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2D扩散模型的评分蒸馏采样（SDS）与变分评分蒸馏（VSD）等文本到3D生成方法已展现出卓越的生成质量。然而，此类算法较长的生成时间严重降低了用户体验。为此，我们提出DreamPropeller——一种即插即用的加速算法，可无缝集成于任何基于评分蒸馏的现有文本到3D生成框架。本方法推广了经典ODE路径并行采样算法Picard迭代，能够处理非ODE路径，例如动量驱动的梯度更新以及三维生成优化过程中常见的维度变化。实验表明，该算法以并行计算换取时钟时间，在几乎所有测试框架中实现了高达4.7倍的加速，而生成质量下降可忽略不计。