  The last decades are marked by massive and diverse image data, which shows
increasingly high resolution and quality. However, some images we obtained may
be corrupted, affecting the perception and the application of downstream tasks.
A generic method for generating a high-quality image from the degraded one is
in demand. In this paper, we present a novel GAN inversion framework that
utilizes the powerful generative ability of StyleGAN-XL for this problem. To
ease the inversion challenge with StyleGAN-XL, Clustering \& Regularize
Inversion (CRI) is proposed. Specifically, the latent space is firstly divided
into finer-grained sub-spaces by clustering. Instead of initializing the
inversion with the average latent vector, we approximate a centroid latent
vector from the clusters, which generates an image close to the input image.
Then, an offset with a regularization term is introduced to keep the inverted
latent vector within a certain range. We validate our CRI scheme on multiple
restoration tasks (i.e., inpainting, colorization, and super-resolution) of
complex natural images, and show preferable quantitative and qualitative
results. We further demonstrate our technique is robust in terms of data and
different GAN models. To our best knowledge, we are the first to adopt
StyleGAN-XL for generating high-quality natural images from diverse degraded
inputs. Code is available at https://github.com/Booooooooooo/CRI.
