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Recent methods such as Score Distillation Sampling (SDS) and VariationalScore Distillation (VSD) using 2D diffusion models for text-to-3D generationhave demonstrated impressive generation quality. However, the long generationtime of such algorithms significantly degrades the user experience. To tacklethis problem, we propose DreamPropeller, a drop-in acceleration algorithm thatcan be wrapped around any existing text-to-3D generation pipeline based onscore distillation. Our framework generalizes Picard iterations, a classicalalgorithm for parallel sampling an ODE path, and can account for non-ODE pathssuch as momentum-based gradient updates and changes in dimensions during theoptimization process as in many cases of 3D generation. We show that ouralgorithm trades parallel compute for wallclock time and empirically achievesup to 4.7x speedup with a negligible drop in generation quality for all testedframeworks.

DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling