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Top1. Introduction
Intangible Cultural Heritage (ICH) encompasses various forms of non-material cultural expressions that represent the diversity of humanity’s living heritage. It serves as a crucial medium for cultural diversity (Aljaberi & Al-Ogaili, 2021). The United Nations Educational, Scientific and Cultural Organization (UNESCO) has significantly increased global awareness of ICH by promoting its protection through international frameworks (Aikawa, 2004). The primary objectives of the UNESCO Convention for the Safeguarding of the Intangible Cultural Heritage include protecting ICH, respecting the cultural heritage of communities, groups, and individuals, enhancing awareness at all levels, and fostering international cooperation and assistance (Jagielska-Burduk et al., 2021). However, under the pressures of modernization and globalization, intangible cultural heritage is increasingly at risk of fading. The lack of technological support and systematic methodologies has hindered the transmission and dissemination of traditional knowledge (Eichler, 2020).
In recent years, artificial intelligence (AI) technologies have advanced rapidly worldwide, demonstrating great potential in cultural heritage preservation and innovation (Deng, 2023). Stable Diffusion, an advanced text-to-image generation model, is capable of producing high-quality, high-resolution images based on textual descriptions (Jadhav et al., 2024). This technology provides new avenues for the digital preservation and innovative design of cultural heritage. International research in this field has focused on virtual restoration of historical artifacts, style transfer, and cultural and creative product design using the SD model. For example, studies have explored the digital restoration of ancient architecture through model training and image generation, reconstructing damaged architectural appearances and supplementing details to assist cultural heritage preservation efforts (Zhang & Romainoor, 2023). Additionally, in the field of cultural and creative design, the SD model has been used to generate modern designs incorporating traditional elements, facilitating the innovative transmission of cultural heritage (Du et al., 2024). The model has also played a crucial role in heritage education and dissemination by generating visual materials related to cultural heritage, enriching educational content, and enhancing public awareness (Du et al., 2024).
Haipai New Year paintings are an essential component of the Shanghai School of Chinese painting, which flourished during the late Qing Dynasty and the early Republican period. These paintings embody the distinctive cultural identity of Shanghai, characterized by an inclusive and diverse aesthetic. Stylistically, Haipai New Year paintings incorporate the artistic traditions of Suzhou’s Taohuawu woodblock prints while integrating Western painting techniques, reflecting the unique aesthetic sensibilities of modern Shanghai (Chen & Luo, 2020). The thematic content of Haipai New Year paintings serves as an encyclopedia of modern Shanghai society. In addition to traditional folk themes, these paintings incorporate urban fashion trends and depict scenes of daily life, reflecting subjects of widespread public interest. As a vital medium for expressing Shanghai’s regional cultural identity, the preservation and development of Haipai New Year paintings play a crucial role in strengthening public recognition of traditional cultural heritage and enhancing cultural soft power.
Current research on Haipai New Year paintings mainly focuses on two areas: historical development and artistic style. From a historical perspective, Zhang and Yan (2016) discuss the rise, prosperity, and decline of Haipai New Year paintings from a macro viewpoint. Zhao (2022) examines the historical evolution of Suzhou Taohuawu and Shanghai Xiaoxiaochang New Year paintings during the late Qing and early Republican periods through a comparative analysis of folk art imagery. In terms of artistic style, Yang (2018) explores the characteristics and artistic value of Haipai New Year paintings in terms of subject matter, composition, color, and linework. Notably, existing literature has yet to investigate the application of generative AI in capturing the stylistic characteristics of Haipai New Year paintings. There remains a significant research gap in key areas such as dynamic interactive dissemination and artistic form innovation.