Negative-prompt Inversion：用于文本引导扩散模型编辑的快速图像反演
Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models
AI总结：
针对扩散模型图像编辑中优化式反演耗时的问题，提出无需优化、仅靠前向传播的 negative-prompt inversion，在保持重建保真度的同时实现比 null-text inversion 快30余倍的反演。
摘要：
在采用扩散模型进行图像编辑时，在改变图像风格的同时保持对原始图像的重建保真度至关重要。尽管现有方法通过优化来确保重建保真度，但其缺点是优化需要大量时间。本文提出 negative-prompt inversion，这是一种无需优化、仅通过前向传播即可实现等效重建的方法，从而支持超快的编辑流程。我们通过实验证明，该方法的重建保真度可与现有方法相媲美，能够在约5秒内完成分辨率为512像素、50个采样步骤的反演，比 null-text inversion 快30多倍。所提方法减少了计算时间，进一步使我们能够在扩散模型中使用更多采样步骤，以适度增加计算时间为代价提高重建保真度。
英文摘要：
In image editing employing diffusion models, it is crucial to preserve the reconstruction fidelity to the original image while changing its style. Although existing methods ensure reconstruction fidelity through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion, a method capable of achieving equivalent reconstruction solely through forward propagation without optimization, thereby enabling ultrafast editing processes. We experimentally demonstrate that the reconstruction fidelity of our method is comparable to that of existing methods, allowing for inversion at a resolution of 512 pixels and with 50 sampling steps within approximately 5 seconds, which is more than 30 times faster than null-text inversion. Reduction of the computation time by the proposed method further allows us to use a larger number of sampling steps in diffusion models to improve the reconstruction fidelity with a moderate increase in computation time.
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。
