Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models

• URL: http://arxiv.org/abs/2410.02055v1
• Date: Wed, 2 Oct 2024 22:05:30 GMT
• Title: Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models
• Authors: James Baker,
• Abstract summary: Teaching text-to-image models to be creative involves using style ambiguity loss. In this work, we explore using the style ambiguity training objective, used to approximate creativity, on a diffusion model. We find that the models trained with style ambiguity loss can generate better images than the baseline diffusion models and GANs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Teaching text-to-image models to be creative involves using style ambiguity loss. In this work, we explore using the style ambiguity training objective, used to approximate creativity, on a diffusion model. We then experiment with forms of style ambiguity loss that do not require training a classifier or a labeled dataset, and find that the models trained with style ambiguity loss can generate better images than the baseline diffusion models and GANs. Code is available at https://github.com/jamesBaker361/clipcreate.

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