  In an effort to improve the efficiency and scalability of single-image
super-resolution (SISR) applications, we introduce AnySR, to rebuild existing
arbitrary-scale SR methods into any-scale, any-resource implementation. As a
contrast to off-the-shelf methods that solve SR tasks across various scales
with the same computing costs, our AnySR innovates in: 1) building
arbitrary-scale tasks as any-resource implementation, reducing resource
requirements for smaller scales without additional parameters; 2) enhancing
any-scale performance in a feature-interweaving fashion, inserting scale pairs
into features at regular intervals and ensuring correct feature/scale
processing. The efficacy of our AnySR is fully demonstrated by rebuilding most
existing arbitrary-scale SISR methods and validating on five popular SISR test
datasets. The results show that our AnySR implements SISR tasks in a
computing-more-efficient fashion, and performs on par with existing
arbitrary-scale SISR methods. For the first time, we realize SISR tasks as not
only any-scale in literature, but also as any-resource. Code is available at
https://github.com/CrispyFeSo4/AnySR.
