GigaGAN
The authors write (https://mingukkang.github.io/GigaGAN/) “Unlike traditional super-resolution techniques [2, 18, 47, 95] that aim to faithfully reproduce low-resolution inputs or handle image degradation like compression artifacts, our upsamplers for large-scale models serve a different purpose. They need to perform larger upsampling factors while potentially leveraging the input text prompt.” During training and inference time, we apply moderate Gaussian noise augmentation to reduce the gap between real and GAN-generated images.
GigaGAN The authors write (https://mingukkang.github.io/GigaGAN/) “Unlike traditional super-resolution techniques [2, 18, 47, 95] that aim to faithfully reproduce low-resolution inputs or handle image degradation like compression artifacts, our upsamplers for large-scale models serve a different purpose. They need to perform larger upsampling factors while potentially leveraging the input text prompt.” During training and inference time, we apply moderate Gaussian noise augmentation to reduce the gap between real and GAN-generated images. image-processing
GigaGAN author write httpsmingukkanggithubioGigaGAN “Unlike traditional superresolution technique 2 18 47 95 aim faithfully reproduce lowresolution input handle image degradation like compression artifact upsamplers largescale model serve different purpose need perform larger upsampling factor potentially leveraging input text prompt” training inference time apply moderate Gaussian noise augmentation reduce gap real GANgenerated image imageprocessing