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Vol. 11, No. 4, November 2026 (Article in Progress)

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Generative Fusion of Two Batik Cual Motifs Using Stable Diffusion

https://doi.org/10.22219/kinetik.v11i4.2763
Haris Azhari Ramadhan
Universitas Muhammadiyah Bangka Belitung
Zikri Wahyuzi
Universitas Muhammadiyah Bangka Belitung
Yudistira Bagus Pratama
Universitas Muhammadiyah Bangka Belitung

Corresponding Author(s) : Haris Azhari Ramadhan

haris.azharir@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 4, November 2026 (Article in Progress)

Abstract

Batik Cual is a traditional textile heritage from Bangka Belitung that contains distinctive ornamental structures, visual identity, and cultural value. However, previous computational studies on batik motifs have mostly focused on recognition, classification, documentation, or cultural interpretation, while controlled generative fusion for creating new Batik Cual motif variations remains limited. This study proposed a Stable Diffusion-based generative fusion framework to combine two Batik Cual motifs through an image-to-image generation process. The method consisted of motif standardisation, linear image blending as a pre-fusion conditioning image, Stable Diffusion image-to-image generation under fixed parameters, and CLIP-based semantic similarity evaluation. Ten generated motif outputs were produced using the same experimental configuration to ensure a reproducible evaluation process. The results showed that the proposed framework generated varied Batik Cual fusion motifs while preserving several visual characteristics of the input motifs, including dominant reddish colour patterns, ornamental repetition, and decorative structures. The CLIP similarity evaluation produced an average score of 0.2555, with the highest score of approximately 0.3050 and the lowest score of approximately 0.1800. These findings indicate that most generated motifs maintained measurable semantic consistency with the intended Batik Cual fusion concept, although one output showed weaker alignment. Therefore, the proposed framework offers a reproducible and measurable approach for AI-assisted Batik Cual motif exploration.

Keywords

Batik Cual Stable Diffusion Generative Fusion Image-to-image Diffusion CLIP Similarity
Ramadhan, H. A., Wahyuzi, Z. ., & Pratama, Y. B. . (2026). Generative Fusion of Two Batik Cual Motifs Using Stable Diffusion. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(4). https://doi.org/10.22219/kinetik.v11i4.2763
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