🐳 更新 Docker 配置到最新项目状态
- 更新 build_docker.sh 到新仓库地址 - 仓库: Sanster/lama-cleaner → let5sne/IOPaint - 镜像标签: cwq1913/lama-cleaner → let5sne/iopaint - 项目名称: lama-cleaner → IOPaint - 重构 Dockerfile (CPU & GPU) - 从源码构建替代 PyPI 安装 - 更新基础镜像到最新版本 - CPU: python:3.11-slim-bookworm - GPU: nvidia/cuda:12.1.0-runtime-ubuntu22.04 - 使用最新的 PyTorch 和依赖版本 - 添加自动启动命令 - 更新 scripts/pack.sh - 构建目录: lama-cleaner → iopaint - 修复拼写错误 (Ensuer → Ensure) - 新增 docker/README.md - 完整的 Docker 部署指南 - 包含 CPU 和 GPU 两种模式 - Docker Compose 配置示例 - 故障排查和安全建议 主要改进: - ✨ 与当前项目完全同步 - 📦 支持从源码构建 - 🔧 更灵活的运行配置 - 📝 完整的使用文档 - 🚀 更新的依赖版本 🤖 Generated with Claude Code
This commit is contained in:
let5sne
@@ -2,32 +2,32 @@
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set -e
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GIT_TAG=$1
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IMAGE_DESC="Image inpainting tool powered by SOTA AI Model"
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GIT_REPO="https://github.com/Sanster/lama-cleaner"
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IMAGE_DESC="IOPaint - Free and open-source inpainting & outpainting tool powered by SOTA AI models"
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GIT_REPO="https://github.com/let5sne/IOPaint"
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echo "Building cpu docker image..."
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echo "Building CPU docker image..."
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docker buildx build \
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--platform linux/amd64 \
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--file ./docker/CPUDockerfile \
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--label org.opencontainers.image.title=lama-cleaner \
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--label org.opencontainers.image.title=IOPaint \
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--label org.opencontainers.image.description="$IMAGE_DESC" \
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--label org.opencontainers.image.url=$GIT_REPO \
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--label org.opencontainers.image.source=$GIT_REPO \
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--label org.opencontainers.image.version=$GIT_TAG \
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--build-arg version=$GIT_TAG \
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--tag cwq1913/lama-cleaner:cpu-$GIT_TAG .
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--tag let5sne/iopaint:cpu-$GIT_TAG .
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# echo "Building NVIDIA GPU docker image..."
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echo "Building NVIDIA GPU docker image..."
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docker buildx build \
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--platform linux/amd64 \
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--file ./docker/GPUDockerfile \
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--label org.opencontainers.image.title=lama-cleaner \
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--label org.opencontainers.image.title=IOPaint \
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--label org.opencontainers.image.description="$IMAGE_DESC" \
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--label org.opencontainers.image.url=$GIT_REPO \
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--label org.opencontainers.image.source=$GIT_REPO \
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--label org.opencontainers.image.version=$GIT_TAG \
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--build-arg version=$GIT_TAG \
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--tag cwq1913/lama-cleaner:gpu-$GIT_TAG .
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--tag let5sne/iopaint:gpu-$GIT_TAG .
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@@ -1,19 +1,29 @@
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FROM python:3.10.11-slim-buster
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FROM python:3.11-slim-bookworm
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common \
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libsm6 libxext6 ffmpeg libfontconfig1 libxrender1 libgl1-mesa-glx \
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curl gcc build-essential
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curl gcc build-essential git \
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&& rm -rf /var/lib/apt/lists/*
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RUN pip install --upgrade pip && \
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pip install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cpu
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pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
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ARG version
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WORKDIR /app
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RUN pip install lama-cleaner==$version
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RUN lama-cleaner --install-plugins-package
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ENV LD_PRELOAD=/usr/local/lib/python3.10/site-packages/skimage/_shared/../../scikit_image.libs/libgomp-d22c30c5.so.1.0.0
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# 复制项目文件
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COPY requirements.txt requirements-dev.txt setup.py main.py ./
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COPY iopaint ./iopaint
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COPY web_app ./web_app
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# 安装依赖
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RUN pip install -r requirements.txt && \
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pip install -e .
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# 安装插件依赖
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RUN iopaint install-plugins-packages || true
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EXPOSE 8080
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CMD ["bash"]
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# 默认启动命令
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CMD ["python3", "main.py", "start", "--model", "lama", "--device", "cpu", "--port", "8080", "--host", "0.0.0.0"]
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@@ -1,19 +1,33 @@
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FROM nvidia/cuda:11.7.1-runtime-ubuntu20.04
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FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && apt-get install -y --no-install-recommends \
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software-properties-common \
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libsm6 libxext6 ffmpeg libfontconfig1 libxrender1 libgl1-mesa-glx \
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curl python3-pip
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curl python3-pip python3-dev git gcc build-essential \
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&& rm -rf /var/lib/apt/lists/*
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RUN pip3 install --upgrade pip
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RUN pip3 install torch==2.1.0 torchvision==0.16.0 --index-url https://download.pytorch.org/whl/cu118
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RUN pip3 install xformers==0.0.22.post4 --index-url https://download.pytorch.org/whl/cu118
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ARG version
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# 安装最新的 PyTorch（CUDA 12.1）
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RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121
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RUN pip3 install lama-cleaner==$version
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RUN lama-cleaner --install-plugins-package
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WORKDIR /app
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# 复制项目文件
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COPY requirements.txt requirements-dev.txt setup.py main.py ./
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COPY iopaint ./iopaint
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COPY web_app ./web_app
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# 安装依赖
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RUN pip3 install -r requirements.txt && \
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pip3 install -e .
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# 安装插件依赖
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RUN iopaint install-plugins-packages || true
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EXPOSE 8080
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CMD ["bash"]
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# 默认启动命令（GPU 模式）
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CMD ["python3", "main.py", "start", "--model", "lama", "--device", "cuda", "--port", "8080", "--host", "0.0.0.0"]
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234
docker/README.md
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234
docker/README.md
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@@ -0,0 +1,234 @@
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# IOPaint Docker 部署指南
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本目录包含 IOPaint 的 Docker 配置文件，支持 CPU 和 GPU 两种模式。
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## 📦 可用镜像
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### CPU 版本
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适用于没有 NVIDIA GPU 的环境。
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### GPU 版本
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适用于有 NVIDIA GPU 的环境，性能更好。
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## 🚀 快速开始
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### 使用预构建的镜像（推荐）
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**CPU 模式：**
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```bash
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docker pull let5sne/iopaint:cpu-latest
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docker run -d -p 8080:8080 let5sne/iopaint:cpu-latest
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```
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**GPU 模式：**
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```bash
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docker pull let5sne/iopaint:gpu-latest
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docker run --gpus all -d -p 8080:8080 let5sne/iopaint:gpu-latest
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```
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访问 `http://localhost:8080` 使用 IOPaint。
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### 从源码构建
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**构建所有镜像：**
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```bash
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# 在项目根目录执行
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bash build_docker.sh 1.0.0 # 替换为版本号
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```
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**构建单个镜像：**
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CPU 版本：
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```bash
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docker build -f docker/CPUDockerfile -t let5sne/iopaint:cpu-latest .
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```
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GPU 版本：
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```bash
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docker build -f docker/GPUDockerfile -t let5sne/iopaint:gpu-latest .
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```
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## 🔧 运行配置
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### 基础运行
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```bash
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# CPU 模式
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docker run -d -p 8080:8080 let5sne/iopaint:cpu-latest
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# GPU 模式（需要 nvidia-docker）
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docker run --gpus all -d -p 8080:8080 let5sne/iopaint:gpu-latest
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```
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### 挂载数据目录
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```bash
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docker run -d \
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-p 8080:8080 \
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-v /path/to/input:/app/input \
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-v /path/to/output:/app/output \
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-v /path/to/models:/root/.cache \
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let5sne/iopaint:gpu-latest
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```
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### 自定义启动参数
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```bash
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docker run -d -p 8080:8080 let5sne/iopaint:gpu-latest \
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python3 main.py start \
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--model runwayml/stable-diffusion-inpainting \
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--device cuda \
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--port 8080 \
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--host 0.0.0.0
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```
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### 使用不同模型
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```bash
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# 使用 SD Inpainting 模型
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docker run -d -p 8080:8080 let5sne/iopaint:gpu-latest \
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python3 main.py start --model runwayml/stable-diffusion-inpainting --device cuda --port 8080 --host 0.0.0.0
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# 使用 SDXL 模型（低内存模式）
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docker run -d -p 8080:8080 let5sne/iopaint:gpu-latest \
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python3 main.py start --model diffusers/stable-diffusion-xl-1.0-inpainting-0.1 --device cuda --low-mem --port 8080 --host 0.0.0.0
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```
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## 📊 Docker Compose
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创建 `docker-compose.yml`：
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```yaml
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version: '3.8'
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services:
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iopaint-gpu:
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image: let5sne/iopaint:gpu-latest
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ports:
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- "8080:8080"
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volumes:
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- ./input:/app/input
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- ./output:/app/output
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- ./models:/root/.cache
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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restart: unless-stopped
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iopaint-cpu:
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image: let5sne/iopaint:cpu-latest
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ports:
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- "8081:8080"
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volumes:
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- ./input:/app/input
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- ./output:/app/output
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- ./models:/root/.cache
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restart: unless-stopped
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```
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启动：
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```bash
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# 启动 GPU 服务
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docker-compose up -d iopaint-gpu
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# 启动 CPU 服务
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docker-compose up -d iopaint-cpu
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```
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## 🛠️ 环境变量
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| 变量名 | 说明 | 默认值 |
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|--------|------|--------|
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| `HF_HOME` | HuggingFace 模型缓存目录 | `/root/.cache` |
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| `PYTORCH_CUDA_ALLOC_CONF` | CUDA 内存分配配置 | - |
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设置环境变量：
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```bash
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docker run -d \
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-p 8080:8080 \
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-e HF_HOME=/models \
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-v /path/to/models:/models \
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let5sne/iopaint:gpu-latest
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```
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## 📋 系统要求
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### CPU 版本
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- RAM: 至少 4GB
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- 磁盘: 至少 10GB 可用空间
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### GPU 版本
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- NVIDIA GPU（支持 CUDA）
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- VRAM:
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- LaMa 模型: 至少 2GB
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- SD Inpainting: 至少 8GB
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- SDXL: 至少 12GB
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- 磁盘: 至少 20GB 可用空间
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- nvidia-docker 或 Docker 19.03+（支持 --gpus）
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## 🔍 故障排查
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### GPU 无法使用
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检查 nvidia-docker 是否正确安装：
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```bash
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docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
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```
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### 端口冲突
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修改端口映射：
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```bash
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docker run -d -p 8888:8080 let5sne/iopaint:gpu-latest
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```
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### 模型下载慢
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使用 HuggingFace 镜像：
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```bash
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docker run -d \
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-p 8080:8080 \
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-e HF_ENDPOINT=https://hf-mirror.com \
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let5sne/iopaint:gpu-latest
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```
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### 内存不足
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对于 SDXL 等大模型，使用低内存模式：
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```bash
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docker run -d -p 8080:8080 let5sne/iopaint:gpu-latest \
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python3 main.py start --model diffusers/stable-diffusion-xl-1.0-inpainting-0.1 --device cuda --low-mem --cpu-offload --port 8080 --host 0.0.0.0
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```
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## 📖 更多信息
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- 项目主页：https://github.com/let5sne/IOPaint
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- 文档：查看项目根目录的 README.md
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- 问题反馈：https://github.com/let5sne/IOPaint/issues
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## 🔐 安全建议
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1. **不要暴露到公网**：默认配置仅用于本地使用
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2. **使用代理**：如需公网访问，建议使用 Nginx 反向代理并配置 HTTPS
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3. **限制资源**：使用 Docker 资源限制避免过度占用系统资源
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```bash
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docker run -d \
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-p 8080:8080 \
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--memory="4g" \
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--cpus="2.0" \
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let5sne/iopaint:cpu-latest
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```
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## 📝 更新日志
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### Version 1.0.0 (2025-11-28)
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- 更新为 IOPaint 项目
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- 使用最新依赖版本
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- 支持 CUDA 12.1
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- 从源码构建而非 PyPI 安装
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- 添加详细的使用文档
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@@ -3,10 +3,10 @@
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set -e
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# Ensuer not use user's python package
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# Ensure not use user's python package
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export PYTHONNOUSERSITE=1
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BUILD_DIST=lama-cleaner
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BUILD_DIST=iopaint
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BUILD_ENV=installer
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USER_SCRIPTS=user_scripts
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Reference in New Issue
Block a user