Advanced Product Personalization in Blockchain-Enabled Metaverse: A Diffusion Model for Automatic Style Generation

The Metaverse is a user-generated virtual world, aiming to provide highly personalized experiences for users. A product personalization design platform is a critical direction for the Metaverse's future development, enhancing user experience by offering personalized services. Blockchain technology ensures the security and privacy of user data, and enables personalized services through smart contracts, offering opportunities for personalization platforms. However, blockchain's decentralization can lead to excessive product data, resulting in ineffective data management and optimization, subsequently confusing personalized product design styles, which diminishes user experience. To address these issues, this study proposes the product style automatic generation system (PSAGS), centered on an image generation unit. The system outputs images with style information based on the input product text, achieving precise quantization and visualization of product styles, thereby enhancing user engagement and loyalty to the Metaverse. The image generation unit, with a standardization module can standardize the product style, namely, the relationship between product design elements and user emotions, addressing the problem of managing vast style data due to blockchain's decentralization. The generation module utilizes a diffusion model enhanced with contrastive language-image pretraining (CLIP) to generate style images, deepening the Metaverse experience. Optimizations include dilation convolution in the UNet architecture to enhance image quality and fine-grained CLIP transformations for improved image and text alignment. Results demonstrate the system's effectiveness in streamlining design processes and improving image quality in personalized product design, with wide applications in the Metaverse.