A Diffusion-Based Generative AI Framework: An Exterior House Design from Textual Descriptions

Abstract: The architectural design process is often iterative, time-consuming, and heavily dependent on effective communication between clients and professionals. Existing design tools, such as Computer-Aided Design (CAD) systems, require technical expertise, limiting accessibility for non-professional users. This study proposes a generative artificial intelligence framework for exterior house design using a diffusion-based text-to-image model. The proposed approach integrates Stable Diffusion for image generation with a vision-language model (BLIP) to enhance semantic alignment between textual descriptions and generated outputs. In addition, an interactive refinement mechanism based on image inpainting is incorporated to allow localized modification of design elements. The system is trained on a dataset of exterior house images and evaluated using quantitative metrics, including CLIP Score and Fréchet Inception Distance (FID), as well as usability assessment. Experimental results demonstrate that the proposed framework is capable of generating semantically relevant and visually coherent architectural designs, while improving accessibility and reducing the time required for design iteration. The findings highlight the potential of generative AI as an effective tool for supporting user-centric architectural visualization and design exploration.

Muhammad Amirul Akmal bin Ajusin, Noor Hasimah Ibrahim Teo, Rosniza Roslan, Raseeda Hamzah and Anita Ahmad Kasim. “A Diffusion-Based Generative AI Framework: An Exterior House Design from Textual Descriptions”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170552

@article{Ajusin2026,
title = {A Diffusion-Based Generative AI Framework: An Exterior House Design from Textual Descriptions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170552},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170552},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Muhammad Amirul Akmal bin Ajusin and Noor Hasimah Ibrahim Teo and Rosniza Roslan and Raseeda Hamzah and Anita Ahmad Kasim}
}