Existing image editing models struggle to meet real-world demands. Despite excelling in academic benchmarks, they have yet to be widely adopted for real user needs. Datasets that power these models use artificial edits, lacking the scale and ecological validity necessary to address the true diversity of user requests. We introduce REALEDIT, a large-scale image editing dataset with authentic user requests and human-made edits sourced from Reddit. REALEDIT includes a test set of 9300 examples to evaluate models on real user requests. Our results show that existing models fall short on these tasks, highlighting the need for realistic training data. To address this, we introduce 48K training examples and train our REALEDIT model, achieving substantial gains - outperforming competitors by up to 165 Elo points in human judgment and 92 percent relative improvement on the automated VIEScore metric. We deploy our model 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 applications.
@article{arxiv.2502.03629,
title = {REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations},
author = {Peter Sushko and Ayana Bharadwaj and Zhi Yang Lim and Vasily Ilin and Ben Caffee and Dongping Chen and Mohammadreza Salehi and Cheng-Yu Hsieh and Ranjay Krishna},
journal= {arXiv preprint arXiv:2502.03629},
year = {2025}
}