  The rapidly evolving fields of e-commerce and metaverse continue to seek
innovative approaches to enhance the consumer experience. At the same time,
recent advancements in the development of diffusion models have enabled
generative networks to create remarkably realistic images. In this context,
image-based virtual try-on, which consists in generating a novel image of a
target model wearing a given in-shop garment, has yet to capitalize on the
potential of these powerful generative solutions. This work introduces
LaDI-VTON, the first Latent Diffusion textual Inversion-enhanced model for the
Virtual Try-ON task. The proposed architecture relies on a latent diffusion
model extended with a novel additional autoencoder module that exploits
learnable skip connections to enhance the generation process preserving the
model's characteristics. To effectively maintain the texture and details of the
in-shop garment, we propose a textual inversion component that can map the
visual features of the garment to the CLIP token embedding space and thus
generate a set of pseudo-word token embeddings capable of conditioning the
generation process. Experimental results on Dress Code and VITON-HD datasets
demonstrate that our approach outperforms the competitors by a consistent
margin, achieving a significant milestone for the task. Source code and trained
models are publicly available at: https://github.com/miccunifi/ladi-vton.
