Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual changes and cannot easily keep specific details of the edited portion. In this paper we make the observation that image-generation models can be converted to image-editing models simply by fine-tuning them on a single image. We also show that initializing the stochastic sampler with a noised version of the base image before the sampling and interpolating relevant details from the base image after sampling further increase the quality of the edit operation. Combining these observations, we propose UniTune, a novel image editing method. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high fidelity to the input image. UniTune does not require additional inputs, like masks or sketches, and can perform multiple edits on the same image without retraining. We test our method using the Imagen model in a range of different use cases. We demonstrate that it is broadly applicable and can perform a surprisingly wide range of expressive editing operations, including those requiring significant visual changes that were previously impossible.
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
JIUTIAN Research, China Mobile（中国移动极天研究院）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
Guide-and-Rescale: Self-Guidance Mechanism for Effective Tuning-Free Real Image Editing
HSE University（高等经济大学）；AIRI（人工智能研究中心）；Constructor University（康斯特大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；UNSW Sydney（新南威尔士大学悉尼分校）
Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model
Communication University of China（中国传媒大学）；National University of Singapore（新加坡国立大学）；University of California at Merced（加州大学默塞德分校）；Yonsei University（延世大学）
Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing
College of Computer Science and Artificial Intelligence, Wenzhou University（温州大学计算机科学与人工智能学院）；Department of Computer Science, Memorial University of Newfoundland（纽芬兰纪念大学计算机科学系）；AI Analytics Team, Nasdaq（纳斯达克人工智能分析团队）；State Key Laboratory of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
Anti-Diffusion: Preventing Abuse of Modifications of Diffusion-Based Models
University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Kuaishou Technology（快手科技）；Macau University of Science and Technology（澳门科技大学）
Tuning-Free Visual Customization via View Iterative Self-Attention Control
Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Nanchang University（南昌大学）；Jiluan Academy, Nanchang University（南昌大学吉鸾学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
针对 LoRA 等方法依赖训练和多张参考图的问题，本文提出免训练的 VisCtrl，通过 DDIM 反演与迭代自注意力特征注入，实现单参考图的图像、视频及 3D 场景个性化编辑。
Bayesian Optimization for Controlled Image Editing via LLMs
University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；University of Washington（华盛顿大学）；Tsinghua University（清华大学）；Skai Intelligence（Skai智能科技）；University of Copenhagen（哥本哈根大学）
EPEdit: Redefining Image Editing with Generative AI and User-Centric Design
University of Science, VNU-HCM（越南胡志明市科学大学）；Vietnam National University, Ho Chi Minh City（越南国家大学，胡志明市）；University of Information Technology, VNU-HCM（越南胡志明市信息科技大学）；University of Dayton（Dayton 大学）
The Promise of RL for Autoregressive Image Editing
Mila – Quebec AI Institute（魁北克AI研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；École de Technologie Supérieure (ETS)（高等技术学院）；Polytechnique Montréal（蒙特利尔理工学院）；ServiceNow（ServiceNow公司）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
