Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
Authors: Jianze Li
Jiezhang Cao
Zichen Zou
Xiongfei Su
Xin Yuan
Yulun Zhang
Yong Guo
Xiaokang Yang
Venue: NeurIPS 2025 poster ()
Keywords: One-Step Diffusion
Image Super-Resolution
Large-Scale Diffusion Discriminator
Primary area: applications

Abstract: Diffusion models have demonstrated excellent performance for real-world image super-resolution (Real-ISR), albeit at high computational costs. Most existing methods are trying to derive one-step diffusion models from multi-step counterparts through knowledge distillation (KD) or variational score distillation (VSD). However, these methods are limited by the capabilities of the teacher model, especially if the teacher model itself is not sufficiently strong. To tackle these issues, we propose a new One-Step \textbf{D}iffusion model with a larger-scale \textbf{D}iffusion \textbf{D}iscriminator for SR, called D$^3$SR. Our discriminator is able to distill noisy features from any time step of diffusion models in the latent space. In this way, our diffusion discriminator breaks through the potential limitations imposed by the presence of a teacher model. Additionally, we improve the perceptual loss with edge-aware DISTS (EA-DISTS) to enhance the model's ability to generate fine details. Our experiments demonstrate that, compared with previous diffusion-based methods requiring dozens or even hundreds of steps, our D$^3$SR attains comparable or even superior results in both quantitative metrics and qualitative evaluations. Moreover, compared with other methods, D$^3$SR achieves at least $3\times$ faster inference speed and reduces parameters by at least 30\%.

Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
Jianze Li Jiezhang Cao²;, Zichen Zou', Xiongfei Su², Xin Yuan", Yulun Zhang"!
Yong Guo Xiaokang Yang' 'Shanghai Jiao Tong University, Harvard University, CChina Mobile Research Institute, *Westlake University, "South China University of Technology
Abstract
Diffusion models have demonstrated excellent performance for real-world image super-resolution (Real-ISR), albeit at high computational costs. Most existing meth- ods trying derive one-step diffusion models from multi-step counterparts through knowledge distillation (KD) or variational score distillation (VSD). How- ever, these methods are limited by the capabilities of the teacher model, especially if the teacher model itself is not sufficiently strong. To tackle these issues, we pro- pose One-Step Diffusion model with larger-scale Diffusion Discriminator for SR, called DRSR. Our diseriminator is able to distill noisy features from any time step of diffusion models in the latent space. In this way, our diffusion dis- criminator breaks through the potential limitations imposed by the presence of teacher Additioldii we improve the perceptual loss with edge-aware DISTS (EA-DISTS) to enhance the model's 's ability to generate fine details. Our experiments demonstrate that, compared with previous requiring dozens or even hundreds of steps, our D attains comparable or even superior results in both quantitative metrics and qualitative evaluations. Moreover, compared with other methods, DR achieves at least 3x faster inference speed and reduces parameters by least 30%. We will release code and models https: / git......... com/ ctm/cianzell Jww.neaaaiiiiiiaee Jimm//iiiziii1///2a.. 114/D3SR.
methods
Introduction
Real-world image super-resolution (Real-ISR) is a challenging task that aims to reconstruct high- resolution (HR) images from their low-resolution (LR) counterparts real-world settings [1]. Most image super-resolution (SR) methods [2, 3, 4, 5, 6] use Bicubic downsampling of HR images to generate LR samples. These methods achieve good results reconstructing simple degraded images. However, they struggle with the complex and unknown degradations widely existing in real-world scenarios. Previous research has predominantly employed generative adversarial networks (GANs) [7, 8, 9, 10] for Real-ISR task. In recent developments, diffusion models (DMs) [11, 12, 13] have emerged as promising alternative. Recent Real-ISR methods have achieved outstanding performance by using diffusion models. In particular, some methods [14, 15, 16, 17, 18] leverage powerful pre-trained diffusion models, such as large-scale text-to-image (T2I) diffusion models like Stable Diffusion [12. 19]. These pre-trained T2I models provide extensive priors and powerful generative abilities. Most Diffusion model based methods generate HR images by employing ControlNet models [20], conditioning on the LR inputs. However, these methods typically require
Equual contribution
CCorresponding author: Yulun Zhang, yulun 100 com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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tens to hundreds of diffusion steps to produce high-quality HR images. The introduction of ControlNet not only increases the number of model parameters but also further exacerbates inference latency. Consequently, Diffusion model based multi-step diffusion methods often incur delays of tens of seconds when processing a single image, which significantly limits their practical application in real-world scenarios for low-level image reconstruction tasks.
To accelerate the generation process of diffusion models, recent research has introduced numerous one-step diffusion methods [21, 22, 23, 24, 25, 26, 27, 28]. These methods are known as diffusion distillation, which distill multi-step pre-traines diffusion models into one-step counterparts. Most of these approaches employ a knowledge distillation strategy, using the multi-step diffusion model as a teacher to train a one-step diffusion student model. These methods significantly reduce inference latency, and the quality of the generated images can be comparable to that of multi-step diffusion models. Real-ISR methods based on one-step diffusion models have become an increasingly popular research direction [29, 30, 31, 32, 33, 34]. These methods employ pre-trained multi-step diffusion models as teachers to guide the training process. They achieve promising results, with performance comparable to that of multi-step models. However, further performance improvement remains challenging task. Existing methods rely on a multi-step teacher to conduct distillation. Nevertheless, this paradigm would inevitably come with several limitations. First, the multi-step teacher may hamper the effectiveness of distillation if the teacher itself is not strong enough. For example, variational score distillation (VSD) [35, 36, 32] is one-step diffusion distillation method. It enhances the realism of generated images by optimizing the KL divergence between the scores of the teacher and student models. This approach heavily relies the prior knowledge of the teacher diffusion model. The limitations of the multi-step diffusion model impose an upper bound on the performance of the aforementioned methods. Second, the widely used VSD approach does not exploit any high resolution (HR) data for training but merely depend on the prior knowledge embedded the pre-trained parameters. If the distribution fitted by the multi-step diffusion model (teacher in VSD) deviates from the high-quality image distribution, it may lead to a loss of realism or the generation of fake textures in the student model's 's images.
To overcome the aforementioned challenges. we propose a novel one-step diffusion model with a large- scale diffusion discriminator. Different from existing one-step diffusion models using distillation techniques with teacher models, we use a larger-scale diffusion model, SDXL [19]. as a discriminator to leverage the powerful priors. Specifically, our diffusion discriminator aims distill latent features with different noises from true data. At the same time, it enables us to perceive the true data distribution of high-quality super-resolution datasets. As result, we overcome the performance limitations imposed by the teacher model's upper bound. Additionally, we propose a simple and effective improvement to the perceptual loss, edge-aware DISTS (EA-DISTS), by capturing high- frequency details from extracted edges. Our comprehensive experiments indicate that DR achieves superior performance and less inference time among one-step DM-based Real-ISR models.
Our main contributions are summarized as follows:
We propose simple but effective one-step diffusion distillation method, DR which uses a large-scale diffusion model as a discriminator for adversarial training. Unlike previous one-step diffusion ISR methods, teacher to guide the training. Instead, we employ a larger-scale diffusion model to guide the training process. This breaks through the performance limitations of teacher models in previous distillation methods.
do not a pre-trained multi-step diffusion model as
We improve the perceptual loss by proposing the edge-aware DISTS (EA-DISTS) loss. Our EA-DISTS leverages image edges to enhance the model's ability and improve the authenticity of reconstructed details.
Experiments show that our method outperforms previous one-step diffusion distillation methods, such as VSD and knowledge distillation. When compared with multi-step DM- based models, DÅSR obtains comparable even better performance with over 7x speedup in inference time. Moreover, our method offers a 3x inference speed advantage over one-step DM-bd methods and reduces parameters by at least 30%.

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2 Related Work
2.1 Real-World Image Super-Resolution
Real-world image super-resolution (Real-ISR) aims to recover high-resolution (HR) images from low- resolution (LR) observations real-world scenarios. The complex and unknown degradation patterns in such scenarios make Real-ISR challenging problem [37, 38, 39, 40]. To address this problem, a variety of methods have been proposed. Early image super-resolution models [2, 41, 42, 5, 6] typically rely on simple synthetic degradations like Bicubic downsampling for generating LR-HR pairs. Although these methods perform well under simple degradation settings, they struggle to achieve satisfactory results in real-world scenarios. Later, GAN-based methods such as BSRGAN [9], Real-ESRGAN [8], and SwinIR-GAN [10] introduce more complex degradation processes. These methods achieve promising perceptual quality but encounter issues such as training instability. Additionally, they have limitations in preserving fine natural details. Recently, Stable Diffusson (SD) [12] is considered for addressing Real-ISR tasks due to its strong ability capture complex data distributions and provide robust generative priors. Approaches such as StableSR [43], DifBBR [16], and SeeSR [14] leverage pre- pre-trained diffusion priors and ControlNet models [20] to enhance HR image generation. One-step diffusion models have gained widespread attention from researchers. Methods such as YONOS-SR SR [29]. SinSR [30]. OESEDiff [32], AddSR [31], TAD-SR [44], and TSD-SR [45] have achieved Real-ISR with diffusion models in a single sampling step.
2.2 Acceleration of Diffusion Models
Acceleration of diffusion models can reduce computational costs and inference time. Therefore, various strategies have been developed to enhance the efficiency of diffusion models in image generation tasks. Fast diffusion samplers [13, 46, 47, 48, 49, 50] have significantly reduced the number of sampling steps from 1,000 to 15~100 without requiring model retraining. However, further reducing the steps below 10 often leads performance drop. Under these circumstances, distillation techniques have made considerable progress in speeding up inference [21, 51, 23, 24, 25, 52, 26, 27, 53, 36]. For instance, Progressive Distillation (PD) methods [23, [4] have distilled pre-trained diffusion models to under 10 steps. Consistency models [25] have further reduced the steps to 2-4 with promising results. Instaflow [27] further achieves one-step generation through reflow [51] and distillation. Recent score distillation-based methods, such as Distribution Matching Distillation (DMD) [54, 55] and Variational Score Distillation (VSD) [56, 36), aim achieve one- step text-to-image generation. They minimize the Kullback-Leibler (KL) divergence between the generated data distribution and the real data distribution. Although these approaches have made notable progress, they still face challenges, like high training costs and dependence on teacher models.
3 Method
In this section, we present our real-world image super-resolution (Real-ISR) model DÅSR. First, in section 3.1, we review the basics of diffusion models and introduce the DRSR generator. sec- tion 3.2, we introduce the diffusion distillation method using large-scale diffusion discriminator. In section 3.3, we introduce the edge-aware DISTS (EA-DISTS) perceptual loss. This loss improves texture details and enhances visual quality. Finally, in section 3.4, we describe the training process for SRR
3.1 Preliminaries: Diffusion Models
Diffusion models include forward and reverse processes. During the forward diffusion process, Gaussian noise with variance Be (0, 1) is gradually injected into the latent variable 2: zt Vā, 2 /I āg c, where c~N((, I), a, can directly predict the clean latent variable Eg from the model's predicted noise è: 20 where is the prediction of the network eg given 2, and E (=,;
Br, and a = IT' ag.. the reverse process, we
- ---1--oé
Vo.
As illustrated in Fig. 1, we first employ the encoder Eg to map the low-resolution (LR) image into the latent space, yielding z1: 27 = (x() Next, we perform a one denoising step to obtain the predicted noise and compute the high-resolution (HR) latent representation بh:
21
oT c c6(21; T2)
Sn
(1)
Var.
3

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Generator Ge
Large-scale Diffusion Discriminator Da
Enooder
diffusion
spatial
VAE Decodder
diffusion
17
SDXL Layers
A1r
Trainabl
Noisy latents
Xir
Frazen
trainable LoRA
MLP
MSE
EA-OISTS
Generator forward
Discriminator forward
A1
F(2..)) 8)
F(Zg. t)
Compute loss
Diflusion Process
Figure 1: Training framework of DR The left side represents the generator Go. which includes the pre-trained VAE and UNet from Stable Diffusion. Only the UNet fine-tuned using LoRA, while other parameters remain frozen. The right side depicts the diffusion discriminator, which guides the training process without participating in inference. The discriminator extracts the UNet Mid-block outputs and processes them through MLP generate realism scores for different image regions. Both the downsample and middle blocks of the UNet the discriminator are fine-tuned with LoRA, whereas the MLP is randomly initialized.
genasted
nel Iaage
noi Image
Tea: magg
0.08
002
60
0.66
00z
-7
Componee 1
Consoned
0.5
-18.3
Figure Visualization features dimensionality reduction for the first 100 channels from the middle block outputs of the Stable Diffusion (SD) UNet. The distributions of the two types of image features are distinctly different.
where ca denotes the denoising network parameterized by 8, and Ty is the diffusion time step. Unlike one-step text-to-image (T21) diffusion models [25, 54], the input to the UNet of the Real-ISR diffusion models is not pure Gaussian noise. We set Tr. to an intermediate time step within the range [0, T), where T is the total number of diffusion time steps. In Stable Diffusion (SD), T 1, 000. Finally, we decode 2н using the decoder Dg to reconstruct the HR image f ân = Do(2H). The entire computation process of the generator can be expressed as 2и Go(IL).
3.2 Distillation with Large Diffusion Discriminator
Currently successful one-step diffusion distillation methods applied image super-resolution include knowledge distillation (KD), variational score distillation (VSD), and others. Among them, VSD stands out due interesting principles and excellent performance. VSD works by training an additional diffusion network to fit the fake score Stake of the generated image, while using frozen pre-trained diffusion model obtain the real score Sreal based on its prior. The method then aligns the two scores' differences using the Kullback-Leibe (KL) divergence, thus enhancing the realism

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of the generated image. However, in VSD, the absence of real image datasets implies that the upper bound of VSD is limited by the prior of the pre-trained diffusion model. The teacher model in VSD restricts the generative capacity of the student model, making further performance improvements challenging.
To address the issue incurred by a weak multi-step teacher, we propose use a large-scale diffusion model to provide stronger guidance for one-step distillation. Figure 2 shows the distributions of real and generated images' latent code at the mid- dle block output of the UNet in the Stable Diffusion (SD) model. There a clear dif- ference in their distribution patterns. This suggests that using pre-trained diffusion model a discriminator has great potential. can also avoid instability issues during the early stages of training. Figure/ I illus- trates our training framework. We append an MLP block after the SD UNet middle block as a classifier to output the authentic- ity score for each patch.
3.0
Farams,
ManigA
Mode
0.60
0.5
0.0
SDXL 1.0 base
SD 2.1 base
SD 1.5
Figure 3: Comparison of the performance SD models with different scales as discriminators. As the model size increases, the performance of the generator im- proves accordingly.
During training, we input the forward dif- fusion results of both the generator's s pre-
dicted latent code HH, and the ground truth latent code 2н ()) The adversarial losses for updating the generator and discriminator are defined as:
Lg -Er, PPmm t~ [0,2] [log Da (F (êH, t))l
(2)
LD B Pam. [0,7]] [og - -Do (F ($H,
Esy p -Pam |0,70 [log Da (F (zH, t)]]
where F(.() denotes the forward diffusion process of at time step I. E [0, 7). specifically,
F(a,t) Von - with c0.
4
Relation to Diffusion GAN Methods. Recently, many methods have combined GANs and Diffusion models, achieving success in image generation and other fields. Diffusion-Gail GGN [57] uses timestep- dependent discriminator to guide the generator's 's training. DDGAN [58] employs multimodal conditional GAN to achieve large-step denoising. ADD [22] and LADD [59] both introduce discrimi- nators to enhance the generative quality of one-step student diffusion models. These discriminators operate in pixel space and latent space, respectively. Both our method and existing Diffusion GAN methods demonstrate the potential of adversarial training in diffusion models. However, there are several essential differencees. First, we use a pre- pre-trained multi-step diffusion model as the discrimi- nator, leveraging the prior of large-scale models to guide the training process. Second, we explore the impact of scaling. Figure shows the performance of different SD models as discriminators. reveals that large-scale diffusion discriminators break through the performance limits of pre- trained diffusion models, achieving superior results.
3.3 Edge-Aware DISTS
To further enhance the quality of the generated images, we aim incorporate perceptual loss. Most image reconstruction methods utilize LPIPS [60] as the perceptual loss. However, to better preserve image texture details and alleviate pseudo-textures in the reconstruction under higher noise levels, we need to focus on the textures on HR images. DISTS [61] can compute the structural and textural similarity images, aligning with human subjective perception of image quality. Furthermore, regions with rich textures or details often exhibit strong edge information. Leveraging image edge information effectively enhances texture quality. Based on this, we propose a novel perceptual loss, termed Edge-Aware DISTS (EA-DISTS) This perceptual loss simultancously evaluates the structure
5

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and texture similarity of the reconstructed and HR images and their edges, thereby enhancing texture detail restoration.
Our proposed EA-DISTS is defined as:
LEA-DISTS (Go( CDISTS (ZL); xH) + DISTS (SGL
(5)
where S(.) represents the Sobel operator used to extract edge information from the images. It consists of two convolution kernels, G and Gy, which detect horizontal and vertical edges, respectively:
--1 0
1
-2
-1
Gx = 2 2
G =
0
(6)
-1 0 1
1
1
The Sobel operator is applied to an image æ as follows:
S(x) = v(Ga = x)² + (G, * x)2,
(7)
where * denotes the convolution operation.
To intuitively demonstrate the effective- ness of EA-DISTS, we visualize the fea- ture maps during the DISTS computation process. Figure presents the visualization results of VGG-16 feature maps. As shown in Fig. 4, areas rich with image details, such the building windows, the feature maps associated with EA-DISTS exhibit more high-frequency information. Com- pared to DISTS, EA-DISTS demonstrates higher contrast in textured and smooth re- gions, further emphasizing the textural de- tails within the images. Our EA-DISTS
08
(8) DISTS
EA
Figure 4: Feature visualization associated with DISTS and EA-DISTS. Our EA-DISTS captures more high- frequency information, like texture and edges. places greater emphasis on texture details within images, guiding the model to generate realistic and rich details.
3.4 Overall Training Scheme
Here, we summarize the whole one-step diffusion model training process. As described in section 3.1, within the generator component, SR obtains स and the decoded high-resolution image è through one-step sampling. The generator then updates its parameters by computing the spatial loss L in pixel space between generated image and the ground truth, as well as the adversarial loss Lg derived from the discriminator in the latent space (Eq. 2). The loss function for updating the generator defined as Sypatial + A₁ Lg. Specifically,, we employ a weighted sum of Mean Squared Error (MSE) loss and perceptual loss to define the spatial loss:
Expatiaa (Go (xL): πH)
LASE Go (11,),.H aH) A2LEA DISTS (Gol TH).
(8)
where A and λ2 hyperparameters used to balance the contributions of each loss component. The suppiementary material provide a detailed description of the algorithm's 's pseudocode and the values of the hyperparameters.
For discriminator training, we utilize paired training features, where each pair consists a negative sample feature . and the corresponding real image's latent representation zи as a positive one. Using Eq. 3, we compute the adversarial loss LDD to update the discriminator's parameters. Furthermore, the discriminator can be initialized with weights from powerful pre-trained models, such as SDXL [19], to achieve superior performance.
This training approach allows our D to overcome the limitations imposed by teacher models, enhancing generator performance without increasing its parameter count or compromising efficiency, Additionally, the integration of a robust discriminator initialized with advanced pre-traine pre-trained models ensures that the generator receives high-quality feedback, facilitating the production of more realistic and detailed nigh-resolution images.

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Table 1: Quantitative results (x4) on the Real-ISR testset with ground truth. The best and second-best
results are colored red and blue. In the one-step diffusion models, the best metric is bolded.
Dataset
Method
PSNR
SSIMI
LPIPSL
DISTSI
NIQEJ
MUSIQT
MANIQAT
CLIPIQAT
SubbleRR=2200
0.7733
0.2622
60.53
0.5570
0.4310
DimBRR-s x50
24.87
0.6466
0.3834
0.2015
3,947
68.02
0.6.099
0.6042
SecSR-x50
0.7555
0.2806
66.37
0.5483
ResShift=115
25.45
0.7246
0.3727
0.2344
7,349
56.18
0.5004
0.4307
ADDSR-s4
23.15
0.6662
0.3769
0.2353
5,256
66.54
0.6581
0.5390
RealSR
SinSR a1
25.83
0.7183
0.3641
0.2193
5.746
61.62
0.5362
0.4691
OSEDifE-s
24.57
0,7202
0.3036
0.1808
4.344
67.31
0.6148
0.5524
ADDSR-s1
25.23
0.7295
0.2900
0,1852
5.223
63.08
0,5457
0.4498
24.11
0,7152
0.2961
0.1782
3.899
68.23
0.6383
0.5647
StableSR-2200
23.68
0.6270
0.4167
0.2023
4.602
49.51
0.4774
0.3778
DifBBIR-s50
22.33
0.5133
0,4681
0.1889
3,156
70.07
0.6307
0.6352
SccSR-250
0.6114
0.3477
67.99
0,5842
ResShift-s15
23.55
0.6023
0.4088
0.2228
6,870
56.07
0.4791
0.4269
ADDSR-s4
0.5578
0,4169
68,26
0.6007
DIVlK-val
SinSR-s1
22.55
0.5405
0.4390
0,2033
5.620
62.25
0.5011
0.5206
OSEDiff-sl
23,10
0.6127
0,3447
0.1250
3,583
66.62
0.5530
0,5350
ADDSR-s1
22.74
0.6007
0.3961
0,1974
4.270
62.08
0,5118
0.4868
D
22.05
0.6031
0.3556
0.1500
3.295
68.51
0.5795
0.5370
4 Experiments
We conduct comprehensive experiments to validate the effecctivnness of D'R in real-world image super-resolution (Real-ISR). We provide a detailed introduction of our experimental setup in sec- tion 4. In section 4.2, evaluate our method and compare it against the current state-of of-the---re methods. In section 4.3, we carry out comprehensive ablation studies to validate the effectiveness and robustness of our proposed approach.
4.1 Experimental Settings
Datasets. We train D²SR on LSDIR [62] and the first 10k images from FFHQ [63], totaling 95k images. During training, we randomly crop patches of size 512x512 pixels from these images. To get low-resolution (LR) and high-resolution (HR) pairs for training. we apply the Real-ESRGAN [64] degradation pipeline. We conduct extensive evaluations of D*SR on synthetic dataset DIV2K- val [65] and two real-world datasets, including RealSR [66] and RealSet65 [67]. In DIV2K-val, we use the ESRGAN degradation pipeline to synthesize the corresponding LR images. We evaluate our model and all other methods by using the whole images from each dataset.
Compared Methods. We compare DR with state-of-the-art DM-based methods for real image super-resolution (Real-ISR), as well as other prominent approaches, including GAN-based and Transformer-basee methods. The DM-based methods include multi-step diffusion models, such as StableSR [43], ResShift [67], DiffBIR [16], and SeeSR [14], alongside recently proposed one-step
diffusion models like SinSR [30], OSEDiff [32], and AddSR [31]. Other methods include GAN-
based approaches, such as BSRGAN [9], RealSR-JPEG [39], Real-ESRGAN [64]. LDL [68], and
FeMASR [69], as well Transformer-based method SwinIR [10].
Evaluation Metrics. To assess the performance of each method, we employ four full-reference (FR) and four no-reference (NR) image quality metrics. The FR metries include PSNR, SSIM, LPIPS [60], and DISTS [61]. Both PSNR and SSIM are computed on the Y channel in the YCbCr color space. The NR metrics include NIQE [70], MUSIQ [71], ManIQA [72], and CLIPIQA [73].
Implementation Details. We initialize the generator network with the SD 2.1 base parameters and the discriminator network with partial parameters from SDXL. We set both the rank and scaling factor a of LoRA to 16 in the generator and discriminator. We use the AdamW optimizer and set the learning rate for both the generator and discriminator to 5e-5. Training is performed with batch size of 8 over 100K iterations with 4 NVIDIA A100-40GB GPUs.
4.2 Comparison with State-of-the-Ar Methods
Quantitative Results. Tables 1 and 2 provide quantitative comparisons of the methods across the three datasets. DASR achieves the best performance among all no-reference (NR) metrics one-step diffusion methods. These NR metrics reflect the details and realism of the images. This indicates that D outperforms all one-step methods and most multi-step methods. Recently, some studies [18] have pointed out that reference- based metrics such PSNR and SSIM cannot accurately reflect the
7

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Bicubic
Real-ESRGAN
SwinIR
StableSR
DinBIR
ResShift
SeeSR
DASR (ours)
Bicubic
Real-ESRGAN
SwinIR
StableSR
DitBIR
ResShift
SccSR
SinSR
OSEDiff
DdSR (ours)
Bicubic
SwinIR
StableSR
DinBIR
Irua aae lus 188
ResShift
SeeSR
SinSR
OSEDiT
D (ours)
Figure 5: Visual comparisons (x4) on Real-ISR task (RealSR [66] dataset). performance of diffusion-basse based Real-ISR methods. We discuss this in the supplementary materials. We compare the quantitative and qualitative results, including those of GAN-based methods. We find that, compared diffusion-baase methods, GAN-base methods generally achieve higher PSNR and SSIM. However, the image quality of these methods is not as high.
Visual Results. Figure 5 presents visual comparison of various diffusion based Real-ISR methods. As observed, most existing methods struggle to generate realistic details and often produce incorrect content in certain regions of the image due to noise artifacts. Notably, our DA demonstrates a significant advantage over others, particularly in the restoration of textual content. Additional visual comparison results
Table 2: Quantitative results (x4) on RealSet65 testset. The best and second-best results are col- ored red and blue. In the one-step diffusion models, the best metric is bolded.
Methed
NIQEL
MUSIQU
MANIQAT
CLIPIQAT
SubbkSR-200
4.985
58 85
0.5366
0.4421
SceSR-s50
4,689
69.79
0.6018
0.5657
ResShin--15
6.730
06.36
0.5071
0.4416
are provided in the supplementary material.
ADDSR-sf
SinSR-a1
5.664
64.22
0.5338
0.5083
Complexity Analysis. Table 3 presents plexity comparison of Stable Diffusion (SD)- based Real-ISR methods, including the number
sD
5.207
64.22
0.5258
0.4718
S'RR-1
3.99M
70.25
0.6298
0.5481
of inference steps, inference time, parameter numbers, and MACs (MultilyyAccummulte Operations). All methods evaluated
NVIDIA A100 GPU. DR achieves the fastest inference speed among all SD-based methods. Furthermore, our method supports using a fixed text embedding as the generation condition. Therefore, do require CLIP and other additional modules (such as DAPE used by OSEDiff and SeeSR, and ControlNet used by DifTBIR) for inference. Our SR has the smallest number of model parameters during inference among Stable Diffusion (SD)-based methods, reducing the parameters by 33% compared to OSEDiff.
In this section, validate the effectiveness of two key components in D² More ablation experi- ments can be found the supplementary materials.
4.3 Ablation Study
Perceptual Loss. Table 4a presents the impact different perceptual loss functions, as well as only mean squared error (MSE) is applied as the spatial loss. Figure 6 showcases the visual outcomes of these experiments. The results indicate that incorporating perceptual loss crucial for training SR models, as it facilitates the generation of more realistic details and enhances overall visual quality.

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Table 3: Complexity comparison (x4) among different methods, including sampling steps during inference, inference time, parameter count, and MACs. Inference time and MACs are tested for an output size of 512x512 with a single A A100-40GB GPU.
StalleR
DIABIR
SeeSR
ResShift
SinSR
OSEDifI
# Step Inference Time /
200
50
50
15
1
1
11.50
7.79
5.93
0.71
0.16
0.35
0.11
Total Param M # MACs/ / G
1.4x 10
1.6110
2.0x 10
173.8
173.8
1,4x10
966.3
75,812
24,528
32,336
4,903
2,059
2,269
2,132
Table 4: Ablation studys on the effects of perceptual losses and different discriminators.
(a) Ablation on different perceptual losses.
(b) Ablation on different discriminators.
Function
LPIPS
NIQEL
MUSIQ1
ManIQAt
Discriminator
LPIPS
NIQEL
MUSIQT
ManIQA1
MSE
0.3626
4,446
65.35
0.5457
None
0.3862
6.962
62.36
0.5597
LPIPS
0,3190
4.123
66.41
0.6383
CNN
0.3402
6.139
64.36
0.5666
EA-LPIPS
0.3175
4,046
67.47
0.64103
Diffusion-AAN
0.3200
4.518
67.51
0.5800
DISTS
0.3463
3,800
(ours)
0.6198
EA-DISTS
0.3150
3.747
68.69
0.6436
SDXI. (ours)
0.3150
3.747
68.69
0.6436
24
20
20
24
26
26
LPIPS
DISTS
LPIPS
DISTS
24
26
26
24
26
28
EA-LPIPS
EA-DISTS
EA-LPIPS
EA-DISTS
Figure 6: Visual results (x4) of DFOSD with different perceptual losses. The left side shows a comparison of the checkerboard. The right one shows content about some numbers, i..., '24, 26, 28',
Our proposed edge-aware DISTS (EA-DISTS) achieves the best performance across various image quality metrics and visual assessments. As shown in Fig. 6, EA-DISTS excels in producing highly realistic details, demonstrating its advantage in perceptual quality. This highlights the effectiveness of EA-DISTS in accurately restoring image textures and details, thereby significantly improving the visual quality.
Diffusion Discriminator. We evaluate the impact of various discriminator modules on the training of D'SR, including our used diffusion discriminator, vanilla discriminator (CNN), diffusion-GAN [57] style discriminator, and training without any discriminator. The CNN discriminator operates in the pixel space, while other discriminators operate in the latent space. The experimental results are shown in Table 4b. The comparison between the Diffusion-GAN discriminator and the SD 2.1 discriminator indicates that using a pre-trained Stable Diffusion (SD) model as the discriminator outperforms randomly initialized parameters.
SDXL 1.0-base the discriminator while keeping the generator size unchanged to verify the impact of scaling for diffusion discriminators. As shown in the last two rows of Table 4b, the performance of the one-step diffusion model trained with SDXL the discriminator outperforms the model trained with SD 2.1 as the discriminator, without requiring any modifications to the generator's architecture. This suggests that SR can effectively leverage the strengths of more powerful pre-trained models, and enhance the performance of generator without compromising its efficiency. This breaks the upper bound imposed by the muti-step teacher diffusion model, making further performance improvement simpler.
Next,
5 Conclusion
In this work, we propose D a One-Step Diffusion model for Real-ISR. Unlike previous methods that use diffusion distillation, our method breaks the limitations of the teacher. We propose the edge- aware DISTS (EA-DISTS) perceptual loss, which enhances the texture realism and visual quality of the generated images. Our adversarial training strategy allows DA to outperform multi-step diffusion models in visual quality. Experiments show that D achieves superior performance and improves image realism. This highlights its potential for efficient image restoration.

---

Acknowledgments
This work was supported by Shanghai Municipal Science and Technology Major Project (201SHZDZZ0102), the Fundamental Research Funds for the Central Universities, the Special Project on Technological Innovation Application for the 15th National Games and the National Paralympic Games under Grant 2025B0/
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Reviews:
[{"review_id": "dYuDcTQfdf", "reviewer": "Reviewer_Cmxu", "summary": "This paper proposes a one-step image super-resolution (SR) framework based on a text-to-image (T2I) diffusion model. Unlike previous methods that rely on the VSD loss for conditional distillation, the authors propose to guide the training process using only a discriminator. The introduced EA-DISTS loss helps improve the reconstruction of border regions and fine details. Experimental results demonstrate that D3SR achieves state-of-the-art performance—particularly on no-reference metrics—among existing one-step diffusion-based SR methods.", "questions": "see weakness", "limitations": "see weakness", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. This paper introduces a new one-step SR method based solely on a large-scale diffusion discriminator, without relying on the reference UNet or VSD loss used in prior works. This simplifies the training pipeline while achieving strong results.\n2. The proposed EA-DISTS loss contributes to improved perceptual quality and better generation of fine-grained textures, especially along object boundaries.\n3. D3SR achieves competitive results in both quantitative metrics and qualitative visual evaluations, showing its effectiveness as a one-step SR solution.\n\nWeaknesses:\n1. The main difference between D3SR and DMD2 lies in the removal of the KL divergence penalty derived from real and fake score functions. As a result, the technical contribution appears incremental rather than fundamentally novel.\n2. By initializing the discriminator with the SDXL encoder, D3SR implicitly incorporates high-resolution priors from SDXL. This makes it difficult to disentangle whether the performance gains are due to the proposed method itself or simply the use of a stronger pre-trained model. Moreover, prior distillation approaches could similarly benefit from incorporating more powerful diffusion models as guidance.\n3. Although the authors claim improved visual quality, several examples in the supplementary material reveal potential issues such as hallucinated textures and color shifts . For instance:\nIn Figure 2 (Case 2) and Figure 3 (Case 3, Case 6), unnatural or fabricated textures appear in the generated images.\nColor distortions are noticeable in Figure 2 (Case 5) and Figure 3 (Case 6).\nThese artifacts may limit the practical applicability of the method, especially in scenarios where faithfulness to the input is critical.", "was_revised": true, "final_justification": "My main concerns have been addressed by the rebuttal. The ablation studies on different discriminator types reveal an interesting finding.", "rebuttal": "# Response to Reviewer Cmxu (denoted as R1)\n\n`Q1-1` The main difference between D3SR and DMD2 lies in the removal of the KL divergence penalty derived from real and fake score functions. As a result, the technical contribution appears incremental rather than fundamentally novel.\n\n`A1-1` There may be some misunderstanding about our contributions and we will clarify them in the following two points.\n1. We highlight that our main contribution lies in exploring the effect of scaling up the discriminator model for one-step diffusion models, instead of removing the KL penalty. We demonstrate that using the same model as the discriminator and generator has its limitations, and employing a larger-scale discriminator can further enhance model performance. \n2. Additionally, we found that the performance improvement brought by DMD's KL penalty is not as significant as that achieved through adversarial training and the training is computationally expensive. Thus, we directly remove it for efficient training. Without DMD's KL penalty, our improved performance merely comes from the proposed adversarial training strategy, which further shows the effectiveness of our method.\n\n\n`Q1-2` By initializing the discriminator with the SDXL encoder, D3SR implicitly incorporates high-resolution priors from SDXL. This makes it difficult to disentangle whether the performance gains are due to the proposed method itself or simply the use of a stronger pre-trained model. Moreover, prior distillation approaches could similarly benefit from incorporating more powerful diffusion models as guidance.\n\n`A1-2` We present the performance of using **SD2.1** as the discriminator in our ablation study. The detailed metrics and comparisons with other methods are shown in the table below. Under the same pretrained discriminator backbone, our method still outperforms other one-step diffusion SR methods. This demonstrates the effectiveness of our proposed approach.\n\n| Method               | PSNR | LPIPS | DISTS  | NIQE | MUSIQ  | MANIQA |\n|----------------------|------|-------|--------|------|--------|--------|\n| SinSR                | 25.83 | 0.3641 | 0.2193 | 5.746 | 61.62 | 0.5362 |\n| OSEDiff              | 24.57 | 0.3036 | 0.1808 | 4.344 | 67.31 | 0.6148 |\n| AddSR                | 25.23 | 0.2990 | 0.1852 | 5.223 | 63.08 | 0.5457 |\n| **D3SR w/ SD2.1 disc.**  | 24.60 | 0.3031 | 0.1775 | 3.925 | 69.21 | 0.6302 |\n| **D3SR w/ SDXL disc.**   | 24.11 | 0.2961 | 0.1782 | 3.899 | 68.23 | 0.6383 |\n\n\n`Q1-3` Although the authors claim improved visual quality, several examples in the supplementary material reveal potential issues such as hallucinated textures and color shifts . For instance: In Figure 2 (Case 2) and Figure 3 (Case 3, Case 6), unnatural or fabricated textures appear in the generated images. Color distortions are noticeable in Figure 2 (Case 5) and Figure 3 (Case 6). These artifacts may limit the practical applicability of the method, especially in scenarios where faithfulness to the input is critical.\n\n`A1-3` In real-world image super-resolution tasks, some scenes exhibit severe degradation, making it unrealistic to fully recover the LR observation back to the ground truth. Compared with other methods, our approach provides better clarity and realism.\n\nIn addition, we conducted a user study as shown in the table below. We selected five representative scene types: **landscape**, **human**, **plant**, **animal**, and **architecture**. A total of 20 participants were involved in the evaluation. Our method achieved a recognition rate of **60.63%**, surpassing all other methods.\n\n| Method     | SinSR | OSEDiff | AddSR | D3SR (ours) |\n|------------|-------|---------|-------|-------------|\n| Percentage/%    | 9.50  | 20.82   | 9.05 |  **60.63**    |"}, {"review_id": "eWJzYF4zbi", "reviewer": "Reviewer_CutL", "summary": "To overcome the limitations imposed by the reliance on a teacher model in existing diffusion-based one-step SISR methods, the authors propose a new one-step diffusion model called D3SR. The authors introduce a Large Diffusion Discriminator that distills noisy features from any time step of diffusion models in the latent space, addressing the potential constraints of using a teacher model. In addition, the authors also introduce an EA-DIST loss for model training to further enhance the model's ability to generate fine details.", "questions": "See weaknesses.", "limitations": "The paper lacks limitation analysis and prospects for real-time inference of the model.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n(1) The author proposed the Large Diffusion Discriminator, which cleverly introduced the idea of GAN into the diffusion model, and it seems to be an optimized alternative to the existing distillation schemes.\n\n(2) The paper is well-written and well-organized. \n\n**Weaknesses:**\n\n(1) The authors claim in the paper that the proposed Large Diffusion Discriminator is superior to the existing VSD method, but there is a lack of corresponding experimental verification in the ablation study. Does the advantage of Large Diffusion Discriminator mainly come from the use of a better model (i.e., SDXL)?\n\n(2) As shown in Tab.1, D3SR achieves lower PSNR/SSIM/LPIPS scores compared to other diffusion-based SISR models.\n\n(3) The proposed methods and OSEDiff are both based on the Stable Diffusion model. Why is there such a big difference in the total parameter in Tab.2 (1.4\\times10_3M vs. 966.3M)? What is the main reason for this? In addition, the total parameter of OSEDiff is also significantly different from the value reported in its official paper. An explanation for these inconsistencies would be helpful.", "was_revised": true, "final_justification": "In this paper, the authors propose a new one-step diffusion model by introducing a Large Diffusion Discriminator, which distills noisy features from any time step of diffusion models in the latent space. The authors have addressed my concerns in the discussion section, including the ablation study on different backbones of the Large Diffusion Discriminator, the reason for lower PSNR/SSIM/LPIPS scores, and inconsistencies in parameter statistics. Therefore, I will maintain my positive rating.", "rebuttal": "# Response to Reviewer CutL (denoted as R2)\n\n`Q2-1` The authors claim in the paper that the proposed Large Diffusion Discriminator is superior to the existing VSD method, but there is a lack of corresponding experimental verification in the ablation study. Does the advantage of Large Diffusion Discriminator mainly come from the use of a better model (i.e., SDXL)?\n\n`A2-1` We present the performance of using **SD2.1** as the discriminator in our ablation study. The detailed metrics and comparisons with other methods are shown in the table below. **OSEDiff** is a representative method based on VSD. Under the same pretrained model used as the discriminator, our method outperforms other one-step diffusion SR approaches. Furthermore, employing a larger discriminator (e.g., **SDXL**) leads to further performance improvements.\n\n| Method               | PSNR | LPIPS | DISTS  | NIQE | MUSIQ  | MANIQA |\n|----------------------|------|-------|--------|------|--------|--------|\n| SinSR                | 25.83 | 0.3641 | 0.2193 | 5.746 | 61.62 | 0.5362 |\n| OSEDiff              | 24.57 | 0.3036 | 0.1808 | 4.344 | 67.31 | 0.6148 |\n| AddSR                | 25.23 | 0.2990 | 0.1852 | 5.223 | 63.08 | 0.5457 |\n| **D3SR w/ SD2.1 disc.**  | 24.60 | 0.3031 | 0.1775 | 3.925 | 69.21 | 0.6302 |\n| **D3SR w/ SDXL disc.**   | 24.11 | 0.2961 | 0.1782 | 3.899 | 68.23 | 0.6383 |\n\nThese results demonstrate that the advantage of the **Large Diffusion Discriminator** stems not only from the stronger pretrained model but also from the adversarial training with a diffusion-based discriminator.\n\n`Q2-2` As shown in Tab.1, D3SR achieves lower PSNR/SSIM/LPIPS scores compared to other diffusion-based SISR models.\n\n`A2-2` In real-world image super-resolution (Real-ISR) tasks, some scenes exhibit severe degradation, making it impractical to fully reconstruct the ground truth from the LR observation. Therefore, for full-reference (FR) metrics, their reliability is often lower than no-reference (NR) metrics in Real-ISR scenarios.\n\nNR metrics better reflect the clarity, realism, and human preference of the generated images. This has been demonstrated in prior works such as **SUPIR** [1] and **TSD-SR** [2].\n\n> [1] *Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild*. CVPR 2024  \n> [2] *TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution*. CVPR 2025\n\n\n`Q2-3` The proposed methods and OSEDiff are both based on the Stable Diffusion model. Why is there such a big difference in the total parameter in Tab.2 (1.4\\times10_3M vs. 966.3M)? What is the main reason for this? In addition, the total parameter of OSEDiff is also significantly different from the value reported in its official paper. An explanation for these inconsistencies would be helpful.\n\n`A2-3` Unlike **OSEDiff**, we remove modules such as the **text encoder** and **DAPE**, which are not essential for one-step diffusion SR. This simplification is also discussed in our main paper. As a result, our method has significantly fewer parameters compared to OSEDiff.\n\nWe use the `thop` library to compute the parameter counts, and the details of each component are listed below. The parameter sizes of **UNet** and **VAE** match those reported in the official **Stable Diffusion** release. The reason for the discrepancy with the numbers reported in the OSEDiff paper remains unclear.\n\n| Component   | Parameters |\n|-------------|------------|\n| SD UNet     | 865.8M     |\n| SD VAE      | 83.6M      |\n| SD CLIP     | 289.7M     |\n| DAPE        | 160.3M     |"}, {"review_id": "TYHlEkvRy0", "reviewer": "Reviewer_Cj9F", "summary": "The paper proposes D3SR, a one-step diffusion model for real-world image super-resolution. It utilizes a large-scale diffusion discriminator for adversarial training, overcoming the capacity limitations of traditional teacher models. The authors also introduce an edge-aware DISTS (which terms as EA-DISTS) perceptual loss to enhance detail generation. D3SR achieves superior performance with faster inference and reduced parameters compared to existing methods. Extensive experiments have validated the effectiveness of the proposed method.", "questions": "For the ablation studies on discriminator types, could the authors consider including some powerful visual feature extraction networks for comparison, such as DINO, in addition to CNN-based discriminators? This would provide a more comprehensive understanding of the impact of different types of discriminators on the performance of the proposed method.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. This article validates the impact of the diffusion discriminator's model size on final performance. By employing a powerful diffusion discriminator, it overcomes the limitations imposed by teacher models in distillation methods.\n\n2. The proposed EA-DISTS somewhat enhances the details of the generated images, avoiding overly smooth outputs and further improving the model's performance.\n\n3. With the use of a powerful diffusion discriminator, the generator does not require an excessive number of parameters to achieve good performance, resulting in at least a 3× faster inference speed and a reduction of parameters by at least 30% compared to other methods.\n\nWeaknesses\n\n   1. The use of a diffusion model as a discriminator has been explored in prior work, such as in UFOGen (CVPR24). This paper's validation that a larger diffusion discriminator yields better performance is somewhat incremental and limited in novelty and significance.\n   2. The paper lacks comparison experiments with the latest works, such as ADCSR and PiSA-SR, which were presented at CVPR 2025. This absence makes it difficult to fully assess the competitiveness of the proposed method in the context of the most recent advancements in the field.", "was_revised": true, "final_justification": "Thanks for the rebuttal, my concerns are addressed by experimental results.", "rebuttal": "# Response to Reviewer Cj9F (denoted as R3)\n\n`Q3-1` The use of a diffusion model as a discriminator has been explored in prior work, such as in UFOGen (CVPR24). This paper's validation that a larger diffusion discriminator yields better performance is somewhat incremental and limited in novelty and significance.\n\n`A3-1` \n1. Our main contribution lies in exploring the impact of scaling up the discriminator model for one-step diffusion models. We demonstrate that using the same model as both the discriminator and the generator has its limitations, and employing a larger-scale discriminator can further enhance model performance. We highlight that, to our knowledge, it is the first work to investigate the impact of discriminator size on the training of one-step diffusion models. We believe that our proposed method is beneficial to many other research works and could be a popular training strategy. \n2. Significance of performance improvement: We highlight that our improvement is competitive with recent works [29, 30, 31] and can be consistently observed across various benchmarks. In Table 1, our method yields the best or second-best in most of the perceptual metrics, including DISTS, NIQE, MUSIQ, etc. More critically, our model is much more efficient than the compared methods in terms of both inference speed and model size (see Table 3). We are convinced that these results are significant.\n \n\n\n\n`Q3-2` The paper lacks comparison experiments with the latest works, such as ADCSR and PiSA-SR, which were presented at CVPR 2025. This absence makes it difficult to fully assess the competitiveness of the proposed method in the context of the most recent advancements in the field.\n\n`A3-2` We have added comparisons with the latest methods, and the results are shown in the table below. Our method achieves the best performance on no-reference (NR) metrics. We highlight that these metrics are often highly related to the perceptual visual quality. Moreover, we also compare the visual results with these methods and observe that our method is able to produce significantly more details, together with obviously better visual quality. Nevertheless, due to the limitation of \"not allowing to show images or links\" in rebuttal, we will include these visual comparisons in the final version and cite these works.\n\n| Method   | PSNR | SSIM | LPIPS | DISTS | NIQE | MUSIQ | MANIQA |\n|----------|------|------|--------|--------|------|--------|---------|\n| AdcSR    | 24.96 | 0.7306 | 0.3100 | 0.1928 | 4.450 | 67.24 | 0.6155 |\n| PiSA-SR  | 25.57 | 0.7493 | 0.2713 | 0.1726 | 4.379 | 67.98 |  0.6284 |\n| **D3SR** | 24.11 | 0.7152 | 0.2961 | 0.1782 | **3.899** | **68.23** | **0.6383** |\n\n*RealSR*\n\n| Method   | PSNR | SSIM | LPIPS | DISTS | NIQE | MUSIQ | MANIQA |\n|----------|------|------|--------|--------|------|--------|---------|\n| AdcSR    | 22.70 | 0.5801 | 0.3687 | 0.2055 | 3.480 | 64.51 | 0.5334 |\n| PiSA-SR  | 23.18 | 0.6135 | 0.3462 | 0.1781 | 3.474 | 67.34 |  0.5707 |\n| **D3SR** | 22.05 | 0.6031 | 0.3556 | **0.1500** | **3.295** | **68.51** | **0.5795** |\n\n*DIV2K-val*\n\n| Method   | NIQE | MUSIQ | MANIQA |\n|----------|------|--------|---------|\n| AdcSR    | 4.876 | 67.89 | 0.5844 |\n| PiSA-SR  | 4.886 | 69.66 | 0.6091 |\n| **D3SR** | **3.998** | **70.25** | **0.6298** |\n\n*RealSet65*\n\n`Q3-3` For the ablation studies on discriminator types, could the authors consider including some powerful visual feature extraction networks for comparison, such as DINO, in addition to CNN-based discriminators? This would provide a more comprehensive understanding of the impact of different types of discriminators on the performance of the proposed method.\n\nThank you for your suggestion. We have conducted additional experiments accordingly. Specifically, we use different versions of **DINO-v2** models as the discriminator in **D3SR**, and the results are shown in the table below.\n\n| Discriminator | LPIPS ↓ | NIQE ↓ | MUSIQ ↑ | ManIQA ↑ |\n|---------------|---------|--------|---------|----------|\n| ViT-S         |   25.40 |  4.251 |   66.50 |   0.6200 |\n| ViT-B         |   24.90 |  3.930 |   67.80 |   0.6250 |\n| ViT-L         |   24.30 |  3.910 |   68.00 |   0.6350 |\n| SD2.1 (ours)  |   24.60 |  3.925 |   69.21 |   0.6302 |\n| SDXL (ours)   |   24.11 |  3.899 |   68.23 |   0.6383 |\n\nConsistent with the conclusions of our paper, as the discriminator model scales up, the performance of the generator gradually improves. The results using **DINO** as the discriminator are slightly inferior to those obtained using a pretrained diffusion model as the discriminator."}, {"review_id": "g0n9wUx2dA", "reviewer": "Reviewer_hd5s", "summary": "This paper proposes D³SR, a novel one-step diffusion model for real-world image super-resolution (Real-ISR). The key contributions include:\n\n(1) Large-scale diffusion discriminator: Replaces traditional teacher models (used in distillation) with an LORA-based SDXL-based discriminator operating in latent space. This avoids the performance upper bound imposed by weaker teachers.\n\n(2) Edge-aware DISTS (EA-DISTS): Enhances perceptual loss by incorporating edge information via Sobel filters, improving texture realism.\n\n(3) Efficiency: Achieves 7× faster inference vs. multi-step diffusion models and 3× faster vs. other one-step methods, with ≥30% fewer parameters.", "questions": "My main concerns are as follows:\n\n1. A similar GAN-based idea can also be found in Seaweed-APT [1], the author should consider making a discussion with this paper.\n\n2. The proposed EA-DISTS requires further decoding into the pixel space during training, which may introduce additional training bottleneck on the training efficiency and memory requirement.\n\n3. While the paper claims that using SDXL as a discriminator for adversarial training overcomes previous teacher-based distillation methods, it is noticeable that previous methods [31, 32, 33] all apply SD2.1 as a teacher model. It is unclear whether the good performance comes from a more powerful model, i.e., SDXL or from the adversarial training without the teacher model.\n\n4. Minor suggestions: (1) Considering that CLIP-IQA [2] has been a metric widely used in the majority of previous methods, the author may consider adding it to the main table to align with previous results for better comparison. (2) If possible, it is better to add a user study to enhance the comparison with human preference.\n\n[1] Diffusion Adversarial Post-Training for One-Step Video Generation. ICML2025\n\n[2] Exploring CLIP for Assessing the Look and Feel of Images. AAAI2023", "limitations": "No.\nThe authors claim that the limitations and potential negative societal impact are listed in the supp. materials but I failed to find related parts.\nThe authors should consider including failure cases and discussing the societal impact in the revision.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n+ Reasonable motivation to use a large-scale diffusion model (SDXL) as a discriminator instead of a teacher, bypassing limitations of distillation-based one-step methods.\n+ EA-DISTS effectively integrates edge information into perceptual loss, addressing texture degradation in high-noise settings.\n+ Comprehensive experiments across 3 datasets (DIV2K-val, RealSR, RealSet65) with 7 metrics (PSNR, SSIM, LPIPS, DISTS, NIQE, MUSIQ, ManIQA).\n+ 0.11s inference time (vs. 0.35s for OSEDiff) and 966M parameters (vs. 1.4B for OSEDiff), making it practical for real-world use.\n+ Eliminates extra modules (e.g., ControlNet, CLIP), reducing complexity.\n\n\nWeakness:\n- A similar GAN-based idea can also be found in Seaweed-APT [1], the author should consider making a discussion with this paper.\n- The proposed EA-DISTS requires further decoding into the pixel space during training, which may introduce training bottleneck on the training efficiency and memory requirement.\n- While the paper claims that using SDXL as a discriminator for adversarial training overcomes previous teacher-based distillation methods, it is noticeable that previous methods [31, 32, 33] all apply SD2.1 as a teacher model. It is unclear whether the good performance comes from a more powerful model, i.e., SDXL or from the adversarial training without the teacher model.\n\n[1] Diffusion Adversarial Post-Training for One-Step Video Generation. ICML2025", "was_revised": true, "final_justification": "The author mostly addressed my concerns. This paper shows the effectiveness of the one-step image SR with adversarial training on a large-scale discriminator to some extent, though some recent works share similar ideas.", "rebuttal": "# Response to Reviewer hd5s (denoted as R4)\n\n`Q4-1` A similar GAN-based idea can also be found in Seaweed-APT [1], the author should consider making a discussion with this paper.\n\n`A4-1` Thank you for your suggestion. The key difference between **D3SR** and **Seaweed-APT** is that we explore the benefits brought by a larger-scale discriminator in training. Additionally, the discriminator in **D3SR** receives the results of latent forward diffusion as input. We will include the appropriate references in the main body of the paper.\n\n`Q4-2` The proposed EA-DISTS requires further decoding into the pixel space during training, which may introduce training bottleneck on the training efficiency and memory requirement.\n\n`A4-2` We highlight that EA-DISTS does not introduce too much training cost or memory consumption. It is worth noting that, besides EA-DISTS, the pixel-level reconstruction loss (e.g., L1/L2 loss) also relies on decoding latents into pixel space, which is exploited in almost all SR methods. In this sense, decoding latents into pixel space is a necessary step and the proposed EA-DISTS will not introduce too much extra training cost. In practice, the increased training cost and memory consumption is negilible and hard to be observed.\n\n\n\n`Q4-3` While the paper claims that using SDXL as a discriminator for adversarial training overcomes previous teacher-based distillation methods, it is noticeable that previous methods [31, 32, 33] all apply SD2.1 as a teacher model. It is unclear whether the good performance comes from a more powerful model, i.e., SDXL or from the adversarial training without the teacher model.\n\n`A4-3` We present the performance of using **SD2.1** as the discriminator in our ablation study. The detailed metrics and comparisons with other methods are shown in the table below. Under the same pretrained model used as the discriminator, our method still outperforms other one-step diffusion SR methods. Furthermore, employing a larger discriminator (e.g., **SDXL**) leads to further performance improvements. \n\n| Method               | PSNR | LPIPS | DISTS  | NIQE | MUSIQ  | MANIQA |\n|----------------------|------|-------|--------|------|--------|--------|\n| SinSR                | 25.83 | 0.3641 | 0.2193 | 5.746 | 61.62 | 0.5362 |\n| OSEDiff              | 24.57 | 0.3036 | 0.1808 | 4.344 | 67.31 | 0.6148 |\n| AddSR                | 25.23 | 0.2990 | 0.1852 | 5.223 | 63.08 | 0.5457 |\n| **D3SR w/ SD2.1 disc.**  | 24.60 | 0.3031 | 0.1775 | 3.925 | 69.21 | 0.6302 |\n| **D3SR w/ SDXL disc.**   | 24.11 | 0.2961 | 0.1782 | 3.899 | 68.23 | 0.6383 |\n\nThis demonstrates that adversarial training with a diffusion-based discriminator can overcome the limitations imposed by teacher models, and using a stronger model as the discriminator further enhances performance.\n\n`Q4-4` If possible, it is better to add a user study to enhance the comparison with human preference.\n\n`A4-4` Thank you for your suggestion. We have added a user study, and the results are shown in the table below. We selected five classic scene types: **landscape**, **human**, **plant**, **animal**, and **architecture**, with a total of 20 participants involved in the evaluation. Our method achieved a recognition rate of **60.63%**, surpassing other methods.\n\n| Method     | SinSR | OSEDiff | AddSR | D3SR (ours) |\n|------------|-------|---------|-------|-------------|\n| Percentage/%    | 9.50  | 20.82   | 9.05 |  **60.63**    |"}]

Author rebuttal:

Meta-review: This paper presents a one-step diffusion model for real-world image super-resolution. It leverages a large-scale diffusion discriminator to overcome the limitations of teacher-based supervision and introduces edge-aware DISTS to enhance perceptual loss for better detail generation. The method achieves 7× faster inference with ≥30% fewer parameters compared to multi-step diffusion models.

All 4 reviewers recognized the effectiveness and efficiency of the proposed approach and gave consistently positive ratings (1 Accept and 3 Borderline Accept). The AC encourages the authors to include additional experiments suggested by the reviewers in the final version, such as comparisons with the latest works (Cj9F), a user study (hd5s), and CLIP-IQA evaluation (hd5s), to further strengthen the completeness of the paper. Overall, the paper makes a solid and timely contribution to advancing one-step diffusion-based super-resolution. Considering the rebuttal and the discussions among reviewers, the AC recommends acceptance of this paper.
Decision: Accept (poster)