InvokeAI: 27.2K+ Stars — 2026 完整安装配置指南

git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI/docker

cp .env.sample .env

# 核心配置
INVOKEAI_ROOT=/opt/invokeai-data
INVOKEAI_PORT=9090
GPU_DRIVER=cuda
CONTAINER_UID=1000
HUGGINGFACE_TOKEN=hf_your_token_here

./run.sh

docker compose up -d

# NVIDIA GPU
docker run --runtime=nvidia --gpus=all \
  --publish 9090:9090 \
  ghcr.io/invoke-ai/invokeai:latest

# AMD GPU
docker run --device /dev/kfd --device /dev/dri \
  --publish 9090:9090 \
  ghcr.io/invoke-ai/invokeai:main-rocm

# 带数据持久化
docker run --runtime=nvidia --gpus=all \
  --publish 9090:9090 \
  --volume /mnt/invokeai-data:/invokeai \
  ghcr.io/invoke-ai/invokeai:latest

pip install invokeai

invokeai-configure

invokeai-web

# 在全新的 Ubuntu 24.04 GPU droplet 上
sudo apt update && sudo apt install -y docker.io docker-compose-plugin
sudo systemctl enable --now docker

# 安装 NVIDIA Container Toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt update && sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

# 部署 InvokeAI
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI/docker
cp .env.sample .env
# 编辑 .env：设置 INVOKEAI_ROOT 和 HUGGINGFACE_TOKEN
sudo docker compose up -d

# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr

x-invokeai: &invokeai
    image: "ghcr.io/invoke-ai/invokeai:latest"
    build:
      context: ..
      dockerfile: docker/Dockerfile
    env_file:
      - .env
    environment:
      - INVOKEAI_ROOT=${CONTAINER_INVOKEAI_ROOT:-/invokeai}
      - HF_HOME
    ports:
      - "${INVOKEAI_PORT:-9090}:${INVOKEAI_PORT:-9090}"
    volumes:
      - type: bind
        source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
        target: ${CONTAINER_INVOKEAI_ROOT:-/invokeai}
        bind:
          create_host_path: true
      - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
    tty: true
    stdin_open: true

services:
  invokeai-cuda:
    <<: *invokeai
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  invokeai-cpu:
    <<: *invokeai
    profiles:
      - cpu

  invokeai-rocm:
    <<: *invokeai
    environment:
      - AMD_VISIBLE_DEVICES=all
      - RENDER_GROUP_ID=${RENDER_GROUP_ID}
    runtime: amd
    profiles:
      - rocm

# 将 .safetensors 或 .ckpt 文件放入模型目录
cp your-model.safetensors /opt/invokeai-data/models/sd-1/main/

# 重启容器
docker compose restart

# 示例：通过 InvokeAI 的 REST API 编程设置生成参数（v6.12.0+）
import requests

response = requests.post(
    "http://localhost:9090/api/v1/sessions",
    json={
        "model": "stable-diffusion-xl-base-1.0",
        "width": 1024,
        "height": 1024,
        "steps": 30,
        "cfg_scale": 7.5,
        "scheduler": "euler_a",
        "positive_prompt": "赛博朋克城市夜景，霓虹灯光，高度细致",
        "negative_prompt": "模糊，低质量，变形"
    }
)
print(response.json()["session_id"])

# 在 .env 中启用多用户模式
INVOKEAI_ENABLE_MULTIUSER=true

# 生产环境 Nginx 配置
server {
    listen 443 ssl http2;
    server_name invokeai.yourdomain.com;

    ssl_certificate /etc/letsencrypt/live/yourdomain.com/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/yourdomain.com/privkey.pem;

    location / {
        proxy_pass http://localhost:9090;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_read_timeout 86400;
    }
}

# /etc/systemd/system/invokeai.service
[Unit]
Description=InvokeAI Creative Engine
After=docker.service
Requires=docker.service

[Service]
Type=oneshot
RemainAfterExit=yes
WorkingDirectory=/opt/InvokeAI/docker
ExecStart=/usr/bin/docker compose up -d
ExecStop=/usr/bin/docker compose down
TimeoutStartSec=0

[Install]
WantedBy=multi-user.target

sudo systemctl daemon-reload
sudo systemctl enable --now invokeai

# docker-compose.monitoring.yml
services:
  prometheus:
    image: prom/prometheus:latest
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    ports:
      - "9091:9090"

  dcgm-exporter:
    image: nvcr.io/nvidia/k8s/dcgm-exporter:latest
    runtime: nvidia
    ports:
      - "9400:9400"

#!/bin/bash
# /opt/invokeai-backup/backup.sh
BACKUP_DIR="/backups/invokeai"
DATE=$(date +%Y%m%d-%H%M%S)

# 备份生成的图像和模型
tar czf "$BACKUP_DIR/images-$DATE.tar.gz" /opt/invokeai-data/images
tar czf "$BACKUP_DIR/models-$DATE.tar.gz" /opt/invokeai-data/models

# 只保留最近 7 天
find "$BACKUP_DIR" -name "*.tar.gz" -mtime +7 -delete

0 2 * * * /opt/invokeai-backup/backup.sh

cd InvokeAI/docker
docker compose pull
docker compose up -d

invokeai-update