†</sup>&emsp;

    <a href='https://github.com/KwaiVGI' target='_blank'><strong>Dingyun Zhang</strong></a><sup> 1,2</sup>&emsp;

    <a href='https://github.com/KwaiVGI' target='_blank'><strong>Xiaoqiang Liu</strong></a><sup> 1</sup>&emsp;

    <a href='https://github.com/KwaiVGI' target='_blank'><strong>Zhizhou Zhong</strong></a><sup> 1,3</sup>&emsp;

    <a href='https://scholar.google.com.hk/citations?user=_8k1ubAAAAAJ' target='_blank'><strong>Yuan Zhang</strong></a><sup> 1</sup>&emsp;

</div>


<div align='center'>
    <a href='https://scholar.google.com/citations?user=P6MraaYAAAAJ' target='_blank'><strong>Pengfei Wan</strong></a><sup> 1</sup>&emsp;

    <a href='https://openreview.net/profile?id=~Di_ZHANG3' target='_blank'><strong>Di Zhang</strong></a><sup> 1</sup>&emsp;

</div>


<div align='center'>
    <sup>1 </sup>Kuaishou Technology&emsp; <sup>2 </sup>University of Science and Technology of China&emsp; <sup>3 </sup>Fudan University&emsp;

</div>


<br>
<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
  <!-- <a href='LICENSE'><img src='https://img.shields.io/badge/license-MIT-yellow'></a> -->
  <a href='https://arxiv.org/pdf/2407.03168'><img src='https://img.shields.io/badge/arXiv-LivePortrait-red'></a>
  <a href='https://liveportrait.github.io'><img src='https://img.shields.io/badge/Project-LivePortrait-green'></a>
  <a href='https://huggingface.co/spaces/KwaiVGI/liveportrait'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
</div>
<br>

<p align="center">
  <img src="./docs/showcase2.gif" alt="showcase">
  <br>
  πŸ”₯ For more results, visit our <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> πŸ”₯
</p>



## πŸ”₯ Updates
- **`2024/07/04`**: πŸ”₯ 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) πŸ’–.

## πŸ”₯ 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

β”œβ”€β”€ insightface

β”‚   └── models

β”‚       └── buffalo_l

β”‚           β”œβ”€β”€ 2d106det.onnx

β”‚           └── det_10g.onnx

└── liveportrait

    β”œβ”€β”€ base_models

    β”‚   β”œβ”€β”€ appearance_feature_extractor.pth

    β”‚   β”œβ”€β”€ motion_extractor.pth

    β”‚   β”œβ”€β”€ spade_generator.pth

    β”‚   └── warping_module.pth

    β”œβ”€β”€ landmark.onnx

    └── retargeting_models

        └── stitching_retargeting_module.pth

```

### 3. Inference πŸš€

```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.

<p align="center">
  <img src="./docs/inference.gif" alt="image">
</p>

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 πŸš€πŸš€πŸš€
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 πŸ’–
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},

}

```