AI for Garden Design Visualization: Development and Validation of the GardenDiff Model
Xiaolong Sun, Xi Chen, Chao Zhou, Shengsha Wu, Hongbo Zhao, Kun Li · Buildings · 2026
The rapid advancement of AI-driven generative design brings new opportunities, but its application in landscape garden design remains limited by two gaps: (1) semantic misalignment between generated images and the designer’s intent, and (2) low-resolution outputs with insufficient details. To address these gaps, we developed GardenDiff, a domain-adapted diffusion model trained via parameter optimization and a specialized landscape garden dataset. Central to this approach is Structured Design Captioning (SDC), a hierarchical annotation system specifically designed for garden design that encodes design elements, style features, and auxiliary scene information. To develop this model, we designed a three-stage experimental framework. In Stage 1, we examined the effects of training caption systems and training resolution on generated landscape garden imagery by controlled experiments. In Stage 2, we conducted joint training across five garden styles (Chinese, Japanese, Mediterranean, Nordic, and English) based on the optimized parameter settings from Stage 1 to construct the GardenDiff model. In Stage 3, we validated the model performance through expert evaluation (N = 36) and public evaluation (N = 136) and analyzed style-specific variations in the generated outcomes. Research results showed that Structured Design Captioning (SDC) improved Spatial Rationale by 19.67–39.46% compared with generic captions, and training at 1536 × 1536 pixels improved image quality by 23.2% compared with 768 × 768-pixel training. GardenDiff trained with these optimized parameters showed notable advantages. Its overall scores (5.06) exceeded those of Stable Diffusion XL base 1.0 (SDXL 1.0) by 16.4% and DreamShaper XL by 22.4%. The model improved across four dimensions, including Design Rationale, Design Professionalism, Design Accuracy, and Design Satisfaction. Our study offers a new model to improve the perspective visualization of generative garden design and provides insights into AI-informed landscape and urban design.