Knowledge-Enhanced Text-to-Image Generation for Digital Cultural Heritage Preservation on Edge Computing Platforms
Keywords:
text-to-image generation, edge computing, digital cultural heritage, knowledge graphs, model adaptation, provenance, sustainabilityAbstract
The preservation of digital cultural heritage increasingly relies on generative artificial intelligence to reconstruct, visualize, and contextualize artifacts whose physical counterparts face deterioration, destruction, or inaccessibility. Text-to-image models offer a promising pathway for generating historically grounded visual content, yet generic models struggle with the domain specificity, contextual accuracy, and cultural sensitivity required by heritage institutions. This paper presents a system-level investigation of knowledge-enhanced text-to-image generation deployed on edge computing platforms, a paradigm that reconciles the need for powerful generative capabilities with the constraints of fieldwork, museums, and heritage sites. We examine how structured domain knowledge, including ontologies, curated metadata, and verified visual references, can be integrated into the generation pipeline through retrieval-augmented conditioning, prompting strategies, and lightweight adapter modules. The architectural discussion foregrounds edge-native deployment trade-offs, encompassing model compression, distributed inference, hardware heterogeneity, and the incorporation of emerging edge-AI accelerator ecosystems. Beyond technical architecture, we analyze governance frameworks for provenance, authenticity labeling, and bias mitigation, emphasizing the risk of erasing marginalized heritage narratives when generative models amplify training data imbalances. The paper further addresses robustness under degraded communication, energy sustainability at remote sites, and long-term digital accessibility. By synthesizing technical, organizational, and ethical dimensions, we argue that knowledge-enhanced generation on edge infrastructure constitutes a sustainable and culturally responsible foundation for next-generation digital heritage preservation systems. The analysis is grounded in contemporary research on multimodal learning, edge computing, and cultural informatics, offering forward-looking perspectives for system architects, heritage professionals, and policy makers.
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