This work introduces "You Only Diffuse Areas" (YODA), a novel method for
partial diffusion in Single-Image Super-Resolution (SISR). The core idea is to
utilize diffusion selectively on spatial regions based on attention maps
derived from the low-resolution image and the current time step in the
diffusion process. This time-dependent targeting enables a more effective
conversion to high-resolution outputs by focusing on areas that benefit the
most from the iterative refinement process, i.e., detail-rich objects. We
empirically validate YODA by extending leading diffusion-based SISR methods SR3
and SRDiff. Our experiments demonstrate new state-of-the-art performance gains
in face and general SR across PSNR, SSIM, and LPIPS metrics. A notable finding
is YODA's stabilization effect on training by reducing color shifts, especially
when induced by small batch sizes, potentially contributing to
resource-constrained scenarios. The proposed spatial and temporal adaptive
diffusion mechanism opens promising research directions, including developing
enhanced attention map extraction techniques and optimizing inference latency
based on sparser diffusion.Comment: Brian B. Moser and Stanislav Frolov contributed equall