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

Ananda Padhmanabhan Suresh, Sanjana Jain, Pavit Noinongyao, Ankush Ganguly

Artistic style transfer is usually performed between two images, a style image and a content image. Recently, a model named CLIPstyler demonstrated that a natural language description of style could replace the necessity of a reference style image. However, their technique requires a lengthy optimisation procedure at run-time for each query, requiring multiple forward and backward passes through a network as well as expensive loss computations. In this work, we create a generalised text-based style transfer network capable of stylising images in a single forward pass for an arbitrary text input making the image stylisation process around 1000 times more efficient than CLIPstyler. We also demonstrate how our technique eliminates the issue of leakage of unwanted artefacts into some of the generated images from CLIPstyler, making them unusable. We also propose an optional fine-tuning step to improve the quality of the generated image. We qualitatively evaluate the performance of our framework and show that it can generate images of comparable quality to state-of-the-art techniques.

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