Stage-Aware Adaptation and Distribution Calibration for Subject-Driven Personalized Text-to-Image Generation
Theoretical analysis shows that timestep-dependent scaling controls the effective perturbation magnitude of a low-rank adapter, while identity-biased candidate selection restricts the radius of selected features around the reference center under explicit conditions, indicating that personalized generation should be evaluated through identity consistency, text alignment, and representation diversity rather than identity metrics alone.