Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

• URL: http://arxiv.org/abs/2602.03139v1
• Date: Tue, 03 Feb 2026 05:45:25 GMT
• Title: Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis
• Abstract summary: Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost.<n>We propose a role-separated distillation framework that explicitly disentangles the roles of distilled steps.<n>We term this approach Diversity-Preserved DMD (DP-DMD), which, despite its simplicity, preserves sample diversity while maintaining visual quality on par with state-of-the-art methods in extensive text-to-image experiments.
• Score: 33.341277146439275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation [52.34982929108336]
  Few-step distillation improves the efficiency of autoregressive (AR) video generation, but often causes diversity collapse.<n>We analyze this degradation in Distribution Matching Distillation (DMD)-distilled AR video generators and find that, in the autoregressive setting, it takes the form of a structured uncertainty collapse.
  arXiv  Detail & Related papers  (2026-09-10T08:59:47Z)
• DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation [29.992105358526715]
  We introduce a distributional evaluation protocol that measures precision and coverage between student and teacher distributions.<n>We propose joint distillation, which combines DMD's mode-seeking objective with a Consistency Distillation-based mode-covering constraint.
  arXiv  Detail & Related papers  (2026-07-29T11:58:47Z)
• Continuous-Time Distribution Matching for Few-Step Diffusion Distillation [57.28746398500951]
  We introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization.<n>CDM achieves this through two continuous-time designs. First, we replace the fixed discrete schedule with a dynamic continuous schedule of random length.<n>Second, we propose a continuous-time alignment objective that performs active off-trajectory matching on latents extrapolated via the student's velocity field.
  arXiv  Detail & Related papers  (2026-05-07T14:56:39Z)
• Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning [41.982957134224904]
  Diffusion distillation has shown great promise in few-step generation but often sacrifices quality for sampling speed.<n>We propose GDMD, a novel framework that redefines the reward mechanism by prioritizing distillation gradients over raw pixel outputs.<n>Our models outperform the quality of their multi-step teacher and substantially exceed previous DMDR results in GenEval and human-preference metrics.
  arXiv  Detail & Related papers  (2026-04-21T02:57:13Z)
• Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shield [54.328202401611264]
  Diffusion model distillation has emerged as a powerful technique for creating efficient few-step and single-step generators.<n>We show that the primary driver of few-step distillation is not distribution matching, but a previously overlooked component we identify as CFG Augmentation (CA)<n>We propose principled modifications to the distillation process, such as decoupling the noise schedules for the engine and the regularizer, leading to further performance gains.
  arXiv  Detail & Related papers  (2025-11-27T18:24:28Z)
• Distribution Matching Distillation Meets Reinforcement Learning [30.960105413888943]
  We propose DMDR, a novel framework that combines Reinforcement Learning (RL) techniques into the distillation process.<n>We show that for the RL of the few-step generator, the DMD loss itself is a more effective regularization compared to the traditional ones.<n>Experiments demonstrate that DMDR can achieve leading visual quality, prompt coherence among few-step methods, and even exhibit performance that exceeds the multi-step teacher.
  arXiv  Detail & Related papers  (2025-11-17T17:59:54Z)
• Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals [48.14879329270912]
  Phased DMD is a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts.<n>Phased DMD is built upon two key ideas: progressive distribution matching and score matching within subintervals.<n> Experimental results demonstrate that Phased DMD preserves output diversity better than DMD while retaining key generative capabilities.
  arXiv  Detail & Related papers  (2025-10-31T17:55:10Z)
• Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis [65.77083310980896]
  We propose Adrial Distribution Matching (ADM) to align latent predictions between real and fake score estimators for score distillation.<n>Our proposed method achieves superior one-step performance on SDXL compared to DMD2 while consuming less GPU time.<n>Additional experiments that apply multi-step ADM distillation on SD3-Medium, SD3.5-Large, and CogVideoX set a new benchmark towards efficient image and video synthesis.
  arXiv  Detail & Related papers  (2025-07-24T16:45:05Z)
• Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation [82.39763984380625]
  We propose textitRestoration Score Distillation (RSD), a principled generalization of Denoising Score Distillation (DSD)<n>RSD accommodates a broader range of corruption types, such as blurred, incomplete, or low-resolution images.<n>It consistently surpasses its teacher model across diverse restoration tasks on both natural and scientific datasets.
  arXiv  Detail & Related papers  (2025-05-19T17:21:03Z)
• Few-Step Diffusion via Score identity Distillation [67.07985339442703]
  Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models.<n>Existing methods rely on real or teacher-synthesized images to perform well when distilling high-resolution T2I diffusion models.<n>We propose two new guidance strategies: Zero-CFG, which disables CFG in the teacher and removes text conditioning in the fake score network, and Anti-CFG, which applies negative CFG in the fake score network.
  arXiv  Detail & Related papers  (2025-05-19T03:45:16Z)
• Scale-wise Distillation of Diffusion Models [26.372118630648338]
  SwD is a scale-wise distillation framework for diffusion models.<n>It effectively employs next-scale prediction ideas for diffusion-based few-step generators.
  arXiv  Detail & Related papers  (2025-03-20T17:54:02Z)
• DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning [57.3467234269487]
  Diffusion models excel at generative modeling (e.g., text-to-image) but sampling requires multiple denoising network passes.<n>Progressive distillation or consistency distillation have shown promise by reducing the number of passes.<n>We show that DDIL consistency improves on baseline algorithms of progressive distillation (PD), Latent consistency models (LCM) and Distribution Matching Distillation (DMD2)
  arXiv  Detail & Related papers  (2024-10-15T18:21:47Z)
• Uncertain Facial Expression Recognition via Multi-task Assisted Correction [43.02119884581332]
  We propose a novel method of multi-task assisted correction in addressing uncertain facial expression recognition called MTAC. Specifically, a confidence estimation block and a weighted regularization module are applied to highlight solid samples and suppress uncertain samples in every batch. Experiments on RAF-DB, AffectNet, and AffWild2 datasets demonstrate that the MTAC obtains substantial improvements over baselines when facing synthetic and real uncertainties.
  arXiv  Detail & Related papers  (2022-12-14T10:28:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.