Controllable Diffusion Models for Generative Design

Abstract

This paper explores the application of controllable diffusion models to generative design, addressing the limitations of traditional stochastic generative systems. Current design systems often struggle with precise control, leading to unpredictable and potentially undesirable outcomes. We propose a novel framework utilizing diffusion models, which offer a powerful mechanism for guiding the design process through explicit control variables. The core idea is to train a diffusion model to generate designs conditioned on parameters such as shape, material properties, and functional requirements. The iterative nature of the diffusion process allows for designer intervention and refinement, ultimately leading to designs that align with specified constraints and aesthetic preferences. This approach represents a significant advancement in generative design, moving beyond purely stochastic generation towards a more directed and controllable workflow. The key contributions of this work lie in the integration of diffusion models with design objectives, enabling a more intuitive and effective design exploration process. We detail the methodology, including the training process, control variable integration, and the iterative refinement strategy, showcasing the potential of this technology for diverse design applications.