Generative Early Architectural Visualizations: Incorporating Architect's Style-trained Models

Abstract

This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image (txt2img) technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, and construction industry. By creating >10,000 images incorporating an architect's personal style and characteristics into a residential house model, the effectiveness of base AI models. Furthermore, various architectural styles were integrated to enhance the visualization process. This method involved additional training for styles with low similarity rates, which required extensive data preparation and their integration into the base AI model. Demonstrated to be effective across multiple scenarios, this technique markedly enhances the efficiency and speed of production of architectural visualization images. Highlighting the vast potential of AI in design visualization, our study emphasizes the technology's shift toward facilitating more user-centered and personalized design applications.