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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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
DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
May 13, 2026Xianbing Sun, Jiahui Zhan, Liqing Zhang +1Virtual Try-On One-Step Generative Modeling
Shanghai Jiao Tong University
Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
May 12, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Md Sajid Ahmed +2Markov Models MeanFlow
Department of Computer Science, University of Kentucky Lexington, Kentucky 40506, USA
DriftXpress: Faster Drifting Models via Projected RKHS Fields
May 12, 2026Ali Falahati, Elliot Creager, Gautam Kamath +1Image Generation One-Step Generative Modeling
Cheriton School of Computer Science, University of Waterloo and Vector Institute. · Department of Electrical and Computer Engineering, University of Waterloo and Vector Institute. · A*STAR Centre for Frontier AI Research, Singapore.
One-Step Generative Modeling via Wasserstein Gradient Flows
May 12, 2026Jiaqi Han, Puheng Li, Qiushan Guo +3Wasserstein Gradient Flows Image Generation
Stanford University · ByteDance
STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models
May 12, 2026Ankit Yadav, Arpit Garg, Ta Duc Huy +1Diffusion Model Guidance One-Step Generative Modeling
Australian Institute for Machine Learning, Adelaide University, Australia
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Synthetic Data Generation One-Step Generative Modeling
AMLab · University of Amsterdam · CuspAI +1
Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
May 10, 2026Wei Zhu, Kai Zhang, Yu Zheng +3Flow Matching Diffusion Model Distillation
Nanjing University of Science and Technology · Nanjing University · Huawei
On Variance Reduction in Learning Mean Flows
May 10, 2026Juanwu Lu, Ziran WangFlow Matching MeanFlow
Purdue University
Coupling Models for One-Step Discrete Generation
May 8, 2026Fred Zhangzhi Peng, Avishek Joey Bose, Anru R. Zhang +1One-Step Generative Modeling Generative Modeling
Duke University · Imperial College London · AITHYRA
Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
May 7, 2026Fan Xu, Yuan Gao, Kun Wang +4Flow Matching Probabilistic Forecasting
1USTC · 2SLAI · 3THU +3
SymDrift: One-Shot Generative Modeling under Symmetries
May 7, 2026Samir Darouich, Vinh Tong, Lluís Pastor-Pérez +3One-Step Generative Modeling Generative Modeling
Institute of Artificial Intelligence Institute of Theoretical Chemistry University of Stuttgart
Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
May 7, 2026Sicheng Ma, Tianyue Yang, Xiuzhe Wu +1Flow Matching One-Step Generative Modeling
Department of Physics University of Cambridge · The Center for Computational Science University College London · Department of Computer Science University College London
Towards Scalable One-Step Generative Modeling for Autoregressive Dynamical System Forecasting
May 7, 2026Tianyue Yang, Xiao XuePDE Surrogate Modeling One-Step Generative Modeling
The Center for Computational Science University College London
A Few-Step Generative Model on Cumulative Flow Maps
May 5, 2026Zhiqi Li, Duowen Chen, Yuchen Sun +1One-Step Generative Modeling Flow-Based Generative Modeling
Georgia Institute of Technology, Atlanta, United States of America
ExpoCM: Exposure-Aware One-Step Generative Single-Image HDR Reconstruction
May 4, 2026Aoyu Liu, Zhen Liu, Ziyi Wang +3Image Enhancement HDR Imaging
University of Electronic Science and Technology of China
Generative Modeling with Orbit-Space Particle Flow Matching
May 4, 2026Sinan Wang, Jinjin He, Shenyifan Lu +3Flow Matching One-Step Generative Modeling
Georgia Institute of Technology, USA
Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
May 1, 2026Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim +8Text-to-Audio Generation One-Step Generative Modeling
National Taiwan University · Amazon AGI
Representation Fréchet Loss for Visual Generation
Apr 30, 2026Jiawei Yang, Zhengyang Geng, Xuan Ju +2One-Step Generative Modeling
USC · CMU · CUHK +1
Analytical Correction for Subsampling Bias in Drifting Models
Apr 29, 2026Jiaru Zhang, Zeyun Deng, Juanwu Lu +2One-Step Generative Modeling Drifting Models
Purdue University
FlowS: One-Step Motion Prediction via Local Transport Conditioning
Apr 28, 2026Leandro Di Bella, Adrian Munteanu, Bruno CornelisFlow Matching Trajectory Generation
Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium · IMEC, Kapeldreef 75, B-3001 Leuven, Belgium
Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows
Apr 27, 2026Yuta Baba, Keiji YanaiFlow Matching One-Step Generative Modeling
The University of Electro-Communications 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
Speech Enhancement Based on Drifting Models
Apr 27, 2026Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2One-Step Generative Modeling Speech Enhancement
Victoria University of Wellington, 3 Lincoln University, New Zealand · GN Advanced Science, Denmark
Bridging Restoration and Generation Manifolds in One-Step Diffusion for Real-World Super-Resolution
Apr 27, 2026Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu +2Diffusion Model Inversion Diffusion Models for Image Restoration
National Yang Ming Chiao Tung University, Hsinchu, Taiwan · MediaTek Inc., Taiwan
Efficient Diffusion Distillation via Embedding Loss
Apr 24, 2026Jincheng Ying, Yitao Chen, Li Wenlin +2Diffusion Model Distillation One-Step Generative Modeling
School of Big Data and Artificial Intelligence Guangdong University of Finance and Economics Guangzhou, China · School of Computer Science Shandong University Qingdao, China
Generative Drifting for Conditional Medical Image Generation
Apr 21, 2026Zirong Li, Siyuan Mei, Weiwen Wu +33D Medical Imaging One-Step Generative Modeling
Department of Orthodontics and Orofacial Orthopedics, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany · Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany · Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany +1
Allo{SR} $^2$ : Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows
Apr 21, 2026Zihan Wang, Xudong Huang, Junbo Qiao +4Flow Matching Diffusion Model Distillation
East China Normal University · Huawei Foundation Model Dept. · Key Laboratory of Advanced Theory and Application in Statistics and Data Science
Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
Apr 20, 2026Chenxi Zhao, Chen Zhu, Xiaokun Feng +6Flow Matching Representation Learning
College of Computer Science, Nankai University · AMAP, Alibaba Group
One-Shot Generative Flows: Existence and Obstructions
Apr 16, 2026Panos Tsimpos, Daniel Sharp, Youssef MarzoukFlow Matching One-Step Generative Modeling
Operations Research Center · Massachusetts Institute of Technology · Cambridge, MA 02139 +2

May 17, 2026, astro-ph.IMJ/K move · Enter open · S save

Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling

Tianyue Yang, Sandro Tacchella, Xiao Xue

The Center for Computational Science University College London · Cavendish Laboratory Kavli Institute for Cosmology University of Cambridge

Conditional Image Generation One-Step Generative Modeling Flow-Based Generative Modeling Diffusion Model Inference Acceleration

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Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical image synthesis using diffusion models and pixel-MeanFlow. We first review the connections between score-based diffusion models, Flow Matching, one-step generative models, and modern diffusion samplers. We then evaluate DDPM, DDIM, DEIS-AB2, DPM++2M, and one-step pixel-MeanFlow on the GalaxiesML-64 dataset using morphology-based metrics, including ellipticity, semi-major axis, Sérsic index, and isophotal area. Our results show a clear accuracy-efficiency trade-off: standard DDPM sampling achieves the best distributional fidelity but requires high computational cost, while second-order samplers substantially improve efficiency over DDIM. Pixel-MeanFlow enables single-step generation and achieves competitive performance on several morphology statistics, though it remains weaker than many-step DDPM for fine-grained structure. Our results demonstrate that one-step generative models can recover key galaxy morphology statistics at orders-of-magnitude lower computational cost, opening a path toward efficient conditional simulators for large cosmological surveys and simulation-based scientific inference.

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Latest papers 97