Recent breakthroughs in diffusion models, multimodal pretraining, and
efficient finetuning have led to an explosion of text-to-image generative
models. Given human evaluation is expensive and difficult to scale, automated
methods are critical for evaluating the increasingly large number of new
models. However, most current automated evaluation metrics like FID or
CLIPScore only offer a holistic measure of image quality or image-text
alignment, and are unsuited for fine-grained or instance-level analysis. In
this paper, we introduce GenEval, an object-focused framework to evaluate
compositional image properties such as object co-occurrence, position, count,
and color. We show that current object detection models can be leveraged to
evaluate text-to-image models on a variety of generation tasks with strong
human agreement, and that other discriminative vision models can be linked to
this pipeline to further verify properties like object color. We then evaluate
several open-source text-to-image models and analyze their relative generative
capabilities on our benchmark. We find that recent models demonstrate
significant improvement on these tasks, though they are still lacking in
complex capabilities such as spatial relations and attribute binding. Finally,
we demonstrate how GenEval might be used to help discover existing failure
modes, in order to inform development of the next generation of text-to-image
models. Our code to run the GenEval framework is publicly available at
this https URL

通过 GenEval 评估框架，我们展示了当前目标检测模型可用于评估各种生成任务的文本到图像模型，并分析其生成能力。我们发现最近的模型在这些任务上有显著改进，但在空间关系和属性绑定等复杂能力方面仍有缺失。最后，我们展示了如何使用 GenEval 来发现现有的失败模式，以促进下一代文本到图像模型的发展。