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<p>We propose a human-in-the-loop AI-generated content (AIGC) system to advance lacquerware design innovation by merging artisan knowledge with controllable diffusion models. Traditional lacquerware design faces challenges in balancing heritage preservation and creative exploration, where artisans often struggle with limited inspiration sources and time-consuming manual processes. The proposed system tackles these problems by employing a closed-loop workflow that integrates AI-driven creation and human adjustment, which fosters iterative cooperation between artisans and the AI engine. The system architecture consists of a front-end interface, an AI engine operating on the back end, and a carefully assembled database of historical and modern lacquerware designs, which undergo preprocessing steps such as normalization, background removal, and detailed annotation to document stylistic and compositional features. Essentially, a diffusion model refined through LoRA adaptation for specialized domain control produces high-quality lacquerware designs without compromising their aesthetic coherence. Furthermore, the system integrates multi-modal control mechanisms, which comprise text-to-image prompts for stylistic direction and image-to-image operations with ControlNet to achieve structural accuracy, thereby granting artisans the capability to submit sketches or reference compositions that the AI subsequently augments with elaborate details. The interactive user interface includes an inspiration gallery, a canvas with dynamic properties, and adjustable parameter controls, granting artisans the ability to efficiently investigate a wide range of design alternatives. Our key achievements consist of an innovative implementation of controllable diffusion models in cultural heritage design, a lacquerware dataset with detailed annotations, and a fine-tuning method empirically proven to elevate generation quality. Experiments show the system’s capacity to generate culturally consistent designs while markedly decreasing the time required for artisans to conceptualize their work. This work holds importance due to its capacity to connect traditional craftsmanship with contemporary AI technologies, thereby promoting innovation while preserving the genuine qualities of lacquerware art. The system broadens availability of sophisticated design tools, thereby hastening creative processes and creating fresh opportunities for cross-disciplinary studies in AI-driven cultural heritage conservation.</p>

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Advancing lacquerware design through human-AI collaboration with controllable diffusion models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

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  • Jun Liang,
  • Yixin Li,
  • Zhileng Xiong,
  • Qing Huang

摘要

We propose a human-in-the-loop AI-generated content (AIGC) system to advance lacquerware design innovation by merging artisan knowledge with controllable diffusion models. Traditional lacquerware design faces challenges in balancing heritage preservation and creative exploration, where artisans often struggle with limited inspiration sources and time-consuming manual processes. The proposed system tackles these problems by employing a closed-loop workflow that integrates AI-driven creation and human adjustment, which fosters iterative cooperation between artisans and the AI engine. The system architecture consists of a front-end interface, an AI engine operating on the back end, and a carefully assembled database of historical and modern lacquerware designs, which undergo preprocessing steps such as normalization, background removal, and detailed annotation to document stylistic and compositional features. Essentially, a diffusion model refined through LoRA adaptation for specialized domain control produces high-quality lacquerware designs without compromising their aesthetic coherence. Furthermore, the system integrates multi-modal control mechanisms, which comprise text-to-image prompts for stylistic direction and image-to-image operations with ControlNet to achieve structural accuracy, thereby granting artisans the capability to submit sketches or reference compositions that the AI subsequently augments with elaborate details. The interactive user interface includes an inspiration gallery, a canvas with dynamic properties, and adjustable parameter controls, granting artisans the ability to efficiently investigate a wide range of design alternatives. Our key achievements consist of an innovative implementation of controllable diffusion models in cultural heritage design, a lacquerware dataset with detailed annotations, and a fine-tuning method empirically proven to elevate generation quality. Experiments show the system’s capacity to generate culturally consistent designs while markedly decreasing the time required for artisans to conceptualize their work. This work holds importance due to its capacity to connect traditional craftsmanship with contemporary AI technologies, thereby promoting innovation while preserving the genuine qualities of lacquerware art. The system broadens availability of sophisticated design tools, thereby hastening creative processes and creating fresh opportunities for cross-disciplinary studies in AI-driven cultural heritage conservation.