Continuous-Time Distribution Matching for Few-Step Diffusion Distillation

Continuous-Time Distribution Matching for Few-Step Diffusion Distillation

From Daily Paper Cast by Jingwen Liang, Gengyu Wang

May 9, 2026 · 15 min · Episode 1843

About this episode

This episode discusses Continuous-Time Distribution Matching for Few-Step Diffusion Distillation, a novel approach to enhance diffusion models.

🤗 Upvotes: 24 | cs.CV, cs.AI Authors: Tao Liu, Hao Yan, Mengting Chen, Taihang Hu, Zhengrong Yue, Zihao Pan, Jinsong Lan, Xiaoyong Zhu, Ming-Ming Cheng, Bo Zheng, Yaxing Wang Title: Continuous-Time Distribution Matching for Few-Step Diffusion Distillation Arxiv: http://arxiv.org/abs/2605.06376v1 Abstract: Step distillation has become a leading technique for accelerating diffusion models, among which Distribution Matching Distillation (DMD) and Consistency Distillation are two representative paradigms. While consistency methods enforce self-consistency along the full PF-ODE trajectory to steer it toward the clean data manifold, vanilla DMD relies on sparse supervision at a few predefined discrete timesteps. This restricted discrete-time formulation and mode-seeking nature of the reverse KL divergence tends to exhibit visual artifacts and over-smoothed outputs, often necessitating complex auxiliary modules -- such as GANs or reward models -- to restore visual fidelity. In this work, we introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization for the first time. CDM achieves this through two…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

  • Diffusion Models
  • Machine Learning
  • Computer Vision
  • Continuous-Time Optimization
  • Data Distribution

Keywords

  • Continuous-Time
  • Distribution Matching
  • Diffusion Distillation
  • Machine Learning
  • Computer Vision

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