Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
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.
6982d8449084cb4f0ecb580d
Tianhe Wu
claimed_verified
2026-02-05T10:55:20.830Z
655de51982afda0fc479fb91
https://cdn-avatars.huggingface.co/v1/production/uploads/655de51982afda0fc479fb91/-t9RLNEBAESO0niQGHoss.png
Tianhe Wu
TianheWu
user
TianheWu
6982d8449084cb4f0ecb580e
Ruibin Li
6982d8449084cb4f0ecb580f
Lei Zhang
6982d8449084cb4f0ecb5810
Kede Ma
Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
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.
A novel distillation framework called DP-DMD is introduced that preserves sample diversity in text-to-image generation by separating the roles of distilled steps, using v-prediction for diversity and standard DMD loss for quality refinement without additional computational overhead.
distribution matching distillation mode collapse reverse-KL formulation v-prediction text-to-image generation sample diversity quality refinement