Background Remover

Background remover tools are everywhere, but almost all of them upload your image to a remote server — and you are trusting a privacy policy you have never read with photos of your face, your client’s product, or your unreleased design. This one does not upload anything.

How on-device background removal actually works

The tool loads a pre-trained segmentation model — Silueta or U2Net-P, your choice — into your browser using WebAssembly and ONNX Runtime. Once loaded, the model classifies every pixel in the image as foreground or background and generates a mask. The mask is applied, the background pixels become transparent, and the result is rendered right in the same browser tab.

No fetch request, no server endpoint, no temporary cloud storage. You can verify this yourself: open the Network tab in DevTools before you drop a file and watch. Nothing fires.

# What you will see in the Network tab after dropping an image:
Requests: 0 new
Transferred: 0 B

The model download happens only once — around 44 MB for Silueta (the sharper one) or 4.6 MB for U2Net-P (the faster one). After the first load, the browser caches it. Repeat visits start processing immediately.

Where the two models differ

Silueta handles hair, fur, lace, and semi-transparent edges noticeably better. It takes a few seconds longer per image, but the edge quality on portraits and pets is visibly cleaner — especially when you toggle to the Mask view and zoom in.

U2Net-P is the right pick when you are batch-processing product shots on plain backgrounds, where edge complexity is low and speed matters more. A product photo on white card rarely needs the extra precision.

You can switch models between images without reloading the page.

Batch processing without an account

Drop multiple files, and each one processes in sequence. There is no per-image gate, no daily cap, and no sign-up prompt after the third file. When you are done, “Download all (ZIP)” packages every result into a single archive.

Most server-based tools throttle free users after a handful of images or downgrade the output resolution. Because processing happens on your own hardware, the only limit here is your device’s available memory.

Choosing the right output format

PNG is the default — transparent background, lossless. This is what you want for layering in a design tool, placing on a webpage, or importing into Canva or Figma.

WebP gives a smaller file with the same transparency. If the cutout is going straight to a website and you want to save bytes, this is the better pick.

JPG drops transparency entirely and fills the background with a colour you choose — white, black, or any hex value. Useful when a marketplace or form requires a JPG with a solid backdrop.

Honest limitations

On-device models are smaller than the cloud models that remove.bg or Photoroom run on their servers. For most images — product shots, headshots, logos, pets — the result is clean and production-ready. On genuinely difficult edges (a person with flyaway hair against a similarly-coloured background, or a glass object with subtle reflections), a server-side tool with a larger model will likely do a better job.

If your image is over 10 MB, resize it first with the resize tool — the quality loss from a modest resize is invisible, and the processing will be faster.

When this is not the right tool

If you need absolute edge perfection on high-fashion hair or transparent glass, remove.bg or Photoroom run larger server-side models that handle those edge cases better — both have free tiers with resolution limits. If you need bulk processing at API scale (thousands of images in a pipeline), Cloudinary or removal.ai offer background-removal APIs with batch endpoints.

For everything else — quick cutouts, private photos, client work you do not want on someone else’s server, or a simple batch of product images — this runs in your browser, stays on your device, and costs nothing.