SinDiffusion: Learning a Diffusion Model from a Single Natural Image
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Microsoft Research Asia（微软亚洲研究院）
Weilun Wang, Jianmin Bao, Wengang Zhou, Dongdong Chen, Dong Chen, Lu Yuan, Houqiang Li
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. SinDiffusion significantly improves the quality and diversity of generated samples compared with existing GAN-based approaches. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales which serves as the default setting in prior work. This avoids the accumulation of errors, which cause characteristic artifacts in generated results. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics, therefore we redesign the network structure of the diffusion model. Coupling these two designs enables us to generate photorealistic and diverse images from a single image. Furthermore, SinDiffusion can be applied to various applications, i.e., text-guided image generation, and image outpainting, due to the inherent capability of diffusion models. Extensive experiments on a wide range of images demonstrate the superiority of our proposed method for modeling the patch distribution.
Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
College of Engineering, Ocean University of China（中国海洋大学工程学院）；School of Information and Control Engineering, Qingdao University of Technology（青岛理工大学信息与控制工程学院）
What's Inside Your Diffusion Model? A Score-Based Riemannian Metric to Explore the Data Manifold
Institut de la Vision & Laboratoire des Systèmes Perceptifs（视觉研究所及感知系统实验室）；Sorbonne Université & École Normale Supérieure（索邦大学及巴黎高等师范学院）；Group for Neural Theory（神经理论小组）；École Normale Supérieure（巴黎高等师范学院）
Synthesis and Perceptual Scaling of High Resolution Naturalistic Images Using Stable Diffusion
Department of Psychology, Humboldt-Universität zu Berlin（柏林洪堡大学心理学系）；Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin（柏林伯恩斯坦计算神经科学中心和柏林先进神经成像中心，查理医学院）；Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany（马克斯·普朗克人类认知与脑科学研究所，莱比锡，德国）；Max Planck School of Cognition, Leipzig, Germany（马克斯·普朗克认知学校，莱比锡，德国）；Kavli Institute for Systems Neuroscience, Centre for Neural Computation, The Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Jebsen Centre for Alzheimer’s Disease, Norwegian University of Science and Technology, Trondheim, Norway（凯斯琳系统神经科学研究所，神经计算中心，埃吉和保罗·布雷athen及弗雷德·卡维利微电路中心，朱贝森阿尔茨海默病中心，挪威科学与技术大学，特罗姆瑟，挪威）；Clinic of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany（柏林查理医学院神经科诊所）；Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany（柏林先进神经成像中心，查理医学院）；Research Cluster of Excellence “Science of Intelligence”, Technische（卓越研究集群“智能科学”，技术）
Enhancing Frequency Forgery Clues for Diffusion-Generated Image Detection
School of Computer and Communication Sciences, EPFL（瑞士联邦理工学院计算机与通信科学学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）
MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion model
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（中国科学技术大学生命科学与医学部生物医学工程学院）；Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州高等研究院）；Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学重点实验室）；Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Visual Intelligence + X International Joint Laboratory of the Ministry of Education（教育部视觉智能与X国际联合实验室）；Picsart AI Research (PAIR)（Picsart人工智能研究院）；Pengcheng Laboratory（鹏城实验室）；Georgia Institute of Technology（佐治亚理工学院）
本文提出 DiffMatte，通过轻量化解码器解耦和均匀时间间隔的自对齐训练，降低扩散抠图计算开销并统一训练推理噪声采样，在 Composition-1k 上取得 SAD 与 MSE 分别提升 5%、15% 的最优性能。
Diffusion-based Adversarial Identity Manipulation for Facial Privacy Protection
School of Computer Science；Engineering, Sun Yat-sen University Guangzhou China；State Key Laboratory of Mathematical Engineering；Engineering, Sun Yat-sen University
UIEDP:Underwater Image Enhancement with Diffusion Prior
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
