Cloud-Computing-Enabled Transformer Architecture for the Design of Functional Clothing Structures

This paper introduces a graphical design model for smart clothing structures based on cloud computing and an integrated approach combining Transformer architecture with conditional Generative Adversarial Networks (cGANs). The model aims to revolutionize the functional clothing design industry by transforming users' diverse needs into machine-understandable vector representations using a multi-head self-attention mechanism. Subsequently, a decoder generates design elements, which are then visualized using cGAN techniques. To evaluate the model's performance, we conducted extensive computational experiments using a comprehensive dataset that includes various design styles and occupational categories, such as medical, catering, aviation, and industrial clothing. The model was trained and validated using K-fold cross-validation, ensuring robustness and generalizability. Key performance metrics were assessed, including design element similarity, layout rationality, and personalization accuracy. Experimental results show that the model achieves an average design element similarity score of over 89%, a layout rationality score of over 90%, and a personalization accuracy of nearly 92%. These performance indicators demonstrate the model's effectiveness in design accuracy, efficiency, personalization, and market adaptability, particularly for occupational clothing design in healthcare, catering, aviation, and industrial applications. The integration of Transformer and cGAN technologies significantly enhances the model's capability to generate high-quality, personalized designs while maintaining robustness and scalability. This approach provides a comprehensive solution for automating the design process, leading to improved design outcomes and enhanced user satisfaction.