  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 https://github.com/thu-nics/FlashEval.
