facechain环境部署

环境安装

# 创建虚拟环境facechain
conda create -n facechain python=3.8
conda activate facechain
# 克隆
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/modelscope/facechain.git --depth 1
# 安装第三方库
cd facechain
pip install -r requirements.txt
pip install -U openmim
# 设置所需的CUDA，因为安装了多个版本的CUDA
export PATH=/home/xxx/.local/cuda-11.7/bin:$PATH
export LD_LIBRARY_PATH=LD_LIBRARY_PATH:/home/xxx/.local/cuda-11.7/lib64
export CUDA_HOME=/home/xxx/.local/cuda-11.7
# 安装mmcv
mim install mmcv-full==1.7.0
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webui的运行方式

• app.py文件增加临时文件存放位置，因为多个人使用同一个服务，gradio的临时文件会存放在/tmp/gradio下存在权限问题。

   import tempfile
   tempfile.tempdir = './tmp'
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• app.py文件修改modelscope和huggingface的目录

  	import os
  	os.environ['MODELSCOPE_CACHE'] = '/opt/buty/work/modelscope'
  	os.environ['HF_HOME'] = '/opt/buty/work/huggingface'
  	os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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• snapdown 文件的路径使用临时文件存放位置还是有问题，需要修改/tmp/snapdown/目录的权限

  sudo chmod -R 777 /tmp/snapdown/
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• 运行脚本

      python app.py
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命令行交互界面的运行方式

• train_text_to_image_lora.py文件修改modelscope和huggingface的目录

  	import os
  	os.environ['MODELSCOPE_CACHE'] = '/opt/buty/work/modelscope'
  	os.environ['HF_HOME'] = '/opt/buty/work/huggingface'
  	os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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• 模型训练

  PYTHONPATH=. sh train_lora.sh "ly261666/cv_portrait_model" "v2.0" "film/film" "./imgs" "./processed" "./output"
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• 推理

  python run_inference.py
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pycharm的运行方式

train_text_to_image_lora.py脚本Edit Configurations设置：
Name:accelerate.commands.launch
Run:facechain module accelerate.commands.launch
Parameters:
		facechain/train_text_to_image_lora.py
		--pretrained_model_name=ly261666/cv_portrait_model
		--revision=v2.0
		--sub_path=film/film
		--train_data_dir=./imgs
		--output_dataset_name=./processed
		--output_dir=./output
Working directory:/opt/buty/work/facechain
Environment variables:
        CUDA_VISIBLE_DEVICES:0
		PYTHONPATH: .
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参考资料
modelscope/facechain