Diffusion models have recently achieved outstanding results in the field of image super-resolution. These methods typically inject low-resolution (LR) images via ControlNet.In this paper, we first explore the temporal dynamics of information infusion through ControlNet, revealing that the input from LR images predominantly influences the initial stages of the denoising process. Leveraging this insight, we introduce a novel timestep-aware diffusion model that adaptively integrates features from both ControlNet and the pre-trained Stable Diffusion (SD). Our method enhances the transmission of LR information in the early stages of diffusion to guarantee image fidelity and stimulates the generation ability of the SD model itself more in the later stages to enhance the detail of generated images. To train this method, we propose a timestep-aware training strategy that adopts distinct losses at varying timesteps and acts on disparate modules. Experiments on benchmark datasets demonstrate the effectiveness of our method. Code: https://github.com/SleepyLin/TASR
CTCal: Rethinking Text-to-Image Diffusion Models via Cross-Timestep Self-Calibration
State Key Laboratory of Complex and Critical Software Environment（复杂与关键软件环境国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Timestep-Aware Block Masking for Efficient Diffusion Model Inference
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Data Science, Fudan University（复旦大学数据科学学院）
VideoAWR: Reinforcing Video Flow Models with Decoupled-Timestep Advantage-Weighted Rectification
Beihang University（北京航空航天大学）；Zhongguancun Academy（中关村学院）；Joy Future Academy（京东探索研究院）；Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）
针对视频流模型扩展 AWR 时的冷启动与引导爆炸问题，提出 VideoAWR 解耦采样与优化锚点，结合解耦时间步校正，在 Wan2.1 上实现稳定优化与更优视频质量。
Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach
City University of Hong Kong（香港城市大学）；Great Bay University（大湾区大学）；National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong（香港中文大学）；Lingnan University（岭南大学）
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Shanghai Jiao Tong University（上海交通大学）；ETH Zürich（苏黎世联邦理工学院）；Singapore Institute of Technology（新加坡理工学院）；Chinese University of Hong Kong（香港中文大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
RPD-Diff: Region-Adaptive Physics-Guided Diffusion Model for Visibility Enhancement under Dense and Non-Uniform Haze
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学智能系统工程学院）；Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology（广东省火灾科学与智能应急技术重点实验室）；The Australian National University（澳大利亚国立大学）
MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising
School of Engineering, Zurich University of Applied Sciences, CH；Bioengineering Department；Imperial-X, Imperial College London, UK；DAMTP, University of Cambridge, UK；Lucerne University Teaching；Research Hospital, CH；Lung Institute, Imperial College London, UK；Cardiovascular Research Centre, Royal Brompton Hospital, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, UK；Yau Mathematical Sciences Center, Tsinghua University, CN
Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training …
Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling
State Key Laboratory of Complex & Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Nanyang Technological University（南洋理工大学）；State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；ETH Zürich（苏黎世联邦理工学院）
A Wavelet Diffusion GAN for Image Super-Resolution
Department of Information Engineering, Electronics and Telecommunications (DIET)（信息工程、电子与电信系）；``Sapienza'' University of Rome（罗马大学萨皮恩扎分校）；Via Eudossiana 18, 00184, Rome（埃杜斯西亚纳街18号，00184，罗马）
