Fidelity Where You Look, Realism Everywhere: Attention-Guided Additive Fidelity Allocation for Generative Image Compression

Generative image codecs produce realistic reconstructions, but spatially uniform distortion pricing cannot distinguish a harmless plausible substitution from the loss of source-specific detail in a face or a word. We introduce FLaRE, a one-step generative codec that adds fidelity on top of a realism floor according to a continuous spatial fidelity-need field. FLaRE spatially extends rate–distortion–perception (RDP) optimization and decomposes its objective into the baseline realism-codec objective and an excess-fidelity allocation problem. A training-only auxiliary decoder makes a dedicated message latent carry the requested source evidence without imposing strong local regression on the delivered reconstruction. Field-conditioned latent gating and scaling control where and how much message rate is spent; no field is transmitted, and the one-step decoding path does not vary with the field. Because the floor objective is retained from the realism codec, FLaRE at zero excess remains competitive with its backbone. Experiments show that coded rate lands where the field points, grows monotonically with its magnitude, and improves fidelity in the requested region without harming the background. For perceived quality, we instantiate the field with off-the-shelf predicted attention, motivated by fixation analysis showing that attention concentrates on faces, text, and other information-bearing content. In a subjective quality assessment with 37 observers, FLaRE improves MOS by 0.29–0.57 over OneDC and AEIC-ME, and at least 35 observers rated it higher on average than either competitor. Our code is included in the supplementary material.