Stable Diffusion WebUI: 159K+ Stars — 2026 완전 설치 가이드

:: 릴리스 페이지에서 sd.webui.zip 다운로드
:: C:\stable-diffusion-webui에 압축 해제
:: 먼저 업데이터 실행
cd C:\stable-diffusion-webui
update.bat

:: WebUI 실행
run.bat

@echo off

set PYTHON=python
set GIT=git
set VENV_DIR=venv
set COMMANDLINE_ARGS=--xformers --autolaunch --update-check

:: VRAM 최적화 옵션 (하나 선택):
:: set COMMANDLINE_ARGS=--medvram    &:: 8GB GPU
:: set COMMANDLINE_ARGS=--lowvram    &:: 4GB GPU  
:: set COMMANDLINE_ARGS=--normalvram &:: 12GB+ GPU

:: RTX 40 시리즈의 경우 --xformers 추가로 20-30% 속도 향상
:: 검은색/녹색 이미지의 경우 --precision full --no-half 추가

call webui.bat

# 종속성 설치 (Ubuntu/Debian)
sudo apt update && sudo apt install -y wget git python3 python3-venv libgl1 libglib2.0-0

# 저장소 클론
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# 가상 환경 생성 및 설치
python3 -m venv venv
source venv/bin/activate
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

# 실행
./webui.sh --xformers --listen

# Dockerfile.stable-diffusion-webui
FROM nvidia/cuda:12.1.1-devel-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y \
    python3 python3-pip python3-venv git wget \
    libgl1 libglib2.0-0 libsm6 libxext6 \
    && rm -rf /var/lib/apt/lists/*

RUN useradd -m -s /bin/bash sduser
WORKDIR /home/sduser

RUN git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
WORKDIR /home/sduser/stable-diffusion-webui

RUN python3 -m venv venv && \
    . venv/bin/activate && \
    pip install --no-cache-dir torch torchvision torchaudio \
    --index-url https://download.pytorch.org/whl/cu121 && \
    pip install -r requirements.txt

RUN chown -R sduser:sduser /home/sduser
USER sduser

EXPOSE 7860

ENTRYPOINT ["bash", "-c", \". venv/bin/activate && python3 launch.py --listen --api --xformers"]

# 이미지 빌드
docker build -f Dockerfile.stable-diffusion-webui -t sd-webui:latest .

# GPU 액세스 및 모델 영속성과 함께 실행
docker run -d \
  --name stable-diffusion-webui \
  --gpus all \
  -p 7860:7860 \
  -v $(pwd)/models:/home/sduser/stable-diffusion-webui/models/Stable-diffusion \
  -v $(pwd)/outputs:/home/sduser/stable-diffusion-webui/outputs \
  -v $(pwd)/extensions:/home/sduser/stable-diffusion-webui/extensions \
  -e NVIDIA_VISIBLE_DEVICES=all \
  sd-webui:latest

# docker-compose.yml
version: '3.8'

services:
  stable-diffusion-webui:
    build:
      context: .
      dockerfile: Dockerfile.stable-diffusion-webui
    container_name: sd-webui
    runtime: nvidia
    ports:
      - "7860:7860"
    volumes:
      - ./models:/home/sduser/stable-diffusion-webui/models/Stable-diffusion
      - ./outputs:/home/sduser/stable-diffusion-webui/outputs
      - ./extensions:/home/sduser/stable-diffusion-webui/extensions
      - ./vae:/home/sduser/stable-diffusion-webui/models/VAE
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    restart: unless-stopped

docker-compose up -d

# 확장 탭을 통해 설치 (권장)
# 1. WebUI 열기 → 확장 → 사용 가능
# 2. "불러오기" 클릭
# 3. "ControlNet" 검색
# 4. "sd-webui-controlnet" 설치 클릭
# 5. UI 재시작

# 또는 수동 설치:
cd extensions
git clone https://github.com/Mikubill/sd-webui-controlnet.git

# 핵심 ControlNet 모델 (SD 1.5)
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose.pth
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth

# SDXL ControlNet 모델
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/diffusers_xl_canny_mid.safetensors
wget -P models/ControlNet/ https://huggingface.co/lllyasviel/sd_control_collection/resolve/main/diffusers_xl_depth_mid.safetensors

// settings.json - ControlNet 구성
{
  "control_net_max_models_num": 3,
  "control_net_model_cache_size": 2,
  "control_net_control_transfer": false,
  "control_net_detectedmap_dir": "extensions/sd-webui-controlnet/detected_maps",
  "control_net_models_path": "models/ControlNet",
  "control_net_modules_path": "extensions/sd-webui-controlnet/annotator",
  "control_net_unit_mode": false,
  "control_net_sync_field_args": true
}

# LoRA 모델을 전용 디렉토리에 다운로드
# .safetensors LoRA 파일을 다음 위치에 배치:
# models/Lora/

# 예시: 인기 있는 스타일 LoRA 다운로드
wget -P models/Lora/ "https://civitai.com/api/download/models/12345"

# 보조 도구로 ComfyUI 설치 (마이그레이션 대신 권장)
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

# 모델 디렉토리를 공유하여 중복 방지
ln -s /path/to/stable-diffusion-webui/models/Stable-diffusion models/checkpoints
ln -s /path/to/stable-diffusion-webui/models/Lora models/loras
ln -s /path/to/stable-diffusion-webui/models/ControlNet models/controlnet

# 다른 포트에서 실행
python main.py --port 8188

# 프로덕션 준비 환경을 위해 다음 확장 설치
cd extensions

# Adetailer - 자동 얼굴/손/세부 수정
git clone https://github.com/Bing-su/adetailer.git

# Ultimate SD Upscale - 고해상도 업스케일
git clone https://github.com/Coyote-A/ultimate-upscale-for-automatic1111.git

# Tiled VAE - 대형 이미지의 VRAM 사용량 감소
git clone https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111.git

# Tag Autocomplete - 프롬프트 도우미
git clone https://github.com/DominikDoom/a1111-sd-webui-tagcomplete.git

# System Info + 벤치마크
git clone https://github.com/vladmandic/sd-extension-system-info.git

# Inpaint Anything - 세그먼트 애니씽 + 인페인팅
git clone https://github.com/Uminosachi/sd-webui-inpaint-anything.git

# 확장 설치 후 WebUI 재시작

# 4GB VRAM GPU (입문):
python3 launch.py --lowvram --precision full --no-half --xformers

# 6-8GB VRAM GPU (메인스트림):
python3 launch.py --medvram --xformers --opt-split-attention

# 12GB+ VRAM GPU (하이엔드):
python3 launch.py --xformers --opt-sdp-attention

# 24GB VRAM GPU (엔터프라이즈):
python3 launch.py --xformers --opt-sdp-attention --no-half-vae

# txt2img API용 Python 클라이언트
import requests
import json

url = "http://localhost:7860/sdapi/v1/txt2img"

payload = {
    "prompt": "masterpiece, best quality, portrait of a cyberpunk samurai, neon city background, 8k uhd",
    "negative_prompt": "blurry, low quality, deformed, ugly, duplicate",
    "steps": 25,
    "cfg_scale": 7.0,
    "width": 1024,
    "height": 1024,
    "sampler_name": "DPM++ 2M Karras",
    "batch_size": 1,
    "seed": -1,
    "override_settings": {
        "sd_model_checkpoint": "sd_xl_base_1.0.safetensors"
    }
}

response = requests.post(url, json=payload)
result = response.json()

# 생성된 이미지 저장
import base64
for i, img_data in enumerate(result['images']):
    with open(f"output_{i}.png", "wb") as f:
        f.write(base64.b64decode(img_data))

# batch_generate.py - 여러 프롬프트 처리
import requests
import csv
import base64

API_URL = "http://localhost:7860/sdapi/v1/txt2img"

def generate_image(prompt, filename, width=1024, height=1024):
    payload = {
        "prompt": prompt,
        "negative_prompt": "blurry, low quality, deformed",
        "steps": 25,
        "cfg_scale": 7.0,
        "width": width,
        "height": height,
        "sampler_name": "DPM++ 2M Karras"
    }
    
    response = requests.post(API_URL, json=payload)
    result = response.json()
    
    with open(filename, "wb") as f:
        f.write(base64.b64decode(result['images'][0]))
    
    return filename

# CSV의 프롬프트 목록 처리
with open("prompts.csv", "r") as f:
    reader = csv.DictReader(f)
    for i, row in enumerate(reader):
        filename = f"output_{i:04d}.png"
        generate_image(row['prompt'], filename)
        print(f"생성됨: {filename}")

# 1. 인증 활성화
python3 launch.py --listen --gradio-auth admin:강력한비밀번호

# 2. HTTPS 리버스 프록시 사용 (nginx)
# /etc/nginx/sites-available/sd-webui
server {
    listen 443 ssl http2;
    server_name sd.example.com;

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

    location / {
        proxy_pass http://127.0.0.1:7860;
        proxy_http_version 1.1;
        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;
        
        # Gradio용 WebSocket 지원
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
    }
}

# 3. 방화벽 규칙 (ufw)
sudo ufw default deny incoming
sudo ufw allow ssh
sudo ufw allow 443/tcp
sudo ufw enable

# 간단한 상태 확인 스크립트 생성
#!/bin/bash
# health_check.sh

STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:7860/)
if [ "$STATUS" != "200" ]; then
    echo "$(date): WebUI 다운 (HTTP $STATUS), 재시작 중..." >> /var/log/sd-webui.log
    systemctl restart sd-webui
fi

# 자동 시작을 위한 systemd 서비스
# /etc/systemd/system/sd-webui.service
[Unit]
Description=Stable Diffusion WebUI
After=network.target

[Service]
Type=simple
User=sduser
WorkingDirectory=/home/sduser/stable-diffusion-webui
ExecStart=/home/sduser/stable-diffusion-webui/venv/bin/python launch.py --xformers --listen --port 7860
Restart=on-failure
RestartSec=30
Environment="PYTHONUNBUFFERED=1"

[Install]
WantedBy=multi-user.target

sudo systemctl daemon-reload
sudo systemctl enable sd-webui
sudo systemctl start sd-webui