Few-Step Diffusion Sampling

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19 papers in the last four weeks, up 280% on the four weeks before. 0.2% of all new papers.

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Velocity Scheduled Flow Matching
Jul 13, 2026Vitalii BondarFlow Matching Conditional Flow Matching
Cherkasy State Technological University Cherkasy, Ukraine
FdAudio: MeanFlow-Anchored Fréchet-Distance Post-Training for One-Step Text-to-Audio Generation
Jul 11, 2026Kuan-Po Huang, Bo-Ru Lu, Ho-Lam Chung +2Text-to-Audio Generation Flow Matching
National Taiwan University · Amazon
IB-Flow: Information Bottleneck-Guided CFG Distillation for Few-Step Text-to-Image Generation
Jul 10, 2026Yiting Wang, Jingyi Zhang, Wenhu Zhang +4Diffusion Model Distillation Few-Step Diffusion Sampling
Tsinghua University · Wan Team, Alibaba Group · HKUST +1
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
Jul 9, 2026Hongyu Liu, Chun Wang, Feng Gao +6Video Diffusion Models Autoregressive Diffusion
Meituan · HKUST · City University of Hong Kong
Bridging Diffusion Pruning and Step Distillation with Teacher-Aligned Repair
Jul 7, 2026Jincheng Ying, Li Wenlin, Minghui Xu +1Diffusion Model Distillation Few-Step Diffusion Sampling
School of Big Data and Artificial Intelligence Guangdong University of Finance and Economics Guangzhou, China · School of Computer Science Shandong University Qingdao, China · GDUFE-UCM Joint Institute Guangdong University of Finance and Economics Guangzhou, China
Dynamic-in-Few-Step: Unifying Dynamic Computation and Few-Step Distillation for Efficient Video Generation
Jul 7, 2026Yu Cheng, Siyue Yao, Zhongang Qi +3Video Diffusion Models Diffusion Model Distillation
Zhejiang University · BlueImage, vivo · Westlake University
x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability
Jul 7, 2026Xin Peng, Ang GaoDiffusion Sampling Few-Step Diffusion Sampling
School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
Fréchet Distance Loss on Speech Representations for Text-to-Speech Synthesis
Jul 7, 2026Ho-Lam Chung, Kuan-Po Huang, Bo-Ru Lu +1Flow Matching TTS Synthesis
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Graduate Institute of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan · Amazon
D2PO: Optimizing Diffusion Samplers via Dynamic Preference
Jul 7, 2026Jinkyu Kim, Jinyoung Choi, Bohyung HanDiffusion Model Alignment Direct Preference Optimization
1ECE & 2IPAI, Seoul National University, Korea · 3AIGS, Ulsan National Institute of Science and Technology, Korea
EMPURPLE: A Free Lunch for Diffusion Distillation based on the Information Bottleneck
Jul 5, 2026Zilai Li, Lujia BaiDistribution Shift Diffusion Model Distillation
Independent researcher, University of Nottingham, Nottingham, UK
Tensor-Train Joint Modeling for Few-Step Discrete Diffusion
Jul 4, 2026Byoungkwon Kim, Minhyuk SungMasked Diffusion Models Tensor Decomposition
KAIST
A Decomposable Probe for Few-Step Diffusion Models: Prompt, Latent, and Score Selectivity across Backbone Families and Distillation Paradigms
Jul 3, 2026Patrick Mu HaojiePrompt Sensitivity Rectified Flow
Independent Researcher.
SwiftAudio: Data-Efficient Caption-Only Distillation for One-Step Text-to-Audio Diffusion-based Generation
Jun 30, 2026Binh Mai, Tran Quoc Bao Le, Hung Dinh +1Text-to-Audio Generation Diffusion Model Distillation
Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
Why Do Few-Step Text Latents Fail When Image Latents Work? Non-Commitment at Sharp Categorical Readouts
Jun 29, 2026Zhongyao WangFew-Step Diffusion Sampling Generative Modeling
Fudan University · Shanghai Innovation Institute
FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On
Jun 28, 2026Jiaxin Liu, Xiaoye Liang, Lai Jiang +2Virtual Try-On Few-Step Diffusion Sampling
School of Electronic Information Engineering, Beihang University, Beijing, China · Zhongguancun Academy, Beijing, China · State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
Beyond Trajectory Matching: Reflow with Marginal Distribution Alignment
Jun 28, 2026Chen Wang, Peiran Yun, Pan Xie +1Diffusion Model Distillation Distribution Matching
Department of Statistics and Data Science, Tsinghua University · Department of Computer Science and Technology, Tsinghua University · Bytedance Inc
SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending
Jun 27, 2026Haoran Bai, Xiaoxu Chen, Xiaoyu Liu +4Video Diffusion Models Few-Step Diffusion Sampling
1Alibaba Group · 2Harbin Institute of Technology · 3Xi’an Jiaotong University
A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
Jun 23, 2026Feiyang Fu, Hehe FanFlow Matching Few-Step Diffusion Sampling
College of Artificial Intelligence, Zhejiang University, Hangzhou, China.
Walk fast but be careful: Understanding Parallel Sampling in Masked Diffusion
Jun 22, 2026Vansh Bansal, Cholyeon Cho, Syamantak Kumar +2Masked Diffusion Models Diffusion Sampling
UT Austin SDS · UT Austin CS · UT Austin ECE
Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning
Jun 21, 2026Songtao Tian, Guhan Chen, Bohan Li +2Curriculum RL Few-Step Diffusion Sampling
1Tsinghua University, Beijing, China · 2Huawei Large Model Data Technology Lab, Shenzhen, China
CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation
Jun 20, 2026Wenhu Zhang, Kun Cheng, Changyuan Wang +7Video Diffusion Models Diffusion Model Distillation
1HKUST · 2Wan Team, Alibaba Group · 3THU +2
Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation
Jun 16, 2026Siyi Chen, Shaowei Liu, Yixuan Jia +4Video Diffusion Models Diffusion Model Distillation
University of Michigan · NVIDIA · University of Illinois Urbana-Champaign
Temporal Difference Learning for Diffusion Models
Jun 13, 2026Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu +1Few-Step Diffusion Sampling RL for Diffusion Models
Department of Engineering Science, University of Oxford, Oxford, United Kingdom · School of Computer Science, Carleton University, Ottawa, Canada.
High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation
Jun 10, 2026Dongyang Liu, Ruoyi Du, David Liu +7Diffusion Model Distillation Few-Step Diffusion Sampling
Z-Image Team, Alibaba Group
Learning to Solve Generative ODEs Beyond the Linear Span
Jun 7, 2026Sihyeon Kim, Seunghun Lee, Vikas Singh +1Few-Step Diffusion Sampling Flow-Based Generative Modeling
Korea University · KAIST · University of Wisconsin–Madison
Few-step Cofolding with All-Atom Flow Maps
Jun 7, 2026Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7Protein Structure Prediction Diffusion Distillation
1Genesis Molecular AI · Massachusetts Institute of Technology · Carnegie Mellon University +2
Mitigating the Contractivity Trap in Diffusion ODEs via Stein Stabilization
Jun 5, 2026Shigui Li, Delu ZengFew-Step Diffusion Sampling Diffusion Model Inference Acceleration
School of Mathematics, South China University of Technology, Guangzhou, China · School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them
Jun 4, 2026Woojung Han, Seil Kang, Youngjun Jun +3Physical Consistency in Video Generation Image-to-Video Generation
1Yonsei University, South Korea · 2NVIDIA, Taiwan.
Tracing the Oracle: Improving Diffusion Timestep Scheduling for 3D CT Reconstruction
Jun 4, 2026Yujia Wu, Zhaoqiang LiuDiffusion Sampling Few-Step Diffusion Sampling
The authors are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China.
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
Jun 2, 2026Weiguo Gao, Ming Li, Lei Shi +1Diffusion Model Distillation Few-Step Diffusion Sampling
School of Mathematical Sciences, Fudan University, Shanghai, 200433, China · Shanghai Key Laboratory of Contemporary Applied Mathematics, Fudan University, Shanghai, 200433, China

Jul 13, 2026, cs.LGJ/K move · Enter open · S save

Velocity Scheduled Flow Matching

Vitalii Bondar

Cherkasy State Technological University Cherkasy, Ukraine

Flow Matching Conditional Flow Matching Few-Step Diffusion Sampling Flow-Based Generative Modeling

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Flow matching trains a neural network to regress the conditional velocity along a linear interpolant between noise and data, and the number of network evaluations~(NFE) sets the cost of sampling. The straight-line interpolant carries an implicit choice: the sample moves at constant speed throughout the trajectory. We relax this choice and introduce Velocity Scheduled Flow Matching~(VSFM), which replaces the conditional target

x_1 - x_0

with

v(t)(x_1 - x_0)

for any nonnegative profile

v:[0,1]\to\mathbb{R}_{\geq 0}

satisfying

\int_0^1 v\,dt = 1

. We study six polynomial profiles drawn from motion planning. The first use of VSFM is at inference time: a pretrained linear flow-matching model can be sampled under any admissible profile by integrating its ODE on a non-uniform

τ

-schedule, with no retraining and no additional computation; on CIFAR-10 this lowers FID by up to

19.8\%

. Training from scratch under a braking profile gives a further reduction of

17.4\%

4

~NFE. Both gains follow from the local truncation error of the Euler integrator on the induced grid.

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