AIGC empowers the sustainable development of traditional Chinese paper-cut

Abstract This research aims to explore the application of artificial intelligence-generated content (AIGC) technology in traditional Chinese paper-cut design and promote the protection and inheritance of traditional Chinese paper-cut culture. First, the paper-cut works of paper-cut artist are analyzed to extract the characteristic factors of her design styles. Second, based on the characteristics of the paper-cut style, a dedicated dataset for model training is constructed and passed into the fine-tuning network to train and generate a Low-Rank Adaptation (LoRA) fine-tuning model with the design style characteristics of the paper-cut artists. Finally, the paper-cut LoRA model is combined with the stable diffusion model to complete the intelligent design practice of traditional paper-cut. Through experimental verification, the paper-cut model trained in this research can effectively realize the migration design of traditional paper-cut artists’ design style and improve the efficiency of paper-cut design. This research proposes a paper-cut style generation method based on AIGC, which provides a new perspective for the protection and development of paper-cut culture. This method reduces the difficulty of paper-cut design, optimizes the design process, and improves design efficiency, providing technical support for the sustainable development of paper-cut art. At the same time, it also has important significance and value for the digital inheritance and innovation of other intangible cultural heritage.