StyleShot: A snapshot on any style

ICLR 2025 Conference Submission389 Authors

Keywords: diffusion model

Abstract:

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.

Primary Area: generative models

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Submission Number: 389