Diffusion Guidance

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing
Jul 2, 2026Anqi Tang, Wenhao Sun, Zhaoqiang LiuDiffusion Model Guidance Text-Guided Image Editing
The authors are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China.
Not All Prediction Targets Keep Training-Free Diffusion Guidance on the Manifold
Jul 1, 2026Yunsung Lee, Hyeongmin LeeDiffusion Model Guidance Diffusion Guidance
MAUM.AI, Republic of Korea · Seoul National University of Science and Technology, Republic of Korea
Intermediate Text Representation Guided Text-to-Image Generation for Enhancing One-and-Only Alignment
Jun 29, 2026Soyoun Won, Aryan Yazdan Parast, Basim Azam +2Text-Image Alignment T2I Generation
The University of Melbourne, Australia
GeoEdit: Geometry-Aware Object Editing via Dual-Branch Denoising
Jun 29, 2026Yi He, Jiangming Wang, Xinyu Wang +5Diffusion Guidance Diffusion-Based Image Editing
Shenzhen International Graduate School, Tsinghua University · Sun Yat-sen University · Peking University +3
Concept Removal Guidance: Evidence-Calibrated Negative Guidance for Safe Diffusion Sampling
Jun 29, 2026Yoonseok Choi, Chaeyoung Oh, Hyunjun Choi +2Diffusion Model Guidance Diffusion Guidance
Kim Jaechul Graduate School of AI, KAIST, Daejeon, Republic of Korea · Center for Humanoid Research, KIST, Seoul, Republic of Korea
Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation
Jun 25, 2026Shubham Vaijanath Phoolari, Aleyna Kara, Christoph Lauer +1Diffusion Guidance Diffusion Model Acceleration
Authors are with CARIAD SE, Germany. · Author is with the Institute of Automotive Engineering (FZD), TU Darmstadt, Germany. · Author is with the Technical University of Munich, Germany.
NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment
Jun 16, 2026Jisung Hwang, Yunhong Min, Jaihoon Kim +2Diffusion Model Alignment Diffusion Model Guidance
1KAIST · †This work was done in part while the author was a visiting researcher at The University of Tokyo. · 2The University of Tokyo
Towards More General Control of Diffusion Models Using Jeffrey Guidance
Jun 11, 2026Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2Diffusion Model Guidance Diffusion Guidance
Inria, CNRS, I3S, Maasai Université Côte d’Azur, France · Technical University of Denmark, Denmark · Inria, CNRS, LJAD, Maasai Université Côte d’Azur, France
Least-Action-Guided Diffusion for Physical Extrapolation
Jun 9, 2026Zhongxin Yang, Yuanwei Bin, Xiang I. A. Yang +1Physics-Informed Diffusion Models Physics-Informed Generative Modeling
College of Engineering, Peking University, Beijing 100871, China · Ningbo Institute for Digital Twin, Eastern Institute of Technology, Ningbo 315200, Zhejiang, China · Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo 315200, Zhejiang, China +2
Guided Discovery of New Behaviors using Diffusion Policies
Jun 7, 2026Dian Yu, Sebastian Sanokowski, Majid KhadivDiffusion Guidance Diffusion Policy
Munich Institute of Robotics and Machine Intelligence, Technical University of Munich
DirectAudioEdit: Inversion-Free Text-Guided Audio Editing via Diffusion Prediction Contrast
Jun 5, 2026Zhengkun Ge, Xiaoqian Liu, Haoran Zhang +5Audio Editing Audio Diffusion Models
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Kunming University of Science and Technology · NiuTrans Research, Shenyang, 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.
ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion
Jun 3, 2026Ruiqing Sun, Sen Yang, Dawei Feng +3Diffusion Guidance Multi-Objective Optimization
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China · Bioinformatics Center of AMMS, Beijing 100080, China
DiBS: Diffusion-Informed Branch Selection
Jun 2, 2026Bo Liu, Yuan Xie, Yuan Gao +4Diffusion Guidance Constraint Satisfaction
1Jilin University · 2The Chinese University of Hong Kong, Shenzhen · 3Southwest University +3
Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking From Sparse Inertial Sensors and Ranging-Based Between-Sensor Distances
Jun 1, 2026Dominik Hollidt, Tommaso Bendinelli, Christian HolzMarkerless Motion Capture Diffusion Guidance
Department of Computer Science, ETH Zurich, Switzerland
Sample-Efficient Diffusion-based Reinforcement Learning with Critic Guidance
May 28, 2026Shutong Ding, Zejia Zhong, Zhongyi Wang +4Diffusion Guidance Robotic RL
1ShanghaiTech University.
Alignment-Guided Score Matching for Text-to-Image Alignment in Diffusion Models
May 28, 2026Jaa-Yeon Lee, Yeobin Hong, Taesung Kwon +1Diffusion Model Alignment Text-Image Alignment
Graduate School of AI, KAIST, South Korea.
Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control
May 28, 2026Hao Ren, Zetong Bi, Yiming Zeng +5Diffusion Model Guidance Diffusion-Based Planning
Sun Yat-sen University, Guangzhou, China · Insta360 Research, Shenzhen, China
Spectral Guidance for Flexible and Efficient Control of Diffusion Models
May 27, 2026Gabriel Moreira, Manuel Marques, João Paulo Costeira +1Diffusion Model Guidance Diffusion Guidance
Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal · Carnegie Mellon University, Pittsburgh, PA, USA
Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization
May 27, 2026Gaurav Rai, Ojaswa SharmaText-to-Motion Generation Diffusion Guidance
Graphics Research Group, IIIT Delhi
Geometry-Correct Diffusion Posterior Sampling with Denoiser-Pullback Curvature Guidance and Manifold-Aligned Damping
May 27, 2026Seunghyeok Shin, Minwoo Kim, Dabin Kim +1Gauss-Newton Optimization Bayesian Inverse Problems
Department of Electrical and Computer Engineering, Inha University, Incheon, 22212, South Korea.
Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal
May 27, 2026Junlin WangDiffusion Model Guidance Diffusion Guidance
School of Engineering and Applied Science University of Pennsylvania
Sum of Costs Diffusion with Dynamic Guidance for Motion Planning
May 23, 2026Aysu Aylin Kaplan, Özgür ErkentTrajectory Generation Diffusion-Based Planning
Computer Engineering Department, Hacettepe University, Ankara, Turkey
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.
Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
May 18, 2026Bin Luo, Runmin Dong, Zhaoyang Luo +4Diffusion Models for Image Restoration Diffusion Transformer
Tsinghua Shenzhen International Graduate School, Shenzhen, China · Sun Yat-sen University, Zhuhai, China · Tsinghua University, Beijing, China +1
Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation
May 16, 2026Shuyin Ouyang, Zhaozhi Qian, Faroq AL-Tam +2RL for Code Generation Diffusion Guidance
King’s College London · Elm Europe
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
Registers Matter for Pixel-Space Diffusion Transformers
May 15, 2026Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy +2Diffusion Transformer Diffusion Guidance
Yandex Research
Physics-Guided Geometric Diffusion for Macro Placement Generation
May 15, 2026Jongho Yoon, Jinsung Jeon, Seokhyeong KangElectronic Design Automation Diffusion Guidance
1POSTECH Institute of Artificial Intelligence · 2KAIST InnoCORE LLM · 3Seoul National University +1
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
May 15, 2026Song Wu, Xinyu Chen, Qian Wang +3Video Diffusion Models Diffusion Guidance
JIUTIAN Research, China · School of Intelligence Science and Technology, Nanjing University · State Key Laboratory of Novel Software Technology, Nanjing University

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

Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing

Anqi Tang, Wenhao Sun, Zhaoqiang Liu

The authors are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China.

Diffusion Model Guidance Text-Guided Image Editing Diffusion Guidance Diffusion-Based Image Editing Image Editing

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Text-guided image editing aims to modify visual content according to a target prompt while preserving the background. Recent inversion-free image editing frameworks such as FlowEdit have demonstrated strong editing capability without requiring inversion. Empirically, FlowEdit can achieve substantial semantic changes under appropriate hyperparameter settings. However, we observe that under certain global attribute shifts, the editing trajectory may not effectively move away from the source distribution in the early timesteps. Our analysis suggests that in the high-noise regime, the dominant manifold-seeking flow toward the data manifold can reduce the influence of the text-conditioned direction, leading to limited global modification while background structures remain only moderately preserved. Inspired by this observation, we propose an inversion-free, frequency-aware semantic compensation strategy that strengthens the effective signal in the early stage of generation, while maintaining structural consistency in the background. The proposed method improves global editing capacity without sacrificing background fidelity.

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