Flow-Based Generative Modeling

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66 papers in the last four weeks, up 288% on the four weeks before. 0.6% of all new papers.

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Latest papers 331

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Strong Stochastic Flow Maps
May 31, 2026Sam McCallum, Zander W. Blasingame, Timothy Herschell +3Stochastic Differential Equations Few-Step Diffusion Sampling
University of Bath · 2AITHYRA
Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
May 29, 2026Jonas Henry Grebe, Tobias Braun, Anna Rohrbach +1Rectified Flow Flow-Based Generative Modeling
Technical University of Darmstadt & hessian.AI, Germany.
Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design
May 28, 2026Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger +2Constrained Generative Modeling Molecular Optimization
Department of Computer Science, ETH Zurich · ETH AI Center · NCCR Catalysis, Switzerland +1
Midpoint Generative Models
May 28, 2026Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2Flow Matching Stochastic Interpolants
1AXXX, Russia · 2Applied AI Institute, Russia · 3Kandinsky Lab, Russia
Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
May 27, 2026Julien Lalanne, David Picard, Lionel Boillot +3Flow Matching Uncertainty Quantification
Navier, CNRS, Univ Gustave Eiffel, ENPC, IP Paris, France · TotalEnergies, OneTech, France · LIGM, CNRS, Univ Gustave Eiffel, ENPC, IP Paris, France
Parameter-Efficient Generative Modeling with Controlled Vector Fields
May 27, 2026Peyman MortezaGenerative Modeling Continuous Normalizing Flows
GenSBI: Generative Methods for Simulation-Based Inference in JAX
May 26, 2026Aurelio AmerioFlow Matching Simulation-Based Inference
Instituto de Física Corpuscular (IFIC) Universitat de València & CSIC València, Spain
Recursive Flow Matching
May 26, 2026Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1Neural Surrogate Modeling Flow Matching
University of California, San Diego · University of Michigan
Two-Parameter Flows for Learning Population Dynamics of Physical Systems
May 25, 2026Paul Schwerdtner, Tobias Blickhan, Benjamin PeherstorferDynamical Systems Continuous Normalizing Flows
Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.
AdvantageFlow: Regularized Advantage-Weighted RL in Flow Models
May 25, 2026Branislav Kveton, Anup Rao, Subhojyoti Mukherjee +2Rectified Flow RL for Image Generation
Adobe Research
Flow-Based Generative Modeling for Optimizing Sampling Policies in Compressed Sensing Applications
May 22, 2026Roman Pavelkin, Luis A. Zavala-Mondragon, Christiaan G. A. Viviers +1Flow Matching Image Reconstruction
Eindhoven University of Technology, Eindhoven
Sparse Compositional Flow Matching by geometric assembly from motion primitives
May 22, 2026Yan Tang, Yuanbo Tang, Tingyu Cao +2Flow Matching Robot Motion Generation
Tsinghua Shenzhen Graduate School, Tsinghua University, Shenzhen, China · School of AI, Chinese University of Hong Kong (Shenzhen)
PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
May 22, 2026Junru Zhang, Lang Feng, Jinbo Wang +6Flow Matching Mixture of Experts
Zhejiang University, China · Nanyang Technological University, Singapore · I2R, Agency for Science, Technology and Research (A*STAR), Singapore
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
May 20, 2026Xuehui Yu, Fucheng Cai, Meiyi Wang +2Constrained Generative Modeling Diffusion Model Guidance
Smart Systems Institute, National University of Singapore, Singapore · Faculty of Computing, Harbin Institute of Technology, Harbin, China · School of Computing, National University of Singapore, Singapore
FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation
May 19, 2026Eric Tillmann Bill, Enis Simsar, Alessio Tonioni +1Flow Matching Image Captioning
ETH Zurich · Google
Probability-Conserving Flow Guidance
May 19, 2026Parsa Esmati, Junha Hyung, Amirhossein Dadashzadeh +2Diffusion Model Guidance Classifier-Free Guidance
University of Bristol · KAIST
Tail Annealing for Heavy-Tailed Flow Matching
May 19, 2026Jean PachebatFlow Matching Generative Modeling
1CMAP, Ecole Polytechnique, Institut Polytechnique de Paris, France.
FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models
May 19, 2026Yi Sun, Zhiqi Zhang, Xinhao Zhong +5Flow Matching T2I Generation
Harbin Institute of Technology, Shenzhen · Jilin University · Tsinghua Shenzhen International Graduate School, Tsinghua University +2
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
Matérn Noise for Triangulation-Agnostic Flow Matching on Meshes
May 19, 2026Tianshu Kuai, Arman Maesumi, Daniel Ritchie +1Flow Matching 3D Mesh Generation
Université de Montréal & Mila, Canada · Brown University, USA
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
Flowing with Confidence
May 18, 2026Friso de Kruiff, Dario Coscia, Max Welling +1Flow Matching Uncertainty Quantification
CuspAI · AMLab, University of Amsterdam · mathLab, SISSA
SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation
May 18, 2026Longtao Jiang, Jianmin Bao, Zhendong Wang +4Normalizing Flows Embedding Compression
University of Science and Technology of China · Kling Team, Kuaishou Technology
Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling
May 17, 2026Tianyue Yang, Sandro Tacchella, Xiao XueConditional Image Generation One-Step Generative Modeling
The Center for Computational Science University College London · Cavendish Laboratory Kavli Institute for Cosmology University of Cambridge
Drift Flow Matching
May 17, 2026Chenrui Ma, Xi Xiao, Lin Zhao +3Flow Matching Inference-Time Scaling
University of California, Irvine · University of Virginia · University of Alabama at Birmingham +1
Learning Unbiased Permutations via Flow Matching
May 16, 2026Yimeng Min, Carla P. GomesFlow Matching Combinatorial Optimization
Department of Computer Science Cornell University Ithaca, NY, USA
CAB: Accelerating Flow and Diffusion Sampling via Rectification and Corrected Adams-Bashforth
May 16, 2026Anuska Roy, Pravin NairFew-Step Diffusion Sampling Generative Flow Networks
Department of Electrical Engineering Indian Institute of Technology Madras, Chennai, India
Wavelet Flow Matching for Multi-Scale Physics Emulation
May 15, 2026Gabriele Accarino, Juan Nathaniel, Carla Roesch +4Flow Matching PDE Surrogate Modeling
Department of Earth and Environmental Engineering Columbia University, NY, USA · University of Edinburgh Edinburgh, Scotland, UK · Courant Institute of Mathematical Sciences New York University, NY, USA +2
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
May 15, 2026Gurjeet Jagwani, Stephen Thorp, Sinan Deger +1Continuous Normalizing Flows Flow-Based Generative Modeling
Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge, Cambridge, UK · Research Computing Services, University of Cambridge, Cambridge, UK · Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK +1
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers
May 15, 2026Bruno Trentini, Dejan Stancevic, Michael M. Bronstein +2Flow Matching Schrödinger Bridges
NVIDIA Corporation, Santa Clara, CA, USA · University of Oxford, Dept. of Computer Science, Oxford, UK · Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, NL +1

May 31, 2026, cs.LGJ/K move · Enter open · S save

Strong Stochastic Flow Maps

Sam McCallum, Zander W. Blasingame, Timothy Herschell, Niklas Rindtorff+2

University of Bath · 2AITHYRA

Stochastic Differential Equations Few-Step Diffusion Sampling Flow Map Learning Diffusion-Based Image Generation Flow-Based Generative Modeling

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Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to numerical integration of an underlying differential equation. Flow maps alleviate this problem by learning the solution map of the differential equation directly, enabling few-step sampling. Yet, current methods are restricted to approximating the solution map of ODEs. These methods can be used to learn the transition kernel of an SDE, thereby obtaining a solution map that recovers the marginal distributions of the process (weak convergence) rather than the solution path (strong convergence). We propose Strong Stochastic Flow Maps (SSFMs) as a novel framework for learning the strong solution map of additive-noise SDEs, directly generalizing deterministic flow maps to the stochastic setting. Further, a polynomial approximation to Brownian motion is introduced and shown to converge pathwise. These results enable a simulation-free training objective for the solution map of diffusion models. We demonstrate that SSFMs outperform previous stochastic flow map methods on image generation and enable few-step sampling of molecular systems.

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Latest papers 331