OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal

Real-world object removal is challenging due to two key dif-ficulties: the target object’s non-local effects (e.g., shadows and reflec-tions) that are hard to model, and the fact that user-provided masksare often inaccurate or incomplete. With billions of parameters and tensof denoising steps, diffusion-based models achieve this goal at the ex-pense of massive computational cost, limiting their use in interactiveapplications and edge devices. To solve this problem, we present OSOR(One-Step Object Removal), which achieves efficient, effect-aware, andmask-robust object removal at the same time. Concretely, OSOR in-troduces (1) an occupancy-guided discriminator for precise boundarysupervision, enabling stable single-step diffusion training, (2) an alphahead, which leverages the knowledge in diffusion models to predict cor-rect removal regions with minimal overhead, handling imperfect masks,and (3) a semantic-anchored verification pipeline (SAVP) that filtersnoisy instruction-based triplets to produce effect-aware supervision atscale. Using SAVP, we curate CORNE with 280K verified removal pairsand further annotate AnimeEraseBench and TextEraseBench to evalu-ate model performance in more complex tasks. Experiments show thatOSOR surpasses strong multi-step diffusion baselines in perceptual qual-ity while achieving 4× to 30× faster inference. Code and resources areavailable at https://github.com/Zhouqm-Git/osor.