How to generate a museum floor plan: LoRA for diffusion models, or GANs?
Li Zhou, Ziyu Song, Shaoming Lu · Frontiers of Architectural Research · 2025
This study utilized a comparative methodology to explore the generative design of museum floor plans, addressing the complexity of meeting curatorial, designer, and visitor demands. A dataset of 263 museum plans was curated, and four generative methods—LoRA for diffusion models, Pix2Pix, CycleGAN, and the generative segmentation model—were trained. The results were evaluated by FID, SSIM, and MAE metrics for image fidelity and diversity. The pixel-based space syntax method was applied to evaluate the connectivity of the functional space, and expert scoring was combined to assess whether the generated plans met the layout characteristics of museums. Findings are: First, Pix2Pix led in terms of the image structural similarity metric SSIM and diversity metric FID but scored lowest in the image detail reconstruction metric MAE. Second, space syntax analysis revealed that the generated layouts had greater exhibition space connectivity but lower public space connectivity than the original images. Third, the expert grading results indicate that the outputs of LoRA better align with the professional expectations of designers regarding functional composition and spatial organization. These findings highlight the potential of generative models in museum design, particularly when enhanced by space syntax evaluations to improve spatial intelligibility and meet diverse stakeholder needs.