mirror of
https://github.com/KwaiVGI/LivePortrait.git
synced 2024-12-23 13:04:23 +00:00
113 lines
4.4 KiB
Markdown
113 lines
4.4 KiB
Markdown
<h1 align="center"> Webcam Live Portrait</h1>
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<p align="center">
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<img src="./assets/docs/showcase2.gif" alt="showcase">
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<br>
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🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥🔥
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</p>
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## 🔥 Updates
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- **`2024/07/10`**: 🔥 I released the initial version of the inference code for webcam. Continuous updates, stay tuned!
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## Introduction
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This repo, named **Webcam Live Portrait**, contains the official PyTorch implementation of author paper [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://arxiv.org/pdf/2407.03168).
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I am actively updating and improving this repository. If you find any bugs or have suggestions, welcome to raise issues or submit pull requests (PR) 💖.
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## 🔥 Getting Started
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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/Mrkomiljon/Webcam_Live_Portrait.git
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cd Webcam_Live_Portrait
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# create env using conda
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conda create -n LivePortrait python==3.9.18
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conda activate LivePortrait
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# install dependencies with pip
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pip install -r requirements.txt
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```
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### 2. Download pretrained weights
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Download 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:
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```text
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pretrained_weights
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├── insightface
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│ └── models
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│ └── buffalo_l
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│ ├── 2d106det.onnx
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│ └── det_10g.onnx
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└── liveportrait
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├── base_models
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│ ├── appearance_feature_extractor.pth
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│ ├── motion_extractor.pth
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│ ├── spade_generator.pth
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│ └── warping_module.pth
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├── landmark.onnx
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└── retargeting_models
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└── stitching_retargeting_module.pth
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```
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### 3. Inference 🚀
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```bash
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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, and generated result.
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<p align="center">
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<img src="https://github.com/Mrkomiljon/Webcam_Live_Portrait/assets/92161283/7c4daf41-838d-4eb8-a762-9188cd337ee6">
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</p>
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https://github.com/Mrkomiljon/Webcam_Live_Portrait/assets/92161283/7c4daf41-838d-4eb8-a762-9188cd337ee6
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Or, you can change the input by specifying the `-s` and `-d` arguments come from webcam:
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```bash
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python inference.py -s assets/examples/source/MY_photo.jpg
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# or disable pasting back
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python inference.py -s assets/examples/source/s9.jpg -d assets/examples/driving/d0.mp4 --no_flag_pasteback
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# more options to see
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python inference.py -h
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```
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### 4. Gradio interface
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We also provide a Gradio interface for a better experience, just run by:
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```bash
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python app.py
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```
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### 5. Inference speed evaluation 🚀🚀🚀
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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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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 listed values of Stitching and Retargeting Modules represent the combined parameter counts and the total sequential inference time of three MLP networks.*
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## Acknowledgements
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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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