ComfyUI

Node-based Stable Diffusion interface with ComfyUI on Clore.ai

Node-based interface for Stable Diffusion with ultimate flexibility on CLORE.AI GPUs.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Server Requirements

Parameter

Minimum

Recommended

RAM

16GB

32GB+

VRAM

8GB (SDXL)

12GB+

Network

500Mbps

1Gbps+

Startup Time

5-10 minutes

Startup Time: First launch downloads dependencies and models (5-10 minutes depending on network speed). HTTP 502 during this time is normal.

Important: ComfyUI with FLUX models requires 16GB+ VRAM. For SDXL with ControlNet, ensure at least 10GB VRAM.

Why ComfyUI?

Node-based workflow - Visual programming for image generation
Maximum control - Fine-tune every step of the pipeline
Efficient - Lower VRAM usage than alternatives
Extensible - Huge ecosystem of custom nodes
Workflow sharing - Import/export as JSON

Quick Deploy on CLORE.AI

Docker Image:

yanwk/comfyui-boot:cu126-slim

Ports:

22/tcp
8188/http

Environment:

CLI_ARGS=--listen 0.0.0.0

Verify It's Working

After deployment, find your http_pub URL in My Orders:

# Check if UI is accessible (may take 5-10 min on first run)
curl https://your-http-pub.clorecloud.net/

If you get HTTP 502 for more than 15 minutes, check:

Server has 16GB+ RAM
Server has 8GB+ VRAM for SDXL, 16GB+ for FLUX
Network speed is adequate for downloading models

Accessing Your Service

When deployed on CLORE.AI, access ComfyUI via the http_pub URL:

Web UI: https://your-http-pub.clorecloud.net/
API: https://your-http-pub.clorecloud.net/prompt
WebSocket: wss://your-http-pub.clorecloud.net/ws

All localhost:8188 examples below work when connected via SSH. For external access, replace with your https://your-http-pub.clorecloud.net/ URL.

Installation

Using Docker (Recommended)

docker run -d --gpus all \
  -p 8188:8188 \
  -v comfyui-data:/root \
  -e CLI_ARGS="--listen 0.0.0.0" \
  yanwk/comfyui-boot:cu126-slim

Manual Installation

# Clone repository
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install PyTorch with CUDA
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124

# Install requirements
pip install -r requirements.txt

# Run
python main.py --listen 0.0.0.0

Directory Structure

ComfyUI/
├── models/
│   ├── checkpoints/     # SD models (.safetensors)
│   ├── loras/           # LoRA models
│   ├── vae/             # VAE models
│   ├── controlnet/      # ControlNet models
│   ├── upscale_models/  # Upscalers
│   └── clip/            # CLIP models
├── input/               # Input images
├── output/              # Generated images
└── custom_nodes/        # Extensions

Download Models

Stable Diffusion Checkpoints

cd ComfyUI/models/checkpoints

# SDXL Base
wget https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors

# SDXL Refiner
wget https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors

# SD 1.5 (smaller, faster)
wget https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.safetensors

# Realistic Vision (photorealistic)
wget https://huggingface.co/SG161222/Realistic_Vision_V6.0_B1_noVAE/resolve/main/Realistic_Vision_V6.0_B1_fp16.safetensors

VAE

cd ComfyUI/models/vae

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

# SD 1.5 VAE (better colors)
wget https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.safetensors

LoRAs

cd ComfyUI/models/loras

# Download from CivitAI or HuggingFace
# Place .safetensors files here

Basic Workflow

Text to Image

Add nodes:
- Load Checkpoint → select model
- CLIP Text Encode (x2) → positive & negative prompts
- Empty Latent Image → set dimensions
- KSampler → connect all
- VAE Decode → latent to image
- Save Image → output
Connect:

[Checkpoint] → MODEL → [KSampler]
[Checkpoint] → CLIP → [CLIP Text Encode +]
[Checkpoint] → CLIP → [CLIP Text Encode -]
[Checkpoint] → VAE → [VAE Decode]
[Text Encode +] → CONDITIONING → [KSampler]
[Text Encode -] → CONDITIONING → [KSampler]
[Empty Latent] → LATENT → [KSampler]
[KSampler] → LATENT → [VAE Decode]
[VAE Decode] → IMAGE → [Save Image]

Image to Image

Replace Empty Latent Image with:

Load Image → your source image
VAE Encode → convert to latent
Adjust denoise in KSampler (0.5-0.8)

ComfyUI Manager

ComfyUI Manager is an essential extension that adds a GUI for installing, updating, and managing custom nodes. It is the standard way to extend ComfyUI.

Installation

cd ComfyUI/custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager.git
# Restart ComfyUI — a "Manager" button will appear in the toolbar

Using ComfyUI Manager

After restart, a Manager button appears in the top-right of the ComfyUI interface.

Key features:

Feature

How to Access

Install custom nodes

Manager → Install Custom Nodes

Update all nodes

Manager → Update All

Disable/enable nodes

Manager → Custom Nodes Manager

Install missing nodes

Manager → Install Missing Custom Nodes

Fetch model info

Manager → Model Manager

Restore snapshot

Manager → Snapshot Manager

Workflow: installing a new node pack

Click Manager button
Select Install Custom Nodes
Search by name (e.g., "FLUX", "AnimateDiff")
Click Install on the desired pack
Click Restart when prompted
New nodes appear in the right-click add menu

Auto-install missing nodes: When you import a workflow JSON that uses nodes you don't have, Manager detects them and offers to install automatically via Install Missing Custom Nodes.

Keeping Nodes Updated

# From CLI (alternative to GUI):
cd ComfyUI/custom_nodes/ComfyUI-Manager
git pull

# Or use Manager → Update All in the UI

FLUX Workflow in ComfyUI

FLUX uses a different node structure than standard SD models. Below is a complete FLUX.1-dev workflow.

Required Files

Before running the workflow, download:

# FLUX model (dev or schnell)
cd ComfyUI/models/unet
wget https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/flux1-dev.safetensors

# Text encoders
cd ../clip
wget https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors
wget https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors

# VAE
cd ../vae
wget https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/ae.safetensors

FLUX.1-dev Workflow JSON

Save as flux_dev_workflow.json and import via Load button in ComfyUI:

{
  "last_node_id": 12,
  "last_link_id": 20,
  "nodes": [
    {
      "id": 1,
      "type": "UNETLoader",
      "pos": [100, 100],
      "size": [300, 60],
      "inputs": [],
      "outputs": [{"name": "MODEL", "type": "MODEL", "links": [1]}],
      "properties": {},
      "widgets_values": ["flux1-dev.safetensors", "default"]
    },
    {
      "id": 2,
      "type": "DualCLIPLoader",
      "pos": [100, 200],
      "size": [350, 80],
      "inputs": [],
      "outputs": [{"name": "CLIP", "type": "CLIP", "links": [2, 3]}],
      "properties": {},
      "widgets_values": ["clip_l.safetensors", "t5xxl_fp16.safetensors", "flux"]
    },
    {
      "id": 3,
      "type": "CLIPTextEncode",
      "pos": [500, 150],
      "size": [425, 180],
      "inputs": [{"name": "clip", "type": "CLIP", "link": 2}],
      "outputs": [{"name": "CONDITIONING", "type": "CONDITIONING", "links": [4]}],
      "properties": {},
      "widgets_values": ["A stunning photorealistic landscape, golden hour lighting, 8K"]
    },
    {
      "id": 4,
      "type": "EmptySD3LatentImage",
      "pos": [100, 350],
      "size": [300, 100],
      "inputs": [],
      "outputs": [{"name": "LATENT", "type": "LATENT", "links": [5]}],
      "properties": {},
      "widgets_values": [1024, 1024, 1]
    },
    {
      "id": 5,
      "type": "ModelSamplingFlux",
      "pos": [500, 350],
      "size": [300, 80],
      "inputs": [{"name": "model", "type": "MODEL", "link": 1}],
      "outputs": [{"name": "MODEL", "type": "MODEL", "links": [6]}],
      "properties": {},
      "widgets_values": [1.15, 0.5, 1024, 1024]
    },
    {
      "id": 6,
      "type": "KSampler",
      "pos": [850, 250],
      "size": [350, 240],
      "inputs": [
        {"name": "model", "type": "MODEL", "link": 6},
        {"name": "positive", "type": "CONDITIONING", "link": 4},
        {"name": "negative", "type": "CONDITIONING", "link": 7},
        {"name": "latent_image", "type": "LATENT", "link": 5}
      ],
      "outputs": [{"name": "LATENT", "type": "LATENT", "links": [8]}],
      "properties": {},
      "widgets_values": [42, "fixed", 20, 3.5, "euler", "simple", 1.0]
    },
    {
      "id": 7,
      "type": "CLIPTextEncode",
      "pos": [500, 500],
      "size": [300, 60],
      "inputs": [{"name": "clip", "type": "CLIP", "link": 3}],
      "outputs": [{"name": "CONDITIONING", "type": "CONDITIONING", "links": [7]}],
      "properties": {},
      "widgets_values": [""]
    },
    {
      "id": 8,
      "type": "VAELoader",
      "pos": [100, 500],
      "size": [300, 60],
      "inputs": [],
      "outputs": [{"name": "VAE", "type": "VAE", "links": [9]}],
      "properties": {},
      "widgets_values": ["ae.safetensors"]
    },
    {
      "id": 9,
      "type": "VAEDecode",
      "pos": [1250, 300],
      "size": [210, 46],
      "inputs": [
        {"name": "samples", "type": "LATENT", "link": 8},
        {"name": "vae", "type": "VAE", "link": 9}
      ],
      "outputs": [{"name": "IMAGE", "type": "IMAGE", "links": [10]}],
      "properties": {}
    },
    {
      "id": 10,
      "type": "SaveImage",
      "pos": [1500, 300],
      "size": [300, 270],
      "inputs": [{"name": "images", "type": "IMAGE", "link": 10}],
      "outputs": [],
      "properties": {},
      "widgets_values": ["flux_output"]
    }
  ],
  "links": [
    [1, 1, 0, 5, 0, "MODEL"],
    [2, 2, 0, 3, 0, "CLIP"],
    [3, 2, 0, 7, 0, "CLIP"],
    [4, 3, 0, 6, 1, "CONDITIONING"],
    [5, 4, 0, 6, 3, "LATENT"],
    [6, 5, 0, 6, 0, "MODEL"],
    [7, 7, 0, 6, 2, "CONDITIONING"],
    [8, 6, 0, 9, 0, "LATENT"],
    [9, 8, 0, 9, 1, "VAE"],
    [10, 9, 0, 10, 0, "IMAGE"]
  ],
  "groups": [],
  "config": {},
  "extra": {"ds": {"scale": 0.8, "offset": [0, 0]}},
  "version": 0.4
}

FLUX.1-schnell Workflow (4 Steps)

For schnell, change KSampler settings in the JSON above:

num_inference_steps: 4
cfg: 1.0
scheduler: "simple"
Model file: flux1-schnell.safetensors

Or set via UI: KSampler → steps: 4, cfg: 1.0, sampler: euler, scheduler: simple

Key Node Differences: FLUX vs SD

Node

SD/SDXL

FLUX

Model loader

Load Checkpoint

UNETLoader

Text encoder

CLIPTextEncode

DualCLIPLoader + CLIPTextEncode

Latent

Empty Latent Image

EmptySD3LatentImage

Extra

—

ModelSamplingFlux

Negative prompt

Required

Optional (leave empty)

Essential Custom Nodes

Recommended Node Packs

Node Pack

GitHub

Use Case

ComfyUI-Manager

ltdrdata/ComfyUI-Manager

Install & manage all other nodes

ComfyUI-FLUX

XLabs-AI/x-flux-comfyui

FLUX ControlNet nodes

was-node-suite

WASasquatch/was-node-suite-comfyui

100+ utility nodes

ComfyUI-Impact-Pack

ltdrdata/ComfyUI-Impact-Pack

Face detection, SAM, ADetailer

ComfyUI-Inspire-Pack

ltdrdata/ComfyUI-Inspire-Pack

Advanced samplers, workflows

ComfyUI-AnimateDiff

Kosinkadink/ComfyUI-AnimateDiff-Evolved

Video / animation generation

ComfyUI-VideoHelperSuite

Kosinkadink/ComfyUI-VideoHelperSuite

Video I/O handling

ComfyUI-GGUF

city96/ComfyUI-GGUF

Run quantized GGUF models

ComfyUI-KJNodes

kijai/ComfyUI-KJNodes

Utility & mask nodes

rgthree-comfy

rgthree/rgthree-comfy

Workflow helpers, better UI

ComfyUI-FLUX (XLabs-AI)

Adds ControlNet support for FLUX inside ComfyUI:

cd ComfyUI/custom_nodes
git clone https://github.com/XLabs-AI/x-flux-comfyui
cd x-flux-comfyui
pip install -r requirements.txt

Adds nodes: Apply ControlNet (FLUX), Load ControlNet Model (FLUX), XFlux Sampler

was-node-suite

Over 100 utility nodes for advanced workflows:

cd ComfyUI/custom_nodes
git clone https://github.com/WASasquatch/was-node-suite-comfyui
cd was-node-suite-comfyui
pip install -r requirements.txt

Key nodes: Image Batch, Text Operations, Image Analyze, Cache Node, Bus Node, Upscale, Mask operations

Install via Manager

Click Manager button
Install Custom Nodes
Search and install
Restart ComfyUI

Advanced Workflows

ControlNet

# Download ControlNet models
cd ComfyUI/models/controlnet

# Canny
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_canny.pth

# Depth
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth

# OpenPose
wget https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_openpose.pth

Workflow:

Load Image → Canny Edge Detector
Apply ControlNet → KSampler
Generate with pose/edge guidance

Upscaling

# Download upscaler
cd ComfyUI/models/upscale_models
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth

Workflow:

Generate image at lower res (768x768)
Upscale Image (Model) node
Optional: img2img pass for detail

SDXL + Refiner

Generate with SDXL base (steps 1-20)
Pass latent to SDXL refiner (steps 21-30)
VAE Decode final result

Keyboard Shortcuts

Key

Action

Ctrl+Enter

Queue prompt

Ctrl+Shift+Enter

Queue prompt (front)

Ctrl+Z

Undo

Ctrl+Y

Redo

Ctrl+S

Save workflow

Ctrl+O

Load workflow

Ctrl+A

Select all

Delete

Delete selected

Ctrl+M

Mute node

Ctrl+B

Bypass node

API Usage

Queue Prompt

import json
import urllib.request

# For external access, use your http_pub URL:
SERVER = "your-http-pub.clorecloud.net"
# Or via SSH: SERVER = "localhost:8188"

def queue_prompt(prompt, server=SERVER):
    data = json.dumps({"prompt": prompt}).encode('utf-8')
    req = urllib.request.Request(f"https://{server}/prompt", data=data)
    urllib.request.urlopen(req)

# Load workflow JSON and queue
with open("workflow.json") as f:
    workflow = json.load(f)
queue_prompt(workflow)

WebSocket for Progress

import websocket
import json

# For external access, use wss:// with your http_pub URL
ws = websocket.WebSocket()
ws.connect(f"wss://{SERVER}/ws")

while True:
    msg = json.loads(ws.recv())
    if msg['type'] == 'progress':
        print(f"Step {msg['data']['value']}/{msg['data']['max']}")
    elif msg['type'] == 'executed':
        print("Done!")
        break

Performance Tips

Enable --lowvram for <8GB VRAM
Use fp16 models when possible
Batch size 1 for limited VRAM
Tiled VAE for high-res images
Disable preview for faster generation

GPU Requirements

Model

Minimum VRAM

Recommended VRAM

Min RAM

SD 1.5

4GB

8GB

16GB

SDXL

8GB

12GB

16GB

SDXL + ControlNet

10GB

16GB

FLUX

16GB

24GB

32GB

GPU Presets

RTX 3060 12GB (Budget)

# Launch with optimizations
python main.py --lowvram --force-fp16

# Recommended settings:
# - SDXL: 768x768, batch 1
# - SD 1.5: 512x512, batch 4
# - Use VAE tiling
# - 20-30 steps

Best for: SD 1.5, SDXL (with limits)

RTX 3090 24GB (Optimal)

# Standard launch
python main.py --force-fp16

# Recommended settings:
# - SDXL: 1024x1024, batch 2
# - FLUX schnell: 1024x1024, batch 1
# - ControlNet + SDXL works well
# - 30-50 steps

Best for: SDXL, ControlNet workflows, moderate FLUX

RTX 4090 24GB (Performance)

# Full speed launch
python main.py

# Recommended settings:
# - SDXL: 1024x1024, batch 4
# - FLUX dev: 1024x1024, batch 1-2
# - Complex workflows with multiple ControlNets
# - 50+ steps for quality

Best for: FLUX, complex workflows, batch generation

A100 40GB/80GB (Production)

# Maximum performance
python main.py --highvram

# Recommended settings:
# - SDXL: 1024x1024, batch 8+
# - FLUX: 1024x1024, batch 2-4
# - Multiple models loaded simultaneously
# - High-res outputs (2048x2048)

Best for: Production workloads, FLUX, high-res generation

Cost Estimate

Typical CLORE.AI marketplace rates:

GPU

VRAM

Price/day

SDXL Speed

RTX 3060

12GB

$0.15–0.30

~15 sec/img

RTX 3090

24GB

$0.30–1.00

~8 sec/img

RTX 4090

24GB

$0.50–2.00

~4 sec/img

A100

40GB

$1.50–3.00

~3 sec/img

Prices in USD/day. Rates vary by provider — check CLORE.AI Marketplace for current rates.

Troubleshooting

HTTP 502 for a long time

Check RAM: Server must have 16GB+ RAM
Check VRAM: 8GB+ for SDXL, 16GB+ for FLUX
Dependencies downloading: First run takes 5-10 min
Model downloading: Large models take longer

Out of Memory

# Run with low VRAM mode
python main.py --lowvram

# Or force fp16
python main.py --force-fp16

Black Images

Check VAE is loaded
Try different VAE
Reduce image size

Slow Generation

Enable CUDA
Use fp16 models
Reduce steps (20-30 is often enough)

Workflow Examples

Import these JSON workflows in ComfyUI:

Next Steps

ControlNet Guide
Real-ESRGAN Upscaling
Kohya Training - Train custom LoRAs
Fooocus - Simpler alternative

PreviousStable Diffusion WebUI NextFooocus

Last updated 6 days ago

Was this helpful?

hashtagServer Requirements

hashtagWhy ComfyUI?

hashtagQuick Deploy on CLORE.AI

hashtagVerify It's Working

hashtagAccessing Your Service

hashtagInstallation

hashtagUsing Docker (Recommended)

hashtagManual Installation

hashtagDirectory Structure

hashtagDownload Models

hashtagStable Diffusion Checkpoints

hashtagVAE

hashtagLoRAs

hashtagBasic Workflow

hashtagText to Image

hashtagImage to Image

hashtagComfyUI Manager

hashtagInstallation

hashtagUsing ComfyUI Manager

hashtagKeeping Nodes Updated

hashtagFLUX Workflow in ComfyUI

hashtagRequired Files

hashtagFLUX.1-dev Workflow JSON

hashtagFLUX.1-schnell Workflow (4 Steps)

hashtagKey Node Differences: FLUX vs SD

hashtagEssential Custom Nodes

hashtagRecommended Node Packs

hashtagComfyUI-FLUX (XLabs-AI)

hashtagwas-node-suite

hashtagInstall via Manager

hashtagAdvanced Workflows

hashtagControlNet

hashtagUpscaling

hashtagSDXL + Refiner

hashtagKeyboard Shortcuts

hashtagAPI Usage

hashtagQueue Prompt

hashtagWebSocket for Progress

hashtagPerformance Tips

hashtagGPU Requirements

hashtagGPU Presets

hashtagRTX 3060 12GB (Budget)

hashtagRTX 3090 24GB (Optimal)

hashtagRTX 4090 24GB (Performance)

hashtagA100 40GB/80GB (Production)

hashtagCost Estimate

hashtagTroubleshooting

hashtagHTTP 502 for a long time

hashtagOut of Memory

hashtagBlack Images

hashtagSlow Generation

hashtagWorkflow Examples

hashtagNext Steps

Server Requirements

Why ComfyUI?

Quick Deploy on CLORE.AI

Verify It's Working

Accessing Your Service

Installation

Using Docker (Recommended)

Manual Installation

Directory Structure

Download Models

Stable Diffusion Checkpoints

VAE

LoRAs

Basic Workflow

Text to Image

Image to Image

ComfyUI Manager

Installation

Using ComfyUI Manager

Keeping Nodes Updated

FLUX Workflow in ComfyUI

Required Files

FLUX.1-dev Workflow JSON

FLUX.1-schnell Workflow (4 Steps)

Key Node Differences: FLUX vs SD

Essential Custom Nodes

Recommended Node Packs

ComfyUI-FLUX (XLabs-AI)

was-node-suite

Install via Manager

Advanced Workflows

ControlNet

Upscaling

SDXL + Refiner

Keyboard Shortcuts

API Usage

Queue Prompt

WebSocket for Progress

Performance Tips

GPU Requirements

GPU Presets

RTX 3060 12GB (Budget)

RTX 3090 24GB (Optimal)

RTX 4090 24GB (Performance)

A100 40GB/80GB (Production)

Cost Estimate

Troubleshooting

HTTP 502 for a long time

Out of Memory

Black Images

Slow Generation

Workflow Examples

Next Steps