Stable Diffusion 容器化部署：Docker Run、Compose 与 Dockerfile 方案详解

# 格式：sudo chown -R 容器内 sduser 的 UID:容器内 sduser 的 GID 宿主机目录路径 sudo chown -R 1000:1000 ~/sd-data/models/ sudo chown -R 1000:1000 ~/sd-data/outputs/

• 启动容器

docker run -d \ --gpus all \ --restart unless-stopped \ -p 7860:8080 \ -e HTTP_PROXY=http://192.168.100.237:7890 \ -e HTTPS_PROXY=http://192.168.100.237:7890 \ -e NO_PROXY=localhost,127.0.0.1 \ -v /home/sdwebui/sd-data/outputs:/app/stable-diffusion-webui/outputs \ -v /home/sdwebui/sd-data/models:/app/stable-diffusion-webui/models \ --name sd-webui \ --user sduser \ universonic/stable-diffusion-webui:full

3、参数详解

4、适用场景

• 快速体验和测试
• 单次临时使用
• 学习 Docker 基础操作

5、镜像保存

如果用官方 universonic/stable-diffusion-webui:full 镜像每次启动都会初始化下载依赖

建议等待下载完成后通过 docker commit 保存为新镜像

三、Docker Compose：生产级编排部署

1、方案概述

使用 Docker Compose 可以定义复杂的多服务架构，适合生产环境部署。

2、文件内容

# 声明 Docker Compose 版本（3.2+ 支持 GPU 相关配置，与原命令兼容性匹配） version: "3.8" # 定义服务列表（单服务场景，服务名可自定义，此处与容器名保持一致） services: # 服务名：sd-webui（与原命令 --name 对应） sd-webui: # 镜像名：sd-webui-new（与原命令最后指定的镜像一致） image: sd-webui-new # 容器运行用户：sduser（与原命令 --user sduser 对应） user: sduser # 端口映射：宿主机 7860 端口 → 容器 8080 端口（与原命令 -p 7860:8080 完全一致） ports: - "7860:8080" # 格式："宿主机端口：容器端口"，默认支持 TCP 协议，无需额外声明 # 目录挂载：对应原命令的两个 -v 参数，保持宿主机与容器路径一致 volumes: - /home/sdwebui/sd-data/outputs:/app/stable-diffusion-webui/outputs - /home/sdwebui/sd-data/models:/app/stable-diffusion-webui/models # GPU 配置：启用所有 GPU（与原命令 --gpus all 功能一致） # runtime: nvidia 用于启用 NVIDIA 运行时，deploy.resources 声明 GPU 资源预留 runtime: nvidia deploy: resources: reservations: devices: - driver: nvidia capabilities: [gpu] # 声明需要 GPU 资源，等价于 --gpus all # 重启策略：容器退出后自动重启（与原命令 --restart unless-stopped 完全一致） restart: unless-stopped # 可选：容器名配置（若不指定，默认生成 "项目名_sd-webui_1"，此处显式指定与原命令一致的容器名） container_name: sd-webui

3、操作命令

# 启动所有服务 docker compose up -d # 查看服务状态 docker compose logs -f # 停止服务 docker compose down # 更新镜像并重启 docker compose pull && docker compose up -d

4、网络配置优势

• 反向代理（Nginx/Traefik）
• 监控服务（Prometheus/Grafana）
• 文件管理系统
• 数据库服务

5、适用场景

• 生产环境部署
• 多服务架构
• 团队协作开发
• 需要高可用性和监控

四、自定义 Dockerfile：高度定制化部署

⭐点击查看

五、故障排除与常见问题

1、指定挂载 GPU 而不是全部挂载

1.1、查看容器内部 GPU 数量

# 进入容器内部 root@gpu-3090-vm09:/home/sdwebui# docker exec -it sd-webui bash # 查看 GPU sduser@bb429aab044d:~/stable-diffusion-webui$ nvidia-smi Sun Sep 28 08:34:40 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 570.153.02 Driver Version: 570.153.02 CUDA Version: 12.8 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 3090 Off | 00000000:01:00.0 Off | N/A | | 36% 31C P8 24W / 350W | 2909MiB / 24576MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA GeForce RTX 3090 Off | 00000000:02:00.0 Off | N/A | | 36% 29C P8 29W / 350W | 3MiB / 24576MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| +-----------------------------------------------------------------------------------------+

1.2、指定 GPU

# 会使用第一张 RTX 3090 docker run -d \ --gpus "device=0" \ --restart unless-stopped \ -p 7860:8080 \ -v /home/sdwebui/sd-data/outputs:/app/stable-diffusion-webui/outputs \ -v /home/sdwebui/sd-data/models:/app/stable-diffusion-webui/models \ --name sd-webui \ --user sduser \ sd-webui-new 

version: "3.8" services: sd-webui: image: sd-webui-new user: sduser ports: - "7860:8080" volumes: - /home/sdwebui/sd-data/outputs:/app/stable-diffusion-webui/outputs - /home/sdwebui/sd-data/models:/app/stable-diffusion-webui/models runtime: nvidia restart: unless-stopped container_name: sd-webui deploy: resources: reservations: devices: - driver: nvidia capabilities: [gpu] device_ids: ["0"] # <-- 核心修改点：指定使用索引为 0 的 GPU

sduser@dec6235c8a3c:~/stable-diffusion-webui$ nvidia-smi Sun Sep 28 08:39:58 2025 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 570.153.02 Driver Version: 570.153.02 CUDA Version: 12.8 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 3090 Off | 00000000:01:00.0 Off | N/A | | 36% 31C P8 24W / 350W | 2909MiB / 24576MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| +-----------------------------------------------------------------------------------------+ 

2、网络波动，一般是因为连接超时

################################################################ Install script for stable-diffusion + Web UI Tested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer. ################################################################ ################################################################ Running on sduser user ################################################################ ################################################################ Repo already cloned, using it as install directory ################################################################ ################################################################ Create and activate python venv ################################################################ ################################################################ Launching launch.py... ################################################################ Python 3.10.12 (main, Feb 4 2025, 14:57:36) [GCC 11.4.0] Version: v1.10.1 Commit hash: 82a973c04367123ae98bd9abdf80d9eda9b910e2 Installing xformers Traceback (most recent call last): File "/app/stable-diffusion-webui/launch.py", line 48, in <module> main() File "/app/stable-diffusion-webui/launch.py", line 39, in main prepare_environment() File "/app/stable-diffusion-webui/modules/launch_utils.py", line 402, in prepare_environment run_pip(f"install -U -I --no-deps {xformers_package}", "xformers") File "/app/stable-diffusion-webui/modules/launch_utils.py", line 144, in run_pip return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live) File "/app/stable-diffusion-webui/modules/launch_utils.py", line 116, in run raise RuntimeError("\n".join(error_bits)) RuntimeError: Couldn't install xformers. Command: "/app/stable-diffusion-webui/venv/bin/python" -m pip install -U -I --no-deps xformers==0.0.23.post1 --prefer-binary Error code: 1 stdout: Collecting xformers==0.0.23.post1 Downloading xformers-0.0.23.post1-cp310-cp310-manylinux2014_x86_64.whl.metadata (1.0 kB) Downloading xformers-0.0.23.post1-cp310-cp310-manylinux2014_x86_64.whl (213.0 MB) 0.0/213.0 MB ? eta -:--:-- stderr: WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProxyError('Cannot connect to proxy.', TimeoutError('_ssl.c:990: The handshake operation timed out'))': /simple/xformers/ WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProxyError('Cannot connect to proxy.', ConnectionResetError(104, 'Connection reset by peer'))': /packages/f4/89/ce8e936d3e64b3b565c16312dd6446d54f6e485f864130702c6b3b3cbe7c/xformers-0.0.23.post1-cp310-cp310-manylinux2014_x86_64.whl.metadata WARNING: Connection timed out while downloading. error: incomplete-download × Download failed because not enough bytes were received (0 bytes/213.0 MB) ╰─> URL: https://files.pythonhosted.org/packages/f4/89/ce8e936d3e64b3b565c16312dd6446d54f6e485f864130702c6b3b3cbe7c/xformers-0.0.23.post1-cp310-cp310-manylinux2014_x86_64.whl note: This is an issue with network connectivity, not pip. hint: Consider using --resume-retries to enable download resumption. ~/stable-diffusion-webui 

3、如何下载模型？

1、Models – Hugging Face

2、AIGate

3、Civitai: The Home of Open-Source Generative AI

Demo 镜像经过 commit 后的总体积仅为 12.2GB，包含一个预置镜像，如需要下载新模型，需要将下载的模型放入 /home/sdwebui/sd-data/models 指定的模型文件夹中

4、内存不足问题

生成图片时容器崩溃，日志显示 OOM（Out of Memory）

version: "3.8" services: sd-webui: image: sd-webui-new user: sduser ports: - "7860:8080" volumes: - /home/sdwebui/sd-data/outputs:/app/stable-diffusion-webui/outputs - /home/sdwebui/sd-data/models:/app/stable-diffusion-webui/models runtime: nvidia restart: unless-stopped container_name: sd-webui deploy: resources: limits: memory: "8G" # 硬限制：容器最多只能使用 8GB 内存 reservations: memory: "4G" # 预留 4GB 内存 devices: # 预留 GPU 资源（与内存预留同级） - driver: nvidia capabilities: [gpu] device_ids: ["0"] # 指定使用索引为 0 的 GPU

5、日志查看和调试

# 查看实时日志 docker logs -f sd-webui # 查看最近 100 行日志 docker logs --tail 100 sd-webui # 进入容器调试 docker exec -it sd-webui bash # 检查容器资源使用情况 docker stats sd-webui

6、无 cuda 支持

--- 0%| | 0/20 [00:00<?, ?it/s] *** Error completing request *** Arguments: ('task(wan01r2cq08hz6h)', <gradio.routes.Request object at 0x7f43ce887850>, '11111', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
  File "/app/modules/call_queue.py", line 74, in f res = list(func(*args, **kwargs))
  ...
  File "/opt/conda/lib/python3.10/site-packages/xformers/ops/fmha/dispatch.py", line 63, in _run_priority_list raise NotImplementedError(msg)
NotImplementedError: No operator found for `memory_efficient_attention_forward` with inputs: query : shape=(2, 4096, 8, 40) (torch.float16) key : shape=(2, 4096, 8, 40) (torch.float16) value : shape=(2, 4096, 8, 40) (torch.float16) attn_bias : <class 'NoneType'> p : 0.0 `decoderF` is not supported because: xFormers wasn't build with CUDA support attn_bias type is <class 'NoneType'> operator wasn't built - see `python -m xformers.info` for more info `[email protected]` is not supported because: xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info` for more info `tritonflashattF` is not supported because: xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info` for more info triton is not available Only work on pre-MLIR triton for now `cutlassF` is not supported because: xFormers wasn't build with CUDA support operator wasn't built - see `python -m xformers.info` for more info `smallkF` is not supported because: max(query.shape[-1] != value.shape[-1]) > 32 xFormers wasn't build with CUDA support dtype=torch.float16 (supported: {torch.float32}) operator wasn't built - see `python -m xformers.info` for more info unsupported embed per head: 40 

新增指定 cuda 兼容的 xformers

# 6. 安装所有 Python 依赖 RUN pip install \ -i https://pypi.tuna.tsinghua.edu.cn/simple \ --trusted-host pypi.tuna.tsinghua.edu.cn \ -r requirements_versions.txt # 7. 【新增】安装支持 CUDA 11.8 的 xformers # 我们使用 xformers 官方提供的预编译 wheel 文件，确保 CUDA 支持 RUN pip install --upgrade --force-reinstall \ --index-url https://download.pytorch.org/whl/cu118 \ --no-deps \ xformers==0.0.22.post7

可正常生成图片

To create a public link, set `share=True` in `launch()`. Startup time: 50.9s (prepare environment: 41.7s, import torch: 3.8s, import gradio: 1.2s, setup paths: 1.6s, initialize shared: 0.5s, other imports: 0.5s, load scripts: 0.7s, create ui: 0.7s, gradio launch: 0.1s). 6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa2 Loading weights [6ce0161689] from /app/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors Creating model from config: /app/configs/v1-inference.yaml /opt/conda/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. warnings.warn( Applying attention optimization: xformers... done. Model loaded in 17.7s (calculate hash: 12.5s, create model: 2.8s, apply weights to model: 1.9s, calculate empty prompt: 0.2s). 100%|██████████| 20/20 [00:01<00:00, 12.15it/s] Total progress: 100%|██████████| 20/20 [00:01<00:00, 15.26it/s] Total progress: 100%|██████████| 20/20 [00:01<00:00, 18.42it/s] 

六、结语

通过这三种容器化部署方案，您可以根据实际需求选择最适合的方式。无论是快速体验还是生产部署，容器化都能提供稳定、可复现的环境保障。

选择建议： 初学者：从 Docker Run 开始，快速上手 生产环境：使用 Docker Compose，便于维护和扩展 定制需求：采用自定义 Dockerfile，实现完全控制

容器化部署不仅解决了环境一致性问题，更为后续的扩展、监控和维护提供了坚实基础。