用预训练大模型提升风格迁移质量与速度，避免结构失真。
SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model
- 引入局部-全局窗口融合模块，精准融合风格与内容特征。
- 设计风格先验损失，加速生成且保持高画质。
- 适合追求快速高质量图像风格迁移的开发者使用。
给定任意内容图和风格图，任意风格迁移旨在生成保留内容结构并具有目标风格的新图像。现有方法要么采用小模型，导致生成图像质量差、出现伪影和不协调图案；要么依赖预训练大模型，虽能生成高质量图像但难以保持内容结构且推理时间长。为此，我们提出SPAST框架，通过设计新颖的局部-全局窗口尺寸风格化模块（LGWSSM）实现风格特征与内容特征的融合，并引入风格先验损失，从预训练大模型中挖掘风格先验信息，引导SPAST在短推理时间内生成高质量图像。大量实验表明，相比当前最优方法，SPAST在图像质量与推理速度上均有显著提升。
原文摘要 · Abstract (English)
Given an arbitrary content and style image, arbitrary style transfer aims to render a new stylized image which preserves the content image's structure and possesses the style image's style. Existing arbitrary style transfer methods are based on either small models or pre-trained large-scale models. The small model-based methods fail to generate high-quality stylized images, bringing artifacts and disharmonious patterns. The pre-trained large-scale model-based methods can generate high-quality stylized images but struggle to preserve the content structure and cost long inference time. To this end, we propose a new framework, called SPAST, to generate high-quality stylized images with less inference time. Specifically, we design a novel Local-global Window Size Stylization Module (LGWSSM)tofuse style features into content features. Besides, we introduce a novel style prior loss, which can dig out the style priors from a pre-trained large-scale model into the SPAST and motivate the SPAST to generate high-quality stylized images with short inference time.We conduct abundant experiments to verify that our proposed method can generate high-quality stylized images and less inference time compared with the SOTA arbitrary style transfer methods.
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