AI for Cultural Heritage Textiles: Fine-Tuned Latent Diffusion for Novel Ulos Motif Synthesis
arXiv:2607.06590v1 Announce Type: cross Abstract: Preserving and revitalising traditional textiles such as Ulos, a cultural heritage of the Batak ethnic group in North Sumatra, Indonesia, requires balancing fidelity to tradition with innovative approaches that meet contemporary design demands. Traditional Ulos weaving faces two key limitations: a narrow range of motifs and a time-intensive design process. This study presents a generative AI framework that fine-tunes two pretrained latent diffusion models: Protogen v3.4 and Stable Diffusion v1.4, on a curated, annotated dataset of high-resoluti
The increasing maturity and accessibility of generative AI models like latent diffusion allow for their application to niche, culturally significant datasets for preservation and innovation.
This exemplifies how AI can be leveraged for cultural preservation and creative industries, potentially democratizing design processes and safeguarding heritage.
Traditional textile design, previously limited by manual processes and established motifs, can now be augmented and expanded by AI-generated patterns.
- · Cultural heritage institutions
- · Generative AI developers
- · Creative industries
- · Designers
- · Traditional textile design schools resistant to technology
- · Counterfeit producers of culturally significant items
AI models are fine-tuned to generate novel motifs for traditional textiles.
This technology can lead to a revitalization of traditional crafts through new designs and increased accessibility.
It could establish a precedent for AI as a tool for cultural authenticity and artistic innovation across various heritage domains, potentially raising new questions about intellectual property and cultural ownership in the age of AI-generated art.
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