  Generative Adversarial Networks (GANs) dominate the research field in
image-based virtual try-on, but have not resolved problems such as unnatural
deformation of garments and the blurry generation quality. While the generative
quality of diffusion models is impressive, achieving controllability poses a
significant challenge when applying it to virtual try-on and multiple denoising
iterations limit its potential for real-time applications. In this paper, we
propose Controllable Accelerated virtual Try-on with Diffusion Model (CAT-DM).
To enhance the controllability, a basic diffusion-based virtual try-on network
is designed, which utilizes ControlNet to introduce additional control
conditions and improves the feature extraction of garment images. In terms of
acceleration, CAT-DM initiates a reverse denoising process with an implicit
distribution generated by a pre-trained GAN-based model. Compared with previous
try-on methods based on diffusion models, CAT-DM not only retains the pattern
and texture details of the inshop garment but also reduces the sampling steps
without compromising generation quality. Extensive experiments demonstrate the
superiority of CAT-DM against both GANbased and diffusion-based methods in
producing more realistic images and accurately reproducing garment patterns.
