Stable Diffusion model has been extensively employed in the study of
archi-tectural image generation, but there is still an opportunity to enhance
in terms of the controllability of the generated image content. A multi-network
combined text-to-building facade image generating method is proposed in this
work. We first fine-tuned the Stable Diffusion model on the CMP Fa-cades
dataset using the LoRA (Low-Rank Adaptation) approach, then we ap-ply the
ControlNet model to further control the output. Finally, we contrast-ed the
facade generating outcomes under various architectural style text con-tents and
control strategies. The results demonstrate that the LoRA training approach
significantly decreases the possibility of fine-tuning the Stable Dif-fusion
large model, and the addition of the ControlNet model increases the
controllability of the creation of text to building facade images. This
pro-vides a foundation for subsequent studies on the generation of
architectural images.

通过 LoRA 对 CMP Facedes 数据集上的 Stable Diffusion 进行优化，然后应用 ControlNet 模型进一步控制输出，通过对各种建筑风格文本内容和控制策略下的结果进行比较，实验证明 LoRA 训练方法显著降低了对 Stable Diffusion 大模型的微调可能性，ControlNet 模型的添加增强了基于文本生成建筑立面图像的可控性。