The development of generative design driven by artificial intelligence algorithms is speedy. There are two research gaps in the current research: 1) Most studies only focus on the relationship between design elements and pay little attention to the external information of the site; 2) GAN and other traditional generative algorithms generate results with low resolution and insufficient details. To address these two problems, we integrate GAN, Stable diffusion multimodal large-scale image pre-training model to construct a full-process park generative design method: 1) First, construct a high-precision remote sensing object extraction system for automated extraction of urban environmental information; 2) Secondly, use GAN to construct a park design generation system based on the external environment, which can quickly infer and generate design schemes from urban environmental information; 3) Finally, introduce Stable Diffusion to optimize the design plan, fill in details, and expand the resolution of the plan by 64 times. This method can achieve a fully unmanned design automation workflow. The research results show that: 1) The relationship between the inside and outside of the site will affect the algorithm generation results. 2) Compared with traditional GAN algorithms, Stable diffusion significantly improve the information richness of the generated results.

翻译：人工智能算法驱动的生成设计发展迅速。当前研究存在两个空白：1）多数研究仅关注设计元素之间的关系，对场地外部信息关注不足；2）GAN等传统生成算法结果分辨率低且细节不足。针对这两个问题，我们整合GAN、Stable Diffusion多模态大规模图像预训练模型，构建了全流程公园生成设计方法：1）首先构建高精度遥感对象提取系统，实现城市环境信息自动化提取；2）其次利用GAN构建基于外部环境的公园设计生成系统，可从城市环境信息快速推断并生成设计方案；3）最后引入Stable Diffusion优化设计方案，补充细节并将方案分辨率扩展64倍。该方法可实现完全无人化的设计自动化工作流程。研究结果表明：1）场地内外关系会影响算法生成结果；2）相较于传统GAN算法，Stable Diffusion显著提升了生成结果的信息丰富度。