In recent years, there has been significant progress in the development of
text-to-image generative models. Evaluating the quality of the generative
models is one essential step in the development process. Unfortunately, the
evaluation process could consume a significant amount of computational
resources, making the required periodic evaluation of model performance (e.g.,
monitoring training progress) impractical. Therefore, we seek to improve the
evaluation efficiency by selecting the representative subset of the text-image
dataset. We systematically investigate the design choices, including the
selection criteria (textural features or image-based metrics) and the selection
granularity (prompt-level or set-level). We find that the insights from prior
work on subset selection for training data do not generalize to this problem,
and we propose FlashEval, an iterative search algorithm tailored to evaluation
data selection. We demonstrate the effectiveness of FlashEval on ranking
diffusion models with various configurations, including architectures,
quantization levels, and sampler schedules on COCO and DiffusionDB datasets.
Our searched 50-item subset could achieve comparable evaluation quality to the
randomly sampled 500-item subset for COCO annotations on unseen models,
achieving a 10x evaluation speedup. We release the condensed subset of these
commonly used datasets to help facilitate diffusion algorithm design and
evaluation, and open-source FlashEval as a tool for condensing future datasets,
accessible at this https URL

近年来，文本到图像生成模型的发展取得了显著进展。我们提出了一种评估效率的方法，通过选择文本 - 图像数据集的代表性子集来改善模型评估过程。我们研究了设计选择，包括选择标准和选择粒度，并提出了一种名为 FlashEval 的迭代搜索算法。在 COCO 和 DiffusionDB 数据集上，我们证明了 FlashEval 在评估扩散模型方面的有效性。发布这些常用数据集的精简子集以促进扩散算法设计和评估，并开源 FlashEval 作为未来数据集精简的工具。