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：近年来，基于得分蒸馏采样（SDS）和变分得分蒸馏（VSD）等方法，利用二维扩散模型进行文本到3D生成的研究取得了显著的质量提升。然而，此类算法的生成时间较长，严重影响了用户体验。为解决这一问题，我们提出DreamPropeller——一种即插即用的加速算法，可无缝集成至任何基于得分蒸馏的现有文本到3D生成流程中。我们的框架对用于并行采样ODE路径的经典算法——Picard迭代——进行了泛化，使其能够处理非ODE路径（如基于动量的梯度更新）以及优化过程中维度变化的情况（这在3D生成案例中常见）。实验证明，我们的算法通过并行计算换取实际运行时间，在所有测试框架中实现了高达4.7倍的速度提升，而生成质量几乎无损。