AI for Cultural Heritage Textiles: Fine-Tuned Latent Diffusion for Novel Ulos Motif Synthesis

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[Submitted on 6 Jul 2026]

Title:AI for Cultural Heritage Textiles: Fine-Tuned Latent Diffusion for Novel Ulos Motif Synthesis

View a PDF of the paper titled AI for Cultural Heritage Textiles: Fine-Tuned Latent Diffusion for Novel Ulos Motif Synthesis, by Humasak Tommy Argo Simanjuntak and 6 other authors

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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-resolution Ulos motifs to generate culturally consistent yet novel designs. Model performance is evaluated quantitatively using Frechet Inception Distance (FID), Inception Score (IS), and qualitatively through assessments by traditional weavers and members of the public. Protogen v3.4 consistently outperforms Stable Diffusion v1.4, achieving substantially lower FID (~10.5x) and higher IS (2.0x), indicating superior visual fidelity, diversity, and closer alignment with the real Ulos motif distribution. We further examine the effects of strength and guidance scale on generation quality across both models. Lower strength values consistently yield higher fidelity (lower FID), while higher strength values increase generative diversity at the cost of realism, revealing a clear fidelity-diversity tradeoff for both models. Across all tested configurations, a guidance scale of 5-9 provides the most effective balance between fidelity and diversity, stabilising FID, KID, and IS, and is recommended as the operating range for high-quality, diverse Ulos motif generation. These findings demonstrate that carefully fine-tuned generative AI can support the creative renewal of intangible cultural heritage while preserving its stylistic and symbolic integrity.

Comments: 21 pages, 8 figures, 3 tables. The manuscript is currently under review at the 2026 4th International Conference on Data, Information and Computing Science (this https URL)

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

ACM classes: I.2; I.4

Cite as: arXiv:2607.06590 [cs.CV]

(or arXiv:2607.06590v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.06590

arXiv-issued DOI via DataCite

Submission history

From: Humasak Simanjuntak [view email] [v1] Mon, 6 Jul 2026 08:04:46 UTC (9,062 KB)

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