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All Journal Inventa: Journal of Science, Technology, and Innovation
Assyfa Febriwanti
Universitas Islam Sultan Agung
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Generasi Desain Pakaian Muslimwear Berbasis Multimodal (Teks dan Gambar) Menggunakan Stable Diffusion v1.5 Assyfa Febriwanti; Sam Farisa Chaerul Haviana
Journal of Science, Technology, and Innovation Vol 1 No 3 (2026): : April: Inventa: Journal of Science, Technology, and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/abf5c845

Abstract

The rapid advancement of Generative Artificial Intelligence has accelerated the adoption of diffusion models in fashion design applications. However, conventional text-to-image approaches often encounter limitations in maintaining visual consistency and controllability during image generation. This study proposes a multimodal Muslimwear design generation system based on Stable Diffusion v1.5 by integrating textual prompts and reference images through a cross-attention fusion mechanism. The training dataset combines DeepFashion1 and a curated Muslimwear dataset, which were preprocessed through image normalization, resolution standardization, and automated caption generation using BLIP. Domain adaptation was performed using the Low-Rank Adaptation (LoRA) technique to enable computationally efficient fine-tuning. Performance evaluation employed Fréchet Inception Distance (FID) and Structural Similarity Index Measure (SSIM) to assess visual quality and structural consistency. Experimental results indicate that the female model achieved a FID score of 176.77 and an SSIM score of 0.311, outperforming the male model with a FID score of 256.22 and an SSIM score of 0.275. The findings demonstrate that multimodal conditioning enhances visual distribution learning and structural preservation, supporting the development of controllable and efficient AI-assisted fashion design systems.
Co-Authors Sam Farisa Chaerul Haviana