Research on the Generation of Ceramic Decorative Patterns from the Perspective of AIGC

Taking Traditional Ornamentation as an Example

Abstract

In traditional ceramic art, the design of decorative patterns is at the core of its aesthetic value. However, traditional design methods are limited due to their low efficiency and singular style. This study aims to promote the development of ceramic decorative pattern design through technological innovation, to meet the personalized and diversified aesthetic demands of modern people for ceramic decorative patterns. Researchers have proposed a method based on fine-tuning the Stable Diffusion large model with the LoRA model, combined with prompt optimization technology, to achieve high-quality generation of ceramic decorative patterns. This method not only significantly improves design efficiency but also breaks through the limitations of traditional styles, creating innovative and personalized decorative patterns. By introducing deep learning and artificial intelligence technologies, this study has brought revolutionary changes to the field of ceramic decorative pattern design, enabling designers to quickly respond to market changes and create works that meet contemporary aesthetics. This has not only propelled the development of ceramic art to a higher level and depth but also provided designers with a fast, flexible, and innovative design tool.