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:cu130-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:

1. Server has 16GB+ RAM

2. Server has 8GB+ VRAM for SDXL, 16GB+ for FLUX

3. 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:cu130-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/cu128

# 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

1. 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

2. 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:

1. Load Image → your source image

2. VAE Encode → convert to latent

3. 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

1. Click Manager button

2. Select Install Custom Nodes

3. Search by name (e.g., "FLUX", "AnimateDiff")

4. Click Install on the desired pack

5. Click Restart when prompted

6. 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

1. Click Manager button

2. Install Custom Nodes

3. Search and install

4. 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:

1. Load Image → Canny Edge Detector

2. Apply ControlNet → KSampler

3. 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:

1. Generate image at lower res (768x768)

2. Upscale Image (Model) node

3. Optional: img2img pass for detail

SDXL + Refiner

1. Generate with SDXL base (steps 1-20)

2. Pass latent to SDXL refiner (steps 21-30)

3. 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

1. Enable --lowvram for <8GB VRAM

2. Use fp16 models when possible

3. Batch size 1 for limited VRAM

4. Tiled VAE for high-res images

5. 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	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

1. Check RAM: Server must have 16GB+ RAM

2. Check VRAM: 8GB+ for SDXL, 16GB+ for FLUX

3. Dependencies downloading: First run takes 5-10 min

4. 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:

• Basic txt2img

• SDXL + Refiner

• ControlNet Canny

• AnimateDiff Video

Next Steps

• ControlNet Guide

• Real-ESRGAN Upscaling

• Kohya Training - Train custom LoRAs

• Fooocus - Simpler alternative