The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models

TL;DR

This paper investigates how prompt complexity affects the quality, diversity, and consistency of images generated by text-to-image models, revealing trade-offs and proposing an evaluation framework.

Contribution

It introduces a new framework for comparing real and synthetic data utility and analyzes the impact of prompt complexity on T2I model outputs across multiple datasets.

Findings

Higher prompt complexity reduces diversity and consistency.

Increasing prompt complexity decreases distribution shift between synthetic and real data.

Prompt expansion with a language model improves diversity and aesthetics.

Abstract

Text-to-image (T2I) models offer great potential for creating virtually limitless synthetic data, a valuable resource compared to fixed and finite real datasets. Previous works evaluate the utility of synthetic data from T2I models on three key desiderata: quality, diversity, and consistency. While prompt engineering is the primary means of interacting with T2I models, the systematic impact of prompt complexity on these critical utility axes remains underexplored. In this paper, we first conduct synthetic experiments to motivate the difficulty of generalization with regard to prompt complexity and explain the observed difficulty with theoretical derivations. Then, we introduce a new evaluation framework that can compare the utility of real data and synthetic data, and present a comprehensive analysis of how prompt complexity influences the utility of synthetic data generated by commonly used T2I models. We conduct our study across diverse datasets, including CC12M, ImageNet-1k, and DCI, and evaluate different inference-time intervention methods. Our synthetic experiments show that generalizing to more general conditions is harder than the other way round, since the former needs an estimated likelihood that is not learned by diffusion models. Our large-scale empirical experiments reveal that increasing prompt complexity results in lower conditional diversity and prompt consistency, while reducing the synthetic-to-real distribution shift, which aligns with the synthetic experiments. Moreover, current inference-time interventions can augment the diversity of the generations at the expense of moving outside the support of real data. Among those interventions, prompt expansion, by deliberately using a pre-trained language model as a likelihood estimator, consistently achieves the highest performance in both image diversity and aesthetics, even higher than that of real data.