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* chore: remove transformers for macOS * chore: specify torch cuda version * chore: update install instruction * chore: update install instruction * chore: update readme * doc: update --------- Co-authored-by: zzzweakman <1819489045@qq.com>
260 lines
14 KiB
Markdown
260 lines
14 KiB
Markdown
<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> 1†</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>†</sup> Corresponding author</small>
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</div>
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<br>
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<div align="center">
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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="./assets/docs/showcase2.gif" alt="showcase">
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<br>
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🔥 For more results, visit our <a href="https://liveportrait.github.io/"><strong>homepage</strong></a> 🔥
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</p>
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## 🔥 Updates
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- `2024/08/05`: 📦 Windows users download the [one-click installer](https://huggingface.co/cleardusk/LivePortrait-Windows/blob/main/LivePortrait-Windows-v20240805.zip) for Humans mode and **Animals mode** now! For details, see [**here**](./assets/docs/changelog/2024-08-05.md).
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- **`2024/08/02`**: 😸 We released a version of the **Animals model**, along with several other updates and improvements. Check out the details [**here**](./assets/docs/changelog/2024-08-02.md)!
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- **`2024/07/25`**: 📦 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`**: 🎨 We support pose editing for source portraits in the Gradio interface. We’ve also lowered the default detection threshold to increase recall. [Have fun](assets/docs/changelog/2024-07-24.md)!
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- **`2024/07/19`**: ✨ We support 🎞️ **portrait video editing (aka v2v)**! More to see [here](assets/docs/changelog/2024-07-19.md).
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- **`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).
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- **`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).
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- **`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)!
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- **`2024/07/04`**: 😊 We released the initial version of the inference code and models. Continuous updates, stay tuned!
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- **`2024/07/04`**: 🔥 We released the [homepage](https://liveportrait.github.io) and technical report on [arXiv](https://arxiv.org/pdf/2407.03168).
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## Introduction 📖
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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) 💖.
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## Getting Started 🏁
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### 1. Clone the code and prepare the environment 🛠️
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> [!Note]
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> Make sure your system has [`git`](https://git-scm.com/), [`conda`](https://anaconda.org/anaconda/conda), and [`FFmpeg`](https://ffmpeg.org/download.html) installed. For details on FFmpeg installation, see [**how to install FFmpeg**](assets/docs/how-to-install-ffmpeg.md).
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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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```
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#### For Linux or Windows Users
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The [X-Pose](https://github.com/IDEA-Research/X-Pose) dependency has **strict limitations** on the CUDA version. To check your current CUDA version, run the following command:
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```bash
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nvcc -V # example versions: 11.1, 11.8, 12.1, etc.
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```
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We provide installation commands for `torch` corresponding to three common CUDA versions. If your version is not listed, please visit [PyTorch Official Website](https://pytorch.org/get-started/previous-versions/) to find the installation command for your CUDA version.
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```bash
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# for Linux and Windows users (choose one based on your CUDA version):
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# for CUDA 11.1
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pip install torch==1.10.1+cu111 torchvision==0.11.2 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
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# for CUDA 11.8
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pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu118
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# for CUDA 12.1
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pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121
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# ...
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```
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Finally, install the remaining dependencies:
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```bash
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pip install -r requirements.txt
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```
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#### For macOS with Apple Silicon Users
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The [X-Pose](https://github.com/IDEA-Research/X-Pose) dependency does not support macOS, so you can skip its installation. While Humans mode works as usual, Animals mode is not supported. Use the provided requirements file for macOS with Apple Silicon:
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```bash
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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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### 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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# !pip install -U "huggingface_hub[cli]"
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huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs"
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```
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If you cannot access to Huggingface, you can use [hf-mirror](https://hf-mirror.com/) to download:
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```bash
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# !pip install -U "huggingface_hub[cli]"
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export HF_ENDPOINT=https://hf-mirror.com
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huggingface-cli download KwaiVGI/LivePortrait --local-dir pretrained_weights --exclude "*.git*" "README.md" "docs"
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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) (WIP). Unzip and place them in `./pretrained_weights`.
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Ensuring the directory structure is as or contains [**this**](assets/docs/directory-structure.md).
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### 3. Inference 🚀
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#### Fast hands-on (humans) 👤
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```bash
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# For Linux and Windows users
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python inference.py
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# For macOS users with Apple Silicon (Intel is not tested). NOTE: 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="./assets/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 ✨
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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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#### Fast hands-on (animals) 🐱🐶
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Animals mode is ONLY tested on Linux and Windows with NVIDIA GPU.
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You need to build an OP named `MultiScaleDeformableAttention` first, which is used by [X-Pose](https://github.com/IDEA-Research/X-Pose), a general keypoint detection framework.
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```bash
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cd src/utils/dependencies/XPose/models/UniPose/ops
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python setup.py build install
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cd - # equal to cd ../../../../../../../
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```
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Then
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```bash
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python inference_animals.py -s assets/examples/source/s39.jpg -d assets/examples/driving/wink.pkl --driving_multiplier 1.75 --no_flag_stitching
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```
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If the script runs successfully, you will get an output mp4 file named `animations/s39--wink_concat.mp4`.
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<p align="center">
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<img src="./assets/docs/inference-animals.gif" alt="image">
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</p>
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#### Driving video auto-cropping 📢📢📢
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> [!IMPORTANT]
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> To use your own driving video, we **recommend**: ⬇️
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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 🤗
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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 # humans mode
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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 # humans mode
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```
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We also provide a Gradio interface of animals mode, which is only tested on Linux with NVIDIA GPU:
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```bash
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python app_animals.py # animals mode 🐱🐶
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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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🚀 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) 🤗**
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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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# For NVIDIA GPU
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python speed.py
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```
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The results are [**here**](./assets/docs/speed.md).
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## Community Resources 🤗
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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-AdvancedLivePortrait](https://github.com/PowerHouseMan/ComfyUI-AdvancedLivePortrait) by [@PowerHouseMan](https://github.com/PowerHouseMan).
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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 💐
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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) and [X-Pose](https://github.com/IDEA-Research/X-Pose) repositories, for their open research and contributions.
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## Citation 💖
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If you find LivePortrait useful for your research, welcome to 🌟 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 📧
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[**Jianzhu Guo (郭建珠)**](https://guojianzhu.com); **guojianzhu1994@gmail.com**
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