Utilizes the Flux-fill model for AI-powered face and background fusion, enabling seamless integration of facial images into ID photo backgrounds
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Face-ID Photo Fusion MCP Servermerge face from selfie.jpg with passport_background.png and save to my_docs folder"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Readme
该项目提供了一个MCP服务器,该服务器可以接受本地指定的人脸图像和证件图像,并交由指定的Comfyui服务器进行人脸与背景的融合,最终在本地保存融合的结果。
项目框架
├── README.md
├── requirements.txt
├── server.py MCP服务器
├── source_test.png 人脸图像示例
├── target_test.jpg 证照图像示例
├── 4.jpg 返回结果示例
└── mask_to_box_node.py 仿射变换模块Related MCP server: MCP Image Placeholder Server
使用方法
1. 安装依赖
pip install -r requirements.txt2. 安装Comfyui相关插件和模型
包含APersonMaskGenerator插件和使用到的flux-fill模型
3. 修改参数
修改server_address为自己的MCP服务器地址
修改mcp.run()中的MCP服务器端口和路径
如有需要,可以适当修改仿射变换中的目标区域坐标dst_pts
4. 运行
python server.pyMCP Client
以Claude为例,在claude_desktop_config.json文件中添加以下内容,替换为自己的MCP server地址
{
"mcpServers": {
"FLUX": {
"command": "npx",
"args": ["mcp-remote", "http://your mcp server address"]
}
}
}示例
提示词需包含三个参数,人脸图像路径,证照图像路径和保存文件夹路径。
source的路径为"D:\study\2025Autumn\code\source_test.jpg",target的文件路径为"D:\study\2025Autumn\code/target_test.jpg",帮我使用flux模型进行人脸与背景的融合,并保存到"D:\study\code\mayi\output"文件夹中返回结果
待改进方向
初次运行时由于要加载大模型,mcp server端运行时间较长,大模型端可能会报错
No result received from client-side tool execution.(但最后成功保存结果没有问题)flux模型填充效果 可以通过调整prompt等参数优化
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