This repository contains the official implementation of the following paper:
Generating Daylight-driven Architectural Design via Diffusion Models
In this paper, we present a novel daylight-driven AI-aided architectural design method. Firstly, we formulate a method for generating massing models, producing architectural massing models using random parameters quickly. Subsequently, we integrate a daylight-driven facade design strategy, accurately determining window layouts and applying them to the massing models. Finally, we seamlessly combine a large-scale language model with a text-to-image model, enhancing the efficiency of generating visual architectural design renderings. Experimental results demonstrate that our approach supports architects' creative inspirations and pioneers novel avenues for architectural design development.
We provide the daylighting dataset in ./dataset for training. For more personal floorplan data, you can refer to ./tools/daylighting_gen.gh to generate more corresponding daylighting maps.
You can refer to the following repositories (sd-scripts and lora-scripts ) for training your models.
This project is distributed under the MIT License. Our work builds upon the foundation laid by others. We thank the contributions of the SD community.
If you find our repo useful for your research, please consider citing our paper:
@article{li2024generating,
title={Generating daylight-driven architectural design via diffusion models},
author={Li, Pengzhi and Li, Baijuan},
journal={arXiv preprint arXiv:2404.13353},
year={2024}
}