  Virtual clothes try-on has emerged as a vital feature in online shopping,
offering consumers a critical tool to visualize how clothing fits. In our
research, we introduce an innovative approach for virtual clothes try-on,
utilizing a self-supervised Vision Transformer (ViT) coupled with a diffusion
model. Our method emphasizes detail enhancement by contrasting local clothing
image embeddings, generated by ViT, with their global counterparts. Techniques
such as conditional guidance and focus on key regions have been integrated into
our approach. These combined strategies empower the diffusion model to
reproduce clothing details with increased clarity and realism. The experimental
results showcase substantial advancements in the realism and precision of
details in virtual try-on experiences, significantly surpassing the
capabilities of existing technologies.
