About Flux Image Generator
Flux Image Generator is an MCP server that creates images with Black Forest Lab's FLUX model. It supports single and batch generation, with configurable image settings and local saving.
Key features
- 01Generate images from text prompts
- 02Adjust dimensions and safety settings
- 03Upsample prompts and set seeds
- 04Generate images in batches
- 05Save generated images locally
Use cases
- 01Creating images from descriptive prompts
- 02Generating several images from a prompt list
- 03Tuning output dimensions and safety tolerance
MCP support
It exposes generateImage, quickImage, and batchGenerateImages tools to MCP clients for generating images with FLUX.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFLUX Image Generator MCP Server
An MCP (Model Context Protocol) server for generating images using Black Forest Lab's FLUX model. Uses the latest MCP SDK (v1.7.0).
Features
- Generate images based on text prompts
- Customize image dimensions, prompt upsampling, and safety settings
- Save generated images locally
- Batch image generation from multiple prompts
Prerequisites
- Node.js (v18.0.0 or higher)
- Black Forest Lab API key (get one at https://api.bfl.ml)
Installation
From Source
- Clone this repository
- Install dependencies:
npm install
- Create a
.envfile based on.env.exampleand add your Black Forest Lab API key:
BFL_API_KEY=your_api_key_here
- Build the project:
npm run build
Using npm
npm install -g @modelcontextprotocol/server-flux-image-generator
Usage
Starting the MCP Server
Start the server with:
npm start
For development with auto-recompilation:
npm run watch
Integrating with MCP Clients
To use this server with MCP clients (like Claude), add the following to your client's configuration:
{
"mcpServers": {
"flux-image-generator": {
"command": "mcp-server-flux-image-generator",
"env": {
"BFL_API_KEY": "your_api_key_here"
}
}
}
}
Available Tools
generateImage
Generates an image based on a text prompt with customizable settings.
Parameters:
prompt(string, required): Text description of the image to generatewidth(number, optional, default: 1024): Width of the image in pixelsheight(number, optional, default: 1024): Height of the image in pixelspromptUpsampling(boolean, optional, default: false): Enhance detail by upsampling the promptseed(number, optional): Random seed for reproducible resultssafetyTolerance(number, optional, default: 3): Content moderation tolerance (1-5)
Example:
{
"prompt": "A serene lake at sunset with mountains in the background",
"width": 1024,
"height": 768,
"promptUpsampling": true,
"seed": 12345,
"safetyTolerance": 3
}
quickImage
A simplified tool for quickly generating images with default settings.
Parameters:
prompt(string, required): Text description of the image to generate
Example:
{
"prompt": "A futuristic cityscape with flying cars"
}
batchGenerateImages
Generates multiple images from a list of prompts.
Parameters:
prompts(array of strings, required): List of text prompts (maximum 10)width(number, optional, default: 1024): Width of the imagesheight(number, optional, default: 1024): Height of the images
Example:
{
"prompts": [
"A serene lake at sunset",
"A futuristic cityscape",
"A magical forest with glowing plants"
],
"width": 1024,
"height": 768
}
Output Format
All tools return responses in this format:
{
"image_url": "https://storage.example.com/generated_image.jpg",
"local_path": "/path/to/output/flux_1234567890.png"
}
For errors:
{
"error": true,
"message": "Error description"
}
The batch tool returns:
{
"total": 3,
"successful": 2,
"failed": 1,
"results": [
{
"prompt": "A serene lake at sunset",
"success": true,
"image_url": "https://storage.example.com/image1.jpg",
"local_path": "/path/to/output/flux_batch_1234567890_0.png"
},
{
"prompt": "A futuristic cityscape",
"success": true,
"image_url": "https://storage.example.com/image2.jpg",
"local_path": "/path/to/output/flux_batch_1234567890_1.png"
},
{
"prompt": "Prohibited content",
"success": false,
"error": "Content policy violation"
}
]
}
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.