Style Ambiguity Loss Using CLIP

James Baker

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arXiv · 2410.02055

Style Ambiguity Loss Using CLIP

James Baker

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Abstract

In this work, we explore using the style ambiguity training objective, originally used to approximate creativity, on a diffusion model. However, this objective requires the use of a pretrained classifier and a labeled dataset. We introduce new forms of style ambiguity loss that do not require training a new classifier or a labeled dataset. Instead of using a classifier, we generate centroids in the CLIP embedding space, and images are classified based on their relative distance to said centroids. We find the centroids via K-means clustering of an unlabeled dataset, as well as using text labels to generate CLIP embeddings, to be used as centroids. Code is available at https://github.com/jamesBaker361/clipcreate

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James Baker. 2024-10-02. Style Ambiguity Loss Using CLIP. https://arxiv.org/abs/2410.02055

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