Abstract
The Chu-style lacquerware (CSL) dataset is a digital resource specifically developed for the digital preservation and inheritance of Chu-style lacquerware, which constitutes an important component of global intangible handicraft heritage. The dataset systematically integrates on-site photographic images from the Hubei Provincial Museum and official digital resources from the same institution, comprising 582 high-resolution images of Chu-style lacquerware, 72 videos of artifacts, and 37 images of traditional Chinese patterns. It comprehensively demonstrates the artistic characteristics of Chu-style lacquerware and provides support for academic research and cultural dissemination. The construction process of the dataset includes data screening, image standardization, Photoshop-based editing and adjustment, image inpainting, and image annotation. Based on this dataset, this study employs the Low-Rank Adaptation (LoRA) technique to train three core models and five style models, and systematically verifies the usability of the CSL dataset from five aspects. Experimental results show that the CSL dataset not only improves the accuracy and detail restoration of Artificial Intelligence (AI)-generated images of Chu-style lacquerware, but also optimizes the generative effect of innovative patterns, thereby validating its application value. This study represents the first dedicated dataset developed for AI generative models of Chu-style lacquerware. It not only provides a new technological pathway for the digital preservation and inheritance of cultural heritage, but also supports interdisciplinary research in archeology, art history, and cultural communication, highlighting the importance of cross-disciplinary collaboration in safeguarding and transmitting Intangible Cultural Heritage (ICH).