A study on the generation of traditional patterns in the perspective of AIGC-ancient Egyptian patterns as an example
Abstract
This study proposes a low-resource pattern generation framework for cultural heritage digital innovation, which effectively solves the problems of resource consumption and data scarcity in traditional pattern generation by integrating the LORA (Low-Rank Adaptation) technology and multi-dimensional evaluation system. The study selects the ancient Egyptian traditional tattoos as the target, and by freezing the parameters of the Stable-Diffusion backbone network and optimizing the iteration of the low-rank matrix only, we achieve the reduction of the training memory and the iteration time, which verifies the efficiency of the low-rank adaptation in the image generation task. Aiming at the scarcity and heterogeneity of pattern data, we propose a preprocessing process based on the two dimensions of content-form, and construct a pattern hierarchical dataset, which effectively eliminates the problems of image background interference and style heterogeneity. A complete workflow including LoRA fine-tuning, parameter optimization and multi-dimensional evaluation (shape and color similarity, aesthetics, innovation and application value) is established, and a label optimization framework assisted by manual annotation is developed. The cross-cultural validation experiments show that the method maintains stable performance in the tasks of generating Chinese Qin-Han patterns and cloud-shouldered dress patterns, providing a technical path that combines professionalism and engineering…
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