Abstract: Existing image editing models struggle to meet real-world demands; despite excelling in academic benchmarks, we are yet to see them adopted to solve real user needs. The datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. In response, we introduce RealEdit, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. RealEdit contains a test set of $9.3$K examples the community can use to evaluate models on real user requests. Our results show that existing models fall short on these tasks, implying a need for realistic training data.So, we introduce $48$K training examples, with which we train our RealEdit model. Our model achieves substantial gains—outperforming competitors by up to $165$ Elo points in human judgment and $92\%$ relative improvement on the automated VIEScore metric on our test set. We deploy our model back on Reddit, testing it on new requests, and receive positive feedback. Beyond image editing, we explore RealEdit's potential in detecting edited images by partnering with a deepfake detection non-profit. Finetuning their model on RealEdit data improves its F1-score by $14$ percentage points, underscoring the dataset's value for broad, impactful applications.