Eta反演：为基于扩散的真实图像编辑设计最优Eta函数
Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
AI总结：
针对现有扩散反演图像编辑方法难兼顾文本保真度与源图相似度的问题，提出基于DDIM采样方程η分析的自适应扩散反演技术，通过时空依赖η函数灵活控编辑程度，性能超现有方法并立新基准。
摘要：
扩散模型在文本引导的图像生成领域已取得显著成功，近期也在文本引导的图像编辑领域表现突出。编辑真实图像的常用策略包含对扩散过程进行反演，以获取原始图像的含噪表征，随后对该表征进行去噪以实现预期编辑效果。然而，当前的扩散反演方法往往难以生成既忠实于指定文本提示、又与源图像高度相似的编辑结果。为克服这些局限，我们提出了一种适用于真实图像编辑的新型自适应扩散反演技术，该技术基于对DDIM（去噪扩散隐式模型）采样方程中$η$作用的理论分析，旨在提升编辑能力。通过设计一种具备时间依赖与区域依赖$η$函数的通用扩散反演方法，我们实现了对编辑程度的灵活控制。通过一系列全面的定量与定性评估（包括与大量近期方法的对比），我们证明了所提方法的优越性。该方法不仅在该领域树立了新的基准，还显著优于现有策略。
英文摘要：
Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for editing real images involves inverting the diffusion process to obtain a noisy representation of the original image, which is then denoised to achieve the desired edits. However, current methods for diffusion inversion often struggle to produce edits that are both faithful to the specified text prompt and closely resemble the source image. To overcome these limitations, we introduce a novel and adaptable diffusion inversion technique for real image editing, which is grounded in a theoretical analysis of the role of $η$ in the DDIM sampling equation for enhanced editability. By designing a universal diffusion inversion method with a time- and region-dependent $η$ function, we enable flexible control over the editing extent. Through a comprehensive series of quantitative and qualitative assessments, involving a comparison with a broad array of recent methods, we demonstrate the superiority of our approach. Our method not only sets a new benchmark in the field but also significantly outperforms existing strategies.
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。
