Stable Diffusion ComfyUI DevSecOps Integration

ComfyUI DevSecOps

Stable Diffusion ComfyUI Node-based Workflow DevSecOps CI/CD Security Model Management SDXL ControlNet LoRA API Automation Container Docker

ComfyUI Setup และ API

# === ComfyUI Setup & API ===

# Docker
# docker run -d --gpus all \
#   -p 8188:8188 \
#   -v ./models:/comfyui/models \
#   -v ./output:/comfyui/output \
#   comfyui/comfyui:latest

# ComfyUI API — Generate Image
# import json
# import requests
# import websocket
# import uuid
#
# COMFYUI_URL = "http://localhost:8188"
# client_id = str(uuid.uuid4())
#
# def queue_prompt(workflow):
#     p = {"prompt": workflow, "client_id": client_id}
#     response = requests.post(f"{COMFYUI_URL}/prompt", json=p)
#     return response.json()
#
# def get_image(filename, subfolder, folder_type):
#     params = {"filename": filename, "subfolder": subfolder, "type": folder_type}
#     response = requests.get(f"{COMFYUI_URL}/view", params=params)
#     return response.content
#
# # Load Workflow JSON
# with open("workflow_api.json") as f:
#     workflow = json.load(f)
#
# # Modify Prompt
# workflow["6"]["inputs"]["text"] = "a beautiful sunset over mountains"
# workflow["3"]["inputs"]["seed"] = 42
#
# # Queue and Wait
# result = queue_prompt(workflow)
# prompt_id = result["prompt_id"]
#
# # WebSocket for progress
# ws = websocket.WebSocket()
# ws.connect(f"ws://localhost:8188/ws?clientId={client_id}")
# while True:
#     msg = json.loads(ws.recv())
#     if msg["type"] == "executing" and msg["data"]["node"] is None:
#         break  # Done

from dataclasses import dataclass
from typing import List

@dataclass
class WorkflowNode:
    node_id: str
    node_type: str
    purpose: str

nodes = [
    WorkflowNode("1", "CheckpointLoaderSimple", "Load SD Model"),
    WorkflowNode("2", "CLIPTextEncode", "Positive Prompt"),
    WorkflowNode("3", "CLIPTextEncode", "Negative Prompt"),
    WorkflowNode("4", "KSampler", "Generate Latent"),
    WorkflowNode("5", "VAEDecode", "Decode to Image"),
    WorkflowNode("6", "SaveImage", "Save Output"),
    WorkflowNode("7", "ControlNetApply", "ControlNet Guidance"),
    WorkflowNode("8", "LoraLoader", "Load LoRA Weights"),
]

print("=== ComfyUI Workflow Nodes ===")
for n in nodes:
    print(f"  [Node {n.node_id}] {n.node_type} — {n.purpose}")

DevSecOps Pipeline

# === DevSecOps Pipeline for AI Image Gen ===

# GitHub Actions CI/CD
# .github/workflows/ai-pipeline.yml
# name: AI Image Pipeline
# on:
#   push: { branches: [main] }
#
# jobs:
#   security-scan:
#     runs-on: ubuntu-latest
#     steps:
#       - uses: actions/checkout@v4
#       - name: Scan Docker Image
#         uses: aquasecurity/trivy-action@master
#         with:
#           image-ref: 'comfyui:latest'
#           severity: 'CRITICAL, HIGH'
#       - name: Model Checksum Verify
#         run: |
#           sha256sum -c models/checksums.sha256
#       - name: NSFW Filter Test
#         run: python tests/test_content_safety.py
#
#   deploy:
#     needs: security-scan
#     runs-on: ubuntu-latest
#     steps:
#       - name: Deploy ComfyUI
#         run: |
#           docker-compose -f docker-compose.prod.yml up -d
#       - name: Health Check
#         run: |
#           curl -f http://localhost:8188/system_stats

# Model Security Scanning
# import hashlib
# import os
#
# def verify_model(model_path, expected_hash):
#     sha256 = hashlib.sha256()
#     with open(model_path, 'rb') as f:
#         for chunk in iter(lambda: f.read(8192), b''):
#             sha256.update(chunk)
#     actual = sha256.hexdigest()
#     if actual != expected_hash:
#         raise SecurityError(f"Model hash mismatch: {actual}")
#     return True

security_checks = {
    "Container Scan": {"tool": "Trivy", "frequency": "Every Build", "severity": "Critical+High"},
    "Model Checksum": {"tool": "SHA256", "frequency": "Every Load", "severity": "Critical"},
    "NSFW Filter": {"tool": "Safety Checker", "frequency": "Every Generation", "severity": "High"},
    "Access Control": {"tool": "OAuth2/API Key", "frequency": "Every Request", "severity": "High"},
    "Audit Log": {"tool": "ELK Stack", "frequency": "Continuous", "severity": "Medium"},
    "License Check": {"tool": "Custom Script", "frequency": "Model Update", "severity": "Medium"},
}

print("\nDevSecOps Security Checks:")
for check, info in security_checks.items():
    print(f"  [{info['severity']}] {check}")
    print(f"    Tool: {info['tool']} | Frequency: {info['frequency']}")

Production Architecture

# === Production Architecture ===

# docker-compose.prod.yml
# version: '3.8'
# services:
#   comfyui:
#     image: comfyui:latest
#     deploy:
#       resources:
#         reservations:
#           devices:
#             - capabilities: [gpu]
#     ports:
#       - "8188:8188"
#     volumes:
#       - ./models:/comfyui/models:ro
#       - ./output:/comfyui/output
#     environment:
#       - COMFYUI_LISTEN=0.0.0.0
#   api-gateway:
#     image: nginx:alpine
#     ports:
#       - "443:443"
#     volumes:
#       - ./nginx.conf:/etc/nginx/nginx.conf
#   redis:
#     image: redis:7-alpine
#     ports:
#       - "6379:6379"
#   monitoring:
#     image: grafana/grafana:latest
#     ports:
#       - "3000:3000"

architecture = {
    "API Gateway": "Nginx + Rate Limiting + Auth",
    "Queue": "Redis Queue สำหรับ Job Management",
    "ComfyUI Workers": "GPU Nodes รัน Stable Diffusion",
    "Model Registry": "S3/HuggingFace เก็บ Models",
    "Storage": "S3 เก็บ Output Images",
    "CDN": "CloudFront/CloudFlare ส่งภาพ",
    "Monitoring": "Grafana + Prometheus Metrics",
    "Logging": "ELK Stack Audit Trail",
}

print("Production Architecture:")
for component, desc in architecture.items():
    print(f"  [{component}]: {desc}")

# Cost Estimation
costs = {
    "GPU Server (A100)": "$2.50/hr",
    "Storage (S3 1TB)": "$23/mo",
    "CDN (10TB transfer)": "$85/mo",
    "Monitoring": "$50/mo",
    "Total (24/7)": "~$1,958/mo",
}

print(f"\n\nCost Estimation:")
for item, cost in costs.items():
    print(f"  {item}: {cost}")

เคล็ดลับ

• Headless: รัน ComfyUI --listen 0.0.0.0 สำหรับ API Server
• Checksum: ตรวจ SHA256 ทุก Model ก่อนใช้งาน
• Queue: ใช้ Redis Queue จัดการ Job ไม่ให้ GPU Overload
• NSFW: เปิด Safety Checker ทุก Generation ใน Production
• Cache: Cache ผลลัพธ์ที่ Prompt เหมือนกัน ประหยัด GPU

ComfyUI คืออะไร

Node-based GUI Stable Diffusion Workflow SDXL ControlNet LoRA API JSON Automation Headless Production ยืดหยุ่น

สรุป

Stable Diffusion ComfyUI Node Workflow DevSecOps CI/CD Security Model Management API Docker GPU Queue NSFW Filter Container Scan Checksum Audit Production Architecture