Few-Step Diffusion Sampling

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Normalizing Trajectory Models
May 8, 2026Jiatao Gu, Tianrong Chen, Ying Shen +3Normalizing Flows Diffusion Model Distillation
Apple · UIUC
Stochastic Transition-Map Distillation for Fast Probabilistic Inference
May 8, 2026George Rapakoulias, Peter Garud, Lingjiong Zhu +1Diffusion Model Distillation MeanFlow
Department of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · Department of Computer Science, Georgia Institute of Technology, Atlanta, GA · Department of Mathematics, Florida State University, Tallahassee, FL
LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling
May 8, 2026Haewon Jeon, Si-Hyeon LeeDiffusion Model Distillation Few-Step Diffusion Sampling
School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon, South 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
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
May 6, 2026Dengyang Jiang, Xin Jin, Dongyang Liu +9Supervised Fine-Tuning Diffusion Model Distillation
1The Hong Kong University of Science and Technology · 2Z-Image Team, Alibaba Group · 4The Chinese University of Hong Kong +1
TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
May 4, 2026Sowmya Vajrala, Akshay Bankar, Manjunath Arveti +5Diffusion Distillation Few-Step Diffusion Sampling
Samsung Research Institute Bangalore, India
Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control
May 2, 2026Jinhao Zhang, Zhexuan Zhou, Huizhe Li +5Visuomotor Policy Learning Diffusion Policy
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University
Consistent Diffusion Language Models
Apr 30, 2026Hasan Amin, Yuan Gao, Yaser Souri +4Few-Step Diffusion Sampling Diffusion Model Acceleration
Department of Computer Science, Purdue University · Microsoft
AdvDMD: Adversarial Reward Meets DMD For High-Quality Few-Step Generation
Apr 29, 2026Xu Wang, Zexian Li, Litong Gong +2Diffusion Model Distillation Few-Step Diffusion Sampling
Shanghai Jiao Tong University · Alimama Tech
VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
Apr 24, 2026Nikoo Moradi, Gijs Luijten, Behrus Hinrichs-Puladi +4Image-to-Image Translation 3D Medical Imaging
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), University of Duisburg-Essen, Essen, Germany · Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria · Center for Virtual and Extended Reality in Medicine (ZvRM), University Hospital Essen (AöR), Essen, Germany +7
Efficient Diffusion Distillation via Embedding Loss
Apr 24, 2026Jincheng Ying, Yitao Chen, Li Wenlin +2Diffusion Model Distillation One-Step Generative Modeling
School of Big Data and Artificial Intelligence Guangdong University of Finance and Economics Guangzhou, China · School of Computer Science Shandong University Qingdao, China
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
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Apr 20, 2026Enshu Liu, Xuefei Ning, Yu Wang +1Few-Step Diffusion Sampling Diffusion Language Model Decoding
Department of EE, Tsinghua University · Microsoft Research
Optimally Bridging Semantics and Data: Generative Semantic Communication via Schrödinger Bridge
Apr 20, 2026Dahua Gao, Ruichao Liu, Minxi Yang +3Semantic Communication Few-Step Diffusion Sampling
School of Artificial Intelligence, Xidian University, Xi’an, Shaanxi 710071 China · Pazhou Lab, Huangpu, Guangdong 510555 China · Peng Cheng Laboratory, Shenzhen, Guangdong 518055 China +1
Reward-Aware Trajectory Shaping for Few-step Visual Generation
Apr 16, 2026Rui Li, Bingyu Li, Yuanzhi Liang +3Diffusion Model Alignment Diffusion Model Distillation
University of Science and Technology of China HeFei, China · TeleAI ShangHai, China
Cross-Resolution Distribution Matching for Diffusion Distillation
Mar 6, 2026Feiyang Chen, Hongpeng Pan, Haonan Xu +3Video Diffusion Models Diffusion Model Distillation
Huawei, Shanghai & Hangzhou, China · Nanjing University of Science and Technology, Nanjing, China
Efficient Generative Modeling beyond Memoryless Diffusion via Adjoint Schrödinger Bridge Matching
Feb 17, 2026Jeongwoo Shin, Jinhwan Sul, Joonseok Lee +2Diffusion Model Sampling Schrödinger Bridges
Seoul National University, Seoul, Korea · Georgia Institute of Technology, Atlanta, GA, USA · Sungkyunkwan University, Seoul, Korea
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Feb 12, 2026Tunyu Zhang, Xinxi Zhang, Ligong Han +10Diffusion Model Distillation Few-Step Diffusion Sampling
Rutgers University · Red Hat AI Innovation · MIT-IBM Watson AI Lab
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
Feb 10, 2026Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2Physics-Informed Generative Modeling PDE Surrogate Modeling
Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan National Center for Theoretical Sciences National Taiwan University Taipei, Taiwan
SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
Jan 13, 2026Dongting Hu, Aarush Gupta, Magzhan Gabidolla +12Diffusion Transformer Diffusion Model Distillation
Snap Inc. · University of Melbourne · MBZUAI
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
Dec 8, 2025Naifu Xue, Zhaoyang Jia, Jiahao Li +4Diffusion Model Guidance Video Diffusion Models
Communication University of China · University of Science and Technology of China · Microsoft Research Asia
Distribution Matching Distillation Meets Reinforcement Learning
Nov 17, 2025Dengyang Jiang, Dongyang Liu, Zanyi Wang +12Diffusion Model Alignment Diffusion Model Distillation
1HKUST · 2Alibaba Group · 3SIAT, CAS +3
SPID: Distilled Protein Backbone Generation
Oct 3, 2025Liyang Xie, Haoran Zhang, Zhendong Wang +2Diffusion Model Distillation Few-Step Diffusion Sampling
The University of Texas at Austin · The Memorial Sloan Kettering Cancer Center

May 8, 2026, cs.CVJ/K move · Enter open · S save

Normalizing Trajectory Models

Jiatao Gu, Tianrong Chen, Ying Shen, David Berthelot+2

Apple · UIUC

Normalizing Flows Diffusion Model Distillation Few-Step Diffusion Sampling Diffusion-Based Image Generation Flow-Based Generative Modeling Diffusion Models

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Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood framework in the process. We introduce Normalizing Trajectory Models (NTM), which models each reverse step as an expressive conditional normalizing flow with exact likelihood training. Architecturally, NTM combines shallow invertible blocks within each step with a deep parallel predictor across the trajectory, forming an end-to-end network trainable from scratch or initializable from pretrained flow-matching models. Its exact trajectory likelihood further enables self-distillation: a lightweight denoiser trained on the model's own score produces high-quality samples in four steps. On text-to-image benchmarks, NTM matches or outperforms strong image generation baselines in just four sampling steps while uniquely retaining exact likelihood over the generative trajectory.

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