Dynamic Attention-Guided Diffusion for Image Super-Resolution

Abstract

Diffusion models in image Super-Resolution (SR) treat all image regions uniformly which risks compromising the overall image quality by potentially introducing artifacts during denoising of less-complex regions. To address this we propose "You Only Diffuse Areas" (YODA) a dynamic attention-guided diffusion process for image SR. YODA selectively focuses on spatial regions defined by attention maps derived from the low-resolution images and the current denoising time step. This time-dependent targeting enables a more efficient 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 methods SR3 DiffBIR and SRDiff. Our experiments demonstrate new state-of-the-art performances in face and general SR tasks across PSNR SSIM and LPIPS metrics. As a side effect we find that YODA reduces color shift issues and stabilizes training with small batches.