Diffusion Models

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Diffusion models learn a data distribution by gradually corrupting examples with noise and training a denoiser to reverse that process. Major approaches include DDPMs and score-based SDEs, latent diffusion and diffusion Transformers, flow and consistency methods, and discrete masked or continuous-time Markov diffusion; conditioning and guidance adapt them to specific tasks. They are used for images, video, audio, language, molecules, and scientific forecasting, with evaluations ranging from ImageNet FID to language perplexity and domain-specific measures of accuracy and uncertainty. A central tension is how to make sampling faster and generation more controllable without sacrificing quality, calibration, or diversity, while theory and ongoing audits address generalization, memorization, and privacy.

Latest papers

1,192 papers152 papers in the last four weeks, up 77% on the four weeks before. 0.9% of all new papers.

Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness
May 21, 2026Hangyeol Lee, Hyojeong Lee, Joo-Young KimDiffusion Transformer Diffusion-Based Image Generation
KAIST, Daejeon, Republic of Korea
RiT: Vanilla Diffusion Transformers Suffice in Representation Space
May 21, 2026Le Zhang, Ning Mang, Aishwarya AgrawalFlow Matching Visual Representation Learning
Mila – Québec AI Institute, UdeM · Utrecht University · Canada CIFAR AI Chair
Noise Schedule Design for Diffusion Models: An Optimal Control Perspective
May 21, 2026Seo Taek Kong, Weina Wang, R. SrikantDiffusion Sampling Diffusion-Based Image Generation
ECE & CSL, University of Illinois Urbana-Champaign · Computer Science Department, Carnegie Mellon University · ECE, CSL & NCSA, University of Illinois Urbana-Champaign
Hierarchical Variational Policies for Reward-Guided Diffusion
May 20, 2026Kushagra Pandey, Farrin Marouf Sofian, Jan Niklas Groeneveld +2Few-Step Diffusion Sampling Diffusion Model Guidance
Department of Computer Science University of California Irvine
One-Step Distillation of Discrete Diffusion Image Generators via Fixed-Point Iteration
May 20, 2026Chaoyang Wang, Yunhai TongDiffusion Model Distillation Few-Step Diffusion Sampling
Peking University
Memorisation, convergence and generalisation in generative models
May 20, 2026Antoine Maillard, Sebastian GoldtNeural Network Memorization Memorization in Generative Models
1INRIA Paris & DI ENS, PSL University, Paris, France · 2International School of Advanced Studies (SISSA), Trieste, Italy
Semantic Granularity Navigation in Image Editing
May 20, 2026Liangsi Lu, Minzhe Guo, Xuhang Chen +1Diffusion Model Sampling Diffusion Timestep Scheduling
Guangdong University of Technology, Guangzhou, China · Huizhou University, Huizhou, China.
DrawMotion: Generating 3D Human Motions by Freehand Drawing
May 20, 2026Tao Wang, Lei Jin, Zhihua Wu +7Text-to-Motion Generation Human Motion Generation
Beijing University of Posts and Telecommunications · University of Science and Technology of China · 3NLP Lab, School of Computer Science and Engineering, Northeastern University, Shenyang, China +4
AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
May 20, 2026Somnath Luitel, Manmeet Singh, Joshua Durkee +11Geospatial Foundation Models Spatiotemporal Forecasting
Department of Earth, Environmental, and Atmospheric Sciences, Western Kentucky University, Bowling Green, KY, USA · NASA Goddard Space Flight Center, Greenbelt, MD, USA · The University of Texas at Austin, Austin, TX, USA +3
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
May 20, 2026Haozhe Jia, Pengyu Yin, Wenshuo Chen +6Physics-Informed Diffusion Models Physics-Informed Generative Modeling
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Shandong University, Jinan, China · Data61/CSIRO, Australia +5
Tippett-minimum Fusion of Representation-space Diffusion Models for Multi-Encoder Out-of-Distribution Detection
May 19, 2026Neelkamal BhuyanDiffusion-Based OOD Detection Distribution Shift Detection
Georgia Institute of Technology
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
May 19, 2026Zachary James, Joseph Guinness, Arthur DeGaetanoProbabilistic Forecasting Diffusion Models
Cornell University, Department of Statistics and Data Science · Washington University in St. Louis, Department of Statistics and Data Science · Cornell University, Department of Earth and Atmospheric Sciences
Landscape-Awareness for Geometric View Diffusion Model
May 19, 2026Yan-Ting Chen, Hao-Wei Chen, Tsu-Ching Hsiao +1Diffusion Model Guidance Camera Pose Estimation
Elsa Lab, National Taiwan University
When Preference Labels Fall Short: Aligning Diffusion Models from Real Data
May 19, 2026Weiyan Chen, Weijian Deng, Yao Xiao +5Diffusion Model Alignment Preference Alignment
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Australian National University, Canberra, Australia +2
Latent Laplace Diffusion for Irregular Multivariate Time Series
May 19, 2026Zinuo You, Jin Zheng, John CartlidgeIrregular Time-Series Modeling Multivariate Time Series Forecasting
School of Computer Science, University of Bristol · School of Engineering Mathematics and Technology, University of Bristol
Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
May 19, 2026David Huk, Dongshan Wang, Miha BresarMultivariate Time Series Forecasting Copula Models
Department of Statistics The University of Warwick · School of Data Science The Chinese University of Hong Kong, Shenzhen
EpiDiffVO: Geometry-Aware Epipolar Diffusion for Robust Visual Odometry
May 19, 2026Prateeth RaoMulti-View Geometry Relative Pose Estimation
International Institute of Information Technology Bangalore, Bengaluru, India
Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection
May 19, 2026Yunzhe Zhang, Hongfu Liu, Pengyu HongDiffusion Model Alignment T2I Generation
Brandeis University
Drifting Objectives for Refining Discrete Diffusion Language Models
May 19, 2026Daisuke Oba, Hiroki Furuta, Naoaki OkazakiDiffusion Model Fine-Tuning Diffusion Language Models
Institute of Science Tokyo · AIST · NII LLMC
Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
May 19, 2026Wenpin Tang, Nizar Touzi, Zikun Zhang +1Stochastic Differential Equations Time Series Generation
Multi-Scale Generative Modeling with Heat Dissipation Flow Matching
May 19, 2026Jun Ma, Hanquan Zhang, Yanjun Qin +2Flow Matching Image Generation
Department of Systems Science, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China · School of Computer Science and Technology, Xinjiang University, Urumqi 830049, China · International Academic Center of Complex Systems, Beijing Normal University, Zhuhai 519087, China +1
Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
May 19, 2026Taegu Kang, Jaesik Yoon, Sungjin AhnInference-Time Scaling Diffusion Models
KAIST · KAIST & SAP · KAIST & NYU
PolycubeNet: A Dual-latent Diffusion Model for Polycube-Based Hexahedral Mesh Generation
May 19, 2026Lu He, Qitao Deng, Junjiang Deng +53D Mesh Generation Diffusion Models
Dalian University of Technology · Jiangxi University of Science and Technology · School of Mathematical Sciences, Guangxi Minzu University +2
What Makes Synthetic Data Effective in Image Segmentation
May 19, 2026Jinjin Zhang, Xiefan Guo, Yizhou Jin +2Synthetic Data Augmentation Image Segmentation
State Key Laboratory of Complex and Critical Software Environment, Beihang University, Beijing 100191, China · School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
May 18, 2026Benjamin Sterling, Mónica F. Bugallo, Tom TirerLangevin Dynamics Memorization in Generative Models
Department of Applied Math & Statistics Stony Brook University Stony Brook, NY 11790 · Department of Electrical and Computer Engineering Stony Brook University Stony Brook, NY 11790 · Faculty of Engineering Bar-Ilan University Ramat-Gan 5290002, Israel
Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
May 18, 2026Suya Li, Kaushik Dutta, Debojyoti Pal +2Diffusion Model Adaptation PET/CT Imaging
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA · Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St. Louis, USA · Department of Surgery, Washington University School of Medicine, St. Louis, USA +1
SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
May 18, 2026Lifu Wei, Yinuo Ren, Naichen Shi +1Particle Filtering Bayesian Filtering
Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States · Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States · Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL, United States
Spectral Progressive Diffusion for Efficient Image and Video Generation
May 18, 2026Howard Xiao, Brian Chao, Lior Yariv +1Video Diffusion Models Diffusion Sampling
Stanford University
SafeDiffusion-R1: Online Reward Steering for Safe Diffusion Post-Training
May 18, 2026Komal Kumar, Ankan Deria, Abhishek Basu +3Diffusion Model Alignment Diffusion Model Fine-Tuning
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE · Michigan State University (MSU), USA
Continuous Diffusion Scales Competitively with Discrete Diffusion for Language
May 18, 2026Zhihan Yang, Wei Guo, Shuibai Zhang +5Language Model Scaling Laws Diffusion Language Models
NVIDIA & Cornell · NVIDIA & Georgia Tech · UW-Madison +3

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Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness

Hangyeol Lee, Hyojeong Lee, Joo-Young Kim

KAIST, Daejeon, Republic of Korea

Diffusion Transformer Diffusion-Based Image Generation Visual Token Pruning Diffusion Model Inference Acceleration Diffusion Models

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Diffusion Transformers (DiTs) achieve superior image generation quality but suffer from quadratic computational complexity relative to token count. While various token reduction (TR) methods have been proposed to mitigate this cost, they overlook the primary objective of generative models: minimizing recovery error, which requires reflecting output token similarity. They rely solely on input token similarity inherited from reduction-only ViT paradigms, leading to a fundamental misalignment with this objective. To bridge this gap, we propose DiTo, a novel TR paradigm that shifts the focus toward output-centric token reduction. Based on the observation that output token similarity is consistently preserved across adjacent timesteps, DiTo utilizes prior-step similarities as an effective proxy to establish token correspondences at a Matching timestep, which are then reused across multiple subsequent Reduction timesteps. To optimize this interleaved scheduling, we propose Pair Match Ratio (PMR)-guided Interval Scheduling to determine the optimal matching frequency. Furthermore, to mitigate localized approximation errors and resulting blocking artifacts caused by repeated reuse, we propose Frequency-aware Token Matching by incorporating a selection-frequency penalty. Extensive experiments demonstrate that DiTo consistently outperforms existing TR methods with 1.6-3.9 dB higher PSNR at comparable speedups, achieving a superior Pareto frontier.