AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion

Abstract

This study is concerned with the restoration of painted pottery images from the Yangshao period. The objective is to enhance the efficiency and accuracy of the restoration process for complex pottery patterns. Conventional restoration techniques encounter difficulties in accurately and efficiently reconstructing intricate designs. To address this issue, the study proposes an AI-assisted restoration workflow that combines Stable Diffusion models (SD) with Low-Rank Adaptation (LoRA) technology. By training a LoRA model on a dataset of typical Yangshao painted pottery patterns and integrating image inpainting techniques, the accuracy and efficiency of the restoration process are enhanced. The results demonstrate that this method provides an effective restoration tool while maintaining consistency with the original artistic style, supporting the digital preservation of cultural heritage. This approach also offers archaeologists flexible restoration options, promoting the broader application and preservation of cultural heritage.

Keywords

• cultural heritage
• artificial intelligence
• Yangshao
• painted pottery
• image restoration
• LoRA