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

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This study shows how generative AI, using Stable Diffusion with ControlNet and LoRA, can create efficient and detailed landscape designs. Fine-tuned AI models produce superior results for conceptual landscape architecture.

Keywords:
Text-to-imageconceptual designgenerative AIgenerative designlandscape designstable diffusion

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Landscape Architecture

Background:

  • Traditional landscape design is time-consuming and creativity-limited.
  • Exploring diverse design solutions lacks efficiency.
  • AI offers potential for innovation in design.

Purpose of the Study:

  • To explore generative AI integration in conceptual landscape design.
  • To demonstrate an efficient AI-driven workflow for landscape visualization.
  • To evaluate the impact of fine-tuned AI models on design quality.

Main Methods:

  • Utilized Stable Diffusion, ControlNet, and LoRA for text-to-image generation.
  • Developed a workflow for conceptual landscape design generation.
  • Conducted qualitative and quantitative evaluations of AI-generated designs.

Main Results:

  • AI workflow efficiently generated detailed and coherent landscape designs (parks, plazas, gardens).
  • Fine-tuned models significantly outperformed non-fine-tuned models.
  • AI-generated designs maintained spatial consistency, scale control, and relevant elements.

Conclusions:

  • Generative AI enhances efficiency and creativity in conceptual landscape design.
  • Fine-tuned AI models are crucial for superior design outcomes.
  • AI integration holds significant potential for landscape architecture innovation.