Blind face restoration usually relies on facial priors, such as facialgeometry prior or reference prior, to restore realistic and faithful details.However, very low-quality inputs cannot offer accurate geometric prior whilehigh-quality references are inaccessible, limiting the applicability inreal-world scenarios. In this work, we propose GFP-GAN that leverages rich anddiverse priors encapsulated in a pretrained face GAN for blind facerestoration. This Generative Facial Prior (GFP) is incorporated into the facerestoration process via novel channel-split spatial feature transform layers,which allow our method to achieve a good balance of realness and fidelity.Thanks to the powerful generative facial prior and delicate designs, ourGFP-GAN could jointly restore facial details and enhance colors with just asingle forward pass, while GAN inversion methods require expensiveimage-specific optimization at inference. Extensive experiments show that ourmethod achieves superior performance to prior art on both synthetic andreal-world datasets.