Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in generated designs. We propose a novel approach combining optimization, constraint satisfaction, and language models to tackle these challenges in architectural design. Our method uses Quality-Diversity (QD) to generate a diverse, high-performing dataset. We then fine-tune a language model with this dataset to generate high-level designs. These designs are then refined into detailed, constraint-compliant layouts using the Wave Function Collapse algorithm. Our system demonstrates reliable adherence to textual guidance, enabling the generation of layouts with targeted architectural and performance features. Crucially, our results indicate that data synthesized through the evolutionary search of QD not only improves overall model performance but is essential for the model's ability to closely adhere to textual guidance. This improvement underscores the pivotal role evolutionary computation can play in creating the datasets key to training generative models for design. Web article at https://tilegpt.github.io

翻译：工程应用中生成式模型面临两个基本挑战：获取高性能、多样化的数据集，以及在生成设计中遵循精确约束。我们提出了一种结合优化、约束满足和语言模型的新方法，以应对建筑设计中的这些挑战。我们的方法使用质量多样性（QD）生成多样化、高性能的数据集，然后利用该数据集微调语言模型以生成高层设计。随后，通过波函数坍缩算法将这些设计细化成详细且符合约束的布局。我们的系统展现出对文本指导的可靠遵循，能够生成具有目标建筑和性能特征的布局。关键的是，我们的结果表明，通过QD进化搜索合成的数据不仅提升了模型整体性能，而且对模型紧密遵循文本指导的能力至关重要。这一改进突显了进化计算在创建训练设计领域生成模型所必需的数据集中可发挥的关键作用。网页文章：https://tilegpt.github.io