## 2 Related Work  
Before 2022, research on architectural floor plan generation based on deep learning mainly employed Generative Adversarial Network (GAN) models [11]. GAN models consist of a generator and a discriminator. The generator is responsible for generating new data samples similar to the training data samples, while the discriminator determines whether the input data is real training data or fake data. The generator accepts random noise input and attempts to generate data that can deceive the discriminator, while the discriminator strives not to be deceived by the generator. The two form an adversarialtraining process, alternating optimization, ultimately achieving high-quality generated data that is difficult to distinguish from real data. The widespread application of GAN models in the field of architectural floor plan generation can be attributed to the pix2 pix model, improved by [PERSON] et al. (2017) from GAN, which realized Image-to-Image translation using paired data [12]. This was followed by the derivation of pix2 pixHD, which can generate higher-resolution images [13]. In recent years, most research applying GAN models for architectural floor plan generation has employed the aforementioned Image-to-Image approach, predominantly focusing on residential buildings. [PERSON] et al. (2018) used pix2 pixHD for residential floor plan generation and first proposed the method of marking functional rooms in apartment floor plans with color blocks to construct training sets and generate results [14]. [PERSON]. (2019) trained a workflow with pix2 pix to gradually generate and associate derivations from building contours to room segmentation to furniture layout, simulating the architect's design process [15]. At the CAADRIA conference in 2020, [PERSON] et al. demonstrated the use of apartment datasets from China and Japan to train the pix2 pixHD model separately, achieving satisfactory generation results [16]. [PERSON] et al. (2021) first attempted to use the upgraded version of the pix2 pixHD model, GauGAN, for generation experiments. They used data on roads, buildings, and other information from northern Chinese communities to train the GauGAN model to generate residential building layouts, aiming for higher resolution and more diverse generation results, as well as adapting to irregular contour conditions [17]. In 2021, [PERSON] et al. introduced the HouseGAN++ model, which improved the model network using relational GANs and conditional GANs, achieving iterative refinement by making the previous generation result a constraint condition for the next generation [2]. [PERSON] et al. (2022) proposed a GAN model framework that combines vector generation and raster image discrimination capable of directly generating vector floor plans, though limited to rectangular room blocks [18]. The use of GAN models for automatic architectural floor plan generation has formed a relatively common and effective methodology, namely dividing architectural floor plans into specific functional areas, establishing segmentation masks for rooms or different functional areas to form identifiable training data, thereby establishing a deep learning model for training and simulation. This approach reduces the complexity of machine recognition and learning of architectural floor plans and significantly improves generation efficiency. From late 2022, the field gradually began to see research using diffusion models as the primary tool. [PERSON] et al. introduced a new method for generating vector floor plans using diffusion models, named HouseDiffusion [1]. The authors represented floor plans as one-dimensional polygonal rings, with each ring corresponding to a room or door. By denoising the two-dimensional coordinates of these two inference targets, vector graphic floor plans were directly generated. Qualitative and quantitative evaluations indicated that the proposed system outperformed the state-of-the-art technology House-GAN++ [2]. In other research directions, [PERSON] et al.