Floorplan-Diffusion: Automatic Floor Plan Generation via Pre-trained Large Latent Diffusion Model

Minyang Xu, Yunzhong Lou, Xiang Gao, Xiangdong Zhou

Abstract: Automatic floor plan generation is a long-standing goal in the field of engineering and architectural design. It has significant value in practical applications. Although extensive research over several decades has introduced numerous innovative methods, the issue remains a significant challenge. In this study, we introduce a novel approach to floor plan image generation, leveraging the capabilities of the large pre-trained Latent Diffusion Model (LDM). Through improvements in the architecture of the pre-trained Latent Diffusion Model (LDM) and subsequent fine-tuning, we achieve an effective multimodal conditional floor plan image generation with a reduced amount of training data. Specifically, we propose a multi-head self-attention graph convolution-based layout embedding and a novel layout fusion contrast learning module integrating to the pre-trained LDM, which not only enhances the constraint generation under layout instructions, but also preserves the balance and consistency of the floor plan elements, such as different kinds of rooms and walls, as specified in the textual instructions. The experiments were conducted on two commonly used data sets, RPLAN and LIFULL. The experimental results show that our method outperforms the traditional methods and SOTA work and achieves the best generation results.

External IDs:dblp:conf/mir/XuLGZ25