We propose a dataset to enable the study of generative models that understand fine-grained individual preferences.We posit that a key challenge hindering the development of such a generative model is the lack of in-the-wild and fine-grained user preference annotations. Our dataset features real-world interaction data from 57K different users, who collectively have built 242K customized LoRAs, written 3M text prompts, and created 5M generated images. Our dataset enables a set of applications. With aggregate-level user preferences from our dataset, we were able to train better preference alignment models. In addition, leveraging individual-level user preference, we benchmark the performance of retrieval models and a vision-language model on personalized image retrieval and generative model recommendation and highlight the space for improvements. Finally, we demonstrate that our dataset enables, for the first time, a generative model personalization paradigm by editing customized diffusion models in a latent weight space to align with individual user preferences.