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× and 122× speedup over ObjectClear and OmniPaint, respectively, while maintaining high visual quality and fidelity.
@article{arxiv.2605.09003,
title = {FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching},
author = {Yixin Tang and Jiawei Guo and Junxian Li and Zhiteng Li and Jixin Zhao and Bingya Zhang and Chenbo Wang and Yulun Zhang and Shangchen Zhou},
journal= {arXiv preprint arXiv:2605.09003},
year = {2026}
}