GFP-GAN is a blind face restoration generative adversarial network that makes use of a generative facial prior (GFP). Through channel-split spatial feature transform layers, this Generative Facial Prior (GFP) is incorporated into the face restoration procedure, allowing for a good balance between fidelity and realness. The GFP-GAN is made up of a degradation removal module (U-Net) and a facial prior using a pretrained face StyleGAN. A latent code mapping and a number of Channel-Split Spatial Feature Transform (CS-SFT) layers connect them.
During training, identity preserving loss is used to keep a face's identity, facial component loss is used with discriminators to improve facial details, and intermediate restoration losses are used to remove complex degradation.

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