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

2026-06-26

AI Summary

This work addresses the challenges of object removal in real-world scenarios, where modeling non-local effects and handling inaccurate user-provided masks remain difficult, while existing diffusion models incur prohibitive computational costs that hinder deployment on interactive or edge devices. The authors propose OSOR, a novel approach that achieves stable training within a single-step diffusion framework for the first time. OSOR integrates an occupancy-guided discriminator, a lightweight alpha head, and a Semantic Anchor Validation Pipeline (SAVP) to jointly optimize generation quality under imperfect masks. Evaluated on the large-scale CORNE dataset and the AnimeEraseBench/TextEraseBench benchmarks, OSOR surpasses multi-step diffusion baselines in perceptual quality while accelerating inference by 4–30×, substantially improving both efficiency and robustness for interactive and edge-based applications.

Abstract

Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete. With billions of parameters and tens of denoising steps, diffusion-based models achieve strong removal performance at the expense of substantial computational cost, limiting their use in interactive applications and on edge devices. To address these challenges, we present OSOR (One-Step Object Removal), which simultaneously achieves efficient, effect-aware, and mask-robust object removal. Concretely, OSOR introduces: (1) an occupancy-guided discriminator for precise boundary supervision, enabling stable single-step diffusion training; (2) an alpha head that leverages knowledge from pretrained diffusion models to predict appropriate removal regions with minimal overhead, thereby handling imperfect masks; and (3) a semantic-anchored verification pipeline (SAVP) that filters noisy instruction-based triplets to produce effect-aware supervision at scale. Using SAVP, we curate CORNE, which contains 280K verified removal pairs, and further annotate AnimeEraseBench and TextEraseBench to evaluate performance on more complex removal tasks. Experiments show that OSOR surpasses strong multi-step diffusion baselines in perceptual quality while achieving $4\times$ to $30\times$ faster inference.

Problem

• object removal
• non-local effects
• inaccurate masks
• diffusion models
• computational efficiency

Innovation

• one-step diffusion
• effect-aware inpainting
• mask-robust removal
• occupancy-guided discriminator
• semantic-anchored verification