A study on the generation of traditional patterns in the perspective of AIGC–ancient Egyptian patterns as an example

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 feasibility for the digital inheritance of intangible cultural heritage. In the revised experimental design, five quantitative indicators are additionally adopted, including FID, CLIPScore, pairwise LPIPS diversity, Inception Score, and a CLIP-based aesthetic score. The proposed framework is further quantitatively compared with DreamBooth, Textual Inversion, HyperNetworks, full fine-tuning, ControlNet-based adaptation, and Adapter tuning, complemented by ablation studies and overfitting/diversity analysis.The pipeline reduces trainable parameters by over 99% (~ 860 M → ~ 3.2 M) and lowers GPU memory from 14.6 GB to 5.8 GB.