  The race to develop image generation models is intensifying, with a rapid
increase in the number of text-to-image models available. This is coupled with
growing public awareness of these technologies. Though other generative AI
models--notably, large language models--have received recent critical attention
for the social and other non-technical issues they raise, there has been
relatively little comparable examination of image generation models. This paper
reports on a novel, comprehensive categorization of the social issues
associated with image generation models. At the intersection of machine
learning and the social sciences, we report the results of a survey of the
literature, identifying seven issue clusters arising from image generation
models: data issues, intellectual property, bias, privacy, and the impacts on
the informational, cultural, and natural environments. We situate these social
issues in the model life cycle, to aid in considering where potential issues
arise, and mitigation may be needed. We then compare these issue clusters with
what has been reported for large language models. Ultimately, we argue that the
risks posed by image generation models are comparable in severity to the risks
posed by large language models, and that the social impact of image generation
models must be urgently considered.
