## 5 Experiment and Discussion
### Discussion  
This study explored the automatic generation of architectural floor plans using SD models, focusing specifically on standard nursing units in general hospitals. The results indicate that deep learning models, particularly diffusion models, can effectively generate preliminary architectural layouts that align with established design principles and functional requirements. The generated layouts demonstrate potential for assisting architects in the early design stages, providing a foundation for more detailed and customized planning.  
The main sections of this paper are concentrated in Sections 4 and 5. Section 4 extensively describes the generation process testing of the SD model, which is crucial for finding the optimal solution. It was ultimately discovered that incorporating the main-corridor into the input image and combining Image-to-Image generation with ControlNet methods is highly effective. By controlling different random seed inputs, diverse layouts meeting the requirements can be generated. Section 5 successfully completed the automated process from parameter-controlled boundary generation to SD-generated functional layouts, further controlling parameters such as area, main-corridor location, and length, making the study practically valuable. Additionally, the effectiveness of the method was demonstrated through a comparative analysis of a large number of generated samples against real samples.  
Two methods were used in the analysis section. Firstly, area assessment is essential, as the proportion of functional areas determines whether the nursing unit meets the project specifications. Different RGB value intervals were set to distinguish color blocks, facilitating the calculation of area ratios. This step also identified results with inaccurate RGB values. Figure 15 shows that most results align with the real area features, though some results deviate significantly, reflecting the instability of the SD model. Further research is needed to improve control in this part. Secondly, user research was conducted on the topological relationships of the generated results, with statistical analysis using classification model performance evaluation methods. The final statistics showed that 82% of the generated  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Subjects id** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Avg.** \\ \hline TP & 13 & 13 & 13 & 12 & 12 & 14 & 11 & 12 & 14 & 13 & \\ FP & 10 & 10 & 10 & 10 & 10 & 10 & 13 & 11 & 9 & 9 & \\ TN & 0 & 1 & 2 & 2 & 0 & 2 & 1 & 1 & 2 & 0 & \\ FN & 7 &  
samples met the evaluation criteria, consistent with preliminary conclusions from the testing chapter.  
While the independence of the nursing unit avoids analyzing the relationship with other functions within the hospital system, the internal relationships among various functions are complex, such as accessibility analysis of the nurse station, visual analysis of the nurse station, the number of patient rooms, and the relationship between traffic flow and the number of serviced patients. This study does not delve into these aspects, but they can serve as interesting starting points for future work.  
The analysis and discussion above indicate that AI-assisted generated floor plans for nursing units are comparable to traditional design methods in terms of spatial layout rationality and clarity of functional zones. At the same time, AI methods demonstrate significant advantages in reducing design time and improving design efficiency. However, since this study focuses on standard nursing units, further research is needed to assess the performance of AI tools when dealing with non-standardized building types and complex engineering conditions.