Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

Tianhe Wu, Ruibin Li, Lei Zhang, Kede Ma

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis: 45 upvotes on Hugging Face Daily Papers, #8 of 53 papers on 2026-02-04. Day-by-day upvote history.

Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost. However, DMD tends to suffer from mode collapse, as its reverse-KL formulation inherently encourages mode-seeking behavior, for which existing remedies typically rely on perceptual or adversarial regularization, thereby incurring substantial computational overhead and training instability. In this work, we propose a role-separated distillation framework that explicitly disentangles the roles of distilled steps: the first step is dedicated to preserving sample diversity via a target-prediction (e.g., v-prediction) objective, while subsequent steps focus on quality refinement under the standard DMD loss, with gradients from the DMD objective blocked at the first step. We term this approach Diversity-Preserved DMD (DP-DMD), which, despite its simplicity -- no perceptual backbone, no discriminator, no auxiliary networks, and no additional ground-truth images -- preserves sample diversity while maintaining visual quality on par with state-of-the-art methods in extensive text-to-image experiments.

Paper page on Hugging Face · arXiv

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