Usage
All three tools follow the same shape: drop an image, adjust a couple of settings, download the result.
Compression and format conversion
Path: /
Drop in one or many images, pick an output format and quality, then download. This is pure encoding — no AI involved — so it is fast and unaffected by your hardware.
Choosing a format
| Format | Best for | Notes |
|---|---|---|
| WebP | The safe default | Excellent compatibility and much better compression than JPEG. If unsure, use this. |
| AVIF | Smallest possible files | Best compression at a given quality, but slower to encode and unsupported in older browsers. |
| JPEG | Maximum compatibility | Opens everywhere. A safe choice for photographic images. |
| PNG | Transparency or lossless | Large files. If you only need transparency, prefer WebP. |
| JPEG XL | Experimental | Promising, but browser support is still limited. |
AI upscaling
Path: /upscale/
Enlarges images 4× with Real-ESRGAN. It reconstructs detail rather than interpolating pixels, which is why old photos and low-resolution assets come out noticeably sharper.
What it is and isn't good at
- Good: low-resolution but otherwise sharp images, assets that need enlarging, mild compression artefacts.
- Not good: out-of-focus or badly blurred photos. Super-resolution rebuilds detail; it does not undo blur. A blurry photo stays blurry, just bigger.
AI background removal
Path: /cutout/
Drop in an image, hit "Remove background", and get a transparent PNG. You can also switch to a white, red or blue backdrop — recolouring is an instant recomposite and does not re-run the model.
The first run downloads a model
The model weights (about 94 MB) are downloaded on first use, with a progress bar showing the percentage. They are cached by the browser afterwards, so later runs start instantly. A different browser or a cleared cache means downloading again.
Reading the result
- Compare — drag the divider to compare the original with the cutout.
- Result — the cutout alone; transparent areas show as a checkerboard.
- Original — just the source image.
- Hold space — temporarily reveal the original to check the edges.
Edges look too soft
The "Edge" setting has two modes: Sharp narrows the semi-transparent transition band for a cleaner look; Soft keeps the natural feathering, which suits hair and fur.
Nothing gets selected
BiRefNet looks for the most salient subject in the frame. That means:
- Multiple objects — only the most prominent one is kept.
- Pure landscapes with no clear foreground — may be judged "no subject" and reported as such, rather than handing you a nearly fully transparent image.
- A very small subject, or one that blends into the background — results degrade. A subject that fills a decent share of the frame and contrasts with the background works best.
In these cases, picking a different photo usually helps more than adjusting settings.
Common questions
Why is the first run so slow?
Upscaling and background removal both download a model and initialise the inference engine first. That happens once; afterwards it comes from cache. See In-browser inference.
Why is it much slower without WebGPU?
With WebGPU, inference runs on the GPU. Without it, the work falls back to the CPU, where being several times slower is expected. Desktop Chrome and Edge have the best WebGPU support; recent Safari versions work too.
How long does a hundred images take?
Compression accepts hundreds of images at once through a batch queue. Upscaling and background removal are one image at a time — they are heavy computation and you want to check each result anyway.