Basic usage
docker run -it --name sd-cpu --env CLI_ARGS="--skip-torch-cuda-test --use-cpu all --no-download-sd-model" zhiqiangwang/stable-diffusion-webui:latest
chmod -R 777 $(pwd)/sd
docker run -it --name sd-gpu --rm \
--gpus all \
-p 7860:7860 \
-v $(pwd)/sd/extensions:/sd/stable-diffusion-webui/extensions \
-v $(pwd)/sd/textual_inversion_templates:/sd/stable-diffusion-webui/textual_inversion_templates \
-v $(pwd)/sd/embeddings:/sd/stable-diffusion-webui/embeddings \
-v $(pwd)/sd/models:/sd/stable-diffusion-webui/models \
-v $(pwd)/sd/localizations:/sd/stable-diffusion-webui/localizations \
-v $(pwd)/sd/inputs:/sd/stable-diffusion-webui/inputs \
-v $(pwd)/sd/outputs:/sd/stable-diffusion-webui/outputs \
-e NVIDIA_VISIBLE_DEVICES=all \
zhiqiangwang/stable-diffusion-webui:latest
GPU driver installation
//系统更新
$ apt -y update && apt -y upgrade
$ apt-get -y install google-perftools
//查看GPU驱动信息
$ NVIDIA_DRIVER_VERSION=$(sudo apt-cache search 'linux-modules-nvidia-[0-9]+-gcp$' | awk '{print $1}' | sort | tail -n 1 | head -n 1 | awk -F"-" '{print $4}')
//安装GPU驱动
$ apt install linux-modules-nvidia-${NVIDIA_DRIVER_VERSION}-gcp nvidia-driver-${NVIDIA_DRIVER_VERSION}
//安装cuda-toolkit
$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
$ dpkg -i cuda-keyring_1.0-1_all.deb
$ apt-get update
$ apt-get -y install nvidia-cuda-toolkit
查看CUDA版本
$ nvidia-smi
$ nvcc --version
issues && pip install
pip install torch==2.0.1 torchvision==0.15.2
//https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13236
pip install httpx==0.24.1
//https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/11855
pip install gradio_client==0.2.7
Using Python to Check PyTorch Environment
python -c "import torch; print(torch.__version__, torch.cuda.is_available())"
Related links
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