Arbitrary-steps Image Super-resolution via Diffusion Inversion
Authors: name: Zongsheng Yue, name: Kang Liao, name: Chen Change Loy

This study presents a new image super-resolution (SR) technique based on
diffusion inversion, aiming at harnessing the rich image priors encapsulated in
large pre-trained diffusion models to improve SR performance. We design a
Partial noise Prediction strategy to construct an intermediate state of the
diffusion model, which serves as the starting sampling point. Central to our
approach is a deep noise predictor to estimate the optimal noise maps for the
forward diffusion process. Once trained, this noise predictor can be used to
initialize the sampling process partially along the diffusion trajectory,
generating the desirable high-resolution result. Compared to existing
approaches, our method offers a flexible and efficient sampling mechanism that
supports an arbitrary number of sampling steps, ranging from one to five. Even
with a single sampling step, our method demonstrates superior or comparable
performance to recent state-of-the-art approaches. The code and model are
publicly available at https://github.com/zsyOAOA/InvSR.