Recent methods such as Score Distillation Sampling (SDS) and Variational Score Distillation (VSD) using 2D diffusion models for text-to-3D generation have demonstrated impressive generation quality. However, the long generation time of such algorithms significantly degrades the user experience. To tackle this problem, we propose DreamPropeller, a drop-in acceleration algorithm that can be wrapped around any existing text-to-3D generation pipeline based on score distillation. Our framework generalizes Picard iterations, a classical algorithm for parallel sampling an ODE path, and can account for non-ODE paths such as momentum-based gradient updates and changes in dimensions during the optimization process as in many cases of 3D generation. We show that our algorithm trades parallel compute for wallclock time and empirically achieves up to 4.7x speedup with a negligible drop in generation quality for all tested frameworks.

翻译：近期基于得分蒸馏采样的Score Distillation Sampling (SDS)和Variational Score Distillation (VSD)等方法，利用2D扩散模型实现文本到3D生成展现出了卓越的生成质量。然而，这类算法的长生成时间显著影响了用户体验。为解决这一问题，我们提出DreamPropeller算法——一种即插即用的加速方案，可无缝集成至任何基于得分蒸馏的文本到3D生成管道中。本框架将经典ODE路径并行采样算法Picard迭代进行泛化，能够处理非ODE路径场景，例如基于动量的梯度更新以及3D生成过程中常见的维度动态变化。实验表明，该算法通过并行计算换取代化解算时间，在所有测试框架中实现了高达4.7倍的加速效果，而生成质量的下降可忽略不计。