  We present OOTDiffusion, a novel network architecture for realistic and
controllable image-based virtual try-on (VTON). We leverage the power of
pretrained latent diffusion models, designing an outfitting UNet to learn the
garment detail features. Without a redundant warping process, the garment
features are precisely aligned with the target human body via the proposed
outfitting fusion in the self-attention layers of the denoising UNet. In order
to further enhance the controllability, we introduce outfitting dropout to the
training process, which enables us to adjust the strength of the garment
features through classifier-free guidance. Our comprehensive experiments on the
VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently
generates high-quality try-on results for arbitrary human and garment images,
which outperforms other VTON methods in both realism and controllability,
indicating an impressive breakthrough in virtual try-on. Our source code is
available at https://github.com/levihsu/OOTDiffusion.
