Disagreement-Aware Distribution Matching Distillation for Few-Step Diffusion Models

Distribution Matching Distillation (DMD) enables strong few-step diffusion generation by matching the student distribution to a pretrained teacher. Existing improvements to DMD often augment its supervision or modify its optimization machinery. We ask a complementary question: can DMD diagnose its own updates using signals already produced during training? DMD inherently computes the discrepancy between a pretrained teacher and a fake model tracking the student distribution, but conventionally uses it primarily to determine the distribution matching direction. We find that the magnitude of this teacher–fake disagreement provides additional diagnostic information. Across extensive experiments, large disagreement in the high-noise regime consistently coincides with substantially higher-energy DMD updates. This reveals a dual role of disagreement: it determines not only where the student should move, but also provides an intrinsic signal of how aggressively the update should be applied. Based on this observation, we introduce Disagreement-Aware Distribution Matching Distillation (DA-DMD), which activates only in the high-noise regime and continuously modulates the update strength according to teacher–fake disagreement. DA-DMD reuses quantities already available during DMD training and requires no additional model, evaluator, or inference-time modification. We show that it exactly recovers vanilla DMD outside the selected region, reduces update energy only through selected samples, and exhibits redescending influence under extreme disagreement. Across multiple diffusion backbones, DA-DMD consistently improves few-step generation at the same inference cost. On SD3.5-L, DA-DMD improves HPSv2 from 28.23 to 30.93, while achieving a 64.4% average win rate over DMD2.