Remove Background

Remove image background (local, offline)

Removes the background from images using rembg (a U^2-Net ONNX model) entirely on this machine. No API key, no network calls after the one-time model download, no rate limits, and it keeps full resolution. This is the robust local alternative to the remove.bg website/API.

The output is always a PNG with a transparent alpha channel (the subject kept, the background knocked out). Confirm success by checking the output exists and is mode RGBA with both transparent and opaque pixels.

The command

Everything runs through this single, self-contained invocation - no install step, no wrapper file needed. Substitute the input/output paths:

# Single image -> transparent PNG
uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i INPUT OUTPUT.png

# Whole folder (batch) -> folder of transparent PNGs
uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg p INPUT_DIR OUTPUT_DIR

rembg subcommands: i = single file, p = folder, b = byte stream, d = download models, s = http server.

Example:

Input: ~/Downloads/product.jpg (a product on a white studio background) Command:

uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i ~/Downloads/product.jpg ~/Downloads/product-nobg.png

Output: ~/Downloads/product-nobg.png - the product on transparency.

Options: finer edges and model choice

Flags go before the input/output paths:

# Alpha matting: softer, more accurate edges for hair, fur, fuzzy outlines. Slower.
uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i -a INPUT OUTPUT.png

# Choose a model with -m:
#   u2net             (default) general purpose, good all-rounder
#   isnet-general-use          heavier, often cleaner edges
#   u2net_human_seg            tuned for people / portraits
#   u2netp                     lightweight and faster, lower quality
uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i -m isnet-general-use INPUT OUTPUT.png

Run uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i --help for all flags.

Dark subjects & solid-background logos (when rembg leaves a halo)

rembg does salient-object detection, not colour keying. On a dark subject sitting on a dark or solid-colour background it often can't find a clean edge and leaves a dark halo/fringe around the subject (and sometimes keeps interior dark holes it should have knocked out). This is worst on JPEGs, where compression sprays near-black noise around the subject that rembg pulls into the cutout. rembg handles most dark images fine - this is the specific edge case where it does not.

If the background is a known, roughly solid colour, don't ask a model to guess the subject - key out the background colour directly. It's deterministic and halo-free whenever the subject colour differs from the background.

Fix A - solid-background subjects (dark OR light bg): colour-key the background

This needs no global install (uvx pulls Pillow on demand, matching the skill's ethos). Tune LO/HI to your background's luminance:

# near-black background -> transparent, feathered edge; tune LO/HI to your bg
uvx --python 3.11 --with pillow python - INPUT OUTPUT.png <<'PY'
import sys
from PIL import Image, ImageFilter
LO, HI, NEUTRAL = 45, 100, 28   # near-black bg luminance window; NEUTRAL guards a colourful subject
im = Image.open(sys.argv[1]).convert("RGBA"); px = im.load(); w, h = im.size
for y in range(h):
    for x in range(w):
        r, g, b, a = px[x, y]; lum = 0.299*r + 0.587*g + 0.114*b; chroma = max(r,g,b) - min(r,g,b)
        if chroma < NEUTRAL:
            if lum <= LO: px[x, y] = (r, g, b, 0)
            elif lum < HI: px[x, y] = (r, g, b, int(255*(lum-LO)/(HI-LO)))
r_, g_, b_, al = im.split(); al = al.filter(ImageFilter.GaussianBlur(1.0))
Image.merge("RGBA", (r_, g_, b_, al)).save(sys.argv[2])
PY

• Light background instead? Key the bright end: flip the test to lum >= HI -> transparent and ramp the alpha down as luminance rises (e.g. LO, HI = 160, 215), so a white/pale background drops out and the dark subject stays.
• chroma < NEUTRAL is the guardrail. It only keys near-grey pixels, so a colourful subject (a red mark, a bright logo detail) is never erased even if it falls in the luminance window. Raise NEUTRAL if some subject edge is still keyed; lower it if background tint survives.
• Scope: this only works when the subject colour differs from the background colour. It is not a universal replacement for rembg - on a near-black detail sitting on a near-black background the luminance test can't tell them apart and will erase the detail. For those, use Fix B.

Fix B - dark photos / complex subjects: stay with rembg

Colour-key wants a solid background; a real photo doesn't have one. Keep using rembg but:

• Try -m isnet-general-use - it often segments dark subjects more cleanly than the default u2net.
• Add -a (alpha matting) for softer, more accurate edges.

uvx --python 3.11 --with "numba>=0.59.1" --from "rembg[cpu,cli]" rembg i -m isnet-general-use -a INPUT OUTPUT.png

If rembg can't even find a very dark subject, brighten a copy, run rembg on the brightened copy to get the mask, then paste that mask's alpha back onto the original so the subject keeps its true colours:

uvx --python 3.11 --with "numba>=0.59.1" --with pillow --from "rembg[cpu,cli]" python - INPUT OUTPUT.png <<'PY'
import sys
from PIL import Image, ImageEnhance
from rembg import remove
orig = Image.open(sys.argv[1]).convert("RGB")
bright = ImageEnhance.Brightness(orig).enhance(2.2)       # brighten a COPY so rembg can see the subject
mask = remove(bright).getchannel("A")                     # rembg finds the subject on the bright copy
out = orig.convert("RGBA"); out.putalpha(mask)            # apply that alpha to the ORIGINAL -> true colours
out.save(sys.argv[2])
PY

Why the invocation looks the way it does

Do not simplify it to a bare uvx rembg - it will fail. The reasons:

• --python 3.11 + --with "numba>=0.59.1" is a required workaround. rembg depends on pymatting -> numba, and on Python 3.12/3.13 uvx otherwise backtracks to an ancient numba 0.53 / llvmlite 0.36 that only builds on Python <3.10 and fails here. Pinning Python 3.11 and forcing a modern numba pulls prebuilt wheels and resolves cleanly.
• rembg[cpu,cli] adds the onnxruntime CPU backend and the file/folder CLI commands; plain rembg reports "onnxruntime backend not found" / "CLI dependencies not installed".
• First run downloads the model (176 MB) to `/.u2net/` and caches it; every run after is fast and fully offline.

Requirements

uv / uvx on PATH is the only prerequisite - it fetches rembg and its dependencies on demand into uv's cache. No global pip install, no API key.

Optional: a reusable wrapper

If you expect to do this often, it is convenient (not required) to save a small wrapper that fills in default output names and folder handling. This repo ships one at scripts/remove-bg.sh for anyone who clones it; a single-file skill install does not include it, so the inline command above is the canonical path.

When NOT to use this

If the user specifically needs remove.bg's own cloud model output (e.g. to match it exactly on a paid plan), that is the remove.bg HTTP API (api.remove.bg, needs a key) - a different path. This skill is the local, key-free route that covers the vast majority of "remove the background" requests.