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Adaptive Stylized Image Generation for Traditional Miao Batik Using Style-Conditioned LCM-LoRA Enhanced Diffusion Models

Author

Listed:
  • Qingqing Hu

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China
    These authors contributed equally to this work.)

  • Yiran Peng

    (Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China
    These authors contributed equally to this work.)

  • Jing Xu

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China)

  • Zichun Shao

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China)

  • Zhen Tian

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Junming Chen

    (Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, Macau 999078, China)

Abstract

As a national intangible cultural heritage in China, traditional Miao batik has encountered obstacles in contemporary dissemination and design due to its reliance on manual craftsmanship and other reasons. Existing generative models are difficult to fully capture the complex semantic and stylistic attributes in Miao batik patterns, which limits their application in digital creativity. To address this issue, we construct the structured CMBP-9 dataset to facilitate semantic-aware image generation. Based on stable diffusion v1.5, Low-Rank Adaptation (LoRA) is used to effectively transfer the structure, sign, and texture features that are unique to the Miao people, and the Latent Consistency model (LCM) is integrated to improve the inference efficiency. In addition, a Style-Conditioned Linear Fusion (SCLF) strategy is proposed to dynamically adjust the fusion of LoRA and LCM outputs according to the semantic complexity of input prompts, thereby overcoming the limitation of static weighting in existing frameworks. Extensive quantitative evaluations using LPIPS, SSIM, PSNR, FID metrics, and human evaluations show that the proposed Batik-MPDM framework achieves superior performance in terms of style fidelity and generation efficiency compared to baseline methods.

Suggested Citation

  • Qingqing Hu & Yiran Peng & Jing Xu & Zichun Shao & Zhen Tian & Junming Chen, 2025. "Adaptive Stylized Image Generation for Traditional Miao Batik Using Style-Conditioned LCM-LoRA Enhanced Diffusion Models," Mathematics, MDPI, vol. 13(12), pages 1-24, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:12:p:1947-:d:1677100
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    References listed on IDEAS

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    1. Buonincontri, P. & Morvillo, A. & Okumus, F. & van Niekerk, M., 2017. "Managing the experience co-creation process in tourism destinations: Empirical findings from Naples," Tourism Management, Elsevier, vol. 62(C), pages 264-277.
    2. Yidan Wang & Yixuan Zhou, 2025. "Artificial Intelligence-Driven Interactive Experience for Intangible Cultural Heritage: Sustainable Innovation of Blue Clamp-Resist Dyeing," Sustainability, MDPI, vol. 17(3), pages 1-18, January.
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