Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models
<|reference_start|>Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models: 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.<|reference_end|>
@article{baker2024using,
    title={Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models},
    author={James Baker},
    journal={arXiv preprint arXiv:2410.02055},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02055},
    primaryClass={cs.CV}
}