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

• URL: http://arxiv.org/abs/2608.01288v1
• Date: Sun, 02 Aug 2026 14:56:13 GMT
• Title: TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion
• Abstract summary: We propose TurboClear, a one-step SDXL-based object-effect removal model.<n>During training, we design Region-Calibrated Distribution Matching (RDM) for region-aware distillation to preserve the teacher model's asymmetric edit-and-preserve behavior.<n>Experiments show that TurboClear significantly improves inference efficiency while maintaining competitive visual quality.
• Score: 40.189383154480154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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