Diffusion Sampling

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Overview

Diffusion sampling starts with noise and repeatedly denoises it to generate data, using reverse stochastic processes such as DDPM or deterministic flows such as DDIM and probability-flow ODEs. Work improves this process with higher-order or adaptive solvers, learned timestep schedules, and methods that reuse or predict model computations; discrete and masked diffusion models also require choices about which tokens to reveal and when. Guidance, particle methods, and search can steer samples toward rewards, constraints, diversity, or solutions to inverse problems, with applications spanning image and text generation, molecules, and scientific modeling. A central tension is reducing steps and computation without losing fidelity, diversity, or correctness, while theory seeks sharper guarantees for error, stability, and sampling cost.

Latest papers

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264 papers49 papers in the last four weeks, up 104% on the four weeks before. 0.3% of all new papers.

  1. Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation

    Mar 23, 2026Armand Rousselot, Joran Wendebourg, Ullrich KötheFeature VisualizationInvariant Representation Learning

    Computer Vision and Learning Lab, Heidelberg University.

  2. Efficient Coarse-to-Fine Diffusion Models with Time Step Sequence Redistribution

    Mar 22, 2026Yu-Shan Tai, An-Yeu, WuDiffusion Model SamplingDiffusion Sampling

    Andy · Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan

  3. Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models

    Mar 22, 2026Qifan Li, Xingyu Zhou, Jinhua Zhang +2Latent-Space RegularizationLatent Diffusion Models

    University of Electronic Science and Technology of China

  4. LPNSR: Learnable Noise Prediction for Diffusion-Based Image Super-Resolution

    Mar 22, 2026Shuwei Huang, Shizhuo Liu, Zijun WeiImage Super-ResolutionDiffusion Models

    Huazhong University of Science and Technology

  5. Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems

    Mar 21, 2026Alankrita Bhatt, Mukur Gupta, Germain Kolossov +1Diffusion SamplingDiffusion Models

    Granica Computing Inc. – granica.ai · Stanford University

  6. Wildfire Spread Scenarios: Increasing Sample Diversity of Segmentation Diffusion Models with Training-Free Methods

    Mar 20, 2026Sebastian Gerard, Josephine SullivanDiffusion SamplingDiffusion Model Guidance

    KTH Royal Institute of Technology, Stockholm, Sweden

  7. Load--Reserve Wasserstein Propagation for Isotropic Diffusion Samplers

    Mar 20, 2026Zicheng Lyu, Zengfeng HuangDiffusion SamplingWasserstein Distance

    School of Data Science, Fudan University · Shanghai Innovation Institute

  8. Alternating Diffusion for Proximal Sampling with Zeroth Order Queries

    Mar 20, 2026Hirohane Takagi, Atsushi NitandaDiffusion Sampling

    Graduate School of Information Science and Technology, The University of Tokyo, Japan · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore +1

  9. Attention-Based Sampler for Diffusion Language Models

    Mar 18, 2026Yuyan Zhou, Kai Syun Hou, Weiyu Chen +1Diffusion SamplingDiffusion Language Model Decoding

    Department of Computer Science and Engineering, The Hong Kong University of Science and Technology

  10. Adaptive Moments are Surprisingly Effective for Plug-and-Play Diffusion Sampling

    Mar 17, 2026Christian Belardi, Justin Lovelace, Kilian Q. Weinberger +1Diffusion SamplingDiffusion Model Guidance

    Cornell University

  11. Chain-of-Trajectories: Unlocking the Intrinsic Generative Optimality of Diffusion Models via Graph-Theoretic Planning

    Mar 16, 2026Ping Chen, Xiang Liu, Xingpeng Zhang +7Diffusion SamplingDiffusion-Based Planning

    Data Science & Artificial Intelligence Research Institute, China Unicom · Unicom Data Intelligence, China Unicom · School of Computer Science and Software Engineering, Southwest Petroleum University +2

  12. Fractals made Practical: Denoising Diffusion as Partitioned Iterated Function Systems

    Mar 13, 2026Ann DoomsDiffusion-Based DenoisingDiffusion Models

    Department of Mathematics & Data Science Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels, BELGIUM

  13. Adaptive Weighted h-Transform Sampling for Coarse-Guided Visual Generation

    Mar 12, 2026Yanghao Wang, Ziqi Jiang, Zhen Wang +1Diffusion Model GuidanceDiffusion Guidance

    The Hong Kong University of Science and Technology

  14. DyWeight: Dynamic Gradient Weighting for Few-Step Diffusion Sampling

    Mar 12, 2026Tong Zhao, Mingkun Lei, Liangyu Yuan +5Diffusion SamplingFew-Step Diffusion Sampling

    Zhejiang University · AGI Lab, Westlake University · TongJi University +1

  15. Free Lunch for Pass@kk? Low Cost Diverse Sampling for Diffusion Language Models

    Mar 5, 2026Sean Lamont, Christian Walder, Paul Montague +2Diversity-Aware SamplingOutput Diversity in Language Models

    Australian National University · Defence Science and Technology Group · Google DeepMind +1

  16. RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions

    Mar 5, 2026Soochul Park, Yeon Ju Lee, SeongJin Yoon +3Diffusion SamplingDiffusion Model Inference Acceleration

    SteAI · MODULABS · Korea University +3

  17. TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration

    Mar 4, 2026Haowei Zhu, Tingxuan Huang, Xing Wang +7Diffusion Model AccelerationDiffusion Model Inference Acceleration

    Tsinghua University · ByteDance

  18. Error as Signal: Stiffness-Aware Diffusion Sampling via Embedded Runge-Kutta Guidance

    Mar 4, 2026Inho Kong, Sojin Lee, Youngjoon Hong +1Diffusion Model GuidanceDiffusion Sampling

    Korea University · KAIST · Seoul National University +1

  19. Toward Early Quality Assessment of Text-to-Image Diffusion Models

    Mar 3, 2026Huanlei Guo, Hongxin Wei, Bingyi JingT2I Generation EvaluationT2I Diffusion Models

    Southern University of Science and Technology · The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute

  20. ReCo-Diff: Residual-Conditioned Deterministic Sampling for Cold Diffusion in Sparse-View CT

    Mar 3, 2026Yong Eun Choi, Hyoung Suk Park, Kiwan Jeon +2Sparse-View CT ReconstructionDiffusion-Based Inverse Problems

    National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea · Department of Computer Engineering, Korea National University of Transportation, Chungju, 27469, Republic of Korea

  21. DUEL: Exact Likelihood for Masked Diffusion via Deterministic Unmasking

    Mar 2, 2026Gilad Turok, Chris De Sa, Volodymyr KuleshovMasked Diffusion ModelsDiffusion Language Models

    Department of Computer Science, Cornell University, New York, New York, USA.

  22. Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching

    Feb 28, 2026Denis Blessing, Lorenz Richter, Julius Berner +2Diffusion Sampling

    Karlsruhe Institute of Technology · Zuse Institute Berlin · dida +1

  23. Sharp Convergence Rates for Masked Diffusion Models

    Feb 26, 2026Yuchen Liang, Zhiheng Tan, Ness Shroff +1Masked Diffusion ModelsDiffusion Sampling

    The Ohio State University

  24. The Diffusion Duality, Chapter II: ΨΨ-Samplers

    Feb 24, 2026Justin Deschenaux, Caglar Gulcehre, Subham Sekhar SahooDiffusion ModelsDiffusion Sampling

    EPFL, Lausanne, Switzerland · Microsoft AI · Cornell Tech, NY

  25. Adaptation to Intrinsic Dependence in Diffusion Language Models

    Feb 23, 2026Yunxiao Zhao, Changxiao CaiDiffusion Language Model InferenceDiffusion Language Models

    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, USA.

  26. Is Your Diffusion Sampler Actually Correct? A Sampler-Centric Evaluation of Discrete Diffusion Language Models

    Feb 23, 2026Luhan Tang, Longxuan Yu, Shaorong Zhang +1Diffusion Language Model BenchmarkingDiffusion Sampling

    University of California, Riverside

  27. Ani3DHuman: Photorealistic 3D Human Animation with Self-guided Stochastic Sampling

    Feb 22, 2026Qi Sun, Can Wang, Jiaxiang Shang +2Human Avatar AnimationVideo Diffusion Models

    City University of Hong Kong

  28. The Geometry of Noise: Why Diffusion Models Don't Need Noise Conditioning

    Feb 20, 2026Mojtaba Sahraee-Ardakan, Mauricio Delbracio, Peyman MilanfarDiffusion ModelsDiffusion Sampling

    Google

  29. Improving Sampling for Masked Diffusion Models via Information Gain

    Feb 20, 2026Kaisen Yang, Jayden Teoh, Kaicheng Yang +2Diffusion SamplingDiffusion Language Model Decoding

    Department of Computer Science and Technology, Tsinghua University · Massachusetts Institute of Technology · Shanghai Jiao Tong University +2

  30. Predict to Skip: Linear Multistep Feature Forecasting for Efficient Diffusion Transformers

    Feb 20, 2026Hanshuai Cui, Zhiqing Tang, Qianli Ma +3Diffusion Model AccelerationDiffusion Sampling

    School of Artificial Intelligence, Beijing Normal University, Beijing, China · Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai, China · Shenzhen University of Advanced Technology, Shenzhen, China