comfyuiNode-based graphical interface for Stable Diffusion workflows. Build complex image generation pipelines by connecting nodes visually. Supports custom nodes, ControlNet, LoRA, upscaling, and advanced workflows with full control over the diffusion process.
Independent GitHub snapshot · Automated classification · Original documentation governs
# install.sh — Clone and set up ComfyUI
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
# Install dependencies (NVIDIA GPU)
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
# Start the server
python main.py --listen 0.0.0.0 --port 8188
# Visit http://localhost:8188
# setup_models.sh — Download and place models in the correct directories
cd ComfyUI
# SDXL base model
wget -P models/checkpoints/ \
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors"
# VAE
wget -P models/vae/ \
"https://huggingface.co/stabilityai/sdxl-vae/resolve/main/sdxl_vae.safetensors"
# LoRA adapters go in models/loras/
# ControlNet models go in models/controlnet/
# Upscale models go in models/upscale_models/
# queue_prompt.py — Submit a workflow to ComfyUI via the API
import json
import requests
import uuid
COMFYUI_URL = "http://localhost:8188"
# Basic txt2img workflow
workflow = {
"3": {
"class_type": "KSampler",
"inputs": {
"seed": 42,
"steps": 25,
"cfg": 7.5,
"sampler_name": "euler_ancestral",
"scheduler": "normal",
"denoise": 1.0,
"model": ["4", 0],
"positive": ["6", 0],
"negative": ["7", 0],
"latent_image": ["5", 0],
},
},
"4": {
"class_type": "CheckpointLoaderSimple",
"inputs": {"ckpt_name": "sd_xl_base_1.0.safetensors"},
},
"5": {
"class_type": "EmptyLatentImage",
"inputs": {"width": 1024, "height": 1024, "batch_size": 1},
},
"6": {
"class_type": "CLIPTextEncode",
"inputs": {
"text": "A majestic mountain landscape at golden hour, photorealistic, 8k",
"clip": ["4", 1],
},
},
"7": {
"class_type": "CLIPTextEncode",
"inputs": {
"text": "blurry, low quality, distorted",
"clip": ["4", 1],
},
},
"8": {
"class_type": "VAEDecode",
"inputs": {"samples": ["3", 0], "vae": ["4", 2]},
},
"9": {
"class_type": "SaveImage",
"inputs": {"filename_prefix": "comfyui_output", "images": ["8", 0]},
},
}
client_id = str(uuid.uuid4())
response = requests.post(
f"{COMFYUI_URL}/prompt",
json={"prompt": workflow, "client_id": client_id},
)
print(f"Queued: {response.json()}")
# get_results.py — Poll for completion and download generated images
import requests
import time
import urllib.request
COMFYUI_URL = "http://localhost:8188"
def wait_for_completion(prompt_id: str) -> dict:
while True:
response = requests.get(f"{COMFYUI_URL}/history/{prompt_id}")
history = response.json()
if prompt_id in history:
return history[prompt_id]
time.sleep(1)
def download_images(history: dict, output_dir: str = "./outputs"):
import os
os.makedirs(output_dir, exist_ok=True)
for node_id, node_output in history["outputs"].items():
if "images" in node_output:
for image in node_output["images"]:
url = f"{COMFYUI_URL}/view?filename={image['filename']}&subfolder={image.get('subfolder', '')}&type={image['type']}"
filepath = os.path.join(output_dir, image["filename"])
urllib.request.urlretrieve(url, filepath)
print(f"Saved: {filepath}")
# Usage after queuing a prompt
prompt_id = "your-prompt-id"
history = wait_for_completion(prompt_id)
download_images(history)
# install_manager.sh — Install ComfyUI Manager for easy custom node management
cd ComfyUI/custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager.git
# Restart ComfyUI — Manager button appears in the UI
# Popular custom node packs:
# - ComfyUI-Impact-Pack: Detection, segmentation, inpainting
# - ComfyUI-AnimateDiff: Animation from static images
# - ComfyUI-IPAdapter: Image prompt adapter for style transfer
# - rgthree-comfy: Workflow organization utilities
# controlnet_workflow.py — Generate images guided by ControlNet (edge detection, depth, pose)
controlnet_nodes = {
"10": {
"class_type": "ControlNetLoader",
"inputs": {"control_net_name": "control_v11p_sd15_canny.pth"},
},
"11": {
"class_type": "LoadImage",
"inputs": {"image": "input_image.png"},
},
"12": {
"class_type": "CannyEdgePreprocessor",
"inputs": {"image": ["11", 0], "low_threshold": 100, "high_threshold": 200},
},
"13": {
"class_type": "ControlNetApply",
"inputs": {
"conditioning": ["6", 0],
"control_net": ["10", 0],
"image": ["12", 0],
"strength": 0.8,
},
},
}
# Connect node "13" output to KSampler positive conditioning instead of "6"
# docker-compose.yml — Run ComfyUI in Docker with GPU support
version: "3.8"
services:
comfyui:
image: ghcr.io/ai-dock/comfyui:latest
ports:
- "8188:8188"
volumes:
- ./models:/workspace/ComfyUI/models
- ./output:/workspace/ComfyUI/output
- ./custom_nodes:/workspace/ComfyUI/custom_nodes
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
.safetensors) placed in models/checkpoints/