rembg-mcp

MCP server wrapping rembg for GPU-accelerated image background removal. Uses onnxruntime-gpu (CUDA) with automatic CPU fallback. No API keys — models run locally.

Install (GPU / CUDA)

Requires an NVIDIA GPU with CUDA 12.x runtime. Python 3.10–3.12.

uv tool install --python 3.12 "git+https://github.com/ReverserID/rembg-mcp.git"

Or from a local clone:

cd rembg-mcp
uv tool install --python 3.12 .

Related MCP server: jgkme/kilo-image-gen-mcp

MCP client config

{
  "mcpServers": {
    "rembg": {
      "command": "rembg-mcp"
    }
  }
}

Claude Code:

claude mcp add rembg -- rembg-mcp

Environment variables

Tools

• remove_background — cut out background from an image on disk, save transparent PNG. Options: model, alpha_matting (better hair/fur edges), post_process_mask, only_mask, bgcolor ("R,G,B,A" to composite onto a flat color).

• remove_background_base64 — same, but image in/out as base64 (no disk).

• list_models — curated model catalog.

• gpu_status — show ONNX Runtime providers and whether CUDA is active.

• unload_sessions — free loaded models from GPU/CPU memory.

Models

u2net (default), u2netp, u2net_human_seg, u2net_cloth_seg, silueta, isnet-general-use, isnet-anime, birefnet-general, birefnet-general-lite, birefnet-portrait, sam. Any other rembg model name also works.

Models download on first use and cache under ~/.u2net.

Notes

• First remove_background call downloads the model and initializes CUDA — slower; subsequent calls reuse the cached session.

• Check gpu_status to confirm CUDAExecutionProvider is active.

License

MIT