Bridging the Distribution Gap to Harness Pretrained Diffusion Priors for Super-Resolution
Authors: JoonKyu Park
Kyoung Mu Lee
Venue: ICLR 2026 Poster (2026)
Keywords: Super-Resolution
diffusion
generative prior
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Diffusion models, well recognized for their strong generative priors, have recently been increasingly applied to super-resolution (SR) tasks. 
However, as diffusion models are trained on Gaussian-corrupted natural images, the distribution gap between low-resolution (LR) inputs and the model’s training distribution hinders direct inference. Prior works address this by conditioning on LR images, but their fine-tuning often weakens generative capability and requires multiple denoising steps. In this work, we present DM-SR, a novel framework that bridges this gap without modifying the pretrained diffusion model. We train an image encoder that maps LR inputs into the latent distribution aligned with the diffusion model’s training space, preserving its full generative power. Furthermore, DM-SR adaptively predicts the appropriate noise level based on the degradation of each input, ensuring optimal alignment with the diffusion model’s timestep-dependent distribution. Extensive experiments show that DM-SR achieves superior perceptual quality with a single-stage diffusion process, setting a new direction for efficient and high-fidelity SR with diffusion models.

Published as a conference paper at ICLR 2026
BRIDGING THE DISTRIBUTION GAP TO HARNESS PRE- TRAINED DIFFUSION PRIORS FOR SUPER-RESOLUTION
Joonkyu Park' and Kyoung Mu Lee Dept. of ECE&ASRI, IIPAI, Seoul National University kkpark0825, kyoungmu/ ) snu. ac.sr
Bicubic
SimSR
InvSR
Figure I: x4 super-resolution comparison on various images. The left half of each image shows the bicubic upsampled input, and the right half shows the output from our DM-SR. Compared to previous methods, DM-SR produces the most perceptually pleasing results. (Zoom-in for best view)
ABSTRACT
Diffusion models, well recognized for their strong generative priors, have recently been increasingly applied to super-resolution (SR) tasks. However, as diffusion models trained Gaussian-corrupter natural images, the distribution gap between low-resolution (LR) inputs and the model's 's training distribution hinders direct inference. Prior works address this by conditioning on LR images, but their fine-tuning often weakens generative capability and requires multiple de- noising steps. In this work, present DM-SR, novel framework that bridges this gap without modifying the pretrained diffusion model. We train an image encoder that maps LR inputs into the latent distribution aligned with the diffusion model's training space, preserving its full generative power. Furthermore, DM-SR adaptively predicts the appropriate noise level based on the degradation of each input, ensuring optimal alignment with the diffusion model's -eeeedepddeeendentt distribution. Extensive experiments show that DM-SR achieves superior perceptual quality with single-stage diffusion process, setting direction for efficient and high-fidelity SR with diffusion models.
INTRODUCTION
In the realm of low-level computer vision, single-image super-resolution (SISR or SR) is funda-
mental task that aims to reconstruct high-resolution (HR) images from their low-resolution (LR)
counterparts. Given the inherently ill-posed nature of this problem, with many plausible solutions, the practical goal SR to generate visually pleasing results from LR inputs Kim et al. (016));
Lim et al. (2017): Wang et al. (2018c); Ma et al. (2020): Ledig et al. (201)); Liang al. (202aa);
Umer & Micheloni (220)) Recently, inspired by the success of powerful diffusion models Ho et al.
(2020); Rombach et al. (2022) in producing realistic images, several studies Li et al. (2022); Wul
ct al. (2024a); Dong et al. (2025); Yue et al. (2025); (W et al. (2024b); Yang et al. (2024) Wang et al.
(2024b) have explored leveraging diffusion models for SR tasks.

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Specifically, diffusion models are trained to progressively denoise images, where the imputs typically a combination of natural high-quality images and Gaussian noise. However, low rewouusottion images follow a substantially different distribution from this training setup, making it difficult to use them directly as inputs to the pretrained diffusion model. Consequently, prior diffusion-based
SR methods [Wang et al.] (2024a): [in et al. (2024); Yang al. (2024); Wu et al. (2024b) still apply noise-corrupeed HR images as direct inputs for denoising during training, using LR images as conditions Zhang et al. (2023a) to guide the diffusion process. On the other hand, during inference, where HR images are unavailable, they start the diffusion process from pure Gaussian noise. By leveraging the generative capabilittes of pretrained diffusion models, these methods produce more realistic HR images compared to traditional non-diffusion-base SR approaches. However, the generative prior may be weakened during the fine-tuning process, and often redundantly regenerates information already present in LR images, resulting in unrealistic outputs. This also leads to the necessity of multiple iterative diffusion steps, which increases overall computational cost.
To address these issues, later approaches propose directly using LR images directly inputs to better preserve the information already present in them, while compressing the diffusion process into only a few steps (or even a single step). Among single-step diffusion- based SR methods, several works Wu jet al. (0244);; Dong et al. (2025) distill the knowledge of diffusion models into SR networks, where the SR network is fine-tuned from pretrained diffusion backbone. However, these methods rely on the diffusion prior only indirectly through the training objective Yin et al.] (2024bla a). and fine-tuning the denoiser (i.e., the SR network) compromises its original generative ability, preventing the SR network from fully retaining the pretrained generative prior. In contrast, InvSR Yue et al. (2025) avoids training the denoiser by predicting the noise under the assumption that noisy HR and LR images are indistinguishable. However, this assumption is not strictly valid: the indistinguishabiiity depends on the input- preservvaen factor (i.e., timestep). When the timestep is small, large portion of the original signal is preserved, causing noisy HR and LR images to follow different distributions.
To address the above limitations, we propose Distribution Matching Super- (DM-SR), a simple yet effective framework. DM-SR is built on fundamental yet important idea: if pretrained diffusion models are optimized to denoise samples drawn from a noise-corrupted image distribution, why not directly transform LR images into that same distribution? Based on this, we focus aligning LR images with the distribution that pretrained diffusion models are familiar with. Specifically, we distribution, which
train only image encoder map LR images into this inffusionncompattine consists of both the image and noise components, where the image and noise parts are differentiated through pretrained diffusion models. Moreover, as the diffusion model's familiar distribution varies across different noise levels (i.e., timesteps), we adaptively map highly degraded LR images to the distributions with higher noise levels accordingly and vice versa. This reflects the fact that the generative power of the diffusion model is more crucial for highly degraded samples.
By fully leveraging the generative prior of diffusion models, our DM-SR can produce highly photo- realistic results on wide range of benchmark datasets, including both synthetically degraded Deng et al. (2009) and real degraded WWi et al. (2020); Cai et al. (2019): Yue al. (2023) LR images. Additionally, extensive ablation studies further validate the effectiveness and generalizability of our DM-SR. Our contributions can be summarized as follows:
We propose DM-SR, which leverages a pretrained diffusion model for super-resolution without modifying the diffusion model. This bypasses the need for fine-tuning the diffusion model and enables full utilization of its generative prior.
We introduce an encoder that maps LR inputs into the diffusion model's native distribution, dynamically aligned to the timestep-dependent prior. This ensures that the generative power of the diffusion model is more leveraged for highly degraded samples.
DM-SR achieves state-of-the-art perceptual quality with a single diffusion step, offering both efficiency and fidelity. This is shown through extensive experiments, confirming the framework's 's effectiveness in achieving high perceptual quality.
2 RELATED WORKS
Deep learning-basse based Resolution. Super-resolution (SR) constitutes a fundamental task in image restoration Park et al. (2022): [ee et al. (2024); Park al. (2023a), it aims recover
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fine-grained details from low-resolution images and plays critical role in numerous real-world applications. After naive SRCNN Dong et al. (2015). innovative CNN-based architectures such as VDSR Kim et al. (2016), EDSR Lim et al. (017)) and SRGAN et al. (207)) were introduced, opening a new horizon for deee--eernnng-beee based SR. With the rise of attention mechanisms Wang et al. (20T8a): Vaswani al. (2017), numerous architectural innovations have been proposed to further improve SR performance. RCAN Zhang et al. (0188) introduced a channel attention mechanism, while SwinIR Liang et al. (202Ta) and SWinFIR Zhang et al. 2022) leveraged Transformer Vaswani et al. (017)- baased architectures to enhance superrresoutionn capabilities. However, most of these methods are trained on datasets generated through simple downsampling methods (i.e., bicubic or bilinear interpolation) Timofte et al. y) Wang et al. (2018b); Lim et al. (2017). making them less effective when dealing with real-world degraded images. To overcome this limitation, later approaches incorporated iggradation-baset data augmentation techniques (e.g., adding noise, JPEG
compression, defocus blur, and varying resizing) Wang et al. (202I); Zhang et al. (2021a); Yue et al. (2023) to better simulate the complex degradations encountered in real scenarios.
GAN-basee Super- Resolutiono Earlier SR methods trained their networks by minimizing pixel-wise losses between the super-resolved and high-resolution images. However, such losses often fail to capture perceptual quality, leading to visually unsatisfying results. To address this, subsequent works Wang al., (2018c); [mer & Micheloni (2020); Pan et al. (202T): WWii et al. (202T); )); Castillo
3) Ma et 20020) et al.]] (2021b); Liang et al. all (2022a) Park et al. (2023) introduced perceptual losses, such as VGG loss and adversarial Goodfellow et al. (2014) loss. Starting with ESRGAN Wang et al. (2018c), which first applied a GAN framework to SR by jointly training a generator (i.e., SK network) and a discriminator, numerous later approaches have explored improved GAN-based strategies. Specifically, DASR Liang et al. (202bb) employed wavelet transforms to focus the GAN on high-frequency components, while Liang et al. (2022a) introduced a discriminator that distinguishes between visual artifacts and realistic details to better regularize adversarial training. SFT-GAN Trang et al. (2018b) incorporated explicit semantic information (e.g., segmentation maps) into the GAN framework, and CAL-GAN Park et al. (2023b) used local discriminators tailored to content regions through a mixture of classifiers JJssifosssJJoooss Jacobs et al. (199I). While these methods achieve more visually realistic SR results compared to those relying solely on pixel-wise losses, they still struggle to generate natural and coherent details.
et al. (2021):
Diffusion-based Super-Resolutie els (DDPMs) Ho et al. (2020). pretrained diffusion models have been widely adopted across various tasks, including SR task. Early methods Kawar al. (2022); Chung et al. (2022); Wang al. (002b)) correlated intermediate diffusion steps with the LR input through variational distribution modeling or null-space decomposition. While effective, they require solving additional optimization problems at every diffusion step, resulting in significant computational overhead. To address this, later works Wang et al. (2024a); Yu al. (2024): Wu et al. (2024b); Yang et al. (2024); Wang et al.) (2024b): Lin et al. (2024) have explored fine-tuning pretrained diffusion models for SR tasks. Instead of updating the full model, which is computationally intensive and may harm the generative prior, most adopt the ControlNet Zhang et al. (2023a) architecture, fine-tuning only a few additional layers. Specifically, StableSR Wang et al. (2024a) fine-tunes a time-aware encoder, SeeSR Wu et al. (2024b) learns degradation- aware prompts, and Yang et al. (2024); Yu et al. (2024) train degradation-awan encoders to provide robust conditions.
Driven by the powerful generative priors of diffusion mod-
Despite their improvements, aforementioned approaches still rely multi-step diffusion process starting from pure noise, which leads to several drawbacks: high computational cost, redundant regeneration of information already present in the LR image, and sensitivity to the choice of initial noise. To mitigate this, more recent methods Dong et al. (2025); ) Wu et al. (2024a); Yue et al. (025) have proposed single-step SR frameworks that either train an SR network (student) to mimic the output distribution of pretrained diffusion model (teacher) or perform noise prediction at fixed timesteps under the assumption that noisy LR and HR images are identical. While these approaches reduce randomness and computational cost, the student networks fine-tuned from pretrained diffusion models often degrade the original generative prior, leading to lower perceptual quality than full diffusion models. Similarly, applying the same noise ratio (i.e., timestep) to all LR samples under this incorrect assumption can further reduce perceptual quality.
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Decompaxiton
Eg
Pretrained VAE decoder
LRR
X
I
Lz
LPIPS
Pretrained VAE encoder
diffusion
Figure 2: The overall architecture of Distribution-Matchin SR (DM-SR), While keeping the pretrained denoiser Hú fixed, we train the image encoder Eg to estimate X from the LR image IRR and the predicted timestep f. DM-SR then leverages μæ to produce realistic latent ZSR, which fed into the pretrained VAE decoder to generate the final SR image IsR. During training, the encoder Eg optimized using both image (Lz) and noise (L,) losses.
3 PROPOSED METHOD
Figure 2 shows the overall pipeline of our DM-SR. Unlike previous fiffuusio-bbsed SR approaches that either train control modules Wang et al. (2024a): Yu et al. (2024); Wu et (2024b); Yang et al. (2024); Lin et al. (2024) or fine-tune an entire SR network Dong et al. (025)) ) Wu et al. (2024a), DM-SR trains only an image encoder Eg. The encoder is trained to map LR inputs I into the distribution that the pretrained diffusion model is familiar with, enabling the generation of super-resolved outputs IsR without fine-tuning the pretrained diffusion model's denoiser pép
3.1 TIMESTEP ESTIMATION
Since pretrained diffusion models by are trained to recover the original images from the corresponding Gaussian-corrupte ones, they are inherently designed to handle mixtures of realistic images and Gaussian noise. However, the proportion of image and noise components that the pretrained diffusion model is familiar with varies depending the timestep:
HH Vo
where I denotes the noisy HR image at timestep t, derived from the clean HR image I with added random Gaussian noise (O, I). The coefficient & {ā ā2..... uv represents the cumulative product of the noise schedule, defined as ā₁ II. (1 - 3s), where 3, determined by predefined variance scheduler.
Therefore, instead of arbitrarily encoding the LR image Lx into a distribution corresponding to any diffusion timestep, first predict appropriate timestep that aligns with the degradation level of the input. As smaller timesteps in the diffusion model preserve more input contents, we assign To do this, as shown in Figure 2
smaller timesteps less degraded images and vice the normalized LPIPS Zhang et al. (2018a) score E [0, 500] between IL and the corresponding HR image IR as the ground truth timestep t. The timestep estimator is then trained to predict to match this ground truth. For details of the timestep estimator and the validation of using LPIPS scores as the ground-truth timestep, please refer to the Appendix A. and Section 4.5) respectively.
3.2 ENCODING LR IMAGES
Given an LR image I and its predicted timestep f, image encoder Eg maps them into a distribution aligned with what the pretrained diffusion model is familiar with. Inspired by stable diffusion models Rombach et al. (022). which significantly reduce computational costs, we operate encoding process into the latent space. Specifically, we encode I into a latent representation X as shown in Figure 2 aiming match the distribution of the corresponding noisy HR latent timestep i, Xi HR-
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Here, the noisy HR latent X can be obtained by applying Equation | in the latent space, where the image domain f and timestep t are replaced with the latent representation XiR and the predicted timestep é. respectively. Here, X is obtained by encoding IR using the pretrained VAE Kingma & Welling )213). For the architecture of Eg. adopt the pretrained VAE encoder and inject features derived from the predicted timestep into each intermediate layer via single linear layers, following the ControlNet Zhang et al. (2023a) design. For details of the encoder architecture, please refer the Appendix A.I
3.3 DECOMPOSING LATENT REPRESENTATION X
While the image encoder Eg maps the LR image I into latent representation X our goal is for this representation align with the distribution of H mixture of latent features from the HR image and Gaussian noise. However, since XHR depends on randomly sampled noise c, as in Equation| directly supervising X SR to match it is non-trivial. To address this, we decompose X into two components: the image part ZyR and the noise part EsR, allowing for targeted supervision. This decomposition is performed using the pretrained diffusion denoiser μug from the stable diffusion model, as follows:
()
ESR
(2)
ZSR
X
V1 (3AESR)
Va
with the assumption that the pretrained diffusion denoiser fe estimates the noise component, and the remaining part corresponds to the latent representation of realistic image. Here, prompts as the text condition for the denoiser 1ág. We then supervise the decomposed components,
fixed text
ZSR and <sR, ensure that their combination X aligns with the distribution of X Note that, during evaluation, we decode ZSR to super-resolved image IsR using the pretrained VAE decoder.
3.4 OBJECTIVES FOR IMAGE PART
For the image component ZSR, supervise directly using the latent representation of the HR image X Specifically, we apply combination of L1 loss LLI, perceptual loss Lper. adversarial loss Ladv. and distribution matching loss Lum
Lx = AI.I + AperCper + Aadv + Adm
where ÀL1 1.0, Aper 2.0, Andv 0.1, and Adm 0.5 are empirically chosen hyperparameters of the corresponding loss functions. For the details about hyperparameters, please refer to the Appendix A.2 Here, for the perceptual loss, since the conventional VGG network [Simonyan & Zisserman (0014) operates in the image domain, we follow Yue et al. (0255) and compute the loss in the latent space instead.
Regarding adversarial loss, we observe that image component ZsR often appears realistic but exhibits significant content discrepancies from the ground truth HR images. To address this, unlike traditional discriminators Goodfellow et al. (2014) used in GAN-based SR methods, which naively distinguish between HR and SR images (or their latent representations), we design a discriminator D& following the ControlNet Zhang et al. (203a)) architecture. Specifically, the discriminator DD takes the LR image I conditional input and distinguishes between either predicted image component ZşR or the real latent X For details of the discriminator architecture, please refer to the supplementary material. Our discriminator Do optimized by the following objective:
min D2
E Ex X
(X0 x [log (D& IRR))]
(4)
E. Ezz log Do IR))]]
Jointly, to fool the discriminator Dr, the image encoder E, is trained with the adversarial loss:
Cady - Ezz [log DD, (ZSR. IRR)],,
(5)
"High-quality, high-contrast,
www.eeottrsssttpoooeeeeeeiiiiice photo-realistic, clean, sharp, highly-detailed, perfect, 8k, ultra HD"
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where gradients propagated only to the encoder Eg; both the diffusion denoiser Me and the discriminator D are kept fixed during this update. This enables the discriminator to assess not only the realism of the samples but also their alignment with the input LR images IRR-
Furthermore, motivated by distillation techniques Yin et al. 2024b a) in diffusion models, we introduce distribution matching loss Lum to align the generated image part latent Zse with the ideal target X To achieve this, we apply the same noise scheduler to both ZSR and X and encourage the pretrained denoiser Hsg predict consistent score functions from their noisy versions. Specifically, following Equation we first corrupt both latents, ZsR and X using shared random Gaussian noise e to obtain:
ZSR Vu + -1- dre, XH VonXH HR + V1 diec
(6)
We then define the distribution matching loss Ldm as:
Lam Znn. ( 8) pw {X
dE
do
where the expectation is computed across E [1, 500]. Note that only the encoder &n is updated while the denoiser My remains fixed.
3.5 OBJECTIVES FOR NOISE PART
While the image part loss L7 guides the encoder to produce image part ZSR close to X and noise that is easier for the pretrained denoiser μ4 to remove, further constraints are needed to regularize the noise itself directly. Considering that diffusion models progressively transform an image into noise, there exists an optimal noise for each image such that, when added to X and passed through pus reconstructs the original HR image Qi et al.] (2024); Yue et al. (2025). Based on this, we corrupt the HR latent X using the predicted noise ESR, and encourage 'sR to approximate the optimal noise that enables reconstruction of the original HR latent. We define the noise loss L. as:
C. EE e
+ v1 CGESR, 2)
ESR
(8)
d0
Finally, we combine the described image and noise part objectives to define the total loss Lut as:
Lit Lz Le.
4 EXPERIMENTS
4.1 DATASETS AND METRICS
Datasets. For training DM-SR, adopt DF2K Timofte et al. (017) and LSDIR Li et al. (0033) For evaluation, we test DM-SR on various benchmarks, including synthetic LR images from Ima- geNet al. (2009) and real-world LR images from DRealSR Wei et al. (2020). RealSR Cai et al. (2019), and RealSet80 Yue et al. (2023). Here, for ImageNet, we follow the degradation protocol of InvSR Yue al. (2025)
Metrics. To evaluate the photo-realistic quality of our super-resolved images, we primarily utilize non-reference--ait perceptual metrics such as BRISQUE Mittal al. (2012). LIQE LIQE al. (NR)
(002b)), CLIP-IQA Wang et al. (2023a). TOPIQ (NR) Chen et al. (2023). NIMA Talebi & Milanfar (2018) MANIQA Yang al. (2022), and MUSIQ Liang et al. (202Tb). Furthermore, also include reference- based metrics like PSNR, SSIM, LPIPS Zhang et al. (2018a), DISTS Ding et al. (2020). and TOPIQ (FR) Chen al. (2023) for completeness. Here, all metrics are evaluated in the standard RGB space, except for SSIM, which is computed on the luminance (Y) channel.
Experimental setups. Like previous diffusion-based SR approaches Wu et al. (2024a): Dong et al. (2025), we initialize our encoder Eg with the pretrained VAE encoder. For the diffusion denoiser Pag-
we adopt the pretrained SD-Turbo Sauer et al.) (2024) due to its efficiency in generating realistic outputs in single step. We fine-tune only the encoder E, using the loss in Equation training, we use LR and HR patch pairs (E R 5112x11223, with a batch size of 16, where LRR patches bicubic upsampled from 4x lower resolution, following the ResShift pipeline Yue al. (2023). The model is trained using the Adam optimizer Loshchilov & Hutter (2017) for 300k iterations, starting with learning rate of 1 10-1, which is reduced by half every 100k steps.
During

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Table SS non-reference metrics comparison various benchmark datasets. Best numbers are denoted with bold.
Method
Dataset
BRISQUE,
LIQU:
CLIPIQA;
TOPIQ (NR),
NIMA,
MANIQA;
MUSIQ;
RealESRGAN
0.526
64.821
Synthetic
StableSR-S0 Wann ot mi 21243
19:067
4.094
0.631
0.594
4.900
0.418
68.247
SiaSR-1 Wang 20210
OSEDit
ImageNet
15.746
3,940
0.661
0.613
0.630
4,893
0.444
67.691
71,750
ImSR-1 Yue ata 2025
15.139
4.560
0.711
0.630
5.225
0.169
72.382
DM-SK-
13.427
4.699
0.785
0.712
5,492
0.633
73,856
RealESRGAN
34.767
2.926
0.451
0.462
4,326
0.344
54,277
StableSR-50
34,060
2,520
0.497
0.432
4,221
0.324
51.242
SinSR-1
55.677
OSFDifff1
23.477
3,940
0.696
0.600
4.676
0.466
64.687
Im:SR-1
18,660
4,056
0.713
0.611
4,857
0.467
$9,998
69.038
RealESRGAN
31.768
3.357
0.448
0.515
4,655
0.374
60.374
StableSR-50
30.874
3,153
0.525
0.515
4,684
0.387
61,876
Real
OSEDiff.
SinSR-1
RealSR
20.501
646
4,068
3.127
0.617
0.609
0.625
0,518
4,894
4.642
0.472
0,403
(99,000
60.391
68.537
DM-SR-1 (Ours)
19.953
4,485
0.732
0.702
5.374
0.601
71.489
ResIESRGAN
18. 18.293
3.797
0.598
0.533
5.219
0,388
64.638
2.976
SinSR- I
OSEDifE-1
Realsct 80
11.196
19.423
4,130
3,586
0.700
0.728
0.570
0.607
5,200
5.276
0,473
0.443
69.204
64,000
InSS--1
11.343
4.291
0.727
0.623
5.459
0.466
79.798
DM-SR-1 (Ours)
11:714
4.652
0.797
0.707
5.575
0.600
70.616
BIKING
BIKING
BIKING
B BIKING
DIAING
BIKING
BIKING
IE CAN
CREEAA
E YOU CAR
OU CAN
F YOU
NE BIUNNCEE
JE BITSETE
IEE BUINIRRE
RE BIVISE
ACE SEIOWIRRE
(a) Bicubic (b) ReaLESRGAN
(c) StableSR
(d) SinSR
(e) OSEDiff
(f) InvSR
(g) DM-SR
Figure 3: x4 super-resolution qualitative comparison on various benchmark dataset. (Zoom-in
for best view) Please refer to supplementary material for more extensive visual comparisons.
4.2 COMPARISON WITH STATE-OF-THE-ART METIIODS
Table T compares DM-SR against prior SR methods, including RealESRGAN Wang et al. (2021) (a non-diffusion model), StableSR Wang et al. (2024a) (an iterative method using 50 DDPM Ho et al.
(220)) steps), and recent single-step diffusion-based SR approaches Wang et al. 2024b); Wu et al.
(2024a); Yue al. (005).. As shown, our DM-SR consistently outperforms others in perceptual quality on both synthetic and real LR images. Moreover, Table 2 presents reference-
metrics. While DM-SR may not always achieve the best scores in reference metrics, the overall results in Tables [ and 2 show that DM-SR maintains competitive distortion performance while delivering superior perceptual quality, highlighting DM-SR's favorable balance in the perceptual-diston trade-off. Specifically, while DM-SR attains reference-basee performance comparable to OSEDiff Wu ctt al. 2024a), it delivers notably superior perceptual quality.
Moreover, Table 2 compares the efficiency of DM-SR with previous super-resolution methods in terms of the number of trainable parameters and runtime. As shown, the GAN-based method Wang al. (202I) achieves the best efficiency due to its lightweight design, but fails to deliver perceptually pleasing results, as shown in Table Among diffusion based methods, DM-SR maintains compara- ble number of trainable parameters and achieves the fastest runtime, while still delivering outstanding perceptual performance, as shown in Table I]
Figure 3 further highlights the perceptual superiority of our DM-SR, showing that method produces sharper and more accurate reconstructions across diverse benchmarks. Specifically, single- step diffusion models Wang al. (2024b); Wu al. (2024a); Yue et al. (2025) fail to recover
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Table 2: (Left) 4 SR reference metries comparison on RealSR dataset. (Right) Efficiency Comparison of DM-SR with previous SR methods on SR task. Specifically, we upsample images of size 128 128x3 using single NVIDIA A100 GPU to measure the runtime of each method.
Method
PSNR,
SSIM
LPIPS_
DISTS;
TOPIQ (FR);
Params (M)
Runtime (ms)
RealESRGAN
26.312
0.777
0.282
0.209
0.517
16.70
StableSR-50
27.064
0.788
0.278
0.212
0.511
152.70
3460
SinSR-1
25.851
0.717
0.368
0.248
0.476
118.58
140
OSEDiff-1
25.938
0.755
0.297
0.216
0.527
8.50
177
ImSR-1
23.740
0.680
0.354
0.246
0.431
33.84
117
DM-SR-1 (Ours)
24.984
0.710
0.317
0.218
0.470
34.16
Table 3: Comparison of DM-SR on Re-
Table 4: Comparison of DM-SR on Realset80 with various number of steps.Despite being controllable, single-step application still produces high-quality results.
alset80 with various timesteps. Our final model adaptively predicts E [0, 500] from I instead of relying on fixed timesteps.
iimesep
LIQE;
CLIPIOA-
TOPIQ (NR)
NIMA;
MUSIO:
steps
LIQE,
CLIPIQA,
TOPIQ (NR),
NIMA.
MUSIQ;
500
4.023
0.706
0.610
5.246
69.078
20
4.152
0.729
0.737
5.081
67,800
250
300
4.471
4.453
0.731
0.733
5.407
5.442
69.990
69.802
10
4.503
0.762
0.743
5.531
68.135
4.314
0.725
69.706
5
4.773
0.768
0.749
5.756
69.948
3.9944
5.201
68.992
2
4.799
0.778
0,744
5,692
69,435
(Ours)
0.797
5.575
70,616
(Ours)
0.707
5.575
70.616
Table 5: final model utilize normalized LPIPS score E [0, 500] for the ground turth for the timesteps.
Comparison of DM-SR on Realset80 with various ground fruth for the timesteps. Our
ground truth
LIQE,
CLIPIQA,
TOPIQ (NR);
NIMA,
MUSIQ;
min'
3.848
0.596
0.563
5.248
65.630
Pixel distance (LI)
4,470
0.782
0.694
5.463
70.328
SSIM
4.628
0.799
0.703
5.572
70.352
LPIPS (Ours)
4.652
0.797
0.707
5.575
70.616
the letter (e.g., "K" in "BIKING" in the first row of Figure reconstructs them, providing better readability. Additionally, prior methods tend to generate severe artifacts the face regions (e.g., in the second row of Figure B). while our DM-SR produces fewer artifacts and delivers significantly more perceptually pleasing results.
3) whereas our DM-SR successfully
4.3 EFFECT OF ADAPTIVE TIMESTEP
Table compares the performance of DM-SR under different timestep settings. While ours adaptively predicts the timestep f based on the degradation level of the input image, the variants use fixed timesteps regardless of input quality. As shown, our adaptive approach consistently achieves the best perceptual SR performance, validating the effectiveness of our timestep estimator T. Interestingly, among the fixed-timestep settings, larger timesteps generally lead to better results than smaller ones. This may be because smaller timesteps tend to preserve more low-resolution content, which often fails to align with the true high-resolution distribution. In contrast, larger timesteps allow the generative model more freedom intervene during super- ruserrresolution, resulting in higher perceptual quality.
4.4 DISCUSSION ON NUMBER OF STEPS
Table a comparison of DM-SR performance using different numbers of sampling steps. For multiple steps, we adopt the DDIM[Song et al.) (2020) sampling, which deterministically generates the SR images without stochastic noise injection. During multi-step sampling, the same estimated noise <sR is reused to form the input for each subsequent iteration. In contrast, the timesteps for later iterations are linearly scheduled in proportion to the initially predicted timestep. Among the tested configurations, the perceptual quality varies across 1, 2, and 5-step settings, indicating that a small number of iterative refinements can still be effective. However, using 10 sampling steps consistently results in the worst perceptual scores across all evaluation metrics. This is likely due to the use of SD-Turbo as the underlying denoiser in our DM-SR framework. While SD-Turbo is capable of strong few-step denoising (e.g.. 1-5 steps), applying it over an excessive number of iterations can lead to over-smoothing, ultimately harming perceptual quality.
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Table Comparison of DM-SR with various ob- jective combinations.
Table 7: Comparison of DM-SR using fixed versus extracted text prompts. Here,
we use LLaVA to extract text prompts from low quality inputs.
Eaii
Edn
f.
LIQE,
CLIPIQA,
TOPIQ (NR)-
NIMA
MUSIQQ
K
3,643
0.726
0.575
5.191
64.153
4.579
0.779
0.691
5.480
69:547
4,171
0.756
0.614
5.308
69.089
Pronps
LIQE;
CLIPIOA;
TOPIO (NR);
NIMA,
ME:S0Q)
70.616
laput specite
0.0
(a) IL and denoised results from ESR
(b) Distribution of ESR
Figure 4: (Left) Validation of the generated noise ESR- Starting from the left LR inputs, encoder transforms them into ZSR and ESR. The diffusion process initialized with ESR produces outputs aligned with the input, as shown in the right images. (Right) Distribution histogram and Q-Q plot of predicted noise esR. Although is not explicitly supervised follow Gaussian distribution, our predicted noise naturally approximates a Gaussian distribution.
4.5 GROUND TRUTH FOR THE PREDICTED TIMESTEPS
Without direct supervision for the predicted timestep i, it difficult provide effective gradients to the timestep estimator T, since the denoiser μu takes the timestep as an integer input. While
we use LPIPS the ground truth (GT) for the predicted timestep. as described in Section 3.1 other
alternatives are also possible. To validate the efficacy of using LPIPS as the GT, Table 5 shows comparisons with other choices. Specifically, we use normalized pixel-distance and SSIM score as alternative GT and observe that ours using LPIPS yields the best performance. Also, we attempt to supervise / by comparing SR results at / |ZsR X (min" in 5). However, this approach produced unstable gradients and collapsed to single timestep across inputs. Nonetheless, we acknowledge that there may exist more optimal formulations for defining the timestep GT, which we leave for future exploration.
and
and selecting the timestep that minimizes
4.6 EFFECT OF THE PROPOSED LOss COMBINATION
6 quantitative analysis of the loss components: Luivs Lam- and Ce. All variants are trained with the baseline losses LL and per. with the three additional loss terms selectively included or omitted. Compared to the first row, which uses only LLI and Epers adding the adversarial loss Ladv (second row) significantly improves perceptual performance. Although including either Lam Le alone leads to only marginal gains compared to adding Luiv. the full DM-SR model (last row), which incorporates all three additional loss terms, achieves the highest perceptual scores. This suggests that each loss provides complementary benefits beyond the baseline, leaving room for others to further enhance performance, thereby demonstrating strong synergy among the proposed loss terms.
4.7 EFFECT OF USED TEXT PROMPTS
While we use fixed text prompts in DM-SR framework, as described Section 3.3 DM-SR does not update the denoiser Me during training. This allows DM-SR RR& to be evaluated using different text prompts at test time without requiring any additional training. As the generative capability investigate the impact of of the pretrained diffusion model vary depending the text prompt, using different prompts. Table shows a comparison between the two settings, where input-specific
prompts are extracted from the LR images using LLaVA Li et al. (2024) 2 As shown, adopting input-specific prompts consistently enhances perceptual quality. indicating that DM-SR has the potential achieve even better results when provided with more informative or refined prompts.

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(a) Bicubic (b) RealESRGAN
(c) StableSR
(d) SinSR
(c) InvSR
(f) DM-SR
(g) DM-SR'
Figure 5: Limitation our DM-SR. While prior methods successfully generate a gray pupil that closely matches the input image, our DM-SR produces black pupil, prioritizing perceptual quality over striet distortion minimization. Here, while (f) DM-SR uses a fixed text prompt, (g) DM-SR employs a customized, input-specific prompt (i.e.. "Old man with full white beard and mustache. He is wearing wire-rimmed glasses. His hair appears white, and his eyes are light-colored gray.").
4.8 DISCUSSION ON PREDICTED NOISE
In the DM-SR framework, unlike previous approaches that initialize with random noise, we transform the input image [ into components: an image ZgR and noise ESR- While the role of the image component is straightforward--iiee it directly contributes to forming the output image IsR-it is less intuitive assess whether the predicted noise ESR reflects the content of the input image.
To validate that our encoder &g adaptively generates a meaningful denoiser He to produce image details aligned with the input, we conduct a simple test, as shown in Figure 4a Specifically, we initialize the diffusion process with ESR and apply DDIM steps. As shown, the resulting image well-aligned with the input LRR, indicating that esR encodes relevant semantic details, and confirming that our encoder effectively conditions the noise for faithful SR.
that guides the subsequent
For further validation of noise CSR. we analyze whether the predicted noise follows Gaussian distribution. As shown in Figure while we do not explicitly supervise ESR to match Gaussian distribution, aligns with a normal distribution, indicating that it exhibits a Gaussian-like structure.
5 LIMITATIONS
While our DM-SR achieves strong performance on perceptual metrics, as shown in Table | by effectively leveraging the generative priors of pretrained diffusion models, it does not attain the best distortion scores, as shown in Table This trade-off may arise because the model prioritizes perceptual quality, which can occasionally lead to minor deviations from the exact appearance of the input LR image. For instance, in Figure 3 whereas other methods (Figures [5b 5e) generate a gray-colored pupil closely matching the LR renuttres in lower distortion DM-SR (Figure (5i) produces a black-colored pupil that aligns better with perceptual realism. This slight discrepancy indicates room for improvement, such as incorporating semantic-level color alignment or adopting adaptive, input-specific text prompts instead of fixed one. Indeed, shown in Figure [5g] simply using an input-specific text prompt that specifies the pupil color better preserves the original content. This observation suggests a promising direction for future work, extracting input-specific text prompts, to balance perceptual quality and distortion.
6 CONCLUSION
In this work, we propose DM-SR, a novel framework that effectively harnesses the generative power of pretrained diffusion models. By keeping the pretrained denoiser fixed and training only an image encoder to project LR inputs into a diffusion-aligned distribution, DM-SR fully preserves the pretrained generative prior without requiring model fine-tuning or iterative denoising steps. Moreover, our adaptive noise-level mapping adjusts the target distribution based on the degradation level of each image, improving perceptual metrics by a large margin across various degraded LR images. Extensive evaluations synthetic and real-world benchmarks confirm that DM-SR achieves of-te-of-the-rr perceptual quality. The framework also lays the groundwork for future extensions to other restoration tasks such deblurring, denoising, and inpainting.
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ACKNOWLEDGMENTS
This work was supported in part by the IITP grants [No. RS-2021-1211133, Artificial Intelligence Graduate School Program (Seoul National University), No. 2025-02303870, No.2022-0-00156] funded by the Korea government (MSIT).
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A APPENDIX
In this appendix, provide discussions, details, and more visual results that could not be included in the main manuscript due to lack of space.
A.1 ARCHITECTURES
Timestep estimator T. We adopt a VGG-style architecture for the timestep estimator T. Specifically, utilize the first five convolutional blocks of VGG19 to extract features from the LR input L which are then projected into a regressed feature vector F; E 5000 To account for the continuous nature of timesteps, we do not apply softmax classification over discrete bins. Instead, compute the predicted timestep as a weighted sum:
= sum(softmaat (F2) [0, 1, ... 488, 499)).
(10)
Encoder Eg. While we use the pretrained VAE encoder as our encoder Eg, we additionally incorporate
the timestep l as an input, depicted in Figure
2 of the main paper. Specifically, we project timestep f
into four embedding vectors (in 128 To 236 121 and 5R112 respectively) using linear layers, and
add these embeddings to the intermediate feature maps within the encoder. This allows our encoder
produce timestep-aware latent representation X
Discriminator Da As described in Section "Objectives for Image Part" of the main paper, our discriminator D& is designed based on the ControlNet architecture. Here, to enable efficient training, we reduce the intermediate output channels of the UNet from [320, 640, 1280, 1280] to [64, 128, 256, 256]. The final output of the ControlNet-stylee discriminator then passed through pooling and linear layers to produce a I-dimensional output vector.
A.2 HYPERPARAMETER FOR LOSs FUNCTIONS
As described in Section 3.4 we set the loss weights as follows: ÀLI 1.00, Apee 2.00, Aaa- 0.10, Aum 0.50, and A, 1.00. For the conventional loss terms, including Lr,1 Lper- and Catv. we follow setting in InvSR Yue et al. (2025) For the newly introduced losses, Latm and L., conduct ablation
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Table 8: Comparison DM-SR Realset80 with various hyperparameter configurations. Our
final model sets Aum 0.50 and X = 1.00.
Amn
0.00
0.25
0.50
0.75
1.00
0.00
0.00
0.00
0.00
0.00
0.50
0.00
MUSIQ-
69.947
70.088
70.146
70.056
70.032
69.929
69.909
70.059
70.069
70.056
70.616
studies by varying each corresponding weight in increments of 0.25. Specifically, we fix one of the terms (e.g.. dm 0.00) while tuning the other, and vice versa. As shown in Table 8 empirically select Adm = 0.50 and X = 1.00 based on the MUSIQ results.
A.3 COMPREHENSIVE ANALYSIS OF L,
As shown in Table of the main paper, the proposed L. exhibits strong synergy with the other loss terms (i.e., Lady and fm addition, Figure 4f the main paper demonstrates that L. enables the predicted noise ESR to align with the input LR image LRR-
Figure 6 further highlights the overall effectiveness of L. As shown in Figure 6cc without C., the predicted noise does not correspond to the input image, leading input-irrelevant artifacts. Since the noise is otherwise unguided, the model tends to predict noise patterns that the pretrained denoiser jé finds easiest to denoise, rather than noise reflecting the input content. Figure [6d] visualizes the distribution of ESR. Here, because the pretrained denoiser (hu optimized to denoise Gaussian noise, EsR also follows Gaussian distribution c (0, I) even when L. is not used. Therefore, to analyze meaningful differences, we plot the distribution at a fixed spatial location (i.j). unlike Figure 4b/of the main paper which shows the distribution of the whole noise tensor. As shown, without L. (i.c., red bar the Figure 6d). the predicted values remain almost identical across different inputs, exhibiting small variance and little diversity. In contrast, with L. (f.e., blue bar in the Figure (dl)r the distribution at the same spatial coordinate (i,j) still follows Gaussian and varies meaningfully according to the input image. This confirms that L. guides the predicted noise to be input-aware, complementing the other loss terms, which operate on the image representation ZSR, in an orthogonal manner.
(a) Li
(b) Denoised from (Wl Ex)
(c) Denoised from (WDD
(d) Distribution of (sR().))
Figure 6: Analysis of the proposed L.. (Left) Following the protocol in Table 4 of the main paper, the encoder transforms the leftmost LR inputs (a) into ZSR using two different <sR (one from the model with L, and one from the model without L.). When only L, is applied, the diffusion process initialized with EsR generates outputs that remain aligned with the input content, as shown in (b) and (c). (Right) We visualize the distribution of the predicted noise EsR specific spatial position (i Without C. (red bar), the predicted noise remains nearly identical across different inputs, indicating that the noise lacks semantic adaptation. contrast, with L. (blue bar), the predicted noise varies according to the input, demonstrating its content-awan behavior.
A.4 COMPREHENSIVE ANALYSTS OF LpM
Similar to C, in Section A.3 the proposed Lam also exhibits strong synergy with the other loss terms, shown in Table of the main paper. Unlike the adversarial loss which encourages ZşR to
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move toward the overall distribution of real HR images, regardless of their semantic content, Lam specifically enforces distribution matching between samples of the same underlying content (i.e.. between ZşR and x$g). Because of this property, Lam only guides ZsR toward the real distribution (as Latv does) but also ensures that it aligns with the content-specific distribution found in the corresponding clean counterpart x
Table further shows how Lm promote this content-consistent distribution alignment, going beyond what can be achieved with the less targeted adversarial loss. As shown, when Lam used alongside Ladv. the model not only improves on non-reference perceptual metrics but also achieves gains on distortion- measures. Although the improvements may seem small, they are consistent across both perceptual and distortion metrics, effectively mitigating the traditional distortion- distortion-pereepion perception trade-off, and therefore represent meaningful enhancement overall SR performance.
Table 9: x 4SR quantitative comparison on RealSR dataset with various objective combinations.
Cady
fim
PSNR,
SSIM,
LPIPS|
LIQE, CLIPIQA,
TOPIQ (NR)-
NIMA,
MUSIQ,
x
24.981
0.710
0.328
4.492 4.485
0.726
0.698 0.702
5.370
70.923
24.984
0.710
0.317
0.732
5.374
71.489
A.5 DISTRIBUTION OF THE PREDICTED TIMESTEPS
Figure 7 shows the overall distribution of the predicted timesteps f. As shown, the predicted timesteps(i.e.. blue lines in Figure (7) closely align with the normalized ground-truth timesteps (i.e., red lines in Figure 7). achieving an RMSE of 7.74 within the range [0, 500]. This demonstrates that our timestep estimator effectively predicts timesteps consistent with the distortion level of the LR input ILR- For this visualization, the predicted and ground-trun truth timesteps. For the rationale behind using the LPIPS score as the ground-truth timestep, please refer to Table 5 and Section 4.5
use the first 100 images from the ImageNet dataset to compare
Predicted
MM
M
WM
300
MW
MAw
M
N
M
200
100
Figure 7: Visualization of ground truth and predicted timesteps.
A.6 ADDITIONAL VISUAL COMPARISON
Figures 8 P and present additional visual comparisons between our DM-SR and previous SR methods. Here, we present comparison real-world low-resolution images by collecting several
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historical photographs from open-soure, resources. The results clearly demonstrate that DM-SR consistently generates perceptually superior outputs compared to existing approaches.
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Bicubic
RealESRGAN
StableSR
SinSR
InvSR
DM-SR
327 cu
327 Cu.
327 cu. INd.
Bicubic
RealESRGAN
StableSR
327 cu:
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Figure 8: x4 super-resolution qualitative comparison various real-world images. (Zoom-in for best view)
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Published as a conference paper at ICLR 2026
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Figure 9: x4 super-resolution qualitative comparison on various real-world images. (Zoom-in for best view)
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Published as a conference paper at ICLR 2026
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Figure 10: x4 super-resolution qualitative comparison various real-world images. (Zoom-in for best view)
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Reviews:
[{"review_id": "fILrSCpHzi", "reviewer": "Reviewer_KAnJ", "summary": "This paper proposes DM-SR, a diffusion-based super-resolution (SR) framework designed to bridge the distribution mismatch between low-resolution (LR) images and the Gaussian-corrupted image space used by pretrained diffusion models.\nInstead of fine-tuning the diffusion model (which may degrade its generative prior), the authors train a lightweight image encoder that projects LR inputs into a diffusion-aligned latent distribution while keeping the pretrained denoiser fixed.", "questions": "NA", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "This paper proposes a diffusion-based SR method without the requirement of modifying the pre-trained Diffusion model weights, and achieves promising results.", "weaknesses": "Q1. The second paragraph identifies the limitations of existing diffusion-based SR methods, such as sampling from pure Gaussian noise and the corruption of generative priors during fine-tuning. This paper aims to address these issues. However, similar challenges have already been discussed in several prior works, even though they have not been completely resolved. Therefore, it is essential for the authors to analyze and discuss these earlier studies to properly position their contribution and demonstrate respect for prior research efforts. Without such discussion, the motivation appears insufficiently justified.\n\nQ2. The paper states that the ground truth for the timestep predictor is obtained using the LPIPS metric. More technical details about this process are needed — for example, how LPIPS is used to determine the optimal timestep.\n\nQ3. The proposed method employs a combination of multiple loss terms. From Table 5, it seems that the performance gain mainly originates from the adversarial loss $L_{adv}$. Given the adversarial loss $L_{adv}$, the necessity of the other two loss components of $L_{dm}$ and $L_{eps}$ should be further validated.\n\nQ4. The reference metric values for the ImageNet dataset should be reported for completeness. Additionally, the synthesis process used to construct the ImageNet dataset for the experiments should be clearly described, including degradation types, resolution settings, and data splits. This information is crucial for ensuring experimental transparency and reproducibility.\n\nQ5. Since this work closely follows the recent InvSR framework, it would be beneficial to include a direct comparison with InvSR in Table 4.  It seems that the authors deliberately weakened the relation of the proposed method to InvSR, as pointed out in Q1.", "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": "9DYs190pP5", "reviewer": "Reviewer_85hy", "summary": "This paper tackles the \"distribution gap\" that arises when applying pre-trained diffusion models (DMs) to image super-resolution (SR). The authors note that DMs are trained on Gaussian-corrupted images, a distribution that low-resolution (LR) inputs do not match. Existing methods attempt to solve this by fine-tuning the DM, which can weaken its powerful generative prior, or by using multi-step inference, which is computationally expensive. The authors propose DM-SR, a novel framework that keeps the pre-trained diffusion model completely frozen. Instead, it trains only an image encoder. This encoder is designed to do two things: (1) map the LR input image into the latent distribution based on the frozen DM, and (2) adaptively predict the appropriate noise level (timestep) based on the degradation of the input image . This approach allows the framework to harness the full generative power of the pre-trained model and achieve state-of-the-art perceptual quality in a single inference step. Extensive experiments show the method is highly efficient (achieving the fastest runtime among diffusion-based methods ) and effective on both synthetic and real-world datasets.", "questions": "(1) Could you provide more justification for using the LPIPS score as the regression target for the timestep estimator? Why was this metric chosen over other, simpler metrics (like PSNR) or a learned degradation classifier?\n\n(2) In Table 4, the model performance varies with the number of steps. The paper explains why many steps (10+) hurt performance due to the SD-Turbo backbone. However, it does not explain the trend for 1, 2, and 5 steps, where performance seems to peak at 2 or 5 steps and then slightly decrease at 1 step (e.g., for LIQE ). Is this slight variation statistically significant, or is it more likely a random fluctuation within a stable performance range for low step counts?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The prevailing paradigms for diffusion-based SR involve either fine-tuning the denoiser (which compromises the prior ) or complex multi-step conditioning. The proposed idea of keeping the denoiser frozen and instead learning to map the input into its native distribution is an elegant and powerful alternative. This approach simultaneously solves two major problems: it preserves the integrity of the powerful generative prior and enables extremely efficient single-step inference. This is a valuable conceptual contribution that will likely be influential for the ICLR community and future work in diffusion-based image restoration.", "weaknesses": "**Major Weaknesses:**\n\n(1) My primary concern is the complexity of the objective function. The final loss is a combination of five different loss terms, which seems to be very complex. The weighting parameters are simply stated as \"empirically chosen\", and the paper provides no clear explanation or justification for why these specific hyperparameters were set. This makes the method seem brittle and difficult to reproduce. While the ablation in Table 5 shows that all components contribute to the final result 3, the paper lacks a strong justification for why this specific combination is necessary, especially for the two loss terms.\n\n(2) The paper acknowledges the method's poor performance on distortion metrics (PSNR/SSIM) and frames it as a standard perceptual-distortion trade-off. However, the limitation shown in Figure 5 (changing the eye's pupil color from gray to black ) is a clear example of sacrificing semantic fidelity for perceptual realism. This is a significant issue. While the black pupil may appear more \"realistic\" (which is why the NR metrics are high), it is an uncontrolled hallucination—the model is generating a feature not present in the input. There is no evidence that this \"fix\" is semantically correct, and no guarantee that it can be correctly generated every time. This lack of faithfulness deserves a more in-depth discussion.\n\n**Minor Weaknesses:**\n\n(1) The analysis in Table 4 regarding the number of steps is confusing. While the paper explains that too many steps (10+) hurt performance due to the SD-Turbo backbone, it fails to explain the trend for 1, 2, and 5 steps. For some metrics (like LIQE), performance peaks at 2 or 5 steps and is slightly worse at 1 step. The paper does not address this fluctuation, making it seem like random noise rather than a significant, well-understood trend.", "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": "f8r87RpVA0", "reviewer": "Reviewer_4wwB", "summary": "The paper introduces a novel framework for single-image super-resolution that leverages pretrained diffusion models without requiring their fine-tuning or iterative denoising steps. DM-SR trains an image encoder to map LR inputs into a latent distribution aligned with the diffusion model’s training space. The method adaptively predicts the appropriate noise level for each input, ensuring optimal alignment with the diffusion model’s timestep-dependent distribution. Extensive experiments on synthetic and real-world benchmarks demonstrate that DM-SR achieves state-of-the-art perceptual results.", "questions": "Please responds to my concerns in Weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper proposes a novel way to leverage pre-trained diffusion model for SR, by aligning the latent representations of a LR image with an intermediate noisy latent representation of diffusion model. It does not require training the pre-trained model which has the merits of take full advantage of the pre-trained knowledge. \n2. The paper is well written, and the work and contribution are clearly presented. \n3. Extensive experiments are conducted showing the superior non-reference performance compared to existing pre-trained diffusion based method.", "weaknesses": "1. It would strengthen the paper to include a more in-depth analysis comparing the proposed model with single-step diffusion-based super-resolution methods such as invSR. Since both approaches share similar motivations and framework designs, a detailed explanation of the differences—and why the proposed method achieves superior results—would help clarify its unique contributions and advantages.\n\n2. Most of the comparison methods in the experiments are single-step diffusion-based super-resolution approaches. However, there are other ways to leverage pretrained diffusion models for super-resolution. Including comparisons with a broader range of diffusion-based SR methods would provide a more comprehensive understanding of how different strategies for utilizing pretrained diffusion models impact SR performance.\n\n3. The proposed method does not demonstrate strong performance on reference-based metrics, and in some cases, it underperforms compared to certain single-step diffusion-based super-resolution approaches. Providing a more detailed explanation for these results would be very helpful in understanding the limitations and potential areas for improvement.", "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": "vGB3Wrj5pf", "reviewer": "Reviewer_1GUs", "summary": "This paper, \"BRIDGING THE DISTRIBUTION GAP TO HARNESS PRE-TRAINED DIFFUSION PRIORS FOR SUPER-RESOLUTION,\" proposes DM-SR, a novel framework for single-image super-resolution (SR) that leverages the strong generative prior of a fixed, pretrained diffusion model (SD-Turbo is used). The core idea is to address the distribution mismatch between low-resolution (LR) inputs and the diffusion model's native training space (Gaussian-corrupted images) by training only an image encoder ($E_{\\theta}$). This encoder adaptively maps the LR image into a timestep-dependent latent distribution, where the timestep $\\hat{t}$ is predicted based on the input's degradation level. The resulting latent is decomposed into an image part ($Z_{SR}$) and a noise part ($\\epsilon_{SR}$) using the fixed denoiser ($\\mu_{\\psi}$). The encoder is optimized using a comprehensive loss function that includes a novel conditional adversarial loss, a distribution matching loss, and a noise regularization loss. DM-SR achieves superior perceptual quality with a single, highly efficient step5", "questions": "1. Timestep Estimator Analysis: The reliance on the LPIPS score [0, 500] as the ground truth for $\\hat{t}$ is novel but requires further analysis. Please show how the performance is affected if the estimated timestep $\\hat{t}$ is replaced by a fixed, arbitrary timestep (e.g., $t=250$) across all samples. This would directly isolate the contribution of the adaptive alignment mechanism.\n2. Multi-Step Comparison: The single-step generation is highly efficient. However, since $X_{SR}^{\\hat{t}}$ is meant to align with the noisy latent $X_{HR}^{\\tilde{t}}$, it is a valid input for a multi-step DDPM process. Please provide a comparison between DM-SR-1 (decoding $Z_{SR}$) and running a full multi-step sampler (e.g., 50 steps) initialized from $X_{SR}^{\\hat{t}}$. This would confirm if the single-step is truly the optimal output.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Preservation of Generative Prior: The core strength is that the method fully utilizes the fixed, pretrained denoiser ($\\mu_{\\psi}$), maintaining its powerful generative capabilities without compromise.\n2. Efficiency: DM-SR is a single-step method, demonstrating the fastest runtime among competitive diffusion-based SR approaches while delivering outstanding perceptual results.\n3. Noise-Adaptive Alignment: The dynamic prediction of a timestep ($\\hat{t}$) based on the input image's degradation level is a clever mechanism to allocate the appropriate amount of generative power.", "weaknesses": "1. Dependency on Timestep Estimator: The success of the method critically depends on the accuracy of the Timestep Estimator ($T$), which is trained to predict a degradation level (normalized LPIPS). The justification for LPIPS as the \"ground truth\" for the degradation level could be elaborated more in the main paper。\n2. Fixed Text Condition: The denoiser $\\mu_{\\psi}$ is conditioned using a fixed, generic text prompt (\"High-quality, high-contrast, photo-realistic, clean...\"). This design choice is simple but potentially limits the full capacity of the diffusion model, which is typically capable of content-aware generation via text conditions.", "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)