LinearSR: Unlocking Linear Attention for Stable and Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2510.08771v2
• Date: Thu, 30 Oct 2025 14:46:21 GMT
• Title: LinearSR: Unlocking Linear Attention for Stable and Efficient Image Super-Resolution
• Authors: Xiaohui Li, Shaobin Zhuang, Shuo Cao, Yang Yang, Yuandong Pu, Qi Qin, Siqi Luo, Bin Fu, Yihao Liu,
• Abstract summary: Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity (O(N2)) creates a major computational bottleneck.<n> Linear Attention offers an O(N) solution, but its promise for photorealistic SR has remained largely untapped.<n>This paper introduces LinearSR, a holistic framework that, for the first time, systematically overcomes these critical hurdles.
• Score: 24.44080642253128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity (O(N^2)) creates a major computational bottleneck. Linear Attention offers an O(N) solution, but its promise for photorealistic SR has remained largely untapped, historically hindered by a cascade of interrelated and previously unsolved challenges. This paper introduces LinearSR, a holistic framework that, for the first time, systematically overcomes these critical hurdles. Specifically, we resolve a fundamental, training instability that causes catastrophic model divergence using our novel "knee point"-based Early-Stopping Guided Fine-tuning (ESGF) strategy. Furthermore, we mitigate the classic perception-distortion trade-off with a dedicated SNR-based Mixture of Experts (MoE) architecture. Finally, we establish an effective and lightweight guidance paradigm, TAG, derived from our "precision-over-volume" principle. Our resulting LinearSR model simultaneously delivers state-of-the-art perceptual quality with exceptional efficiency. Its core diffusion forward pass (1-NFE) achieves SOTA-level speed, while its overall multi-step inference time remains highly competitive. This work provides the first robust methodology for applying Linear Attention in the photorealistic SR domain, establishing a foundational paradigm for future research in efficient generative super-resolution.

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