Diffusion-Based Image Generation

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15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

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Continuous-Time Distribution Matching for Few-Step Diffusion Distillation
May 7, 2026Tao Liu, Hao Yan, Mengting Chen +8Diffusion Distillation Few-Step Diffusion Sampling
1VCIP, College of Computer Science, Nankai University · 2Alibaba Group · College of Artificial Intelligence, Jilin University
InkDiffuser: High-Fidelity One-shot Chinese Calligraphy via Differentiable Morphological Optimization
May 7, 2026Kunchong Shi, Jing ZhangImage Generation Diffusion-Based Image Generation
Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237 China
Taming Outlier Tokens in Diffusion Transformers
May 6, 2026Xiaoyu Wu, Yifei Wang, Tsu-Jui Fu +3Diffusion Transformer Activation Outliers
Rice University · Apple
UniPCB: A Generation-Assisted Detection Framework for PCB Defect Inspection
May 6, 2026Huan Zhang, Lianghong Tan, Yichu Xu +5Conditional Image Generation Industrial Inspection
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China · School of Computer Science, Wuhan University, Wuhan 430072, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Efficient Geometry-Controlled High-Resolution Satellite Image Synthesis
May 6, 2026Vlad Vasilescu, Daniela Faur, Teodor CostachioiuRemote Sensing Diffusion-Based Image Generation
Univ. POLITEHNICA Bucharest · SIGMA Lab, CAMPUS Institute · GEOSENSE, CAMPUS Institute
Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions
May 4, 2026Jiaxiang Jiang, Mahesh Subedar, Omesh TickooSkin Lesion Classification Synthetic Data Augmentation
Intel · Google
SIFT-VTON: Geometric Correspondence Supervision on Cross-Attention for Virtual Try-On
May 2, 2026Kosuke Takemoto, Takafumi KoshinakaVirtual Try-On Diffusion-Based Image Generation
The Graduate School of Data Science, Yokohama City University, Kanagawa, Japan
SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection
Apr 29, 2026Paul Julius Kühn, Mika Pommeranz, Arjan Kuijper +1Synthetic Data Augmentation Industrial Anomaly Detection
Fraunhofer IGD, 64283 Darmstadt, Germany
ACPO: Anchor-Constrained Perceptual Optimization for Diffusion Models with No-Reference Quality Guidance
Apr 29, 2026Yang Yang, Feifan Meng, Han Fang +1Diffusion Model Guidance Diffusion Model Fine-Tuning
School of Electronic and Information Engineering, Anhui University · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center · School of Cyber Science and Technology, University of Science and Technology of China
SpatialFusion: Endowing Unified Image Generation with Intrinsic 3D Geometric Awareness
Apr 29, 2026Haiyi Qiu, Kaihang Pan, Jiacheng Li +3Conditional Image Generation Image Generation
Zhejiang University Hangzhou, China · HiThink Research Hangzhou, China
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
LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
Apr 22, 2026Inclusion AI, Tiwei Bie, Haoxing Chen +15Unified Multimodal Models Multimodal Generation
AGI Research Center, Inclusion AI
Accelerating New Product Introduction for Visual Quality Inspection via Few-Shot Diffusion-Based Defect Synthesis
Apr 22, 2026Serkan Hamdi Güğül, Kemal Levi, Burak AcarSynthetic-to-Real Domain Adaptation Synthetic Data Augmentation
Relimetrics, Inc.
HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition
Apr 21, 2026Xiaoqi Zhuang, Jefersson A. Dos Santos, Jungong HanCompositional Image Generation Remote Sensing
The University of Sheffield · Tsinghua University
Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Apr 21, 2026Johannes Schusterbauer, Ming Gui, Yusong Li +3Diffusion Model Sampling Diffusion Sampling
MetaCloak-JPEG: JPEG-Robust Adversarial Perturbation for Preventing Unauthorized DreamBooth-Based Deepfake Generation
Apr 20, 2026Tanjim Rahaman Fardin, S M Zunaid Alam, Mahadi Hasan Fahim +1Adversarial Robustness Diffusion-Based Image Generation
Computer Science and Engineering BRAC University Dhaka, Bangladesh
UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement
Apr 20, 2026Jingwei Yang, Ruoxi Wu, Wei Shen +4Constrained Generative Modeling Diffusion Transformer
China University of Mining and Technology, Beijing, China · OPPO Artificial Intelligence Center, Beijing, China
Coevolving Representations in Joint Image-Feature Diffusion
Apr 19, 2026Theodoros Kouzelis, Spyros Gidaris, Nikos KomodakisRepresentation Learning Latent Diffusion Models
Archimedes, Athena RC · National Technical University of Athens · University of Crete +1
Elucidating the SNR-t Bias of Diffusion Probabilistic Models
Apr 17, 2026Meng Yu, Lei Sun, Jianhao Zeng +2Diffusion Model Sampling Diffusion Sampling
Lanzhou University · AMAP Alibaba Group
An Analysis of Regularization and Fokker-Planck Residuals in Diffusion Models for Image Generation
Apr 16, 2026Onno Niemann, Gonzalo Martínez Muñoz, Alberto Suárez GonzalezDiffusion-Based Image Generation Score-Based Generative Modeling
Universidad Autónoma de Madrid
PoseDreamer: Scalable and Photorealistic Human Data Generation Pipeline with Diffusion Models
Mar 30, 2026Lorenza Prospero, Orest Kupyn, Ostap Viniavskyi +2Synthetic Data Generation Diffusion-Based Image Generation
The Podium Institute for Sports Medicine and Technology University of Oxford Oxford, UK · Visual Geometry Group University of Oxford Oxford, UK · Ukrainian Catholic University Lviv, Ukraine
SHARP: Spectrum-aware Highly-dynamic Adaptation for Resolution Promotion in Remote Sensing Synthesis
Mar 23, 2026Bingxuan Zhao, Qing Zhou, Chuang Yang +1Rotary Positional Embeddings Diffusion Transformer
School of Computer Science, Northwestern Polytechnical University, Xi’an, China · School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
Mar 17, 2026Han Lin, Xichen Pan, Zun Wang +4Diffusion Model Alignment Pixel-Space Diffusion Models
1UNC Chapel Hill · 2NYU · 3Meta +1
CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
Mar 17, 2026Mahmoud K. Ibrahim, Bart Elen, Chang Sun +5Healthcare Compositional Image Generation
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · VITO, Belgium
WiT: Waypoint Diffusion Transformers for Alleviating Trajectory Conflict in Pixel-Space Image Generation
Mar 16, 2026Hainuo Wang, Mingjia Li, Xiaojie GuoFlow Matching Diffusion Transformer
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
Cross-Resolution Distribution Matching for Diffusion Distillation
Mar 6, 2026Feiyang Chen, Hongpeng Pan, Haonan Xu +3Video Diffusion Models Diffusion Model Distillation
Huawei, Shanghai & Hangzhou, China · Nanjing University of Science and Technology, Nanjing, China
LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation
Mar 5, 2026Anugunj Naman, Ayushman Singh, Gaibo Zhang +1Image Segmentation Healthcare
Elmore Family School of Electrical and Computer Engineering Purdue University, West Lafayette, USA · Sesame AI, New York, USA · Department of Computer Science Purdue University, West Lafayette, USA +1
Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
Mar 2, 2026Jiaqi Han, Juntong Shi, Puheng Li +3Video Diffusion Models Diffusion-Based Image Generation
Stanford University · ByteDance
Dual-End Consistency Model
Feb 11, 2026Linwei Dong, Ruoyu Guo, Ge Bai +3One-Step Generative Modeling Consistency Distillation
1Zhejiang University · 2Bytedance Inc. · 3Zhejiang Lab
Learning on the Manifold: Unlocking Standard Diffusion Transformers with Representation Encoders
Feb 10, 2026Amandeep Kumar, Vishal M. PatelFlow Matching Diffusion Transformer
Johns Hopkins University

May 7, 2026, cs.CVJ/K move · Enter open · S save

Continuous-Time Distribution Matching for Few-Step Diffusion Distillation

Tao Liu, Hao Yan, Mengting Chen, Taihang Hu+7

1VCIP, College of Computer Science, Nankai University · 2Alibaba Group · College of Artificial Intelligence, Jilin University

Diffusion Distillation Few-Step Diffusion Sampling Diffusion-Based Image Generation Distribution Matching Distillation

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Step distillation has become a leading technique for accelerating diffusion models, among which Distribution Matching Distillation (DMD) and Consistency Distillation are two representative paradigms. While consistency methods enforce self-consistency along the full PF-ODE trajectory to steer it toward the clean data manifold, vanilla DMD relies on sparse supervision at a few predefined discrete timesteps. This restricted discrete-time formulation and mode-seeking nature of the reverse KL divergence tends to exhibit visual artifacts and over-smoothed outputs, often necessitating complex auxiliary modules -- such as GANs or reward models -- to restore visual fidelity. In this work, we introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization for the first time. CDM achieves this through two continuous-time designs. First, we replace the fixed discrete schedule with a dynamic continuous schedule of random length, so that distribution matching is enforced at arbitrary points along sampling trajectories rather than only at a few fixed anchors. Second, we propose a continuous-time alignment objective that performs active off-trajectory matching on latents extrapolated via the student's velocity field, improving generalization and preserving fine visual details. Extensive experiments on different architectures, including SD3-Medium and Longcat-Image, demonstrate that CDM provides highly competitive visual fidelity for few-step image generation without relying on complex auxiliary objectives. Code is available at https://github.com/byliutao/cdm.

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