基于Docker安装的Stable Diffusion使用CPU进行AI绘画

由于博主的电脑是为了敲代码考虑买的，所以专门买的高U低显，i9配核显，用Stable Diffusion进行AI绘画的话倒是专门有个CPU模式，不过安装过程经历了许多坎坷，特此记录一下

博主的环境是Windows 11附带的WSL2中安装的Ubuntu 20.04 LTS,安装的Stable Diffusion Docker版本为2.1.0

安装条件

安装Docker
安装DockerCompose
会Docker（博主说的比较粗略，不建议没有docker经验的按本博客进行）

一、下载代码

地址：https://github.com/AbdBarho/stable-diffusion-webui-docker/archive/refs/tags/2.1.0.zip

解压到你需要的路径

二、修改路径

wsl2对应windows的路径如: D:/soft --> /mnt/d/soft

在你上个解压的文件里面的docker-compose.yml中更改路径

例如build: ./services/download/

改为

build: /mnt/d/wsl/stable-diffusion-webui/stable-diffusion-webui-docker-2.1.0/services/download/

三、构建下载

命令为

docker compose --profile download up --build

四、构建镜像

主要的坑也是这里了，需要修改service/AUTOMATIC1111/Dockerfile，修改为下方代码

博主删除了不必要的xformers(必须要google drive，而且开了代理也不行,cpu模式不需要这个东西)，然后对git加了代理，然后修改了git的配置

# syntax=docker/dockerfile:1

FROM alpine/git:2.36.2 as download

SHELL ["/bin/sh", "-ceuxo", "pipefail"]

RUN git config --global http.version HTTP/1.1

RUN git config --global http.postBuffer 524288000

RUN git config --global https.postBuffer 524288000

RUN git config --global core.compression -1

RUN git clone https://github.91chi.fun/https://github.com/salesforce/BLIP.git repositories/BLIP && cd repositories/BLIP && git reset --hard 48211a1594f1321b00f14c9f7a5b4813144b2fb9

RUN git clone https://github.91chi.fun/https://github.com/sczhou/CodeFormer.git repositories/CodeFormer && cd repositories/CodeFormer && git reset --hard c5b4593074ba6214284d6acd5f1719b6c5d739af

RUN git clone https://github.91chi.fun/https://github.com/CompVis/stable-diffusion.git repositories/stable-diffusion && cd repositories/stable-diffusion && git reset --hard 69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc

RUN <<EOF

# because taming-transformers is huge

git config --global http.postBuffer 1048576000

git clone https://github.91chi.fun/https://github.com/CompVis/taming-transformers.git repositories/taming-transformers

cd repositories/taming-transformers

git reset --hard 24268930bf1dce879235a7fddd0b2355b84d7ea6

rm -rf data assets

EOF

RUN git clone https://github.91chi.fun/https://github.com/crowsonkb/k-diffusion.git repositories/k-diffusion && cd repositories/k-diffusion && git reset --hard f4e99857772fc3a126ba886aadf795a332774878

FROM python:3.10-slim

SHELL ["/bin/bash", "-ceuxo", "pipefail"]

ENV DEBIAN_FRONTEND=noninteractive PIP_PREFER_BINARY=1 PIP_NO_CACHE_DIR=1

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

RUN apt-get update && apt install fonts-dejavu-core rsync git -y && apt-get clean

RUN <<EOF

git clone https://github.91chi.fun/https://github.com/AUTOMATIC1111/stable-diffusion-webui.git

cd stable-diffusion-webui

git reset --hard 1eb588cbf19924333b88beaa1ac0041904966640

pip install -r requirements_versions.txt

EOF

ENV ROOT=/stable-diffusion-webui \

  WORKDIR=/stable-diffusion-webui/repositories/stable-diffusion

COPY --from=download /git/ ${ROOT}

RUN pip install --prefer-binary --no-cache-dir -r ${ROOT}/repositories/CodeFormer/requirements.txt

# TODO: move to top

RUN apt-get install jq moreutils -y

# Note: don't update the sha of previous versions because the install will take forever

# instead, update the repo state in a later step

ARG SHA=36a0ba357ab0742c3c4a28437b68fb29a235afbe

RUN <<EOF

cd stable-diffusion-webui

git pull --rebase

git reset --hard ${SHA}

pip install --prefer-binary --no-cache-dir -r requirements_versions.txt

EOF

RUN pip install --prefer-binary --no-cache-dir opencv-python-headless \

  git+https://github.91chi.fun/https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379 \

  git+https://github.91chi.fun/https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1 \

  pyngrok

COPY . /docker

RUN <<EOF

chmod +x /docker/mount.sh && python3 /docker/info.py ${ROOT}/modules/ui.py

EOF

ENV CLI_ARGS=""

WORKDIR ${WORKDIR}

EXPOSE 7860

# run, -u to not buffer stdout / stderr

CMD /docker/mount.sh && \

  python3 -u ../../webui.py --listen --port 7860 --ckpt-dir ${ROOT}/models/Stable-diffusion ${CLI_ARGS}

修改以后运行

docker compose --profile auto-cpu up --build

结果

放下测试的结果（一个奇奇怪怪的小猫）

基于Docker安装的Stable Diffusion使用CPU进行AI绘画的相关教程结束。