GFPGAN (Generative Facial Prior GAN) is a landmark AI face restoration model created by Xintao Wang, Yu Li, Honglun Zhang, and Ying Shan at Tencent Research (ARC Lab) and first presented at CVPR 2021.
The model solves a fundamental challenge in face restoration: how do you recover realistic, high-fidelity detail from a photo where that detail no longer exists? GFPGAN's answer is the Generative Facial Prior — a rich facial knowledge base extracted from a pre-trained StyleGAN2 that contains the natural statistics of real faces. By incorporating this prior into the restoration pipeline, GFPGAN can reconstruct eyes, skin texture, hair, and expression with extraordinary realism.
Unlike simple sharpening or upscaling tools, GFPGAN understands what faces should look like. It detects and aligns facial landmarks, encodes the specific degradation pattern of the input, applies the generative prior to fill in missing or corrupted detail, and seamlessly composites the result back onto the original image — all while preserving the subject's unique identity with 98.2% FaceNet cosine similarity.
Since its release, GFPGAN has become one of the most widely used face restoration models in the world, with over 10 million downloads, citations in 400+ academic papers, and integration into tools ranging from Stable Diffusion WebUI to professional broadcast pipelines. It is open-source, MIT-licensed, and freely available to everyone.
This site provides a browser-native implementation of GFPGAN's restoration pipeline using the Canvas API and WebAssembly-accelerated processing. Your images are processed entirely on your device — no upload, no server, no data retention. Just upload, restore, and download.