  In this paper, we show that, a good style representation is crucial and
sufficient for generalized style transfer without test-time tuning. We achieve
this through constructing a style-aware encoder and a well-organized style
dataset called StyleGallery. With dedicated design for style learning, this
style-aware encoder is trained to extract expressive style representation with
decoupling training strategy, and StyleGallery enables the generalization
ability. We further employ a content-fusion encoder to enhance image-driven
style transfer. We highlight that, our approach, named StyleShot, is simple yet
effective in mimicking various desired styles, i.e., 3D, flat, abstract or even
fine-grained styles, without test-time tuning. Rigorous experiments validate
that, StyleShot achieves superior performance across a wide range of styles
compared to existing state-of-the-art methods. The project page is available
at: https://styleshot.github.io/.
