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 this https URL

本文提出了一种基于 StyleGAN-XL 的 GAN 反演框架，使用聚类和正则化反演（CRI）来生成高质量的自然图像。 CRI 方案在多个恢复任务（修补，着色和超分辨率）中验证了其有效性，并展示了优越的定量和定性结果。

适用于大规模多样化数据集的高分辨率 GAN 反演技术对退化图像的应用

High-Resolution GAN Inversion for Degraded Images in Large Diverse  Datasets

Computer graphics has experienced a recent surge of data-centric approaches
for photorealistic and controllable content creation. StyleGAN in particular
sets new standards for generative modeling regarding image quality and
controllability. However, StyleGAN's performance severely degrades on large
unstructured datasets such as ImageNet. StyleGAN was designed for
controllability; hence, prior works suspect its restrictive design to be
unsuitable for diverse datasets. In contrast, we find the main limiting factor
to be the current training strategy. Following the recently introduced
Projected GAN paradigm, we leverage powerful neural network priors and a
progressive growing strategy to successfully train the latest StyleGAN3
generator on ImageNet. Our final model, StyleGAN-XL, sets a new
state-of-the-art on large-scale image synthesis and is the first to generate
images at a resolution of $1024^2$ at such a dataset scale. We demonstrate that
this model can invert and edit images beyond the narrow domain of portraits or
specific object classes.

使用强大的神经网络先验和渐进增长策略，成功地在 ImageNet 上训练了最新的 StyleGAN3 生成器，创造了新的 StyleGAN-XL 模型，并在大规模图像合成方面取得最新的技术突破。