# Image Super-resolution

InvSR is a diffusion inversion-based image super-resolution technology that leverages the rich image priors from large pre-trained diffusion models to enhance super-resolution performance. This technology constructs intermediate states of the diffusion model through a partial noise prediction strategy, serving as starting sampling points, while employing a deep noise predictor to estimate an optimal noise map, thus initializing sampling during the forward diffusion process to generate high-resolution results. InvSR supports any number of sampling steps, ranging from one to five, and even demonstrates performance superior to or comparable with existing state-of-the-art methods when using just a single sampling step.

AuraSR-v2 is a GAN-based image super-resolution model designed for enlarging generated images, and it is a variant of the GigaGAN paper. The PyTorch implementation of this model is based on the unofficial lucidrains/gigagan-pytorch repository. It significantly enhances the resolution of images while preserving their quality, which is particularly important for applications requiring high-definition image outputs.