ReAL: Reference-to-Image (R2I) Aware Latent Diffusion for Image Super-Resolution

Abstract

Recent di_x001B_usion-based super-resolution (SR) methods oftenemploy large text-to-image (T2I) backbones as generic priors. While thisyields strong perceptual quality, restoration performance can becomeoverdependent on text-driven semantics. T2I pipelines typically rely onclassi_x001C_er-free guidance (CFG), doubling the number of function evalu-ations at inference time. To address these limitations, we propose theReference-to-Image Aware Latent Di_x001B_usion Model for Image Super-Resolution (ReAL), a purely Reference-to-Image (R2I) model condi-tioned only on the LR input and a retrieved reference. The ReferenceFusion Module encodes the reference once, caches its key_x0015_value tensors,and injects them into the denoising model's self-attention layers at everytimestep, providing strong feature-level guidance for texture and struc-ture recovery. The R2I design substantially reduces dependence on T2Ipriors and improves semantic consistency, achieving state-of-the-art per-formance compared to T2I-based SR baselines.