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Owing to the power of vision-language foundation models, e.g., CLIP, the areaof image synthesis has seen recent important advances. Particularly, for styletransfer, CLIP enables transferring more general and abstract styles withoutcollecting the style images in advance, as the style can be efficientlydescribed with natural language, and the result is optimized by minimizing theCLIP similarity between the text description and the stylized image. However,directly using CLIP to guide style transfer leads to undesirable artifacts(mainly written words and unrelated visual entities) spread over the image. Inthis paper, we propose SpectralCLIP, which is based on a spectralrepresentation of the CLIP embedding sequence, where most of the commonartifacts occupy specific frequencies. By masking the band including thesefrequencies, we can condition the generation process to adhere to the targetstyle properties (e.g., color, texture, paint stroke, etc.) while excluding thegeneration of larger-scale structures corresponding to the artifacts.Experimental results show that SpectralCLIP prevents the generation ofartifacts effectively in quantitative and qualitative terms, without impairingthe stylisation quality. We also apply SpectralCLIP to text-conditioned imagegeneration and show that it prevents written words in the generated images. Ourcode is available at https://github.com/zipengxuc/SpectralCLIP.

SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective