RCEdit-500K: Reference Completion for Image-Conditioned Image Editing

Image-conditioned image editing (ICIE) guides edits with a reference image to convey visual attributes—such as style, tone, or object identity—that are difficult to specify through text alone. Despite growing practical demand, no large-scale unified ICIE dataset currently exists in the open-source ecosystem; existing efforts cover only a narrow subset of edit types at small scale, typically constructed via costly forward synthesis. We reformulate ICIE data construction as a reference-completion problem: high-quality text-conditioned image editing (TCIE) datasets already supply the input image, instruction, and edited target, and can be augmented with aligned reference images through type-specific synthesis and lightweight instruction adaptation. Building on this insight, we propose a scalable pipeline equipped with weak-instruction augmentation and five-dimensional VLM-based post-filtering, and use it to construct RCEdit-500K—the first large-scale unified open ICIE dataset comprising 477K quadruplets across six edit categories (add, remove, replace, background, style, alter) with both concrete and abstract reference types. Training on RCEdit-500K consistently improves reference-guided editing: LoRA adaptation on diffusion models yields up to +1.22 average gain, and an autoregressive model without native editing ability acquires competitive ICIE performance, demonstrating that data availability is the primary bottleneck for open-source ICIE.