Generating conceptual landscape design via text-to-image generative AI model

abstract

• This study explores the integration of text-to-image generative AI, particularly Stable Diffusion, in conjunction with ControlNet and LoRA models in conceptual landscape design. Traditional methods in landscape design are often time-consuming and limited by the designers individual creativity, also often lacking efficiency in the exploration of diverse design solutions. By leveraging AI tools, we demonstrate a workflow that efficiently generates detailed and visually coherent landscape designs, including natural parks, city plazas, and courtyard gardens. Through both qualitative and quantitative evaluations, our results indicate that fine-tuned models produce superior designs compared to non-fine-tuned models, maintaining spatial consistency, control over scale, and relevant landscape elements. This research advances the efficiency of conceptual design processes and underscores the potential of AI in enhancing creativity and innovation in landscape architecture.

authors

• Song, Yang
• Ye, Xinyue

published proceedings

• Environment and Planning B Urban Analytics and City Science

author list (cited authors)

• Ye, X., Huang, T., Song, Y., Li, X., Newman, G., Wu, D. J., & Zeng, Y.

complete list of authors

• Ye, Xinyue||Huang, Tianchen||Song, Yang||Li, Xin||Newman, Galen||Wu, Dayong Jason||Zeng, Yijun

publisher

• SAGE Publications  Publisher

published in

• Environment and planning. B: Urban analytics and city science  Journal

keywords

• 33 Built Environment And Design
• 3301 Architecture
• 3303 Design

Digital Object Identifier (DOI)

• 10.1177/23998083251316064

URL

• https://doi.org/10.1177/23998083251316064