算力云平台部署—SadTalker的AI数字人视频

选择算力

部署选择

选择镜像

机器管理

控制台

通过平台工具进入服务器

认识管理系统

打开命令行

进入目录 stable-diffusion-webui#

cd

增加执行权限

chmod +x ./webui.sh

运行命令

bash ./webui.sh

sudo apt install -y python3 python3-venv git

安装软件

1. Creating the env and install the requirements.

git clone https://github.com/OpenTalker/SadTalker.git

cd SadTalker 

conda create -n sadtalker python=3.8

conda activate sadtalker

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

conda install ffmpeg

pip install -r requirements.txt

运行安装环境

pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

conda install ffmpeg

pip install -r requirements.txt

### Coqui TTS is optional for gradio demo. 
### pip install TTS

系统盘不够用

目录结构

╔═════════════════╦════════╦════╦═════════════════════════════════════════════════════════════════════════╗

║目录 ║名称 ║速度║说明 ║

╠═════════════════╬════════╬════╬═════════════════════════════════════════════════════════════════════════╣

║/ ║系 统 盘║一般║实例关机数据不会丢失，可存放代码等。会随保存镜像一起保存。 ║

║/root/autodl-tmp ║数 据 盘║ 快 ║实例关机数据不会丢失，可存放读写IO要求高的数据。但不会随保存镜像一起保存 ║

║/root/autodl-nas ║网 盘║ 慢 ║可以实现多实例间的文件同步共享，不受实例开关机和保存镜像的影响。 ║

║/root/autodl-fs ║文件存储║一般║可以实现多实例间的文件同步共享，不受实例开关机和保存镜像的影响。 ║

╚═════════════════╩════════╩════╩═════════════════════════════════════════════════════════════════════════╝

cat: /sys/fs/cgroup/cpu/cpu.cfs_quota_us: No such file or directory

移动数据到数据盘

mv ./sadtalker/ /root/autodl-tmp/cyberwin/

查看目录

ls

迁移成功

资源和路径

# All requested packages already installed.                                             
                                                                                        
(sadtalker) root@autodl-container-a37840aad8-029fab27:~/autodl-tmp/cyberwin/SadTalker# pip install -r requirements.txt           
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安装网页

Install script for SadTalker + Web UI
Tested on Debian 11 (Bullseye)
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Running on root user
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Repo already cloned, using it as install directory
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Create and activate python venv
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Launching launcher.py...
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Python 3.8.20 (default, Oct  3 2024, 15:24:27) 
[GCC 11.2.0]
Commit hash: cd4c0465ae0b54a6f85af57f5c65fec9fe23e7f8
Installing torch and torchvision
Looking in indexes: https://mirrors.aliyun.com/pypi/simple, https://download.pytorch.org/whl/cu113
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下载模型

Windows

A video tutorial in chinese is available here. You can also follow the following instructions:

1. Install Python 3.8 and check "Add Python to PATH".
2. Install git manually or using Scoop: scoop install git.
3. Install ffmpeg, following this tutorial or using scoop: scoop install ffmpeg.
4. Download the SadTalker repository by running git clone https://github.com/Winfredy/SadTalker.git.
5. Download the checkpoints and gfpgan models in the downloads section.
6. Run start.bat from Windows Explorer as normal, non-administrator, user, and a Gradio-powered WebUI demo will be started.

macOS

A tutorial on installing SadTalker on macOS can be found here.

Docker, WSL, etc

Please check out additional tutorials here.

2. Download Models

You can run the following script on Linux/macOS to automatically download all the models:

bash scripts/download_models.sh

We also provide an offline patch (gfpgan/), so no model will be downloaded when generating.

Pre-Trained Models

• Google Drive
• GitHub Releases
• Baidu (百度云盘) (Password: sadt)

GFPGAN Offline Patch

• Google Drive
• GitHub Releases
• Baidu (百度云盘) (Password: sadt)

Model Details

Model explains:

New version

Model	Description
checkpoints/mapping_00229-model.pth.tar	Pre-trained MappingNet in Sadtalker.
checkpoints/mapping_00109-model.pth.tar	Pre-trained MappingNet in Sadtalker.
checkpoints/SadTalker_V0.0.2_256.safetensors	packaged sadtalker checkpoints of old version, 256 face render).
checkpoints/SadTalker_V0.0.2_512.safetensors	packaged sadtalker checkpoints of old version, 512 face render).
gfpgan/weights	Face detection and enhanced models used in facexlib and gfpgan.

Old version

Model	Description
checkpoints/auido2exp_00300-model.pth	Pre-trained ExpNet in Sadtalker.
checkpoints/auido2pose_00140-model.pth	Pre-trained PoseVAE in Sadtalker.
checkpoints/mapping_00229-model.pth.tar	Pre-trained MappingNet in Sadtalker.
checkpoints/mapping_00109-model.pth.tar	Pre-trained MappingNet in Sadtalker.
checkpoints/facevid2vid_00189-model.pth.tar	Pre-trained face-vid2vid model from the reappearance of face-vid2vid.
checkpoints/epoch_20.pth	Pre-trained 3DMM extractor in Deep3DFaceReconstruction.
checkpoints/wav2lip.pth	Highly accurate lip-sync model in Wav2lip.
checkpoints/shape_predictor_68_face_landmarks.dat	Face landmark model used in dilb.
checkpoints/BFM	3DMM library file.
checkpoints/hub	Face detection models used in face alignment.
gfpgan/weights	Face detection and enhanced models used in facexlib and gfpgan.

The final folder will be shown as:

3. Quick Start

Please read our document on best practices and configuration tips

WebUI Demos

Online Demo : HuggingFace | SDWebUI-Colab | Colab

Local WebUI extension : Please refer to WebUI docs.

Local gradio demo (recommanded) : A Gradio instance similar to our Hugging Face demo can be run locally:

## you need manually install TTS(https://github.com/coqui-ai/TTS) via `pip install tts` in advanced.
python app_sadtalker.py

You can also start it more easily:

• windows: just double click webui.bat, the requirements will be installed automatically.
• Linux/Mac OS: run bash webui.sh to start the webui.

CLI usage

Animating a portrait image from default config:

python inference.py --driven_audio <audio.wav> \
                    --source_image <video.mp4 or picture.png> \
                    --enhancer gfpgan 

The results will be saved in results/$SOME_TIMESTAMP/*.mp4.

Full body/image Generation:

Using --still to generate a natural full body video. You can add enhancer to improve the quality of the generated video.

python inference.py --driven_audio <audio.wav> \
                    --source_image <video.mp4 or picture.png> \
                    --result_dir <a file to store results> \
                    --still \
                    --preprocess full \
                    --enhancer gfpgan