Diffusion models significantly improve the quality of super-resolved images
with their impressive content generation capabilities. However, the huge
computational costs limit the applications of these methods.Recent efforts have
explored reasonable inference acceleration to reduce the number of sampling
steps, but the computational cost remains high as each step is performed on the
entire image.This paper introduces PatchScaler, a patch-independent
diffusion-based single image super-resolution (SR) method, designed to enhance
the efficiency of the inference process.The proposed method is motivated by the
observation that not all the image patches within an image need the same
sampling steps for reconstructing high-resolution images.Based on this
observation, we thus develop a Patch-adaptive Group Sampling (PGS) to divide
feature patches into different groups according to the patch-level
reconstruction difficulty and dynamically assign an appropriate sampling
configuration for each group so that the inference speed can be better
accelerated.In addition, to improve the denoising ability at each step of the
sampling, we develop a texture prompt to guide the estimations of the diffusion
model by retrieving high-quality texture priors from a patch-independent
reference texture memory.Experiments show that our PatchScaler achieves
favorable performance in both quantitative and qualitative evaluations with
fast inference speed.Our code and model are available at
https://github.com/yongliuy/PatchScaler.

基于扩散模型的独立于补丁的单图像超分辨率方法 PatchScaler 显着提高了图像质量，降低了计算成本，并实现了快速推理速度。