DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Authors: Zheng Chen
Zichen Zou
Kewei Zhang
Xiongfei Su
Xin Yuan
Yong Guo
Yulun Zhang
Venue: NeurIPS 2025 poster ()
Keywords: One-Step Diffusion
Real-World
Video Super‑Resolution
Primary area: applications
TL;DR: An efficient one-step diffusion model for real-world vsr.

Abstract: Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely slow. Sampling acceleration techniques, particularly single-step, provide a potential solution. Nonetheless, achieving one step in VSR remains challenging, due to the high training overhead on video data and stringent fidelity demands. To tackle the above issues, we propose DOVE, an efficient one-step diffusion model for real-world VSR. DOVE is obtained by fine-tuning a pretrained video diffusion model (*i.e.*, CogVideoX). To effectively train DOVE, we introduce the latent-pixel training strategy. The strategy employs a two-stage scheme to gradually adapt the model to the video super-resolution task. Meanwhile, we design a video processing pipeline to construct a high-quality dataset tailored for VSR, termed HQ-VSR. Fine-tuning on this dataset further enhances the restoration capability of DOVE. Extensive experiments show that DOVE exhibits comparable or superior performance to multi-step diffusion-based VSR methods. It also offers outstanding inference efficiency, achieving up to a **28$\times$** speed-up over existing methods such as MGLD-VSR. Code is available at: https://github.com/zhengchen1999/DOVE.

DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Zheng Chen'; Zichen Zou?; Kewei Zhang', Xiongfei Su Xin Yuan', Yong Guo Yulun Zhang' Schooo of Computer Science, Shanghai Jiao Tong University, Zhiyuan College, Shanghai Jiao Tong University, *Chna Mobile Research Institute, *Wettlake University, HHuawei Consumer Business Group
Abstract
Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely slow. Sampling acceleration techniques, particularly single- step, provide a potential solution. Nonetheless, achieving one step in VSR re- mains challenging, due to the high training overhead video data and strin- gent fidelity demands. To tackle the above issues, we propose DOVE, an effi- cient one-step diffusion model for real-world VSR. DOVE is obtained by fine- tuning a pretrained video diffusion model (i.e., Cog VideoX.. To effectively train DOVE, introduce the latent-pixx pixel training strategy. The strategy employs two-stage scheme to gradually adapt the model to the video super-resolution task. Meanwhile, design video processing pipeline to construct a high-quality dataset tailored for VSR, termed HQ-VSR, Fine-tuning on this dataset further enhances the restoration capability of DOVE Extensive experiments show that DOVE exhibits comparable or superior performance to multi-step diffusion- VSR methods. It also offers outstanding inference efficiency, achieving up a 28x speed-up over existing methods such as MGLD-VSR. Code is available httpps///gihuuu..c////eengngi/i
Introduction
Video super-resolution (VSR) is a long-standing task that aims to reconstruct high-resolution (HR) videos from low-resolution (LR) inputs [6). With the rapid growth of smartphone photography and streaming media, real-world VSR has become increasingly critical. Unlike the synthetic degrada- tions (e.g., bicubic), real-world videos often suffer from complex and unknown degradation. This makes high-quality video restoration difficult. To tackle this issue, numerous methods have been proposed [5] 26 37 47 60). Among them, generative models, e.g., generative adversarial networks
(GANs), are widely adopted for their ability to synthesize fine details [8 23 5).
Recently, new generative model, the diffusion model (DM), has rapidly gained popularity [10] Compared with GANs, diffusion models exhibit stronger generative capabilities, especially those pretrained large-scale datasets 28 27
52) Therefore, leveraging pretrained diffusion models for VSR has become an increasingly popular direction. For instance, some methods adopt pretrained text-to-image (T2I) models (50) 63 and incorporate temporal layers and optical flow constraints to ensure consistency across frames. Meanwhile, some approaches directly employ the text-to-video (T2V) model [ 48), and use ControlNet constrain video generation. By exploiting the natural priors in pretrained models, these methods can realize more realistic restorations.
2
59
"qqual contribution.
CCorresponding author: Yulun Zhang, yulun 100@ gmail.com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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RAAVS
VOLLD
US VILDOW
STAR
DOVER
DJ
Figure I: Efficiency and performance comparisons on the real-world benchmark (i.e., VideoLQ (5) We provide qualitative (left) and quantitative (right) results. The running time (Time) is measured on one A100 GPU using a 33-frame x20x 1280 video. Our method achieves impressive performance and excellent efficiency. Compared with MGLD-VSI VSR [50]. DOVE is approximately 28x faster.
However, existing diffusion-based VSR methods face several critical challenges: (1) Multi-step sampling restricts efficiency. To generate high-quality HR videos, these models typically require dozens of sampling steps. This seriously hinders the running efficiency. Moreover, for some methods (50 48 long videos are processed in time segments. which further amplifies this inefficiency. (2) Additional modules increase overhead. Whether based on T2I or T2V models, many methods often introduce auxiliary components (63 9 e.g., ControlNet [57] temporal layers, realize VSR. These additional modules further slow down inference. For example, when processing a 33- frame 720p x720x 1280) video on an NVIDIA A100 GPU, MGLD- -VR (50) takes 425.23 seconds, while STAR 488 requires 173.07 seconds. Such high latency severely hinders the application of diffusion- based video super-resolution methods in the real world.
One common approach to accelerating the diffusion model is to reduce the number of inference steps [33 [22. In this context, single-step inference has attracted widespread attention as an extreme acceleration form. Prior studies have demonstrated that single-step diffusion models achieve impres-
sive results in image/video generation [30 55 61 18] and image restoration [39 13 (e.g.. image
super-resolution) tasks. However, in VSR, single-step diffusion models have rarely been studied. There are two critical difficulties in realizing one-step inference in VSR: (1) Excessive video-training cost. To enhance single-step models performance, some methods (e.g.. DMD (55) and VSD [4] 45))
jointly optimize multiple networks. While manageable in the image domain, such overhead becomes burdensome and unacceptable in video due to the multi-frame setting. (2) High-fidelity demands in VSR. Some single-step video generation models improve generation quality via adversarial training rather than multi-network distillation [6 [8]. However, the inherent instability of adversarial training may introduce undesired details results, hindering VSR performance 445)
To address these challenges, we propose DOVE, an efficient one-step diffusion model for real-world video super-resolution. It is built by fine-tuning an advanced pretrained video generation model (i.e., Cog VideoX (52). Considering the strong representation and prior knowledge of the pretrained model, we do not introduce additional components (e.g., optical flow module (50 63 or ControlNet (48).
This design can further improve the inference efficiency.
For effective DOVE training, we introduce the latent- latent-pixel training strategy. Based on the above analysis, we opt for the regression loss instead of distillation or adversarial losses to enhance training efficiency. The strategy consists of two stages: Stage-1: Adaptation. In the latent space, minimize the gap between the predicted and HR latent representation. This enables the model to learn one-step LR-to-HR mapping. Stage-2: Refinement. In the pixel space, we perform mixed training using both images and short video clips. This stage enhances model restoration performance. With the proposed training strategy, we can complete model fine-tuning within only 10K iterations.
Moreover, fine-tuning pretrained models on high-quality datasets is crucial for achieving strong performance. However, in the field of VSR, suitable public datasets [63 25 remain scarce. To address this issue, we design a systematic video processing pipeline. Using the pipeling, we curate a high-quality dataset of 2,055 videos, HQ-VSR, from existing large-scale sources. Equipped with the latent- pixel training strategy and the HQ-VSR dataset, our DOVE achieves impressive performance.
As shown in Fig. DOVE outperforms state-of-the-an multi-step diffusion-based VSR methods. Simultaneously, due to one-step inference, our DOVE is up to 28x faster over previous diffusion- based VSR methods, e.g., MGLD-VSR [50]. In summary, our contributions include:
2

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We propose a novel one-step diffusion model, DOVE, for real-world VSR. To our knowledge, this the first diffusion- based VSR model with one-step inference.
We design a latent- latent-pixel training strategy and develop a video processing pipeline to construct a high-quality dataset tailored for VSR, enabling effective fine-tuning of DOVE.
Extensive experiments demonstrate that DOVE achieves state-of-the -ftth-rft performance across multiple benchmarks with remarkable efficiency.
2 Related Work
2.1 Video Super- Repper-esooltion
With the advancement of deep learning, numerous video super-resolution (VSR) methods have emerged [11] 24] 16, 4.. These approaches (e.g., Bas [4] and Vrt [16] utilize a variety of
architectures, including recurrent-based [17 32 and booddng-window-bee
[4 53) models, and
have demonstrated promising results. However, these methods typically assume a fixed degradation
which limits their performance when confronted with real-world degradation, which is often more complex. To better address such scenarios, some methods, such as RealVSR (51] and
process 40 53
MVSR4x (37. have utilized HR-LR paired data from real environments. In contrast, others (e.g..
RealBasic (5I) have proposed variable degradation pipelines to enhance the model's 's adaptability to the complex degradations in real-world VSR. In addition, real-world VSR methods [60] 26
incorporate structural modifications to tackle these challenges. Despite the considerable development in these domains, these methods still face persistent challenges in generating delicate textures.
2.2 Diffusion Model
Diffusion models [0]] have demonstrated strong performance in visual tasks, driving the development of both image generation models (28] 27 [29 and video generation models 2 59] These pretrained models (e.g., Stable Diffusion 29 and 12 XL [59]) offer rich generative priors, significantly advancing downstream tasks such as video restoration. By leveraging pretrained diffusion models,
many video restoration methods 50 63
can recover more realistic video textures. Some approaches [50] 63 15 adapt pretrained image generation models for VSR tasks, enhancing them to address temporal inconsistencies between frames. For example, Upscale-A-Video [63 employs temporal layers train on frozen pretrained model, thereby enhancing the temporal consistency of the video. Meanwhile, other approaches [9] 48) utilize video generation models (59) and incorporate ControlNet (57) constrain video generation. However, these methods remain limited by multi-step sampling, which hampers efficiency, and by the added modules, which increase computational overhead, thereby impacting inference speeds.
48
36 15
2.3 One-Step Acceleration
common method to accelerate diffusion models is reducing the number of inference steps, with one-step acceleration gaining significant attention as extreme approach. This technique leverages methods such as rectified flow 199 20 adversarial training [166), and score distillation [55]
Building on these methods, recent image super-resolution (ISR) studies 40 13 45 7 have integrated
one-step diffusion. For instance, OSEDiff (45) applics variational score distillation [41] to improve inference efficiency. However, directly applying these methods to VSR is impractical due to the high computational cost associated with processing multiple video frames. Additionally, some methods (e.g., SF-V [61] and Adversarial Post-Training (18) have employed adversarial training explore one-step diffusion in video generation. However, adversarial training methods are inherently unstable. Applying them in VSR, instead of multi-networt distillation [55] 41), may introduce unwanted artifacts that degrade the VSR performance [45). In this paper, we introduce a novel and effective one-step diffusion model that successfully accelerates the inference process of video super-resolution.
3 Method
In this section, we introduce the proposed efficient one-step diffusion model, DOVE, First, we present the overall framework of DOVE, which is developed based on Cog VideoX to achieve one-step video super-resolution (VSR). Then, we describe two key designs that facilitate high-quality fine-tuning: the latent-pixel training strategy and the video processing pipeline.
3

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VAE Emcoder
Difusion
VAEE Erooodor
Transformer
Xir
Stage-1 (Latent-Space): Adaptation
Eiuare
VÁE Encodda
VAE Decoder
Diftusion Transfomer
Image Branch
Edios
Stage-2 (pacel-Space): Refinement
Sar
Figure 2: Overview the framework and training strategy of DOVE. Our method performs one-step sampling to reconstruct HR videos (X,r) from LR inputs (x;,). To enable effective training, adopt the two-stage latent-pixel training strategy. Stage-1 (latent-space); Minimize the difference between the predicted and HR latents. Stage-2 (pixel-space); Improve detail generation using mixed image / video training, where the data branch at each iteration is controlled by image ratio (ş)). To reduce memory cost, video is processed frame-by-frane
the encoder and decoder.
3.1 Overall Framework
We construct our one-step diffusion network, DOVE, based on powerful pretrained text-to-video model (i.e. VogVideoX (52]). CogVideoX employs 3D causal VAE compress videos into the latent space and uses Transformer denoiser Vo for diffusion. Leveraging its strong priors, our method can better handle the complexities of real-world scenarios. Meanwhile, enhance inference efficiency, we do not introduce any auxiliary modules like previous methods [50 63 [
such as temporal layers or ControlNet. Instead, we design two-stage training strategy and curate high-quality dataset specifically for the VSR task, enabling strong performance with high efficiency.
The overall architecture of DOVE is illustrated in Fig. 2 Specifically, given low-resolution (LR) video Xly, we first upscale it to the target high resolution using bilinear interpolation. The upscaled video then encoded into a latent representation Zír by the VAE encoder E. Following previous works [66 [5], we take as the starting point of the diffusion process. That is, we treat Z/r- as the noised latent z₁ a specific timestep 1, which originally formulated as:
2, v 0, + VI OrE, ar II (at): ==1
= 1-
where is the Gaussian noise, is the "clean" latent sample, and 9 is the noise factor. Afterwards, single denoising step is performed through Transformer Ve. yielding the "clean" latent zer (i.e., z in Eq. (T). Since CogVideoX adopts the u-prediction formulation, the denoising process is defined as:
gr Vā,ūir VI &g (Ztr: c.
(2)
Unlike the previous approach [55 that set to the total diffusion step (i.e., 999 in CogVideoX), we choose smaller value. This is based the observation that early diffusion steps focus on global structure while later steps refine fine details. Since the LR video already contains sufficient structural information, starting from the beginning not needed. Conversely, a tiny t (i.e., late step) would hinder the removal of degradation. Therefore, we empirically set t-3 Finally, the latent z,r is decoded by the VAE decoder D obtain the output video X the restoration results.
3.2 Latent-Pixel Training Strategy
The framework described above enables to adapt generative model for the VSR task. However, since the model is designed for multi-step sampling and the distribution of Zir and Z, are not consistent, the pretrained model cannot be directly applied. Thus, fine-tuning is required to ensure that the reconstructed output X closely matches the high-resolution (HR) ground truth X/r-
Nevertheless, many one-step fine-tuning strategies developed for images (e.g., DMD (55) and VSD 41 are not available in the video domain, as the video data volume larger (due multiple frames). Besides, adversarial training, which is commonly applied in single-step video generation [6] 18 is less suitable for VSR. This because the inherent instability of adversarial training may lead to undesired details in the results, which are misaligned with the high-fidelity requirements of VSR.

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To enable effective training for DOVE, we design novel latent- pixel training strategy. We adopt the regression loss, instead of distillation or adversarial learning, improve training efficiency. In addition, we only fine-tune the Transformer component, preserving the priors in the pretrained VAE. The strategy is illustrated in Fig. 2
which consists of a two-stage training process.
Stage-1: Adaptation. With the VAE decoder D fix, making the predicted Xay close to the high-quality Xhr, corresponds minimizing the difference between Zar and the HR video latent Zhr-
Due to the high compression ratio of the VAE, training in latent space is more efficient than in pixel space. Therefore, we first minimize the difference between 2 and Zhr- As illustrated in Fig. 2 both the LR and HR videos are first encoded into latent representations via the VAE encoder E. We then train the Transformer vo using the MSE loss (denoted as L, The process is denoted as:
L1 Lase (Zar, Zhr)
Zsr
Zhr l
Zhhr
Benefiting from the high computational efficiency in the latent space, can train the model on videos with longer frame sequences. This enables the model to better handle long-duration video inputs, which are common but challenging in video super-resolution tasks.
Stage-2:
Refinement, After the first training stage, the model can achieve LR to HR video mapping to certain extent. However, we observe that the output Xgr exhibits noticeable gaps from the ground-truth XAr- This may be because in the latent space, although Zer is close to Zhr, the slight gap will be further amplified after passing through the VAE decoder D. Therefore, further fine-tuning in pixel space is necessary to reduce reconstruction errors. However, the large volume of video data makes the training overhead in pixel space unacceptable.
To address this issue, we introduce image data for the second stage fine-tuning. An image can be treated as a single-frame video, whose data volume is much smaller than that of multi-frame sequences, making pixel-domain training feasible. As illustrated in Fig. 2 we minimize the MSE loss between the output image &ar and the HR image &hr. Meanwhile, we additionally adopt the perceptual DISTS 6 loss, Ldisss, to better preserve texture details. The total image loss is defined as:
Luz-image = mms ( &nr) + X Laists (xrr &
(4)
where Å is the perceptual loss scaler. After fine-tuning on images in pixel space, the restoration detail is greatly improved. However, since the model is trained only on single-frame data, exhibits instability when handling multi-frame videos, which adversely affects overall performance. To resolve this issue, we reintroduce video data and adopt a mixed image/video training strategy.
Specifically, we revisited the memory bottlenecks during video training. We find that the VAE is the primary constraint. Inspired by the success of image-only training, we process videos frame-by- through the VAE encoder & and decoder D. This avoids multi-frame memory spikes. Meanwhile, the Transformer continues to operate on the complete latent. The process (as in Fig. 2 is defined as:
Zrr E(x (!) t 1,..., n, Zir (1) Zir
(t)
(m)
Zgr (o(2))),
(5)
_ = (2) 1 I,..., XSr
x
where x and x!9 mean the 1-th frame of LR and SR video, respectively; is the frame number; (-) denotes the one-step denoising process based on Transformer ve defined in Eq. ( As with the image-level training, we apply MSE loss and the perceptual DISTS loss. Additionally, to enforce frame consistency, we introduce frame difference loss:
(Xar, Xhrr)
Ax (t)
(0 IIp Ax!"
Ax = x -x(""" (1-))
(6)
2
where (x captures the frame-to-frame change. The total loss for video training is computed as: Lxz-video = Lmse (Xar, Xhr) + A1 (Xsss Xhr) + >2 Lirame (X.r, Xtrr.
(7)
where A is the frame loss scaler. By including video data in training, the stability of video processing is improved, further enhancing video restoration performance.
In summary, our mixed training strategy (combining image and video) in the pixel domain, effectively enhances restoration performance and robustness on videos. Besides, we introduce a hyperparameter to control the ratio between image and video samples. We study the effect of &o in Sec. 4.2
5

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S1: Meta Filter
S2: Scene Filter
S4: Motion Process
Dataset: HQ-VSR
Heght 220
With 720
Frame 50
10 Notion Huesnn Sackpound
3 Biuncing Bux Mittinn Aroot Region
63: Quality Filtering
Austhelic
CL P.P.P-DD
Finke VGA +0
DIDEVER
Nobion
Figure 3: The illustration of the video processing pipeline (four steps). Based on this pipeline, we construct HQ-VSR, high-quality dataset tailored for the VSR task.
3.3 Video Processing Pipeline
Motivation. Fine-tuning high-quality datasets significantly improve the performance of pretrained models on downstream tasks. However, in the domain of video super-resolution (VSR),
suitable public datasets remain scarce. Current VSR methods typically apply the following data: (1)
conventional video datasets, e.g., REDS [24]; (2) self-collected videos, e.g., YouHQ [63 and (3)
publicly text-video datasets, e.g., WedVid-IOM I and Open Vid- IM [25).
Nevertheless, these datasets have some limitations: (1) some contain relatively few data and scenarios; (2) some lack proper curation filtering specifically for VSR. As result, fine-tuning on these datasets cannot fully realize the potential of pretrained models in the VSR task.
Pipeline. To address the aforementioned limitations, we propose systematic video processing pipeline to construct high qualityaltty dataset tailored for VSR. As shown in Fig. 3 the pipeline is:
Step I: Metadata Filtering. We begin with a coarse filtering process based on metadata, i.e., video resolution and frame count. We extract all videos with shorter side that exceeds 720 pixels and over 50 frames. As VSR often targets large-sized videos, the matching training data is required.
Step 2: Scene Filtering. Following prior work 22 we perform scene detection, segmenting videos into distinct scenes. We discard short clips with fewer than 50 frames. This step reduces cuts and transitions, which are unsuitable for the model to learn coherent video semantics.
Step 3: Quality Filtering. Next, we score each video with multiple quality metrics. While previous works [62, 225 have used metrics like the LAION aesthetic model [31]. these are insufficient, since VSR pays more attention detail quality. Therefore, we incorporate more metrics: CLIP-IQA [35) Faster VQA 43,, and DOVER [44]. With more metrics, we achieve stricter filtering.
Step 4: Motion Processing. Finally, eliminate videos with insufficient motion. We first estimate optical flow to calculate the motion score, following prior methods 622 2). Although the score can filter some still videos, the global score is suboptimal for VSR. In VSR, training data is typically generated via cropping rather than resizing from HR video to preserve fine details. However, high- motion videos may contain static regions (e.g., speech backgrounds in Fig. B yielding static crops.
To address this, we introduce the motion area detection algorithm for localized processing (see Fig. B}. We generate motion intensity map M from the optical flow F, and apply threshold T to produce motion mask. Then, we determine the motion areas based on the mask. To ensure sufficient context, expand the bounding box by fixed padding p. The procedure is defined as:
Mij [2,l,2, S= (i.
Mij- 7),
B
min P, min j- P. max i + P. max + p), (UJ)S
(8)
(Kj)sS
(i.j)ES
(i.jES
where ?C2² is the set of all pixel indices; Fij denotes the optical-flow vector pixel (i,j); S is the motion mask; and B is the bounding box corresponding to motion areas. Finally, we crop the video according to the bounding box B. and discard the cropped region with resolution lower than 720p.
We apply the proposed pipeline to the public dataset Vidn Vid-IM M 225,, which contains diverse scenes. Based on this, we extract 2,055 high-quality video samples suitable for VSR, forming a new dataset, HQ-VHS VSR. The detailed pipeline configuration provided in the supplementary material. Fine-tuning DOVE the HQ-VSE yields superior performance compared to other datasets.
HQ-VSR.

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Training Sluge
S1
S14S2-1
S1+S2-IVV
Image Ratio
0% (video)
205
50%
80%
100S (imuge)
PSNR
27.20
26.41
26.41
26.44
LPIPS
0.3037
0.2784
0.2696
LPIPS L
0.2624
0.2617
0.2686
0.2696
0.2784
CLIP-IQA f
0.3236
0,5085
0.5107
CLIP-IQA
0.4800
0,5012
0.5027
0.5107
0,5085
DOVER
0.6154
0.7694
0.7809
DOVER
0.7647
0.7701
0.7751
0.7809
0.7694
(a) Ablation training strategy.
(b) Ablation image ratio (4p) stage-2.
Dalaset
PSNR
LPIPS 1 CLIP-IQA 1
DOVER
Pipeline
PSNR
LPIPS 1
CLIP-IQA
DOVER
YouIQQ
26.88
0.3383
0.2496
0.3965
Open IM
27.04
0.3376
0.2683
0.4363
OpenVid-IM
27.04
0.3376
0.2683
0.4363
+Fiiter
27.09
0.3236
0.2894
0,5357
HQ-VSR
27.20
0.3037
0.3236
0.6154
+Motion
27.20
0.3037
0.3236
0.6154
(c) Ablation training dataset.
(d) Ablation on processing pipeline.
Table I: Ablation study. Evaluation is conducted on UDM10. (a) S1/S2: stage- 1/2: I: image-only training: I/V: image-video mixed training. (b) 0%: video-only; 100%: image-only. (c): Experiments
on stage-1. (d) + +ilttr: apply steps 1-3; + Mootoo further apply step 4 (motion processing).
4 Experiments
4.1 Experimental Settings
Datasets. The training dataset comprises video and image datasets. The video dataset, HQ-VSR, includes 2,055 high-quality videos, and adopts the RealBasicVSR (5) degradation pipeline to syn- thesize LQ-HQ pairs. The image dataset is DIV2K [3], with 900 images, which follows the Real- ESRGAN (38] degradation process. For evaluation, we apply both synthetic and real-world datasets. The synthetic datasets include UDMI0 (34), SPMCS [53], and YouHQ40 663, using the same degra- dations as training. For real-world datasets, we apply RealVSR (II) MVSR4x 377, and VideoLQ [5]. RealVSR and MVSR4x contain weal-world LQ-HQ pairs captured via mobile phones, while VideoLQ is Internet-sourced without HQ references. All experiments are conducted with a scaling factor x4.
Evaluation Metrics. We adopt multiple evaluation metrics assess model performance, which are categorized into two types: image quality assessment (IQA) and video quality assessment (VQA). The IQA metrics include two fidelity measures: PSNR and SSIM 422. We also some perceptual quality IQA metrics: LPIPS [58]. DISTS [6) and CLIP-IQA 35 For VQA, we employ FasterVQA (43 and DOVER [44) to evaluate overall video quality. Meanwhile, we adopt the flow warping error Ewarp' refers to Ewarp (x10~3) [12. to assess temporal consistency. Through these metrics, conduct a comprehensive evaluation of video quality.
Implementation Details. Our DOVE is based on the text-to- model, CogVideoX1.5 [52. We empty text the prompt, which is pre-encoded in advance to reduce inference overhead. The proposed two-stage training strategy is then applied for fine-tuning. Both stages are trained on 4 NVIDIA A800-80G GPUs with the total batch size 8. We use the AdamW optimizer [21] with 31=09, 00995 and 33=9.88. In stage- 1, training is conducted on video data. The videos have a resolution of 320x640 and frame length of 25. The model is trained for 10,000 iterations with learning rate of 2x 55 In stage-2, both video and image data are used, with 0-0.8 (i.e., images comprising 80% of the input). All inputs have a resolution 320 640. The model is trained for 500 iterations with learning rate of 5x 10-6, The loss weights A₁ and A2 are set to 1.
4.2 Ablation Study
We investigate the effectiivnnes of the proposed latent-pixel training strategy and video processing pipeline. All training configurations are kept consistent with settings described Sec. 4.1 We evaluate all models on UDM10 34] Results are presented in Tab.
Training Strategy. We study the effects of the latent-pixel training strategy, as shown in Tab. In the stage-1 1 (S1), where training is conducted in latent space with MSE loss, the results tend to be overly smooth, leading to lower perceptual performance. After fine-tuning in pixel space during 2 (S2), perceptual metrics (i.e,, LPIPS, CLIPP-QA, and DOVER), improve significantly. Furthermore, using a mixed training scheme with both images and videos in stage-2 (S2-I/V) leads to further performance gains, demonstrating the effectiveness of hybrid training.
7

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Dotnset
Metrie
ReaEESRGAN
[8]
ResShift [56
RealBasie VSR
[
Upscale---Vibe [3]
MGLD-VSR
50
VEalancer 9
STAR ESS
DOVE
(ours)
PS
24.04
23.65
24.15
21.72
24.23
21
23.47
26.48
SSIM
0.7107
0.6016
0.6801
0,5913
0.6457
0.6811
0.6804
0.7827
LPIPS
0.3877
0.3908
0.4116
0.2696
UDMIO
DISTS L
0.2184
0.2898
0.2067
0.2230
0.1677
0.2310
0.2156
0.14 1492
CLIP-IQA
0:4184
04364
0,3494
0.4607
0.4557
0.2852
0.2417
0.5107
VAsterVQA
0.7386
0.7744
0.6009
8.7489
0:5493
0.7042
0.8064
DOVER
0,7060
0.3290
0.7564
0.7291
0.7261
0.1576
0.4830
0,7809
4,83
6.12
3.10
3.97
3.59
1.03
2,08
1.77
PSNR f
21.22
21.68
22.17
18.81
22.39
18.58
21.24
23.11
SSIM
0:5613
0.5153
0.5638
0.4113
0.5896
0,4850
0.5441
0.6210
I.PIPS
0.3721
0.4667
0.3662
0.4468
0.3263
0.5358
0.5257
0,2888
SPMCS
DISTS L
CLIP-IQA
0.5238
0.2220
0,5442
0.2697
0,3513
0.2164
0,5248
0.2455
0.434K
0.1960
0.3188
0.2669
0.2672
0.2646
0.1713
0.5660
VOst
0.2213
0.7307
0.6556
0.6745
0,4658
0.4076
0.7245
DOVER
0,2490
0.4930
0.6753
0.7171
0.6754
0.4284
0.3204
0,7828
Hearp
5.61
8,07
1.88
4.22
1.68
1.19
1,01
1.04
PSNRT
22,82
23.32
22.39
19.62
23.17
19.78
22.64
24.30
0.6337
0.5895
0,4524
0:5911
LPIPS
0.3571
0.4211
0.4091
0.4268
0,3608
0.4742
0.4600
0,2997
YoaHQ40
DISTS L
0,1790
0.2159
0.1933
0,2012
0.1685
0.21-10
0.2267
0.17
CLIP-IQA
0.4704
0,4633
0.3964
0.5258
0.4657
0.3309
0.2739
0.4985
PasterVQA
0.8401
0,7024
0.8423
0.8660
0,8363
0,7022
0.5586
0.8494
DOVER
0,8572
0.6855
0.8596
0,8596
0,8146
0.6957
0.5594
0,8574
5,91
5.75
3,08
6.84
3.45
0.95
2.21
2.05
PSNR
20.85
20.81
22.12
20.29
22.02
15.75
17:43
22.33
0.7105
0.7163
0.5945
0.5215
LPIPS
0,2011
0.2312
0.1870
0.2671
0.2182
0.3784
0.2943
0.55
RealVSR
DISTS 1
0.1279
0.1435
0,0983
0.1425
0.1169
0.1688
0.1599
0.0278
CLIP-IQA
0.7472
0.2905
0.4855
0.3880
VosterVOA
0,2436
0.6988
0.7789
0.7403
0.7707
0,8018
0.2338
0,7959
DOVER
0,7542
0.7099
0,7636
0,7114
0.7508
0,7637
0.2051
0,7867
6.32
4.45
6:25
5.15
9.88
PSNR
22.47
21.58
21.80
20.42
22.77
20:50
22,42
22.42
0.7412
0.7045
0.6117
0.7418
0.7117
0.7421
1.PIPS
0.4534
0 0.5945
0.4235
0.4717
0.3568
D.4471
04311
0.3476
MVSR4x
CLIP-IQA
DISTS
0,3021
0.4396
0.3351
0.2:98
0,2673
0.6106
0.2245
0.3769
0,2800
0.2714
0.2363
RaservVQ
0.3371
044723
0.7497
0.7666
0.6764
0.3584
0.2840
0.7742
DOVER
0,2111
0.3255
0.6846
0,7221
0.6214
0,3164
0,2137
0.6984
Mearp
1.64
1.69
5.10
0.62
CLIP-IQA
0.3617
0,3433
0.1132
0.3166
0,3031
0,3184
Videol.Q
FasterVQA
0.7381
0.5909
0.7586
0.7501
0.7412
0.6769
0.7028
0.7764
DOVER
0,2310
0.6160
0.7388
0,2370
0,7421
0.6912
0.7080
0,7435
7.58
7.79
5.97
13.47
6.79
6.495
5.96
5.85
Table 2: Quantitative comparison with state-of-the-art methods. The best and second best results are colored with red and blue. Our method outperforms on various datasets and metrics.
Image Ratio (ç)). We further investigate the impact of the image data ratio in stage-2. The results are presented in Tab. Tb Specifically, ratio-0% denotes training with video data only, while 100% uses only image data. Neither alone yields optimal results, since videos suffer from lower quality due to hardware limitations; while with higher quality are limited by the single-fran frame nature. Conversely, mixing both offers complementary strengths and improves overall performance. Based on the experiments, we finally set the image ratio to 80% (i.e., 0.8).
Training Dataset. We compare different training datasets. To eliminate the influence of image data, we conduct training only in stage-1. The results are listed in Tab. Tcl For Vid- (25], we select videos with a resolution higher than 1080p (1080x 1920), resulting in approximately 0.4M videos. The YouHQ dataset [63] contains 38,576 1080p videos. We observe substantial performance variation across datasets. Notably, our proposed HQ- VSR dataset achieves superior performance despite containing 2,055 videos, which is significantly fewer than the other datasets.
Video Processing Pipeline. We also performed an ablation on the proposed video processing pipeline. The results are presented in Tab. [d First, we apply the filtering steps (i.e., +Filter), including metadata, scene, and quality filtering) to the raw dataset (i.e., Vid- 1M (25).. Although Oie has undergone some preprocessing, applying more filtering tailored to VSR further improves data quality. Then, we apply motion processing on the filtered data to exclude static videos and regions. This leads to further performance gains, confirming the benefit of motion detection cropping.
4.3 Comparison with State-of-the-Art Methods
We compare our efficient one-step diffusion model, DOVE, with recent state-of-the-art image and video super-resolution methods: RealESRGAN (38), ResShift (56), RealBasie [5) Upscale-A-
Video (63) MGLD-VE VSR [50], VEnhancer
[9] and STAR (48).
8

---

HR
1.R
ResShift
Upscale- A. Video L
YouHQuU: 036
MGI AGGLD-VSR 50
VEnhancer
STAR
45
DOVE (ours)
CC-24-55
4
EC:24-55
4
EC-24-55
4
IL:24555
4
BENH
LR
ResShift
RealBasic VSR
Upscale-A- A-iddo
EC22455
4
2
55
4
2455
4
2C-24-55 BET
4
V: D41
MGLD-VSR 50
VEnhancer
STAR AX
DOVE (ours)
Figure 4: Visual comparison on synthetic (YouHQ40 63 and real-world (Videol (5] datasets.
The videos Videol.Q Q are sourced from the Internet without high-resolution (HQ) references.
Method
Step
Time (8)
Performance
Upscalo-A-Viee
S
279.32
0.7370
MGLD-VSR 50
425.23
0.7421
VEahancer
0
121.27
0.6912
WOLD-VSR
STAR AS
173.07
0.7080
STAR
DOVE (ours)
14.90
0.7435
DOVE
Table 3: Comparison of inference step (Step), run- ning time (Time), and DOVER on VideoL. (Per- formance) different diffusion-based methods.
Figure 5: Comparison of temporal consistency (stacking the red line across frames).
Quantitative Results. We present quantitative comparisons Tab. 2 Our DOVE achieves out- standing performance across diverse datasets. For fidelity metrics (i.e., PSNR and SSIM), model achieves the best performance on most datasets. For perceptual metrics (i.e., LPIPS, DISTS, and CLIP-IQA), the DOVE ranks first second on five datasets. Furthermore, for video-specific metrics regarding quality (f.e., FasterVQA and DOVER) and consistency (e.g., Emarp)) the DOVE also performs strongly. Besides, provide more comparison results in the supplementary material.
Qualitative Results. We provide visual comparisons on both synthetic (i.e., YouHQ40) and real- world (i.e., VideoL.Q) videos in Fig. 4 Our DOVE produces more realistic results. For instance, in the first case, DOVE successfully reconstructs the brick pattern, while other methods yield overly smooth or inaccurate results. Similarly, in the second example, our method delivers sharper restoration. More visual results are provided in the supplementary material.
Temporal Consistency. We also visualize the temporal profile in Fig. 5 We can observe that existing methods struggle under complex degradations, exhibiting misalignment (e.g., Upscale-A-Vin Video [63] and STAR [48 or blurring (e.g., MGLD-VSD VSR [50]). In contrast, our method achieves excellent temporal consistency, with smooth transitions and rich details across frames. This due the strong prior of the pretrained model and our effective latent-spacs training strategy.
Running Time Comparisons. We compare the inference step (Step), running time (Time), and performance (DOVER on VideoLQ (5)) of different diffusion-based video super-resolution methods Tab. For fairness, all methods are measured running time on the same A100 GPU, generating 33-frame 720x 1280 video. Our method is approximately 28x faster than MGLD-VSR [50). Even compared with the fastest compared method, VEnhancer [9) the DOVE is 8 times faster. More comprehensive analyses are provided in the supplementary material.
5 Conclusion
In this paper, we propose an efficient one-step diffusion model, DOVE, for real-world video super- resolution (VSR). DOVE is constructed based on the pretrained video generation model, Cog VideoX. To enable effective fine-tuning, we introduce the latent-pixel training strategy. It is a two-stage scheme that gradually adapts the pretrained video model to VSR task. Moreover, we construct a high-quality dataset, HQ-VSR, to further enhance performance. The dataset is generated by our proposed video processing pipeline, which tailored for VSR. Extensive experiments demonstrate that our DOVE outperforms state-of-the- art methods with high efficiency.

---

Acknowledgments
This work was supported by Shanghai Municipal Science and Technology Major Project (2011H2D22X0102) 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 20055
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Reviews:
[{"review_id": "KhnxbByQCn", "reviewer": "Reviewer_1Gww", "summary": "This paper proposes DOVE, a one-step diffusion model for real-world video super-resolution (VSR). By fine-tuning a pretrained video diffusion model (CogVideoX) using a two-stage latent–pixel training strategy, and curating a new dataset (HQ-VSR) with a dedicated video processing pipeline, the method achieves competitive perceptual quality with significantly improved inference speed. Experiments on several benchmarks demonstrate strong performance.", "questions": "- How does DOVE perform on un-seleted no-reference IQA metrics such as MUSIQ, MANIQA, and Q-ALIGN? Since the current evaluation mainly reports metrics (CLIP-IQA, DOVER, FasterVQA) that were also used in data filtering, it raises concerns about potential bias. To better assess the model’s generalization in perceptual quality, it would be valuable to compare DOVE against other diffusion-based baselines (Upscale-A-Video, MGLD-VSR, VEnhancer, STAR) under these additional NR-IQA metrics. This would help confirm whether DOVE maintains its superiority across diverse perceptual criteria not seen during data construction.\n\n- Is the choice of **t = 399** in one-step inference empirically justified? Providing a comparison with other values could help readers better understand the effect of this hyperparameter and assess whether the choice generalizes across datasets or degradation types.\n\n\n- Will the authors release HQ-VSR, pretrained models, and training code for reproducibility?", "limitations": "yes", "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- Clear motivation: Addresses the critical inefficiency of multi-step diffusion models for VSR.\n\n- Effective training strategy: Latent-to-pixel two-stage fine-tuning is well justified and validated.\n\n- Strong results: Shows superior performance and speed across multiple datasets.\n\n- HQ-VSR dataset: Well-designed pipeline with multi-stage filtering and motion-aware cropping.\n\nWeaknesses: \n\n- Evaluation bias: Key no-reference metrics (CLIP-IQA, FasterVQA) are also used in dataset filtering, raising concerns about selection bias.\n\n- Limited NR-IQA diversity: Lacks evaluation with other no-reference metrics like MUSIQ, MANIQA, Q-ALIGN.\n\n- No subjective quality study: Lacks human evaluation (e.g., MOS) to support perceptual quality claims.\nOver-reliance on pretrained prior: Performance gains may stem largely from CogVideoX, not from method design alone.", "was_revised": true, "final_justification": "The author addressed my concerns, so I keep the positive score.", "rebuttal": "# Response to Reviewer 1Gww (denoted as R1)\n\n`Q1-1` Evaluation bias: Key no-reference metrics (CLIP-IQA, FasterVQA) are also used in dataset filtering, raising concerns about selection bias.\n\n`A1-1` Thank you for the comment.\n\nThe metrics in our dataset pipeline are general and diverse, covering both frame-level quality (CLIP-IQA, Aesthetics) and overall video quality (FasterVQA, DOVER). This ensures a comprehensive enhancement of the perceptual performance of the model, rather than overfitting to specific metrics. The results in `A1-2` further support this point.\n\n\n\n`Q1-2` Limited NR-IQA diversity: Lacks evaluation with other no-reference metrics like MUSIQ, MANIQA, Q-ALIGN.\n\n`A1-2` Thank you for the suggestion. We conduct more evaluations and clarify the reasons for only CLIP-IQA.\n\n**(1) Evaluation.**\nWe evaluate our method (DOVE), and current SOTA approaches (Upscale-A-Video, MGLD-VSR, VEnhancer, and STAR) on the UDM10 dataset using additional NR-IQA metrics: MUSIQ, MANIQA, and Q-Align. The results are as follows:\n\n| Method          |  MUSIQ↑   |  MANIQA↑   |  Q-Align↑  |\n| --------------- | :-------: | :--------: | :--------: |\n| Upscale-A-Video |   59.06   |   0.2868   |   0.7625   |\n| MGLD-VSR        |   60.55   |   0.2783   |   0.7794   |\n| VEnhancer       |   37.25   |   0.2120   |   0.5513   |\n| STAR            |   41.98   |   0.2088   |   0.5340   |\n| DOVE (ours)     | **61.68** | **0.3284** | **0.8250** |\n\nOur method also achieves strong performance, confirming the absence of bias.\n\n\n\n**(2) Clarification.**\n\nIn the paper, we apply a wide range of metrics, including FR-IQA, NR-IQA, and VQA, to ensure comprehensive evaluation. Due to space limitations and the fact that NR-IQA typically measures only single-frame quality rather than full video quality, we primarily report the **representative** NR-IQA (CLIP-IQA) results.\n\nThanks again for your suggestion. We will include more no-reference metrics in the **revision**.\n\n\n\n`Q1-3` No subjective quality study: Lacks human evaluation (e.g., MOS) to support perceptual quality claims. Over-reliance on pretrained prior: Performance gains may stem largely from CogVideoX, not from method design alone.\n\n`A1-3` Thank you for the comment. We address both concerns by conducting a **user study** and clarifying the role of pretrained **priors**.\n\n**(1) User-Study.**\n\n***Setup.*** We conduct a user study involving 20 volunteers. DOVE is compared against four SOTA methods: RealBasicVSR, Upscale-A-Video, MGLD-VSR, and STAR. We randomly select 50 videos from three real-world datasets (RealVSR, MVSR4x, and VideoLQ). Each participant is shown 10 randomly selected samples (from the 50 videos). We show the LR input and recovery results of all methods for LR (shown in a randomly anonymous order). Participants are asked to vote for the best quality result for each sample.\n\nIn total, we collect **200** votes. The results are as follows:\n\n| Method          | RealBasicVSR | Upscale-A-Video | MGLD-VSR | STAR  | DOVE (ours) |\n| --------------- | :----------: | :-------------: | :------: | :---: | :---------: |\n| Number of votes |      4       |       11        |    32    |  25   |   **128**   |\n| Percentage      |     2.0%     |      5.5%       |  16.0%   | 12.5% |  **64.0%**  |\n\n***Result.*** DOVE receives **128 votes**, accounting for **64%** of the total, significantly outperforming other methods. These findings align with the quantitative and qualitative results in the paper.\n\n\n\n**(2) Pretrained Prior.**\n\n1. Leveraging pretrained models is a **feature** of our method and also a corresponding **advantage**. Besides, many recent VSR approaches also build upon pretrained backbones.\n2. However, to achieve the performance of DOVE, it is not enough to only utilize pretrained models. It depends on our proposed **latent-pixel training strategy** and the tailored **video processing pipeline** (*i.e.*, HQ-VSR). Tables 1a and 1c in the main paper highlight the contributions of these designs.\n3. Moreover, the non-specific model design feature of our method also ensures **generalizability**. To validate this, we extend our method (training strategy and dataset) to another pretrained T2V model, **WAN2.1-T2V-1.3B** [**ref1**]. The resulting model (denoted as **DOVE (WAN2.1)**), trained with our approach, also performs well. We show results on UDM10:\n\n| Method          |   PSNR↑   |   LPIPS↓   | CLIP-IQA↑  |   DOVER↑   |\n| --------------- | :-------: | :--------: | :--------: | :--------: |\n| Upscale-A-Video |   21.72   |   0.4116   |   0.4697   |   0.7291   |\n| MGLD-VSR        |   24.23   |   0.3272   |   0.4557   |   0.7264   |\n| STAR            |   23.47   |   0.4242   |   0.2417   |   0.4830   |\n| DOVE (WAN2.1)   | **25.28** | **0.2555** | **0.5133** | **0.7563** |\n\n[**ref1**] Wan: Open and Advanced Large-Scale Video Generative Models, 2025.\n\n\n\n`Q1-4` How does DOVE perform on un-seleted no-reference IQA metrics such as MUSIQ, MANIQA, and Q-ALIGN? Since the current evaluation mainly reports metrics (CLIP-IQA, DOVER, FasterVQA) that were also used in data filtering, it raises concerns about potential bias. To better assess the model’s generalization in perceptual quality, it would be valuable to compare DOVE against other diffusion-based baselines (Upscale-A-Video, MGLD-VSR, VEnhancer, STAR) under these additional NR-IQA metrics. This would help confirm whether DOVE maintains its superiority across diverse perceptual criteria not seen during data construction.\n\n`A1-4` Thank you for the comment.\n\nThe metrics used in data filtering are general and diverse, which minimizes the risk of selection bias. We provide additional evaluations using other NR-IQA metrics: MUSIQ, MANIQA, and Q-Align.\n\nFor details, refer to `A1-1` and `A1-2`.\n\n\n\n`Q1-5` Is the choice of t = 399 in one-step inference empirically justified? Providing a comparison with other values could help readers better understand the effect of this hyperparameter and assess whether the choice generalizes across datasets or degradation types.\n\n`A1-5` Thank you for the suggestion.\n\n**(1) Ablation on Different Values of $t$.** We conduct an ablation study on the denoising step *t*, as reported in Tab. 3 of the **supplementary material**. We show part of the primary results below:\n\n| Step (t)  |   99   |    399     |    599    |\n| --------- | :----: | :--------: | :-------: |\n| PSNR↑     | 27.22  |   27.20    | **27.28** |\n| LPIPS↓    | 0.3115 | **0.3037** |  0.3138   |\n| CLIP-IQA↑ | 0.3116 | **0.3236** |  0.3036   |\n| DOVER↑    | 0.5862 | **0.6154** |  0.5543   |\n\nSetting $t=399$ achieves a better balance between fidelity and perceptual quality. These results align with our analysis in Sec. 3.1 in the main paper. More detailed results and analyses are in the supplementary material.\n\n**(2) Generalization across Datasets (REDS).**  We further conduct the same experiment on another training dataset (**REDS**), with other settings remaining unchanged. The results are as follows:\n\n| Step (t)  |    99     |    399     |  599   |\n| --------- | :-------: | :--------: | :----: |\n| PSNR↑     | **26.45** |   26.35    | 26.28  |\n| LPIPS↓    |  0.3478   | **0.3378** | 0.3435 |\n| CLIP-IQA↑ |  0.3747   | **0.3853** | 0.3813 |\n| DOVER↑    |  0.6435   | **0.6611** | 0.6331 |\n\nThe results are close to those trained on HQ-VSR, which shows that the choice of $t$ is generalizable.\n\n\n\n`Q1-6` Will the authors release HQ-VSR, pretrained models, and training code for reproducibility?\n\n`A1-6` Yes, we will release the code, HQ-VSR dataset, and pretrained models to support full reproducibility."}, {"review_id": "iXqJdRJGr5", "reviewer": "Reviewer_Xx99", "summary": "The paper presents DOVE, a one-step diffusion model designed for real-world video super-resolution. DOVE builds on the pre-trained diffusion model CogVideoX and introduces two key contributions. First, a two-stage training strategy is proposed: the model is initially adapted using latent representations obtained by the frozen encoders (and the diffusion encoder), followed by training on a combination of images and video frames. Second, the authors introduce a new Video High-Resolution dataset (HQ-VSR), which improves the performance of the downstream task.", "questions": "* Table 1c provides an initial indication of the significance of the proposed HQ-VSR dataset. My question focuses on two key aspects: how much the performance of the proposed model degrades without training or adaptation using HQ-VSR, and to what extent related work or baseline methods might improve if they were evaluated using the same dataset.\n\n* L_S2 has three components (MSE, Dists, and frame).  How does the model perform if one or two elements are removed?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 1, "strengths_and_weaknesses": "The paper has the following strengths based on the model description and results:\n* The model effectively multiple representations of low-resolution video, including latent space features of a pretrained diffusion model as a first step to the downstream task. Then, the model combines both image reconstructions as a single unit and frame differences as sequence information.\n\n* The results show outperforming several baselines in both quantitative metrics and qualitative results, considering that the model is a one-step diffusion.\n\nThe only weakness is related to novelty.  L_S1 and L_S2 have elements that have already been applied. In the case of L_S1, comparing the latent representations between the prediction and the ground truth is not totally new (i.e., StableSR or DiffBIR).  For the case of L_S2, this stage incorporates established components like pixel-space MSE loss and perceptual loss. [1] uses the differences between frames.  \n\n[1] Isobe et al. (2022). Look Back and Forth: Video Super‑Resolution with Explicit Temporal Difference Modeling (CVPR 2022)", "was_revised": true, "final_justification": "After reading the author's rebuttal, my concerns were fully addressed. I upvoted to weak accept", "rebuttal": "# Response to Reviewer Xx99 (denoted as R2)\n\n`Q2-1` The only weakness is related to novelty. L_S1 and L_S2 have elements that have already been applied. In the case of L_S1, comparing the latent representations between the prediction and the ground truth is not totally new (i.e., StableSR or DiffBIR). For the case of L_S2, this stage incorporates established components like pixel-space MSE loss and perceptual loss. [1] uses the differences between frames.\n\n`A2-1` Thank you for the comment. We clarify our **novelty** as follows:\n\n1. **Loss Function.** The specific loss functions, such as MSE, DISTS, and Frame losses, are not claimed to be innovative. We adopt these standard losses to ensure general applicability.\n\n2. **(Novelty) Training Strategy.** Our core contribution is the proposed **latent-pixel training strategy**. Unlike common latent-space losses (*e.g.*, StableSR, DiffBIR), our approach enhances restoration in the pixel space. Additionally, we introduce **image-video mixed training** and the **frame-by-frame VAE encoder-decoder** to better adapt to VSR training.\n\n3. **Experiment Results.** Table 1a in the main paper demonstrates the effectiveness of our training strategy. We further show the results here.\n\n   | Training Stage |  Stage-1  | Stage-1+Stage-2(Image) | Stage-1+Stage-2(Image/Video) |\n   | -------------- | :-------: | :--------------------: | :--------------------------: |\n   | PSNR↑          | **27.20** |         26.39          |            26.48             |\n   | LPIPS ↓        |  0.3037   |         0.2784         |          **0.2696**          |\n   | CLIP-IQA↑      |  0.3236   |         0.5085         |          **0.5107**          |\n   | DOVER↑         |  0.6154   |         0.7694         |          **0.7809**          |\n\n4. **(Another Novelty) Data Pipeline.** Another contribution is our **video processing pipeline**, through which we construct the **HQ-VSR dataset**. The dataset also contributes to the performance.\n\nIn general, while individual loss functions are standard, the overall training strategy, which is adapted for VSR, is novel.\n\n\n\n`Q2-2` Table 1c provides an initial indication of the significance of the proposed HQ-VSR dataset. My question focuses on two key aspects: how much the performance of the proposed model degrades without training or adaptation using HQ-VSR, and to what extent related work or baseline methods might improve if they were evaluated using the same dataset.\n\n`A2-2` Thank you for the insightful question. We conduct two new experiments: **(1)** train our DOVE on the REDS dataset; **(2)** retrain the recent method (MGLD-VSR) with our proposed HQ-VSR dataset.\n\n\n\n**(1) DOVE Trained on REDS.**\n\nWe train DOVE using the REDS dataset with the same training settings described in our paper. The stage-2 image dataset is still DIV2K. We refer to the resulting model as **DOVE (REDS)**. The results on UDM10 are shown below:\n\n| Method               | PSNR↑             | LPIPS ↓              | CLIP-IQA↑            | DOVER↑               |\n| -------------------- | ----------------- | -------------------- | -------------------- | -------------------- |\n| MGLD-VSR             | 24.23             | 0.3272               | 0.4557               | 0.7264               |\n| DOVE (REDS)          | 25.28             | 0.3113               | 0.4801               | 0.7510               |\n| DOVE (HQ-VSR, paper) | **26.48** (+1.20) | **0.2696** (−0.0417) | **0.5107** (+0.0306) | **0.7809** (+0.0299) |\n\nThese results show that training on HQ-VSR boosts both fidelity and perceptual quality.\n\nMeanwhile, thanks to the proposed training strategy and pretrained model prior, DOVE (REDS) still outperforms existing methods when trained on REDS.\n\n\n\n**(2) MGLD-VSR Trained on HQ-VSR.**\n\nWe retrain MGLD-VSR using its official codebase, replacing the original training dataset (REDS) with HQ-VSR.\n\nWe use 4 A6000 GPUs with a batch size of 16. Other training settings are consistent with the official configuration.\n\nThe resulting model is denoted as **MGLD-VSR (HQ-VSR)**. The performance on UDM10 is:\n\n| Method                    | PSNR↑             | LPIPS ↓              | CLIP-IQA↑            | DOVER↑               |\n| ------------------------- | ----------------- | -------------------- | -------------------- | -------------------- |\n| MGLD-VSR (official, REDS) | 24.23             | 0.3272               | 0.4557               | 0.7264               |\n| MGLD-VSR (HQ-VSR)         | **24.91** (+0.68) | **0.3097** (−0.0175) | **0.4712** (+0.0155) | **0.7383** (+0.0119) |\n\nTraining MGLD-VSR on HQ-VSR also results in improvements across all metrics, demonstrating the general utility of our HQ-VSR.\n\n\n\n`Q2-3` L_S2 has three components (MSE, Dists, and frame). How does the model perform if one or two elements are removed?\n\n`A2-3` Thank you for the suggestion. We conduct an ablation study on the loss functions used in stage-2. The results are as follows:\n\n| Method          | PSNR↑ |  LPIPS ↓   | CLIP-IQA↑  |   DOVER↑   |\n| --------------- | :---: | :--------: | :--------: | :--------: |\n| MSE             | 27.50 |   0.3095   |   0.2934   |   0.5455   |\n| MSE+DISTS       | 26.33 |   0.2722   |   0.5044   |   0.7779   |\n| MSE+DISTS+Frame | 26.48 | **0.2696** | **0.5107** | **0.7809** |\n\nWe observe that MSE alone has poor perceptual performance, although PSNR is high. This may be because the model tends to output smoother results. The DISTS loss significantly improves **perceptual** quality, as reflected in LPIPS, CLIP-IQA, and DOVER scores. Meanwhile, the Frame loss also brings certain performance improvements by enhancing frame consistency."}, {"review_id": "qjyJVpFeHo", "reviewer": "Reviewer_WLko", "summary": "An efficient one-step diffusion model DOVE was proposed to achieve the real-world video super resolution. A latent–pixel training strategy was designed to effectively train DOVE and a video processing pipeline was proposed to construct a high-quality dataset to enhance the restoration capability. Extensive experiments show that DOVE exhibits comparable or superior performance.", "questions": "Comparison of Fairness\n\nDOVE model proposed the video processing pipeline to fine-tuning the VSR models on high-quality datasets. In state-of-the-art VSR comparison in Table 2, the DOVE model using the proposed video processing pipeline achieves the best performance in most scenarios, while other methods without used.  Can other existing methods also use this pipeline to improve performance? The more results and discussion should be provided.", "limitations": "Please see weaknesses and questions.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \n\nThe first one-step diffusion model was proposed for VSR task, this model is at least 11× faster over existing diffusion-based VSR models. The efficient latent-pixel training strategy and a video processing pipeline were developed to enable effective fine-tuning proposed model. These designs make the DOVE achieve the state-of-the-art performance on multiple benchmarks.  \n\n\nWeaknesses\n\nInsufficient novelty. \nOne-step diffusion models were proposed in many existing methods, i.e., [1]-[4]. The difference between the proposed DOVE and existing methods should be clarified and discussed. Additionally, in video processing pipeline, the metadata/scene/quality filtering were proposed by existing methods, i.e., Stable video diffusion, Openvid-1m, Open-sora. The DOVE only added more metrics, which is insufficient novelty. \n\n[1] Liu, Y., Pan, J., Li, Y., Dong, Q., Zhu, C., Guo, Y. and Wang, F., 2025. UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space. arXiv preprint arXiv:2505.19958.\n\n[2] Sun, Y., Sun, L., Liu, S., Wu, R., Zhang, Z. and Zhang, L., 2025. One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution. arXiv preprint arXiv:2506.15591.\n\n[3] Wu, R., Sun, L., Ma, Z. and Zhang, L., 2024. One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Processing Systems, 37, pp.92529-92553.\n\n[4] Wang, Y., Yang, W., Chen, X., Wang, Y., Guo, L., Chau, L.P., Liu, Z., Qiao, Y., Kot, A.C. and Wen, B., 2024. Sinsr: diffusion-based image super-resolution in a single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 25796-25805).\n\n\nExperimental results.\nIn Table 1-3 provide the corresponding results that proves the effectiveness of DOVE model. Some detailed results should be provided. For example, The image ratio interval in Table 1 (b) is too large, and setting a smaller interval is beneficial for obtaining the optimal ratio. The experimental results of each filter operation in video processing pipeline should be provided in Table 1(d) for verifying the effectiveness of each filter.  Besides, in Table 1 (d), the \"+filter\" (steps 2-4) only brings a PSNR gain of 0.05dB. How to verify the effectiveness of the proposed filters?", "was_revised": true, "final_justification": "After reading the author's rebuttal, the detailed explanation and experimental results are provided, my concerns were addressed. I decide my score as accept.", "rebuttal": "# Response to Reviewer WLko (denoted as R3)\n\n`Q3-1` Insufficient novelty. One-step diffusion models were proposed in many existing methods, i.e., [1]-[4]. The difference between the proposed DOVE and existing methods should be clarified and discussed. Additionally, in video processing pipeline, the metadata/scene/quality filtering were proposed by existing methods, i.e., Stable video diffusion, Openvid-1m, Open-sora. The DOVE only added more metrics, which is insufficient novelty.\n\n`A3-1` Thank you for the comment. We clarify the novelty of our **one-step** model and data **pipeline** below.\n\n**(1) One-Step Novelty.**\n\n***General Difference.***\n\n1. OSEDiff [3] and SinSR [4] are designed for **image** SR and are not tailored to the video task. Meanwhile, UltraVSR [1] and DLoRAL [2] are one-step VSR methods released on arXiv **after** the NeurIPS submission deadline.\n2. Existing methods [1]–[4] rely on pretrained **image** models and incorporate specifically designed modules to enhance performance.\n3. In contrast, our method is built upon a pretrained **video** model. We propose a well-designed **training strategy** that activates and enhances the VSR ability of the pretrained model.\n\n***Detailed Difference.***\n\n1. **UltraVSR [1]** enhances temporal consistency by inserting temporal layers into a pretrained **image** model (*i.e.*, stable diffusion) and uses a degradation-aware **module** to improve performance.\n2. **DLoRAL [2]** also relies on the **image** model (stable diffusion) uses a dual-stage process to train consistency-LoRA and detail-LoRA modules. However, it only supports two-frame inference.\n3. **OSEDiff [3]** is an **image** SR method using LoRA and variational score distillation, leveraging pretrained image priors.\n4. **SinSR [4]** distills a multi-step **image** SR model (*i.e.*, ResShift) into a one-step student within the image domain.\n5. **DOVE (ours)** utilizes a pretrained **video** model and introduces the **latent-pixel training strategy** designed explicitly for VSR, achieving high-quality one-step restoration.\n\n***Comparison Results.***\n\nWe compare DOVE against existing one-step SR methods: OSEDiff, SinSR, and DLoRAL (UltraVSR code is not publicly available). The comparison results are as follows:\n\n| Dataset     |   UDM10    |                   |   SPMCS    |                   |  RealVSR   |                   |   MVSR4x   |                   |\n| ----------- | :--------: | :---------------: | :--------: | :---------------: | :--------: | :---------------: | :--------: | :---------------: |\n| **Method**  | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** |\n| SinSR [4]   |   0.4091   |       8.59        |   0.6052   |       11.20       |   0.7192   |       11.97       |   0.3974   |       5.86        |\n| OSEDiff [3] |   0.7240   |       5.73        |   0.7380   |       4.77        |   0.7650   |       7.21        |   0.6729   |       2.75        |\n| DLoRAL [2]  |   0.7219   |       3.40        |   0.6741   |       2.98        |   0.7539   |       4.73        |   0.6267   |       1.60        |\n| DOVE (ours) | **0.7809** |     **1.77**      | **0.7828** |     **1.04**      | **0.7867** |     **3.52**      | **0.6984** |     **0.78**      |\n\nWe observe that **image SR methods** (OSEDiff and SinSR) perform poorly on video-specific metrics, *e.g.*, DOVER or $E^*_{warp}$, confirming their unsuitability for VSR tasks. Compared to DLoRAL, our DOVE achieves superior performance, validating the effectiveness of our method.\n\n\n\n**(2) Pipeline Novelty.**\n\n1. **Metadata Filtering Difference.** Unlike previous pipelines, our process emphasizes high-resolution video, which is essential for VSR and not explicitly focused on in other pipelines.\n2. **Quality Filtering Difference.** As the eviewer (WLko) noted, we incorporate more diverse quality metrics. This is consistent with the goal of VSR, which is to recover fine-grained details.\n3. **Motion Filtering Difference.** This step is unique to our pipeline. This fits the characteristics of VSR clipping training and is not included in previous pipelines.\n4. **Ablation Results.** Table 1d in the main paper demonstrates the effectiveness of the filtering and motion process. Further ablation for each filter component is provided in `A3-2`.\n5. **Comparison with Openvid-1M.** As shown in Tab. 1c (main paper), models trained on HQ-VSR outperform those trained on the existing dataset, Openvid-1M. This demonstrates the value of our video processing pipeline.\n\n\n\n`Q3-2` Experimental results. In Table 1-3 provide the corresponding results that proves the effectiveness of DOVE model. Some detailed results should be provided. For example, The image ratio interval in Table 1 (b) is too large, and setting a smaller interval is beneficial for obtaining the optimal ratio. The experimental results of each filter operation in video processing pipeline should be provided in Table 1(d) for verifying the effectiveness of each filter. Besides, in Table 1 (d), the \"+filter\" (steps 2-4) only brings a PSNR gain of 0.05dB. How to verify the effectiveness of the proposed filters?\n\n`A3-2` Thank you for the suggestions. We conduct additional ablations for the image ratio and each filtering step. We also clarify the filter gains.\n\n\n\n**(1) Ablation: Fine-Grained Image Ratio.**\n\nWe conduct more fine-grained experiments on the image ratio. Based on the preliminary results in Tab. 1b, we compare the cases of 70%, 75%, 80%, 85%, and 90%, and the results are as follows:\n\n| Image Ratio |  70%   |  75%   |    80%     |    85%     |  90%   |\n| ----------- | :----: | :----: | :--------: | :--------: | :----: |\n| PSNR↑       | 26.33  | 26.36  |   26.48    | **26.54**  | 26.39  |\n| LPIPS↓      | 0.2713 | 0.2680 |   0.2696   | **0.2644** | 0.2715 |\n| CLIP-IQA↑   | 0.5053 | 0.5074 | **0.5107** |   0.5083   | 0.5023 |\n| DOVER↑      | 0.7785 | 0.7801 |   0.7809   | **0.7870** | 0.7776 |\n\nWe find that 80% and 85% yield a good balance between frame fidelity and perceptual quality. This is close to the settings (80%) in our paper. For consistency, we still adopt the image ratio of 80%.\n\n\n\n**(2) Ablation: Data Filtering Steps.**\n\nWe perform a **breakdown** ablation of the three filtering components in our data pipeline: Metadata, Scene, and Quality filtering. The results are as follows:\n\n| Method            |   PSNR↑   |   LPIPS↓   | CLIP-IQA↑  |   DOVER↑   |\n| ----------------- | :-------: | :--------: | :--------: | :--------: |\n| OpenVid-1M        |   27.04   |   0.3376   |   0.2683   |   0.4363   |\n| + Metadata Filter | **27.22** |   0.3368   |   0.2765   |   0.4511   |\n| + Scene Filter    |   27.20   |   0.3363   |   0.2771   |   0.4529   |\n| + Quality Filter  |   27.09   | **0.3236** | **0.2894** | **0.5357** |\n\nDifferent filters have different functions:\n\n- **Metadata filtering** removes low-resolution videos that are unsuitable for VSR. This improves fidelity (*e.g.*, PSNR) and slightly enhances perceptual quality.\n- **Scene filtering** has a limited effect since OpenVid-1M already includes scene cuts. However, it is essential to extend the pipeline to other unprocessed data.\n- **Quality filtering** significantly enhances perceptual metrics, particularly DOVE. The slight drop in PSNR may result from imperfect alignment between restored details and the ground truth. But it still performs better than the unfiltered dataset (*i.e.*, OpenVid-1M).\n\n\n\n**(3) Clarification on Filter Effectiveness.**\n\n1. **Caption Correction:** The label, \"+Filter (steps 2–4)\"  in Tab. 1 (main paper) is a typo. Actually, it is \"+Filter (steps 1-3)\". We will revise it.\n2. **Filter-Specific Effects:** As shown above, each filter contributes differently to fidelity and perceptual performance.\n3. **Perceptual Improvement:** The primary purpose of filtering is to improve perceptual quality. While the overall PSNR gain is 0.05 dB, perceptual improvements are significant. For example, DOVER improves by 0.0994.\n\n\n\n`Q3-3` Comparison of Fairness: DOVE model proposed the video processing pipeline to fine-tuning the VSR models on high-quality datasets. In state-of-the-art VSR comparison in Table 2, the DOVE model using the proposed video processing pipeline achieves the best performance in most scenarios, while other methods without used. Can other existing methods also use this pipeline to improve performance? The more results and discussion should be provided.\n\n`A3-3` Thank you for the comment.\n\n1. Our training strategy is one of the innovations in our method. Therefore, we directly compare DOVE with existing approaches that do not use our training strategy.\n\n2. The training strategy of existing diffusion VSR methods is tightly bound to model design. Thus, directly applying our training strategy to those models is not easy and requires extensive adaptation.\n\n3. But our method is well-suited for pretrained text-to-video (T2V) models. To reveal the generalizability of our method, we conduct experiments using another pretrained T2V model, **WAN2.1-T2V-1.3B** [**ref1**], with the same training settings as DOVE. We refer to the resulting model as **DOVE (WAN2.1)**.\n\n   The evaluation results on UDM10 are listed below:\n\n   | Method          |   PSNR↑   |   LPIPS↓   | CLIP-IQA↑  |   DOVER↑   |\n   | --------------- | :-------: | :--------: | :--------: | :--------: |\n   | Upscale-A-Video |   21.72   |   0.4116   |   0.4697   |   0.7291   |\n   | MGLD-VSR        |   24.23   |   0.3272   |   0.4557   |   0.7264   |\n   | STAR            |   23.47   |   0.4242   |   0.2417   |   0.4830   |\n   | DOVE (WAN2.1)   | **25.28** | **0.2555** | **0.5133** | **0.7563** |\n\n   The WAN-based model also achieves strong VSR performance, further confirming the generalizability of our proposed method.\n\n   \n\n[**ref1**] Wan: Open and Advanced Large-Scale Video Generative Models, 2025."}, {"review_id": "DgERofdOsa", "reviewer": "Reviewer_svnJ", "summary": "The paper proposes an efficient one-step diffusion model, DOVE, based on CogVideoX for real-world video super-resolution. The DOVE introduces no extra modules but keeps the simple CogVideoX's Encoder-DiT-Decoder schema. To adapt the T2V-based CogVideoX to SR task, the DOVE employs a two-stage training with high-quality video obtained by a video processing pipeline.", "questions": "- My main question is about the utilization of diffusion priors. Since the DOVE is simply adapted from commonly used perceptual VSR/ISR losses and does not use CogVideoX to calibrate the output, I have doubts about identifying the DOVE as a \"diffusion\" model. For instance, replacing the CogVedeo with a GAN,  like GigaGAN to VideoGigaGAN, the pipeline still works. It seems like the improvement is brought by the scaling laws of changing the base model from weak GigaGAN to powerful CogVideoX.\n- In Table 1 (b), the DOVE (80%) leads by DOVE (100%) in a rather small margin. However, Table 2 compares only ResShift, which is a rather weak baseline even for image SR. Comparison with more recent diffusion-based SR and GAN-based VSR will raise my confidence in DOVE.\n- The visual results in sup tend to be extremely sharp and lose details, making it look unnatural. Moreover, the high sharpness and smoothness will induce higher subjective scores.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n- The overall pipeline is simple but effective. The DOVE, by avoiding introducing complicated modules, can achieve higher efficiency.\n- The video processing pipeline is interesting and ought to work for most video restoration tasks.\n- The experimental results are satisfactory to show DOVE advancing recent diffusion-based VSR.\n\nWeakness:\n- Despite the simplicity and intuitiveness, the originality is limited. The DOVE is simply adapted from commonly used perceptual VSR/ISR losses. It barely uses the abundant diffusion priors from CogVideoX except for initialization.\n- The visual results tend oversharp and smooth.", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns.", "rebuttal": "# Response to Reviewer svnJ (denoted as R4)\n\n`Q4-1` Despite the simplicity and intuitiveness, the originality is limited. The DOVE is simply adapted from commonly used perceptual VSR/ISR losses. It barely uses the abundant diffusion priors from CogVideoX except for initialization.\n\n`A4-1` Thank you for the comment. We clarify our **novelty** and **prior** utilization below.\n\n**(1) Novelty Clarification.**\n\n1. **Common Loss.** The commonly used loss functions, such as MSE, DISTS, and Frame losses, are not our novelty. We adopt these commonly used losses to ensure **general applicability**.\n\n2. **(Novelty) Training Strategy.** Our main contribution lies in the proposed **latent-pixel training strategy**, which consists of two stages. Stage-1 learns the one-step mapping in the latent space. Stage-2 enhances reconstruction in pixel-space. Additionally, we introduce **image-video mixed training** and a **frame-by-frame VAE encoder-decoder** to better adapt to VSR training.\n\n3. **Experiment Results.** Table 1a in the main paper demonstrates the effectiveness of our training strategy. We provide the results below:\n\n   | Training Stage |  Stage-1  | Stage-1+Stage-2(Image) | Stage-1+Stage-2(Image/Video) |\n   | -------------- | :-------: | :--------------------: | :--------------------------: |\n   | PSNR↑          | **27.20** |         26.39          |            26.48             |\n   | LPIPS↓         |  0.3037   |         0.2784         |          **0.2696**          |\n   | CLIP-IQA↑      |  0.3236   |         0.5085         |          **0.5107**          |\n   | DOVER↑         |  0.6154   |         0.7694         |          **0.7809**          |\n\n4. **(Another Novelty) Data Pipeline.** We also design a **video processing pipeline** to construct the HQ-VSR dataset. This dataset also contributes to the performance.\n\n\n\n**(2) Diffusion Prior.**\n\n1. **Prior Utilization.** We apply pretrained diffusion priors for initialization to enhance the restoration capability of DOVE.\n2. **Task Gap.** Since the original model (*i.e.*, CogVideoX) is designed for generation rather than restoration, we propose the **latent-pixel training strategy** to better adapt it to the VSR task.\n3. **Effective Training Design.** Our design ensures architectural simplicity, training efficiency, and impressive performance.\n\n\n\n`Q4-2` The visual results tend oversharp and smooth.\n\n`A4-2` Thank you for the comment. In some challenging cases, the visual results of our DOVE may appear oversharpened and smooth. However, this typically occurs in highly degraded scenarios where existing methods struggle to recover the right structure.\n\nWe provide several examples in the **supplementary material** to illustrate this point.\n\n- **Case 1** *(Fig. 5, first sample, UDM10: 000)*: The LR input is heavily blurred, and the wall textures are barely visible. Our DOVE successfully reconstructs the correct structural lines, although slightly oversharpened. In comparison, other methods fail to recover the lines and even introduce artifacts.\n- **Case 2** *(Fig. 7, fourth sample, VideoLQ: 016)*: While DOVE produces a slightly oversharpened result, it restores the text correctly and without artifacts. In contrast, methods like STAR misrecognize the word \"CRIPPLE\" as \"CHIPPLE\", while other methods produce artifacts.\n\n**Overall**, DOVE achieves superior restoration quality compared to existing methods. Of course, we acknowledge the reviewer's observation that some challenging cases may exhibit oversharpening or smoothing. This is an important direction for future improvement.\n\n\n\n`Q4-3` My main question is about the utilization of diffusion priors. Since the DOVE is simply adapted from commonly used perceptual VSR/ISR losses and does not use CogVideoX to calibrate the output, I have doubts about identifying the DOVE as a \"diffusion\" model. For instance, replacing the CogVedeo with a GAN, like GigaGAN to VideoGigaGAN, the pipeline still works. It seems like the improvement is brought by the scaling laws of changing the base model from weak GigaGAN to powerful CogVideoX.\n\n`A4-3` Thank you for the question. Our approach is a diffusion model. We explain it below.\n\n**(1) Following the Diffusion Paradigm.**\n\n1. **Diffusion Formulation.** DOVE follows the core principle of diffusion models: generating results through denoising. Specifically, our model adopts the v-prediction scheme, where a Transformer predicts the velocity (a variant of noise). The restored latent **${z}_{sr}$** is obtained using: \n\n   $z_{sr} = \\sqrt{ᾱ_t} z_{lr} - \\sqrt{1-ᾱ_t} v_θ(z_{lr}, t)$.\n\n   This formulation is consistent with the definition of the diffusion model.\n\n2. **Consistency with Existing Methods.** Our method aligns with the framework of other one-step diffusion models (*e.g.*, those discussed by **Reviewer-WLko** in `Q3-1`: UltraVSR [1], DLoRAL [2], and OSEDiff [3]). From the framework definition perspective, the main difference between these diffusion methods and our DOVE is the loss function, but this does not affect the overall definition.\n\n\n\n**(2) Pipeline Transferability.**\n\n1. The pipeline is transferable. One strength of our method is its **generalizability**. The proposed training strategy can be adapted to other pretrained T2V backbones, not just CogVideoX.\n\n2. Although pipeline transfer is possible, **diffusion priors** are central to our approach. Compared to GANs, diffusion models offer more stable training and stronger generative priors. These properties contribute significantly to the impressive performance of DOVE.\n\n3. Motivated by the reviewer's comment, we extend DOVE to another pretrained diffusion T2V model, **WAN2.1-T2V-1.3B** [**ref1**]. We refer to the resulting model as **DOVE (WAN2.1)**. The evaluation results on UDM10 are as follows:\n\n   | Method          |   PSNR↑   |   LPIPS↓   | CLIP-IQA↑  |   DOVER↑   |\n   | --------------- | :-------: | :--------: | :--------: | :--------: |\n   | Upscale-A-Video |   21.72   |   0.4116   |   0.4697   |   0.7291   |\n   | MGLD-VSR        |   24.23   |   0.3272   |   0.4557   |   0.7264   |\n   | STAR            |   23.47   |   0.4242   |   0.2417   |   0.4830   |\n   | DOVE (WAN2.1)   | **25.28** | **0.2555** | **0.5133** | **0.7563** |\n\n   The WAN-based model shows strong VSR performance, validating the generality and effectiveness of our method.\n\n\n\n[**ref1**] Wan: Open and Advanced Large-Scale Video Generative Models, 2025.\n\n\n\n`Q4-4` In Table 1 (b), the DOVE (80%) leads by DOVE (100%) in a rather small margin. However, Table 2 compares only ResShift, which is a rather weak baseline even for image SR. Comparison with more recent diffusion-based SR and GAN-based VSR will raise my confidence in DOVE.\n\n`A4-4` Thank you for the feedback. We provide detailed clarifications below.\n\n**(1) Improvement Clarification.** Compared with DOVE (100%), DOVE (80%) has a quantitative improvement of 0.0115 in the DOVER metric, which is not very small. Meanwhile, image-video mixed training leads to more stable and consistent results.\n\n\n\n**(2) More Comparison Methods.** We include comparisons with more recent methods:\n\n- **SinSR** [**ref2**]: Image SR, diffusion-based, CVPR 2024\n- **OSEDiff** [**ref3**]: Image SR, diffusion-based, NeurIPS 2024\n- **RealViformer** [**ref4**]: Video SR, GAN-based, ECCV 2024\n\n(Note: VideoGigaGAN is not publicly released, so we cannot include it in the evaluation.) \n\nThe comparison results are as follows:\n\n| Dataset                     |   UDM10    |                   |   SPMCS    |                   |  RealVSR   |                   |   MVSR4x   |                   |\n| --------------------------- | :--------: | :---------------: | :--------: | :---------------: | :--------: | :---------------: | :--------: | :---------------: |\n| **Method**                  | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** | **DOVER↑** | **$E^*_{warp}$↓** |\n| SinSR (SR, CVPR'24)         |   0.4091   |       8.59        |   0.6052   |       11.20       |   0.7192   |       11.97       |   0.3974   |       5.86        |\n| OSEDiff (SR, NeurIPS'24)    |   0.7240   |       5.73        |   0.7380   |       4.77        |   0.7650   |       7.21        |   0.6729   |       2.75        |\n| RealViformer (VSR, ECCV'24) |   0.7303   |       2.29        |   0.5905   |       1.46        |   0.7191   |       3.93        |   0.6451   |       1.25        |\n| DOVE (ours)                 | **0.7809** |     **1.77**      | **0.7828** |     **1.04**      | **0.7867** |     **3.52**      | **0.6984** |     **0.78**      |\n\nThe results show that DOVE outperforms recent diffusion- and GAN-based methods in video quality and temporal consistency.\n\n\n\n[**ref2**] SinSR: Diffusion-Based Image Super-Resolution in a Single Step, CVPR 2024.\n\n[**ref3**] One-Step Effective Diffusion Network for Real-World Image Super-Resolution, NeurIPS 2024.\n\n[**ref4**] RealViformer: Investigating Attention for Real-World Video Super-Resolution, ECCV 2024.\n\n\n\n`Q4-5` The visual results in sup tend to be extremely sharp and lose details, making it look unnatural. Moreover, the high sharpness and smoothness will induce higher subjective scores.\n\n`A4-5` Thank you for the comment.\n\nIn challenging cases with severe degradation, DOVE may produce results that appear oversharpened or overly smooth. However, it still outperforms existing methods regarding fidelity and visual quality.\n\nAdditionally, we agree with the reviewer that better balancing sharpness and detail preservation is a valuable direction for future work. \n\nFor more details, please see `A4-2`."}]

Author rebuttal:

Meta-review: This paper introduces DOVE, a one-step diffusion model for video super-resolution. The motivation is clear: multi-step diffusion methods are too slow for real-world deployment, and the proposed approach achieves much faster inference while maintaining strong perceptual quality. The main strengths are the effective latent-to-pixel fine-tuning strategy, strong benchmark results, and the introduction of the HQ-VSR dataset. Concerns were raised about the performance gains being largely attributable to the pretrained base model, limited technical novelty, and possible evaluation bias. During rebuttal, the authors provided additional comparisons with other SOTA methods using more diverse metrics and included human studies, which addressed most concerns and strengthened their claims. Reviewers converged towards acceptance after these clarifications. Overall, despite modest novelty, the paper’s contributions are solid and well-supported after rebuttal, and I recommend acceptance as a poster.
Decision: Accept (poster)