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.
翻译：本文提出了一种多网络组合的文本到建筑外立面图像生成方法，以增强生成图像内容的可控性。我们首先使用Low-Rank Adaptation方法在CMP Facades数据集上微调了稳定扩散模型，然后应用ControlNet模型进一步控制输出。最后，我们比较了不同建筑风格文本内容和控制策略下的外立面生成结果。结果表明，LoRA训练方法显著降低了微调稳定扩散大模型的可能性，而ControlNet模型的添加增加了文本到建筑外立面图像创建的可控性。为后续关于建筑图像生成的研究打下了基础。
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