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arXiv · 2306.09935

Drag-guided diffusion models for vehicle image generation

Nikos Arechiga·Frank Permenter·Binyang Song·Chenyang Yuan
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Abstract

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.

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Nikos Arechiga, Frank Permenter, Binyang Song, Chenyang Yuan. 2023-06-16. Drag-guided diffusion models for vehicle image generation. https://arxiv.org/abs/2306.09935

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