The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for interactive adaptation to user modifications. This paper introduces ChatHouseDiffusion, which leverages large language models (LLMs) to interpret natural language input, employs graphormer to encode topological relationships, and uses diffusion models to flexibly generate and edit floor plans. This approach allows iterative design adjustments based on user ideas, significantly enhancing design efficiency. Compared to existing models, ChatHouseDiffusion achieves higher Intersection over Union (IoU) scores, permitting precise, localized adjustments without the need for complete redesigns, thus offering greater practicality. Experiments demonstrate that our model not only strictly adheres to user specifications but also facilitates a more intuitive design process through its interactive capabilities.

翻译：平面图的生成与编辑在建筑规划中至关重要，需要高度的灵活性与效率。现有方法需要大量输入信息，且缺乏对用户修改进行交互式适应的能力。本文提出ChatHouseDiffusion，该方法利用大语言模型（LLMs）解析自然语言输入，采用Graphormer编码拓扑关系，并运用扩散模型灵活生成与编辑平面图。该方案支持基于用户想法的迭代式设计调整，显著提升了设计效率。与现有模型相比，ChatHouseDiffusion取得了更高的交并比（IoU）分数，允许进行精确的局部调整而无需完全重新设计，因而具有更强的实用性。实验表明，我们的模型不仅严格遵循用户规范，还通过其交互能力促进了更直观的设计流程。