CRAudit: Predicting, Preventing, and Repairing Conditional Response Collapse in One-Step Image Editing

One-step distilled text-to-image models are fast, but often collapse distinct editing instructions into nearly identical responses. The central challenge is to identify vulnerable text responses early to direct resources toward their preservation or repair. We address this gap through the conditional response operator, the Jacobian from text-embedding perturbations to predicted image-residual changes. Conditional Controllability Spectra (CCS) audits this operator in a fixed text-derived basis and exposes contraction of weak response modes. Across four distillation families, CCS effective rank tracks an editing-quality drop. Using only the first three of 15 checkpoints, terminal-risk prediction yields Brier 0.088 and AUROC 0.89; threshold-rank prediction correlates at $r=0.94$. The same aligned coordinates guide spectrum-preserving distillation (SPD) toward at-risk modes and spectral residual adaptation (SRA) toward frozen-student repair with 0.54% additional parameters. On PIE-bench, SPD+SRA reaches 25.06 dB PSNR and 24.58 CLIP-E in 0.35 s; it obtains 75.3% participant-clustered preference over FlowEdit and 71.7% in an opponent-balanced tournament ($N=48$). Compression audits characterize contraction; interventions test selected response directions. Matched allocation yields 0.49/0.47 CLIP-E gains for SPD/SRA; fixed-architecture controls further test predictive selection, separating allocation from adapter capacity.