Diffusion Model Guidance

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28 papers in the last four weeks, up 75% on the four weeks before. 0.3% of all new papers.

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AttriStory: Fine-grained Attribute Realization for Visual Storytelling with Diffusion Models
May 20, 2026Manogna Sreenivas, Rohit Kumar, Soma BiswasDiffusion Model Guidance Controllable Image Generation
Indian Institute of Science, Bengaluru, India
Conflict-Aware Additive Guidance for Flow Models under Compositional Rewards
May 20, 2026Xuehui Yu, Fucheng Cai, Meiyi Wang +2Constrained Generative Modeling Diffusion Model Guidance
Smart Systems Institute, National University of Singapore, Singapore · Faculty of Computing, Harbin Institute of Technology, Harbin, China · School of Computing, National University of Singapore, Singapore
Probability-Conserving Flow Guidance
May 19, 2026Parsa Esmati, Junha Hyung, Amirhossein Dadashzadeh +2Diffusion Model Guidance Classifier-Free Guidance
University of Bristol · KAIST
Structural Energy Guidance for View-Consistent Text-to-3D Generation
May 19, 2026Qing Zhang, Jinguang Tong, Jing Zhang +2Multi-View Consistency Diffusion Model Guidance
Australian National University, Canberra, 2601, ACT, Australia. · CSIRO, Canberra, 2601, ACT, Australia. · The University of Hong Kong, Hong Kong SAR.
Landscape-Awareness for Geometric View Diffusion Model
May 19, 2026Yan-Ting Chen, Hao-Wei Chen, Tsu-Ching Hsiao +1Diffusion Model Guidance Camera Pose Estimation
Elsa Lab, National Taiwan University
Stitched Value Model for Diffusion Alignment
May 19, 2026Hyojun Go, Hyungjin Chung, Prune Truong +8Diffusion Model Alignment Diffusion Model Guidance
1ETH Zurich · 2Google · University of Copenhagen
Improved Baselines with Representation Autoencoders
May 18, 2026Jaskirat Singh, Boyang Zheng, Zongze Wu +3Diffusion Model Guidance Diffusion Transformer
Adobe Research · ANU · New York University
Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
May 18, 2026Chenyang Wang, Weizhong Wang, Yinuo Ren +2Diffusion Model Guidance Importance Sampling
School of Mathematical Sciences, Peking University, Beijing, China · School of Mathematical Sciences, Fudan University, Shanghai, China · Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, United States +2
TILT: Model-Intrinsic Reward Alignment For Compositional Diffusion
May 16, 2026Debottam Dutta, Jianchong Chen, Jaehoon Hahm +1Diffusion Model Alignment Diffusion Model Guidance
Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA · Zhejiang University, China · Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA
DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer
May 15, 2026Qingji Dong, Hang Dong, Mingqin Chen +2Diffusion Models for Image Restoration Diffusion Model Guidance
ByteDance Inc.
Compositional Video Generation via Inference-Time Guidance
May 14, 2026Ariel Shaulov, Eitan Shaar, Amit Edenzon +2Diffusion Model Guidance Video Diffusion Models
1Tel-Aviv University · 2Independent Researcher · 3Bar Ilan University +1
HDRFace: Rethinking Face Restoration with High-Dimensional Representation
May 14, 2026Zirui Wang, Xianhui Lin, Yi Dong +7Diffusion Models for Image Restoration Diffusion Model Guidance
City University of Hong Kong · vivo BlueImage Lab, vivo Mobile Communication Co., Ltd
Probing into Camera Control of Video Models
May 14, 2026Chen Hou, Christian RupprechtDiffusion Model Guidance Camera-Controlled Video Generation
Visual Geometry Group · Visual Geometry Group University of Oxford
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
IG-Diff: Complex Night Scene Restoration with Illumination-Guided Diffusion Model
May 14, 2026Yifan Chen, Fei Yin, Chunle Guo +2Diffusion Models for Image Restoration Diffusion Model Guidance
Tsinghua Univerisity · NanKai University
Guided Diffusion Sampling for Precipitation Forecast Interventions
May 14, 2026Ayumu Ueyama, Kazuhiko Kawamoto, Hiroshi KeraPrecipitation Forecasting Diffusion Model Guidance
Chiba University · National Institute of Informatics
PanoPlane: Plane-Aware Panoramic Completion for Sparse-View Indoor 3D Gaussian Splatting
May 13, 2026Adil Qureshi, Dongki Jung, Jaehoon Choi +13D Gaussian Splatting Diffusion Model Guidance
University of Maryland, College Park
Drag within Prior Distribution: Text-Conditioned Point-Based Image Editing within Distribution Constraints
May 13, 2026Haoyang Hu, Masataka Seo, Yen-Wei ChenDiffusion Model Guidance Text-Guided Image Editing
† Ritsumeikan University, Graduate School of Information and Engineering · ⋆Osaka Institute of Technology
Image AID via continuous-time reinforcement learning
May 13, 2026Yilie Huang, Xun Yu ZhouDiffusion Model Guidance Masked Diffusion Models
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA. · Department of Industrial Engineering and Operations Research & Data Science Institute, Columbia University, New York, NY 10027, USA.
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
GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
May 11, 2026Hantao Zhang, Weidong Guo, Yuhe Liu +5Cross-Modality Medical Image Segmentation Diffusion Model Guidance
CVLab, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Fudan University, Shanghai, China · Beihang University, Beijing, China +3
Inverse Design for Conditional Distribution Matching
May 10, 2026Ori Meidler, Shaul Tolkovsky, Or ZukDiffusion Model Guidance Distribution Matching
Department of Statistics and Data Science Hebrew University of Jerusalem
TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers
May 8, 2026Daniel Wesego, Pedram RooshenasDiffusion Model Guidance Adversarial Robustness
Department of Computer Science · University of Illinois Chicago · Chicago, IL 60607
Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
May 8, 2026Atsushi Nitanda, Dake Bu, Yueming Lyu +1Diffusion Model Guidance Langevin Dynamics
Agency for Science, Technology and Research (A⋆STAR) · Nanyang Technological University · City University of Hong Kong
Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
May 8, 2026Kim Yong Tan, Yueming Lyu, Ivor Tsang +1Diffusion Model Guidance Black-Box Optimization
College of Computing and Data Science, Nanyang Technological University, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
ActCam: Zero-Shot Joint Camera and 3D Motion Control for Video Generation
May 7, 2026Omar El Khalifi, Thomas Rossi, Oscar Fossey +6Diffusion Model Guidance Camera-Controlled Video Generation
Kinetix, France · University of Oxford, United Kingdom · MBZUAI, United Arab Emirates
Diverse Sampling in Diffusion Models with Divergence-Free Particle Guidance
May 7, 2026Gal Vinograd, Idan Achituve, Ethan FetayaDiffusion Model Guidance Diffusion Sampling
Faculty of Engineering Bar-Ilan University Ramat Gan, Israel · Independent Researcher
DCR: Counterfactual Attractor Guidance for Rare Compositional Generation
May 7, 2026Taewon Kang, Matthias ZwickerDiffusion Model Guidance Compositional Image Generation
University of Maryland at College Park, United States
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
May 7, 2026Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2Diffusion Model Guidance Inference-Time Optimization
Politecnico di Milano, AIRLab, Italy
Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion
May 7, 2026Justin Sanders, Luca Giancardo, Lan Guo +4Diffusion Model Guidance Protein Language Models
Paul G. Allen School of Computer Science and Engineering, University of Washington · ∗Work performed during internship at Amazon Web Services · Life Sciences, Amazon Web Services

May 20, 2026, cs.CVJ/K move · Enter open · S save

AttriStory: Fine-grained Attribute Realization for Visual Storytelling with Diffusion Models

Manogna Sreenivas, Rohit Kumar, Soma Biswas

Indian Institute of Science, Bengaluru, India

Diffusion Model Guidance Controllable Image Generation

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Visual storytelling with diffusion models has made impressive strides in maintaining character consistency across narrative scenes. However, a critical gap remains: while these methods ensure a character remains consistent across scenes, they provide no systematic method to ensure if fine-grained attributes such as color and textures of clothing, accessories are faithfully rendered in the generated images. Towards this goal, we introduce AttriStory, a benchmark enabling attribute realization in visual storytelling. We curate 200 multi-scene stories across 10 distinct artistic styles using Large Language Model. Each scene is constructed with detailed attribute specifications to enable rich visual narratives. Further, to address attribute realization, we propose a plug-and-play latent optimization module that operates during early denoising steps, when the model establishes structural and semantic content. We achieve this through AttriLoss objective designed to maximize alignment between the cross-attention maps for desired attribute-object pairs while suppressing spurious associations, guiding models to localize attributes correctly. This approach operates orthogonally to existing consistency mechanisms, integrating seamlessly with current story generation pipelines without requiring architectural modifications. Our experiments demonstrate consistent improvements on incorporating AttriLoss across all baselines. This work positions attribute realization as a distinct, complementary dimension of visual storytelling, alongside character consistency, advancing the field toward fine-grained attribute-controlled story generation. Project-page:https://manogna-s.github.io/attristory/

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