论文标题
FastClipStyler：使用样式表示
FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations
论文作者
论文摘要
近年来，语言驱动的艺术风格转移已成为一种新型的样式转移技术，通过使用该样式的自然语言描述消除了对参考样式图像的需求。实现这一目标的第一个模型，称为ClipStyler，已显示出令人印象深刻的风格化结果。但是，其在每个查询时运行时的冗长优化程序限制了其对许多实际应用的适用性。在这项工作中，我们介绍了FastClipStyler，这是一种基于文本的图像样式传输模型，该模型旨在在单个正向通行证中进行任意文本输入的单个图像。此外，我们介绍了EdgeClipstyler，这是一种轻巧的模型，旨在与资源约束设备兼容。通过与最新方法的定量和定性比较，我们证明了我们的模型基于可测量的指标实现了卓越的样式质量，同时提供了显着提高的运行时效率，尤其是在边缘设备上。
In recent years, language-driven artistic style transfer has emerged as a new type of style transfer technique, eliminating the need for a reference style image by using natural language descriptions of the style. The first model to achieve this, called CLIPstyler, has demonstrated impressive stylisation results. However, its lengthy optimisation procedure at runtime for each query limits its suitability for many practical applications. In this work, we present FastCLIPstyler, a generalised text-based image style transfer model capable of stylising images in a single forward pass for arbitrary text inputs. Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed for compatibility with resource-constrained devices. Through quantitative and qualitative comparisons with state-of-the-art approaches, we demonstrate that our models achieve superior stylisation quality based on measurable metrics while offering significantly improved runtime efficiency, particularly on edge devices.
