Diffusion Sampling

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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.

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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.

MGD: Moment Guided Diffusion for Maximum Entropy Generation
Feb 19, 2026Etienne Lempereur, Nathanaël Cuvelle--Magar, Florentin Coeurdoux +2Generative Modeling Constrained Generative Modeling
D´epartement d’informatique, ENS, Universit´e PSL, Paris, France · Capital Fund Management, Paris, France · Coll`ege de France, Paris, France +3
GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation
Feb 19, 2026Ye Zhu, Kaleb S. Newman, Johannes F. Lutzeyer +3T2I Generation T2I Diffusion Models
Laboratoire d’Informatique (LIX), CNRS, École Polytechnique, IPP, France · Department of Computer Science, Princeton University, USA · FAIR at Meta - Montreal, Canada +3
Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models
Feb 18, 2026Yu Xie, Ludwig Winkler, Lixin Sun +10Free Energy Calculation Diffusion Sampling
Microsoft Research AI for Science
Steering diffusion models with quadratic rewards: a fine-grained analysis
Feb 18, 2026Ankur Moitra, Andrej Risteski, Dhruv RohatgiDiffusion Sampling Diffusion Models
Fast and Scalable Analytical Diffusion
Feb 18, 2026Xinyi Shang, Peng Sun, Jingyu Lin +1Diffusion Models Diffusion Model Inference Acceleration
University College London · Mohamed bin Zayed University of Artificial Intelligence · Zhejiang University +2
Training-Free Adaptation of Diffusion Models via Doob's $h$ -Transform
Feb 18, 2026Qijie Zhu, Zeqi Ye, Han Liu +2Diffusion Model Adaptation Diffusion Models
Department of Statistics and Data Science, Northwestern University. · Department of Industrial Engineering and Management Sciences, Northwestern University. · Department of Computer Science, Northwestern University.
Discrete Stochastic Localization for Non-autoregressive Generation
Feb 18, 2026Yunshu Wu, Jiayi Cheng, Longxuan Yu +4Non-Autoregressive Text Generation Diffusion Language Models
University of California Riverside · New York University
Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance
Feb 17, 2026Haiqi Lu, Ying TangDiffusion Models Diffusion Sampling
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China · Yingcai Honor College, University of Electronic Science and Technology of China, Chengdu 611731, China · School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China +2
Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
Feb 16, 2026Daniil Dmitriev, Zhihan Huang, Yuting WeiSample Complexity Diffusion Model Sampling
Department of Statistics and Data Science, the Wharton School, University of Pennsylvania
Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
Feb 13, 2026Sangwoo Jo, Sungjoon ChoiDiffusion Model Sampling Diffusion Sampling
Department of Artificial Intelligence, Korea University, Seoul, South Korea.
Prediction--Loss Alignment for Sampler--Robust Flow Matching Training
Feb 11, 2026Jiadong Hong, Lei Liu, Xinyu Bian +2Flow Matching Diffusion Sampling
Zhejiang University, Hangzhou, China · Huawei Technologies Co., Ltd, Hong Kong
ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
Jan 26, 2026Yilie Huang, Wenpin Tang, Xunyu ZhouDiffusion Sampling Adaptive Sampling
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. · Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University, New York, NY 10027, USA.
A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models
Jan 20, 2026Qiang Sun, H. Vincent Poor, Wenyi ZhangDiffusion Sampling Kullback-Leibler Divergence
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China · Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
Systematic Hazard Sampling: Minimal-Variance Inference for Discrete Diffusion and Flow Models
Jan 6, 2026Seunghwan Jang, Wonje Jeung, SooJean HanMarkov Models Diffusion Sampling
Department of EE KAIST Daejeon, South Korea
Fast Score-Based Sampling via Log-Concave Reductions
Dec 30, 2025M. J. WainwrightSample Complexity Log-Concave Sampling
Laboratory for Information and Decision Systems · Statistics and Data Science Center · EECS & Mathematics +1
VEDA: Generation of 3D Molecules via Variance-Exploding Diffusion with Annealing
Nov 11, 2025Peining Zhang, Jinbo Bi, Minghu Song3D Molecular Generation Diffusion Models
University of Connecticut, Storrs, Connecticut 06269, USA · Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei 230601, China
Demystifying MaskGIT Sampler and Beyond: Adaptive Order Selection in Masked Diffusion
Oct 6, 2025Satoshi Hayakawa, Yuhta Takida, Masaaki Imaizumi +2Diffusion Model Sampling Diffusion Sampling
Sony Group Corporation · Sony AI · The University of Tokyo
IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed
Sep 15, 2025Yongzhe Lyu, Yu Wu, Yutian Lin +1Image Inpainting Diffusion Models
School of Computer Science, Wuhan University, Wuhan 430072, China
MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
Jul 29, 2025Junzhe Li, Yutao Cui, Tao Huang +8Diffusion Model Alignment Flow Matching
Hunyuan, Tencent, China · School of Computer Science, Peking University, China · Computer Center, Peking University, China +2
Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations
Jun 25, 2025Shunqi Mao, Wei Guo, Chaoyi Zhang +3Diffusion Sampling Conditional Diffusion Models
School of Computer Science, The University of Sydney, Australia
Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
Jun 16, 2025Daniel Zhengyu Huang, Jiaoyang Huang, Zhengjiang LinDiffusion Model Sampling Diffusion Sampling
Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking University, Beijing, China · Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA · Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Ave, 02139 Cambridge MA, USA
The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis
Jun 13, 2025Bernardo P. Schaeffer, Ricardo M. S. Rosa, Glauco ValleStochastic Differential Equations Diffusion Sampling
Instituto de Matem´atica, Universidade Federal do Rio de Janeiro
Low-dimensional adaptation of diffusion models: Convergence in total variation
Jan 22, 2025Jiadong Liang, Zhihan Huang, Yuxin ChenDiffusion Sampling Diffusion Models
Department of Statistics and Data Science, the Wharton School, University of Pennsylvania
ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts
Nov 26, 2024Jinho Chang, Changsun Lee, Hyungjin Chung +1Diffusion Model Guidance Classifier-Free Guidance
Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, Seoul, South Korea · EverEx, Seoul, South Korea

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MGD: Moment Guided Diffusion for Maximum Entropy Generation

Etienne Lempereur, Nathanaël Cuvelle--Magar, Florentin Coeurdoux, Stéphane Mallat+1

D´epartement d’informatique, ENS, Universit´e PSL, Paris, France · Capital Fund Management, Paris, France · Coll`ege de France, Paris, France+3

Generative Modeling Constrained Generative Modeling Diffusion Sampling

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Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from moment constraints but require sampling via MCMC or Langevin dynamics, which typically exhibit exponential slowdown in high dimensions. In contrast, generative models based on diffusion and flow matching efficiently transport noise to data but offer limited theoretical guarantees and can overfit when data is scarce. We introduce Moment Guided Diffusion (MGD), which combines elements of both approaches. Building on the stochastic interpolant framework, MGD samples maximum entropy distributions by solving a stochastic differential equation that guides moments toward prescribed values in finite time, thereby avoiding slow mixing in equilibrium-based methods. We formally obtain, in the large-volatility limit, convergence of MGD to the maximum entropy distribution and derive a tractable estimator of the resulting entropy computed directly from the dynamics. Applications to financial time series, turbulent flows, and cosmological fields using wavelet scattering moments yield estimates of negentropy for high-dimensional multiscale processes.