通过语义特征提示和LoRA培训来增强产品概念图像生成
Jiangnan Li1, Shuo Zhang1, Li Sun2
1School of Arts and Design, Yanshan University, Haigang District, Qinhuangdao, 066004, Hebei province, China.
Scientific reports
|November 19, 2025
概括
这项研究增强了用于产品设计的文本到图像生成,使用细粒度语义解码和低级适应 (LoRA) 微调. 这种创新方法提高了生成人工智能 (GAI) 概念化产品视觉图像的能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 当前的生成人工智能 (GAI) 模型在精确的产品概念图像设计方面扎.
- 在控制特定设计应用的大型模型的输出方面存在限制.
研究的目的:
- 提出一项创新策略,将精细的语义特征解码与低级适应 (LoRA) 微调集成.
- 显著提高产品概念设计的文本到图像技术性能.
- 为产品设计提供大型模型中可控发电的解决方案.
主要方法:
- 使用E-Prime软件进行语义初始化,以提取关键的产品设计词.
- 雇佣DeepSeek提示工程来解码跨心理,功能和物理图像维度的语义特征.
- 应用低级调整 (LoRA) 技术用于基于专家衍生语义特征提示的独立数据集培训.
主要成果:
- 通过智能脉冲诊断仪器示范了该策略在概念设计中的应用.
- 进行多维评估和比较实验以验证疗效.
- 通过基于语义特征提示的LoRA微调实现了最佳模型配置.
结论:
- 拟议的策略有效地提高了产品概念设计的文本到图像生成.
- 语义解码和LoRA的整合为设计中控制的GAI提供了可行的解决方案.
- 通过经验测试和案例研究验证了潜力和疗效.
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