Pro‐DG: Procedural Diffusion Guidance for Architectural Facade Generation

Abstract 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.

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Publication Details

Journal
Computer Graphics Forum
Published
2026-05-01
DOI
https://doi.org/10.1111/cgf.70487
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

Pro‐DG: Procedural Diffusion Guidance for Architectural Facade Generation

J. Swidzinski, A. Plocharski, P. Musialski
Computer Graphics Forum
Generative Adversarial Networks and Image Synthesis
article

Pro‐DG: Procedural Diffusion Guidance for Architectural Facade Generation

J. Swidzinski, A. Plocharski, P. Musialski
article en

Abstract

Abstract 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.

Computer Graphics Forum
Warsaw University of Technology (PL), New Jersey Institute of Technology (US), NIHR Imperial Biomedical Research Centre (GB), Akces Lab (PL), Imperial College London (GB)
Sustainable cities and communities
Openalex Percentile: Top 8%
Generative Adversarial Networks and Image Synthesis
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