Almost everyone has a box of old family photographs somewhere. Faded prints from the 1970s, scanned negatives from a disposable camera, low-resolution JPEGs from an early digital camera, or screenshots from a video call that came out blurry. The faces in those images are the parts we most want to preserve, and they are also the parts that suffer the most when image quality is poor. A few years ago, the best you could do was a careful manual retouch, one pixel at a time. Today, a specialized AI model called GFPGAN can reconstruct a face from almost any degraded image in seconds. This guide explains how that works and why it is different from regular upscaling.
Why faces are a special case
Generic image upscaling and restoration models treat every part of an image the same way. They try to sharpen edges, reduce noise, and fill in missing detail based on patterns they have learned from training on millions of general photographs. For most subjects, this works reasonably well. But faces are special. Humans are exquisitely sensitive to facial details. We can spot when an eye is the wrong shape, when a nose looks asymmetric, or when a mouth has a strange curve, even at a glance. A result that would be acceptable for a brick wall or a sky looks deeply wrong on a face.
This sensitivity is why a model trained specifically on faces tends to outperform a general-purpose restoration model when it comes to portraits. The model learns the statistical structure of human faces: where eyes sit relative to noses, how skin texture varies with lighting, what eyelashes and eyebrows look like from different angles, and so on. When it encounters a blurry face, it uses that learned prior knowledge to reconstruct the features that should be there, not just sharpen what is already there.
What GFPGAN actually does
GFPGAN (Generative Facial Prior GAN) is a face restoration model developed by Tencent ARC Lab. The word GAN in the name refers to Generative Adversarial Network, an architecture in which two neural networks are trained together: a generator that produces images, and a discriminator that tries to tell real images from generated ones. As training progresses, the generator gets better at producing convincing outputs, and the discriminator gets better at spotting fakes. The result is a model that can synthesize highly realistic faces.
GFPGAN builds on this idea but adds a crucial component: a pre-trained face generator (specifically, StyleGAN2) that already knows how to produce realistic human faces from scratch. When GFPGAN receives a low-quality input, it uses the blurry image as a guide and asks the pre-trained generator to produce a sharp version that matches. Because the generator has already learned the full space of what faces look like, it can fill in missing detail in a way that is anatomically plausible. The output is not just a sharpened version of the input. It is a newly synthesized face that is consistent with the input but enriched with realistic detail.
Restoration vs. upscaling: a real distinction
It is worth dwelling on why this is different from simple upscaling. An upscaler like Real-ESRGAN looks at a low-resolution image and produces a high-resolution version of the same image. It can sharpen edges and add plausible texture, but it cannot invent information that is not there. If an eye is blurred to the point where you cannot tell if it is open or closed, an upscaler will produce a sharper blurry eye.
A face restoration model like GFPGAN, by contrast, has learned that eyes are either open or closed and has strong priors about what each looks like. It can make a decision. The risk, of course, is that the decision might be wrong. If the original person had an unusual facial feature that the model has not seen before, it may be smoothed away during restoration. In extreme cases, a heavily degraded input can even be restored as a face that does not look like the same person. This is the tradeoff of using a generative model: you get detail, but that detail is synthesized, not recovered.
Best practices for restoring old photos
If you are using GFPGAN to restore old family photos, a few practical tips will help you get the best results:
- Scan at the highest possible resolution. Even though the final output will be upscaled by the model, starting from a high-quality scan gives the model more information to work with. A 600 dpi scan of a photographic print will produce better results than a quick phone snapshot of the same print.
- Crop tight on the face. Face restoration models work best when the face occupies most of the frame. If your photo has several people, consider processing each face separately.
- Fix obvious damage first. If the photo has large scratches or tears crossing the face, a round of manual cleanup before running it through the AI can dramatically improve the outcome.
- Compare before and after critically. Because the model can invent detail, always compare the restored version to the original. If something looks wrong, trust the original.
- Save the original. Always keep the source file. The model may improve in the future, and you will want to re-run it with a better version one day.
Ethical considerations
Any technology that can reconstruct a face from low-quality inputs raises ethical questions. Used on family photos, it is a heartwarming tool for preserving memories. Used on surveillance footage or non-consensual images, it is something very different. It is worth remembering that the restored face is, in part, a fabrication. It looks like what the model thinks the face should look like, not necessarily what the face actually was. For historical or sentimental restoration, that is fine. For anything with legal or identification consequences, it is not a substitute for the real data.
Try face restoration on your own photos
Our free face restore tool runs GFPGAN on our servers and returns the result in seconds. You can use it on old family photos, low-resolution screenshots, compressed social media images, or anywhere else you have a face that needs help. There is no sign-up, no watermark, and your uploaded image is deleted immediately after processing completes.