基于Docker安裝的Stable Diffusion使用CPU進行AI繪畫

• 由于博主的電腦是為了敲代碼考慮買的，所以專門買的高U低顯，i9配核顯，用Stable Diffusion進行AI繪畫的話倒是專門有個CPU模式，不過安裝過程經歷了許多坎坷，特此記錄一下

• 博主的環境是Windows 11附帶的WSL2中安裝的Ubuntu 20.04 LTS,安裝的Stable Diffusion Docker版本為2.1.0

安裝條件

1. 安裝Docker
2. 安裝DockerCompose
3. 會Docker（博主說的比較粗略，不建議沒有docker經驗的按本博客進行）

一、下載代碼

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

解壓到你需要的路徑

二、修改路徑

wsl2對應windows的路徑如: D:/soft --> /mnt/d/soft

在你上個解壓的文件里面的docker-compose.yml中更改路徑

例如build: ./services/download/

改為

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

三、構建下載

命令為

docker compose --profile download up --build

四、構建鏡像

1. 主要的坑也是這里了，需要修改service/AUTOMATIC1111/Dockerfile，修改為下方代碼

博主刪除了不必要的xformers(必須要google drive，而且開了代理也不行,cpu模式不需要這個東西)，然后對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}

2. 修改以后運行

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

結果

放下測試的結果（一個奇奇怪怪的小貓）