Direct Inversion：用3行代码提升基于扩散模型的编辑
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
AI总结：
本文提出Direct Inversion技术，通过解耦源和目标扩散分支仅需3行代码提升编辑效果，并发布PIE-Bench基准，在8种编辑方法上实现更优性能与近一个数量级加速。
摘要：
文本引导的扩散模型已经彻底改变了图像生成和编辑，提供了出色的真实性和多样性。具体而言，在基于扩散模型的编辑中，源图像根据目标提示词进行编辑，该过程首先通过扩散模型获取与源图像对应的噪声潜向量。随后，该向量被输入到独立的源扩散分支和目标扩散分支中进行编辑。这一反演过程的准确性显著影响最终的编辑结果，既影响源图像的本质内容保留，也影响根据目标提示词的编辑保真度。先前的反演技术旨在为源扩散分支和目标扩散分支寻找统一解。然而，我们的理论和实证分析表明，解耦这两个分支会导致本质内容保留和编辑保真度保障的职责明确分离。基于这一洞察，我们引入了“Direct Inversion”，这是一种新颖的技术，仅需3行代码即可实现两个分支的最佳性能。为了评估图像编辑性能，我们提出了PIE-Bench，这是一个包含700张图像的编辑基准，展示了多样的场景和编辑类型，并配备了多功能标注和全面的评估指标。与最先进的基于优化的反演技术相比，我们的解决方案不仅在8种编辑方法中均取得了更优的性能，还实现了近一个数量级的加速。
英文摘要：
Text-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process commences by acquiring a noisy latent vector corresponding to the source image via the diffusion model. This vector is subsequently fed into separate source and target diffusion branches for editing. The accuracy of this inversion process significantly impacts the final editing outcome, influencing both essential content preservation of the source image and edit fidelity according to the target prompt. Prior inversion techniques aimed at finding a unified solution in both the source and target diffusion branches. However, our theoretical and empirical analyses reveal that disentangling these branches leads to a distinct separation of responsibilities for preserving essential content and ensuring edit fidelity. Building on this insight, we introduce "Direct Inversion," a novel technique achieving optimal performance of both branches with just three lines of code. To assess image editing performance, we present PIE-Bench, an editing benchmark with 700 images showcasing diverse scenes and editing types, accompanied by versatile annotations and comprehensive evaluation metrics. Compared to state-of-the-art optimization-based inversion techniques, our solution not only yields superior performance across 8 editing methods but also achieves nearly an order of speed-up.
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。
