Information Theoretic Text-to-Image Alignment
CHAO WANG Giulio Franzese Alessandro Finamore Massimo Gallo Pietro Michiardi
Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved
tremendous success. Yet, aligning these models with user’s intentions still involves a
laborious trial-and-error process, and this challenging alignment problem has attracted
considerable attention from the research community. In this work, instead of relying on
fine-grained linguistic analyses of prompts, human annotation, or auxiliary vision-language
models, we use Mutual Information (MI) to guide model alignment. In brief, our method
uses self-supervised fine-tuning and relies on a point-wise MI estimation between prompts
and images to create a synthetic fine-tuning set for improving model alignment. Our
analysis indicates that our method is superior to the state-of-the-art, yet it only requires
the pre-trained denoising network of the T2I model itself to estimate MI, and a simple
fine-tuning strategy that improves alignment while maintaining image quality. Code available at https://github.com/Chao0511/mitune.
Diffusion model, Text-image alignment, Mutual information
A novel fine-tuning method for text-to-image generative diffusion models, that uses mutual information to align generated images to user intentions through natural prompts.