Personalized Adaptive Generation of Peking Opera Facial Makeup Using Generative Artificial Intelligence
Abstract
Abstract The intelligent generation of Peking Opera facial makeup serves as a representative case of stylized image generation, offering valuable insights into the integration of artificial intelligence with traditional cultural expressions. This study focuses on the intelligent generation of Peking Opera facial makeup images by addressing these key challenges. First, two random number generators are designed to independently generate colors for bright and dark regions; Second, multiple attention mechanism enhanced U-Net models of the Stable Diffusion model are employed, which refine the generation process by capturing fine details and improving authenticity. Third, a labeled dataset of Peking Opera facial makeup is used to train a text-guided image generation model. Finally, LoRA fine-tuning network is implemented to optimize the model’s performance, accelerating the generation process while maintaining image quality. In subsequent experiments, the proposed model was compared with SOTA models. The proposed model achieved scores of 16.34, 9.44, 0.4912, and 0.2917 on the FID, KID, SSIM, and MS-SSIM metrics, surpassing other SOTA models. The improved Stable Diffusion model presented in this study effectively enhances both the quality and speed of Peking Opera facial makeup generation.
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