PocketSR: The Super-Resolution Expert in Your Pocket Mobiles
Authors: Haoze Sun
Linfeng Jiang
Fan Li
Renjing Pei
Zhixin Wang
Yong Guo
Jiaqi Xu
Haoyu Chen
Jin Han
Fenglong Song
Yujiu Yang
Wenbo Li
Venue: NeurIPS 2025 poster ()
Keywords: Image Super-resolution
Diffusion Model
Model Pruning
Primary area: applications

Abstract: Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones. While existing methods leveraging large generative models demonstrate impressive results, the high computational cost and latency make them impractical for edge deployment. In this paper, we introduce PocketSR, an ultra-lightweight, single-step model that brings generative modeling capabilities to RealSR while maintaining high fidelity. To achieve this, we design LiteED, a highly efficient alternative to the original computationally intensive VAE in SD, reducing parameters by 97.5\% while preserving high-quality encoding and decoding. Additionally, we propose online annealing pruning for the U-Net, which progressively shifts generative priors from heavy modules to lightweight counterparts, ensuring effective knowledge transfer and further optimizing efficiency. To mitigate the loss of prior knowledge during pruning, we incorporate a multi-layer feature distillation loss. Through an in-depth analysis of each design component, we provide valuable insights for future research. PocketSR, with a model size of 146M parameters, processes 4K images in just 0.8 seconds, achieving a remarkable speedup over previous methods. Notably, it delivers performance on par with state-of-the-art single-step and even multi-step RealSR models, making it a highly practical solution for edge-device applications.

PocketSR: The Super-Resolution Expert in Your Pocket Mobiles
Haoze Sun'; Linfeng Jiang", Fan Li', Renjing Pei?, Zhixin Wang', Yong Guo?, ;
Jiaqi Xu², Haoyu Chen', Jin Han Fenglong Song", Yujiu Yang 1s Wenbo L
'Tsinghua University "Huawei *Joy Future Academy *HKUST (GZ)
shz228tsinghua org.cn cn yang. aogg.yujjussz yujiuesz.tsinghuus wwinttsu.nnneiiee.e edu.
fenglinglww@@maillcco com
Abstract
Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such those captured by mobile phones. While existing methods leveraging large generative models demonstrate impressive results, the high computational cost and latency make them impractical for edge deployment. In this paper, we introduce PocketSR, an altra-lightweight single-step model that brings generative modeling capabilities RealSR while maintaining high fidelity. To achieve this, we design LiteED, highly efficient alternative to the original computationally intensive VAE SD, reducing parameters by 97.5% while preserving high- high-quality encoding and decoding. Additionally, we propose online annealing pruning for the U-Net, which progressively shifts generative priors from heavy modules to lightweight counterparts, ensuring effective knowledge transfer and further optimizing efficiency. To mitigate the loss of prior knowledge during pruning, we incorporate multi-layer feature distillation loss. Through an in-depth analysis of each design component, we provide valuable insights for future research. PocketSR, with model size of 146M parameters, processes 4K images in just 0.8 seconds, achieving a remarkable speedup over previous methods. Notably, it delivers performance on par with state-of-the- of-ttheart single-step and even multi-step RealSR models, making it highly practical solution for edge-device applications.
Introduction
Real-world image super-resolution (RealSR) [1, 2, 5, 7] fundamental task in computer vision that reconstructs high-quality images from low-quality inputs. With applications spanning smartphone photography, medical imaging, and remote sensing, RealSR has driven extensive research interest and development. Recent brcakthroughs in generative modeling parttcclaal diffusion models [8, 10, 11, 12, 13, hav-have opened new frontiers in SR by leveraging powerful generative priors to recover intricate textures and realistic structures, significantly enhancing image quality.
Scaling up text-to-image (T2I) diffusion models (e.g., from SD1.5 [10] to SD3 [15] FLUX [16]) has been shown to markedly improve SR performance [17]. Additionally, integrating multimodal large language models to generate detailed and accurate image descriptions further unlocks these models' generative potential [18, 19, 20, 21, 22]. However, the substantial computational cost and slow inference speed of such large-scale models limit their practical deployment. Consequently, research efforts have shifted toward efficient SR diffusion methods, focusing on lightweight architectures [23] and reduced sampling steps [24, 25, 26, 27, 28]. Yet, deploying these models resource-constrained edge devices remains a formidable challenge.
Given that low-resolution (LR) inputs provide rich and detailed prior- far more informative than the sparse textual cues in T2I generation-in is possible to achieve competitive SR performance with
Equual Contribution
Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Low- Low-Resolution Input
Low- New-Resolution Input
BeckettR Output
LR
LR
PocketSR Output
PocketSR
PocketSR
MACs/G
Params/M
step SD-Turbo
2221
950
1.1x
Lite Encoder
1670
Lite Decoder
481
3.5x
868
1.7x
Channel Pruning
279
430
Online Annealing
1.2x
Pruning
225
10x
6.5x
Figure 1: Visualization of the real-world image super-resolution results and efficiency of our proposed method. To enable the practical application of diffusion-based SR models, introduce PocketSR, an ultra-lightweight, single-step solution. The top visual examples demonstrate that PocketSR achieves high-quality super-resolution across diverse scenes, preserving fine details and textures. Best viewed zoomed in. The bottom section highlights the significant reduction in parameters (10x) and com- putational cost x). allowing our model to process 4K images in just 0.8 seconds dramatically outpacing existing methods.
a significantly compressed model. Motivated by this insight, we conduct a thorough analysis of the core components of diffusion models, including the variational autoencoder (VAE) and the U-Net architecture, and propose tailored strategies to maintain high-fidelity and realistic generation while substantially reducing the model size. Our approach is built upon three key innovations:
Lite Encoder & Decoder (LiteED). We introduce highly compressed encoder-decoder design featuring an ultra-compact encoder for efficient LR feature extraction and conditioning, coupled with tiny decoder for high-quality reconstruction. To mitigate potential losses in representational capacity, we incorporate a Dual-Path Feature Injection mechanism that enriches U-Net inputs with additional high-dimensional feature channels and Adaptive Skip Connections that retain critical information. The resulting LiteED contains only 2M parameters, drastically improving model efficiency while preserving image quality.
Online Annealing Pruning for U-Net. Recognizing the extensive generative priors embedded within U-Net architectures, we go beyond straightforward channel pruning [23] by introducing an online annealing pruning strategy. In this approach. lightweight modules are gradually integrated alongside existing components (e.g., residual blocks, self oeff-attention layers, and feed-forward networks), while the contributions of original modules are progressively annealed to zero. This smooth transition ensures stable knowledge transfer to the pruned architecture, Additionally, we conduct a comprehensive ablation study to determine optimal pruning positions, ensuring that our lightweight model retains strong generative capabilities.
Multi-layer Feature Distillation. Prior studies [23, 29] have demonstrated that feature-space distillation offers more stable optimization process compared distillation the image domain. Motivated by this observation, we adopt multi-layer, multi-scale distillation scheme to facilitate more reliable knowledge transfer. When combined with our online annealing pruning strategy, this approach substantially reduces computational cost while effectively preserving generative priors.

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Through these architectural optimizations, we further integrate adversarial training to develop Pock- etSR, an light-high,,,ight, one-step diffusion- based SR model with only 146M parameters- 10.4% of the size of StableSR [3] and 8.2% of OSEDiff [27]. Despite its compactness, PocketSR achieves performance on par with state-of- afrth approaches while significantly reducing computa- tional complexity and inference time, as illustrated in Sect. 4.2. Notably, it processes a 4K image in just 0.8 seconds-7 times faster than OSEDiff [27]. Our findings demonstrate that with carefully designed modifications, diffusion-based SR models can be both lightweight and powerful, paving the way for practical deployment in real-world applications.
Related Work
2.1 Real-world Image Super-Resolution
Real-world image super-resolution (SR) aims to recover realistic details from degraded inputs while maintaining overall fidelity. Early image super-resolution (ISR) methods [30, 31, 32, 33] often converge to the statistical mean of plausible solutions, resulting overly smoothed outputs and loss of fine details in real-world scenarios. To overcome this, several works [34, 2, 1, 35, 36, 37] have explored generative adversarial networks (GANs) for texture enhancement. However, due to inherent limitations such as mode collapse and training instability [38, 391, GAN-based methods still struggle to produce realistic textures. More recently, diffusion models [8. 9, 10, 11, 12, 40] have shown strong generative capabilities in synthesizing fine-grained details and realistic textures, making them promising alternative for real-world SR.
2.2 Diffusion-based ISR
Recent advances in diffusion models, especially in text-to-image (T2I) synthesis (e.g.. SD3 [15], FLUX [16]), have led to the development pre-trained diffusion-based SR methods such as Sta- bleSR [3], DiffBIR [4], PASD [5], CoSeR [41], SeeSR [19], and SUPIR [18]. These approaches benefit from the strong image priors of T2I models, achieving impressive results. However, their reliance on multi-step inference introduces high latency and substantial computational cost.
To address this, recent works aim to reduce inference time by distilling multi-step models into one- or few-step variants. ResShift [6] accelerates inference by learning residual transitions from LR to HR images via Markov chain. Building on this. Wang et al.[24] condense multi-step capabilities into single-step networks. OSEDiff[27] further leverages VSD [42] to incorporate T2I knowledge efficiently. PISA-SR [43] decouples structure restoration and texture enhancement using dual LoRA sets. Despite these advances, single-step SR methods still remain computationally heavier than traditional GAN-based models, limiting their deployment on edge devices and highlighting the need for better ffffiienncy-uallli trade-offs.
2.3 Efficient Diffusion Models
Recent efforts on efficient diffusion models [44, 45, 46, 47, 48, 49, 29] focus on architectural opti- mization to reduce redundancy in large-scale models. SnapFusion [49] disentangles the contributions of individual modules balance efficiency and accuracy. MifileDiff [47] improves efficiency by relocating Transformer blocks to lowerrtesolution stages. SnapGen [29] cuts computation and model size by removing high-resolution attention and replacing standard convolutions with depthwise sepa- rable ones. AdeSR [23] further explores one-step SR model efficiency. In Sect. 4.3, we compare our design with AdeSR and show that our approach achieves better performance with higher compression.
3 Method
Driven by deployment considerations, our PocketSR framework utilizes SDDTurbo? as the backbone, which achieves extreme model compression through two-stage training pipeline. the first stage, we replace the original Stable Diffusion's 's (SD) variational autoencoder with the Lite Encoder-Decoe (LiteED), with all parameters set to be trainable. In the second stage, we freeze LiteBD and apply our pruning strategies to the U-Net, gradually removing redundant components.
3

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Figure 2: Overview of PocketSR framework. We replace the original Stable Diffusion variational autoencoder with LiteED, and apply online annealing pruning and multi-layer feature distillation strategies to the diffusion U-Net, effectively reducing model parameters and computational complexity while maintaining excellent super-resolution performance.
3.1 Lite Encoder & Decoder
We propose LiteED, an lighteitgtt encoder-decoder architecture designed for edge-side super- resolution. To reduce complexity, we simplify the SD) encoder to several convolutional layers, while maintaining effective feature extraction. Additionally, we replace the original decoder with lightweight alternative that offers substantial improvements in efficiency while preserving comparable image fidelity (detailed in the supplementary materials). Notably, the decoder LiteED is modular and can be substituted with a more powerful variant to enhance reconstruction quality, albeit at the cost of efficiency. The decoder initialized from an open-source model", while the encoder is randomly initialized.
To supplement the ultra-light encoder and enrich the representational capacity, we propose an adaptive skip connection mechanism in LiteED, as depicted in Figure 2. Specifically, four control coefficients are generated from the encoder output via an MI.P to modulate the multi-scale skip connections. This adaptive mechanism allows the model to selectively integrate input features during decoding, effectively mitigating the loss of information due to encoder compression. Additionally, stabilize training. we incorporate zero-convolution into the skip connections.
Dual-path Feature Injection. We identify an information bottleneck between the original SD encoder and the U-Net. For 512 x 512 image, the output feature size of the SD encoder's last ResBlock is [N, 512, 64, 64], but it is compressed reduction in feature dimensionality may lead to substantial information loss, negatively impacting super-resolution performance. To address this issue and improve the fidelity of the result, propose a dual-path feature injection mechanism. Specifically, our encoder, an additional high-dimensiona feature of size N, 320, 64, 64) is extracted after the second convolutional layer of LiteED and injected into the U-Net following the initial block. This strategy enhances the information flow, allowing for more robust feature extraction. As for feature injection, employ cross normalization [50], which has been shown to facilitate fast and stable convergence.
NN 4, 64, 64 in the final convolutional layer. This 128x
Despite its lightweight design, LiteED proves to be more effective in practice than networks with several times higher computational complexity. Moreover, the decoder in LiteED can be replaced with larger-capacity network, which leads to better performance demonstrated in experiments.
3.2
U-Net Pruning
3.2.1
Online Annealing Pruning
Pruning is straight-forward and effective model compression technique, which widely used in efficient image generation [45, 48, 47, 49, 29, 51, 52]. In this paper, we propose online annealing
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MACS 201
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Table Comparison of the computa- tional efficiency of the SD network and its modules before and after our stream- lining. We highlight the computational efficiency of the original SD and the improvements brought by PocketSR.
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279
30
243
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Timee (ms)
Computational Effictency
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Figure 3: Analysis of the impact of pruning residual blocks at different depths using the widely adopted RealSR [53] test
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1996
(995) 8.1
1986
SD
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359.5
54.2
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12.5
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SD U.Na
1908
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401.9
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The inference resolution is 512 x 512, and we report the maximum inference speed on an A100 GPU.
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10.3
1289
89.4
PocketsR
190%
137
1906 234.5
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Original SD
79.7
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pruning, stable and efficient pruning strategy specifically designed for SR. Existing Diffusion U-Net pruning methods typically simply discard [47, 49] or replace pruned modules [48, 291, which leads to a significant loss of prior information. Our pruning strategy preserves the prior information through online knowledge transfer. As shown in Figure 2, we connect the original module M in parallel with a lightweight replacement module Mp. During the training process, we continuously increase the contribution of the lightweight module, while gradually annealing the contribution of the original module to zero:
y= ((x + (1 11-00 σ) -p((x)
where x and y represent the module input and output, respectively. The annealing coefficient is defined as min (0, (T t) /T'), where T' is the total number of annealing steps, and & denotes the current training step. The parameters of the original module are frozen during training. Once training is complete, the original module is replaced with the lightweight module. The pruned modules are permanently removed at inference time, yielding a highly compact and efficient model. Our experiments show that this online pruning approach can better preserve the diffusion prior and generate more realistic textures (see Sect. 4.3).
3.2.2 Pruning Implementation
Diffusion U-Net comprises four computationally expensive module types: residual blocks, cross- attention layers, self-attention layers, and feed-forward networks (FFNs). To reduce the number of parameters and computational cost while preserving performance, we replace each module with carefully chosen lightweight counterpart. Lightweight layers (e.g.. normalization, activation) remain unchanged. For residual blocks, we replace convolutions with depthwise separable convolutions [55]. significantly reducing the number of parameters. Since text input is entirely discarded, cross-attention layers are replaced with MLPs of two linear layers. Self-attention layers are approximated using linear attention [56] to reduce computational cost. Finally, the hidden dimension of the FFN is reduced to one-fourth of its original size.
We also observe that the pruning location has significant impact on model performance, with its effect varying according to the module's depth within the network. Specifically, shallower modules exert greater influence super-resolation quality, while deeper blucks can be pruned with minimal impact. Taking SD's U-Net as an example, we label the depths as (I, II, III, and IV), from shallow to deep. Figure 3 illustrates the trade-off between performance and efficiency when pruning residual blocks at different depths. Our results show that pruning at deeper levels (III and IV) significantly reduces parameter count and computational cost while having negligible impact on performance. While pruning shallower modules yields greater efficiency gains, it leads to a more noticeable drop in final performance.
We interpret this phenomenon as deeper layers in the U-Net of generative diffusion model primarily process high-level information [57, 58], such as layout and style, which contributes less to the SR task. This characteristic makes free lunch for pruning. Our findings extend beyond residual blocks to other network modules, egg., self-attention, cross-attention, and FFNs, indicating a general pruning strategy for SR networks. Balancing efficiency and effectiveness, we prune residual blocks and FFNs depths III and IV, while self-attention layers are pruned depth IV. Additionally, prune
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all cross-attention layers, as their impact on super-resolution quality is minimal. Detailed ablation experiments on all network module pruning locations are provided in the supplementary material.
3.2.3 Multi-layer Feature Distillation
To better preserve generative priors during pruning, we introduce a feature-level knowledge distillation strategy. Due the architectural mismatch between the models before and after pruning, single-layer distillation may result in training instability. Instead, we adopt a multi-layer global distillation scheme enhance robustness and enable stable knowledge transfer:
disull
E
Σ | pro-penning & (J post-pruning
|
where f preeppruning and f post-pruning denote the feature representations from the 1-th layer of the models before and after pruning, respectively. Following [59], we implement the mapping function (6-) as lightweight, trainable projection module composed of single convolutional layer.
3.3 Training Details
Beyond the aforementioned lightweight optimizations, further investigate the impact of channel reduction on model performance, which is compatible with our online annealing pruning strategy. Notably, direct channel reduction substantially weaken the model's 's learned priors. However, this adverse effect is markedly mitigated when integrated with the proposed multi-layer feature distillation strategy. Comprehensive experimental results are provided the supplementary materials. To strike a balance between efficiency and performance, we reduce the channel width in the U-Net to 70% of its original size. Table presents the final latency, computational cost, and parameter count of our model in comparison to the original SD-Turbo.
Building on recent progress in one-step diffusion- beased generation [60, 61]. we introduce adversarial loss during training to improve texture fidelity. In the first stage, the model is trained using a combination of MSE loss, LPIPS [62], and adversarial loss. In the second stage, incorporate the multi-layer feature distillation loss to retain knowledge from the full-capacity model during pruning.
In the first training phase, train the unpruned SD U-Net equipped with LiteED for 80,000 steps. In the second phase, we first apply channel pruning over 80,000 steps, followed by module-wise online annealing pruning for additional 8,000 steps. The total number of annealing steps is set to = 8000. A fixed batch size of 64 is used throughout the entire training process. We employ the AdamW optimizer with a learning rate of x 10 and the timestep is fixed at 999 for one-step diffusion. Additionally, the original text embedding is replaced with a learnable embedding vector.
Experiments
4.1
Experimental Settings
The training dataset comprises approximately 500K high-quality images from LSDIR [63] and 10K images from FFHQ [64]. During training, images are randomly cropped into 512 x 512 patches. Low-quality counterparts are synthesized using the widely adopted degradation pipeline from Real-ESRGAN [1]. For evaluation, we follow the protocols in [23, 27] and report results on the DRealSR [65] and RealSR [53] benchmarks.
We compare the proposed PocketSR with four state-of-the-art multi-step methods-Stablled [3]. DiffBIR [4], SeeSR [19], and ResShift [6]-as well three leading single-step methods: SinSR [24]. AdeSR [23], and OSEDiff [27]. We also compare our model with GAN-based SR methods in the supplementary material. All models are evaluated using a comprehensive set of metrics, including
perceptual similarity metrics (LPIPS [62], DISTS [66]). fidelity metrics (PSNR, SSIM [67]), and no-reference quality metries (NIQE [68]. MUSIQ [69]).
4.2 Comparison with State-of-the-Arts
Quantitative and Efficiency Comparison. Table reports quantitative results on RealSR [53] and DRealSR [65], with efficiency metrics listed in the last five rows. The results show that our

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Table 2: Quantitative and efficiency comparisons with state-of-ther artt diffusion-basee based methods on real-world datasets are presented. The best results for each metric are highlighted in bold. PocketSR delivers real-time inference at over 60 FPS A100 GPU for 512 x 512 inputs, while maintaining
competitive quantitative performance.
Datasets
Metrics
StableSR [3] DifBBIR [4]
SecSR [19]
ResShift [6]
SinSR [24]
OSEDif [27]
AdcSR [23]
PocketSR
LPIPS.
0.3018
0.3636
0.3009
0.3460
0.3188
0.2921
0.2885
0.2713
DISTSL
0.2288
0,2312
0.2223
0.2498
0,2353
0,2128
0.2129
0.2094
PSNRT
24.70
24.75
28.18
26.31
26.28
25.15
25.47
25.47
RealSR [53]
SSIMT
0,7085
0.6567
0.7216
0,7421
0,7347
0.7341
0.7301
0,7330
NIQE,
5,912
5.535
5.408
7.264
6.287
5.648
5.350
5.067
MUSIQT
65.78
64.98
69.77
58.43
60.80
69.09
69.90
67.07
LPIPS.
0.3284
0.4557
0.3189
0.4006
0,3665
0.2968
0.3046
0.2962
DISTS
0.2269
0.2748
0.2315
0.2656
0.2485
0.2165
0,2200
0.2139
PSNKT
28.03
26.71
28.17
28.46
28.36
27.92
28.10
28.05
DRealSR (65)
SSIM
0.7536
0.6571
0.7691
0.7673
0,7515
0,7835
0.7726
0.7675
NIQE_
6.524
6.312
6.397
8.125
6.991
6,490
6,450
5,809
MUSIQT
58.51
61.07
64.93
50,60
55.33
64.65
66.26
63.85
Parameters (M)
1410
1717
2524
119
119
1775
456
146
MACs (G)
79940
24234
65857
5491
2649
2265
496
225
Sampling Steps
200
50
1
Inference Time (8)
11.5
2.7
4.3
0.71
0.13
0.11
0.03
0.016
FPS
0.09
0.37
0.23
1.4
7.7
9.1
33.3
62.5
method achieves strong super-resolution performance with excellent computational efficiency. First, the proposed single-step model, PocketSR, has only 146M parameters, achieves the lowest MACs among all methods, and processes a 512 x 512 image in 0.016 seconds on an A A100 GPU-nearly twice as fast as AdeSR. This low latency and lightweight design make it ideal for real-time and edge deployment. PocketSR achieves an inference time of only 140ms on newly released smartphone model from 2025, representing an over 80% reduction compared to the original, non- non-lightweight backbone. Second, PocketSR attains the best LPIPS, DISTS, and NIQE scores, demonstrating superior perceptual quality. Notably, the DRealSR dataset, PocketSR surpasses the second-best method by 10% % in NIQE, indicating clearer texture restoration. Third, delivers competitive fidelity, achieving PSNR and SSIM on par with AdeSR on RealSR and clearly outperforming multi-step models such as StableSR, DifIBIR, and SeeSR. Although ResShift and SinSR perform better than PocketSR in PSNR and SSIM, their texture are not as realistic PocketSR.
Qualitative Comparison. Figure 4 shows qualitative comparisons, demonstrating that method consistently maintains high fidelity and excels in detail reconstruction. In the first row, only DIffBIR, ResShift, SinSR, and PocketSR successfully recover the diagonal striped texture in the upper-right corner. However, the textures produced by DiffBIR and SinSk appear visually unrealistic. Among single-step methods, only PocketSR reconstructs accurate and perceptually convincing pattern, highlighting its strength in preserving fine details. The second row further shows that PocketSR the only method able restore the building's 's structural details, reinforcing its high fidelity and detail recovery capabilities. Figure showcases results across a broader range of natural scenes, including animals and animes. These results further verify that PocketSR not only achieves faithful reconstructions but also delivers rich, fine- fine-grained textures across diverse visual contexts.
4.3 Ablation Study
We carefully analyze the effects of the proposed lite encoder and decoder, online annealing pruning, and multi-layer feature distillation, demonstrating an excellent trade-off between super-resolution quality and efficiency through extensive experiments. The experiments in this section follow the first-stage training in Section 3.3, where the unpruned SD U-Net is jointly trained with various encoder and decoder architectures for 80,000 steps. All settings share the same model and training configuration, differing only in encoder/decoder architecture. RealSR is used as the test dataset.
Effeet of Adaptive Skip Connection and Dual-path Feature Injection. The proposed lite encoder simplifies the original SD encoder to improve efficiency, which inevitably introduces information
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StableSR-sa2 $200
DifIBIR
SeeSR-s50
ResShift-s15
SinSR-s1
OSEDiff-s1
AdcSR-s1
PocketSR-si
StableSR-s200
DiffBIR-s50
SeeSR-s50
ResShift-s15
LR
SinSR-s1
OSEDiff-s1
AdcSR-s1
PocketSR-s1
Figure 4: Qualitative results on real-world images. Single-step methods are highlighted for clarity. PocketSR delivers competitive performance, generating well-preserved structures and fine-grained textures, even when compared to multi-step models.
Table 3: We conducted ablation studies on the core architectural components of LiteED, including the Adaptive Skip Connection (ASC) and Dual-path Feature Injection (DFI). Additionally, compared the proposed lightweight encoder with alternative designs to validate the effectiveness of architecture. Finally, replaced the decoder assess the structural robustness of LiteED.
Decoder
Encoder
PSNRT
SSIM
LPIPS]
DISTS
NIQEL
Time (ms8)
MACs (G)
Param. (M)
Lite Enc. (Ours)
25.61
0,7431
0.2474
0.1911
5.200
31.4
481.6
868.3
Lite Enc. w/o ASC
25.42
0.7426
0.2737
0.2050
5.329
30.7
480.8
868.3
Lite (Ours)
Enc. wfo
25.35
0.7427
0.2580
0.1940
478.6
867.6
PixelUnshufle
25.23
0.7417
0.2697
0.2028
5,552
30.5
474.8
867.7
SD Enc.
25.19
0.7259
0.2685
0.1981
5.092
51.7
1032.2
901.4
Lite Enc. (Ours)
25.95
0.7503
0.2390
0.1843
6.899
58.1
1669.7
916.2
SD Dec.
SD Enc.
26.00
0.7548
0.2445
0.1892
6.898
79.7
2221.2
949.6
loss. To address this, we introduce the Adaptive Skip Connection (ASC), which flexibly transfers essential features to the decoder during encoding. This not only eases the training of the diffusion U-Net but also improves both fidelity and perceptual quality in the super-resolution results.
Additionally, we design the Dual-path Feature Injection (DFT) module to alleviate the information bottleneck between the encoder and U-Net (see Sect. 3.1). our Dual-path Feature Injection (DFT) design, the lite path provides compressed, information-dent guidance and align well with the VAE feature distribution in SD. making them easier for the pretrained U-Net to utilize. In contrast, the heavy path contains richer details but lower information density, making it harder for generative models to use directly.
features that offer global structural
As shown in Table 3, both ASC and DFI consistently improve performance across nearly all metrics with minimal overhead. Figure further proves: removing either module causes visible degradation, particularly in the word "Sausage" (first row) and the plastic chair edges (second row).
Comparison between the Lite Encoder and Alternative Encoding Strategies. We compare our lite encoder with the PixelUnshuffle approach and the original SD encoder (SD Enc.) PixelUnshuffle, introduced by AdeSR [23], is lightweight method for injecting low-resolution (LR) features. Despite similar computational cost, our encoder consistently outperforms PixelUnshuffle across all metries, with up 8.3 % improvement in LPIPS. Moreover, design achieves superior performance

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Sausage
Sausage
Sansage
Dumpling
Dumpling
Dumpling
Dumpling
Dumpling
Dumpling
Dumpling
Beef ket
Beef bricket ket
Beef bris ket
Beef bricket
Beef bricket
Beef brisket
Beef bris ket
Pig skin
Pig skin
Pig skin
Pig skin
Pig skin
Pig skin
Pig Mignkin
Original LiteED (Ours)
w/o Adaptive Skip Connection
w/o Dusl-path
Pixellunstufllle
Stabie Diffusion Encoder
Stable Diffusion Decoder
Festure Irjection
Figure 5: Ablation study of the LiteED design on "Canon 003" from RealSR (top) and "DSC 1286" from DRealSR (bottom).
Table 4: Ablation study on pruning strategies. We conduct ablation studies on the online annealing strategy and the multi-layer feature distillation loss employed during pruning. Additionally, we compare the effectiveness of multi-layer distillation against single-layer distillation applicd solely to the output layer of the U-Net.
Online Annealing
Multi-layer Distillaioon
Strategy
LPIPS
DISTS)
NIQE!
Praning
Strategy
Anneainng Pruning Feature Distillatoon
Ours (2)
0.2713
0.2094
5.067
Figure Ablation study our pruning strategy on "Nikon 004" and "Nikon 015" images from RealSR.
0.2732
0.2120
0.2816
0,2162
5.097
Single-layer
0.2762
0.2116
reference-based metrics than the SD encoder, with only 46.7% of its computational cost. We attribute this to the absence of skip connections and the potential bottleneck in the SD encoder, which may result in the loss of fidelity-critial information. As shown in Figure 5, PixclUnshuffle often causes fine detail loss (first row) and oversmoothing (sccond row), while the SD encoder tends to produce unnatural textures. These results highlight that well-designed lightweight encoder can outperform complex counterparts in SR tasks, offering valuable insights for efficient model design.
Compatibility with more powerful image decoders. Prior studics [70, 23] have shown that decoder complexity often has greater impact performance than encoder complexity. Building on this, we investigate the decoder flexibility of LiteED by replacing its decoder with the original SD decoder, while keeping the lite encoder (w/ ASC & DFI) unchanged. This substitution leads to improved SR quality, albeit with a 247% increase in computational overhead. These results underscore the generalizability of LiteED, enabling flexible decoder adjustment balance efficiency and performance. We also compare our design with the original SD VAE (w/o ASC & DFI). The variant using the SD decoder and lite encoder achieves comparable performance with 24.8% reduction in computational cost, further validating the effectiveness of the LiteED architecture.
Effect of Online Annealing Pruming. In Table 4, we compare the proposed online annealing pruning strategy with direct lightweight module replacement, denoted as Strategy (1). Our approach consistently outperforms the baseline across all metrics, especially the no-reference NIQE score, thanks its progressive knowledge transfer that better preserves the SD model's generative prior. As shown in Figure 6, while direct pruning fails to recover clear brick patterns and wheat textures, our method yields visually superior and high-quality results.
Effect of Multi-layer Feature Distillation. Multi-layer feature distillation is introduced to enhance the stability and robustness of knowledge transfer during pruning. As shown in Table 4, removing it (Strategy 2) degrades performance across metries, with LPIPS showing the largest drop (3.8%). We also evaluate single-layer distillation at the U-Net output (Strategy 3), which slightly outperforms Strategy but still lags behind the full multi-layer setup. As illustrated in Figure 6, removing multi-layer distillation results in noticeable noise and artifacts due to unstable training dynamics.

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Conclusion and limitation
We present PocketSR, highly efficient single-step model for real-world image super-resolution. By replacing the heavy VAE in SD with LiteED, PocketSR reduces parameters and latency while preserving fidelity. Our proposed pruning strategy further enhances efficiency by progressively transferring generative priors to lightweight modules, optimizing performance without compromising quality. Experiments show that it achieves significant speedup and performance comparable to state-of-the-an RealSR models, making it practical for broad deployment.
One limitation of PocketSR is that its detail generation capability under severe degradations remains to be improved. Moreover, the current framework has not yet been optimized in conjunction with edge hardware, which identify a promising avenue for future research.
Acknowledgments. This work was supported by the National Key Research and Development Program of China (No. 2024YFB2808903), Thanks for the computing resources provided by Lei Ke during the rebuttal.
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Reviews:
[{"review_id": "pTtJCEmAe7", "reviewer": "Reviewer_2iJ7", "summary": "This paper introduces PocketSR, an ultra-lightweight diffusion-based model for real-world image super-resolution targeting edge deployment. The core methods include:\nLiteED: A highly efficient VAE alternative reducing parameters by 97.5% while preserving encoding/decoding quality via adaptive skip connections and dual-path feature injection.\nOnline Annealing Pruning: A strategy to progressively shift generative priors from heavy U-Net modules to lightweight counterparts, minimizing knowledge loss.\nMulti-layer Feature Distillation: Stabilizes knowledge transfer during pruning by aligning features across layers.\nPocketSR achieves 146M parameters, processes 4K images in 0.8s, and matches SOTA single/multi-step methods in fidelity and perceptual quality.", "questions": "Please see Cons.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Pros: \n\n1.Novelty: The online annealing pruning and LiteED design offer fresh solutions to diffusion model compression. The dual-path feature injection effectively mitigates information bottlenecks.\n\n2.Practical Impact: PocketSR’s efficiency (62.5 FPS for 512×512 images on A100) makes it viable for mobile/edge devices. The 10× parameter reduction over StableSR is compelling.\n\n3.Rigorous Evaluation: Extensive comparisons on RealSR/DRealSR benchmarks show PocketSR outperforms SOTA in LPIPS, DISTS, and NIQE. \n\n\nCons：\n\n1.Insufficient reproducibility of the experiment. The code is not open-source: the author declares that it cannot be open-sourced due to company policy (Page 15), which seriously affects reproducibility. It is recommended to provide pseudo-code or schematic diagrams of the inference process. And Section 4.1 does not adequately describe the training details.\n\n2.Results Analysis: In Table 2 PocketSR has lower PSNR/SSIM than ResShift, but better LPIPS/NIQE. Discussion is needed on whether this trade-off affects specific application scenarios.\n\n3.Writing. Section 3.1 “LiteAE” (Page 2&4) and “LiteED” (Abstract) are suspected to be mixed and need to be corrected. Comparative Clarity: Is the “Parameters (M)” line in Table 2 mislabeled, with 119M for both ResShift/SinSR?\n\n4.Edge Deployment Realism: The paper overlooks comparisons with non-diffusion SR models (e.g., transformer-based and CNN-based), leaving the efficiency breakthrough incompletely contextualized. Also, can authors report on the inference speeds on common GPUs such as 3090 and 4090, since the authors claim it is for edge computing?", "was_revised": true, "final_justification": "The rebuttal addressed some of my concerns. After reading the other reviewers' comments, I decided to raise the score to borderline accept.", "rebuttal": "We sincerely thank the reviewer for taking the time to read our paper and provide valuable feedback. We are pleased to see that the reviewer finds our work both novel and practically valuable. We deeply appreciate your insights, and in the following, we address each of your concerns carefully. All discussions and additional experiments presented in this rebuttal will be incorporated into the camera-ready version of the paper.\n\n---\n> **[W1] Insufficient reproducibility.**\n\nWe fully understand and agree with the reviewer’s concern. In response, we have decided to conduct a code reimplementation, within the boundaries of our company’s policy, and plan to release it publicly upon acceptance to better support the community. \n\nRegarding the training details mentioned by the reviewer, we would like to clarify that they are indeed provided in Section 3.3 rather than Section 4.1. Nonetheless, we will further enrich the description of training settings and implementation details in the camera-ready version to improve clarity and reproducibility.\n\n---\n> **[W2] Performance trade-off (better LPIPS/NIPE but lower PSNR/SSIM than ResShift).**\n\nWe sincerely thank the reviewer for the insightful and professional comments. We would like to clarify this concern from two perspectives:\n\n1. PSNR and SSIM primarily reflect pixel-level similarity, which does not always align with human visual perception—a limitation that has been widely discussed in prior works on image super-resolution and quality assessment [1,2,3]. Moreover, it is possible for overly smoothed outputs to yield higher PSNR/SSIM scores. For example, we found that **simply upsampling the low-resolution images in DRealSR using bicubic interpolation achieves a PSNR of 30.58 and SSIM of 0.8312**—surpassing all super-resolution methods reported in Table 2. A similar phenomenon is observed on RealSR. This clearly contradicts the fundamental goal of super-resolution, which is to recover fine and realistic image details.\n2. Given the limitations of PSNR/SSIM, we advocate for a multi-metric evaluation strategy rather than relying on a single indicator. From the perceptual similarity perspective, our method achieves the best performance in both LPIPS and DISTS (which measure different aspects from PSNR/SSIM), indicating that our results are most consistent with human perception. On the pixel-wise alignment side, while our method slightly lags behind ResShift and SinSR, it performs on par with previous multi-step diffusion-based methods such as StableSR and DiffBIR, as well as single-step approaches like OSEDiff and AdcSR—demonstrating comparable pixel-level fidelity. In terms of image detail and texture, our method achieves the best NIQE score and comparable MUSIQ, further reflecting its ability to produce high-quality outputs.\n\nIn summary, our method delivers competitive or even superior performance across multiple dimensions, while being significantly more efficient in computation, highlighting its practical value on common application scenarios.\n\n[1] Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild, CVPR 2024\n\n[2] CoSeR: Bridging Image and Language for Cognitive Super-Resolution, CVPR 2024\n\n[3] Image quality assessment: Unifying structure and texture similarity, TPAMI 2020\n\n---\n> **[W3] Writing and clarity.**\n\nWe sincerely appreciate the reviewer’s careful examination. The mention of LiteAE was a typographical error, and we will correct it in the camera-ready version.\n\nRegarding the parameter counts reported in Table 2, we would like to clarify that the numbers are accurate. This is because SinSR is distilled from ResShift, and thus they share the same number of parameters. We respectfully invite the reviewer to refer to Table 2 in OSEDiff[4], where the reported values are consistent with ours.\n\n[4] One-Step Effective Diffusion Network for Real-World Image Super-Resolution, NeurIPS 2024\n\n---\n> **[W4-1] Comparisons with non-diffusion SR models.**\n\nWe sincerely thank the reviewer for the insightful and professional feedback. To address your concern, we have conducted additional comparisons on the DRealSR dataset with several representative non-diffusion-based methods, including a CNN-based method (DASR [5]) and a Transformer-based method (FeMaSR [6]). The comparison results are presented in the table below (FPS measured on an A100 server).\n\nOur method significantly outperforms the competing approaches in terms of perceptual quality metrics such as LPIPS, DISTS, NIQE, and MUSIQ. While DASR shows stronger results in PSNR, SSIM, and efficiency, we believe this is partly due to its extremely lightweight design, which—while advantageous in terms of speed and model size—also limits its ability to recover fine-grained details from complex real-world degradations, resulting in smoother outputs. As discussed in **[W2]**, such characteristics can sometimes lead to higher PSNR and SSIM scores that may not fully reflect perceptual quality.\n\n$$\n\\begin{array}{l|cccccc|ccc}\n\\hline\n\\textbf{Methods} & \\textbf{PSNR} \\uparrow & \\textbf{SSIM} \\uparrow & \\textbf{LPIPS} \\downarrow & \\textbf{DISTS} \\downarrow & \\textbf{NIQE} \\downarrow & \\textbf{MUSIQ} \\uparrow & \\textbf{Params (M)} & \\textbf{MACs (G)} & \\textbf{FPS} \\\\\\\\\n\\hline\n\\text{DASR[5] (CNN-based)} & \\mathbf{29.75} & \\mathbf{0.8262} & 0.3099 & 0.2275 & 7.586 & 42.41 & \\mathbf{8} & \\mathbf{46} & \\mathbf{148.0} \\\\\\\\\n\\text{FeMaSR[6] (Trans-based)} & 26.87 & 0.7569 & 0.3157 & 0.2239 & 5.910 & 53.71 & 34 & 384 & 16.8 \\\\\\\\\n\\text{PocketSR (Ours)} & 28.05 & 0.7675 & \\mathbf{0.2962} & \\mathbf{0.2139} & \\mathbf{5.809} & \\mathbf{63.85} & 146 & 225 & 62.5 \\\\\\\\\n\\hline\n\\end{array}\n$$\n\n[5] Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution, ECCV 2022\n\n[6] Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors, ACMMM 2022\n\n---\n> **[W4-2] Inference speed on common GPUs.**\n\nWe sincerely thank the reviewer for the valuable suggestion. The table below presents the inference time of different methods on an RTX 4090 GPU. We observe that the inference speed on the 4090 is comparable to that on the A100, demonstrating that our method maintains high efficiency even when deployed on consumer-grade GPUs—highlighting its practicality for real-world applications.\nIf the reviewer is interested in the performance of PocketSR in more constrained edge-computing environments such as mobile NPUs, we kindly refer you to our response to **Reviewer nGC4’s Weakness 2**. Specifically, PocketSR takes approximately 140ms to super-resolve a 512×512 image on a mobile NPU.\n\n$$\n\\begin{array}{l|cccccc}\n\\hline\n\\textbf{4090 GPU} & \\textbf{DASR} & \\textbf{FeMaSR} & \\textbf{SinSR} & \\textbf{OSEDiff} & \\textbf{AdcSR} & \\textbf{PocketSR (Ours)} \\\\\\\\\n\\hline\n\\text{Inference Speed (ms)} & 7.23 & 59.83 & 129.05 & 112.70 & 41.67 & 16.78 \\\\\\\\\n\\hline\n\\end{array}\n$$"}, {"review_id": "ISKBFOeajS", "reviewer": "Reviewer_2VLv", "summary": "PocketSR is a lightweight and efficient model for the RealSR task, using a compact encoder-decoder and efficient pruning to reduce size and latency. It introduces LiteED, a compact alternative to the VAE used in Stable Diffusion, reducing parameters by 97.5% while maintaining high quality. The model also uses online annealing pruning and multi-layer feature distillation to optimize performance and retain generative knowledge. With only 146M parameters, PocketSR can process 4K images in 0.8 seconds, offering speed and quality comparable to state-of-the-art methods, making it ideal for real-time mobile and edge applications.", "questions": "1. How to obtain the evaluation values in Table 3? Are they used to evaluate the reconstruction results of the VAE? What test dataset was used? For the encoders and decoders compared with LiteED, were they all trained together with the unpruned SD U-Net for 80000 steps as well?\n\n2. Which version of Stable Diffusion is PocketSR based on?\n\n3. The paper repeatedly emphasizes PocketSR's speed advantage in processing 4K images. What is the performance of PocketSR in handling 4K images SR? Is its performance on different resolutions affected by the choice of the pretrained SD model?\n\n4. The design of ASC is interesting, but it requires more detailed ablation studies and explanations to give readers a clearer understanding:\n\nA. Compare the performance between skip connections (without the control coefficients) and adaptive skip connections.\n\nB. Why are the control coefficients learned from the output of the lite path? What would happen if they were learned from the heavy path instead?\n\nC. What are the characteristics of the learned control coefficients? Do they show any patterns based on different input contents or statistical properties?\n\nD. During inference, how would modifying the control coefficients affect the output?\n\n5. The purpose of the dual-path design is not clearly explained. What are the respective roles of the lite path and the heavy path? What would happen to performance if either the lite or heavy path were removed?", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. PocketSR demonstrates impressive speed, making it suitable for real-time applications on edge devices.\n\n2. The introduction of LiteED significantly reduces model size and computational cost while maintaining high-quality results.\n\n3. The paper proposes novel methods, such as online annealing pruning and adaptive skip connections (ASC), which contribute to both efficiency and performance.\n\n4. Despite its simplicity and single-step design, PocketSR performs on par with more complex, multi-step RealSR models.\n\nWeaknesses:\n\n1. The methodology behind some evaluations (e.g., Table 3) is unclear, specifically which datasets were used, and whether all encoder-decoder pairs were trained under the same conditions.\n\n2. The paper doesn't specify which version of Stable Diffusion PocketSR is based on.\n\n3. While speed is emphasized, the quality of 4K image super-resolution results is not thoroughly analyzed. It’s also unclear how performance varies across resolutions or with different pretrained SD models.\n\n4. The ASC module is underexplored. Key aspects such as the function of control coefficients and their effect during inference are not fully examined.\n\n5. The roles of the lite path and heavy path in the dual-path structure are not well justified. The impact of removing either path is not studied.", "was_revised": true, "final_justification": "Most of my concerns have been satisfactorily addressed. I encourage the authors to provide further clarification on the DFI and ASC components in the revised version to enhance clarity. Additionally, applying the proposed strategy to a more recent model, such as FLUX, could further strengthen the contribution.\n\nAfter this discussion, I have decided to raise my rating from Borderline Accept to Accept.", "rebuttal": "We sincerely thank the reviewer for the insightful and constructive feedback, which has prompted us to further reflect on our approach. We also appreciate your recognition of our work’s effectiveness and novelty. Below, we address your concerns point by point (with related issues grouped for clarity). All discussions and new experiments will be included in the camera-ready version.\n\n---\n> **[W1&Q1] Explanation for Table 3.**\n\nWe apologize for not clearly specifying the experimental setup of Table 3, and we will include this information in the camera-ready version. The experiments in Table 3 follow the first-stage training described in Section 3.3, where the unpruned SD U-Net is jointly trained with various encoder and decoder architectures for 80,000 steps. \n\nTable 3 evaluates the impact of different encoder and decoder designs on super-resolution performance (not VAE reconstruction), serving as an ablation for our proposed LiteED. U-Net pruning is not applied at this stage. All settings share the same model and training configuration, differing only in encoder/decoder architecture. RealSR is used as the test dataset.\n\n---\n> **[W2&Q2] Explanation for SD version.**\n\nThank you for the question. As mentioned in line 101 of the paper, PocketSR uses SD-Turbo as the pretrained model.\n\n---\n> **[W3&Q3] Performance on handling high-resolution SR.**\n\nThank you for the valuable suggestion. To assess PocketSR at higher resolutions, we constructed a multi-resolution benchmark with three 4× SR tasks: upscaling to 1K, 2K, and 4K. Each setting includes 20 test images with ground-truths. The 1K and 2K samples are selected from RealSR, ensuring resolution and scene diversity; 4K samples are from DRealSR. Notably, this benchmark uses original full-resolution images, unlike Table 2 which evaluates on center-cropped patches—a common practice in SR [1].\n\nAs shown below, PocketSR consistently achieves superior LPIPS, DISTS, and NIQE scores, and competitive PSNR, SSIM, and MUSIQ results. Its stable performance across resolutions demonstrates strong robustness, making it well-suited for real-world use cases like mobile photography.\n\n$$\n\\begin{array}{l|l|cccccc}\n\\hline\n\\textbf{Tasks} & \\textbf{Methods} & \\textbf{PSNR} \\uparrow & \\textbf{SSIM} \\uparrow & \\textbf{LPIPS} \\downarrow & \\textbf{DISTS} \\downarrow & \\textbf{NIQE} \\downarrow & \\textbf{MUSIQ} \\uparrow \\\\\\\\\n\\hline\n\\text{1K Resolution SR} & \\text{SinSR} & \\mathbf{25.53} & 0.7015 & 0.3934 & 0.2286 & 5.794 & 62.82 \\\\\\\\\n& \\text{OSEDiff} & 24.73 & 0.7088 & 0.3226 & 0.1933 & 3.943 & 70.50 \\\\\\\\\n& \\text{AdcSR} & 25.01 & 0.6995 & 0.3154 & 0.1886 & \\mathbf{3.409} & \\mathbf{72.05} \\\\\\\\\n& \\text{PocketSR (Ours)} & 25.07 & \\mathbf{0.7162} & \\mathbf{0.2709} & \\mathbf{0.1662} & 3.880 & 65.23 \\\\\\\\\n\\hline\n\\text{2K Resolution SR} & \\text{SinSR} & \\mathbf{26.96} & 0.7473 & 0.3884 & 0.2102 & 6.563 & 55.25 \\\\\\\\\n& \\text{OSEDiff} & 25.81 & \\mathbf{0.7756} & 0.2775 & 0.1661 & 4.333 & 63.78 \\\\\\\\\n& \\text{AdcSR} & 26.35 & 0.7732 & 0.2729 & 0.1630 & 4.158 & 63.52 \\\\\\\\\n& \\text{PocketSR (Ours)} & 26.41 & 0.7736 & \\mathbf{0.2467} & \\mathbf{0.1510} & \\mathbf{4.060} & \\mathbf{63.85} \\\\\\\\\n\\hline\n\\text{4K Resolution SR} & \\text{SinSR} & 27.36 & 0.7086 & 0.4753 & 0.2379 & 6.246 & 29.56 \\\\\\\\\n& \\text{OSEDiff} & 26.37 & \\mathbf{0.7951} & 0.3200 & 0.1696 & 4.291 & \\mathbf{36.69} \\\\\\\\\n& \\text{AdcSR} & \\mathbf{27.37} & 0.7888 & 0.3258 & 0.1784 & 4.325 & 34.34 \\\\\\\\\n& \\text{PocketSR (Ours)} & 26.76 & 0.7752 & \\mathbf{0.3174} & \\mathbf{0.1664} & \\mathbf{4.064} & 34.25 \\\\\\\\\n\\hline\n\\end{array}\n$$\n\nWe adopt SD-Turbo for SD pretraining, a widely used default in many super-resolution frameworks [2,3]. The reviewer raises an insightful question about whether model performance across resolutions is influenced by the resolution of the pretrained SD. We believe the answer is likely yes, as SD models trained on different resolutions may encode distinct resolution-specific priors. We consider this a valuable direction for future work.\n\n[1] Exploiting Diffusion Prior for Real-World Image Super-Resolution, IJCV 2024\n\n[2] Arbitrary-steps image super-resolution via diffusion inversion, CVPR 2025\n\n[3] Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors, Arxiv 2024\n\n---\n> **[W4&Q4-1] Detailed ablation studies for the Adaptive Skip Connection (ASC).**\n\nWe sincerely thank the reviewer for the constructive suggestions. In response, we provide additional ablation studies on the Adaptive Skip Connection (ASC) module in the table below. The experiments are conducted on the RealSR dataset, following the same settings as in Table 3 of the main paper for ease of comparison. We omit efficiency-related metrics here, as the differences are minimal.\n1. We investigate the effect of removing the learnable control coefficients in ASC. We observe that using naive skip connections hinders the model’s ability to adaptively control the contribution of skip features based on the degradation level of the input. As a result, the model performs poorly when handling diverse real-world degradations.\n2. We explore an alternative design where the control coefficients are predicted from features in the heavy path. However, we find that this strategy leads to instability during training, as heavy path features are uncompressed and contain redundant information, which interferes with the learning of accurate control signals. Although this variant performs relatively well on the no-reference metric NIQE, **the significantly lower LPIPS and PSNR scores suggest compromised perceptual and pixel-level fidelity**, with more noticeable artifacts in the output.\n\n$$\n\\begin{array}{l|ccccc}\n\\hline\n\\textbf{LiteED Design} & \\textbf{PSNR} \\uparrow & \\textbf{SSIM} \\uparrow & \\textbf{LPIPS} \\downarrow & \\textbf{DISTS} \\downarrow & \\textbf{NIQE} \\downarrow \\\\\\\\\n\\hline\n\\text{Original} & \\mathbf{25.61} & \\mathbf{0.7431} & \\mathbf{0.2474} & \\mathbf{0.1911} & 5.200 \\\\\\\\\n\\text{w/o control coefficients in ASC} & 25.16 & 0.7276 & 0.2486 & 0.1999 & 5.321 \\\\\\\\\n\\text{w/ control coefficients learning from the heavy path} & 25.08 & 0.7347 & 0.2667 & 0.1995 & \\mathbf{4.952} \\\\\\\\\n\\hline\n\\end{array}\n$$\n\n---\n> **[W4&Q4-2] Characteristics of the learned control coefficients.**\n\nThank you for the excellent question. Due to space constraints, we couldn’t include this analysis in the paper, but we provide more details below:\n\n1. Control Coefficients vs. Outputs: Larger control coefficients make outputs more similar to the low-resolution (LR) input—preserving fidelity but reducing generative strength. Smaller coefficients shift reliance to the U-Net output, enhancing generative capacity at the cost of fidelity.\n2. Control Coefficients vs. Inputs: For severely degraded LR inputs (e.g., high-scale SR, real-world degradations), control coefficients are generally smaller in early layers and slightly larger in deeper ones. In mild degradation cases, the pattern reverses, with higher coefficients in shallow layers.\n\nThis behavior aligns with intuition: mildly degraded inputs benefit from more skip features to preserve details, while heavily degraded ones require suppressing skips to rely more on generative priors for plausible reconstruction.\n\nRegarding Q4-D, we agree that manually adjusting the control coefficients can reveal a similar fidelity–generation trade-off. However, such tuning is sensitive and may cause instability, so we recommend using the model-learned adaptive coefficients, which are more robust across varied degradations.\n\n---\n> **[W5&Q5] Explanation and ablation studies for the Dual-path Feature Injection (DFI).**\n\nThank you for the thoughtful question. In our Dual-path Feature Injection (DFI) design, the lite path provides compressed, information-dense features that offer global structural guidance and align well with the VAE feature distribution in SD, making them easier for the pretrained U-Net to utilize. In contrast, the heavy path contains richer details but lower information density, making it harder for generative models to use directly.\n\n**The lite path serves as the primary guidance source, while the heavy path complements it with fine-grained textures. Both are essential for high-quality super-resolution.** To validate this, we conducted ablation studies following the experimental settings in Table 3 of the main paper. We observe that removing the lite path leads to a significant drop in fidelity, as the model loses its primary information source. In this case, the model overly relies on the pretrained SD’s generative prior, often hallucinating textures, which results in perceptual artifacts and inflated non-reference scores like NIQE. On the other hand, removing the heavy path causes a moderate degradation in detail reconstruction, reflected by a noticeable drop in perceptual metrics such as LPIPS and DISTS.\n\n$$\n\\begin{array}{l|ccccc|ccc}\n\\hline\n\\textbf{LiteED Design} & \\textbf{PSNR} \\uparrow & \\textbf{SSIM} \\uparrow & \\textbf{LPIPS} \\downarrow & \\textbf{DISTS} \\downarrow & \\textbf{NIQE} \\downarrow & \\textbf{Time (ms)} & \\textbf{MACs (G)} & \\textbf{Param. (M)} \\\\\\\\\n\\hline\n\\text{Original DFI} & \\mathbf{25.61} & \\mathbf{0.7431} & \\mathbf{0.2474} & \\mathbf{0.1911} & 5.200 & 31.4 & 481.6 & 868.3 \\\\\\\\\n\\text{w/o Lite Path} & 24.75 & 0.7229 & 0.2596 & 0.1991 & \\mathbf{4.860} & 31.3 & 481.6 & 868.3 \\\\\\\\\n\\text{w/o Heavy Path} & 25.35 & 0.7427 & 0.2580 & 0.1940 & 5.136 & \\mathbf{31.1} & \\mathbf{478.6} & \\mathbf{867.6} \\\\\\\\\n\\hline\n\\end{array}\n$$\n\n\nIn summary, DFI effectively balances structural guidance and detail injection, significantly improving fidelity with minimal computational overhead."}, {"review_id": "9Smy4d5lh0", "reviewer": "Reviewer_nGC4", "summary": "This work proposes PocketSR, a lightweight, single-step, diffusion-based model for image super-resolution. \nThis paper introduces three key contributions: \n\n(i) LiteED, which is an efficient encoder-decoder architecture that significantly reduces the parameter count of the standard SD VAE by 97.5%. It incorporates dual-path feature injection to enrich the U-Net with high-dimensional features and adaptive skip connections to retain critical information, mitigating the impact of the compressed encoder.\n\n(ii) Online annealing pruning for U-Net, which progressively shifts generative priors from heavier, original U-Net modules to lightweight replacements. This method ensures a stable transfer of knowledge, preserving the generative capability. The paper also identifies that pruning deeper layers of the U-Net has a minimal impact on performance, offering an effective strategy for model compression.\n\n(iii) Multi-layer Feature Distillation: This technique is used in conjunction with online annealing pruning to facilitate a more stable and reliable knowledge transfer from the larger model to the pruned one, preserving the generative priors and avoids the instability that can occur with single-layer distillation.", "questions": "1. As mentioned in weakness, please consider report mobile benchmark. \n2. What is the detailed structure for the \"lightweight\" block? Is it the same for VAE and Unet? Is it Mobilenet-like? What are the norm layer and activations? And what is the ultra-lightweight FFN in line 158? \n3. In line 159 what is the linear attention employed here? There are different variants. And how about the tradeoff here? Is it difficult to adapt?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 4, "originality": 2, "strengths_and_weaknesses": "Strength: \n1. The motivation and proposed methods are technically sound and easy to follow. \n2. Efficient diffusion-based SR is an important application, the investigation of its efficiency and quality is timely and of great research value. \n3. The empirical results are comprehensive. Under significant efficiency gain, the SR quality still achieves state-of-the-art. \n4. The authors provide comprehensive ablation studies that validate each of their design choices including adaptive skip connection, dual-path feature injection, and the online annealing pruning strategy.\n\nWeakness: \n1. The authors state that this work will not be open-sourced, which limits the reproducibility. In fact as a low-level vision application, opensourcing the model can benefit the community a lot, while having less risks compared to generative models. \n2. There is a mismatch between the claimed \"pocket\" scope and the reported server GPU benchmarks, which is problematic. I think this work should either report mobile benchmark, or re-scope the contribution to \"GPU-efficient\" SR. \n3. Novelty is a minor concern. The methods in this work (pruning, feature distillation, step distillation) are not brand new. Overall the successful integration of these methods to obtain an efficient, single-step diffusion-based SR model can be viewed as sufficient contribution, but not quite novel.", "was_revised": true, "final_justification": "The authors addressed most of my concerns in the rebuttal, after reading other reviews, I will keep my voting for acceptance.", "rebuttal": "We sincerely thank the reviewer for the time and effort dedicated to evaluating our work. We are encouraged to see that our paper was described as “technically sound” and of “great research value.” We will address each of the reviewer’s concerns and identified weaknesses in detail. All additional experiments and discussions presented in this rebuttal will be incorporated into the camera-ready version of the paper.\n\n---\n> **[W1] Reproducibility.**\n\nWe fully understand and agree with the reviewer’s concern. In response, we have decided to conduct a code reimplementation, within the boundaries of our company’s policy, and plan to release it publicly upon acceptance to better support the community. \n\n---\n> **[W2&Q1] Efficiency on mobile devices.**\n\nWe fully understand the reviewer’s concern. However, we hope you can appreciate that migrating and deploying all existing super-resolution methods to mobile devices would require a significant amount of time and engineering effort. As an alternative, we provide the measured inference efficiency of PocketSR on a newly released smartphone model from 2025. For comparison, we also report the runtime of the uncompressed SD-Turbo backbone on the same device across corresponding modules.\n\nThe results are shown in the table below (measured on a single 512×512 image; unit: milliseconds). As can be observed, **PocketSR achieves an inference time of only 140ms on-device, representing an over 80% reduction compared to the original, non-lightweight backbone**—demonstrating the practical efficiency of PocketSR for real-world deployment.\n\n$$\n\\begin{array}{l|rrrr}\n\\hline\n\\textbf{Models} & \\textbf{Encoder} & \\textbf{U\\-Net} & \\textbf{Decoder} & \\textbf{Total} \\\\\\\\\n\\hline\n\\text{SD-Turbo} & 320ms & 270ms & 200ms & 790ms \\\\\\\\\n\\text{PocketSR} & \\textbf{\\downarrow 97 \\\\%} \\\\:11ms & \\textbf{\\downarrow 67 \\\\%}\\\\:88ms & \\textbf{\\downarrow 80 \\\\%}\\\\:41ms & \\mathbf{\\downarrow 82\\\\%}\\\\:140ms \\\\\\\\\n\\hline\n\\end{array}\n$$\n\n---\n> **[W3] Concern on novelty.**\n\nWe sincerely thank the reviewer for the thoughtful question. However, we believe there may have been a misunderstanding regarding the core contributions of our work.\n\nOur paper makes three primary contributions:\n\n1. We propose **a lightweight encoder-decoder framework, LiteED, specifically designed for the super-resolution task**. LiteED integrates carefully designed components, including Adaptive Skip Connections and Dual-Path Feature Injection, and serves as an effective replacement for the original SD VAE. Remarkably, despite its significantly reduced parameter count, LiteED matches or even outperforms the original VAE in certain aspects.\n2. We introduce **Online Annealing Pruning, a pruning strategy tailored for low-level vision tasks**. While similar pruning concepts have been explored in efficient text-to-image (T2I) generation, our Figure 6 and Table 4 show that such methods fail to retain the image priors encoded in pretrained generative models when applied to super-resolution. This discrepancy stems from the fundamental difference between T2I and SR training objectives.\n3. Our proposed **Multi-layer Feature Distillation enhances the transfer of generative knowledge by guiding the student model with multi-scale features across several layers**. In contrast, prior methods typically distill only from the model’s output—a relatively weak constraint that may fail to preserve rich prior information after pruning. In Table 4, we compare our approach with a variant that distills solely from the U-Net output (Strategy 3). The results clearly demonstrate the superior performance of our method, validating its effectiveness in retaining the pretrained generative prior in a lightweight setting.\n\nTo the best of our knowledge, all contributions are novel and have not been addressed in prior work. While we do adopt step distillation, it is not the core innovation of our paper.\n\n---\n> **[Q2] Detailed structure for the “lightweight” block.**\n\nThank you for the insightful question. We provide an explanation of the “Lightweight Block” in Figure 2 (lines 153–160), and would like to clarify the following:\n\n1. **Scope of Pruning:** The Online Annealing Pruning strategy is applied only to the U-Net, not to LiteED, so the notion of “Lightweight Block” does not apply to LiteED.\n\n2. **Pruned Components:** Pruning targets the four most computationally expensive U-Net components: residual blocks, cross-attention, self-attention, and FFNs. Lightweight layers (e.g., normalization, activation) remain unchanged.\n\n3. **Pruning Details:**\n\n   * **Residual Blocks:** All convolutions are replaced with depthwise separable convolutions (as in MobileNet), forming the “Lightweight Block.”\n   * **Cross-Attention:** Since text input is not used in SR, we replace these layers with an MLP of two linear layers.\n   * **Self-Attention:** Approximated using linear attention to reduce cost.\n   * **FFNs:** Hidden dimensions are reduced to one-fourth.\n\nWe acknowledge that the term “ultra-lightweight FFN” may have caused confusion and will revise it for clarity in the camera-ready version.\n\n---\n> **[Q3] Questions on linear attention.**\n\nThank you for the question. We adopt the basic form of linear attention as described in [1].\n\nAs shown in Table 3 of the supplementary material, replacing standard attention with linear attention involves performance trade-offs. Our experiments indicate that self-attention—especially in the shallow layers of the U-Net—is crucial for capturing fine-grained textures. Replacing it with linear attention in early layers leads to noticeable degradation, likely because these layers extract low-level features that lightweight alternatives cannot easily replicate.\n\nTo balance efficiency and performance, we apply linear attention only at depth 4 of the U-Net. This modification preserves model performance while improving training stability and efficiency.\n\n[1] Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, ICML 2020"}, {"review_id": "f62vrrmrKl", "reviewer": "Reviewer_rEby", "summary": "The paper proposes to adapt an existing few-step image generation diffusion model to 1-step image super-resolution, while simultaneously significantly reducing its parameters. First, the encoder and decoder size are drastically reduced to only 2M parameters, followed by the proposed online annealing pruning of the UNet with multi-layer feature distillation. The approach achieves comparable results with SOTA in terms of quality, while reducing the inference time to half of the previous approach at 16 milliseconds for 128x128->512x512 super-resolution.", "questions": "- Could the proposed LiteED be used for generation as well?\n- The used VAE encode, decode and UNet is a highly symmetric network. However, the low-quality and high-quality images provide inherently non-symmetric information. Was this taken into account in the design for pruning?\n- Which findings can be extended to DiTs?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Comparable results with SOTA with twice the speed and third of the parameters\n- A novel approach of transitioning from a few-step generator to a 1-step super-resolution model, rather than previous approaches that start from a multi-step generator and distill it to 1-step.\n- Simplicity and generalizability of the proposed online annealing pruning that stabilizes pruning for blocks and channel width\n\nWeaknesses:\n- Contributions from theoretical side are somewhat limited. The proposed online annealing pruning seems to be an extension of [2]. Similarly the multi-layer feature distillation has been used in previous work that utilize distillation [4]\n- The paper uses \"older\" base model with an UNet, whereas existing approaches mostly use DiTs for improved image quality, therefore making the contributions not directly applicable\n- The choice of pruning seems to have been done somewhat arbitrarily based on high-level findings, rather than careful analysis of MACs and importance of each block.\n\nMinor:\n- Include CLIPIQA metric for fairer comparison\n- Missing concurrent work PisaSR [1]. Not necessary to add comparison, but it would benefit the community.\n- Similar approaches to online annealing pruning have been proposed for channel width compression in [2, 3] that can be mentioned in related work\n\n\n[1] L Sun, R Wu, Z Ma, S Liu, Q Yi, L Zhang. Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach, CVPR 2025.\n[2] Julian Knodt, Structural Dropout for Model Width Compression, 2022\n[3] AN Gomez, I Zhang, SR Kamalakara, D Madaan, K Swersky, Y GAL, GE Hinton. Learning Sparse Networks Using Targeted Dropout, 2019\n[4] YLiu, J Cao, B Li, W Hu, J Ding, L Li. Cross-Architecture Knowledge Distillation, ACCV 2022.", "was_revised": true, "final_justification": "After reading the rebuttal and other reviews I will retain my original score. The authors clarified my concerns, and I believe the method will serve as a strong baseline and most of the proposed techniques can be further extended to stronger base models in the future.", "rebuttal": "We sincerely thank the reviewer for recognizing the value of our work and for providing constructive feedback. We will carefully address each of the identified weaknesses and concerns in the following responses. All discussions and additional experiments presented in this rebuttal will be incorporated into the camera-ready version of the paper.\n\n---\n> **[W1] Contributions of online annealing pruning and multi-layer feature distillation.**\n\nWe sincerely thank the reviewer for the valuable comments. However, we believe there may be some misunderstandings regarding the core contributions of our work.\n\nOur main contribution lies in the design of the LiteED architecture, and the integration of Online Annealing Pruning and Multi-layer Feature Distillation, which together enable effective compression for super-resolution (SR) while preserving the generative prior.\n\n1. While general pruning strategies (as cited in [1,2]) have been explored for efficient text-to-image (T2I) generation, our Figure 6 and Table 4 show that **these general strategies fail to preserve the pretrained prior when applied to SR**—likely due to fundamental differences in training objectives between T2I and SR. To our knowledge, no prior work proposes a pruning strategy specifically tailored to retain image priors in the SR setting, as we do.\n\n2. Our Multi-layer Feature Distillation further improves prior preservation by supervising the student with multi-scale features across multiple layers. In contrast, **existing methods such as SnapGen[3] and [4] typically distill only from the output**, which imposes a weak constraint. As shown in Table 4, our method outperforms a variant using output-only distillation (Strategy 3), validating its effectiveness under a lightweight setting.\n\n[1] Learning sparse networks using targeted dropout, Arxiv 2019\n\n[2] Structural Dropout for Model Width Compression, Arxiv 2022\n\n[3] Snapgen: Taming high-resolution text-to-image models for mobile devices with efficient architectures and training, CVPR 2025\n\n[4] Cross-Architecture Knowledge Distillation, ACCV 2022\n\n\n---\n> **[W2] “Older” base model: U-Net.**\n\nWe sincerely thank the reviewer for the valuable suggestion. Our choice not to adopt DiT as the base model is primarily driven by deployment considerations. This work aims to reduce computational overhead and enable on-device SR, especially on mobile NPUs, which currently lack sufficient support for DiT-based architectures—making them less practical for edge deployment.\n\nIn addition, while some recent single-step SR models use DiT, most state-of-the-art methods—including OSEDiff, AdcSR, and PiSA-SR (as cited by the reviewer)—still rely on U-Net architectures. By using the same backbone, we ensure fair and meaningful comparisons with these widely accepted baselines.\n\n\n---\n> **[W3] Arbitrary choice of pruning.**\n\nWe appreciate the reviewer’s thoughtful analysis. While we understand the concern, we would like to clarify the rationale behind our pruning strategy.\n\nOur pruning design (i.e., where to prune) is guided by empirical observations: shallow modules have greater impact on SR quality, while deeper ones can be pruned with minimal degradation. We support this with ablation studies across different module types and depths, as shown in Tables 1–4 of the supplementary material.\n\nWe acknowledge that we did not conduct independent ablations for every single module, mainly for two reasons:\n\n1. Such exhaustive experiments would require significant resources beyond the scope of this work.\n\n2. More importantly, we aim to offer conceptual insights rather than purely engineering-driven optimizations, which we believe are more meaningful for the research community.\n\n---\n> **[W4] CLIPIQA metric.**\n\nWe sincerely thank the reviewer for the professional and constructive feedback. We will include a comparison on CLIPIQA in the camera-ready version. \n\nThe table below reports the results on the DRealSR dataset. CLIPIQA leverages the pretrained image-text alignment model CLIP to evaluate the consistency between an image and a given textual prompt. **This metric tends to favor results with richer texture details, regardless of their alignment with the ground truth.**\n\nWhile our method performs slightly worse than OSEDiff and AdcSR on this metric—both of which emphasize stronger generative capability—**we achieve better results on perceptual fidelity metrics such as LPIPS and DISTS, which are more indicative of human-perceived visual quality.** Importantly, all these improvements are achieved under significantly lower computational budgets, highlighting the efficiency and practicality of our approach.\n\n$$\n\\begin{array}{l|ccccc}\n\\hline\n\\textbf{Metrics} & \\textbf{FeMaSR} & \\textbf{ResShift} & \\textbf{OSEDiff} & \\textbf{AdcSR} & \\textbf{PocketSR} \\\\\\\\\n\\hline\n\\text{CLIPIQA↑} & 0.5464 & 0.5342 & 0.6963 & 0.7049 & 0.6050 \\\\\\\\\n\\text{LPIPS↓}  & 0.3157 & 0.4006 & 0.2968 & 0.3046 & 0.2962 \\\\\\\\\n\\text{DISTS↓}  & 0.2239 & 0.2656 & 0.2165 & 0.2200 & 0.2139 \\\\\\\\\n\\text{FPS↑}    & 16.8   & 1.4    & 9.1    & 33.3   & 62.5   \\\\\\\\\n\\hline\n\\end{array}\n$$\n\n---\n> **[W5&W6] Missing citations.**\n\nWe sincerely thank the reviewer for the helpful suggestion. We will cite the papers mentioned by the reviewer in the Related Work section of the camera-ready version.\n\n---\n> **[Q1] Could the proposed LiteED be used for generation?**\n\nWe thank the reviewer for the thoughtful and forward-looking question.\n\n**The proposed lite decoder can be directly applied to efficient image generation tasks.** However, the lite encoder includes components specifically tailored for super-resolution—such as Adaptive Skip Connections and Dual-Path Feature Injection—which may not transfer well to generative settings and would likely require adaptation.\n\nIn image generation, encoders and decoders are typically used asymmetrically: the encoder is only used during training, while the decoder is involved in both training and inference. This prevents the use of skip connections as employed in super-resolution.\n\nAdditionally, generative models often operate in compact latent spaces. The heavy-path features from our encoder may introduce redundancy that could hinder generation quality rather than help it.\n\n---\n> **[Q2] Network symmetry.**\n\nThank you for the excellent question. We explicitly considered this asymmetry when designing LiteED. Empirically, we observed that in super-resolution, the decoder plays a more critical role than the encoder in determining performance.\n\nAs shown in Table 1, the decoder accounts for 8.7× more MACs than the encoder. Moreover, Table 3 shows that enlarging the decoder significantly improves reconstruction quality, while increasing encoder complexity yields only marginal gains and adds training overhead.\n\nThis likely stems from the inherent nature of SR: low-resolution inputs contain less information than the high-resolution outputs. **Under a fixed computational budget, it is thus more effective to prioritize the decoder, which reconstructs the richer content.**\n\n\n---\n> **[Q3] Can findings be extented to DiTs?**\n\nThank you for the insightful question. We agree that extending our approach to a DiT-based backbone for lightweight image super-resolution is a promising direction. **In principle, both components of our method—LiteED and online annealing pruning—are transferable to other base models.** LiteED targets the VAE-style decoder, and the pruning strategy is architecture-agnostic.\n\nHowever, certain pruning decisions—such as where to prune and which lightweight modules to use—would need to be revisited for DiT, taking its structural characteristics into account. Recent work [5] suggests that DiT exhibits layer-wise specialization, with intermediate layers contributing most to semantic alignment. Such insights could help guide pruning when adapting our method to DiT.\n\nWe appreciate the reviewer’s suggestion, which points to a valuable future direction for improving the generalizability of our framework.\n\n[5] Revelio: interpreting and leveraging semantic information in diffusion models, Arxiv 2024"}]

Author rebuttal:

Meta-review: Summary:
- This paper presents PocketSR, an ultra-lightweight, single-step generative model for real-world image super-resolution. It achieves state-of-the-art quality with only 146M parameters. It replaces the heavy VAE in Stable Diffusion with LiteED (97.5% fewer parameters), applies online annealing pruning to optimize the U-Net, and uses multi-layer feature distillation to preserve priors. PocketSR processes 4K images in 0.8s, matching or surpassing existing single- and multi-step methods while being practical for edge deployment.

Strength: 
- Technical novelty: new approach for model compression using online annealing pruning and LiteED design.
- Practicality: efficient inference makes it practical for mobile and edge devices.

Weakness: 
- The paper lacks clarity in several areas: the evaluation methodology and dataset details are unclear, the Stable Diffusion version used is unspecified, and the 4K SR quality analysis is insufficient.
- Reproducibility is limited by its decision not to open-source the model.

Overall, all reviewers are positive and appreciate the contributions preseted in the paper. The AC checks the reviews and agrees with the concensus.
Decision: Accept (poster)