LEDITS++：利用文本到图像模型实现无限图像编辑
LEDITS++: Limitless Image Editing using Text-to-Image Models
AI总结：
LEDITS++提出了一种无需调优、支持多编辑且架构无关的文本图像编辑方法，通过新颖反演和隐式掩蔽技术实现高保真编辑，并引入TEdBench++基准验证其优越性。
摘要：
文本到图像的扩散模型最近因其仅从文本输入就能生成高保真图像的惊人能力而受到越来越多的关注。后续的研究工作旨在利用并将这些能力应用于真实图像的编辑。然而，现有的图像到图像方法往往效率低下、不精确且通用性有限。它们要么需要耗时的微调，要么偏离输入图像过强，和/或缺乏对多个同时编辑的支持。为了解决这些问题，我们引入了LEDITS++，一种高效、通用且精确的文本图像操控技术。LEDITS++新颖的反演方法无需调优或优化，并能在几步扩散中产生高保真结果。其次，我们的方法支持多个同时编辑，且与架构无关。第三，我们使用一种新颖的隐式掩蔽技术，将变化限制在相关图像区域。作为我们详尽评估的一部分，我们提出了新颖的TEdBench++基准。我们的结果展示了LEDITS++的能力及其相对于先前方法的改进。
英文摘要：
Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming finetuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
