Enlarge images without the blur
Traditional resizing (the kind your operating system, image editor or web browser does for free) interpolates between existing pixels using algorithms like bilinear or bicubic. They're fast, but they can't invent detail that isn't already there — so a 400 × 300 photo stretched to 1600 × 1200 ends up soft, smeared, and obviously upscaled. AI upscaling solves that by training a neural network on millions of pairs of low- and high-resolution images. The model learns what plausible "high-resolution" detail looks like and regenerates it during the enlargement.
Why this tool runs in your browser
Most AI upscalers send your image to a server, run inference there, and send the result back. That's expensive (someone has to pay for the GPU) and it means your photo lives on a third-party machine, even if briefly. This tool uses UpscalerJS on top of TensorFlow.js to run the same kind of model directly in your browser, on your CPU or GPU. The model downloads once, gets cached, and never needs to leave your device after that.
2× vs 4× — which should you pick?
2× is the default. It quadruples the pixel count and is enough for almost every "share larger on social media" or "print bigger" scenario. It also runs in roughly half the time of 4× and uses much less memory.
4× shines when your source is genuinely small — say a thumbnail, an old web image, a tiny avatar — and you need a result that holds up at full screen. Under the hood the tool runs the 2× model twice in succession. The trade-off is time (about 2× as long) and memory (significantly more). If you hit an out-of-memory error, drop back to 2× or shrink the source first.
Best results: what photos work well?
- Portraits and faces — the model has seen a lot of these and recovers eye, hair and skin detail beautifully.
- Landscapes with natural texture — leaves, water, clouds and rocks all upscale well.
- Product photos — fabric, leather, packaging, and clean studio shots stay crisp.
- Old web images and screenshots — sharp text might still be soft (use a vector source if you have one).
What it can't do
AI upscaling is regeneration, not magic restoration. If your source is already heavily compressed, the model will sometimes lock onto the JPEG artefacts and amplify them. Faces from very small thumbnails can come back looking subtly different from the real person — the model is filling in plausible detail, not recovering the truth. Treat the output as "a believable larger version" rather than "the original at higher resolution".