We use hierarchical procedural rules for the generation of control maps within the stable diffusion framework to produce photo-realistic architectural facade images. Starting from a single input image and its segmentation, we apply an inverse procedural module to identify the facade's hierarchical layout. Leveraging this hierarchy and structural features, we introduce a novel ControlNet pipeline that generates new facade imagery guided by procedural transformations. Our method enables various structural edits, including floor duplication and window rearrangement, by integrating hierarchical alignment directly into control maps. This precisely guides the diffusion-based generative process, ensuring local appearance fidelity alongside extensive structural modifications. Comprehensive evaluations, including comparisons with inpainting-based approaches and synthetic benchmarks, confirm our approach's superior capability in preserving architectural identity and achieving accurate, controllable edits. Quantitative results and user feedback validate our method's effectiveness.

翻译：我们利用层级式程序化规则在稳定扩散框架内生成控制图，以产生逼真的建筑立面图像。从单张输入图像及其分割结果出发，我们应用逆程序化模块来识别建筑的层级布局。借助这一层级结构与结构特征，我们引入一种新型ControlNet管道，该管道通过程序化变换引导生成新的立面图像。我们的方法通过将层级对齐直接整合到控制图中，实现了多种结构编辑操作，包括楼层复制与窗户重新排列。这精准引导了基于扩散的生成过程，在确保局部外观保真度的同时支持大规模结构修改。综合评估（包括与基于修补方法的对比及合成基准测试）证实，本方法在保持建筑特征一致性及实现精确可控编辑方面具有卓越能力。定量结果与用户反馈验证了该方法的有效性。