SimInversion：一种基于反演的文本到图像编辑的简单框架
SimInversion: A Simple Framework for Inversion-Based Text-to-Image Editing
AI总结：
针对原生DDIM反演因无分类器引导优化不足、误差累积导致编辑效果差的问题，提出解耦源与目标分支引导尺度的SimInversion框架，理论得出最优引导尺度0.5，在PIE-Bench上可在保效率的同时大幅提升编辑性能。
摘要：
扩散模型在文本引导下展现出出色的图像生成性能。受扩散学习过程启发，现有图像可通过DDIM反演（去噪扩散隐式模型反演）根据文本进行编辑。然而，原生DDIM反演未针对无分类器引导进行优化，累积误差会导致性能不佳。尽管已有诸多算法改进DDIM反演的编辑框架，本研究探究了DDIM反演中的近似误差，提出在保留原有框架的前提下，解耦源分支与目标分支的引导尺度以降低误差，且可从理论上推导出优于默认设置的引导尺度（即0.5）。在PIE-Bench上的实验表明，该方法能在不牺牲效率的情况下，显著提升DDIM反演的性能。
英文摘要：
Diffusion models demonstrate impressive image generation performance with text guidance. Inspired by the learning process of diffusion, existing images can be edited according to text by DDIM inversion. However, the vanilla DDIM inversion is not optimized for classifier-free guidance and the accumulated error will result in the undesired performance. While many algorithms are developed to improve the framework of DDIM inversion for editing, in this work, we investigate the approximation error in DDIM inversion and propose to disentangle the guidance scale for the source and target branches to reduce the error while keeping the original framework. Moreover, a better guidance scale (i.e., 0.5) than default settings can be derived theoretically. Experiments on PIE-Bench show that our proposal can improve the performance of DDIM inversion dramatically without sacrificing efficiency.
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。
