Decoupled DMD: How 8-Step Diffusion Outperforms 100-Step Models Without Extra Parameters

Decoupled DMD: Why 8-Step Diffusion Can Outperform 100-Step Teachers Without Extra Parameters Central question: How can a student network with no additional parameters generate images that look better than its 100-step teacher in only 8 forward passes? Short answer: By decomposing the training objective into two cooperative mechanisms—CFG Augmentation (the engine) and Distribution Matching (the seat-belt)—and giving each its own noise schedule. 1. The Misleading Success of DMD Core question: If DMD was supposed to match distributions, why does it only work when you add an asymmetric CFG term that breaks the theory? Short answer: Theory describes the DM term; …