Where the Examples Were: Local Loss Weighting Inherits the Spatial Layout of Scarce Supervision in Diffusion Models

Local loss weighting emphasizes fine detail, but it applies to whatever the scarce detailed examples contain, including where they are. We test this in a controlled diffusion task where four texture classes, each in a cyan frame at one of four panel positions, share an identical low-resolution observation. Moving the same 32 highresolution photograph–detail pairs to another panel, without adding conditions or changing the budget, moves the extra frames that region-of-interest (ROI) weighting draws at unrequested panels. Their shift toward the new panel exceeds uniform weighting’s by +81.25, +70.31 and +98.44 percentage points in three SD1.5 training seeds, with paired intervals excluding zero; uniform weighting’s own shift is +1.56 in every seed. The effect is confined to this unrequested structure: the same contrast on joint class-and-location success crosses zero in all three seeds. Moving only the weighted region removes the extra frames instead of relocating them, in two seeds under each of two arrangements, while the layout code moves them consistently under neither. How the same examples are arranged also changes how much the weighting helps, in an interaction whose balanced half replicates. Our readouts are frame and classifier proxies rather than human judgments of texture, the target region is marked in the coarse observation, and native 4K is a boundary test.