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

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

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 designer’s 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.

Suggested Citation

  • Xinyue Ye & Tianchen Huang & Yang Song & Xin Li & Galen Newman & Dayong Jason Wu & Yijun Zeng, 2025. "Generating conceptual landscape design via text-to-image generative AI model," Environment and Planning B, , vol. 52(8), pages 1903-1919, October.
    • Handle: RePEc:sae:envirb:v:52:y:2025:i:8:p:1903-1919
    DOI: 10.1177/23998083251316064
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