Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation

TL;DR

This paper introduces VisionPrefer, a high-quality, multi-aspect preference dataset created using multimodal large language models, which improves text-to-image alignment and generalizes better than previous metrics.

Contribution

The paper presents VisionPrefer, a novel AI-annotated preference dataset capturing multiple aspects, and demonstrates its effectiveness in enhancing text-to-image generative model alignment.

Findings

VP-Score achieves human-level preference prediction accuracy.

VisionPrefer improves compositional image generation across multiple aspects.

Synthetic AI-generated data enhances model alignment with human preferences.

Abstract

Recent studies have demonstrated the exceptional potentials of leveraging human preference datasets to refine text-to-image generative models, enhancing the alignment between generated images and textual prompts. Despite these advances, current human preference datasets are either prohibitively expensive to construct or suffer from a lack of diversity in preference dimensions, resulting in limited applicability for instruction tuning in open-source text-to-image generative models and hinder further exploration. To address these challenges and promote the alignment of generative models through instruction tuning, we leverage multimodal large language models to create VisionPrefer, a high-quality and fine-grained preference dataset that captures multiple preference aspects. We aggregate feedback from AI annotators across four aspects: prompt-following, aesthetic, fidelity, and harmlessness to construct VisionPrefer. To validate the effectiveness of VisionPrefer, we train a reward model VP-Score over VisionPrefer to guide the training of text-to-image generative models and the preference prediction accuracy of VP-Score is comparable to human annotators. Furthermore, we use two reinforcement learning methods to supervised fine-tune generative models to evaluate the performance of VisionPrefer, and extensive experimental results demonstrate that VisionPrefer significantly improves text-image alignment in compositional image generation across diverse aspects, e.g., aesthetic, and generalizes better than previous human-preference metrics across various image distributions. Moreover, VisionPrefer indicates that the integration of AI-generated synthetic data as a supervisory signal is a promising avenue for achieving improved alignment with human preferences in vision generative models.