Penerapan Low-Rank Adaptation (LoRA) pada Stable Diffusion untuk Generasi Citra Motif Batik Indonesia
DOI:
https://doi.org/10.24114/cess.v11i2.75575Kata kunci:
Low-Rank Adaptation (LoRA);, Stable Diffusion, Generasi Citra, Batik Indonesia;, Kecerdasan BuatanAbstrak
Perkembangan model text-to-image berbasis difusi memberikan peluang dalam pengembangan motif batik Indonesia menggunakan kecerdasan buatan. Penelitian ini bertujuan menerapkan Low-Rank Adaptation (LoRA) pada Stable Diffusion 1.5 untuk menghasilkan citra enam motif batik Indonesia, yaitu Parang, Ceplok, Kawung, Buketan, Lasem, dan Mega Mendung. Dataset yang digunakan terdiri atas 300 citra yang telah melalui tahap pra-pemrosesan sebelum dilatih menggunakan KohyaSS. Evaluasi dilakukan menggunakan Frechet Inception Distance (FID) dan Contrastive Language–Image Pre-training (CLIP) Score. Hasil penelitian menunjukkan bahwa motif Buketan memperoleh performa terbaik dengan nilai FID sebesar 24,81 dan CLIP Score sebesar 0,351, sedangkan motif Parang memperoleh nilai FID sebesar 41,52 dan CLIP Score sebesar 0,286. Hasil tersebut menunjukkan bahwa metode LoRA mampu menghasilkan citra motif batik dengan kualitas visual dan kesesuaian semantik yang baik pada beberapa kategori motif, meskipun performanya masih bervariasi pada setiap motif.
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