Ayman Fathy Ashour, Wael Rashdan
Recent text to image systems can synthesize Islamic heritage elements with high visual fidelity, but their outputs rarely translate into fabricable geometry or integrate into interiors without substantial redrawing. We present an end-to-end workflow that links historically grounded precedent retrieval, controllable tileable generation, semantic segmentation and vectorization, and geometry-aware mapping into Computer-Aided Design (CAD) environments. Contributions include the following: (i) a license-audited dataset schema and a retrieval classifier for common Islamic motif families and architectural elements; (ii) precedent retrieval via a ResNet 50 and Vision Transformer (ViT) embedding pipeline; (iii) a Low-Rank Adaptation (LoRA) tuned diffusion model that generates tileable motifs with motif/region controls; (iv) a raster-to-vector pipeline that enforces curve closure and minimum feature widths for CNC/laser fabrication; and (v) a rubric and domain metrics (symmetry coherence, seam/tileability error, spline closure and junction valence, UV distortion, feature width compliance) that quantify “depth of integration” beyond surface texture. Quantitative metrics and blinded expert ratings compare the workflow against strong parametric baselines, while scripts translate images to fabrication-ready vectors/solids across walls, ceilings, partitions, floors, and furniture. Cultural safeguards cover calligraphy handling, regional balance audits, and provenance/credit. The workflow advances heritage-aware generative design by carrying imagery across the last mile into buildable detail and by providing practical checklists for adoption in interior architecture and conservation.