Drag-guided diffusion models for vehicle image generation
Nikos Arechiga Frank Permenter Binyang Song Chenyang Yuan
Denoising diffusion models trained at web-scale have revolutionized image
generation. The application of these tools to engineering design is an
intriguing possibility, but is currently limited by their inability to parse
and enforce concrete engineering constraints. In this paper, we take a step
towards this goal by proposing physics-based guidance, which enables
optimization of a performance metric (as predicted by a surrogate model) during
the generation process. As a proof-of-concept, we add drag guidance to Stable
Diffusion, which allows this tool to generate images of novel vehicles while
simultaneously minimizing their predicted drag coefficients.