本文研究如何通过人类美学评价引导的低秩自适应（LoRA）微调，提升生成式AI模型在实体产品设计中的输出效果，以灯具设计为例，探讨将人类反馈融入AI模型，优化设计的美观性与吸引力。实验聚焦于Stable Diffusion模型的LoRA微调，并研究将AI生成设计转化为实体产品的3D打印实现方法。结果表明，LoRA微调有效提升了AI生成设计的美学偏好匹配度，显著提高了设计的美观性和吸引力。

arXiv:2507.02865v1 Announce Type: cross Abstract: This study explores how Low-Rank Adaptation (LoRA) fine-tuning, guided by human aesthetic evaluations, can enhance the outputs of generative AI models in tangible product design, using lamp design as a case study. By integrating human feedback into the AI model, we aim to improve both the desirability and aesthetic appeal of the generated designs. Comprehensive experiments were conducted, starting with prompt optimization techniques and focusing on LoRA fine-tuning of the Stable Diffusion model. Additionally, methods to convert AI-generated designs into tangible products through 3D realization using 3D printing technologies were investigated. The results indicate that LoRA fine-tuning effectively aligns AI-generated designs with human aesthetic preferences, leading to significant improvements in desirability and aesthetic appeal scores. These findings highlight the potential of human-AI collaboration in tangible product design and provide valuable insights into integrating human feedback into AI design processes.