文献类型：期刊文献

英文题名：An intelligent generative method of fashion design combining attribute knowledge and Stable Diffusion Model

作者：Chen, Yumiao[1];Ma, Jingyi[1]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China

年份：2025

卷号：95

期号：11-12

起止页码：1231

外文期刊名：TEXTILE RESEARCH JOURNAL

收录：;EI(收录号：20244517330904);WOS:【SCI-EXPANDED(收录号:WOS:001346287900001)】；

基金：The author(s) disclosed receipt of the following financial sup-port for the research, authorship, and/or publication of this article: This study was partly supported by the Research Project of Humanities and Social Sciences of the Ministry of Education (No. 24YJA760013), the National Natural Science Foundation of China (No. 51905175), Shanghai Soft Science Key Project (No. 24692109400), and Xie Youbai Design Science Research Foundation (No. XYB-DS-202301).

语种：英文

外文关键词：Fashion attribute knowledge; fashion design; intelligent generative methods; prompt template; Stable Diffusion Model

摘要：Artificial intelligence generation technology has brought new opportunities to the field of fashion design. Attribute knowledge has a significant impact on the overall effect of fashion design. Contemporary generative methods of fashion design frequently yield results lacking semantic information or missing specific attributes. To address the problem, this study aims for an intelligent generative method of fashion design through constructing prompt templates and a specific attribute low-rank adaption (LoRA) to combine fashion attribute knowledge into the generative process of the Stable Diffusion Model. First, a fashion attribute knowledge graph is constructed to establish prompt templates, and natural language descriptions are transformed into professional and complete prompt through GPT-4. Second, the fashion dataset is annotated with templates to filter specific attributes for LoRA training, followed by controlling fashion attributes in generation. Furthermore, analyses of the generation of women's jacket designs show that the proposed method consistently improves the accuracy and stability of attributes in fashion design generation.