An Empirical Study on the Applicability of Image Generation AI in Fashion Design: A Comparative Analysis of GPT, LOOK AI, and Diffusion
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This study examines the applicability of generative AI in fashion design through a comparative analysis of GPT-based generators, LOOK AI, and Stable Diffusion. A dress sketch was chosen as a complex test item, and images were evaluated by 15 professional designers using a structured survey across three domains: design reproduction, visual fidelity, and practical usability. ANOVA results showed that LOOK AI achieved the highest accuracy and usability, GPT-based tools performed moderately with strengths in structural interaction, and Stable Diffusion, while creative, showed lower fidelity. By combining expert evaluation with a complex garment type under controlled conditions, the proposed method provides a practical framework for validating AI image tools, offering implications for fashion practice and education as well as potential extension to other design fields.
Abstract
This study examines the applicability of generative AI in fashion design through a comparative analysis of GPT-based generators, LOOK AI, and Stable Diffusion. A dress sketch was chosen as a complex test item, and images were evaluated by 15 professional designers using a structured survey across three domains: design reproduction, visual fidelity, and practical usability. ANOVA results showed that LOOK AI achieved the highest accuracy and usability, GPT-based tools performed moderately with strengths in structural interaction, and Stable Diffusion, while creative, showed lower fidelity. By combining expert evaluation with a complex garment type under controlled conditions, the proposed method provides a practical framework for validating AI image tools, offering implications for fashion practice and education as well as potential extension to other design fields.