Information Theoretic Text-to-Image Alignment

• URL: http://arxiv.org/abs/2405.20759v3
• Date: Tue, 11 Feb 2025 07:27:41 GMT
• Title: Information Theoretic Text-to-Image Alignment
• Authors: Chao Wang, Giulio Franzese, Alessandro Finamore, Massimo Gallo, Pietro Michiardi,
• Abstract summary: Mutual Information (MI) is used to guide model alignment.<n>Our method uses self-supervised fine-tuning and relies on a point-wise (MI) estimation between prompts and images.<n>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.
• Score: 49.396917351264655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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