More than anything: Advocating for synthetic architectures within large-scale language-image models

Abstract

Large-scale language-image (LLI) models have the potential to open new forms of critical practice through architectural research. Their success enables designers to research within discourses that are profoundly connected to the built environment but did not previously have the resources to engage in spatial research. Although LLI models do not generate coherent building ensembles, they offer an esthetic experience of an AI infused design practice. This paper contextualizes diffusion models architecturally. Through a comparison of approaches to diffusion models in architecture, this paper outlines data-centric methods that allow architects to design critically using computation. The design of text-driven latent spaces extends the histories of typological design to synthetic environments including non-building data into an architectural space. More than synthesizing quantic ratios in various arrangements, the architect contributes by assessing new categorical differences into generated work. The architects’ creativity can elevate LLI models with a synthetic architecture, nonexistent in the data sets the models learned from.

Understanding the Impact of Deep Learning Models on Building Information Modeling Systems: A Study on Generative Artificial Intelligence Tools

“…In their research, Jang and Lee (2023, p. 7) [26] examined the process of incorporating pre-trained large language models into BIM authoring tools. Besides, architects can utilize large-scale language-image (LLI) models to generate synthetic designs, which can provide multifaceted solutions to complex issues [27]. Exploring the use of generative AI instruments for architectural concept generation and early design phases is a significant concern within this subject.…”

Synthetic Data Meets Architectural Typology: An Exploratory Computational Workflow with a Carbon Footprint Inference Case Study

“…While acknowledging valid concerns, I emphasize AI's potential to build on, not supplant, human creativity and critical thinking. 10 Further interdisciplinary research is needed to delineate appropriate applications of generative models that augment, rather than replace, the architect's unique capacities. Overall, a nuanced examination of the continuities and ruptures between past technical shifts and current AI reveals significant unrealized potential.…”