SPECTRA: Source-Preserving Editing with Cross-Scale Transform and Representation Adaptation

Text-guided image editing must follow the target instruction while retaining source information that should remain unchanged. Existing training-free methods use feature injection, attention control, or predefined frequency regions, but typically apply coarse or fixed preservation policies. We study source trajectories and find that preservation evidence differs across transform scales, is non-uniform across orientations within a scale, and changes over sampling time. SPECTRA therefore defines preservation over scale, direction, space, and time. At each native step, source-only multiscale transform energies determine threshold-free scale– direction weights, while target-token attention supplies spatial evidence through Semantic Structure Mass-Odds (SSMO). The fused clean prediction is returned to the backbone’s native DDIM or FlowMatch scheduler, enabling one interface across Stable Diffusion and transformer flow backbones; appearance-preservation tasks optionally use decoder-cycle residual chroma injection (DCRCI). Across two appearance-changing and two appearance-preserving benchmarks, SPECTRA shifts the preservation–editability operating point toward stronger source retention. Controlled analysis separates a strong cross-scale effect from a smaller conditional directional refinement: directional benefit tends to increase with source anisotropy, while a shuffled-direction control shows that structural preservation depends on matching source-derived weights to their corresponding bands.