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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DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders
May 21, 2026Tianhang Wang, Yitong Chen, Wei Song +3Latent Diffusion Models Autoencoders
Zhejiang University · Shanghai Innovation Institute · Fudan University +1
Rethinking Token Reduction for Diffusion Models via Output-Similarity-Awareness
May 21, 2026Hangyeol Lee, Hyojeong Lee, Joo-Young KimDiffusion Transformer Diffusion-Based Image Generation
KAIST, Daejeon, Republic of Korea
RiT: Vanilla Diffusion Transformers Suffice in Representation Space
May 21, 2026Le Zhang, Ning Mang, Aishwarya AgrawalFlow Matching Visual Representation Learning
Mila – Québec AI Institute, UdeM · Utrecht University · Canada CIFAR AI Chair
Noise Schedule Design for Diffusion Models: An Optimal Control Perspective
May 21, 2026Seo Taek Kong, Weina Wang, R. SrikantDiffusion Sampling Diffusion-Based Image Generation
ECE & CSL University of Illinois Urbana-Champaign · Computer Science Department Carnegie Mellon University · ECE, CSL & NCSA University of Illinois Urbana-Champaign
Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
May 21, 2026Gang Dai, Yining Huang, Yiming Xia +2Test-Time Scaling Diffusion-Based Image Generation
Guangdong University of Technology · South China University of Technology · Nanyang Technological University
UniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image Generation
May 20, 2026Jiayun Wang, Yu Wang, Weijie Gan +2Image Generation Vision-Language Grounding
Center for Advanced AI, Accenture
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
OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation
May 20, 2026Ziye Li, Henghui DingDiffusion Transformer Occlusion Reasoning
Institute of Big Data, College of Computer Science and Artificial Intelligence, Fudan University, China.
Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model
May 18, 2026Suya Li, Kaushik Dutta, Debojyoti Pal +2PET/CT Imaging Diffusion-Based Image Generation
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, USA · Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St. Louis, USA · Department of Surgery, Washington University School of Medicine, St. Louis, USA +1
Spectral Progressive Diffusion for Efficient Image and Video Generation
May 18, 2026Howard Xiao, Brian Chao, Lior Yariv +1Video Diffusion Models Diffusion Sampling
Stanford University
Improved Baselines with Representation Autoencoders
May 18, 2026Jaskirat Singh, Boyang Zheng, Zongze Wu +3Diffusion Model Guidance Diffusion Transformer
Adobe Research · ANU · New York University
Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
May 18, 2026Grigory Bartosh, David Ruhe, Emiel Hoogeboom +3Diffusion Sampling Diffusion-Based Image Generation
*Work done while interning at Google DeepMind Amsterdam, 1University of Amsterdam, 2Google DeepMind Amsterdam · 2Google DeepMind Amsterdam
FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion
May 18, 2026Lichen Ma, Zipeng Guo, Yu He +5Pixel-Space Diffusion Models Diffusion-Based Image Generation
Designing streetscapes from street-view imagery using diffusion models
May 17, 2026Yuzhou Chen, Yuebing Liang, Lingqian Hu +4Image Generation Diffusion-Based Image Generation
Department of Urban and Regional Planning, University of Florida · Singapore-MIT Alliance for Research and Technology Centre (SMART) · Department of Landscape Architecture and Urban Planning, Texas A&M University +1
AnimeAdapter: A Modular Adapter for Appearance-Consistent Anime Character Generation
May 17, 2026Yixuan HanSubject-Driven Image Generation Image Generation
National University of Singapore, Singapore
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
RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations
May 15, 2026Yanhao Ge, Shanyan Guan, Weihao Wang +2Implicit Neural Representations Neural Rendering
College of Electronic and Information Engineering, Tongji University · vivo Mobile Communication Co., Ltd. · Nanjing University.
HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
May 15, 2026Yu He, Lichen Ma, Zipeng Guo +5Diffusion Transformer Image Generation
ElasticDiT: Efficient Diffusion Transformers via Elastic Architecture and Sparse Attention for High-Resolution Image Generation on Mobile Devices
May 15, 2026Kunpeng Du, Haizhen Xie, Sen Lu +11Diffusion Transformer Diffusion-Based Image Generation
Huawei Technologies
VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
May 15, 2026Yan Luo, Ahmadou Aidara, Jingyi Lu +3Classifier-Free Guidance Diffusion-Based Image Generation
Harvard AI and Robotics Lab, Harvard University · School of Computing and Data Science, The University of Hong Kong · Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University
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
LPH-VTON: Resolving the Structure-Texture Dilemma of Virtual Try-On via Latent Process Handover
May 14, 2026Yixin Liu, Baihong Qian, Jinglin Jiang +6Diffusion Model Alignment Virtual Try-On
Shanghai Jiao Tong University
UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation
May 14, 2026Ping Zhou, Haoyu Wang, Mengmeng Zheng +4Cross-Modal Alignment Multimodal Generation
School of Computer Science, Northwestern Polytechnical University · School of Computer Science & Technology, Xi’an University of Posts & Telecommunications · MMLab, The Chinese University of Hong Kong
AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors
May 14, 2026Zuopeng Zhao, Ying Liu, Xiaoyu Li +3Diffusion Model Guidance Image Generation
School of Computer Science and Technology / School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, China · Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou, China · Jiangsu Provincial Industrial Technology Engineering Center for Intelligent Sensing and Emergency IoT in Underground Space, Xuzhou, China
L2P: Unlocking Latent Potential for Pixel Generation
May 12, 2026Zhennan Chen, Junwei Zhu, Xu Chen +7Latent Diffusion Models Pixel-Space Diffusion Models
Nanjing University · Tencent Youtu Lab · Hainan-biuh +1
RealDiffusion: Physics-informed Attention for Multi-character Storybook Generation
May 12, 2026Qi Zhao, Jun Chen, Ivor Tsang +1Diffusion-Based Image Generation Temporal Consistency in Video Generation
Xi’an Jiaotong University, China · Zhejiang Normal University, China · SGIT AI Lab, State Grid Corporation of China +1
Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods
May 9, 2026Md Abu Obaida Zishan, Jannatun Noor, Annajiat Alim RaselLatent Diffusion Models Diffusion-Based Image Generation
School of Data and Sciences BRAC University, Dhaka · Computing for Sustainability and Social Good (C2SG) Research Group Department of Computer Science and Engineering United Internation University, Dhaka
Normalizing Trajectory Models
May 8, 2026Jiatao Gu, Tianrong Chen, Ying Shen +3Normalizing Flows Diffusion Model Distillation
Apple · UIUC
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
May 8, 2026Zhengrong Yue, Taihang Hu, Mengting Chen +8Latent Diffusion Models Autoencoders
Shanghai Jiao Tong University · Alibaba Group · Beihang University +4
Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations
May 8, 2026Yuan Zhang, Chenyi Li, Guoqing Ma +7Diffusion Model Distillation Diffusion Sampling
JD Explore Academy, China · The Hong Kong University of Science and Technology · Tsinghua University +1

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DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders

Tianhang Wang, Yitong Chen, Wei Song, Zuxuan Wu+2

Zhejiang University · Shanghai Innovation Institute · Fudan University+1

Latent Diffusion Models Autoencoders Image Reconstruction Diffusion-Based Image Generation

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Representation Autoencoders (RAEs) leverage frozen vision foundation models (VFMs) as tokenizer encoders, providing robust high-level representations that facilitate fast convergence and high-quality generation in latent diffusion models. However, freezing the VFM inherently constrains its spatial reconstruction capacity, limiting fine-grained generation and image editing; in contrast, incorporating reconstruction-oriented signals via fine-tuning disrupts the pretrained semantic space and degrades generative fidelity. To address this trade-off, we propose DecQ, a simple yet effective framework for RAEs. Specifically, DecQ introduces lightweight detail-condensing queries that extract fine-grained information from intermediate VFM features through condenser modules. These queries are incorporated into the decoder to support reconstruction and are jointly generated with patch tokens during generative modeling. By aggregating information from both shallow and deep layers, DecQ effectively mitigates the reconstruction--generation trade-off, improving both reconstruction quality and generative performance. Our experiments demonstrate that: (1) with only 8 additional queries and 3.9% extra computation, DecQ improves reconstruction over the frozen DINOv2-based RAE, increasing PSNR from 19.13 dB to 22.76 dB; and (2) for generative modeling, DecQ achieves 3.3

\times

faster convergence than RAE, attaining an FID of 1.41 without guidance and 1.05 with guidance.

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Latest papers 189