Fine-tuning diffusion model to generate new kite designs for the revitalization and innovation of intangible cultural heritage.

Abstract

Traditional kite creation often relies on the hand-painting of experienced artisans, which limits the revitalization and innovation of this intangible cultural heritage. This study proposes using an AI-based diffusion model to learn kite design and generate new kite patterns, thereby promoting the revitalization and innovation of kite-making craftsmanship. Specifically, to address the lack of training data, this study collected ancient kite drawings and physical kites to create a Traditional Kite Style Patterns Dataset. The study then introduces a novel loss function that incorporates auspicious themes in style and motif composition, and fine-tunes the diffusion model using the newly created dataset. The trained model can produce batches of kite designs based on input text descriptions, incorporating specified auspicious themes, style patterns, and varied motif compositions, all of which are easily modifiable. Experiments demonstrate that the proposed AI-generated kite design can replace traditional hand-painted creation. This approach highlights a new application of AI technology in kite creation. Additionally, this new method can be applied to other areas of cultural heritage preservation. Offering a new technical pathway for the revitalization and innovation of intangible cultural heritage. It also opens new directions for future research in the integration of AI and cultural heritage.