Score distillation sampling (SDS) has emerged as an effective framework in text-driven 3D editing tasks due to its inherent 3D consistency. However, existing SDS-based 3D editing methods suffer from extensive training time and lead to low-quality results, primarily because these methods deviate from the sampling dynamics of diffusion models. In this paper, we propose DreamCatalyst, a novel framework that interprets SDS-based editing as a diffusion reverse process. Our objective function considers the sampling dynamics, thereby making the optimization process of DreamCatalyst an approximation of the diffusion reverse process in editing tasks. DreamCatalyst aims to reduce training time and improve editing quality. DreamCatalyst presents two modes: (1) a faster mode, which edits the NeRF scene in only about 25 minutes, and (2) a high-quality mode, which produces superior results in less than 70 minutes. Specifically, our high-quality mode outperforms current state-of-the-art NeRF editing methods both in terms of speed and quality. See more extensive results on our project page: https://dream-catalyst.github.io.

DreamCatalyst是一种新颖的框架，将基于Score distillation sampling的3D编辑解释为扩散反向过程，通过考虑采样动力学优化过程，以减少训练时间并提高编辑质量，其中高质量模式在不到70分钟内产生优越的结果，并在速度和质量方面超过了当前的最先进NeRF编辑方法。

DreamCatalyst：通过控制可编辑性和身份保护实现快速高质量的3D编辑