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<p>To address the digitization needs of Cantonese embroidery, a human intangible cultural heritage, and resolve the limitations of existing simulation techniques—insufficient stitch diversity, unnatural pattern transitions, and inaccurate structure-color reproduction—this study proposes a diffusion-based method that generates high-quality Cantonese embroidery-style images with hundreds of labeled samples. In this method, lightweight LoRA fine-tuning endows the large model with ultrahigh-fidelity texture reproduction; SAM semantic segmentation imposes high-precision spatial semantic constraints on generation; ControlNet multi-condition guidance performs accurate structure‒color restoration. This synergistic combination achieves superior feature reconstruction and detail generation, a balance that existing models struggle to maintain under limited data. It outperforms existing approaches in key metrics (LPIPS: 0.244; FID: 95.57; PSNR: 16.38), with remarkable visual and user evaluation advantages. This work enables applications such as relic restoration, design reference, and intelligent manufacturing simulation, providing a critical path for the digital preservation of intangible cultural heritage and for innovative design.</p>

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Diffusion model-based image generation method for Cantonese embroidery artistic styles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

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  • Yongsheng Rao,
  • Sailan Chen,
  • Yingshuang Xuan,
  • Bing Hu,
  • Ranran Wang,
  • Maoning Li

摘要

To address the digitization needs of Cantonese embroidery, a human intangible cultural heritage, and resolve the limitations of existing simulation techniques—insufficient stitch diversity, unnatural pattern transitions, and inaccurate structure-color reproduction—this study proposes a diffusion-based method that generates high-quality Cantonese embroidery-style images with hundreds of labeled samples. In this method, lightweight LoRA fine-tuning endows the large model with ultrahigh-fidelity texture reproduction; SAM semantic segmentation imposes high-precision spatial semantic constraints on generation; ControlNet multi-condition guidance performs accurate structure‒color restoration. This synergistic combination achieves superior feature reconstruction and detail generation, a balance that existing models struggle to maintain under limited data. It outperforms existing approaches in key metrics (LPIPS: 0.244; FID: 95.57; PSNR: 16.38), with remarkable visual and user evaluation advantages. This work enables applications such as relic restoration, design reference, and intelligent manufacturing simulation, providing a critical path for the digital preservation of intangible cultural heritage and for innovative design.