Attention-map KL losses plus a PAC-Bayes-style regularizer give small CLIP similarity gains for compositional text-to-image generation, but the theoretical derivation is invalid and the evaluation is under-powered.
Karanam, Kshitijh Joseph, Ak- shara Saxena, Karan Goswami, and Balaji Vasan Srinivasan
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Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory
Attention-map KL losses plus a PAC-Bayes-style regularizer give small CLIP similarity gains for compositional text-to-image generation, but the theoretical derivation is invalid and the evaluation is under-powered.