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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Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation
Jun 2, 2026Tianhe Wu, Zikai Zhou, Kun Yan +23Diffusion Model Distillation Few-Step Diffusion Sampling
Flicker-DDPM: Accelerating Denoising Diffusion via 1/f Colored Noise Injection
Jun 2, 2026KeXiang Mao, FanCheng LiFew-Step Diffusion Sampling Diffusion Model Acceleration
School of Physics and Technology, Wuhan University, Wuhan 430072, China · Hongyi Honor College, Wuhan University, Wuhan 430072, China
RMPrior: Bridging Propagation Priors and Diffusion Refinement for Efficient Radio Map Construction
Jun 2, 2026Zixuan Guo, Xiucheng Wang, Nan ChengFew-Step Diffusion Sampling Wireless Communications
School of Physics, Xidian University, Xi’an, 710071, China · State Key Laboratory of ISN and School of Telecommunications Engineering, Xidian University, Xi’an, 710071, China
BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers
Jun 1, 2026Justin Deschenaux, Caglar GulcehreMasked Diffusion Models Diffusion Sampling
EPFL Lausanne, Switzerland · EPFL, Lausanne, Switzerland · Microsoft AI
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Jun 1, 2026Shucheng Li, Iolo Jones, Alexander Tong +1Diffusion Model Distillation Few-Step Diffusion Sampling
Department of Computer Science, University of Oxford, Oxford, UK · AITHYRA, Research Institute for Biomedical AI, Vienna, AT
Strong Stochastic Flow Maps
May 31, 2026Sam McCallum, Zander W. Blasingame, Timothy Herschell +3Stochastic Differential Equations Few-Step Diffusion Sampling
University of Bath · 2AITHYRA
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
May 30, 2026Jinyang Du, Shenghao Jin, Ziqian Xu +5Video Diffusion Models Diffusion Model Quantization
SenseTime
SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation
May 28, 2026Zhuguanyu Wu, Ruihao Gong, Yang Yong +5Video Diffusion Models Diffusion Model Distillation
Beihang University · SenseTime Research · Hong Kong University of Science and Technology
On the Error-Correcting Effects of Stochasticity in Discrete Diffusion
May 26, 2026William Yuan, Sungwon Jeong, Amirali AghazadehMarkov Models Diffusion Sampling
Georgia Institute of Technology
Reinforcing Few-step Generators via Reward-Tilted Distribution Matching
May 25, 2026Yushi Huang, Xiangxin Zhou, Ruoyu Wang +3Diffusion Model Alignment Diffusion Model Distillation
Tencent Hunyuan · Hong Kong University of Science and Technology · Westlake University
PixelWizard: Towards Efficient High-Fidelity Video Generation at Ultra-Large Spatial Resolution
May 25, 2026Wenxue Li, Jingjing Ren, Peng Zhang +4Video Diffusion Models Few-Step Diffusion Sampling
The Hong Kong University of Science and Technology (Guangzhou) · MiLM Plus, Xiaomi Inc · The Hong Kong University of Science and Technology
Three-Step Conditional Diffusion 3D Reconstruction for Light-Field Microscopy
May 24, 2026Qihong Zhao, Shaokang Yan, Zhimin Qiao +23D Reconstruction Few-Step Diffusion Sampling
1Yanshan University · 2Peking University
Hierarchical Variational Policies for Reward-Guided Diffusion
May 20, 2026Kushagra Pandey, Farrin Marouf Sofian, Jan Niklas Groeneveld +2Diffusion Models for Image Restoration Diffusion Model Guidance
Department of Computer Science University of California Irvine
One-Step Distillation of Discrete Diffusion Image Generators via Fixed-Point Iteration
May 20, 2026Chaoyang Wang, Yunhai TongDiffusion Model Distillation Few-Step Diffusion Sampling
Peking University
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
Generative Pseudo-Force Fields for Molecular Generation
May 18, 2026Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4Machine Learning Interatomic Potentials Diffusion Model Sampling
Machine Learning Group, Technische Universität Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany · Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany +6
Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
May 18, 2026Grigory Bartosh, Teodora Pandeva, Sushrut Karmalkar +1Few-Step Diffusion Sampling Diffusion Model Acceleration
University of Amsterdam · Microsoft Research, Cambridge
Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation
May 18, 2026Xiao He, Yang Li, Peizhen Zhang +3Flow Matching Diffusion Model Distillation
1State Key Laboratory of Integrated Services Networks, Xidian University · 2Tencent Hunyuan
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
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
Efficient Image Synthesis with Sphere Latent Encoder
May 15, 2026Tung Do, Thuan Hoang Nguyen, Hao LiLatent Diffusion Models Few-Step Diffusion Sampling
Mohamed Bin Zayed University of Artificial Intelligence, UAE
Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation
May 14, 2026Min Zhao, Hongzhou Zhu, Kaiwen Zheng +6Autoregressive Diffusion Real-Time Interactive Video Generation
Tsinghua University · ShengShu · Renmin University of China
AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation
May 13, 2026Yuchao Gu, Guian Fang, Yuxin Jiang +4Video Diffusion Models Diffusion Model Distillation
1NVIDIA · 2Show Lab, National University of Singapore · 3MIT
Covariance-aware sampling for Diffusion Models
May 13, 2026Andrea Schioppa, Tim SalimansDiffusion Model Sampling Diffusion Sampling
GDM - Amsterdam · work done at GDM - Amsterdam
FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity
May 12, 2026Jian Tang, Jiawei Fan, Qingbin Liu +1Video Diffusion Models Diffusion Transformer
Platform and Content Group, Tencent
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
Infinite Mask Diffusion for Few-Step Distillation
May 11, 2026Jaehoon Yoo, Wonjung Kim, Chanhyuk Lee +1Masked Diffusion Models Diffusion Model Distillation
Korea Advanced Institute of Science and Technology (KAIST)
When Few Steps Are Enough: Training-Free Acceleration of Identity-Preserved Generation
May 10, 2026Dongqi ZhengIdentity-Preserving Image Generation Few-Step Diffusion Sampling
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
May 9, 2026Yixin Tang, Jiawei Guo, Junxian Li +6Diffusion Model Distillation Few-Step Diffusion Sampling
Shanghai Jiao Tong University · Nanyang Technological University · Honor Device Co., Ltd
Geometry-Aware Discretization Error of Diffusion Models
May 8, 2026Samuel Hurault, Thomas Moreau, Gabriel PeyréDiffusion Sampling Few-Step Diffusion Sampling
Univ. Gustave Eiffel, CNRS, LIGM · Univ. Paris-Saclay, Inria, CEA · CNRS, ENS, PSL Université

Jun 2, 2026, cs.CVJ/K move · Enter open · S save

Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation

Tianhe Wu, Zikai Zhou, Kun Yan, Kaiyuan Gao+22

Diffusion Model Distillation Few-Step Diffusion Sampling Diffusion-Based Image Editing Distribution Matching Distillation T2I Diffusion Models

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Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially reducing inference overhead while enabling real-time generation and cost-efficient deployment across a broad range of practical scenarios. However, prior work has predominantly focused on advancing training objectives, while comparatively overlooking the training recipe, which has become increasingly critical in the era of large-scale foundation models. In this work, we systematically revisit the training recipe under the well-established distribution matching distillation (DMD) framework for both text-to-image generation and image editing, focusing on three key dimensions: training data composition, teacher guidance within DMD, and task mixture. Our empirical analysis reveals several non-obvious and counterintuitive phenomena, ultimately motivating the development of Qwen-Image-Flash. These findings highlight that effective few-step distillation depends not only on carefully designed objectives, but also on a principled training recipe.

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Latest papers 114