  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.
