Distribution Matching Distillation

Also known as DMD

Distribution Matching Distillation transfers the behavior of a slow diffusion or flow-model teacher to a student that generates images, video, or other data in only a few steps. Many methods use teacher and student score estimates to form distribution-matching gradients, while alternatives learn density ratios adversarially or use consistency and flow-map objectives. Recent approaches also combine matching with diversity-preserving constraints, preference rewards, or temporal guidance for autoregressive video. The central tension is to gain speed without sacrificing sample quality, diversity, or long-range dynamics, especially when critic errors or limited student capacity distort training.

Latest papers

27 papers8 papers in the last four weeks, up 60% on the four weeks before. 0.0% of all new papers.

Multi-Bandwidth Distribution Matching Distillation: On the Equivalence of Distribution Matching Distillation and Drifting Models
Oct 7, 2026Jialin Zhu, Xing Liu, Feixiang He +1Distribution Matching Distillation Diffusion Model Distillation
Baidu Inc., China · Central South University, China · AI Centre, University College London, UK
Consistent Distribution Matching for Data-Free Diffusion Distillation
Oct 6, 2026Yuxiang Fu, Qi Yan, Zike Wu +4Diffusion Model Distillation Diffusion Model Inference Acceleration
University of British Columbia · Vector Institute for AI · Canada CIFAR AI Chair
DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation
Oct 2, 2026Jiahao Zhan, Yan Wang, Yongrui Ma +5Single-Step Video Generation Streaming Video Generation
MMLab, CUHK · ByteDance Inc. · HKUST +1
DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation
Oct 1, 2026Zhengming Yu, Junkun Yuan, Haotian Yang +8Diffusion Model Distillation Few-Step Diffusion Sampling
Texas A&M University · Intelligent Creation, ByteDance
Distribution Matching Distillation for Continuous Diffusion Language Models
Sep 30, 2026Paul Le Van Kiem, Dario Shariatian, Umut Simsekli +1Continuous Diffusion Language Models Diffusion Language Model Distillation
Inria, PSL Research University · Cohere · CMAP, Ecole Polytechnique
LongTake: Learning to Sustain Dynamics in Long-Horizon Video Generation
Sep 29, 2026Byoungwoo Park, Jaemoo Choi, Juho Lee +1Video Diffusion Models Long-Horizon Video Generation
KAIST · Georgia Tech
DMA $^2$ : Pixel-space Distribution Matching with Adversarial and Anchor Losses
Sep 29, 2026Xin Lin, Zhifei Zhang, Yuqian Zhou +6Diffusion Model Distillation Pixel-Space Diffusion Models
UC San Diego · Adobe Research · University of Virginia +1
PDMD: Projected Distribution Matching Distillation for Video Diffusion Models
Sep 28, 2026Zimo Wang, Junkun Yuan, Angtian Wang +9Video Diffusion Models Diffusion Model Distillation
University of California, San Diego · ByteDance Inc.
DyMD: Preserving Interaction Dynamics through Distribution Matching Distillation in Few-Step Video World Models
Sep 25, 2026Haojun Xu, Jie Huang, Xin Lu +4Diffusion Model Distillation Video World Models
Beihang University · JD Future Academy
Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation
Sep 24, 2026Zhenyu Zhou, Can Wang, Chun Chen +2Diffusion Model Distillation Few-Step Diffusion Sampling
Zhejiang University · University of California, Berkeley
Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation
Sep 10, 2026Zixuan Duan, Xunzhi Xiang, Yabo Chen +6Diffusion Model Distillation Few-Step Diffusion Sampling
Nanjing University, Institute of Artificial Intelligence, China Telecom (TeleAI), China · Institute of Artificial Intelligence, China Telecom (TeleAI), China · Fudan University, Institute of Artificial Intelligence, China Telecom (TeleAI), China
Mask Forcing: Improving Autoregressive Video Diffusion Distillation via Dual-Noise Masking Rollout
Sep 8, 2026Zhuoran Zhao, Shengju Qian, Tongtong Liang +7Video Diffusion Models Diffusion Model Distillation
1HKUST(GZ) · 2HKUST · 3LIGHTSPEED +3
Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching
Sep 3, 2026Jiuzhou Lin, Junlong Wu, Fei Zuo +11Video Generation Diffusion Model Alignment
Tsinghua University · Kuaishou Technology · Harbin Institute of Technology (ShenZhen) +1
DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation
Jul 29, 2026Jiaxing Li, Kai Zou, Cindy Zhou +7Generative Modeling Autoregressive Video Generation
Riemann Dynamics · Nanyang Technological University · 1Riemann Dynamics, 2Nanyang Technological University +2
CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation
Jun 20, 2026Wenhu Zhang, Kun Cheng, Changyuan Wang +7Video Diffusion Models Diffusion Model Distillation
1HKUST · 2Wan Team, Alibaba Group · 3THU +2
BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression
Jun 8, 2026Shaohao Rui, Xiaofeng Mao, Zhanyu Zhang +5Action-Conditioned Video Generation Camera-Controlled Video Generation
LynnReal AI · Shanghai Innovation Institute · Shanghai Jiao Tong University +1
Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Jun 2, 2026Tianhe Wu, Zikai Zhou, Kun Yan +23Diffusion Model Distillation Few-Step Diffusion Sampling
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Jun 1, 2026Shucheng Li, Iolo Jones, Alexander Tong +1Diffusion Model Distillation Few-Step Diffusion Sampling
Department of Computer Science, University of Oxford, Oxford, UK · AITHYRA, Research Institute for Biomedical AI, Vienna, AT
Reinforcing Few-step Generators via Reward-Tilted Distribution Matching
May 25, 2026Yushi Huang, Xiangxin Zhou, Ruoyu Wang +3Diffusion Model Alignment Diffusion Model Distillation
Tencent Hunyuan · Hong Kong University of Science and Technology · Westlake University
Distribution Matching Distillation without Fake Score Network
May 19, 2026Youngjoong Kim, Deokyeong Lee, Jaesik ParkDiffusion Model Distillation Few-Step Diffusion Sampling
Department of Computer Science and Engineering, Seoul National University · Department of Computer Science and Engineering, Sogang University Seoul, Republic of Korea
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
May 7, 2026Xinyuan Wei, Zian Li, Shaoheng Yan +2Diffusion Distillation Few-Step Diffusion Sampling
Institute for Artificial Intelligence, Peking University · Yuanpei College, Peking University · School of Intelligence Science and Technology, Peking University +2
Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
May 7, 2026Tao Liu, Hao Yan, Mengting Chen +8Diffusion Distillation Few-Step Diffusion Sampling
1VCIP, College of Computer Science, Nankai University · 2Alibaba Group · College of Artificial Intelligence, Jilin University
Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning
Apr 21, 2026Linwei Dong, Ruoyu Guo, Ge Bai +3Diffusion Model Distillation Few-Step Diffusion Sampling
1Zhejiang University · 2Bytedance Inc. · 3Zhejiang Lab
1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation
Apr 5, 2026Haoyu Li, Tingyan Wen, Lin Qi +6Diffusion Model Distillation Diffusion Sampling
Tsinghua University · Central Media Technology Institute, Huawei
Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation
Apr 3, 2026Xingtong Ge, Yi Zhang, Yushi Huang +6Diffusion Model Distillation Video Generation
Hong Kong University of Science and Technology · Vivix Group Limited
$R_\text{dm}$ : Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation
Mar 30, 2026Linqian Fan, Peiqin Sun, Tiancheng Wen +2Diffusion Model Distillation Distribution Matching Distillation
KlingAI Research · Tsinghua University
DUO-VSR: Dual-Stream Distillation for One-Step Video Super-Resolution
Mar 23, 2026Zhengyao Lv, Menghan Xia, Xintao Wang +1Video Super-Resolution Diffusion Model Distillation
The University of Hong Kong · Huazhong University of Science and Technology · Kling Team, Kuaishou Technology

Oct 7, 2026J/K move · Enter open · S save

Multi-Bandwidth Distribution Matching Distillation: On the Equivalence of Distribution Matching Distillation and Drifting Models

Jialin Zhu, Xing Liu, Feixiang He, He Wang

Baidu Inc., China · Central South University, China · AI Centre, University College London, UK

Distribution Matching Distillation Diffusion Model Distillation One-Step Generative Modeling Drifting Models

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Researchers are exploring effective one-step generative model continuously, and, Drifting Models (Deng et al., 2026), demonstrate great potential in one-step generation recently. There are works that reveal the connection between Diffusion & Flow Style Generative Models (DFSGMs) (Ho et al., 2020; Song et al., 2020a;b; Lipman et al., 2022; Liu et al., 2022) and Drifting Models (Li & Zhu, 2026; Lai et al., 2026; Turan et al., 2026). But no one has yet established a precise correspondence between the Drifting Model and the widely used distillation method- Distribution Matching Distillation (DMD/DMD2) (Yin et al., 2024b;a) to the best of our knowledge, even though their optimization objective formulas are virtually identical. In this paper, we prove that by converting the velocity-field / noise-field from the pre-trained DFSGMs into the attraction force field in Drifting Models and estimating the repulsion force field from the generative distribution, training the Drifting Model is naturally equivalent to the Distribution Matching Distillation. With this equivalent concept, we propose an improved method based on DMD from the Drifting Model's perspective- Multi-Bandwidth Distribution Matching Distillation (MBDMD).