unbagrnd erases the background from any photo — instantly, privately, and entirely on your machine. No cloud API, no account, no upload, ever.
Why unbagrnd
Every feature below is designed around one rule: your images never leave your device.
Background removal runs through a real neural network via ONNX Runtime, inferred locally — no image, filename, or metadata is ever sent anywhere.
MIT-licensed, built on a free, open-source stack — no API keys, no paid tiers, no usage limits.
Choose from several background-removal models in Settings, trading off speed vs. accuracy — from a lightweight 43 MB default up.
The only network request unbagrnd ever makes is the one-time model download. No analytics, no update checks, no telemetry.
Process one image, or drop an entire folder, from the same window — with live previews as each file finishes. Batches can share one background and shadow, name files from a template, be cancelled or retried, and exported as a ZIP.
Native installers for macOS (Apple Silicon), Windows, and Linux, plus an Android build — one small app, no Electron.
A look inside
Single-image mode, batch mode, and the model picker in Settings.
Beyond the cutout
On-device editors for the spots the model gets almost right — brush, edge controls and background styling, with no AI and no network, just the result you already have.
Brush over parts of the subject to erase or restore, with full undo/redo history.
Shift the outline in or out, smooth jagged edges, or feather them — a live preview makes hair and fur easy to dial in.
Fill in a solid color - basic, pastel, neutral, or lifted straight from the photo - and add a soft drop shadow computed on-device, with angle and opacity presets.
Save the cutout's transparency as a black-and-white alpha matte, ready to use as a layer mask in your design tool.
Batch
Set a background, a shadow and a naming scheme once, then drop in a whole folder. It all runs on your device, one image at a time.
Apply one background color and drop shadow to every image in the batch.
Name outputs with a template such as {index}_{name}-studio; collisions are renamed instead of overwritten.
Stop a run at any time, then retry only the files that failed or were not processed.
Bundle the finished images into a single archive in one click (desktop).
See it in action
A quick showcase of the app, and a full walkthrough of how background removal works end-to-end.
A quick tour of the interface, single & batch modes, and the on-device model picker.
A hands-on demo showing background removal from start to finish, entirely offline.
Under the hood
unbagrnd runs IS-Net "general use"
as an ONNX model via the Rust ort bindings for ONNX Runtime —
the same model family used by the popular rembg tool.
Your image is loaded and resized to the model's input size, fully in memory.
The neural network predicts a foreground mask, entirely on your CPU.
The mask becomes an alpha channel, composited back onto the original pixels.
The result is written straight to disk — the frontend never touches the network.
Get started
Pick your platform. Every build is free, and the source is right there on GitHub.
xattr -cr /Applications/unbagrnd.app once — see the video for the full fix.
unbagrnd is MIT-licensed. Read the code, file an issue, or send a pull request — it's all on GitHub.