  Social reward as a form of community recognition provides a strong source of
motivation for users of online platforms to engage and contribute with content.
The recent progress of text-conditioned image synthesis has ushered in a
collaborative era where AI empowers users to craft original visual artworks
seeking community validation. Nevertheless, assessing these models in the
context of collective community preference introduces distinct challenges.
Existing evaluation methods predominantly center on limited size user studies
guided by image quality and prompt alignment. This work pioneers a paradigm
shift, unveiling Social Reward - an innovative reward modeling framework that
leverages implicit feedback from social network users engaged in creative
editing of generated images. We embark on an extensive journey of dataset
curation and refinement, drawing from Picsart: an online visual creation and
editing platform, yielding a first million-user-scale dataset of implicit human
preferences for user-generated visual art named Picsart Image-Social. Our
analysis exposes the shortcomings of current metrics in modeling community
creative preference of text-to-image models' outputs, compelling us to
introduce a novel predictive model explicitly tailored to address these
limitations. Rigorous quantitative experiments and user study show that our
Social Reward model aligns better with social popularity than existing metrics.
Furthermore, we utilize Social Reward to fine-tune text-to-image models,
yielding images that are more favored by not only Social Reward, but also other
established metrics. These findings highlight the relevance and effectiveness
of Social Reward in assessing community appreciation for AI-generated artworks,
establishing a closer alignment with users' creative goals: creating popular
visual art. Codes can be accessed at
https://github.com/Picsart-AI-Research/Social-Reward
