AI Face Restorer

In most treasured old photos, the faces are the smallest important thing in the frame. A group portrait from a film camera might give each face only a few hundred pixels, and every generation of copying, scanning, and compressing chips away at exactly that region. Printing softens it, time fades it, and early digital cameras added noise on top.

Generic sharpening cannot repair this, because sharpening only exaggerates edges that still exist. A face that has collapsed into a smudge has no edges left to exaggerate. What it needs is reconstruction: a system that knows what human faces look like and can rebuild plausible eyes, skin, and hair that stay faithful to the person.

That is what a face restoration network does, and it is why the results look dramatic compared to any filter. The model brings knowledge of faces in general and applies it to the specific face in your photo.

The pipeline runs in three stages, all inside your browser. First a lightweight detector scans the photo and finds each face along with five landmark points: the eyes, the nose tip, and the mouth corners. Up to 4 faces are handled per photo.

Second, each face is rotated and scaled onto a standard position using those landmarks, and the aligned crop passes through GFPGAN, a network with a generative facial prior. In plain words, it carries a deep memory of how sharp faces look, and it uses your blurry face to decide which sharp face to reconstruct. Identity, pose, and expression come from your photo, crispness comes from the model.

Third, the restored face is warped back to its exact place and blended in with a soft edge, so there is no visible patch boundary. The rest of the photo stays untouched, only the faces change. Because reconstruction generates fine detail, treat micro-features like skin texture as plausible rather than documentary, the person remains recognizably themselves.

Face photos are the most personal images most people own, which makes the usual upload-to-server restoration flow an uncomfortable trade. This tool removes it. Both models, the small detector and the 163 MB restoration network, download to your browser once, and every photo is then processed locally. Nothing you restore is ever transmitted.

Restoration is heavier than the other image tools here, expect roughly ten seconds per face on a desktop, longer on phones. The step indicator shows the model download on first use and then per-face progress. Photos up to 2,048 pixels on the longest side are accepted, larger scans should be resized down first.

Deliberate design choice worth knowing: the restoration runs on the processor rather than the graphics chip, because this particular network needs the extra numerical precision to produce correct colors. Slower, but right.

Frontal and near-frontal faces restore beautifully, they match what the network saw most in training. Strong profiles, heavily tilted heads, and faces partially covered by hands or objects restore less reliably. Small faces are fine as long as the detector can find them, if one is missed, crop the photo so the face fills more of the frame and run again.

Very degraded photos benefit from preparation. Denoise a grainy scan with the AI Image Denoiser first so the detector sees structure instead of speckle. After restoration, black and white portraits pair naturally with the AI Photo Colorizer, and if the whole photo needs to be bigger, finish with the AI Image Upscaler.

Keep the original file alongside the restored one. Reconstruction is honest about identity but generative about fine detail, and for family archives the pair of before and after tells the fuller story.