## 3 Experiments and Results
### Experimental Results
Figure 11 compares the generated conceptual sketches with the real sketches in the training dataset, demonstrating that the DSTF-GAN does not memorize the samples in the dataset but rather understands the semantics of textual descriptions and the features of the high-rise sketches. Notably, the generated sketches exhibited uniqueness, indicating that the DSTF-GAN can learn the attributes of the design concepts in the training dataset and does not lose the creative requirements of the architectural design process. Subsequently, as shown in Figure 12, the PR module effectively transformed the generated conceptual sketches into lifelike high-rise buildings using the pre-trained _stable-diffusion-v1-5_ model, with parameters set to \(strength=0.6\sim 0.8\), \(prompt=\)_'A high -rise building, 3D', \(scale=30\)_, and \(ddim\_steps=100\). The experimental outcomes confirmed the practical viability of the proposed approach for generating a diverse and authentic collection of high-rise building concepts from textual descriptions. Figure 13 provides a direct comparison of the high-rise building images generated by the pre-trained latent diffusion model and the proposed method. The pre-trained model employs the text-to-image functionality of the _stable-diffusion-v1-5_ model, with the input consisting of the text description corresponding to the sketch, using parameters \(scale=5\) and \(ddim\_steps=100\). The results demonstrate that our approach generates high-rise building renderings with improved semantic consistency compared to those produced directly from the text-to-image functionality of the diffusion model, thus highlighting the superior performance of the proposed method. The limitations of the pre-trained latent diffusion model, such as lacking a comprehensive understanding of architecture and precise correspondence between architectural design concepts and visual features, were evident. In contrast, the proposed method, which utilizes conceptual sketches as an intermediary representation, proved to be a more effective approach. Authenticity assessment of generated results. To further validate the authenticity of the generated results, an investigation is conducted to assess the applicability of the generated architectural sketches and renderings by comparing them with real high-rise buildings not included in the dataset. Figure 14 demonstrates the utility of the generated sketches and renderings, showing similarities with existing buildings. This indicates that the proposed methodology captures essential characteristics found in real buildings while maintaining innovation and diversity in conceptual design. For instance, Figure 14a highlights similarities between a generated rendering sample and Metropol Tower Istanbul in the Atasehir district of Istanbul, Turkey, showcasing a cutting architectural design approach at the top and similarly decorated surfaces. These findings validate the feasibility of applying this method. Figure 11: Creative comparison of generated high-rise conceptual sketches with real sketches. Figure 10: Comparison of semantic consistency between generated high-rise conceptual sketches and textual descriptions. The semantic correspondences highlighted in the text are indicated by colored bounding boxes in the sketches. Figure 12: Rendering high-rise building appearances from generated conceptual sketches using the pre-trained _stable-diffusion-v1-5_ model. Figure 13: Comparisons of generated high-rise building appearances directly from the pre-trained latent stable diffusion model and the proposed two-stage method. (**a**) Comparison of high-rise type building generation results. (**b**) Comparison of tower type building generation results.