Stable Diffusion ComfyUI Scaling Strategy วิธี Scale

Stable Diffusion ComfyUI Scaling

ComfyUI Node-based GUI สำหรับ Stable Diffusion สร้างภาพ AI Visual Workflow SDXL SD1.5 ControlNet LoRA IPAdapter Scale สำหรับ Production Multi-GPU Multi-Node

Docker Setup

# === ComfyUI Docker Setup ===

# Dockerfile
# FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04
# 
# RUN apt-get update && apt-get install -y \
#     python3 python3-pip git wget \
#     && rm -rf /var/lib/apt/lists/*
# 
# WORKDIR /app
# RUN git clone https://github.com/comfyanonymous/ComfyUI.git .
# RUN pip3 install -r requirements.txt
# RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121
# 
# EXPOSE 8188
# CMD ["python3", "main.py", "--listen", "0.0.0.0", "--port", "8188"]

# docker-compose.yml
# version: '3.8'
# services:
#   comfyui-worker-1:
#     build: .
#     runtime: nvidia
#     environment:
#       - NVIDIA_VISIBLE_DEVICES=0
#     ports:
#       - "8188:8188"
#     volumes:
#       - ./models:/app/models
#       - ./output:/app/output
#     deploy:
#       resources:
#         reservations:
#           devices:
#             - driver: nvidia
#               count: 1
#               capabilities: [gpu]
#
#   comfyui-worker-2:
#     build: .
#     runtime: nvidia
#     environment:
#       - NVIDIA_VISIBLE_DEVICES=1
#     ports:
#       - "8189:8188"
#     volumes:
#       - ./models:/app/models
#       - ./output:/app/output
#     deploy:
#       resources:
#         reservations:
#           devices:
#             - driver: nvidia
#               count: 1
#               capabilities: [gpu]
#
#   nginx:
#     image: nginx:alpine
#     ports:
#       - "80:80"
#     volumes:
#       - ./nginx.conf:/etc/nginx/nginx.conf
#     depends_on:
#       - comfyui-worker-1
#       - comfyui-worker-2
#
#   redis:
#     image: redis:7-alpine
#     ports:
#       - "6379:6379"

# nginx.conf — Load Balancer
# upstream comfyui_workers {
#     least_conn;
#     server comfyui-worker-1:8188;
#     server comfyui-worker-2:8188;
# }
#
# server {
#     listen 80;
#     location / {
#         proxy_pass http://comfyui_workers;
#         proxy_http_version 1.1;
#         proxy_set_header Upgrade $http_upgrade;
#         proxy_set_header Connection "upgrade";
#         proxy_read_timeout 300s;
#     }
# }

# Commands
# docker compose up -d
# docker compose logs -f comfyui-worker-1
# docker compose scale comfyui-worker-1=4
# docker compose down

import json
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class WorkerConfig:
    name: str
    gpu_id: int
    port: int
    vram_gb: float
    status: str = "idle"

class ComfyUICluster:
    def __init__(self):
        self.workers: List[WorkerConfig] = []

    def add_worker(self, worker: WorkerConfig):
        self.workers.append(worker)

    def get_idle_worker(self) -> WorkerConfig:
        for w in self.workers:
            if w.status == "idle":
                w.status = "busy"
                return w
        return None

    def release_worker(self, name: str):
        for w in self.workers:
            if w.name == name:
                w.status = "idle"

    def show_status(self):
        print("ComfyUI Cluster Status:")
        for w in self.workers:
            print(f"  {w.name} | GPU:{w.gpu_id} | Port:{w.port} | "
                  f"VRAM:{w.vram_gb}GB | {w.status}")

cluster = ComfyUICluster()
cluster.add_worker(WorkerConfig("worker-1", 0, 8188, 24.0))
cluster.add_worker(WorkerConfig("worker-2", 1, 8189, 24.0))
cluster.add_worker(WorkerConfig("worker-3", 2, 8190, 24.0))
cluster.add_worker(WorkerConfig("worker-4", 3, 8191, 24.0))
cluster.show_status()

Kubernetes Deployment

# === Kubernetes ComfyUI Deployment ===

# comfyui-deployment.yaml
# apiVersion: apps/v1
# kind: Deployment
# metadata:
#   name: comfyui-worker
#   labels:
#     app: comfyui
# spec:
#   replicas: 3
#   selector:
#     matchLabels:
#       app: comfyui
#   template:
#     metadata:
#       labels:
#         app: comfyui
#     spec:
#       containers:
#       - name: comfyui
#         image: comfyui:latest
#         ports:
#         - containerPort: 8188
#         resources:
#           limits:
#             nvidia.com/gpu: 1
#             memory: "16Gi"
#           requests:
#             nvidia.com/gpu: 1
#             memory: "8Gi"
#         volumeMounts:
#         - name: models
#           mountPath: /app/models
#         env:
#         - name: COMFYUI_LISTEN
#           value: "0.0.0.0"
#       volumes:
#       - name: models
#         persistentVolumeClaim:
#           claimName: comfyui-models-pvc
#       nodeSelector:
#         gpu: "true"
#       tolerations:
#       - key: "nvidia.com/gpu"
#         operator: "Exists"
#         effect: "NoSchedule"
#
# ---
# apiVersion: v1
# kind: Service
# metadata:
#   name: comfyui-service
# spec:
#   selector:
#     app: comfyui
#   ports:
#   - port: 80
#     targetPort: 8188
#   type: ClusterIP
#
# ---
# apiVersion: autoscaling/v2
# kind: HorizontalPodAutoscaler
# metadata:
#   name: comfyui-hpa
# spec:
#   scaleTargetRef:
#     apiVersion: apps/v1
#     kind: Deployment
#     name: comfyui-worker
#   minReplicas: 2
#   maxReplicas: 10
#   metrics:
#   - type: Pods
#     pods:
#       metric:
#         name: queue_length
#       target:
#         type: AverageValue
#         averageValue: "5"

# kubectl commands
# kubectl apply -f comfyui-deployment.yaml
# kubectl get pods -l app=comfyui
# kubectl scale deployment comfyui-worker --replicas=5
# kubectl logs -f deployment/comfyui-worker
# kubectl top pods -l app=comfyui

# Queue System with Redis
import time
from typing import Optional

class ImageQueue:
    """Queue System สำหรับ ComfyUI"""

    def __init__(self, redis_url: str = "redis://localhost:6379"):
        self.queue = []
        self.results = {}
        self.job_counter = 0

    def submit_job(self, workflow: dict, priority: int = 0) -> str:
        self.job_counter += 1
        job_id = f"job-{self.job_counter:06d}"
        self.queue.append({
            "id": job_id,
            "workflow": workflow,
            "priority": priority,
            "status": "queued",
            "submitted_at": time.time(),
        })
        return job_id

    def get_next_job(self) -> Optional[dict]:
        if not self.queue:
            return None
        self.queue.sort(key=lambda x: -x["priority"])
        job = self.queue.pop(0)
        job["status"] = "processing"
        return job

    def complete_job(self, job_id: str, result: dict):
        self.results[job_id] = {
            "status": "completed",
            "result": result,
            "completed_at": time.time(),
        }

    def get_status(self, job_id: str) -> dict:
        if job_id in self.results:
            return self.results[job_id]
        for job in self.queue:
            if job["id"] == job_id:
                return {"status": job["status"], "position": self.queue.index(job)}
        return {"status": "not_found"}

queue = ImageQueue()
job1 = queue.submit_job({"prompt": "a cat", "steps": 20}, priority=1)
job2 = queue.submit_job({"prompt": "a dog", "steps": 30}, priority=2)
print(f"Submitted: {job1}, {job2}")
print(f"Status: {queue.get_status(job1)}")

next_job = queue.get_next_job()
print(f"Processing: {next_job['id']}")

Performance Optimization

# performance.py — ComfyUI Performance Tips
optimizations = {
    "Model Loading": {
        "tip": "ใช้ --highvram หรือ --gpu-only ให้ Model อยู่ใน VRAM",
        "impact": "ลดเวลา Load 80%",
        "command": "python main.py --highvram --listen 0.0.0.0",
    },
    "xformers": {
        "tip": "ติดตั้ง xformers สำหรับ Memory Efficient Attention",
        "impact": "ลด VRAM 30% เร็วขึ้น 20%",
        "command": "pip install xformers",
    },
    "FP16/BF16": {
        "tip": "ใช้ Half Precision ลด VRAM และเพิ่มความเร็ว",
        "impact": "ลด VRAM 50% เร็วขึ้น 30%",
        "command": "python main.py --force-fp16",
    },
    "Batch Processing": {
        "tip": "รวม Requests เป็น Batch ประมวลผลพร้อมกัน",
        "impact": "เพิ่ม Throughput 40%",
        "command": "ตั้ง batch_size ใน Workflow",
    },
    "Model Caching": {
        "tip": "Cache Models ใน RAM/VRAM ไม่ต้อง Load จาก Disk",
        "impact": "ลดเวลา First Image จาก 30s เหลือ 3s",
        "command": "python main.py --highvram --disable-smart-memory",
    },
    "TensorRT": {
        "tip": "Compile Model เป็น TensorRT Engine",
        "impact": "เร็วขึ้น 40-60%",
        "command": "ใช้ ComfyUI-TensorRT Node",
    },
}

print("ComfyUI Performance Optimization:")
for opt, info in optimizations.items():
    print(f"\n  [{opt}]")
    print(f"    Tip: {info['tip']}")
    print(f"    Impact: {info['impact']}")
    print(f"    Command: {info['command']}")

# VRAM Requirements
vram_req = {
    "SD 1.5 (512x512)": {"min": "4GB", "rec": "6GB", "speed": "~5 img/min"},
    "SD 1.5 (768x768)": {"min": "6GB", "rec": "8GB", "speed": "~3 img/min"},
    "SDXL (1024x1024)": {"min": "8GB", "rec": "12GB", "speed": "~2 img/min"},
    "SDXL + Refiner": {"min": "12GB", "rec": "16GB", "speed": "~1 img/min"},
    "SDXL + ControlNet": {"min": "12GB", "rec": "24GB", "speed": "~1.5 img/min"},
    "Flux.1 Dev": {"min": "16GB", "rec": "24GB", "speed": "~0.5 img/min"},
}

print(f"\n\nVRAM Requirements:")
for model, req in vram_req.items():
    print(f"  {model}: Min {req['min']} | Rec {req['rec']} | {req['speed']}")

เคล็ดลับ

• GPU: RTX 4090 24GB ดีที่สุดสำหรับ Price/Performance
• Queue: ใช้ Redis Queue จัดคิว ป้องกัน GPU Overload
• Models: แชร์ Models ผ่าน NFS/PVC ไม่ต้อง Download ทุก Worker
• Monitoring: ใช้ Prometheus + Grafana Monitor GPU VRAM Queue
• Autoscale: Scale ตาม Queue Length ไม่ใช่ CPU Usage

ComfyUI คืออะไร

Node-based GUI สำหรับ Stable Diffusion Visual Workflow SDXL SD1.5 ControlNet LoRA IPAdapter Custom Node Python Browser

สรุป

Stable Diffusion ComfyUI Scaling Strategy Docker Compose Multi-GPU Kubernetes HPA Redis Queue Nginx Load Balancer TensorRT xformers Performance Optimization Production Deployment