Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to quantify and evaluate the uncertainty of T2I models with respect to the prompt. Alongside adapting existing approaches designed to measure uncertainty in the image space, we also introduce Prompt-based UNCertainty Estimation for T2I models (PUNC), a novel method leveraging Large Vision-Language Models (LVLMs) to better address uncertainties arising from the semantics of the prompt and generated images. PUNC utilizes a LVLM to caption a generated image, and then compares the caption with the original prompt in the more semantically meaningful text space. PUNC also enables the disentanglement of both aleatoric and epistemic uncertainties via precision and recall, which image-space approaches are unable to do. Extensive experiments demonstrate that PUNC outperforms state-of-the-art uncertainty estimation techniques across various settings. Uncertainty quantification in text-to-image generation models can be used on various applications including bias detection, copyright protection, and OOD detection. We also introduce a comprehensive dataset of text prompts and generation pairs to foster further research in uncertainty quantification for generative models. Our findings illustrate that PUNC not only achieves competitive performance but also enables novel applications in evaluating and improving the trustworthiness of text-to-image models.

翻译：文本到图像（T2I）生成模型中的不确定性量化对于理解模型行为和提高输出可靠性至关重要。本文首次针对提示词对T2I模型的不确定性进行了量化与评估。除了适配现有的、旨在图像空间中测量不确定性的方法外，我们还提出了基于提示词的文本到图像模型不确定性估计方法（PUNC），这是一种利用大型视觉语言模型（LVLM）来更好地处理由提示词和生成图像的语义所引发不确定性的新方法。PUNC利用LVLM为生成的图像生成描述，然后在语义更丰富的文本空间中比较该描述与原始提示词。PUNC还能够通过精确率和召回率来解耦偶然不确定性和认知不确定性，这是图像空间方法无法做到的。大量实验表明，PUNC在各种设置下均优于最先进的不确定性估计技术。文本到图像生成模型中的不确定性量化可应用于多种场景，包括偏见检测、版权保护和分布外（OOD）检测。我们还引入了一个包含文本提示词与生成图像对的综合数据集，以促进生成模型不确定性量化领域的进一步研究。我们的研究结果表明，PUNC不仅实现了有竞争力的性能，还为评估和提高文本到图像模型的可信度开启了新的应用途径。