  While image-based virtual try-on has made significant strides, emerging
approaches still fall short of delivering high-fidelity and robust fitting
images across various scenarios, as their models suffer from issues of
ill-fitted garment styles and quality degrading during the training process,
not to mention the lack of support for various combinations of attire.
Therefore, we first propose a lightweight, scalable, operator known as Hydra
Block for attire combinations. This is achieved through a parallel attention
mechanism that facilitates the feature injection of multiple garments from
conditionally encoded branches into the main network. Secondly, to
significantly enhance the model's robustness and expressiveness in real-world
scenarios, we evolve its potential across diverse settings by synthesizing the
residuals of multiple models, as well as implementing a mask region boost
strategy to overcome the instability caused by information leakage in existing
models. Equipped with the above design, AnyFit surpasses all baselines on
high-resolution benchmarks and real-world data by a large gap, excelling in
producing well-fitting garments replete with photorealistic and rich details.
Furthermore, AnyFit's impressive performance on high-fidelity virtual try-ons
in any scenario from any image, paves a new path for future research within the
fashion community.
