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arXiv · 2506.08520

Plug-and-play linear attention with provable guarantees for training-free image restoration

Srinivasan Kidambi·Karthik Palaniappan·Pravin Nair
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Abstract

Multi-head self-attention (MHSA) is a key building block in modern vision Transformers, yet its quadratic complexity in the number of tokens remains a major bottleneck for real-time and resource-constrained deployment. We present PnP-Nystra, a training-free Nystr\"{o}m-based linear attention module designed as a plug-and-play replacement for MHSA in {pretrained} image restoration Transformers, with provable kernel approximation error guarantees. PnP-Nystra integrates directly into window-based architectures such as SwinIR, Uformer, and Dehazeformer, yielding efficient inference without finetuning. Across denoising, deblurring, dehazing, and super-resolution on images, PnP-Nystra delivers $1.8$--$3.6\times$ speedups on an NVIDIA RTX 4090 GPU and $1.8$--$7\times$ speedups on CPU inference. Compared with the strongest training-free linear-attention baselines we evaluate, our method incurs the smallest quality drop and stays closest to the original model's outputs.

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Srinivasan Kidambi, Karthik Palaniappan, Pravin Nair. 2025-06-10. Plug-and-play linear attention with provable guarantees for training-free image restoration. https://arxiv.org/abs/2506.08520

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