AI 中文总结
本文提出基于SDXL的单步对象-效果去除模型TurboClear，通过区域校准分布匹配与可学习空间融合，大幅降低计算开销且保持良好视觉质量，效率优于相关基线方法。
AI 中文摘要
近期，基于扩散模型的去除方法在去除目标对象及其关联效果方面已取得令人满意的视觉质量，但这类方法通常依赖多步去噪，导致推理成本较高。直接应用现有的单步蒸馏方法效果欠佳，因为其全局目标缺乏显式的区域校准，可能会削弱对象-效果去除所需的不对称编辑与保留行为。为应对这些挑战，本文提出TurboClear，一种基于SDXL的单步对象-效果去除模型。训练过程中，我们设计了区域校准分布匹配（RDM）用于区域感知蒸馏，以保留教师模型的不对称编辑与保留行为；此外，我们提出可学习空间融合（LSF）用于轻量级推理时融合。大量实验表明，TurboClear在保持竞争力的视觉质量的同时，显著提升了推理效率：与ObjectClear相比，其计算开销最多降低40.04倍；与基于Flux的方法OmniPaint相比，最多降低665倍，且视觉去除质量相当或更优。代码可在指定URL获取。
英文摘要
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.
CommentsCode: https://github.com/GuoCalix/TurboClear
