Harnessing Multimodal AI for Creative Design: Performance Evaluation of Stable Diffusion and DALL-E 3 in Fashion Apparel and Typography

In recent years, multimodal AI (Artificial Intelligence) models have exhibited promising capabilities in generating diverse forms of creative content. This review paper critically evaluates and compares the performance of two prominent multimodal models, Stable Diffusion and DALL-E, in the context of fashion apparel and typography design generation. The evaluation methodology encompasses both human judgment and automatic evaluation metrics, including CLIP Score, Image Reward, and Inception Score, to provide a comprehensive assessment of the generated outputs. Through a systematic analysis of the results, this paper highlights the strengths and limitations of each model, shedding light on their respective abilities to capture design nuances, maintain coherence, and exhibit creativity. Additionally, the review discusses the impact of training data, model architecture, and hyperparameters on the quality and diversity of generated designs. The findings of this review contribute to a deeper understanding of the capabilities of multimodal AI models in creative design tasks and offer insights into their potential applications in the fashion and design industries. This paper aims to guide researchers and practitioners in selecting suitable models for specific design generation tasks while fostering advancements in multimodal AI research.