ChatHouseDiffusion: Prompt-guided generation and editing of floor plans

Abstract:

The generation and editing of floor plans are critical in intelligent architectural planning, requiring a high degree of flexibility and efficiency. In response to the limitations of existing methods—such as excessive reliance on input information, lack of interactivity, and insufficient support for precise local modifications—this paper presents ChatHouseDiffusion. The proposed approach first utilizes a large language model to parse natural language instructions into structured JSON prompts, then applies Graphormer to capture the topological relationships between rooms, and finally adopts a diffusion model to generate floor plans under room boundary constraints. The results indicate that during the editing phase, the cross-attention maps replacement mechanism enables localized modifications without reconstructing the entire layout. Experiments conducted on the RPLAN dataset demonstrate that ChatHouseDiffusion outperforms existing models in both Micro-IoU and Macro-IoU, especially when accurate input prompts are used, achieving results highly consistent with ground truth, which exhibits strong practical utility and generalization performance. The model not only strictly follows user requirements but also enables intuitive and iterative design through interactive operations, providing a novel pathway for intelligent floor plan design. Based on this method, we further developed a visual platform enabling the drawing of outlines, inputting text prompts, and generating and editing floor plans, enhancing the practicality and usability of floor plan design.