BiRefNet Background Removal Service

GPU-accelerated background removal as an HTTP API. Two pipelines:

• Auto — BiRefNet / RMBG-2.0 salient-object matting.
• Prompt — GroundingDINO
  • SAM: segment whatever a text prompt describes.

Served with LitServe, packaged for the NVIDIA container runtime.

Requirements

• NVIDIA GPU + driver, Docker, and the nvidia container runtime
• ~5 GB free disk for model weights (downloaded on first use, cached in a volume)

Quick start

make build      # build the Docker image
make run        # start the service on :8000 (GPU)
make logs       # watch startup — first run downloads model weights
make test       # send test.jpg, save output.png

make test waits for /health before sending, so the first call may block while a model downloads and loads.

Web UI

Open http://localhost:8000/ — a single-page test app (handy over SSH):

• Auto remove — pick a model variant + resolution.
• Prompt segment — type what to keep (e.g. the dog), tune the GroundingDINO box / text thresholds.

Both modes support a transparency checkerboard preview, click-to-zoom lightbox, optional crop-to-subject, and download.

Keyboard shortcuts

The UI is fully keyboard-drivable. Shortcuts are ignored while typing in a field and while Ctrl/Cmd/Alt is held.

In the zoom view:

API

POST /predict — automatic background removal

{
  "image": "<base64 image bytes>",   // required
  "model": "HR",                     // general|HR|portrait|matting|lite|rmbg2
  "resolution": 2048,                // inference resolution (×32)
  "background": "alpha",             // alpha|white|black|gray|green|blue|red
  "mask_blur": 0,                    // Gaussian blur radius on mask edges
  "crop": false,                     // crop to the foreground bounding box
  "crop_margin": 0.0,                // crop margin in inches (uses image DPI)
  "return_mask": false               // include the raw mask in the response
}

POST /segment — prompt-conditioned segmentation

{
  "image": "<base64 image bytes>",   // required
  "prompt": "the dog",               // required — object(s) to keep
  "box_threshold": 0.3,              // GroundingDINO detection threshold
  "text_threshold": 0.25,
  "background": "alpha",
  "mask_blur": 0,
  "crop": false,
  "crop_margin": 0.0
}

Response (both): image (base64 PNG), format, width, height, plus model/resolution (/predict) or detections/prompt (/segment).

GET /health returns 200 when the service is ready.

CLI

python3 scripts/client.py --input photo.jpg --output cut.png --model HR --resolution 2048 --crop
python3 scripts/client.py --input photo.jpg --output dog.png --prompt "the dog" --crop

Configuration (environment variables)

Local development (no Docker)

Requires a local CUDA-capable PyTorch environment.

make dev        # uv sync + run the server locally

Layout

src/rmbg_as_a_service/model.py           BiRefNet / RMBG-2.0 wrapper + compositing
src/rmbg_as_a_service/prompt_segment.py  GroundingDINO + SAM pipeline
src/rmbg_as_a_service/server.py          LitServe /predict + /segment + web UI
src/rmbg_as_a_service/static/            web UI (index.html + styles.css)
scripts/client.py                       stdlib-only test client
Dockerfile / compose.yml                CUDA image + nvidia runtime
Makefile                                build / run / test shortcuts