rembg-bg-removal

name

rembg-bg-removal

description

Remove image backgrounds with rembg, producing transparent-PNG cutouts. Use this whenever the user wants to remove/erase/delete a background, cut out or isolate a subject, make a transparent PNG, extract a foreground object, or batch-process a folder of images into cutouts — including phrasings like "抠图", "去背景", "remove background", "cut out this object", "make this transparent", "isolate the product", or when they hit rembg errors (missing onnxruntime backend, CUDA/libcudart version mismatch, gradio proxy crash). It sets up an ISOLATED conda/venv environment (never touching existing envs), installs the correct GPU (CUDA 12) or CPU backend for the machine, prefetches models, and runs single-image or whole-folder background removal.

rembg background removal

Removes image backgrounds using rembg. The hard part isn't the removal itself — it's getting a working, isolated environment with a GPU backend that matches the machine's CUDA version. This skill bundles that setup so it's a one-time script, then a thin run wrapper.

Workflow

1. Ensure the environment exists. Check for it before running anything:

   # conda (default when conda is present):
   conda env list | grep -q '\brembg\b' && echo EXISTS
   # venv fallback:
   test -x ~/.venvs/rembg/bin/rembg && echo EXISTS
   

   If it doesn't exist, run the setup script (see below). If it exists, skip straight to running.

2. Run background removal via the wrapper (auto-detects file vs folder):

   bash <skill>/scripts/run_rembg.sh <input> <output> [-m MODEL] [flags]
   

3. Report where the outputs went and which model was used. If the user cares about quality, point them at the tuning options.

Setup (first time only)

Run the bundled installer. It creates a dedicated environment and installs only there — it never modifies the user's active or base environment.

bash <skill>/scripts/setup_env.sh

What it does, and why:

• Isolation. Creates a dedicated conda env named rembg (or a venv at ~/.venvs/rembg if conda isn't available). It computes an explicit interpreter path and installs with <that-python> -m pip, so nothing lands in whatever env happens to be active.
• Backend matched to CUDA. Reads the driver's CUDA version from nvidia-smi. For CUDA 12.x it installs onnxruntime-gpu==1.22.0 plus the matching nvidia-cudnn-cu12 / cublas / cuda-runtime wheels. This matters: the latest onnxruntime-gpu (1.23+) is built for CUDA 13 and fails to import on a CUDA-12 box with libcudart.so.13: cannot open shared object file. For CUDA ≥13 it uses the latest GPU build; with no GPU it uses the CPU build.
• Loader path. onnxruntime-gpu needs the pip NVIDIA libs on LD_LIBRARY_PATH. The installer adds a conda activate.d hook, and the run wrapper sets it too (so venv works), so the user never has to export anything.
• Model prefetch. Downloads birefnet-general (~928MB) and u2net with wget -c resume + md5 verification, because the GitHub-release links routinely drop mid-download.

Useful overrides (env vars): ENV_TYPE=venv, ENV_PATH=~/.venvs/rembg, FORCE_CPU=1, PROXY=http://127.0.0.1:7890 (route downloads through a proxy; empty by default), RECREATE=1 (rebuild from scratch), MODELS="birefnet-general isnet-anime".

Running

The wrapper handles single files and folders identically — pass a file to get a file, a directory to get a directory of cutouts. The output path is optional: omit it and the wrapper writes next to the input with a _rembg suffix (photo.jpg → photo_rembg.png, icons/ → icons_rembg/), so you don't have to invent one.

# single image, auto output -> photo_rembg.png
bash <skill>/scripts/run_rembg.sh photo.jpg

# explicit output
bash <skill>/scripts/run_rembg.sh photo.jpg photo_cutout.png

# whole folder, auto output -> icons_rembg/
bash <skill>/scripts/run_rembg.sh ./icons -m birefnet-general -ppm

# pick a model / pass rembg flags (flags may follow the input directly)
MODEL=isnet-anime bash <skill>/scripts/run_rembg.sh char.png -ppm

The wrapper unsets any socks:// proxy first (it crashes rembg's gradio import). It uses no proxy by default; set PROXY to route on-demand model downloads through an http proxy.

Choosing a model & tuning quality

Default is birefnet-general (best general edges). If the user is unhappy with edges, mentions hair/soft edges, anime, or portraits, or wants a solid-color background, read references/models_and_flags.md for the full model table and the flag-tuning order (start with -ppm, then a stronger model, then -a alpha matting with -ae erode tuning).

Troubleshooting

• No onnxruntime backend found — the GPU wheel failed to import (usually a CUDA-major mismatch). Re-run setup_env.sh; it picks the matching backend.
• libcudart.so.13: cannot open shared object file — onnxruntime-gpu is built for CUDA 13 but the box has CUDA 12. This is exactly what the version pin in setup_env.sh fixes.
• Unknown scheme for proxy URL 'socks://...' — a socks:// all_proxy breaks gradio/httpx. The run wrapper unsets it; if running rembg by hand, do unset all_proxy ALL_PROXY first.
• The CLI dependencies are not installed — rembg was installed without the [cli] extra. setup_env.sh installs rembg[cpu,cli]; re-run it.
• Model download stalls / SSL EOF — set a working PROXY, or pre-download with wget -c into ~/.u2net/<name>.onnx (the installer does this).