一拍即合：用区域校准加速图像去物去效，快665倍还画得更好
TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion
- 区域校准分布匹配，让模型懂哪里改哪里留
- 推理时轻量融合，速度提升40倍以上
- 适合要快且画质稳的图像编辑场景
基于扩散模型的去物去效方法虽有良好视觉效果，但多依赖多步去噪，推理成本高。直接套用现有一步蒸馏方法效果不佳，因其全局目标缺乏区域级校准，削弱了去物去效所需的非对称编辑-保留行为。为此，我们提出TurboClear，一种基于SDXL的一步式去物去效模型。训练中采用区域校准分布匹配（RDM），实现区域感知的蒸馏，以保留教师模型的非对称编辑-保留特性；推理时引入可学习空间融合（LSF），实现轻量级融合。大量实验表明，TurboClear显著提升推理效率，同时保持优异视觉质量：相比ObjectClear，计算开销降低最高达40.04倍；相较Flux-based方法OmniPaint，降低最高达665倍，且画质相当或更优。代码已开源：https://github.com/GuoCalix/TurboClear。
原文摘要 · Abstract (English)
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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