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In recent years, language-driven artistic style transfer has emerged as a newtype of style transfer technique, eliminating the need for a reference styleimage by using natural language descriptions of the style. The first model toachieve this, called CLIPstyler, has demonstrated impressive stylisationresults. However, its lengthy optimisation procedure at runtime for each querylimits its suitability for many practical applications. In this work, wepresent FastCLIPstyler, a generalised text-based image style transfer modelcapable of stylising images in a single forward pass for arbitrary text inputs.Furthermore, we introduce EdgeCLIPstyler, a lightweight model designed forcompatibility with resource-constrained devices. Through quantitative andqualitative comparisons with state-of-the-art approaches, we demonstrate thatour models achieve superior stylisation quality based on measurable metricswhile offering significantly improved runtime efficiency, particularly on edgedevices.

FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations