ImagenHub: Standardizing the evaluation of conditional image generation
  models
Authors: name: Max Ku, name: Tianle Li, name: Kai Zhang, name: Yujie Lu, name: Xingyu Fu, name: Wenwen Zhuang, name: Wenhu Chen

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 prominent tasks
and curate high-quality evaluation datasets for them. Secondly, we built a
unified inference pipeline to ensure fair comparison. Thirdly, we design two
human evaluation scores, i.e. Semantic Consistency and Perceptual Quality,
along with comprehensive guidelines to evaluate generated images. We train
expert raters to evaluate the model outputs based on the proposed metrics. Our
human evaluation achieves a high inter-worker agreement of Krippendorff's alpha
on 76% models with a value higher than 0.4. We comprehensively evaluated a
total of around 30 models and observed three key takeaways: (1) the existing
models' performance is generally unsatisfying except for Text-guided Image
Generation and Subject-driven Image Generation, with 74% models achieving an
overall score lower than 0.5. (2) we examined the claims from published papers
and found 83% of them hold with a few exceptions. (3) None of the existing
automatic metrics has a Spearman's correlation higher than 0.2 except
subject-driven image generation. Moving forward, we will continue our efforts
to evaluate newly published models and update our leaderboard to keep track of
the progress in conditional image generation.