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Text-to-image models are trained on extensive amounts of data, leading themto implicitly encode factual knowledge within their parameters. While somefacts are useful, others may be incorrect or become outdated (e.g., the currentPresident of the United States). We introduce ReFACT, a novel approach forediting factual knowledge in text-to-image generative models. ReFACT updatesthe weights of a specific layer in the text encoder, only modifying a tinyportion of the model's parameters, and leaving the rest of the modelunaffected. We empirically evaluate ReFACT on an existing benchmark, alongsideRoAD, a newly curated dataset. ReFACT achieves superior performance in terms ofgeneralization to related concepts while preserving unrelated concepts.Furthermore, ReFACT maintains image generation quality, making it a valuabletool for updating and correcting factual information in text-to-image models.

ReFACT: Updating Text-to-Image Models by Editing the Text Encoder