Generative Architectural Design from Textual Prompts: Enhancing High-Rise Building Concepts for Assisting Architects

In the early stages of architectural design, architects convert initial ideas into concrete design schemes, which heavily rely on their creativity and consume considerable time. Therefore, generative design methods based on artificial intelligence are promising for such tasks. However, effectively communicating design concepts to machines is challenging. To address this challenge, this paper proposes a novel cross-model approach for architectural design concepts using textual descriptions to assist architects, comprising a design concept extraction module and an architectural appearance generation module. The design concept extraction module adopts a contrastive learning framework to yield a text encoder with semantic extraction. Subsequently, the architectural appearance generation module proposes a novel deep sparse and text fusion generative adversarial network to convert the extracted design concept semantics into conceptual sketches, utilizing the unique sparsity of sketches. Additionally, it employs the pre-trained latent stable diffusion model to generate realistic and diverse high-rise building renderings, simulating the recreation processes of architects. The generated designs are evaluated qualitatively and quantitatively and further compared with existing real-life buildings to demonstrate the effectiveness of the proposed method. Furthermore, we demonstrate the feasibility of applying the proposed methodology in the early stages of architectural design by modeling a generated design.