ComfyUI MCP Server

Yes, this image was generated by the tool. Yes, we know AI art is ethically fraught at best and genuinely dangerous at worst. We built this for internal asset generation, not to replace artists. Use responsibly or don't use at all.

When your Mac renders black frames, you learn to distribute.

I wanted Claude to generate images through my local ComfyUI setup. Ran Sonic. Ten seconds of pure black frames. The GPU couldn't keep up.

So I distributed it.

The MCP server runs on Fly.io—stateless, auto-scaling. GPU compute lives on RunPod, pay-per-second. Generated assets go to Supabase with signed URLs. Tailscale meshes it all together securely. What started as "let me generate some images" became a production distributed system because the alternative was a space heater that outputs nothing.

Now Claude can generate images, upscale them, run ControlNet pipelines, do intelligent inpainting/outpainting, transfer styles with IP-Adapter, synthesize speech, and create lip-synced talking head videos—all through natural conversation. 40 tools. 796 tests. No API fees. Full parameter control.

You: "Generate a cyberpunk cityscape at sunset and save it to ./assets/hero.png"

Claude: I'll generate that image for you.
        [Calls generate_image with prompt, saves to ./assets/hero.png]
        Done! The cyberpunk cityscape has been saved to ./assets/hero.png

Features

Imagine Tool: The ultimate generation tool—describe what you want in natural language and get optimized results with auto-detected model settings
Smart Prompting: Auto-generates optimized prompts based on your model (Illustrious, Pony, Flux, SDXL, Realistic, SD1.5)
Pipeline Execution: Chain txt2img → hi-res fix → upscale in a single command
Quality Presets: From "draft" (fast) to "ultra" (full pipeline with upscaling)
Text-to-Image: Generate images from text prompts with full parameter control
Image-to-Image: Transform existing images with AI guidance
AI Upscaling: Enhance resolution using RealESRGAN and other models
LoRA Support: Apply custom style and character LoRAs with adjustable weights
ControlNet: Guide generation with edge detection, depth maps, poses, and more
IP-Adapter: Transfer style and composition from reference images
Inpainting: Selectively regenerate masked regions while preserving context
Outpainting: Extend canvas in any direction with coherent AI generation
Intelligent Masks: Auto-generate masks using GroundingDINO + SAM segmentation
Text-to-Speech: Clone voices with F5-TTS from short audio samples
Lip-Sync Video: Create talking head videos with SONIC
Portrait Generation: Multi-backend avatar creation (SDXL, Flux GGUF, Flux FP8)
Model Discovery: List available checkpoints, LoRAs, samplers, and schedulers
Queue Monitoring: Check generation status and pending jobs

Prerequisites

ComfyUI running locally
Node.js 20+
At least one Stable Diffusion checkpoint model

Required Custom Nodes

Some features require additional ComfyUI custom nodes. Install these via ComfyUI Manager or manually:

Feature	Required Node	Repository
IP-Adapter (style transfer)	ComfyUI_IPAdapter_plus	cubiq/ComfyUI_IPAdapter_plus
ControlNet preprocessing	comfyui_controlnet_aux	Fannovel16/comfyui_controlnet_aux
Intelligent masks (SAM)	comfyui_segment_anything	storyicon/comfyui_segment_anything
Text detection (GroundingDINO)	ComfyUI-GroundingDINO	IDEA-Research/GroundingDINO
Lip-sync (SONIC)	ComfyUI-SONIC	smthemex/ComfyUI-SONIC
TTS (F5-TTS)	ComfyUI-F5-TTS	chaojie/ComfyUI-F5-TTS

Quick install with ComfyUI Manager:

Open ComfyUI web interface
Click "Manager" button
Search for node name and install
Restart ComfyUI

Core features (txt2img, img2img, upscaling, basic ControlNet) work without any custom nodes.

Quick Start

Option A: Install from npm (Recommended)

npx @peleke.s/comfyui-mcp

Add to ~/.claude/settings.json (global) or .claude/settings.local.json (project-specific):

{
  "mcpServers": {
    "comfyui": {
      "command": "npx",
      "args": ["-y", "@peleke.s/comfyui-mcp"],
      "env": {
        "COMFYUI_URL": "http://localhost:8188",
        "COMFYUI_MODEL": "dreamshaper_8.safetensors"
      }
    }
  }
}

Option B: Run from Source

git clone https://github.com/Peleke/comfyui-mcp.git
cd comfyui-mcp
npm install
npm run build

{
  "mcpServers": {
    "comfyui": {
      "command": "node",
      "args": ["/absolute/path/to/comfyui-mcp/dist/index.js"],
      "env": {
        "COMFYUI_URL": "http://localhost:8188",
        "COMFYUI_MODEL": "dreamshaper_8.safetensors"
      }
    }
  }
}

Start ComfyUI

Ensure ComfyUI is running at the configured URL (default: http://localhost:8188).

Restart Claude Code

Important: Claude Code loads MCP servers at startup. You must restart Claude Code (exit and relaunch) after adding the configuration.

Generate Images

"Generate a portrait with warm lighting and save it to ./images/portrait.png"

Available Tools

generate_image

Generate an image from a text prompt (txt2img).

Parameter	Type	Default	Description
prompt	string	required	What to generate
negative_prompt	string	"bad quality, blurry"	What to avoid
width	number	512	Image width in pixels
height	number	768	Image height in pixels
steps	number	28	Sampling steps
cfg_scale	number	7	Classifier-free guidance scale
sampler	string	"euler_ancestral"	Sampling algorithm
scheduler	string	"normal"	Noise scheduler
model	string	env default	Checkpoint model name
seed	number	random	Random seed for reproducibility
loras	array	none	LoRAs to apply (see below)
output_path	string	required	Where to save the image
upload_to_cloud	bool	true	Upload to cloud storage and return signed URL

Returns: { success, path, remote_url?, seed, message }

When upload_to_cloud is true and cloud storage is configured, remote_url contains a signed URL valid for 1 hour.

LoRA format:

{
  "name": "style_lora.safetensors",
  "strength_model": 0.8,
  "strength_clip": 0.8
}

img2img

Transform an existing image with AI guidance.

Parameter	Type	Default	Description
prompt	string	required	What to generate
input_image	string	required	Filename in ComfyUI input folder
denoise	number	0.75	0.0 = no change, 1.0 = full regeneration
output_path	string	required	Where to save the result
(plus all txt2img params)

upscale_image

Upscale an image using AI upscaling models.

Parameter	Type	Default	Description
input_image	string	required	Filename in ComfyUI input folder
upscale_model	string	"RealESRGAN_x4plus.pth"	Upscaling model
target_width	number	native	Optional resize width
target_height	number	native	Optional resize height
output_path	string	required	Where to save the result

Discovery Tools

Tool	Description
list_models	Available checkpoint models
list_loras	Available LoRA adapters
list_samplers	Sampling algorithms (euler, dpm++, etc.)
list_schedulers	Noise schedulers (normal, karras, etc.)
list_upscale_models	Upscaling models (RealESRGAN, etc.)
get_queue_status	Running and pending jobs

imagine (Recommended)

The easiest way to generate images. Describe what you want in natural language, and it handles everything: auto-detects your model family, crafts optimized prompts, applies quality presets, and runs the full pipeline.

Parameter	Type	Default	Description
description	string	required	Natural language description of what to generate
output_path	string	required	Where to save the final image
model	string	env default	Checkpoint model (auto-detected if not set)
model_family	string	auto	"illustrious", "pony", "flux", "sdxl", "realistic", "sd15"
style	string	none	"anime", "cinematic", "portrait", "landscape", etc.
artist_reference	string	none	Artist style, e.g., "studio ghibli"
quality	string	"standard"	"draft", "standard", "high", "ultra"
loras	array	none	LoRAs to apply
seed	number	random	For reproducibility
upload_to_cloud	bool	true	Upload to cloud and return signed URL

Returns: { success, imagePath, remote_url?, seed, prompt, modelFamily, pipelineSteps, settings, message }

Quality presets:

draft: Fast generation, txt2img only
standard: Balanced quality (default)
high: Includes hi-res fix pass
ultra: Full pipeline with hi-res fix + upscaling

execute_pipeline

Run a multi-step generation pipeline: txt2img → hi-res fix → upscale.

Parameter	Type	Default	Description
prompt	string	required	The positive prompt
model	string	required	Checkpoint model
output_path	string	required	Final output location
enable_hires_fix	bool	false	Add img2img refinement pass
hires_denoise	number	0.4	Denoise for hi-res (0.3-0.5 recommended)
enable_upscale	bool	false	Add AI upscaling step
(plus all txt2img params)

craft_prompt

Generate an optimized prompt from a natural description. Useful when you want to see/edit the prompt before generating.

Parameter	Type	Default	Description
description	string	required	Natural language description
model_name	string	none	For auto-detection of model family
model_family	string	auto	Explicit family override
style	string	none	Style preset to apply
rating	string	"safe"	Content rating (for Pony models)

Returns: optimized positive prompt, negative prompt, recommended settings, LoRA suggestions.

get_prompting_guide

Get tips and example prompts for a specific model family.

Parameter	Type	Required	Description
model_family	string	yes	"illustrious", "pony", "flux", etc.

list_prompting_strategies

List all supported model families and their prompting characteristics.

ControlNet Tools

Guide image generation using structural information from reference images.

Tool	Description
generate_with_controlnet	Single ControlNet conditioning (canny, depth, pose, etc.)
generate_with_multi_controlnet	Combine multiple ControlNet conditions
preprocess_control_image	Preview control signal (edge map, skeleton, etc.)
generate_with_hidden_image	Embed hidden images using QR Code ControlNet
stylize_photo	Transform photos to artistic styles (anime, oil painting)
generate_with_pose	Copy exact pose from reference using OpenPose
generate_with_composition	Match layout/composition using semantic segmentation
list_controlnet_models	List available ControlNet models by type

Supported control types: canny, depth, openpose, qrcode, scribble, lineart, semantic_seg

IP-Adapter

Transfer style, composition, or character likeness from reference images.

Requires: ComfyUI_IPAdapter_plus custom node

Parameter	Type	Default	Description
prompt	string	required	Text prompt for generation
reference_image	string	required	Reference image filename
influence	string	"balanced"	"subtle", "balanced", "strong", "dominant"
transfer_type	string	"style"	"style", "composition", "face"
output_path	string	required	Where to save the result

Inpainting & Outpainting

Selectively edit regions of images or extend canvas boundaries.

Tool	Description
inpaint	Regenerate masked regions (white = regenerate, black = keep)
outpaint	Extend canvas in any direction with AI-generated content
create_mask	Generate masks using AI segmentation or manual regions

inpaint parameters:

Parameter	Type	Default	Description
prompt	string	required	What to generate in masked region
source_image	string	required	Source image filename
mask_image	string	required	Mask image (white = inpaint)
denoise_strength	number	0.75	0.0 = no change, 1.0 = full regen
output_path	string	required	Where to save result

outpaint parameters:

Parameter	Type	Default	Description
prompt	string	required	What to generate in extended regions
source_image	string	required	Source image filename
extend_left/right/top/bottom	number	0	Pixels to extend
feathering	number	40	Edge blending in pixels
output_path	string	required	Where to save result

create_mask parameters:

Parameter	Type	Default	Description
source_image	string	required	Image to create mask from
preset	string	none	"hands", "face", "eyes", "body", "background", "foreground"
text_prompt	string	none	Custom detection ("red shirt", "the cat")
region	object	none	Manual {x, y, width, height} percentages
expand_pixels	number	0	Grow mask outward
feather_pixels	number	0	Blur mask edges
invert	bool	false	Swap white/black
output_path	string	required	Where to save mask

Text-to-Speech (F5-TTS)

Generate speech with voice cloning from short reference audio.

Tool	Description
tts_generate	Generate speech from text with cloned voice
list_tts_models	Available TTS models
list_voices	Available voice samples

tts_generate parameters:

Parameter	Type	Default	Description
text	string	required	Text to speak
voice_reference	string	required	Reference audio for voice cloning
voice_reference_text	string	none	Transcript of reference (improves quality)
speed	number	1.0	Speech speed (0.5-2.0)
output_path	string	required	Where to save audio

Lip-Sync Video

Create talking head videos from portraits and audio.

Tool	Description
lipsync_generate	Generate lip-synced video from image + audio
talk	Full pipeline: text → TTS → lip-sync → video
list_lipsync_models	Available lip-sync models
list_avatars	Portrait images in input/avatars/
list_voices_catalog	Voice samples with metadata

Portrait Generation

Generate AI portraits optimized for lip-sync and avatar use.

Tool	Description
create_portrait	Single portrait with style/expression control
batch_create_portraits	Generate multiple portraits in batch

Backend options: sdxl (checkpoints), flux_gguf (quantized), flux_fp8 (full precision)

Health & Diagnostics

Tool	Description
check_connection	Full health check with GPU info and latency
ping_comfyui	Quick connectivity check

Usage Examples

Using Imagine (Recommended)

"Imagine a cozy coffee shop with warm lighting and plants,
save to ./assets/coffee_shop.png with high quality"

"Create an anime-style portrait of a warrior princess in a
fantasy setting, style: anime, quality: ultra"

"Generate a professional product photo of a sneaker on white
background using my realistic model, artist reference: apple product photography"

Basic Generation

"Generate a mountain landscape at golden hour, save to ./assets/landscape.png"

With LoRAs

"Create an anime-style portrait using the animeStyle.safetensors LoRA
at 0.8 strength, save to ./output/anime_portrait.png"

Image Transformation

"Take the sketch in ComfyUI's input folder called sketch.png and turn
it into a detailed illustration with 0.7 denoise"

Upscaling

"Upscale hero.png to 4K using RealESRGAN"

Batch Workflow

"Generate 3 variations of a forest scene with different lighting:
1. Misty morning
2. Harsh noon sun
3. Sunset through trees
Save them to ./scenes/forest_*.png"

Architecture

┌─────────────┐     MCP Protocol    ┌─────────────┐     REST/WS     ┌──────────┐
│   Claude    │ ◄─────────────────► │  MCP Server │ ◄─────────────► │ ComfyUI  │
│   (Client)  │    Tool Calls       │  (Bridge)   │   Workflows     │  (API)   │
└─────────────┘                     └─────────────┘                 └──────────┘

The MCP server:

Exposes tools to the AI client
Receives requests with parameters
Builds ComfyUI workflow JSON
Queues workflows via REST API
Monitors progress via WebSocket
Retrieves and saves generated images

How ComfyUI Workflows Work

ComfyUI represents image generation as a graph of nodes. Each node performs an operation:

CheckpointLoader → CLIPTextEncode → KSampler → VAEDecode → SaveImage
       ↓                                ↑
    LoraLoader(s) ──────────────────────┘

Our server dynamically constructs these graphs based on your parameters. When you specify LoRAs, we inject LoraLoader nodes into the chain. The workflow is submitted as JSON to ComfyUI's /prompt endpoint.

Why This Architecture

Some decisions here didn't come from the AI—they came from knowing what breaks at scale:

Distributed rate limiting (Upstash Redis): In-memory rate limiters fail when you have multiple Fly.io instances. Sliding window algorithm, tier-based limits, per-API-key with IP fallback.
Quirrel for long-running jobs: Fly.io has connection limits (25 hard, 20 soft). Portrait generation, TTS, and lipsync run async through job queues. Otherwise HTTP timeouts kill requests.
Storage abstraction layer: Single interface, three implementations (Supabase, GCP, local). Swap providers with an env var. No vendor lock-in.
Tailscale mesh: RunPod URLs are public. Tailscale adds a security layer between Fly.io and the GPU node.
Strategy pattern for prompting: Illustrious wants tags, Flux wants natural language, Pony needs score tags. Six model families, six strategies, auto-detected from checkpoint name. The alternative was unmaintainable spaghetti.

The AI suggested "use Playwright locally" for browser automation. That's not remotely plausible in a distributed context. Knowing when to distribute matters.

Environment Variables

Core

Variable	Default	Description
COMFYUI_URL	http://localhost:8188	ComfyUI API endpoint
COMFYUI_MODEL	(none)	Default checkpoint model
COMFYUI_OUTPUT_DIR	/tmp/comfyui-output	Fallback output directory

Cloud Storage (Optional)

Variable	Default	Description
STORAGE_PROVIDER	(none)	Set to "supabase" to enable cloud uploads
SUPABASE_URL	(none)	Your Supabase project URL
SUPABASE_SECRET_KEY	(none)	Supabase service role key (sb_secret_...)
SUPABASE_BUCKET	generated-assets	Storage bucket name

HTTP Server

Variable	Default	Description
PORT	3001	HTTP server port
NODE_ENV	development	Set to "production" for Fly.io

Project Structure

comfyui-mcp/
├── src/
│   ├── index.ts              # MCP server entry point (40 tools)
│   ├── comfyui-client.ts     # ComfyUI REST/WebSocket client
│   ├── workflows/
│   │   ├── txt2img.json      # Text-to-image template
│   │   ├── img2img.json      # Image-to-image template
│   │   ├── upscale.json      # Upscaling template
│   │   ├── inpaint.json      # Inpainting template
│   │   ├── outpaint.json     # Outpainting template
│   │   └── builder.ts        # Workflow parameterization & LoRA injection
│   ├── prompting/            # Smart prompt generation system
│   │   ├── generator.ts      # Main PromptGenerator class
│   │   ├── model-detection.ts# Auto-detect model family
│   │   └── strategies/       # Per-model prompting strategies
│   ├── storage/              # Cloud storage abstraction
│   │   ├── index.ts          # Provider factory
│   │   └── supabase.ts       # Supabase implementation
│   └── tools/
│       ├── imagine.ts        # Main generation tool
│       ├── pipeline.ts       # Multi-step pipeline executor
│       ├── controlnet.ts     # ControlNet tools
│       ├── ipadapter.ts      # IP-Adapter style transfer
│       ├── inpaint.ts        # Inpaint/outpaint/mask tools
│       ├── tts.ts            # Text-to-speech (F5-TTS)
│       ├── lipsync.ts        # Lip-sync video generation
│       ├── avatar.ts         # Portrait generation
│       └── health.ts         # Connection diagnostics
├── deploy/                   # RunPod deployment
│   ├── serverless/           # Serverless handler
│   ├── terraform/            # Infrastructure as code
│   └── scripts/              # Deployment utilities
├── buildlog/                 # Development journal
├── .github/workflows/        # CI/CD pipelines
├── vitest.config.ts          # Test configuration
├── BUILD_JOURNAL.md          # Feature narratives
└── README.md

Development

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

# Run production build
npm start

# Run tests
npm test

# Run tests in watch mode
npm run test:watch

# Run tests with coverage
npm run test:coverage

796 tests covering all tools, prompting strategies, storage providers, and pipeline execution.

Troubleshooting

Tools not showing in Claude Code

Ensure you've restarted Claude Code after adding the MCP configuration
Verify the server starts manually: npx @peleke.s/comfyui-mcp (should print "ComfyUI MCP server running on stdio")
Try killing all Claude Code instances and restarting fresh

"Connection refused"

Ensure ComfyUI is running at the configured COMFYUI_URL.

"Model not found"

Run list_models to see available checkpoints. Model names must match exactly, including file extension.

"No image in output"

Check ComfyUI's web interface for workflow errors. The queued prompt may have failed due to missing nodes or invalid parameters.

"Node does not exist" errors

Some features require custom nodes. If you see errors like IPAdapterModelLoader does not exist or DWPreprocessor does not exist, install the required custom nodes (see Prerequisites section).

Slow generation

Generation time depends on hardware, model size, and step count. Reduce steps for faster drafts.

LoRA not applying

Verify the LoRA filename with list_loras. Ensure strength values are reasonable (0.5-1.2 typically).

Extending

The codebase is designed for extension:

Video Generation: ComfyUI supports AnimateDiff—same workflow pattern applies
Custom nodes: Any ComfyUI custom node can be integrated into workflow templates
New model families: Add prompting strategies in src/prompting/strategies/
Additional backends: Extend portrait generation with new model backends

Cloud Deployment

Architecture Overview

flowchart TB
    subgraph Client["Client Layer"]
        CC[Claude Code<br/>MCP Client]
        WEB[Web App<br/>landline-landing]
    end

    subgraph Service["Service Layer (Fly.io)"]
        MCP[comfyui-mcp<br/>MCP Server]
        HTTP[HTTP Server<br/>REST API]
    end

    subgraph GPU["GPU Layer (RunPod)"]
        COMFY[ComfyUI<br/>RTX 4090]
    end

    subgraph Storage["Storage Layer (Supabase)"]
        BUCKET[Storage Bucket<br/>generated-assets]
        SIGNED[Signed URLs<br/>1hr expiry]
    end

    CC -->|MCP Protocol| MCP
    WEB -->|HTTPS REST| HTTP
    MCP -->|Proxy URL| COMFY
    HTTP -->|Proxy URL| COMFY
    MCP -->|Upload| BUCKET
    HTTP -->|Upload| BUCKET
    BUCKET -->|Generate| SIGNED
    SIGNED -->|View in Browser| WEB
    SIGNED -->|View in Browser| CC

How It Works

Client Request: Claude Code (via MCP) or web apps (via HTTP) send generation requests
Service Processing: The service builds ComfyUI workflows and queues them
GPU Execution: RunPod executes the workflow on a rented GPU (RTX 4090)
Cloud Storage: Results are uploaded to Supabase Storage
Signed URLs: A 1-hour signed URL is returned for secure browser viewing

RunPod Setup

Don't have a local GPU? Run ComfyUI on RunPod and connect remotely.

Create a RunPod pod with PyTorch template
SSH in and run:

curl -fsSL https://raw.githubusercontent.com/Peleke/comfyui-mcp/main/deploy/quick-deploy.sh | bash -s -- --dreamshaper

Get your pod URL: https://<POD_ID>-8188.proxy.runpod.net
Configure locally:

./deploy/scripts/configure-local.sh https://<POD_ID>-8188.proxy.runpod.net

See deploy/README.md for detailed instructions.

Cloud Storage (Supabase)

Generated images can be automatically uploaded to Supabase Storage with signed URLs for secure sharing.

Setup:

Create a Supabase project
Create a storage bucket named generated-assets
Add the RLS policy: true (or auth.role() = 'service_role' for restricted access)
Set environment variables (see below)

Usage:

MCP tools (imagine, generate_image) include upload_to_cloud: true by default
Results include a remote_url field with a 1-hour signed URL
URLs can be opened directly in browsers for viewing

HTTP Server (Web Integration)

For web apps that can't use MCP directly, an HTTP server is available:

# Start the HTTP server
npm run serve
# or
node dist/http-server.js

Endpoints:

Endpoint	Method	Description
`/health`	GET	Health check with GPU info
`/imagine`	POST	Natural language image generation
`/image`	POST	Direct txt2img generation
`/portrait`	POST	AI portrait generation
`/tts`	POST	Text-to-speech with F5-TTS
`/lipsync`	POST	Video lip-sync with MuseTalk

Example:

curl -X POST https://your-service.fly.dev/imagine \
  -H "Content-Type: application/json" \
  -d '{
    "description": "A cyberpunk cityscape at sunset",
    "output_path": "/tmp/cyberpunk.png"
  }'

# Response:
{
  "success": true,
  "imagePath": "/tmp/cyberpunk.png",
  "signedUrl": "https://xxx.supabase.co/storage/v1/object/sign/...",
  "seed": 1234567890,
  "message": "✨ Image generated successfully!"
}

Fly.io Deployment

Deploy the HTTP server to Fly.io for production web integration:

# Install flyctl
curl -L https://fly.io/install.sh | sh

# Deploy
fly launch
fly secrets set \
  COMFYUI_URL="https://<POD_ID>-8188.proxy.runpod.net" \
  COMFYUI_MODEL="novaFurryXL.safetensors" \
  SUPABASE_URL="https://xxx.supabase.co" \
  SUPABASE_SECRET_KEY="sb_secret_xxx" \
  SUPABASE_BUCKET="generated-assets" \
  STORAGE_PROVIDER="supabase"

fly deploy

License

MIT

Full Tutorial: See ARTICLE.md for a detailed walkthrough of building this server from scratch, including architecture decisions and implementation details.

Name		Name	Last commit message	Last commit date
Latest commit History 57 Commits
.github/workflows		.github/workflows
.serena		.serena
assets		assets
buildlog		buildlog
coverage		coverage
deploy		deploy
docs		docs
output/test-heads		output/test-heads
src		src
test-output		test-output
.dockerignore		.dockerignore
.gitignore		.gitignore
.npmrc		.npmrc
ARTICLE-CONTROLNET.md		ARTICLE-CONTROLNET.md
ARTICLE-DRAFT.md		ARTICLE-DRAFT.md
ARTICLE.md		ARTICLE.md
BUILD_JOURNAL.md		BUILD_JOURNAL.md
CLAUDE.md		CLAUDE.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
docker-entrypoint.sh		docker-entrypoint.sh
fly.toml		fly.toml
package-lock.json		package-lock.json
package.json		package.json
test-lipsync.ts		test-lipsync.ts
tsconfig.json		tsconfig.json
vitest.config.ts		vitest.config.ts

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ComfyUI MCP Server

Features

Prerequisites

Required Custom Nodes

Quick Start

Option A: Install from npm (Recommended)

Option B: Run from Source

Start ComfyUI

Restart Claude Code

Generate Images

Available Tools

generate_image

img2img

upscale_image

Discovery Tools

imagine (Recommended)

execute_pipeline

craft_prompt

get_prompting_guide

list_prompting_strategies

ControlNet Tools

IP-Adapter

Inpainting & Outpainting

Text-to-Speech (F5-TTS)

Lip-Sync Video

Portrait Generation

Health & Diagnostics

Usage Examples

Using Imagine (Recommended)

Basic Generation

With LoRAs

Image Transformation

Upscaling

Batch Workflow

Architecture

How ComfyUI Workflows Work

Why This Architecture

Environment Variables

Core

Cloud Storage (Optional)

HTTP Server

Project Structure

Development

Troubleshooting

Tools not showing in Claude Code

"Connection refused"

"Model not found"

"No image in output"

"Node does not exist" errors

Slow generation

LoRA not applying

Extending

Cloud Deployment

Architecture Overview

How It Works

RunPod Setup

Cloud Storage (Supabase)

HTTP Server (Web Integration)

Fly.io Deployment

Related

License

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