ImagenHub: Standardizing the evaluation of conditional image generation models

arXiv:2310.01596v4 Announce Type: replace
Abstract: Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc. However, we observe huge inconsistencies in experimental conditions: datasets, inference, and evaluation metrics - render fair comparisons difficult. This paper proposes ImagenHub, which is a one-stop library to standardize the inference and evaluation of all the conditional image generation models. Firstly, we define seven …

abstract arxiv control cs.cv cs.gr cs.mm datasets editing etc evaluation evaluation metrics experimental however image image generation image generation models inference metrics observe serve tasks text text-to-image type