VARestorer: One-Step VAR Distillation for Real-World Image Super-Resolution
Authors: Yixuan Zhu
Shilin Ma
Haolin Wang
Ao Li
Yanzhe Jing
Yansong Tang
Lei Chen
Jiwen Lu
Jie Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: visual autoregressive model
image restoration
Primary area: generative models

Abstract: Recent advancements in visual autoregressive models (VAR) have demonstrated their effectiveness in image generation, highlighting their potential for real-world image super-resolution (Real-ISR). However, adapting VAR for ISR presents critical challenges. The next-scale prediction mechanism, constrained by casual attention, fails to fully exploit global low-quality (LQ) context, resulting in blurry and inconsistent high-quality (HQ) outputs. Additionally, error accumulation in the iterative prediction severely degrades coherence in ISR task. To address these issues, we propose VARestorer, a simple yet effective distillation framework that transforms a pre-trained text-to-image VAR model into a one-step ISR model. By leveraging distribution matching, our method eliminates the need for iterative refinement, significantly reducing error propagation and inference time. Furthermore, we introduce pyramid image conditioning with cross-scale attention, which enables bidirectional scale-wise interactions and fully utilizes the input image information while adapting to the autoregressive mechanism. This prevents later LQ tokens from being overlooked in the transformer. By fine-tuning only 1.2\% of the model parameters through parameter-efficient adapters, our method maintains the expressive power of the original VAR model while significantly enhancing efficiency. Extensive experiments show that VARestorer achieves state-of-the-art performance with 72.32 MUSIQ and 0.7669 CLIPIQA on DIV2K dataset, while accelerating inference by 10 times compared to conventional VAR inference.

Published as a conference paper at ICLR 2026
VARESTORER: ONE-STEP VAR DISTILLATION FOR REAL- WORLD IMAGE SUPER-RESOLUTION
Yixuan Zhu, Shilin Ma, Haolin Wang, Ao Li, Yanzhe Jing, Yansong Tang", Lei Chen, Jiwen Lu, Jie Zhou Tsinghua University
ABSTRACT
Recent advancements in visual autoregressive models (VAR) have demonstrated their effectiveness in image generation, highlighting their potential for real-world image super-resolution (Real-ISR). However, adapting VAR for ISR presents crit- ical challenges. The next-scale prediction mechanism, constrained by causal at- tention, fails to fully exploit global low-quality (LQ) context, resulting in blurry and inconsistent high-quality (HQ) outputs. Additionally, error accumulation the iterative prediction severely degrades coherence in ISR task. To address these issues, we propose VARestorer, a simple yet effective distillation framework that transforms pre-trained text-to-image VAR model into one-step ISR model. By leveraging distribution matching, our method eliminates the need for iterative refinement, significantly reducing error propagation and inference time. Further- more, we introduce pyramid image conditioning with cross-scale attention, which enables bidirectional scale-wise interactions and fully utilizes the input image in- formation while adapting to the autoregressive mechanism. This prevents later L. tokens from being overlooked the transformer. By fine-tuning only 1.2% of the model parameters through parameter-efficient adapters, our method maintains the expressive power of the original VAR model while significantly enhancing efficiency. Extensive experiments show that VARestorer achieves state-of-the-an performance with 72.32 MUSIQ and 0.7669 CLIPIQA on DIV2K dataset, while accelerating inference by 10 times compared to conventional VAR inference. Our code is available at https: /tt///gtttu /githbb..... com/ htt//EeenaalBlnn waw.aaa/ellaa//eeeeete...e
INTRODUCTION
Real-world image super-resolution (Real-ISR) aims to enhance visual quality, as images captured in the wild suffer from noise, blur, downsampling, and compression due to device limitations and com- plex environments. In recent years, great development has been achieved in the image enhancement field using deep learning methods (Dong et al., 2014; Zhang al., 2017; Liang et al., 2021; Chen et al., 2021; ; Lin et al., 2023; Zamir et al., 2022; Chen et al., 2023). While these methods yield com- mendable outcomes under specific, well-defined degradations, they often fall short when faced with the complex conditions of real-world scenarios. Thus, our basic goal is to construct an effective and robust image enhancement framework capable of addressing various degradation conditions. This framework aims to deliver high-quality, visually appealing, and structurally consistent results within limited computational budget, making more practical for diverse real-world use cases.
Real-ISR is inherently ill-posed due to unknown degradation processes, allowing for various pos- sible high-quality (HQ) results from low-quality (LQ) inputs. To address this problem, researchers have explored a wide range of deep learning methods, which can be generally categorized into three classes: predictive (Huang al., 2020; Gu et al., 2019; Zhang et al., 2018a), GAN-based (Wang et al., 2021a; Yuan et al., 2018; Fritsche et al., 2019; Zhang et al., 2021b; Wang et al., 2021c) and diffusion-based (Kawar et al., 2022: Wang et al., 2022: Fei et al., 2023: Lin et al., 2023; Yue et al., 2023). Predictive methods estimate blur kernels via convolutional networks but struggle in real- world conditions. To better handle real-world challenges, some approaches leverage Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) to jointly learn the data distribution of the images and various degradation types. GAN-based methods significantly enhance image quality but require careful tuning of sensitive hyper-parameters during training. In recent years, diffusion

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Published as a conference paper at ICLR 2026
A blue car driving through a quiet neighbortiedd
green fieid with a river under serene sky
cute dog standing front of motobike
Real Input
VARestorer
Real Input
VARestorer
Real Input
VARestorer
Figure Our VARestorer showcases remarkable image restoration capabilities across complex degradations. With highly effective VAR distillation framework, VARestorer adeptly harnesses the rich knowledge within the pre-trained VAR model for real-world image super-resolution a single step. Full input-output comparisons are provided in the supplementary material.
models (DMs) (Ho et al., 2020; Rombach et al., 2022) have shown impressive visual generation ca- pacity for image synthesis tasks. Some methods (Yu al., 2024a; Wu et al., 2024a; Lin al., 2023; Yue et al., 2023) adopt the pre-trained diffusion models and restore the images during the denoising sampling. Most recently, visual autoregressive models (VAR) (Tian et al., 2025) arise powerful image synthesis framework with the next-scale prediction mechanism, which is inherently potential for super-resolution. However, the error accumulation and the sequential prediction nature of VAR model harm its performance to generate consistent and high-quality result.
To address the aforementioned challenges, we propose a novel VAR distillation framework that dis- tills a pre-trained VAR model into an efficient one-step model for real-world image super-resolution. To maintain the image quality of VAR models, we leverage the token-level distribution matching to align the generation quality of the one-step student model and the pre-trained VAR. Our ap- proach eliminates error accumulation during iterative sampling and significantly accelerates infer- ence, while fully utilizing the learned generative priors of VAR, as depicted in Figure 2. Moreover, we devise the cross-scale pyramid conditioning mechanism to fully leverage the information low and high scales. This also preserves the original architecture and capabilities of VAR, reducing the difficulty VAR distillation. After the VAR distillation, our framework establishes direct one- step mapping from low -uw-qualtty inputs high-quality results. This mapping carefully optimized to ensure that the output images maintain the fidelity and realism of the teacher model while distinctly diverging from undesirable visible artifacts.
We comprehensively evaluate our framework on both real-world and synthetic datasets. Experimen- tal results underscore the proficiency of VARestorer across these datasets. For the synthesis dataset, VARestorer achieves 72.32 MUSIQ and 0.7669 CLIPIQA on the synthetic DIV2K- Val. Additionally, it sets new benchmarks with an NIQE of 4.41, highlighting its ability to restore high- fidelity textures. For real-world datasets, we attain 0.5638 and 0.5655 MANIQA on the DrealSR and RealSR datasets, demonstrating both high restoration quality and efficiency storer consistently delivers visually appealing, perceptually convincing, and semantically plausible enhancements, underscoring the proficiency of our framework, as illustrated Figure 1.
single step. VARe-
RELATED WORKS
Real-world image super-resolution. Real-world image super-resolution involves tasks like denois- ing, deblurring, and super-resolution, etc. Conventional works (Dong et al., 2014; Huang et al., 2020; Zhang al., 2018a; Dong et al., 2015) utilize predictive models to estimate blur kernels and restore HQ images. With the rise of vision transformers (Dosovitskiy et al., 2020; Liu et al., 2021), some methods (Chen al., 2021) incorporate the attention mechanism into basic architec- tures, yielding high-quality results. However, these models struggle with real-world degradations. The advent of generative models has introduced two main approaches in real-ISR, achieving signifi- cant success in complex blind image restoration tasks. One approach is GAN-based methods (Wang et al., 2021a; Yuan et al., 2018; Fritsche et al., 2019; Zhang et al., 2021b; Wang et al., 202;c;;; Yang et al., 2021; Zhou al., 2022), which process images in the latent space handle tasks like
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condition
x(K 5)
step
LQ input
Prediction
Prediction
Prediction
Transformer
Transformer
Transformet
Diställed
(n) Zero-shot restoration
(b) Emar accumulation
(c) Distilled VAR model
Figure 2: Comparison of VAR-based ISR approaches. (a) Zero-shot upsampling uses LQ tokens at scale 8, but severe degradations limit effectiveness. (b) loage-conditioned VAR improves clarity but suffers from error accumulation and artifacts. (c) VARestorer distills VAR into a one-step model, minimizing errors while preserving generative capability without extra conditioning.
tuning and they often tailored to specific tasks, limiting their versatility. The other approach involves diffusion mod- els (Ho et al., 2020; Rombach et al., 2022). known for their impressive image generation capabilities. Methods like (Wang et al., 2023b; Yue & Loy, 2022; Yue et al., 2023; Kawar et al., 2022; Fei et al., 2023; Lin al., 2023; Yu et al.. 2024a) design specific denoising structures transfer the image generation framework to image restoration tasks. However, the sampling of diffusion models is time-consuming. To address this, some methods like (Xie al., 2024; Wang al., 2024: Zhu al., 2024; Wu et al., 2024a) employ diffusion distillation frameworks to process images in fewer steps. Nevertheless, distillation results of diffusion models often suffer from oversmoothing and reduced diversity, especially when facing complex degeneration.
real-ISR. However, GAN-based methods require meticulous
Visual autoregressive models. Building on the success LLMs (Touvron al., 2023a;b; Brown et al., 2020; Radford et al., 2019), autoregressive models adopt discrete quantizers such as VQ- VAE (Van Den Oord et al., 2017) to encode image patches into tokenized representations, enabling image generation through next-token prediction (Van Den Oord et al., 2017; Esser et al., 2021; Razavi et al., 2019). However, the sequential nature of flattened token prediction can disrupt spa- tial coherence and structure consistency. Recently, visual autoregressive models (VAR) (Tian al., 2025) shift from the next-token prediction to the next-scale prediction, greatly improving image generation quality while ensuring superior scalability. VAR-based methods (Han et al., 2024; Li et al., 2024b; Ma et al., 2024) have expanded to other conditional generation tasks (e.g.. C21, T2I), achieving results comparable to diffusion models. Despite this progress. VAR remains underex- plored for super-resolution due to limited cross-scale dependency modeling, error accumulation. and less effective conditioning (Li al., 2024a; Yao et al., 2024). To overcome these issues, we propose an effective VAR distillation framework to minimize eтror accumulation. We also design the cross-scale pyramid conditioning which not only preserves the generative capability of VAR but also enables bidirectional attention across scales.
METHOD
In this section, present VARestorer, a one-step VAR-based framework that achieves real-world image super-resolution with distribution matching distillation of VAR models. Our key idea is to investigate the inherent knowledge in pre- trained text-to-image VAR model and minimize the error accumulation with single-step inference for real-ISR. We will start by reviewing the background of visual autoregressive models, and then describe our designs of VARestorer, including one-step VAR distillation via distribution matching and the cross-scale pyramid conditioning for fully leveraging the LQ input information. The overall framework of our VARestorer illustrated in Figure 3.
3.1 PRELIMINARY: VISUAL AUTOREGRESSIVE MODELS.
Autoregressive models formulate data generation as a next-token prediction process, where data sample (æ) ,2, FT) ) is modeled as a product of conditional probabilities:
T
p(x)= II
p( 11, 22 0t-1))
(1)
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prompt y
Image decoder
restorer
captioner
"now monntainss walley"
D
Cpura & CASSR
x
E
Image encoder
Student Model F
CKL
Teacher Model F.
(Fx)
(ru)
(TH))
(a) VAR distillaion
cross-scale full attention
scale-wise causal attention
Trainable
Frozen
Fs
(Fcr)
VAE
Image lokens
(FLO)
(b) Crass-scale Conditioning
The overall framework of VARestorer. (a) VARestorer utilizes VAR distillation frame- work for real-ISR. During training, we employ the pre-trained text-to-image VAR model as the teacher to predict the high-quality tokens and calculate the token-level KL divergence for distribu- tion alignment. (b) To fully exploit the LQ input, we introduce cross-scale pyramid conditioning, which allows the student model to use full attention to learn relationships among features produced by the multi-scale VAE. During inference, VARestorer produces high-quality results in single step.
Figure 3:
where æ; is the token at step AR models have been widely used in sequence modeling tasks, such as natural language processing and generative modeling. However, their sequential nature makes them computationally expensive and prone error accumulation over long sequences.
Visual autoregressive (VAR) models extend the AR framework for image generation by introducing next-scale prediction mechanism, where image E RR is represented sequence of discrete token maps at different scales æ (T1, ,,...,, TK). The generation process follows sequential dependency across scales:
K II p(ra 71, Tk-1), k=1
p(x)=
where 7kk E [v]l is the token map at scale k with dimensions he and wk, conditioned on previous scales (r1, ra....,,k-1.... Pk-11) Each token in T& is index from the VQVAE codebook V, which is learned via multi-scale quantization and shared across scales.
3.2 IMAGE RESTORATION VIA VAR DISTILLATION
The pre-trained VAR model encodes rich information about real-world data distributions and cels at detailed image synthesis. Its next-scale prediction mechanism aligns naturally with super- resolution, making a promising candidate for image restoration. Our goal is to leverage the gen- crative priors of pre-trained diffusion model for image restoration while significantly reducing the computational overhead of VAR-based upsampling. To achieve this, we propose an efficient distil- lation framework with tailored conditioning to transform the autoregressive transformer F from text-to-image pre-trained VAR model into one-step image enhancer. This approach enables direct upsampling, predicting all high-quality image tokens in single pass.
However, we observe that while the VAR model learns to predict next-scale tokens, it cannot directly upscale real-world images like dedicated super-resolution model. As shown in Figure 2, we input low-resolution (LR) tokens at scale level s into the pre-trained VAR model and use autoregres- sive predictions to complete high-resolution (HR) components. However, this zero-shot approach produces noticeable artifacts and suboptimal results. We hypothesize that this limitation arises be- cause LR tokens fail to provide sufficiently detailed conditioning for the autoregressive transformer to predict finer scales accurately. Additionally, LR tokens from real-world images may not align well with the learned token maps due to other degradations in low-quality (IQQ) inputs like noise and

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Published as a conference paper at ICLR 2026
blurring, Another key limitation of VAR models is error accumulation during iterative inference. In text and image generation tasks, AR models can produce suboptimal tokens at intermediate steps as long as the final output remains plausible. However, in image restoration, the model must generate deterministic output that precisely aligns with the groundtruth. Consequently, iterative sampling in VAR can lead severe mismatches and artifacts due error propagation. To address this, we aim to minimize the upsampling process to a single step, eliminating room for error accumulation. This motivates our exploration of VAR distillation, which compresses the rich generative knowledge of VAR into a lightweight one-step model tailored for real-world image restoration.
Since our goal is to distill a multi-step generative model to one-step model, we follow the pre- vious diffusion distillation method like (Yin et al., 2023; Nguyen & Tran, 2024) to incorporate the distribution matching framework to transfer the generative knowledge from teacher model T-a pre-trained VAR model-to the student one-step model S. This process can be formulated as an optimization problem for the KI. divergence between real image distribution Pt and generated distri- bution gt. DKL (Pellg.). In diffusion models, the density of the data distribution can be estimated by the denoising model (Song et al., 2020), allowing the KL gradient to be estimated via two denois- ing models. However, due to the fundamental differences in image and distribution formulations between VAR and diffusion models, a direct application of this approach is impractical. Instead, we explore alternative pathway to solve this optimization, starting with the KL divergence formula- tion in the VAR model based on predicted token distributions across scales:
LKI. Σ DKI (PI(PT(TX (rk <HQ Ps(#x (r.))), Je
where PHQ < is the GT token maps before scale kt Å& is the student's predicted tokens and Ft. is the token maps of the input IQQ image. The teacher model generates high-resolution tokens tk sequentially from low-resolution tokens <HQ while our one-step student model directly predicts all tokens at once given the LQ image input:
pT(rk <HQ.<<)= = Fr
Ps(f (ro)) = Fs((r.))
(4)
With token-level distribution matching, force the student's token predictions different scales aligned with the teacher model. This ensures that the student directly learns to generate high- resolution tokens in single pass, mimicking the teacher's step-by-step generation process. Un- like pixel-wise losses, KL loss encourages the student model to learn diverse, high-quality pre- dictions rather than just averaging outputs. Besides distribution matching, we also adopt the widely used perception and MSE loss to improve the consistency between the predicted image as = (f1, ....... fx) and the groundtruth ØGT. The final loss function combines token-level KL alignment with additional consistency losses:
L AKLLLKK + XpereCpere- + AMSE | xGT 1l.
(5)
By optimizing these terms, the student model learns to enhance image quality, fidelity, and consis- tency in an end-to-end manner. Once trained, it enables one-step inference given LQ input, signifi- cantly accelerating the traditional VAR prediction process.
3.3 CROSS-SCALE PYRAMID CONDITIONING
We then explore how to effectively incorporate the low-quality (LQ) image into the student model. In diffusion- models, various image conditioning strategies have been developed to embed image information into the denoising process, thereby guiding the denoising trajectory. A straightforward approach is to concatenate the control image with the noisy image. Another effective method is ControlNet (Zhang et al., 2023), which injects conditioning features into the intermediate layers of the model. These strategies can be adapted similarly to the VAR model. However, fundamental challenge arises due to VAR'S hierarchical token prediction: How many control tokens should be used at each scale? A naive solution is to maintain the number of control tokens equal to r at each scale k to match the pre-trained model and merge these control tokens with Tx. However, this limits the ability to fully exploit the input image's 's information, particularly at lower scales, where the guidance signal becomes ineffective. While ControlNet offers a powerful way to inject detailed image features, prior works like (Yao et al., 2024) and (Li et al., 2024a) have shown that directly applying ControlNet to VAR can disrupt the autoregressive generation rather than enhancing it, requiring extensive modifications and retraining.
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To address this, we propose cross-scale pyramid conditioning, inspired by VAR's zero-shot upsam- pling. Instead of modifying the model's architecture extensively, we finetune the VAE encoder of the pre-trained VAR model to generate multi-scale token maps. forming a pyramid representation of the input image. Fach level of this pyramid captures different levels of detail, ensuring both high- level semantics and fine-grained structures are effectively utilized. To fully exploit these tokens, modify the causal attention mask in VAR to allow full attention across scales. This modification enables direct interaction between all resolution levels, ensuring that the model retains its generative prior while making full use of the conditioning information. By employing this strategy, our method preserves the original architecture and capabilities of the pre-trained VAR model, while significantly improving its ability to handle real-world degradations and achieve high-quality restoration.
3.4 IMPLEMENTATION
To leverage VAR's generative power, we initialize both student S and teacher T models with a pre-trained text-to-image VAR (Han et al., 2024). In order construct pyramid representation of the input image, we fine-tune the VAE encoder of the student model, enabling the extraction of high-quality multi-scale token maps. We use BLIP (Li al.. 2022) to generate textual prompts, enabling the model to exploit pre-trained lision-language knowledge. With the textual prompt as an additional hint, the model can better exploit the rich pre-trained knowledge of image understanding relationships acquired during the generation task. To tune the student model, we unfreeze the cross-attention layers of S by LoRA (Hu et al., 2022), which consists of merely 1.2% trainable parameters of the transformer, and freeze other parameters. For the restorer, we employ the lightweight module in (Liang et al., 2021) following (Lin et al., 2023) to perform a coarse restoration. To approximate the degradation conditions in BFR and BSR, we produce the synthetic data from HQ image by æLQ = [(k * THQ) lr +n JPEG which consists of blur, noise, resize and JPEG compression. Further details are provided in Section A.
and rision-languaa
EXPERIMENTS
We conduct experiments across various datasets to verify the effectiveness of method. We will start by describing the experimental settings and then present our main quantitative and qualitative results. We will also provide detailed ablation studies and in-depth analyses of our approach to highlight each component's contribution and validate our overall design choices.
4.1 EXPERIMENT SETUPS
Datasets. For a fair comparison, we follow previous work (Wu et al., 2024a) to establish our training and evaluation datasets. For simplicity, we train our model using the LSDIR (Li et al., 2023) dataset, which consists of about 85,000 high-quality images. We evaluate our model and compare it with competing methods using the test set provided by StableSR (Wang et al., 2023c), including both synthetic and real-world data. The synthetic data includes 3000 images of size 512x512, whose GTs randomly cropped from DIV2K-VA Val (Agustsson & Timofte, 2017) and degraded using the Real- ESRGAN pipeline (Wang et al., 2021c). The real-world data include LQ-HQ pairs from RealSR (Cai al., 2019) and DRealSR (Wei et al., 2020) validate performance under genuine degradations.
Training details. We unfreeze the cross-attention layers and self-attention layers in the student model S in our framework during training, which allows the student better capture both global dependencies and fine tne-grained interactions across modalities. With a batch size of 32 and learning rate of le-6 using AdamW optimizer with weight decay of le-2, we initialize the student and teacher models by replicating the autoregressive transformer blocks in (Han et al., 2024). For LQ- HQ pair synthesis, we employ the high-order degradation model (Wang et al., 2021c), training for 10K steps with 8 Nvidia L20 GPUs, respectively. We set the KL term weight AKL to 0.1, the perception term weight Apere to 0.25 and the MSE term weight AMSE to 0.5 for balance and optimal performance. To unfreeze the student model, we utilize LoRA to inject trainable parameters into the cross-attention modules and self-attention modules, with the rank set to 32, thereby enabling efficient adaptation without introducing excessive computational cost.
Metrics. To evaluate our VARestorer's performance on image restoration, we calculate three tra- ditional metries, including PSNR, SSIM and LPIPS (Zhang et al., 2018b). However, these metrics

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison on both synthetic and real-world benchmarks. The best and second-best values for each metric are highlighted in red and blue, respectively. The number after each method denotes the inference steps. Our framework achieves high-quality results and outperforms existing methods in various image quality metrics.
Datasets
Methods
PSNR
SSIM
LPIPS]
MANIQAT
MUSIQ?
NIQEJ
CLIPIQAT
LIQE?
QALIGNT
FTD
DiIBBR-50
21.48
0.5050
0.3670
0.5664
69.87
5.003
0.7303
4.346
4,070
32,75
Sec5R-50
21.97
0.5673
0.3193
0.5036
68.67
4.808
0.6936
4.274
4.035
25.90
PASD-20
22.31
0.5675
0.3296
0.4371
67.78
4.581
0.6459
3.947
3.895
35.47
DIVIK-Val Val
ResShift-15
22,66
0,5888
0.3077
0.3693
58.90
0.5715
3,082
3.309
30.81
OSEDiT-11
22.06
0.5735
0.2942
0.4410
67.96
4.711
0.6680
4.117
3.926
26.34
SinSR-1
22.52
0,5680
0.3240
0.42166
62.77
6.005
0.6483
3.493
3.553
35.45
VARSR-10
22.41
0.5724
0.3177
0,5173
71.48
5.977
0.7330
4.282
3.853
33.86
VARestorer-
21.08
0.5355
0.3131
0,5590
72.32
4,410
0.7669
4.664
4,363
31,11
DifBBRR-50
24.05
0,5831
0.4669
0.5543
66. 14
6.329
0,7072
4.101
3,734
180.4
SecSR-50
PASD-20
26.14
0.7466
0.3081
0,4404
62.34
6.126
0.6293
3.603
3.572
164.1
DrealSR
OSEDifT-1
25.85
0.7548
0.2966
0.4657
64.69
6.464
0.6962
3,939
3,746
135.4
SinSR-1
0.6447
VARSR-10
26.05
0.7353
0.3536
0,5361
68.14
6,971
0.7215
4.137
3,480
156.5
VARestorer-1
0.3584
DHfBIR--0 50
23.33
0.6180
0.3650
0,5583
69.28
5,839
0.7054
4.101
3,760
130.8
SecSR-50
23.60
0.6947
0.3007
0.5437
5,396
6696
4.136
3,789
125.4
PASD-20
24.83
0.7247
0.2709
0.4423
66.93
5.349
0.5815
3.575
3,705
131.9
RealSR
ResShift-15
23.67
0.6931
0.3451
0.3538
56.90
0,5350
3.111
129.5
OSEDiff-
23.59
0.7074
0.29920
0.4716
69.08
5.652
0.6685
4.070
3,801
123.5
SinSR-1
24.50
0.7076
0.3219
0.4045
61.07
0.6178
3.200
3.299
VARSR-10
24.12
0.7001
0.3216
0,5465
71.16
6.063
0.7004
4.148
3.551
130.6
VAResterer-1
22.78
0.6453
0.3249
0.5655
71.37
0.7423
4.180
Zoomed I.Q
VARSR-10
DiMBIR--0
OSEDif-1
LQ Input
PASD-20
ReyShinn-15
SuSRR1
Ouns-1
Zoamed
VARSR-10
DiMBBR-55
OSEDifE1
LQ Inpul
PASD-20
ResSbifl-15
SinSR-1
Oune1
Figure 4: Qualitative comparisons real-world datasets. Our VARestorer delivers exceptional details with just one-step inference. The numbers following each method indicate the corresponding inference steps. Please zoom in for a better view.
have their limitations in assessing visual quality as they often penalize high-frequence details in our generated images, e.g., hair texture. Therefore, we also include the widely-used FID (Heusel al., 2017) score to provide distribution-level evaluation of overall image quality and realism. Addi- tionally, we leverage six widely adopted non-reference metrics, including MANIQA (Yang et al., 2022), MUSIQ (Ke et al., 2021), CLIPIQA (Wang et al., 2023a). LIQE, NIQE and QALIGN, to assess image quality. Finally, order to highlight the practicality of VARestorer, we also compare the inference time of framework with other competing approaches.

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Published as a conference paper at ICLR 2026
LQ input
w/O distill
W/O Cross
w/O L KL
VARestorer
GT
Figure 5: Visual results of the ablations. Our distillation method, cross-scale attention, and dis- tribution matching collectively enhance the visual quality of the generated images by reducing arti- facts, preserving fine details, and ensuring better structural consistency.
4.2 MAIN RESULTS
Quantitative Comparisons. As shown in Table present comprchensive comparison with DiffBIR (Lin et al., 2023), SeeSR (Wu et al., 2024b), PASD (Yang et al., 2024), ResShift (Yue et al,, 2023), VARSR (Qu et al., 2025), OSEDiff (Wu al., 2024a) and SinSR (Wang et al., 2024) on the three benchmark datasets (DIV2K-Val. DrealSR, and RealSR). Our method, VARestorer, achieves consistently strong performance across various metrics, particularly in the no-reference perceptual metric. On all three datasets, VARestorer attains the highest CLIPIQA and NIQE scores, as well as top-2 LIQE and MUSIQ scores, indicating that our restorations are both perceptually appealing and well-aligned with aesthetic preferences. Although some methods (e.g.. PASD and OSEDiff) perform well on certain reference quality metries (such as PSNR or LPIPS), their advantages often come at the expense of either lower visual quality or more inference steps. In contrast, VARe- storer strikes a more favorable balance, delivering top-tier perceptual quality and aesthetics while maintaining competitive
by our best FID on RealSR. Parameter and inference efficiency (Table 2) further reinforce our advantage: despite using fewer trainable parameters and faster inference than Qu et al. (2025), VARestorer delivers superior performance. These results un- derscore the effectiveness of our design, which unifies high perceptual quality, fidelity, efficiency, and distribution alignment into single, efficient framework.
Qualitative Comparisons. Figure presents a visual comparison with several representative meth- ods on two challenging real-world image restoration cases. In the first row, most methods struggle with preserving the intricate petal structuressom introduce blurriness (e.g., PASD and SinSR), others misinterpret colors (e.g.. OSEDiff), or generate unrealistic textures (e.g.. DiffBIR). While certain methods, such as PASD, retain the overall flower shape and color, they still struggle with generating convincing petal details. In contrast, our approach effectively restores sharp and natural petal structures, accurately capturing subtle folds and smooth color transitions. A similar conclusion can be drawn from the second row. While other methods over-smooth textures or introduce artifacts near transitions (rocks and water), our method yields more authentic textures and clearer boundaries with fewer artifacts. Such fidelity and detail preservation highlight the effectiveness our design in leveraging powerful image priors while maintaining robust semantic consistency. Overall, VARe- storer consistently produces high-quality, visually appealing results, confirming its superiority over competing methods. Additional comparisons are provided in Section B.
4.3 ANALYSIS
In this section, we will conduct detailed ablation experiments DIV2K- dataset further eval- uate the effectiveness of each of the components in VARestorer. We further extend VARestorer to tasks such as deraining and low-light enhancement, with details provided in Section C.
VAR compression in one step. The VAR achieves high-qualiv outcomes with a multi-step next- scale prediction approach. We experiment with several model structures using the next-scale predic- tion approach-e.. e.g., ControlNet, adapter, or directly concatenating them with tokens-to to inject low- quality image features. However, these attempts yielded worse restoration results, with pronounced artifacts and blurriness. The primary reason lies in the error propagation intrinsic to next-scale pre- diction: a subtle mistake made in the initial token predictions is carried over to subsequent scales, accumulating at each step. Unlike image generation, which can tolerate some deviations, restoration requires a high degree of alignment between the output and the input image, making such error ac- cumulation particularly detrimental. To mitigate this issue, distilled VAR into one-step model
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Table 2: Parameter and inference analysis. Our generator demonstrates balance between computational efficiency and performance.
Table 3: Ablation studies. We evaluate VARe- storer's 's components and demonstrate that it out- performs multi-step VAR and scale-wise causal attention. Additionally, distribution matching further improves image quality and consistency.
Trainable Parums.
Inference Time (S)
Method
MANIQAT
MUSIQI
DiABIR
380.0M
10.27
0.5664
69.87
SeeSR
749.9M
7.18
0.5036
68.67
Method
LPIPS.
MUSIQ1
NIQEL
CLIPIQAT
PASD
625.0M
4.58
0.4371
67.78
weo distill
0.3723
62.22
6,283
0,4794
ResShift
118.6M
1.13
0,3693
58.90
wlo cross
0.4224
63.72
6.029
0.3910
OSEDiff 8.5M
0.18
0,4410
67.96
LxL.
0.3214
4.372
0.6682
VARSR
1101.9
0.63
0.5173
VARestorer
27.3M
0.23
0,5590
72.32
VARestorer
0.3131
72.32
4,410
0.7669
that generates tokens for all scales in single inference step. Although this inference strategy de- viates from the VAR's training procedure, the resulting images remain well-aligned with the input while preserving high visual quality. We report the performance of multi-step VAR with ControlNet in Table 3 and Figure 5 (w/o distill). While promising in diffusion- based models, this approach produces unsatisfactory results with noticeable artifacts. Moreover, compared with VAR-based Qu et al. (2025) (10 steps, > 1B parameters) in Table 2, our VARestorer delivers superior results in just one step. These findings demonstrate the effectiveness of our method.
Enhanced distillation via distribution matching. In the training phase, relying solely on LPIPS and MSE losses between the generated and ground-truth images effectively constrains the model to learn one-to-one mapping from the low-quality input to a single high-quality output. However, this approach conflicts with the inherent one-to-man nature of image restoration, substantially in- creasing training difficulty and limiting the diversity of generated images. To address this issue, introduce a distribution matching strategy that leverages a pre-trained VAR model as the teacher. Specifically, incorporate the KL divergence between the probability distributions predicted by our model and those from the teacher into the training loss. By integrating the generative capabilities of the 12V model, transition from strict one-to-one training paradigm a disstibuution-matee approach, thereby accelerating training and improving the visual quality of the generated outputs. As shown in Table and Figure 5, we conduct an ablation study by removing this design (w/o (KL)- The student model can still give clean images but lack realness and some unrealistic texture occurs. This underscores the crucial role of LKL. in enhancing image quality and preserving realistic details.
Cross-scale conditioning for high quality. Due to the sequential generation nature of the VAR model, low-level scales lack direct access to information from the higher-level scales. This absence of cross-scale interaction leads to unawareness of the low-level token maps about how to construct proper foundation map for later scales, making difficult to reconstruct high-quality images during the ISR process. To address this, we replace scale-wiss wise causal attention in the autoregressive trans- former with full attention, allowing information to flow across different scales. To assess the impact of this change, conduct an ablation experiment where structure (w/o cross). As shown in Table and Figure 5, our results demonstrate that cross-scale conditioning can bring significant improvement in both quantitative metrics and visual quality. The causal attention captures the relationship between different resolution levels, resulting in global blur- ring and patch-like artifacts. We hypothesize this issue arises due the disharmony within token maps across scales, further highlighting the importance of enabling cross-scale communication.
maintain the original causal attention
Limitations. VARestorer delivers high-quality, one-step restoration but may struggle with severe noise or heavy compression. Some failure cases are shown in Section E.
5 CONCLUSION
We propose VARestorer, one-step real-world image super-resolution framework that achieves both efficiency and effectiveness. By distilling a pre-trained VAR model with distribution matching and integrating cross-scale pyramid conditioning, VARestorer effectively aligns the generated token dis- tributions with the pre-trained VAR, ensuring high-fidelity restoration with minimal computational cost. This approach mitigates error accumulation in autoregressive models while fully leveraging VAR's generative priors. We hope our work inspires future research to further explore efficient generative priors for image restoration and enhancement.

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blue car driving through a quiet neighbortood
green field with a rier under serene
A cute dog stonding front of motobile
Real Input
Real Input
Real Input
VARcstorer
Figure A: VARestorer achieves strong one-step restoration by effectively leveraging the knowledge of the pre-trained VAR model.
A DETAILED IMPLEMENTATIONS
We employ the widely used Visual Autoregressive (VAR) model, Infinity-2B (Han et al., 2024), which has 32 transformer layers, an embedding dimension of 2048, and 16 attention heads to lever- age its generative capabilities for image restoration. The training process is conducted on the LS- DIR (Li al., 2023) dataset, which consists of approximately 8,5000 images. All images are resized to resolution of 512 x 512, and we utilize BLIP (Li et al., 2022) to generate corresponding image captions. To mitigate error accumulation caused by next-scale prediction, we distill the pretrained model into one-step model. Specifically, we concatenate the input image tokens from all scales and predict the output single step. Cross-scale attention is incorporated to ensure that all input in- formation contributes to the prediction of each token. To preserve the visual priors of the pretrained model, we integrate LoRA parameters with rank four into the transformer while freezing all other parameters. The trainable parameters amount to approximately 27. 27.3M, accounting for only 1.2% the total model parameters. We employ distribution matching method to effectively in- corporate the generative capabilities of the 12V model and prevent one-to-one mapping- Since the Infinity model originally designed to generate images at a resolution of 1024 x 1024, we fine-tune the 512 512 dataset and use the fine-tuned model the teacher. The KL divergence between the probability distributions predicted by our model and those from the teacher is computed and used loss term during training. The training process conducted each equipped with 48GB of VRAM. With just single epoch of training on the LSDIR dataset, the model attains strong restoration capabilities, requiring approximately 4.5 hours. During inference, the model processes each image in an average of 0.33 seconds, making it comparable with GAN- based and diffusion distillation methods. Figure demonstrates the model's ability effectively restore high-quality images from degraded inputs.
8 NVIDIA LS20 GPUs,
Details of the cross-scale attnetion. Our cross-scale full attention a deliberate architectural de- viation from the original VAR's block-wise causal attention. In the standard VAR design, each scale attends only to past scales through a block-wise causal mask, enforcing an autoregressive de- pendency structure. While this preserves strict generative ordering for upsampling, it also limits feature interaction across scales, often resulting in suboptimal restoration quality due to insufficient multi-scale information flow. In VARestorer, instead employ full attention mask across all

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scales (illustrated in Figure 3), allowing features from every scale to interact bidirectionally. This modification is crucial for image restoration: unlike generation from tokens, ISR benefits from rich cross-scale fusion, where both coarse structures and fine textures reinforee each other. Importantly, although this departs from VAR's autoregressive mechanism, we find that: (1) The modification preserves most of the pre-trained VAR representational capacity, validated by our strong quanti- lative and qualitative results (Figure 5 and Table 3). (2) It significantly improves restoration quality, addressing the limitations caused by causal masking and enabling stronger multi-scale consistency. Overall, our design prioritizes effective feature integration over strict autoregressive ordering, which more suitable for the one-step restoration setting.
B MORE QUALITATIVE COMPARISONS
We provide additional qualitative results to further demonstrate the effectiveness and versatility of our framework. As shown in Figure B, our method produces highly realistic, high-quality outputs that closely match the input image. In the first example, for instance, AddSR and DiftBIR strug- gle to restore texture, resulting in oversmoothed or artifact-laden details. OSEDiff recovers some structural information but lacks tend to generate either unnatural patterns or blurry edges, while SinSR fails to adequately restore the boundary regions. In contrast, our approach effectively preserves subtle textures and structural fidelity, yielding a more realistic and visually coherent outcome. This highlights the strength of our framework in capturing both global structure and fine- mne-grained details.
details and intricate patterns. PASD and ResShift
C GENERALIZATION TO MORE TASKS
To assess generalization, we apply VARestorer several challenging super-resolution (SR) sce- narios that deviate from the training degradations. Below, we provide representative visual examples in the supplement and summarize results for four special cases.
We apply our model to deraining and low-light enhancement with slight fine-tuning. As shown in Figure C, our VARestorer consistently produces satisfactory results.
We additionally test on RealBlur-SR, heavy JPEG compression, and samples from social media. As shown in Figure D, our model performs consistently well across these challenging scenarios.
Generalization to unseen degradations is challenging. However, recent advances in large-scale pre- training allow generative models (e.g., diffusion, VAR) encode robust priors across diverse sce- narios. By distilling from such models, we benefit from this generality. We demonstrate this on salt-and-pepper noise, which is unseen during training and structurally different from our degrada- tion pipeline. As shown in Figure E, our model produces strong results.
Our model uses fixed resolution during evaluation to align with standard practice in prior SR works (e.g., DifBBIR, OSEDiff, AddSR). These methods all operate on fixed image size during inference. However, our model can indeed handle diverse resolutions: (1) The pretrained VQVAE (default size 1024) can effectively encode and reconstruct smaller images (e.g., 512, 768). (2) During inference, we first resize the LR inputs to 512² like OSEDiff, enabling arbitrary lower-resolution inputs. (3) For higher resolutions, methods and fine-tuning on larger and mixed scales, both supported by the teacher (default size 1024), as shown in Figure F.
adopt two approaches: illnng-basdd inference like diffusion-based
D MORE DISCUSSIONS
D.1 ABLATION ON TEXTUAL PROMPTS
To evaluate the contribution of textual conditioning, we conduct an ablation study by removing the prompt at inference time and feeding empty text input ("w/o prompt"). All other settings remain unchanged. Across benchmarks, the absence of prompt guidance leads to noticeable per- formance drops, particularly in semantic consistency and fine- mn-ggrained structure recovery. Without textual cues, the model tends to produce overly smooth textures and occasionally drifts toward am- biguous object shapes. In contrast, using the prompt provides high-level contextual signals that help
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Ours-1
Figure More qualitative comparisons. Our VARestorer delivers exceptional details with just one-step inference. The numbers following each method indicate the corresponding inference steps.
model stabilize its predictions, preserve object identities, and generate sharper details. These results demonstrate that the textual prompt is not merely auxiliary, but provides meaningful seman- tic constraints that improve both fidelity and consistency in the reconstructed images. We include quantitative comparisons and visual examples in Table A.
D.2 FULL TUNING VS. LORA TUNING
further compare full fine-tuning with parameter-efficient LoRA tuning under the same training setup in Table A. Interestingly, full tuning yields slightly lower quantitative metrics and requires substantially longer time to converge. We attribute this behavior to the fact that updating all pa- rameters disturb the pre-trained generative prior, making optimization less stable and occasion- ally leading to degraded image quality. In contrast, LoRA tuning preserves most of the pretrained weights and introduces only lightweight, task-specific adaptations. This allows the model to retain

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Published as a conference paper at ICLR 2026
Deraining
Low- light enhancement
Figure C: More tasks supported by VARestorer. VARestorer supports various tasks, including deraining and low-light enhancement with slight fine-tuning. Please zoom in for better view.
Heavy JEEG compresssion
Social media
RealBBlu SR
Output
Output
Qutput
Figure D: JPEG artifact removal. VARestorer can effectively remove JPEG artifacts and recover clear textures. Please zoom in for better view.
its generative capability while efficiently learning ISR-specific behaviors, resulting in faster conver- gence and better performance.
D.3 1024 x 1024 RESOLUTION.
We also study the effect of using 1024x1022 1024 training resolution. Although the base VAR model is pretrained to generate 1024px images, experiments show that 1024px training yields competi- tive results but does not provide notable performance improvements for ISR. (Table A). However, introduces substantial increase in training time and computational cost (Figure H, left, Ours-1024). Interestingly, the pre-trained model already exhibits strong generation capability at smaller resolu- tions (e.g., 512 and 768). We attribute this the multi-scale VAE design, which provides stable and high-quality representations across resolutions. As result, training at lower resolutions achieves comparable quality while being far more efficient.
D.4 TRAINING SUFFIIENCY
VARestorer fine-tunes only 1.2% of the pre-trained VAR model, so it does not require long training schedules to reach convergence. This behavior consistent with prior tuning tuning-based based ISR approaches. For example, OSEDiff (Wu et al., 2024a) fine-tunes diffusion models using a batch size of 16 for 20K steps (~1 day on 4xA100). comparison, our setup (8xL20, batch size 32) reaches conver- gence within 10K steps (~2 days, 3.7 epochs). To validate this, include a training MSE loss curve in Figure H (left, Ours-512), which clearly shows that the loss stabilizes well before 10K steps. We also trained variants for 20K and 25K steps and observed no meaningful improvement across any metric. This confirms that the model has already converged under our standard schedule and that additional training provides negligible benefit. These results demonstrate that the proposed
4

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Published as a conference paper at ICLR 2026
Input
Output
Input
Output
Figure E: Salt & Pepper noise removal. VARestorer can effectively remove salt & pepper noise and recover clear textures. Please zoom for better view.
1024 x 1024
Fine-tuning
1800 x 1200
Tiled inference
Figure F: High-resolution image restoration. VARestorer can effectively restore high-resolution
images (e.g., 1024x 1024) with high-quality details. Please zoom in for better view.
brown umbrello the net ground the
Real Input
VARestorer
Ground Truth
A motoroycle fromt of brick building
Input
VARestorer
Truth
Figure G: Failure cases. VARestorer may struggle with certain extremely challenging tasks, such as severe degradation or complex noise patterns. Please zoom in for a better view.
lightweight fine-tuning procedure is sufficient and efficient for adapting the VAR backbone to the ISR task.
D.5
COMPUTATION ANALYSIS
We report the FLOPs of VARestorer and competing methods on 512 512 images in Table B. Since FLOPs vary slightly with textual prompt len estimate the computational cost by averaging

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Published as a conference paper at ICLR 2026
MSE Loss
MSE Loss
2.0
Ours-512
2.0
Aua 0..05
Ours-1024
Ax 0.11
1.5
1.5
Auu - 0.3
Aua 0.6
0.5
0.5
0.0
0.0
0k 2k
7
10
12
15
0k 2k
5.
7k
10
12
15
Tralning Steps
Training Stops
Figure H: Loss curves of the ablations. We present the MSE loss curves for key ablation settings, including training with 1024px inputs and sensitivity analysis of the super-parameter ÅKL.
Table A: Additional Ablation Studies. We conduct ablations to evaluate the impact of textual prompts, tuning strategies, and training image resolution on VARestorer's performance.
Method
LPIPS
MUSIQU
NIQEJ
CLIPIQAT
prompt
0.3201
67.05
4.831
0.7332
w'o restorer
0,3150
72.27
4.458
0.7624
full tuning
0.3127
70.58
4.976
0.6864
1024 resolution
0.3082
72.11
4.427
0.7645
VARestorer
0.3131
72.32
4.410
0.7669
over 10 randomly sampled prompts. Our method requires only 1536 GFLOPs, which is roughly 10% of diffusion-based approaches such as SeeSR (Wu al., 2024b) and PASD (Yang et al., 2024), both of which rely on multi-step sampling. Despite this sigmificant reduction in computation, VARestorer still achieves state-of-the-art performance on perceptual metries, including MANIQA and MUSIQ, demonstrating strong balance between efficiency and image quality. We further analyze the com- putational overhead of the cross-scale pyramid conditioning: compared with the original block- wise causal attention (w/o cross in Table 3, ~ 1020 GFLOPs for 512 x 512 images), full cross-scale at- tention adds roughly 330 GFLOPs, resulting 1530 GFLOPs. This moderate increase is justified by the notable gains in perceptual metrics and visual fidelity.
D.6 DISCUSSION ABOUT METRICS
Traditional full-reference metrics such as PSNR and SSIM have long been used to evaluate image restoration performance. While these metrics measure pixel-wise similarity structural consis- tency, they do not fully capture human perceptual preference. In particular, they often favor overly smooth blurry reconstructions that minimize low-level errors, istic textures and high-frequency details. This limitation has been extensively discussed in prior works (Yu et al., 2024b; Blau & Michacli, 2018; Jinjin et al., 2020; Gu et al., 2022; Liang et al., 2021; Lin et al., 2023).
if such outputs lack real-
Recent generative restoration methods, including VARestorer, produce richer, visually appealing de- tails that align better with human perception (Figure and Figure B). As result, such methods may score lower on PSNR SSIM (Table 1) despite providing outputs that are sharper, more natural, and more faithful to real-world image distributions. To provide more comprehensive evaluation, we also report non-reference perceptual metrics such as MANIQA and CLIPIQA, where VARestorer achieves substantial improvements (Table 1).
common concern whether the improvement perceptual metrics arises from artificial high- frequency patterns rather than genuine detail recovery. In our case, the gains do not stem from hallucinated noise. As shown in our qualitative analyses (Figure I), the model reliably reconstructs both high- frequenyyyyncy details (e.g., flowers) and low-frequency structures (c.g., desert, sea), demon- strating balanced reconstruction across frequency components. However, in extremely degraded cases where the input is too blurry reveal its original content, perfect fidelity is fundamentally

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Published as a conference paper at ICLR 2026
Table B: Computation and performance analysis. Our one-step generator achieves an effective trade-off between computational efficiency and restoration performance.
Method
Trainable Params.
Inference Time (8)
GFL.Ps
MANIQA1
MUSIQU
DifIBIR
380.0M
10.27
12117
0.5664
69.87
SeeSR
749.9M
7.18
32928
0.5036
68.67
PASD)
625.0M
4.58
14562
0.4371
67.78
ResShift
118. 6M
1.13
2745
0,3693
58,90
OSEDiff
8.5M
0.18
1133
0.4410
67.96
VARestorer
27.3M
0.23
1536
0.5590
72.32
Figure I: Qualitative results across frequency components. VARestorer generates visually pleas- ing outputs, preserving both high-frequency details (top: flowers) and low-frequency structures (bot- tom: desert and sea). Each example shows the low-quality input the left and our restored result on the right.
unattainable. these scenarios, the model leverages its generative capability to produce plausible, natural-looking structures that align with real-world image statistics. While this may lead to results that are slightly less faithful to the exact ground truth, they are visually coherent and far closer to the natural image distribution, avoiding the overly smooth and unusable outputs typical of PSNR- oriented methods. We argue that such perceptual reconstruction-aaae prrouccinngggg realistic textures when the signal is insufficient-is more aligned with real-world practical needs than strictly preserving low-level metrics at the cost of visual quality.
To clarify the LPIPS behavior of generative-prior SR models, include a comparison with BSR- GAN (Zhang et al., 2021a) (Table C, Figure J). Under heavy degradation, BSRGAN attains higher fidelity scores (PSNR/SSIM) mainly because its outputs become overly smooth and blurry, reducing feature-space distance to the ground truth. In contrast, our method restores natural high high-frequency textures and sharper structures, which improves perceptual quality but can increase LPIPS when the true details are unrecoverable. Importantly, this behavior is a general feature of generative mod- els rather than arbitrary hallucination: under light degradation our model shows no semantic drift, and under heavy degradation all generative-prior approaches necessarily rely on learned distribu- tions. The resulting textures may deviate from the exact GT pixels but remain plausible, statistically natural, and structurally consistent with the input. As shown in Figure J, the LPIPS increase DReaISR/RealSR mainly results from realistic high-frequency variations (c.g., foliage, brick pat- terns, window textures) rather than incorrect semantic content.

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Published as a conference paper at ICLR 2026
Light
Heavy
Input image
BSRGAN
Ours
Figure Qualitative comparison with BSRGAN (Zhang et al., 2021a) under light (top) and heavy (bottom) degradation. We show cases under both light (top) and heavy degradation (bottom). BSR- GAN fails under severe degradation, while our method produces plausible restorations.
Table C: Quantitative comparison with BSRGAN (Zhang et al., 2021a). VARestorer achieves substantially higher perceptual quality metries.
Datasets
Methods
PSNRT
SSIM
LPIPS,
MANIQAT
MUSIQI
NIQEJ
CLIPIQA!
FID,
BSRGAN
24.58
0.6269
0.3351
0.5071
61.20
4.7518
0.5247
44.23
DIV2K- Val
VARestorer-1
21.08
0.5355
0.3131
0,5590
72.32
4.410
0.7669
31.11
BSRGAN
28.75
0.8031
0.2883
0.4878
$7.14
6.5192
0.4915
155.63
DrealSR
VARestorer-1
24.31
0.6894
0.3584
0.5638
69.49
5.494
0.7810
149.7
BSRGAN
26,39
0.7654
0.2670
0.5399
63.21
5,6567
0.5001
141.28
ReaiSR
VARestorer-1
22.78
0.6453
0.3249
0.5655
71.37
4.763
0.7423
117.2
D.7 SENSITIVITY ANALYSIS ÄS
We conduct a sensitivity analysis to evaluate how the choice loss weights affects training stability and final performance. In VARestorer, the total training loss combines KI. divergence (LKL). MSE loss (CMSE), and perceptual loss (Lpere), weighted by hyperparameters ÀKL. AMSE, and Xpero- We vary ÀKL. in {0.05, 0.1, 0.3, 0.6} while keeping the other weights fixed. As shown in Figure H. (right), larger AKL tends to slightly slow down convergence, but all configurations eventually reach similar MSE loss levels, indicating that the final performance is robust to moderate changes in AKI.- A broader hyperparameter search further confirms that the final restoration quality relatively insensitive long the weights are within reasonable ranges: AKL. (0.05, 0.8), AMSE [0.3. 1).
8

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Published as a conference paper at ICLR 2026
and Apere E [0.2, The optimal combination selected in experiments is λKL 0.1, AMSE 0.5, and Aperc = 0.25, which balances convergence speed and restoration quality.
D.8 EFFECT OF SWINIR PREPROCESSING
We conduct an ablation to assess the impact of the SwinIR preprocessor on VARestorer's perfor- mance (w/o restorer). The preprocessor applied coarsely remove simple degradations (e.g., noise) before extracting textual prompts, allowing the generative stage to focus on high-frequency details.
Our experiments in Table A show that while SwinIR improves the accuracy of prompt extraction and slightly enhances restoration quality, the absence of SwinlR results in only modest perfor- mance drop across evaluated metrics. Importantly even without the preprocessor, VARestorer still surpasses prior methods on most quantitative metries, indicating that the generative framework itself is robust.
E
FAILURE CASES
While our framework demonstrates impressive performance across wide range of image types, it exhibits certain limitations when handling particularly complex tasks. As illustrated Figure G, present two representative failure cases that highlight these challenges. In the first instance, the input image suffers from severe degradation and substantial noise contamination. This condition leads entirely incorrect prompt from the caption model. Consequently, due both the low- quality input and the erroneous prompt, VARestorer generates an image that significantly deviates from the ground truth, thereby revealing its limitations in restoring images with substantial noise interference.
The second case presents scenario where the bicycle's skeletal structure, particularly the han- dlebars, appears relatively slender against the brick building background. The noise interference disrupts the bicycle's structural integrity during the degradation process, making it challenging to recognize. While VARestorer successfully restores other components of the image, it fails to recon- struct the complete bicycle structure.
These observations indicate that the limitations under severe degradation stem from two main fac- tors: (1) insufficient informative content the input, which reduces the ability of model to condition its generation accurately, and (2) the inherent one-step inference and generative mecha- nism, which, while efficient, cannot fully compensate for missing or ambiguous signals. In such scenarios. VARestorer relies learned priors to generate plausible structures, which may result in outputs that deviate from the original content while still being realistic.
Although VARestorer consistently produces high-quality and coherent results in most restoration tasks, it encounters difficulties with certain exceptionally challenging scenarios. These observations underscore the need for further refinement of the framework to enhance its robustness in handling complex and noisy image restoration tasks.
F
CODE
The complete source code for our method is provided in the /code folder, which contains the required files reproduce experiments with the VARestorer framework.


Reviews:
[{"review_id": "UucoaCfSv2", "reviewer": "Reviewer_RnKJ", "summary": "This paper proposes a distilled VAR framework for real-world image super-resolution. The method distills a single-step student model from a multi-step VAR teacher to achieve faster inference while maintaining visual quality. A multi-step guidance strategy is introduced to reduce accumulated errors during training. Experiments on several benchmarks demonstrate competitive no-reference metrics and visually sharp results compared with existing SR methods.", "questions": "please see the weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written and tackles an important problem in real-world image super-resolution. The idea of distilling a multi-step VAR model into a single-step version is practical and well-motivated for efficiency. Experiments are thorough, covering multiple benchmarks, and the proposed method achieves strong results on several perceptual quality metrics.", "weaknesses": "- **Inconsistency in the core motivation and training design.**\n\nThe paper claims that multi-step VAR models suffer from cumulative errors, yet the proposed approach distills knowledge from a multi-step VAR teacher. Since the teacher’s outputs are already affected by the same multi-step bias, the student may inherit those errors rather than eliminating them. This raises a conceptual inconsistency between the stated problem and the actual training setup, making it unclear whether the proposed method truly mitigates error accumulation or simply compresses it into a single step.\n\n- **Mismatch between metrics and visual realism.**\nAlthough the method achieves significantly higher no-reference quality scores on RealSR, the visual results (e.g., the flower example in Fig. 4) exhibit unrealistic, hallucinated textures. The generated details appear sharp but not natural, suggesting that the improvement in perceptual metrics may come from artificial high-frequency patterns rather than faithful detail recovery. This discrepancy questions whether the claimed perceptual gains reflect genuine visual quality improvements.\n\nIf these concerns are properly addressed and clarified, I would be willing to consider raising my score.", "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": "IwKNSzGG8x", "reviewer": "Reviewer_YB72", "summary": "This paper proposes VARestorer, a one-step visual autoregressive (VAR) distillation framework for real-world image super-resolution (Real-ISR). The method addresses two key challenges in applying VAR to ISR: (1) the next-scale prediction mechanism's limited ability to exploit global low-quality context, and (2) error accumulation during iterative prediction. VARestorer distills a pre-trained text-to-image VAR model into a one-step model using distribution matching, and introduces cross-scale pyramid conditioning with full attention to better leverage input information. Experiments on DIV2K-Val, DrealSR, and RealSR show improvements on perceptual metrics (72.32 MUSIQ, 0.7669 CLIPIQA) with 10× faster inference than vanilla VAR.", "questions": "1. In Equation (3), pT and pS have different conditioning. How do you compute DKL(pT(rk | rHQ,<k) || pS(rˆk | rLQ))? Is this importance sampling, or are you assuming some form of equivalence?\n2. How does \"cross-scale full attention\" preserve the autoregressive property of VAR? Don't future scales condition on past scales in VAR? Providing the attention mask explicitly would help.\n3. Teacher fine-tuning: Why fine-tune the 1024×1024 VAR on 512×512? Doesn't this lose the high-resolution knowledge? Have you tried using the original teacher directly?\n4. Training sufficiency: Why only 10K steps (1 epoch)? Have you verified convergence? What happens with more training?\n5. Which is correct: 1.2% (abstract), 0.09% or 1.78M (Appendix A), or 27.3M (Table 2)?\n6. Table 3 shows \"w/o distill\" performs very poorly. Does this mean VAR is inherently unsuitable for ISR without distillation?\n7. Why does VARSR need 10 steps? Have you tried VARSR with 1 step for fair comparison?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper has clear problem motivation, Identifies specific limitations of VAR for ISR (error accumulation, limited context)\n2. Solid experimental setup: LODO protocol, multiple datasets, standard metrics\n3. Good visual quality: Figure 1, 4 show appealing results\n4. Ablations validate components: Table 3 shows each component contributes\n5. Practical efficiency: One-step inference is desirable for deployment", "weaknesses": "1. This paper has weak theoretical foundation, including the following points:\n- KL divergence formulation (Eq. 3) with mismatched conditioning lacks justification\n- No analysis of distillation convergence or optimality\n- Cross-scale full attention's compatibility with autoregressive generation unclear\n- Missing theoretical characterization of when method should succeed/fail\n\n2. Insufficient experimental rigor:\n- Training 1 epoch (10K steps) without convergence analysis is concerning\n- Teacher fine-tuning on 512×512 (vs. original 1024×1024) impact not studied\n- Baseline comparison potentially unfair (VARSR-10 vs VARestorer-1)\n- No sensitivity analysis of loss weights (λKL, λperc, λMSE)\n- \"w/o distill\" baseline terrible but not investigated\n\n3. Method limitations underexplored:\n- Caption dependency critical but only qualitatively discussed (Figure G), also failure on severe degradation acknowledged but not analyzed.\n- Fixed resolution (512×512) limits practical utility\n- PSNR/SSIM degradation vs. baselines suggests fidelity issues\n\n\n4. For presentation:\n- Parameter count inconsistencies (1.2% vs 0.09% vs 27.3M)\n- Cross-scale conditioning implementation vague\n- Missing algorithmic details for key components\n- No discussion of why VAR is preferable to diffusion", "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": "IN0Ri4fufs", "reviewer": "Reviewer_BsAZ", "summary": "This paper proposes a VAR-based distillation framework that distills the generative prior of a pretrained VAR model into a one-step model to achieve more efficient real-world image super-resolution. In addition, the authors propose a full-attention-based cross-scale conditioning mechanism to further improve generation quality. Experiments on multiple datasets demonstrate significant improvements on no-reference metrics.", "questions": "Why is a restorer used as a preprocessing step? How does the VAR model perform without it?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper’s narrative is coherent, the language is clear and fluent, and it is easy to follow.\n\n2. Beyond super-resolution, the authors also conduct experiments on de-raining and low-light enhancement, demonstrating the effectiveness of the method.\n\n3. Through ablation studies, the paper validates the effectiveness of each component.", "weaknesses": "From Table 1, although the method shows clear gains on no-reference metrics, its performance on reference-based metrics is poor. This result may suggest a fidelity–perception trade-off; the authors should explain possible reasons for the weak reference-based performance.", "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": "Na4D5kzlSU", "reviewer": "Reviewer_gJQV", "summary": "This paper proposes VARestorer, a one-step super-resolution framework that addresses error accumulation in visual autoregressive models through distribution matching distillation. The method incorporates cross-scale pyramid conditioning to enable multi-scale information interaction while maintaining the original model's generative capabilities. Experiments demonstrate superior performance, achieving an optimal balance between efficiency and restoration quality.", "questions": "1. How does the distillation process prevent the student model from learning and reproducing any inherent errors or suboptimal predictions made by the teacher model during distribution matching?\n2. By transforming a sequential decision process into a one-shot mapping, the model might sacrifice some expressive power for computational efficiency. Could you discuss the theoretical limitations this imposes, especially when addressing highly complex or unseen degradations?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The paper adapts VAR models to the real-world image super-resolution (Real-ISR) task. The proposed distillation approach directly tackles the critical issue of error accumulation inherent in autoregressive models for restoration.\n2. The pyramid image conditioning with cross-scale attention enables bidirectional scale-wise interactions and fully utilizes the information of the input image while adapting to the autoregressive mechanism.", "weaknesses": "1. The computational cost of the cross-scale pyramid conditioning should be discussed, with quantitative analysis to justify its overhead in relation to performance gains.\n2. The labels and arrows in Figure 3 are unclear. Please define $\\mathcal{D}$ and $\\mathcal{E}$ (possibly Encoder and Decoder) in the caption or figure, and redraw the arrows to unambiguously show the process flow and component interactions.\n3. The paper employs BLIP to generate textual prompts, yet its necessity remains unverified. The claim that text guidance improves restoration quality lacks support from ablation studies.\n4. Although the high efficiency of training only 1.2% of the parameters was emphasized, no performance comparison was made with full parameter fine-tuning. It is suggested to add the baseline experimental results of full parameter fine-tuning to conclusively prove that LoRA can balance efficiency while maintaining performance.\n5. The computational efficiency claims lack comprehensive quantification. While inference time is reported, metrics like FLOPs or MACs are missing, making cross-architecture comparison difficult.", "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)