ComfyUI: 87K+ Stars — 노드 기반 Stable Diffusion 설정 가이드 2026

{
  "1": {
    "inputs": {
      "ckpt_name": "sd_xl_base_1.0.safetensors"
    },
    "class_type": "CheckpointLoaderSimple",
    "_meta": { "title": "Load Checkpoint" }
  },
  "2": {
    "inputs": {
      "text": "a cyberpunk cityscape at night, neon lights, 8k",
      "clip": ["1", 1]
    },
    "class_type": "CLIPTextEncode",
    "_meta": { "title": "Positive Prompt" }
  }
}

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

# 가상 환경 생성
python3 -m venv venv
source venv/bin/activate

# PyTorch 설치 (CUDA 12.1)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# 의존성 설치
pip install -r requirements.txt

# 서버 시작
python main.py --listen 0.0.0.0 --port 8188

# 다음에서 최신 데스크톱 릴리스를 다운로드합니다:
# https://github.com/Comfy-Org/ComfyUI-Desktop/releases

# 데스크톱 앱은 Python, CUDA, 의존성 관리를 자동으로 처리합니다.
# 첫 실행에 약 15분이 소요됩니다 (모델 다운로드 및 환경 설정).

# GPU 패스스루 확인
nvidia-smi
docker run --rm --gpus all nvidia/cuda:12.0-base nvidia-smi

# 영구 저장소 디렉토리 생성
mkdir -p comfyui-deploy/{models/checkpoints,models/loras,models/vae,models/controlnet,output,custom_nodes,workflows}
cd comfyui-deploy

# docker-compose.yml
version: "3.8"

services:
  comfyui:
    image: ghcr.io/ai-dock/comfyui:latest-cuda
    container_name: comfyui
    ports:
      - "8188:8188"
    volumes:
      - ./models:/workspace/ComfyUI/models
      - ./output:/workspace/ComfyUI/output
      - ./custom_nodes:/workspace/ComfyUI/custom_nodes
      - ./workflows:/workspace/ComfyUI/user
    environment:
      - CLI_ARGS=--listen 0.0.0.0 --preview-method auto
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

# 서비스 시작
docker compose up -d

# 시작 로그 모니터링
docker compose logs -f comfyui

# 컨테이너 내 GPU 사용률 확인
docker exec comfyui nvidia-smi

# SDXL Base (6.9 GB)
wget -P models/checkpoints \
  "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"

# Flux Dev (23 GB) — 16+ GB VRAM 필요
wget -P models/unet \
  "https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors"

# SDXL VAE
wget -P models/vae \
  "https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors"

# ControlNet OpenPose
wget -P models/controlnet \
  "https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/diffusion_pytorch_model.safetensors"

# refiner 파이프라인이 있는 SDXL 로딩 구성
CheckpointLoaderSimple:
  ckpt_name: "sd_xl_base_1.0.safetensors"

KSampler:
  seed: 42
  steps: 30
  cfg: 7.0
  sampler_name: "dpmpp_2m"
  scheduler: "karras"
  denoise: 1.0

# Flux 워크플로우 노드
UNETLoader:
  unet_name: "flux1-dev.safetensors"
  weight_dtype: "fp8_e4m3fn"  # VRAM을 24GB에서 12GB로 감소

DualCLIPLoader:
  clip_name1: "t5xxl_fp8_e4m3fn.safetensors"
  clip_name2: "clip_l.safetensors"
  type: "flux"

EmptySD3LatentImage:
  width: 1024
  height: 1024
  batch_size: 1

# Wan 커스텀 노드 설치
cd custom_nodes
git clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git
pip install -r ComfyUI-WanVideoWrapper/requirements.txt

# Wan 텍스트-투-비디오 워크플로우
WanVideoSampler:
  model: "wan_2.1_14b_fp8.safetensors"
  positive: "slow motion aerial shot of ocean waves"
  width: 1280
  height: 720
  frames: 81
  steps: 30

# 강도 제어가 있는 다중 LoRA 적용
LoraLoaderModelOnly:
  model: ["CheckpointLoader", 0]
  lora_name: "add_detail.safetensors"
  strength_model: 0.8

ControlNetApplyAdvanced:
  positive: ["CLIPTextEncode", 0]
  control_net: ["ControlNetLoader", 0]
  image: ["LoadImage", 0]
  strength: 1.0
  start_percent: 0.0
  end_percent: 0.8

# API를 통한 워크플로우 제출
curl -X POST http://localhost:8188/prompt \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": {
      "1": {"inputs": {"ckpt_name": "sd_xl_base_1.0.safetensors"}, "class_type": "CheckpointLoaderSimple"},
      "2": {"inputs": {"text": "cyberpunk city, neon lights", "clip": ["1", 1]}, "class_type": "CLIPTextEncode"}
    }
  }'

# 큐 상태 확인
curl http://localhost:8188/queue

# 생성된 이미지 가져오기
curl http://localhost:8188/view?filename=ComfyUI_00001_.png&subfolder=output&type=output

# 공유 스타일 LoRA가 있는 배치 워크플로우
LoadCheckpoint → LoadLoRA → CLIPTextEncode → KSampler → VAE Decode
                    ↓
            LoadPromptList (50개 프롬프트)
                    ↓
            SaveImage (메타데이터 + 파일명 패턴 포함)

# /etc/nginx/sites-available/comfyui
server {
    listen 443 ssl http2;
    server_name comfyui.yourdomain.com;

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

    location / {
        proxy_pass http://127.0.0.1:8188;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $host;
        proxy_read_timeout 86400;
    }

    # API 접근 제한
    location /prompt {
        allow 10.0.0.0/24;
        deny all;
        proxy_pass http://127.0.0.1:8188;
    }
}

# localhost 전용 + API 키로 시작
python main.py --listen 0.0.0.0 --port 8188 \
  --api-key "your-secure-api-key-here" \
  --disable-xformers

# custom_nodes/my_custom_node/nodes.py
class MyUpscaleNode:
    """Real-ESRGAN을 사용한 간단한 4x 업스케일 노드."""

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "model": (["RealESRGAN_x4plus", "RealESRGAN_x2plus"],),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "upscale"
    CATEGORY = "image/upscaling"

    def upscale(self, image, model):
        # 구현 코드
        return (upscaled_image,)

NODE_CLASS_MAPPINGS = {"MyUpscaleNode": MyUpscaleNode}
NODE_DISPLAY_NAME_MAPPINGS = {"MyUpscaleNode": "My Upscale (Real-ESRGAN)"}

# custom_nodes/my_custom_node/__init__.py
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS

__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

# GPU 사용률 대시보드 (ComfyUI와 함께 실행)
watch -n 1 nvidia-smi

# 큐 깊이 로그
curl -s http://localhost:8188/queue | jq '.queue_running | length'

# 모델 저장소 디스크 공간 모니터링
df -h models/ output/

#!/bin/bash
# backup-comfyui.sh
BACKUP_DIR="/backup/comfyui-$(date +%Y%m%d)"
mkdir -p "$BACKUP_DIR"

# 워크플로우 (경량, Git으로 버전 관리)
cp -r workflows "$BACKUP_DIR/"

# 커스텀 노드
cp -r custom_nodes "$BACKUP_DIR/"

# 생성된 출력 (대용량 디렉토리용 rsync)
rsync -av --progress output/ "$BACKUP_DIR/output/"

# 모델 (선택 — 큼, 보통 재다운로드 가능)
# rsync -av --progress models/ "$BACKUP_DIR/models/"

echo "백업 완료: $BACKUP_DIR"

cd ComfyUI
git pull origin master
pip install -r requirements.txt
# 서버 재시작