TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion

2026-08-02

AI Summary

This work addresses the inefficiency of existing diffusion models in object and effect removal tasks, which typically rely on multi-step denoising, as well as the inability of single-step distillation methods to preserve the asymmetric behavior between edited and retained regions. To overcome these limitations, the authors propose TurboClear—a single-step removal model based on SDXL—that introduces Region-aware Distribution Matching (RDM) to enable region-sensitive distillation, effectively retaining the teacher model’s asymmetric editing characteristics. Additionally, a lightweight Learnable Spatial Fusion (LSF) mechanism is incorporated to enhance inference efficiency. Experimental results demonstrate that TurboClear achieves 40.04× and 665× speedups over ObjectClear and OmniPaint, respectively, while delivering comparable or superior visual removal quality.

Abstract

Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However, they typically rely on multi-step denoising, leading to high inference cost. Directly applying existing one-step distillation methods is also suboptimal, since their global objectives lack explicit region-wise calibration and may weaken the asymmetric edit-and-preserve behavior required by object-effect removal. To address these challenges, we propose TurboClear, a one-step SDXL-based object-effect removal model. During training, we design Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model's asymmetric edit-and-preserve behavior. Furthermore, we propose Learnable Spatial Fusion (LSF) for lightweight inference-time fusion. Extensive experiments show that TurboClear significantly improves inference efficiency while maintaining competitive visual quality. TurboClear reduces the computational overhead by up to $40.04\times$ compared to ObjectClear, and by up to $665\times$ against the Flux-based method OmniPaint, all while maintaining comparable or better visual removal quality. Code is available at https://github.com/GuoCalix/TurboClear.

Problem

• object-effect removal
• one-step distillation
• region-wise calibration
• asymmetric edit-and-preserve
• inference efficiency

Innovation

• Region-Calibrated Distribution Matching
• Learnable Spatial Fusion
• One-Step Distillation
• Object-Effect Removal
• Diffusion Model Acceleration