  Recently, using diffusion models for zero-shot image restoration (IR) has
become a new hot paradigm. This type of method only needs to use the
pre-trained off-the-shelf diffusion models, without any finetuning, and can
directly handle various IR tasks. The upper limit of the restoration
performance depends on the pre-trained diffusion models, which are in rapid
evolution. However, current methods only discuss how to deal with fixed-size
images, but dealing with images of arbitrary sizes is very important for
practical applications. This paper focuses on how to use those diffusion-based
zero-shot IR methods to deal with any size while maintaining the excellent
characteristics of zero-shot. A simple way to solve arbitrary size is to divide
it into fixed-size patches and solve each patch independently. But this may
yield significant artifacts since it neither considers the global semantics of
all patches nor the local information of adjacent patches. Inspired by the
Range-Null space Decomposition, we propose the Mask-Shift Restoration to
address local incoherence and propose the Hierarchical Restoration to alleviate
out-of-domain issues. Our simple, parameter-free approaches can be used not
only for image restoration but also for image generation of unlimited sizes,
with the potential to be a general tool for diffusion models. Code:
https://github.com/wyhuai/DDNM/tree/main/hq_demo
