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
Venue: ICLR 2026 Poster (2026)
Keywords: Image Super-Resolution
Linear Attention
Training Stability
Primary area: applications to computer vision, audio, language, and other modalities

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.

Published as a conference paper at ICLR 2026
LINEARSR: UNLOCKING LINEAR ATTENTION FOR STABLE AND EFFICIENT IMAGE SUPER-RESOLUTION
Xiaohui Li
Shaobin Zhuang' Shuo Cao
Yang Yang*.
Yuandong Pu 1.2 Qi Qin Siqi Luo
Bin Fu²
Yihao Liu
Shanghai Jiao Tong University Shanghai Artificial Intelligence Laboratory
University of Science and Technology of China
The Australian National University
250-
Vanilla Attention
287.
200
Linear Atteation
150
Time (mis)
Sec5R
SUPIR
DreamClear
20.5
1.8
4.2
2.4
65
160
AdeSR
TSD-SR SR
LinearSR
Input Size
3.5k
-O-
Linear Atlention
2k
GFLOPs
1.5k
1k
13c
SeeSR
SUPIR
Dicomtllear
0.5k
70
140
218
32
64
96
128
160
Input Size
Figure 1: LinearSR enables high-fidelity super-resolution at a linear computational cost. Left: LinearSR produces high-fig fideliiyliity visual results, restoring fine details and textures. Right: The plots highlight the dramatic efficiency advantage of our Linear Attention. As input size grows, its cost in time and GFLOPs scales linearly, versus the quadratic growth of vanilla attention.
ABSTRACT
Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity (O(N2)) creates ma- jor computational bottleneck. Linear Attention offers O(N) solution, but its promise for photorealistic SR has remained largely untapped, historically hin- dered by 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, ing instability that causes catastrophic model divergence using our novel "knee point"-based Early- Starl-Stopping Guided Fine-tuning (ESGF) strategy. Furthermore. we mitigate the classic perception-distoriin trade-off with a dedicated -hsee Mixture of Experts (MoE) architecture. Finally, establish effective and lightweight guidance paradigm, TAG, derived from our prrccssion-over-olumme 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-leve speed, while its overall multi stepi-step inference time re- mains highly competitive. This work provides the first robust methodology for applying Linear Attention the photorealistic domain, establishing founda- tional paradigm for future research in efficient generative super-resolution.
resolve fundamental, train-
"Corresponding author. This work done during internship Shanghai AT Laboratory.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Recent advancements in Image Super-Resolution (SR) are dominated by generative models (Chen al. 2023 Rombach al. 2022 Duan et al. 2025) that leverage the powerful self-attention mech- anism to synthesize photorealistic details. However, this power comes at steep price: the quadratic complexity (O(22) of self-attention imposes a major computational bottleneck. Linear Attention (Shen et al., 2021 Katharopoulos et al. 2020 Wang et al., 2020 Cai et al. (022), with its O(N) complexity, has emerged as a compelling alternative. Its potential was successfully shown gen- eral image generation by models like SANA (Xie et al.] 2022), which validated its ability to capture global dependencies efficiently. This work addresses the central challenge: how can the efficiency of Linear Attention be fully unlocked to satisfy the extreme fidelity requirements of super-resolution?
Translating this theoretical promise into practice, however, required overcoming a significant cas- cade of technical hurdles. Our initial exploration into guidance was driven by the scarcity of high- resolution images paired with high- figh-precision annotations. This motivated us to test information- agnostic extractors like DINO, whose surprising success led to the "precisioon-over-ollum"" prin- ciple, which was ultimately validated by the concise TAG model. Subsequently, a more formidable hurdle soon emerged: a critical training instability. When fine-tuning converged model-a standard industry practice-the loss would abruptly diverge to NaN, halting all progress and revealing fun- damental flaw in applying conventional methods to Linear Attention SR. Finally, even after resolv- ing this, a persistent final barrier remained: the classic perception-distorioon trade-off. The model struggled to improve perceptual realism (e.g., finer textures) without simultaneously sacrificing re- construction fidelity (e.g.. PSNR), making it the last obstacle to unlocking top-tier performance.
To conquer these challenges, we propose LinearSR, a framework designed to harmnnize efficiency, stability, and performance. As encapsulated by our teaser in Fig. [ LinearSR achieves two goals: it produces photorealistic results while demonstrating an efficiency advantage. The plot highlights our model's linear (O(N) scaling, in stark contrast the quadratic (O(N2) cost of standard attention.
Crucially, this linear scaling advantage is not merely theoretical but is directly reflected in the per- formance of the core architecture, independent of orthogonal optimizations like model distillation. Specifically, for megapixel- scale synthesis (1024x (0044x1024), our model's fundamental diffusion for- ward pass (1-NFE) sets a new SOTA-level time of 0.036s. This metric precisely benchmarks our structural contribution to the attention mechanism's 's efficiency. Consequently, the overall multi-step inference time remains highly competitive 0.830s, demonstrating the practical viability of our ap- proach. This achievement is built upon a triad of core contributions: (i) an Early-Stopping Guided Fine-tuning (ESGF) strategy that resolves the critical training instability; (ii) an SNR-based Mixture of Experts (MoE) architecture to mitigate the perception -isttrrtin trade-off; and (iii) the adoption of the effective TAG-based guidance paradigm, validated by our "precision-or cetiiiiinoove--voon -oee--oovv--eeeee principle.
Equipped with this framework, LinearSR sets a new powerful benchmark for efficiency in the core diffusion forward pass. This work, for the first time, provides a robust and repeatable methodology to successfully apply Linear Attention in the high-fieelity SR domain. By establishing this foundational paradigm, we pave the way for numerous future optimizations, such as model distillation, to further push the boundaries of both speed and perceptual quality in generative super-resolution.
RELATED WORK
The paradigm in image restoration has shifted from traditional methods towards powerful diffusion- based generative priors (Lin el al.) 2024 Wu et al., 20224b Yu et al.) 2024 Ai et al., [024).. While these models achieve state-of-the-at art perceptual quality, their prohibitive computational cost spurred a new line of research focused on accelcration through methods like knowledge distillation and dif-
fusion inversion (Wang et al. 2024b) Wu et al., 2024a| Dong et al., 2025; Yue et al. 2025 Chen et al. 2025a). However, these post-hoc optimizations do not resolve the fundamental architectural bottleneck: the quadratic complexity of the standard self-attention mechanism, which remains a se- vere bottleneck for high-resolution inputs. To address this limitation, linear attention methods offer a compelling O(N) alternative. Pioneered in NLP (Wang et al. 2020 Katharopoulos et al. 2020) and successfully extended to other vision tasks (Shen et al. [202] Cai et al.) 2022) and generative modeling (Xie et al., 2024). this approach provides a strong foundation. Yet, translating this theoret- ical efficiency to the demanding super-resolution task has proven notoriously difficult, historically
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Flow Matching Schedule
Linear Attention
Vanilla Attention
7
Vs.
MasMul
Linear Allen Low
Linear Pittttiioon
MatMal
Sontaaa
DC-AE
DC-AE
Actortionn
MatMul
MatMul
ReL
Text Encoder
(a) MoE inference pipeline.
(b) Linear attention illustration.
Fewer Steps
Prestraining
Quahh-Quality Dataset
SET
MUSIO
Traleinn Iterations
Trbleing Reraioon
Lange-Scal
Stage 1: Pre Pre-oraining
Stage 2: Contimuore Moč Pre-truining Stage 3: Supervised Finetuning
(c) Multi-Stage training strategy.
The Integrated LinearSR Framework. This figure illustrates how our contributions synergize: the tag-guided Mixture of Experts (MoE) architecture (a), built upon an efficient lin- ear attention backbone (b), is made stable and effective by our Early-Stopping Guided Fine-tuning (ESGF) strategy (c), which initiates fine-tuning at the critical "knee point" to maximize performance.
Figure 2:
plagued by training instability and a severe prrcepiionddistorton trade-off. Our work aims to bridge this critical gap, demonstrating the first successful integration of linear attention for high-fidelity diffusion- -asodd super-resolution. A more detailed literature review is provided in Appendix B
3 METHOD
LinearSR, our framework designed to dismantle the long-standing trade-off between computational efficiency and generative fidelity in super-resolution. Motivated by the need for robust and practical O(N) solution, we architected system that seamlessly integrates a lightweight guidance paradigm with stable, multi-stage training methodology. The synergy of these components, summarized in Fig. 2 establishes the first effective application of linear attention in high-fidelity SR.
3.1 LINEARSR FRAMEWORK
LinearSR conditional Diffusion Transformer (DiT) whose architecture is shown in Fig. 2(a). Its core is DiT backbone using ReLU-based Linear Attention, a mechanism validated for efficiency in high-resolution domains like dense prediction (Wang et al., 2020 Shen et al. 202T; Cai al. 2022) and generative synthesis (Xie et al., 2024). Our work, for the first time, adapts this established architecture for the distinct challenges of high-fidelity generative super-resolution.
Standard self-attention computes pairwise similarity matrix with O(N3) complexity. As con- trasted in Fig. (bb), linear attention avoids this bottleneck using the associative property of matrix multiplication. Given query, key. and value vectors qı, kj, V E Rd the output o, is:
)(q.)
Σ N د o(k,)T
0, Φqq)) Ey=n ((k))
where (6-) ReLU(.). Instead of associating each query q with every key kj. the operations TT v, and its normalizer computed first, forming global summary
reordered. The terms Σ (T,

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Published as a conference paper at ICLR 2026
in fixed-size tensor. Each query q, then interacts with this pre-computed context, reducing the overall complexity to O(N). To enhance performance, our backbone pairs linear attention with a Mix-FFN module. This module uses a 3 x 3 depth-wise convolution to bolster local information proesssigg-compensating for a known weakness of linear attention-and accelerate convergence.
A critical adaptation for SR is injecting the low-resolution (LR) image condition. We introduce a lightweight conditioning stem, Econu. to process the LR input 2tr- This stem transforms T/r into feature map with spatial dimensions matching the noisy latent zt. The feature map is then concatenated with ž along the channel dimension to provide the DiT backbone with structural guidance, expressed as:
(2) The Econe stem consists of three strided convolutional layers with SiLU activations. This learnable, sulti-scale approach captures salient structural and content information from the LR image, offering superior guidance compared to fixed, non-learned upsampling techniques like bilinear interpolation.
2 = Concat (z,, (xr))
3.2 GUIDANCE: A "PRECISION-OVER VOLUME" APPROACH
Let our model be vector field prediction network no Et, t. e) trained with the Conditional Flow Matching (CFM) objective (Lipman et al. [022,) Tong et al.) 2023):
fCrM = (e,.
[[ - z0) 0o ((1 =)=0 + t2 1, [
(3)
where 21 is sample from the data distribution and 2g is sampled from prior, typically N(O,, T)- The model learns to approximate the vector field of a probability path that transports samples from the prior to the data distribution. A pivotal design choice for super-resolution (SR) is the nature of the conditioning context c. Unlike text-to-image synthesis, which creates content from external prompts, SR begins with strong visual prior: the low-resolution (LR) image itself. This raises a fundamental question: is it more effective to supplement the model with rich, external descriptions, or to guide it by instead precisely extracting features already inherent to the LR input?
To investigate this, we explore two distinct guidance paradigms. The first is external semantic guidance using descriptive captions. The second, termed self-contained feature guidance, extracts features directly from the LR image. We evaluate spectrum of models for this: at one end is CLIP RRadford et al. 202I), whose features are aligned with language concepts via contrastive learning. At the other DINO (Caron et al. 2021). self-supervised model that learns purely visual representations without textual supervision. Through self self-distillation on augmented views, is forced to learn features for object parts and structures, often unaligned with linguistic semanties. Bridging these two extremes, we consider a tag-style model inspired by SeeSR (Wu et al. 2024h), which uses tagger like RAM (Zhang al. 2024) to extract concise set of object labels.
Previous work (Wu et al.) 2024a b) has shown tag- tag-based guidance surpasses long-text captions. Our investigation expands on this by directly comparing it against powerful, vision-only models like CLIP and DINO. Our empirical findings, detailed in Sec. 4.3. revealed that interestingly, both DINO and CLIP features outperformed descriptive text. This suggests the core challenge in SR is not an information deficit but its effective utilization; adding external context is less effective than precisely extracting intrinsic semantics from the LR image. The tag-style model, providing struc- principle:
tured object vocabulary, yielded the best results, validating our prrecision-over-vlumee a smaller, targeted guidance signal is indeed more effective and efficient for the SR task.
3.3 EARLY-STOPPING GUIDED FINE-TUNING (ESGF) FOR STABILITY
Fine-tuning the linear attention model for high-fideliyy SR presented a challenge: the training invari- ably collapsed. We hypothesized this instability stemmed from the model converging to sharp min- imum in the loss landscape, known to cause poor generalization and adaptation instability (Keskar ct al. 2016). In this state, the model over-specializes on artifacts instead of learning robust features.
To validate this critical hypothesis, extensively analyzed the training dynamics. By tracking vali- dation metrics against the ever-decreasing training loss, we discovered a universal pattern (Fig. b)): performance metries would improve, plateau, and then begin to erratically oscillate, proving conclu- sively that relying on loss alone is a deceptive guide for model selection. This observation led us to define the "knee-point" the iteration of optimal generalization before performance degrades.
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Training Dynamies:: PSNR and Loss Ivolution
ORO
Dilider Foxt
Kisobea Hiu
Training Racatioon it
Training Dynomics: LPIPS and Loss Evelution
hat 10t
Truit ae Loo
Inpur LR
48k (Knee-Point)
224k (Unstable-Pens Peak)
(8) Linear Attention Features: Knce-Point vs. Unstable Peak
(b) Training Dynamics
Figure 3: Justification for ESGF through Instability Analysis. (a) Representative feature maps from the same linear attention layer reveal a stark structural degradation from the knee-point later unstable peak. (b) The training dynamics confirm this phenomenon is universal, with PSNR and LPIPS metrics exhibiting the characteristic "Plateau and Oscillation Phase" post-knee-point.
As definitive proof, we compared internal model states. We visualized feature maps from the same linear attention layer, contrasting the knee-point model with one from a later "unstable peak (Fig-B]a) The results striking: features from the knee-point structurally coherent, while those from the unstable peak are noisy and degraded, confirming catastrophic loss of representa- tional quality. Based on this evidence, we propose Early-Stopping Guided Fine-tuning (ESGF): all fine-tuning must initialize from the knee-point checkpoint. This model, residing in a flatter, more robust region of the loss landscape, provides a stable foundation for adaptation.
This theoretically grrroudd strategy resolves the training collapse. This instability was persistent bottleneck that suggested linear attention was fundamentally ill-suited for multi-stage SR training. Therefore, ESGF is not merely an enabler but a core innovation that makes our framework viable.
3.4 SNR-BASED MIXTURE OF EXPERTS FOR PERCEPTION-DISTORTOOO TRADF-OFF
final obstacle to top-tier performance was the perception-distortion trade-off, where im- proving perceptual realism sacrificed fidelity. Our insight is that this trade-off is dynamic: carly, high-noise stages (low SNR) demand coarse structure generation, while later, low-
SNR-based Timestep Division for MoE
Aver
Expert
Export Refinement
(ogiSNR)
noise stages (high SNR) require detail refine- ment. To address this, introduce an SNR- based Mixture of Experts (MoE) architecture.
Detail Polishing
1g. 0)
Am.
Our approach, visualized in Fig. 4 parti- tions the generative trajectory within the log- Signal-to-Noise Ratio (log-SNR) space, A(t). The process operates over an effective range [Amin; Amax] dictated by the noise schedule; for instance, the "scaled_linear" variant yields a characteristic asymmetric range (Diffusion) 2024). The partitioning is hierarchical: first, primary anchor Xuneber at to bisects the range into high-noise (structure) and low-noise (refinement) regimes, division supported by related work (Wan- Video (025). We then further bisect these two sub-intervals in the log-SNR space, mapping their midpoints back to the time domain via ((A) to derive the final boundaries i and 13-
Am
Expert Texture Generation
Amin
T
12
7/2
Denoising Timestep
Figure 4: Hierarchical log-SNR bisection de- fines operational boundaries for 4-expert MoE.
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This hierarchical derivation yields the time boundaries (t1, f2, t/s for our four experts, {Ex) (de- tailed in App. A) A gating network uses these boundaries to deterministically route inputs, enabling specialized processing without inference overhead, as only one expert is active per timestep.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Training and Implementation Details. For the pre-training and MoE stages, we use the pub- lic datasets DIV2K (Agustsson & Timofte. 2017). LSDIR (Li ct al. 2023), and ReLAION- Resolution (laion 2024). along with a custom high-resolution dataset crawled from Unsplash. The final SFT stage employs curated set of high-quality images collected from the Internet. We syn- thesize 4x LR-HR pairs (256x256 from 1024x 1024) using the Real-ESRGAN (Wang et al. (02T)) degradation pipeline. Our model is trained with flow-based objective (Lipman et al., 2022), em- ploying the CAME optimizer (Luo et al., (202)) with a constant learning rate of le-4 and a batch size of 14. Stage utilized NVIDIA A800 GPUs, while each of the four experts Stage was trained on 6 A800 GPUs. Following our ESGF strategy, each stage proceeds until its performance 'knee-point" is reached. For inference, we use 20 sampling steps generate results.
Evaluation Datasets and Compared Methods. We conduct evaluations on RealSR (Cai et al. 2019). DrealSR (Wei et al.) 2020). RealLQ250 (Ai et al.] 2024), and synthetic test set of 100 images from DIV2K- Val (Agustsson & Timofte 2017) generated with our training degradation. For
RealSR and DrealSR, we follow the established protocol (Wu et al.) 2024a b) Chen et al. 2025a)
of center-cropping imputs to 256x256. We compare LinearSR against ten tfateoo-thhe-as methods:
StableSR (Wang et al. 2024a). DifiBIR (Lin et al., 2024).. SceSR (Wu et al. 2024b). SUPIR YYu
al. 2024), DreamClear (Ai et al. 2024), SinSR (Wang et al. (024b)) OSEDifi (Wu et al.
2024a).
AdeSR (Chen et af. 2025a), InvSR (uue et al. 2025). and TSD-SR (Dong et al. 2025).
PSNR, SSIM (Wang al. 2004). and LPIPS (Zhang et al.) 2018a) as full-reference metrics and MANIQA (Yang et al. 20222. CLIPIQA WWan et al.] (023), and MUSIQ (Ke et al. (021) as non-reference metries for comprehensive eval- uation. The former evaluate pixel-level fidelity, while the latter are designed to align with human perception. Our method, like other generative SR approaches (Ai et al. 2024 Lin al. 2024 Wang et al., 2024a Yu et al., [024] Chen et al. 2025a Yue et al. 2025 Dong et al. 2025). ) achieves strong results in non-reference metrics but performs less competitively on full-reference metrics.
Evaluation Metrics. Following (Yu et al. 2024
4.2 COMPARISON WITH STATE-OF-THE-
Table 1: Quantitative comparison with SOTA methods. Best and second-best are highlighted.
Datasels
Metrics
StableSR
DilTBIR
SeeSRt
SUPIR
DreamClear
SinSR
OSEDiIT
AdkSit
InSRR
TSD-SR
LimearSR
PSNR1
26.329
25.450
26.150
25.179
25.486
26.098
25.724
25.782
25.481
24.199
25.262
SSIM1
0.6:16
0.680
0.711
0.656
0.658
0.634
0.688
0.671
0.695
0.621
0.681
DIVIK-Val
MANIQAT
LPIPS]
0.281
0.474
0.473
0.374
0,400
0.397
0.376
0.393
0.499
0.403
0.397
0.429
0.426
0.438
0.401
0:475
MUSIQ1
$2,101
64.131
68.356
63.593
60.304
60.296
66.761
66. 168
65.155
69.277
69.166
CLIPIQA
0.487
0.670
0.682
0.563
0.609
0.668
0.646
0,636
0.675
0.686
0.683
PSNR
23301
24,200
SSIM1
0.738
0.681
0.751
0.688
0.696
0.727
0.737
0.737
0.730
0.711
0.696
ReaSR
LPIPS]
0.272
0.535
0.267
0.340
0.312
0.350
0.280
0,280
0.271
0.265
0.313
MANIQAT
0.372
0.534
0.519
0.419
01471
0,400
0.484
0.508
01445
D.493
0.578
MUSIQU
63.352
67.241
254
65.213
59.313
68.670
70.493
70.552
CLIPEQA
0.561
0.686
PSNRT
25.75%
25.158
212
24.
25.186
25.734
25 455
25. 768
74.4K3
24.264
25.235
SSIMI
0.675
0.636
0.745
0.700
0.633
0.661
0.739
0.730
(..93
0.681
0.719
DreaISR
MANIQA!
LPIPS]
0.308
0.444
0.495
0.320
0.375
0.363
0.476
0.320
0.326
0.364
0.331
0.359
MUSIQU
$6,500
63.
67.429
57.164
58.505
05.051
69 02.
68.016
68.195
69.073
CLIPIQA
0.530
0.704
0.702
0.564
0.624
0.673
0.723
0.736
0.738
0.757
0.713
MANIOAI
0.289
0.495
0.502
0,393
0,450
0.421
0.433
0,450
0.421
0,470
0.515
RealLQ2500
MUSIQU
70.912
67.126
71.914
CLIPIQA
0.508
0.706
0.703
0.574
0.688
0.698
0.673
0.682
0.677
0.704
0.720
Quantitative Analysis.
We perform a comprehensive guantitative evaluation against state-of-the- art (SOTA) methods, with results detailed in Tab. [ While many models compete on traditional fi-

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Published as a conference paper at ICLR 2026
Table 2: Efficiency comparison for 1024x1024 SR (tested on NVIDIA H-series GPUs). Best, second, and third are highlighted. LinearSR's s SOTA I-NFE time validates its core efficiency.
Metries (L)
StableSR
DiMBIR
SeeSk
SUPIR
DrenmClear
SinSR
OSEDIf
AdeSR
ImSS
TSD-SR
LimearSR
1 Image Inference Time (S)
404
25,541
13,632
13,632 2.662
16.319
8,999
1 1,866
01561
(.667
12,635
0,830
NPE Forward Time
0.428
0.499
0.273
1,873
0.929
0.150
0.046
0.615
9,434
0.036
delity metrics, LinearSR consistently demonstrates superior performance in no-reference perceptual quality, which more accurately reflects human visual assessment. On the challenging ReaILQ250 benchmark, LinearSR achieves clean sweep, ranking first across the board in MANIQA (0.515), MUSIQ (71.914), and CLIPIQA (0.720). This trend of perceptual dominance is consistent across all tested datasets, such as achieving top scores in MANIQA and MUSIQ on both DIV2K-Val and DrealSR. This proves our model's exceptional ability to generate realistic and aesthetically pleasing images, successfully translating its architectural innovations into state-of-the-ar generative quality without compromising on a strong balance in reference-bacee metries like LPIPS.
Efficiency Analysis. The core advantage of our work is validated in Tab. 2 which benchmarks computational efficiency. To ensure a fair and rigorous comparison, all measurements were con- ducted on the same GPU with no other tasks running. The reported times are the average over 100 runs, each processing a 256x256 input to a large-scale 1024x1024 output. Crucially, we focus on the I-NFE (Number of Function Evaluations) forward time, metric that isolates the performance of the core diffusion step by excluding the VAE decoder. LinearSR establishes a new state-of-the-ar in this metric at just 0.036s for 1024x 1024 image in 1-NFE time, significantly outperforming pre- vious methods that often benchmarked at smaller 5 512x512 resolutions. This highlights the architec- tural efficiency of our linear attention, especially when compared to one-step methods like TSD-SR, whose extensive time is attributable to its tile-based processing logic. This achievement is purely architectural; our method is orthogonal to, not mutually exclusive with, distillation techniques. This indicates that substantial room for further optimization exists by applying future distillation methods to our already efficient base model. While AdeSR and InvSR show faster overall inference due to model distillation and optimized sampling strategies respectively, LinearSR's s total time of 0.830s remains highly competitive and is orders of magnitude faster than heavyweight models like SUPIR.
Qualitative Analysis. Beyond metrics, a qualitative comparison in Fig. 5 reveals the practical impact of our approach. While competing methods can produce plausible results, they often suf- fer from common generative pitfalls, such as introducing unnatural artifacts or an overly smooth, "painterly" effect that erases fine details. This dichotomy is evident, where some methods fail to re- solve the initial degradation, leaving behind blur, while others sacrifice authentic detail for a smooth finish. In contrast, LinearSR excels at restoring crisp, realistic textures across diverse scenes. For example, in the case of the flower, our model reconstructs the delicate stamens with high clarity and preserves the subtle curvature and shadows of the petals, which are lost in other methods. Likewise, renders the axolotl's 's eye with sharp definition and faithfully captures the fine, porous texture of its skin and the intricate details of its external gills. This qualitative edge is a direct result of our holis- tic framework, where the stable training enabled by ESGF and the specialized refinement from the based MoE effectively translate the efficiency of linear attention into superior visual fidelity.
4.3 ABLATION STUDY
To systematically validate our core contributions, we conducted a series of targeted ablations. This section dissects the impact of our guidance paradigm and the ESGF training strategy, demonstrating that they are integral components underpinning the performance and stability of LinearSR.
4.3.1 VALIDATION OF THE "PRECISION-OVER-VOLLU
GUIDANCE PRINCIPLE
We investigate different guidance methods, with results in Tab. B. An interesting trend emerged: guidance from raw visual features (DINO,
Table 3: Quantitative comparison of guidance methods.
Method
PSNR1
SSIM
LPIPS)
MANIQAT
MUSIQ
CLIPIQAT
CLIP) significantly outperformed
Origin
22.05
0.4267
0.6324
0.4541
60.10
0.6964
CLUP
23.79
0.6270
0.4260
0.3510
60.75
0.5520
DINO
23.83
0.6560
0.3860
0.3370
62.76
0.5560
TAG
24.85
0.6910
0,3740
0.3630
63.93
0.5720

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Zoomed LQQ
SecSR
SUPIR
DreamClear
LQ
AdcSR
InvSR
TSD-SR
LinearSR
Zoomed LQ
SccSR
SUPIR
DreamClear
LincarSR Figure 5: Qualitative comparison with state-of-the-art methods. Our LinearSR consistently re- stores intricate textures and realistic details, outperforming competing methods across diverse real- world degradations. This is particularly evident in its ability to reconstruct the flower's delicate stamens and petal textures, as well as the axolotl's complex skin pattern and sharp eye.
LQ
AdeSR
InvSR
TSD-SR
Origin baseline using verbose sentence-level descriptions. This progression culminates with the TAG model, which provides concise object labels and is the definitive winner across nearly all critical principle, demonstrating that for the metrics. This outcome validates our "precision--vve--vlumm effective and efficient guidance signal. SR task, concise, high-recall of object labels is The qualitative evidence in Fig. (((a) corroborates this, as the TAG-guided model is shown to clearly restore intricate details, such the flower's stamens and previously illegible text.
Table 4: Comparison of training strategies for the second stage. Our strategy selecting the checkpoint at the knee-point (48k) ensures stable training, whereas naive selection from seem- ingly optimal ltte- Unstable-Peat Peak checkpoint (224k) inevitably leads to training collapse.
1st Stage Checkpoint
2nd Stage
Strategy
Training Status
PSNR
SSIM
LPIPS
MANIQA!
MUSIQU CLIPIQA!
Naive Selection
224k (Unstable-Pedl Peakk)
Collapse (2k)
23.59
0.664
0.403
0.459
60.39
0.663
Our Strategy
48k (Knce-Point)
Stable (Completed)
24.78
0.667
0.410
0.452
64,59
0,690
4.3.2 NECESSITY OF THE ESGF STRATEGY FOR STABLE FINE-TUNING
Next, we demonstrate the critical role of our Early-Stopping Guided Fine-tuning (ESGF) strategy. We compare two approaches for initiating second-stage fine-tuning: a naive selection from a late- stage "Unstable-Peake checkpoint versus our ESGF-guided selection from the "Knee-Point". As shown in Tab. the outcome is decisive. This checkpoint selection is the decisive factor for training stability. The naive approach quickly leads to a training Collapse, yielding a poor final model. In contrast, strategy ensures Stable and complete fine-tuning process, resulting in significantly better-performing model. This proves that ESGF not merely an optimization but foundational enabler, resolving the inherent instability of multi-stage training for linear attention SR models.
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AATHII GOBO PEDA
YDA 13 YDA 13 YDA 13
YDA 13 10235
0235 10235
10235
CLIP
DINO
TAG
(a) Ablation on guidance methods
(b) Ablation on MoE configurations
Figure 6: Qualitative ablation study of our key components. (a) Visual comparison of guidance methods, where our TAG-based approach, validating the precision-over-vllume principle, restores superior texture and illegible details. (b) Visual comparison of MoE designs, demonstrating that our SNR-based 4-expert architecture yields the most realistic results by avoiding generative artifacts.
Table 5: Ablation study Mixture-of-Experts (MoE) configurations DrealSR Dataset.
Partitioning Strategy
Boundaries
Exp.
Configuration
PSNR
SSIM
LPIPS,
MANIQA1
MUSIQU
CLIPIQA1
(i)
(a)
Buseline
N/A
N/A
24.85
0.691
0.374
0.363
63.93
0,572
(b)
2-Expert MoE
SNR-bused
[0.875]
25.02
0.671
0.377
0.374
63.18
0,591
(c)Ours
4-Expert MoE
SNR-based
[0.223, 0.875.0.939]
25.00
0,682
0.375
0.371
64.02
0,598
(d)
4-Expert MoE
Nuive Uniform
[0.25, 0.5,0.75]
24.84
0.666
0.389
0.368
62.51
0.582
4.3.3 EFFECTIVENESS OF THE SNR-BASED MOE ARCHITECTURE
We evaluate our SNR-based Mixture of Experts (MoE) architecture by first examining simpler ap- proaches. As shown in Tab. 5 and Fig- 6(b)) the model without MoE fails to generate fine details, while naive uniform partitioning yields blurry and distorted results, failing to properly handle the distinct generative stages. This demonstrates that our core strategy of SNR- -bsed expert specializa- tion is essential for success. Building on this validated approach, our 4-expert model proves superior to simpler 2-expert version. renders visibly finer details the woman's 's face and the dog eye, ultimately achieving the highest perceptual scores and the best overall performance.
Table 6: Progressive ablation study of our main contributions. We demonstrate ESGF is pre- requisite for stable fine-tuning, as the naive approach (Exp. 3) results in training collapse.
TAG Prompt
SNR-based MoF
Exp.
ESGF
PSNR1
SSIM
LPIPS,
MANIQAT
MUSIQ CLIPIQAT
4-MoE
SFT
(1) Baseline
22.05
0.427
0.632
0.454
60.10 63.93
0.696
(2) Add Guidance
24.85
0.691
0.374
0.363
0.572
(3) Naive FT
Training Collapse
(4) Add MoE
1.
25.00
0.682
0.375
0.371
64.02
0.598
LinearSR
4
4.
25.24
0.719
0.359
0,510
69.07
0.713
4.3.4 PROGRESSIVE CONTRIBUTION OF COMPONENTS
Finally, we analyze the progressive contributions of our framework's components in Tab, 6 The first step, replacing original sentences with the TAG Prompt (Exp. 2 vs. 1), yields a dramatic boost across key fidelity metries by improving guidance. Next, we introduce the SNR-based 4-MoE (Exp. 3 & 4), fine-tuned from the Exp. 2 checkpoint. It is critical to note that this stage is only made possible by leveraging ESGF to select stable starting point. This intervention crucial, as our attempts direct fine-tuning proved unstable and would invariably collapse without this essential safeguarding mechanism. The final LinearSR model then applies the full, ESGF- BoGGeeg two-stage MoE fine-tuning (MoE-SFT), pushing all metrics their peak, especially in perceptual quality. This step-by-step analysis confirms that each component is indispensable and synergistic, with their interplay culminating in the superior performance of our complete framework.

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5 CONCLUSION
In this work, we introduce LinearSR, the first framework to successfully unlock the potential of linear atfention for high-fidelity super-resolution. By combining precision guidance, specialized expert architecture, and a multi-stage, early-stopping fine-tuning strategy, we systematically disman- lle the technical barriers that have historically hindered its application. Crucially, our architectural approach is orthogonal to, not mutually exclusive with, post-hoc optimizations like model distil- lation and pruning. By forging the first viable pathway for linear attention in the super-resolution domain, our work establishes foundational and efficient baseline for future research to build upon.
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ACKNOWLEDGMENTS
This work is supported by Shanghai Artificial Intelligence Laboratory.
ETHICS STATEMENT
Our LinearSR is the first framework to successfully unlock the potential of linear attention for high- fidelity super-resolution. To ensure ethical compliance, our training data was curated from a com- bination of established public datasets and publicly accessible online platforms, such as Unsplash. We have taken deliberate measures to minímize potential biases in this data, fully aligning with uni- versal ethical guidelines. We explicitly emphasize that this framework is not intended for misuse in achieving harmful purposes; downstream users are encouraged to adhere to ethical principles when applying the technology. Additionally, all authors declare no conflicts of interest related to this work.
REPRODUCIBILITY STATEMENT
To enable direct reproduction of the quantitative metrics in our tables and the visual comparisons in our figures, we plan to release the corresponding pre-trained model checkpoints and evaluation scripts, pending institutional approval. To ensure a consistent testing environment, we will also provide detailed specifications of the hardware used, such GPU models, and the exact versions of all software libraries. This commitment ensures that our reported results can be precisely verified by the community.
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A A HIERARCHICAL FRAMEWORK FOR MOE BOUNDARY DETERMINATION
In our main paper, we propose a Mixture-of-Eaie -x-eerts (MoE) architecture to assign specialized sub- networks to distinct phases of the flow- sow-matching-basee generation process. The efficacy of this approach is contingent upon a principled partitioning of the continuous time variable t E [0. 1]. This derivation of the boundaries for our 4-expert model, rooted
section provides a detailed, in hierarchical partitioning of the log-Signal-to-NNe Ratio (log-SNR) space.
Our framework begins with the log-SNR definition for our Flow Matching model, A(t), and its inverse, (A)::
X(t) = 2(log 1 - t) - log(t))
(4)
(A) cxp ( +
(5)
A.1 DERIVATION OF THE EFFECTIVE LOG-SNR RANGE
The operational domain of our model defined by an effective log-SNR range, [Amin Amax]. This range is not arbitrary but is derived from the noise schedule of foundational models like Stable Diffusion 1.5 (Diffusion) 2024), whose practices we adopt. Specifically, the log-SNR is directly related to the noise level in many diffusion frameworks by:
X = log
(6)
The Stable Diffusion 1.5 repository and its common implementations (c.g., in k-diffusion) define an effective noise schedule bounded by min and maxx For our model's configuration, the effective boundaries correspond to min.ellf 2 0.0118 and Cml 2 33.78. Plugging these into the equation yields our operational range:
Amax 2 llog(oomnn (mminottt) 2 log (0.0188 8.87
Amin - 2 lo = -2 log((33.8)) R -7.04
(8)
Thus, our effective log-SNR range is established as [Amin Amax] ~ [-7.04, 8.87].
A.2 FOUNDATIONAL BISECTION: THE 2-EXPERT CONCEPTUAL MODEL.
The most fundamental division of labor in the generative process is separating the high-noise regime (where global structure is formed) from the low-noise regime (where details are refined). This motivates conceptual 2-expert model. This principle of partitioning the generation process based on noise levels has been shown to be highly effective. For instance, eDiff-I (Balaji et al. (0022) successfully employed an ensemble of specialized denoisers, one for high-noise steps and another for low-noise steps, validating the benefit of such separation.
Inspired by this, we establish a logical boundary between these two regimes, which can be con- ceptualized as "perceptual turning point" where the model's 's output transitions from being noise- dominated to struttue-dommnated. We instantiate this idea by selecting a concrete anchor point, fanchor 0.875. This value represents state where the coarse structure is largely formed, but fine details are still absent. We define its corresponding log-SNR value as our primary anchor, Aunchor
Annchor A(fanchor) A(0.875) ~ -3.89
This anchor point partitions the effective log-SNR range into two primary operational zones:
High-Noise Zone (Structure Formation): À [-7.04,-33 -3.89]
LLo--Noiee Zone (Detail Refinement): À E [-3.89,8. 8.87]
A.3 REFINED QUADRISECTION: EXTENDING TO 4-EXPERT ARCHITECTURE
To achieve finer-grained specialization, we extend the 2-expert model to a 4-expert architecture by further partitioning each of the two primary zones. To ensure that the task complexity is distributed equitably among the new experts, point bisection strategy, also empirically validated related work (Wan- Video 2025), introduces two secondary boundaries, A and Ag:
bisect each log-SNE sub-interval its midpoint. This mid-
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1. Low-Noise Zone Bisection (A1): To separate detail-refinement from texture eexture-generatoon we define A as the midpoint of the low-noise interval (Anchorr Amax]:
Annchor + Amax 3.89 + 8.87
2.49
(10)
2. High-Noise Zone Bisection (3z): To distinguish between initial denoising and coarse structure formation, we define A3 as the midpoint of the high-noise interval [Amin- Aancbor]:
A Amia
Aanchor
-7.04 + (-3.89
~ -5.47
(11)
2
2
A.4 FINAL EXPERT BOUNDARIES
The three calculated log-SNR values-A1 = 2.49, Aanchor - -3.99 (renamed to X2 for consistency), and A3 -e-ve-eerve the final boundaries for our 4-expert model. We use the inverse function *(A) (Eq. 5 to map these back to the time domain, yielding the final partitions summarized in Table
Table 7: Final MoE Boundaries in log-SNR and Time Domains, ordered by generation process.
Expert
Primary Task
log-SNR Range
Time (/) Range
Expert 1
Initial Denoising
-7.04, -..04.5.444 55447
[0.939, 1.0]
Expert 2
Coarse Structure
-5.47, -3.89
0.875, 0.939
Expert 3
Texture Generation
[3389, 2.49]
0.222,, 0.875
Expert 4
Detail Refinement
[2.49, 8.87]
[0.0, 0.223]
B DETAILED RELATED WORK
B. IMAGE RESTORATION WITH DIFFUSION MODELS
Recent years have witnessed paradigm shift in image restoration, moving from traditional methods
based on Convolutional Networks (Dong et al. 2015) 2014; 2016 Zhang et al. (0188) and Trans-
formers (Chen et 2021 Liang al. 2021 Zamir al. 2022 Chen et al. 2025b) approaches that leverage large-scale generative models as powerful priors. The emergence of diffusion models has been particularly transformative, enabling the generation of visually realistic and semantically consistent outputs even under severe degradation. A prevailing trend has been to build increasingly large models tackle blind restoration, exemplified by works like DifIBRR (Lin et al. (024) and SeeSR (Wu et al. 2024b). This paradigm culminated in models like SUPIR Yu et al. (0024), which leverages the powerful SDXL prior to achieve an exceptional balance between perceptual quality and fidelity, and DreamClear (Ai et al. 2024), the first to apply pure Diffusion Transformer (DiT) architecture directly to super-resolution.
As these large-scale models demonstrated state-of-the-art capabilities, their prohibitive inference costs became a major bottleneck. Consequently, the research focus began to shift towards acceler- ation. Subsequent works have pursued this direction by employing techniques such as knowledge distillation, model compression, and diffusion inversion to significantly reduce inference costs while maintaining high performance (Wang et al. 2024b; Wu et al. 2024a Dong et al., 2025 Yue al.) 2025 Chen et al., 2025a). However, common thread in these models is their heavy reliance on the standard self-attention mechanism. The quadratic computational complexity of this mechanism be- comes a severe bottleneck as input resolution increases, motivating the exploration of more efficient alternatives.
B.2 LINEAR ATTENTION
To address the quadratic complexity (O(N2)) of standard self-attention, linear attention methods were developed to reduce the computational cost to O(V). The core idea, shared across many vari- ants, is exploit the associative property of matrix multiplication. Instead of explicitly computing
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SUPIR
DreanClear
10%
SeeSR
10%
AdeSR
16%
%%
InvSR
10%
29%
68
TSD-SR
LinearSR
(a) I.Q images selected in the study
(b) Voting results
Figure 7: User study on perceptual preference. (a) The 20 LQ images used for evaluation. (b) Score
proportion across methods from 50 participants with Top-3 voting (3/2/1).
the N attention matrix in (QKT)V, these methods reorder the computation to QVKTV), thus avoiding the expensive quadratic term (Shen et al.] (021). This concept was pioneered in the NLP domain with methods like Linformer (Wang et al. 2020), which used low-rank projections, and the
work of Katharopoulos et al. (Katharopoulos et al. 2020), which introduced kernel-based feature
maps to linearize the attention calculation.
Building these foundations, linear attention has been successfully extended into the computer vision domain, proving particularly effective for high-resolution tasks. For instance, Efficient At- tention (Shen al. 2021 improved performance on object detection, while EfficientViT (Cai al., 2022) achieved sfate-of-the-an efficiency in dense prediction tasks like semantic segmentation. More recently, the application of linear attention has expanded into generative modeling, where Sana (Xie et al.] 2024) demonstrated its effectiveness for efficient, high-resolution text-to-image synthesis within Diffusion Transformer (DiT). The work establishes linear attention mature and effective technique for reducing the computational burden of Transformers, providing a strong foundation for our work.
C USER STUDY
To comprehensively evaluate the perceptual quality and content fidelity of our method, we conducted controlled user study, following the established practices of recent Real-ISR works (Dong et al.,
2025
Chen et al. 2025a Wu et 2024a Ai al. 2024).
Experimental Setup. We curated diverse test set 20 low-quality (LQ) images, which shown in Figure This set was specifically designed to cover different degradation types, com- prising 3 synthetically degraded images from the DIV2K validation set and 17 real-world degraded images from the RealLQ250 dataset. We compared our method against six strong diffusion-basee baselines: TSD-SR, InvSR, AdeSR, DreamClear, SUPIR, and SeeSR.
Evaluation Protocol. We recruited 50 participants for the study. For each of the 20 source images, participants were presented with the results from all seven methods in randomized order, alongside the original LQ input for reference. The presentation order reshuffled for each participant and each image to prevent bias. Participants were instructed to evaluate the results based on two equally weighted criteria: (1) overall perceptual quality (e.g., clarity, detail, and realism) and (2) content consistency with the input (i.c., faithful restoration of structure and texture).
Scoring Mechanism. We employed a Top-3 voting scheme. For each source image, participants selected their top three preferred results, assigning 3 points their first choice, points to the second, and point to the third. This means each participant distributed points per image. The total points for each method across all participants and images, denoted as Sm. were aggregated.
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To quantify the overall preference, calculated the final score proportion Pm. as:
Sm
Pm
Σn Sk
With 50 participants, 20 images, and 6 points awarded per image, total of 6,000 points were distributed among the methods. This score proportion, representing a method's relative share of user preference, is visualized in Figure (bb.. As shown, method achieves the highest score proportion, indicating a strong user preference for its superior restoration quality.
D ROBUSTNESS THE KNEE-POINT SELECTION STRATEGY
In this section, elaborate the rigorous methodology used determine the optimal training termination step, referred to as the "Knee-Point." Rather than relying on arbitrary heuristics or manual guesswork, we employ systematic two-stage strategy: (I) Automated Detection via Metric Variance Analysis, followed by (2) Human Verification.
D.1 AUTOMATED DETECTION ALGORITHM
To identify the precise transition from the convergence phase to the oscillation phase, we track 6 metries (PSNR, SSIM, LPIPS, MANIQA, MUSIQ. CLIPIQA) across diverse datasets (DIV2K-
Val, RealSR, DrealSR, RealLQ250). This covers both synthetic and real-world distributions.
We define the Knee-Point as the critical step where the model achieves maximum stability before performance degradation begins. The detection process is formalized in Algorithm [ Let M = {m1, m....... mt) } be the sequence validation metric recorded at steps t. We utilize a sliding window of size W monitor the local variance and the future trend slope.
Algorithm Automated Knee- Point Detection Strategy
Require: Metric sequence M {m+]_ Window size W, Stability threshold Cytable
Ensure: Optimal Knee-Point t
1: Initialize candidates set C +-
2: for 6= W to T W do
3: [Calculate local variance over the past window]
4: Vi + Var(m;
5: (Calculate slope trend over the future window)
6: S. Slope(m4:++v)
7:
{Identify stability region before negative trend]
8:
if < Estable and S, then
9:
C C- (t)
end if
11: end for
(Select the latest step satisfying criteria)
13: 1" -- --max())
14: return
Following the automated proposal of t*, we conduct a Human Verification phase. We visually inspect the validation curves and generated samples around t* ensure the detected point does not correspond to temporary local fluctuation but represents genuine structural convergence.
D.2 EMPIRICAL VALIDATION AND UNIVERSALITY
To demonstrate the universality of this phenomenon, present the training log of separate opti- mization experiment in Table 8 and visualize the trend in Figure
While the Knee-Point in main paper detected at 48k steps, this separate experimental run exhibits the Knee- Point at 58k steps. Despite the shift in the absolute step number due to different hyperparameter settings, the underlying pattern remains consistent:
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1. Rapid Ascent Phase: Metrics improve significantly in the early stages.
2. Knee-Point (t"): Performance peaks and variance is minimized (highlighted in bold in Table
3. Oscillation Phase: Beyond the metries enter phase of high variance or slight degra- dation, indicating overfitting or instability inherent to the adversarial/perceptua loss com- ponents.
As shown in Table at step 58k, the model achieves the optimal trade-off across perception (MANIQA, MUSIQ) and fidelity (PSNR, SSIM) metrics. For instance, on the RealSR dataset, MUSIQ peaks 60.231 at 58k before dropping to 51.643 at 242k, confirming the necessity of our early stopping strategy.
E BENCHMARKING PROTOCOLS AND QUALITY-LATENCY ALIGNMENT
In this section, we provide a detailed clarification of our benchmarking methodology, specifically focusing on the measurement of inferene latency and the structural differences between the original and distilled models.
E.1 DEFINITION OF 1-NFE FORWARD TIME
The reported "I "I-NFE Forward Time" in our experiments strictly measures the core diffusion de- noising step. This measurement excludes auxiliary computational costs such as the Text Encoder, VAE decoding, and pre-processing steps. This isolation ensures that the metric purely reflects the architectural efficiency of the diffusion backbone itself.
E.2 THE "QUALITY-LATENCY ALIGNMENT" STANDARD
To ensure a rigorous and fair comparison, we adhere to Quality-Latency Alignment standard. This principle dictates that the latency reported for model must be measured using the con- figuration (e.g., byperparameters, guidance strategies) required to achieve the optimal visual quality presented in the qualitative comparisons.
Impact of Classifier-Free Guidance (CFG). A critical factor inftuencing this measurement is the use of Classifier- Free Guidance (CFG). Based on this principle, the "unit work" per step differs across methods:
Methods with CFG (Heavier): Like LinearSR, DifBBIR, DreamClear, SeeSR, and SUPIR, these models default to using CFG. Their "core denoising step" physically necessitates computing both conditional and unconditional branches (effective batch size 2).
Methods without CFG (Lighter): Distilled single-branch methods like AdeSR, OSED iff, InvSR, TSD-SR, and SinSK typically use single branch (effective batch size = 1).
E.3 EFFICIENCY ANALYSIS
is acknowledged that single-branch models naturally possess a computational advantage 1-NFE time due to their reduced workload. Despite this structural difference, our model achieves state- of-the-art I-NFE efficiency of 0.036s. This performance highlights the intrinsic speed of LinearSR. It indicates that architectural optimizations are orthogonal to distillation, suggesting significant potential for further acceleration when combined with such techniques.
F MORE VISUAL COMPARISONS
To further demonstrate the robustness and superiority of our proposed LinearSR, we provide exten- sive visual comparisons against state-of-the-- the-tth-rtt cefffusion-bssee methods. Figures 9 showcase results on challenging real-world and synthetic degradation scenarios, respectively. These qualita- tive results comprehensively validate method's effectiveness.
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Across numerous examples, observe several distinct patterns among the competing methods. Firstly, highly generative models like SUPIR and SeeSR, while capable of producing sharp details, often suffer from tendency hallucinate. They may introduce plausible but factually incorrect textures or even alter fine structures, sacrificing fidelity for perceptual sharpness. For instance, in several cases, they generate overly stylized patterns fabries unnatural sheens surfaces that are not present in the original content.
Secondly, method such InvSR tends be more conservative. While it generally maintains good structural consistency, it often yields over-smoothed results. This is particularly evident in its failure restore eigh-frequency details, such the intricate texture of sculpture, the fine strands of animal fur, or delicate patterns in foliage, leading to a less realistic and visually flat appearance.
Thirdly, other competitive methods like AdcSR, TSD-SR and DreamClear deliver strong perfor- mance but are not without flaws. They occasionally introduce subtle color shifts minor artifacts, especially when dealing with complex textures or severe degradation, indicating a lack of robustness in the most challenging scenarios.
In stark contrast, our LinearSR consistently strikes an exceptional balance between fidelity and real- ism. As shown in Figure 9 real-world degraded images, our method excels at restoring authentic and natural-looking details without introducing distracting artifacts. successfully reconstructs crisp textures and clean edges where other methods struggle. Furthermore, demonstrated in Fig- ure 10, even under severe synthetic degradation, LinearSR shows remarkable fidelity to the ground truth. It faithfully recovers complex structures and avoids the distortions blurring effects that plague other approaches. This robust performance across diverse and challenging conditions high- lights the significant advantage of our method in producing perceptually pleasing and structurally coherent super-resolution results.
G DECLARATION OF USE OF LARGE LANGUAGE MODELS (LLM)
We affirm that this paper was primarily written by the authors. Large Language Models (LLMs) were utilized solely as eeneral-purpose assistive tools for language refinement, grammar correction. and stylistic improvements during the writing process. Specifically, Gemini 2.5 Pro (Comanici et al., 2025) was employed for minor text polishing and rephrasing enhance clarity and readability. No LLM was used for conceptual ideation, experimental design, data analysis, or generating any substantive content of the research.
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Published as a conference paper at ICLR 2026
242k
0.65
0.388
0.505
54,708
01.82
25.079
0,700
01302
51.643
(1428
26,087
0.707
51,800
0.320
60,373
0.527
226k
0.674
0.350
03303
54.641
0.462
24,660
0,700
0.236
03188
$4,880
0457
26,077
0.7
0311
54,312
0.98
0.307
58 461
212k
0.664
0.335
0.204
53.996
0.485
24,668
0.689
0.311
53.755
0.401
25,892
0.705
0.297
52 24.455
0.315
196k
159
0.666
0.57
56.229
0.49
24.726
0.695
0.31
53.589
0.441
0.784
0.318
55.631
59.506
0.519
0.
180k
25.165
0,663
0.37
54.95
0.492
24,609
0,695
0.306
0,302
52.18
25.92%
0.704
0.298
53.331
0.312
59.5 363
0.514
158k
0.655
0.314
566799
0..1
24.541
0.691
0.399
0.331
55.995
0.48
25,610
0.690
0.316
54332
0.329
59,840
Table 8: Full training log across all steps. The 58k step nrighlighted as the Knee-P Point.
142k
0.648
0.389
57.691
0.516
24,282
0.683
0.321
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0.687
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57,054
0.325
68.124
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128k
0.638
54.476
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21,172
0.681
53.097
0.459
0.692
0.298
53.078
29.334
59.35
96k
0.650
0.41
0.201
$2.949
0.461
24.233
0.636
0.331
0.306
$3.289
01444
25.056
0.699
0.279
50,745
0.313
59.013
0.639
0.400
0.323
56 36.890
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0.663
0.327
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61.716
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0.377
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0.677
0330
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0.478
25,735
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53.87
0.49
0.31
56 009
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0.38
56.515
0.513
24.038
0.675
03366
53
0.500
25.758
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0.33
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59.247
0.535
70k
0.648
0.397
54.426
0.491
0.679
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0.505
20k
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60.355
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66 190
22.
MANIQAT
MANIQAT
MANIQAI
MANIQAI
Metries
PSNNR
SSIMI
I.IPS
MUSIQ
CI LPQQ
PSNR
SSIMI
IPIPS
MUSIQ
C1 IPQQ
PSNR
SSIMI
LPPPS
C MUSIQ
MUSIQ
Reall Q250
RealSR
DrealR

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Published as a conference paper at ICLR 2026
DIV2Kvval Training Dynamics:
Normalized value
55M
UPP
CLIN
Training steps
RealSR Training Dynamics: Metric
MIKKC
Normalized Normalized
5PM
USSS
CURO
Training steps
DrealSR Training Dynamics: Curves
13-
Training steps
RealLQ250 Training Dynamics: Metric Curves
13-
Normalized
MUSI
Training steps
Figure 8: Visualization of the Knee-Point Phenomenon. The curves illustrate the evolution of
6metrics throughout the training process. A distinct "Knee-Point" observable around step 58k, marked by peak in performance and stability, followed by oscillation phase where metrics fluctuate or degrade. This visual evidence corroborates the quantitative data in Table
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Published as a conference paper at ICLR 2026
Zoomed
SceSR
SUPIR
DreamClear
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AdeSR
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LineurSR
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SUPIR
DreamClear
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AdcSR
InvSR
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Zooned LQ
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Figure 9: Visual comparisons real-world degraded images. Our LinearSR consistently pro- duces more realistic details and textures while maintaining high fidelity.
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Zoomed LQ
SecSR
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Figure 10: Visual comparisons on synthetically degraded images. Compared against the GT, LinearSR demonstrates superior performance in restoring faithful details under severe degradation.
24


Reviews:
[{"review_id": "nV0LG5wvUM", "reviewer": "Reviewer_NwBS", "summary": "This paper aims to unlock the potential of linear attention in high-fidelity image super resolution (SR) and solve the computational bottleneck of the traditional SR generation model based on self-attention. The author attempts to solve three problems that hinder the application of linear attention in realistic SR: catastrophic training instability, the classic perception-distortion trade-off, and the lack of an efficient guidance paradigm. The authors then propose LinearSR, a framework integrating three core innovations: an Early-Stopping Guided Fine-tuning (ESGF) strategy that initializes fine-tuning from the \"knee point\" of training dynamics to resolve instability, an SNR-based Mixture of Experts (MoE) architecture that partitions the generative trajectory by noise level to mitigate the perception-distortion trade-off, and finally a lightweight TAG guidance paradigm derived from the \"precision-over-volume\" principle for effective feature extraction from low-resolution inputs. Evaluations show it achieves SOTA perceptual quality and efficiency, setting a foundational paradigm.", "questions": "Please clarify my concerns in the weakness part.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Overall, this paper tackles a significant bottleneck in generative super-resolution. The core idea is simple yet powerful. The author identify that the potential of linear attention is hindered by training instability and the perception-distortion trade-off. They then systematically dismantle these barriers using a novel early-stopping strategy (ESGF) and an SNR-based expert architecture, successfully unlocking the efficiency of linear attention for high-fidelity image generation.", "weaknesses": "**Major Weaknesses:**\n\nI appreciate the author's effort in unlocking linear attention for efficient super-resolution. However, I have some concerns that I summarize in three parts.\n\n1.  The paper states that each expert in the Mixture-of-Experts (MoE) architecture specializes in a different SNR range. However, it's unclear if the experts share the same architecture. If so, this might be suboptimal as different generative stages (e. g., structure formation vs. detail polishing) could benefit from structurally different networks. The paper could provide more justification for using a homogeneous structure.\n\n2. The paper's MoE employs a deterministic, rule-based gating mechanism that routes inputs based on fixed, pre-calculated time boundaries(t). While this cleverly avoids the load-balancing problems of sparse MoEs, it seems overly simplistic and rigid. The optimal boundaries for generative stages are likely task-dependent and data-dependent. A hard-coded if-else structure lacks the flexibility to adapt to different degradations or content types. A learnable(yet still efficient) gating network could potentially discover more optimal, dynamic routing strategies, leading to better specialization and overall performance. The paper could benefit from discussing why this rule-based approach was chosen over a more adaptive one and exploring its limitations.\n\n3. The Early-Stopping Guided Fine-tuning (ESGF) strategy relies on identifying a \"knee-point\" from validation metrics. This approach seems effective but might be sensitive to the choice of validation set and evaluation metrics. The paper could discuss the robustness of this method and how reliably this \"knee-point\" can be identified across different datasets and model configurations in practical scenarios.\n\n**Minor Weaknesses:**\n\nIn the ablation study for the SNR-based MoE (Table 5), the time boundaries t are provided for different expert configurations. However, the paper could be more explicit about how these specific boundary values (e.g., [0.223, 0.875, 0.939]) were derived or chosen for each experimental setting, beyond the general hierarchical bisection method described in the appendix.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dZs3Nws3uZ", "reviewer": "Reviewer_UxiE", "summary": "This paper introduces LinearSR, a novel framework that enables robust application of linear attention in high-fidelity image super-resolution (SR). Addressing key limitations of linear attention—training instability, the perception-distortion trade-off, and inefficient guidance, by integrating three core innovations: Early-Stopping Guided Fine-tuning (ESGF), an SNR-based Mixture of Experts (MoE) architecture, and a TAG guidance paradigm. Experiments show LinearSR achieves linear computational complexity $\\(O(N)\\)$, with a state-of-the-art (SOTA) 1-NFE forward time of 0.036s for 1024×1024 SR and top performance on non-reference perceptual metrics (e.g., MANIQA 0.515, MUSIQ 71.914 on RealLQ250) against 10 SOTA methods.", "questions": "1. How might LinearSR’s performance on full-reference metrics (e.g., PSNR) be improved? Could integrating pixel-level loss terms (e.g., L1) with the CFM objective help balance perception and distortion without undermining efficiency?\n2. The \"knee point\" in ESGF is identified via validation metrics—what automated methods could detect this point dynamically, especially for diverse datasets where manual tracking may be impractical?\n3. Since LinearSR is orthogonal to distillation, have the authors tested combining it with model distillation? If so, what efficiency gains (e.g., reduced inference time) or quality trade-offs were observed?\n4. For the SNR-based MoE, how sensitive is performance to variations in the noise schedule (e.g., switching from \"scaled linear\" to cosine)? Would the log-SNR partitioning need re-calibration for different schedules?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The idea proposed in this paper is straightforward and easy to understand. LinearSR fills a critical gap by providing the first viable methodology for applying linear attention to high-fidelity SR, overcoming long-standing technical barriers (e.g., training instability) that previously hindered this approach.\n+ Its linear complexity delivers dramatic efficiency gains (e.g., 0.830s multi-step inference for 1024×1024 SR) without sacrificing perceptual quality, outperforming heavyweight models like SUPIR by orders of magnitude in speed while leading on key perceptual metrics.\n+ Comprehensive ablations (e.g., verifying ESGF’s necessity for stability, TAG’s superiority over text/CLIP/DINO guidance) and user studies confirm the synergistic value of each component, enhancing the work’s credibility.", "weaknesses": "- While strong on perceptual metrics, LinearSR lags behind some baselines (e.g., SeeSR on PSNR for DrealSR: 26.212 vs. 25.235) in full-reference metrics, suggesting room to improve pixel-level fidelity without compromising perception.\n\n- The choice of 4 experts (over 2 or more) and the specific log-SNR boundaries (e.g., $\\(t_{anchor}=0.875\\)$) lacks extensive ablation, why this configuration outperforms alternatives is not fully justified beyond empirical results.\n\n- Though efficient for 1024×1024, the paper does not evaluate LinearSR on larger resolutions (e.g., 2048×2048), leaving uncertainty about whether its linear scaling holds for extremely high-resolution SR.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N6FN4tp5qL", "reviewer": "Reviewer_cyVt", "summary": "This paper introduces a novel framework for image super-resolution that leverages linear attention to achieve high-fidelity results with linear computational cost. In order to achieve good performance with linear attention, this paper address three major challenges introduced by linear attention, namely, the choice of external guidance, training stability, perception-distortion trade-off. Extensive experiments are conducted on a wide range of datasets to evaluate the proposed method's performance and efficiency. The ablation study also validate the proposed methods can successfully address the challenges mentioned in the paper.", "questions": "Please responds to my concerns in the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper proposes a way to apply linear attention into image super resolution with diffusion based model. I think it is a very promising and important research direction with great potential. \n2. This paper provides extensive analysis on the challenges introduced by applying linear attention, which is quite insightful. \n3. Several new techniques are proposed to address these challenges and are validated by ablation studies. \n4. Experiments show competitive performance of proposed method in terms of both SR quality and model efficiency.", "weaknesses": "1. The efficiency comparison results reported in this paper are relatively simple. \n  1.1 I think the authors should elaborate more on the details of this evaluation. Otherwise, there may be some reproducibility and fairness concerns of this experiment.  For example, how each compared model is deployed? What kind of deployment framework is used? What is the numerical precision of the deployed model? Any acceleration techniques adopted at kernel level?  \n  1.2 The efficiency is compared only on one type of hardware setting. Comparison on different hardware setting will demonstrate the generalization ability on more practical scenarios of the method. \n  1.3 Inference speed on different resolution settings will also be a helpful analysis. \n  1.4 I think it will also be helpful if authors provide results in terms of model complexity such as FLOPs or parameter counts. This will provide another perspective of the model efficiency and also it is independent of hardware and deployment configurations. \n\n2. From table 1, the proposed method seems to achieve better perceptual quality than fidelity metrics, in the sense that it ranks much higher on perceptual quality metrics. Are there any explanations on these results? Does this have anything to do with the proposed MoE-based method?  \n3. From table 2, I am not sure I am able to observe a significant improvement on SR performance when comparing methods with similar efficiency like AdcSR. Authors may need to provide stronger arguments on the value of LinearSR based on this observation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZqsUHUfuIB", "reviewer": "Reviewer_Spqu", "summary": "This paper introduces LinearSR, a novel and holistic framework for high-fidelity Image Super-Resolution (SR) that successfully integrates $O(N)$ Linear Attention into a conditional Diffusion Transformer (DiT) backbone. The primary motivation is to overcome the quadratic computational bottleneck ($O(N^2)$) imposed by traditional self-attention in generative SR models. The authors identify and systematically address three key challenges that have previously hindered the adoption of linear attention for photorealistic SR: training instability, the perception-distortion trade-off, and guidance effectiveness. LinearSR's solutions include: (i) Early-Stopping Guided Fine-tuning (ESGF) to resolve training divergence by stopping at the \"knee-point\"; (ii) an SNR-based Mixture of Experts (MoE) architecture to balance fidelity and distortion ; and (iii) a lightweight, effective TAG-based guidance paradigm based on the \"precision-over-volume\" principle. LinearSR achieves state-of-the-art efficiency, notably setting a new speed record for the core diffusion forward pass (1-NFE)", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. LinearSR achieves a new state-of-the-art speed for the core diffusion forward pass (1-NFE) at $0.036$s for a $1024 \\times 1024$ output, demonstrating the practical benefit of $O(N)$ linear scaling.\n2. The paper holistically addresses the long-standing issues of linear attention in SR: instability (ESGF), distortion/perception trade-off (SNR-MoE), and sub-optimal guidance (TAG).\n3. The \"knee-point\" observation and the resulting Early-Stopping Guided Fine-tuning (ESGF) strategy offer a novel and insightful solution to a fundamental training divergence problem unique to this architecture.\n4. The model consistently outperforms SOTA methods on no-reference perceptual quality metrics (MANIQA, MUSIQ, CLIPIQA) across multiple benchmarks.", "weaknesses": "1. While the 1-NFE time is SOTA, the overall multi-step inference time (0.830s) remains competitive but is not the absolute fastest compared to some distilled single-step methods like AdcSR and InvSR. The benefit of architectural efficiency is clear, but the user-facing speed remains a mixed result. Meanwhile, can LinearSR be applied into one-step inference?\n2. The \"knee-point\" is defined as the iteration of optimal generalization before performance degrades. This is highly dependent on tracking external validation metrics (PSNR/LPIPS evolution, Figure 3(b)), which makes it a non-trivial, potentially hyper-sensitive process in practice. More implementation detail (e.g., how the knee-point is automatically detected or whether a fixed iteration is sufficient) is needed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)