通过区域感知蒸馏与特征缓存，实现图像物体移除的超快推理。
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
- 引入区域感知对抗蒸馏，仅对前景区域进行高效去噪。
- 在OBER数据集上比ObjectClear快8.26倍，比OmniPaint快122倍。
- 无需训练即可加速，适合实时图像编辑场景。
基于扩散模型的物体移除方法虽效果出色，但普遍对所有标记在所有时间步进行无差别去噪，导致计算开销大、推理慢。针对此问题，本文提出潜空间判别器实现区域感知对抗蒸馏（RAD），构建名为FlashClear的少步高效模型。此外，为适配少步扩散模型，设计了无需训练的加速策略FPAC（前景优先非对称注意力与缓存）。大量实验表明，该框架在保持或超越基线模型ObjectClear性能的同时，实现显著加速：在OBER基准上，相比ObjectClear最高提速8.26倍，相比OmniPaint提速122倍，同时维持高视觉质量和保真度。
原文摘要 · Abstract (English)
Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, they indiscriminately denoise all tokens across all timesteps, ignoring that removal usually involves small foreground regions. This strategy introduces substantial computational overhead and prolonged inference times. To overcome this computational burden, we propose a latent discriminator to implement Region-aware Adversarial Distillation (RAD), yielding a highly efficient few-step model named FlashClear. Furthermore, tailored to few-step diffusion models, we propose FPAC (Foreground-Prioritized Asymmetric Attention and Caching), a training-free acceleration strategy. Extensive experiments demonstrate that our framework provides massive acceleration while maintaining or exceeding the performance of our base model, ObjectClear. Notably, on the OBER benchmark, our FlashClear achieves up to 8.26$\times$ and 122$\times$ speedup over ObjectClear and OmniPaint, respectively, while maintaining high visual quality and fidelity.
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