  While recent advancements in vision-language models have had a transformative
impact on multi-modal comprehension, the extent to which these models possess
the ability to comprehend generated images remains uncertain. Synthetic images,
in comparison to real data, encompass a higher level of diversity in terms of
both content and style, thereby presenting significant challenges for the
models to fully grasp. In light of this challenge, we introduce a comprehensive
dataset, referred to as JourneyDB, that caters to the domain of generative
images within the context of multi-modal visual understanding. Our meticulously
curated dataset comprises 4 million distinct and high-quality generated images,
each paired with the corresponding text prompts that were employed in their
creation. Furthermore, we additionally introduce an external subset with
results of another 22 text-to-image generative models, which makes JourneyDB a
comprehensive benchmark for evaluating the comprehension of generated images.
On our dataset, we have devised four benchmarks to assess the performance of
generated image comprehension in relation to both content and style
interpretation. These benchmarks encompass prompt inversion, style retrieval,
image captioning, and visual question answering. Lastly, we evaluate the
performance of state-of-the-art multi-modal models when applied to the
JourneyDB dataset, providing a comprehensive analysis of their strengths and
limitations in comprehending generated content. We anticipate that the proposed
dataset and benchmarks will facilitate further research in the field of
generative content understanding. The dataset is publicly available at
https://journeydb.github.io.
