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

翻译：社交奖励作为一种社区认可形式，为在线平台用户参与和贡献内容提供了强大的激励。近期文本条件图像合成领域的进展开启了协作时代，人工智能赋能用户创作需寻求社区验证的原创视觉艺术作品。然而，在集体社区偏好情境下评估此类模型带来了独特挑战。现有评估方法主要聚焦于有限规模用户研究，其导向标准为图像质量与提示对齐度。本研究开创范式转变，提出"社交奖励"——一种创新性奖励建模框架，利用参与生成图像创意编辑的社交网络用户的隐式反馈。我们基于在线视觉创作与编辑平台Picsart展开大规模数据集构建与精炼工作，首次产出百万用户量级的隐式人类偏好数据集（命名为Picsart Image-Social），用于评估用户生成视觉艺术。分析揭示了现有指标在建模文本到图像模型输出的社区创意偏好方面的缺陷，促使我们提出专为克服这些局限而设计的新型预测模型。严格的量化实验与用户研究表明，我们的社交奖励模型在社交流行度对齐方面优于现有指标。此外，我们利用社交奖励微调文本到图像模型，产出的图像不仅更受社交奖励模型青睐，也更符合其他成熟指标的评估标准。这些发现凸显了社交奖励在评估AI生成艺术社区认可度方面的相关性与有效性，使评估更贴近用户创作目标——创作流行视觉艺术。代码可通过https://github.com/Picsart-AI-Research/Social-Reward获取。