One-Step Generative Modeling

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16 papers in the last four weeks, against 1 the four weeks before. 0.2% of all new papers.

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Generative wave propagator
Jul 5, 2026Shijun Cheng, Tariq AlkhalifahPDE Surrogate Modeling One-Step Generative Modeling
Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900, Saudi Arabia
High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching
Jul 4, 2026Yuran Chen, Xinye Cai, Zhonglin Gong +1Flow Matching Visuomotor Policy Learning
School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China · State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China · School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China
Representation Distribution Matching for One-Step Visual Generation
Jul 2, 2026Lan Feng, Wuyang Li, Eloi Zablocki +2Maximum Mean Discrepancy One-Step Generative Modeling
1EPFL, Switzerland · 2Valeo.ai, France · 3Sorbonne Universit´e, France
Self-conditioned Flow Map Language Models via Fixed-point Flows
Jul 1, 2026Jaehoon Yoo, Wonjung Kim, Floor Eijkelboom +4Normalizing Flows One-Step Generative Modeling
KAIST · University of Amsterdam · Carnegie Mellon University
One-Step Flow Matching for Generative Modeling of Path-Dependent Physical Fields
Jun 22, 2026Yijing Zhou, Jasmin JelovicaFlow Matching Structural Mechanics
aDepartment of Mechanical Engineering, The University of British Columbia, Vancouver, Canada · bDepartment of Civil Engineering, The University of British Columbia, Vancouver, Canada
NullFlow: One-Step Generative Reconstruction
Jun 21, 2026Xiao Shi, Edward P. Chandler, Chicago Y. Park +2Flow Matching Image Reconstruction
Computational Imaging Group (CIG) · Washington University in St. Louis
CrossFlow: One-Step Generation Across Latent and Pixel Spaces
Jun 18, 2026Xiyuan Wang, Xiao Zhang, Yang Li +4Flow Matching Latent Diffusion Models
Institute for Artificial Intelligence, Peking University · Tencent · Fudan University
Rethinking One-Step Image Editing through ChordEdit: Reproduction, Simplification, and New Insights
Jun 12, 2026Minghan Li, Jeremy Moebel, Mengyu WangText-Guided Image Editing One-Step Generative Modeling
Harvard AI and Robotics Lab
Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models
Jun 9, 2026An Zhao, Shengyuan Zhang, Zhongjian Sun +5Flow Matching One-Step Generative Modeling
College of Computer Science and Technology, Zhejiang University, Zhejiang, China · School of Software Technology, Zhejiang University, Zhejiang, China · Princeton University, Princeton, USA +1
MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation
Jun 8, 2026Dohwan Kim, Jung-Woo ChoiFlow Matching MeanFlow
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea
SwiftVR: Real-Time One-Step Generative Video Restoration
Jun 8, 2026Jiaqi Yan, Xiangyu Chen, Xinlin Zhong +5Video Diffusion Models One-Step Generative Modeling
State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau · Institute of Artificial Intelligence (TeleAI), China Telecom · State Key Laboratory for Novel Software Technology, Nanjing University
Vault: One-Step Latent Generation with Positive-Anchored Rewards for Autonomous Driving
Jun 4, 2026Yining Xing, Zehong Ke, Zhiyuan Liu +1End-to-End Autonomous Driving One-Step Generative Modeling
SB-RF: Schrödinger Bridge Rectified Flow for One-Step Robust Speech Enhancement
Jun 4, 2026Caixia Lu, Xueyang Lv, Penglong Hu +1Rectified Flow Schrödinger Bridges
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Jun 2, 2026Cheng Jiang, Sitian Qian, Kevin Pedro +3Physics-Informed Generative Modeling Flow Matching
School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, United Kingdom · Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA · Fermi National Accelerator Laboratory, Batavia, IL 60510, USA +2
Optimal Transport Flow Matching by Design
Jun 2, 2026Shimon Malnick, Matan Rusanovsky, Ohad Fried +1Flow Matching One-Step Generative Modeling
1Tel Aviv University · 2Reichman University
MariData: One-Step Unpaired Image Translation for Maritime Environments
Jun 2, 2026Santeri Henriksson, Mehdi Asadi, Amin Majd +1Image-to-Image Translation Synthetic Data Generation
AIS Lab, Turku University of Applied Sciences, Turku, Finland
Drifting Preference Optimization for One-Step Generative Models
Jun 1, 2026Zhou Jiang, Yandong Wen, Zhen LiuDiffusion Model Alignment One-Step Generative Modeling
Westlake University · The Chinese University of Hong Kong, Shenzhen
Training-free image inversion for one-step diffusion models
May 31, 2026Tao Wu, Senmao Li, Yaxing Wang +3Diffusion Model Inversion Text-Guided Image Editing
Computer Vision Center, Universitat Autonoma de Barcelona, Barcelona, 08193, Spain · Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates · Jilin University, 130012, Changchun, Jilin, China +3
Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
May 30, 2026Hugues Van Assel, Edward De Brouwer, Saeed Saremi +2One-Step Generative Modeling Distribution Matching
Genentech
Midpoint Generative Models
May 28, 2026Daniil Shlenskii, Nikita Gushchin, Lev Novitskiy +2Flow Matching Stochastic Interpolants
1AXXX, Russia · 2Applied AI Institute, Russia · 3Kandinsky Lab, Russia
Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Prediction
May 27, 2026Mufan Qiu, Genhui Zheng, Yinuo Xu +4World Model Learning Latent Dynamics Modeling
University of North Carolina at Chapel Hill · The University of Texas at Austin · University of Pennsylvania
Recursive Flow Matching
May 26, 2026Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1Neural Surrogate Modeling Flow Matching
University of California, San Diego · University of Michigan
Cross-scale Aligned Supervision for Training GANs
May 26, 2026Sangeek Hyun, MinKyu Lee, Jae-Pil HeoConditional Image Generation One-Step Generative Modeling
Sungkyunkwan University
DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
May 24, 2026Jiangjie Qiu, Yijun Li, Wentao Li +1One-Step Generative Modeling Molecular Generation
Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials
One-Forcing: Towards Stable One-Step Autoregressive Video Generation
May 22, 2026Jiaqi Feng, Justin Cui, Yuanhao Ban +1Video Diffusion Models One-Step Generative Modeling
Tsinghua University · UCLA
Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
May 21, 2026Krishnakumar BalasubramanianOne-Step Generative Modeling Drifting Models
Department of Statistics, University of California, Davis
On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures
May 20, 2026Likun Lin, Zhongjian Wang, Jack Xin +1One-Step Generative Modeling Optimal Transport
Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, P.R. China. · Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371. · Department of Mathematics, University of California at Irvine, Irvine, CA 92697, USA. +1
Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL
May 18, 2026Junyi Wu, Weijian Luo, Haoyang Zheng +2Diffusion Model Alignment One-Step Generative Modeling
Purdue University · hi-lab, Xiaohongshu Inc.
DCFold: Efficient Protein Structure Generation with Single Forward Pass
May 18, 2026Zhe Zhang, Yuanning Feng, Yuxuan Song +3Protein Structure Prediction AI-Assisted Scientific Research
Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · School of Computer Science and Technology, Huazhong University of Science and Technology +1
A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
May 18, 2026Chenguang Wang, Tianshu YuWasserstein Gradient Flows One-Step Generative Modeling
School of Data Science The Chinese University of Hong Kong, Shenzhen

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Generative wave propagator

Shijun Cheng, Tariq Alkhalifah

Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal 23955-6900, Saudi Arabia

PDE Surrogate Modeling One-Step Generative Modeling Seismology Conditional Diffusion Models Physics-Based Simulation

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Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and stability constraints, which often require dense spatial grids and small time steps and thereby severely limit the effectiveness of iterative inversion workflows. We introduce a conditional diffusion-based wavefield propagator that advances seismic wavefields recursively from one time step to the next. Instead of learning an unconditional data distribution of wavefield evolution, the model is conditioned by a short history of recent wavefield time steps (snapshots), the velocity model, and the wavefield time step index, allowing it to represent the conditional transition between adjacent physical states. By training the network to directly predict the clean next wavefield snapshot, this strong physical conditioning makes it possible to replace the iterative reverse diffusion process with a single network evaluation for each predicted snapshot. To improve stability over long recursive rollouts, we further introduce a causal time-weighted loss, in which adaptive weights, accumulated as exponential moving averages of per-snapshot training errors, emphasize training directions that are consistent with the forward propagation sequence and reduce the amplification of one-step prediction errors. Because the learned propagator is tied to the temporal spacing of the training snapshots rather than to the FD stability limit, it can advance the wavefield using a physical time step ten times larger than that required by the underlying solver. Experiments on the Overthrust, SEG/EAGE, and Marmousi models show that the proposed method accurately reproduces wavefield snapshots and shot gathers and achieves an end-to-end speedup of 2.17 x over a GPU-accelerated tenth-order staggered-grid FD implementation under matched hardware conditions.

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