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
this https URL

社交奖励作为一种社区认可形式，是在线平台用户参与和贡献内容的强大动力。最近文本驱动的图像合成的进展开启了人工智能赋予用户创作原创视觉艺术，并寻求社区认可的协作时代。本研究在集体社区偏好的背景下评估这些模型带来了独特的挑战。现有的评估方法主要集中在用户研究上，以图像质量和提示对齐为导向。本研究首创了一种新的奖励建模框架 —— 社交奖励，利用社交网络用户对生成图像进行创作编辑的隐式反馈。通过从在线视觉创作和编辑平台 Picsart 搜集数据集，我们发现了当前度量社区创作偏好的指标的不足之处，并引入了一种专门解决这些限制的预测模型。严格的定量实验和用户研究表明，我们的社交奖励模型比现有的度量指标更符合社交受欢迎程度。此外，我们利用社交奖励来微调文本驱动的图像生成模型，得到不仅社交奖励喜欢，而且其他已有度量指标也更看好的图像。这些发现突显了社交奖励在评估人工智能生成的艺术品中社区认可的相关性和效果，与用户的创作目标 —— 创作热门视觉艺术更为接近。

社交奖励：通过千万用户的在线创意社区反馈评估和增强生成型人工智能

Social Reward: Evaluating and Enhancing Generative AI through  Million-User Feedback from an Online Creative Community

Due to the difficulty in scaling up, generative adversarial networks (GANs)
seem to be falling from grace on the task of text-conditioned image synthesis.
Sparsely-activated mixture-of-experts (MoE) has recently been demonstrated as a
valid solution to training large-scale models with limited computational
resources. Inspired by such a philosophy, we present Aurora, a GAN-based
text-to-image generator that employs a collection of experts to learn feature
processing, together with a sparse router to help select the most suitable
expert for each feature point. To faithfully decode the sampling stochasticity
and the text condition to the final synthesis, our router adaptively makes its
decision by taking into account the text-integrated global latent code. At
64x64 image resolution, our model trained on LAION2B-en and COYO-700M achieves
6.2 zero-shot FID on MS COCO. We release the code and checkpoints to facilitate
the community for further development.

Aurora 是一种基于 GAN 的文本到图像生成器，利用一组专家进行特征处理学习，并使用稀疏路由器来选择每个特征点最合适的专家，以实现对采样随机性和文本条件的忠实解码，该模型在 64x64 图像分辨率下，使用 LAION2B-en 和 COYO-700M 数据集进行训练，在 MS COCO 上达到了 6.2 的零样本 FID 指标。