What runtime does it require?
It requires Python 3 and a virtual environment. The project uses the `mcp` package and Stable Diffusion dependencies installed via `pip install -e .`.
@aymec
基本信息
This project provides an HTTP server for image generation using Stable Diffusion, along with a Model Context Protocol (MCP) server that enables AI agents to request image generation.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"image_gen_with_mcp": {
"command": "python",
"args": [
"generate_image.py"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
This project is an MCP (Model Context Protocol) server with integrated Stable Diffusion image generation capabilities. It enables AI agents to request and receive generated images via a standardized MCP interface. It is based on Block's Goose Custom Extension tutorial.
Set up a virtual environment (mandatorily named .venv), install the MCP package (pip install -e .), then run the combined service with image-gen-mcp after setting the IMAGE_GEN_DIR environment variable. Alternatively, use development mode with the FastMCP Inspector for testing and debugging. Integrate with Goose as a StandardIO extension by providing the executable path and the IMAGE_GEN_DIR variable.
It requires Python 3 and a virtual environment. The project uses the `mcp` package and Stable Diffusion dependencies installed via `pip install -e .`.
Set the environment variable `IMAGE_GEN_DIR` to the absolute path of the target folder before starting the service.
Yes, use the `--port` flag when running `image-gen-mcp` (e.g., `image-gen-mcp --port 5001`).
Yes, it can run locally with Ollama. The only model tested successfully is mistral-small3.1, though it is slower than using OpenAI GPT-4o.
Press Ctrl+C to stop both the MCP server and the image generation service, as they run within the same process.
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