  Recall that most of the current image style transfer methods require the user
to give an image of a particular style and then extract that styling feature
and texture to generate the style of an image, but there are still some
problems: the user may not have a reference style image, or it may be difficult
to summarise the desired style in mind with just one image. The recently
proposed CLIPstyler has solved this problem, which is able to perform style
transfer based only on the provided description of the style image. Although
CLIPstyler can achieve good performance when landscapes or portraits appear
alone, it can blur the people and lose the original semantics when people and
landscapes coexist. Based on these issues, we demonstrate a novel framework
that uses a pre-trained CLIP text-image embedding model and guides image style
transfer through an FCN semantic segmentation network. Specifically, we solve
the portrait over-styling problem for both selfies and real-world landscape
with human subjects photos, enhance the contrast between the effect of style
transfer in portrait and landscape, and make the degree of image style transfer
in different semantic parts fully controllable. Our Generative Artisan resolve
the failure case of CLIPstyler and yield both qualitative and quantitative
methods to prove ours have much better results than CLIPstyler in both selfies
and real-world landscape with human subjects photos. This improvement makes it
possible to commercialize our framework for business scenarios such as
retouching graphics software.
