  Our world is marked by unprecedented technological, global, and
socio-political transformations, posing a significant challenge to
text-to-image generative models. These models encode factual associations
within their parameters that can quickly become outdated, diminishing their
utility for end-users. To that end, we introduce ReFACT, a novel approach for
editing factual associations in text-to-image models without relaying on
explicit input from end-users or costly re-training. ReFACT updates the weights
of a specific layer in the text encoder, modifying only a tiny portion of the
model's parameters and leaving the rest of the model unaffected. We empirically
evaluate ReFACT on an existing benchmark, alongside a newly curated dataset.
Compared to other methods, ReFACT achieves superior performance in both
generalization to related concepts and preservation of unrelated concepts.
Furthermore, ReFACT maintains image generation quality, making it a practical
tool for updating and correcting factual information in text-to-image models.
