  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.
