Title: DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Venue: NeurIPS 2025
Accepted: yes
Keywords: One-Step Diffusion

Real-World

Video Super‑Resolution
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

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 singlestep, 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 finetuning 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 × speed-up over existing methods such as MGLD-VSR. Code is available at: https://github.com/zhengchen1999/DOVE .

1 Introduction

Video super-resolution (VSR) is a long-standing task that aims to reconstruct high-resolution (HR) videos from low-resolution (LR) inputs [11, 16]. With the rapid growth of smartphone photography and streaming media, real-world VSR has become increasingly critical. Unlike the synthetic degradations ( 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 [51, 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, a new generative model, the diffusion model (DM), has rapidly gained popularity [10]. Compared with GANs, diffusion models exhibit stronger generative capabilities, especially those pretrained on large-scale datasets [28, 27, 2, 59, 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 [9, 48], and use ControlNet to constrain video generation. By exploiting the natural priors in pretrained models, these methods can realize more realistic restorations.

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STAR

DOVE (ours)

MGLD-VSR

DOVE (ours)

Real-World Video

↑

UAV

MGLD-VSR

STAR DOVE (ours)

0.70

0.73

0.76

0.7370

0.7421

0.7080

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FasterVQA

↑

UAV

MGLD-VSR

STAR DOVE (ours)

0.69

0.73

0.77

0.7501

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279.32

425.23

173.07

14.90

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0

200

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13.47

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5.4

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-7.62

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] or temporal layers, to realize VSR. These additional modules further slow down inference. For example, when processing a 33frame 720p (720 × 1280) video on an NVIDIA A100 GPU, MGLD-VSR [50] takes 425.23 seconds, while STAR [48] 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 impressive results in image/video generation [30, 54, 61, 18] and image restoration [39, 45, 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 [41, 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 [61, 18]. However, the inherent instability of adversarial training may introduce undesired details in results, hindering VSR performance [45].

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. , CogVideoX [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-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, we 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. 1, DOVE outperforms state-of-the-art multi-step diffusion-based VSR methods. Simultaneously, due to one-step inference, our DOVE is up to 28 × faster over previous diffusionbased VSR methods, e.g. , MGLD-VSR [50]. In summary, our contributions include:

Wepropose a novel one-step diffusion model, DOVE, for real-world VSR. To our knowledge, this is the first diffusion-based VSR model with one-step inference.

Wedesign a 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-art performance across multiple benchmarks with remarkable efficiency.

2 Related Work

2.1 Video Super-Resolution

With the advancement of deep learning, numerous video super-resolution (VSR) methods have emerged [11, 24, 16, 4]. These approaches ( e.g. , BasicVSR [4] and Vrt [16]) utilize a variety of architectures, including recurrent-based [17, 32] and sliding-window-based [14, 53] models, and have demonstrated promising results. However, these methods typically assume a fixed degradation process [49, 53], 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 MVSR4x [37], have utilized HR-LR paired data from real environments. In contrast, others ( e.g. , RealBasicVSR [5]) have proposed variable degradation pipelines to enhance the model's adaptability to the complex degradations in real-world VSR. In addition, real-world VSR methods [60, 26, 47] 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 [10] 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 I2VGen-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, 9, 48, 36, 15] 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 to train on a 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] to 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.

2.3 One-Step Acceleration

A common method to accelerate diffusion models is reducing the number of inference steps, with one-step acceleration gaining significant attention as an extreme approach. This technique leverages methods such as rectified flow [19, 20], adversarial training [18, 61], and score distillation [55, 41]. Building on these methods, recent image super-resolution (ISR) studies [40, 13, 45, 7] have integrated one-step diffusion. For instance, OSEDiff [45] applies 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 to explore one-step diffusion in video generation. However, adversarial training methods are inherently unstable. Applying them in VSR, instead of multi-network 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 CogVideoX 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.

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3.1 Overall Framework

We construct our one-step diffusion network, DOVE, based on a powerful pretrained text-to-video model ( i.e. , CogVideoX [52]). CogVideoX employs a 3D causal VAE to compress videos into the latent space and uses a Transformer denoiser v θ for diffusion. Leveraging its strong priors, our method can better handle the complexities of real-world scenarios. Meanwhile, to enhance inference efficiency, we do not introduce any auxiliary modules like previous methods [50, 63, 9], such as temporal layers or ControlNet. Instead, we design a two-stage training strategy and curate a 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 a low-resolution (LR) video x lr , we first upscale it to the target high resolution using bilinear interpolation. The upscaled video is then encoded into a latent representation z lr by the VAE encoder E . Following previous works [46, 45], we take z lr as the starting point of the diffusion process. That is, we treat z lr as the noised latent z t at a specific timestep t , which is originally formulated as:

where ϵ is the Gaussian noise, z is the 'clean' latent sample, and β is the noise factor. Afterwards, a single denoising step is performed through Transformer v θ , yielding the 'clean' latent z sr ( i.e. , z in Eq. (1)). Since CogVideoX adopts the v -prediction formulation, the denoising process is defined as:

Unlike the previous approach [45] that set t to the total diffusion step ( i.e. , 999 in CogVideoX), we choose a smaller value. This is based on 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 is not needed. Conversely, a tiny t ( i.e. , late step) would hinder the removal of degradation. Therefore, we empirically set t =399 . Finally, the latent z sr is decoded by the VAE decoder D to obtain the output video x sr , as the restoration results.

3.2 Latent-Pixel Training Strategy

The framework described above enables us to adapt a generative model for the VSR task. However, since the model is designed for multi-step sampling and the distribution of z lr and z t are not consistent, the pretrained model cannot be directly applied. Thus, fine-tuning is required to ensure that the reconstructed output x sr , closely matches the high-resolution (HR) ground truth x hr .

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 is larger (due to multiple frames). Besides, adversarial training, which is commonly applied in single-step video generation [61, 18], is less suitable for VSR. This is 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.

To enable effective training for DOVE, we design a novel latent-pixel training strategy. We adopt the regression loss, instead of distillation or adversarial learning, to improve training efficiency. In addition, we only fine-tune the Transformer component, preserving the priors in the pretrained V AE. 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 x sr close to the high-quality x hr , corresponds to minimizing the difference between z sr and the HR video latent z hr .

Due to the high compression ratio of the V AE, training in latent space is more efficient than in pixel space. Therefore, we first minimize the difference between z sr and z hr . As illustrated in Fig. 2, both the LR and HR videos are first encoded into latent representations via the V AE encoder E . We then train the Transformer v θ using the MSE loss (denoted as L s1). The process is denoted as:

Benefiting from the high computational efficiency in the latent space, we 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 a certain extent. However, we observe that the output x sr exhibits noticeable gaps from the ground-truth x hr . This may be because in the latent space, although z sr is close to z hr , the slight gap will be further amplified after passing through the V AE 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 ˆ x sr and the HR image ˆ x hr . Meanwhile, we additionally adopt the perceptual DISTS [6] loss, L dists, to better preserve texture details. The total image loss is defined as:

where λ 1 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, it 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 V AE is the primary constraint. Inspired by the success of image-only training, we process videos frame-by-frame through the VAE encoder E 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:

where x ( t ) lr and x ( t ) sr mean the t -th frame of LR and SR video, respectively; n is the frame number; Φ θ ( · ) denotes the one-step denoising process based on Transformer v θ defined in Eq. (2). As with the image-level training, we apply MSE loss and the perceptual DISTS loss. Additionally, to enforce frame consistency, we introduce a frame difference loss:

( t )

where ∆ x captures the frame-to-frame change. The total loss for video training is computed as:

where λ 2 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 φ to control the ratio between image and video samples. We study the effect of φ in Sec. 4.2.

S1: Meta Filter

S3: Quality Filtering

S2: Scene Filter

S4: Motion Process

Dataset: HQ-VSR

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C: CLIP-IQA > 0.4

F: FasterVQA > 0.6

D: DOVER > 0.7

Height > 720

Width > 720

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3.3 Video Processing Pipeline

Motivation. Fine-tuning on high-quality datasets can 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. , WebVid-10M [1] and OpenVid-1M [25].

Nevertheless, these datasets have some limitations: (1) some contain relatively few data and scenarios; (2) some lack proper curation or filtering specifically for VSR. As a 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 a systematic video processing pipeline to construct a high-quality dataset tailored for VSR. As shown in Fig. 3, the pipeline is:

Step 1: Metadata Filtering. We begin with a coarse filtering process based on metadata, i.e. , video resolution and frame count. We extract all videos with a 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 [2], 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, 25] have used metrics like the LAION aesthetic model [31], these are insufficient, since VSR pays more attention to detail quality. Therefore, we incorporate more metrics: CLIP-IQA [35], FasterVQA [43], and DOVER [44]. With more metrics, we achieve stricter filtering.

Step 4: Motion Processing. Finally, we eliminate videos with insufficient motion. We first estimate optical flow to calculate the motion score, following prior methods [62, 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, highmotion videos may contain static regions ( e.g. , speech backgrounds in Fig. 3), yielding static crops.

To address this, we introduce the motion area detection algorithm for localized processing (see Fig. 3). We generate a motion intensity map M from the optical flow F , and apply a threshold τ to produce a motion mask. Then, we determine the motion areas based on the mask. To ensure sufficient context, we expand the bounding box by a fixed padding p . The procedure is defined as:

where Ω ⊂ Z 2 is the set of all pixel indices; F ij denotes the optical-flow vector at 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.

HQ-VSR. We apply the proposed pipeline to the public dataset OpenVid-1M [25], which contains diverse scenes. Based on this, we extract 2,055 high-quality video samples suitable for VSR, forming a new dataset, HQ-VSR. The detailed pipeline configuration is provided in the supplementary material. Fine-tuning our DOVE on the HQ-VSR yields superior performance compared to other datasets.

(a) Ablation on training strategy.

(b) Ablation on image ratio ( φ ) in stage-2.

(c) Ablation on training dataset.

(d) Ablation on processing pipeline.

Table 1: 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) + Filter: apply steps 1~3; + Motion: 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 synthesize LQ-HQ pairs. The image dataset is DIV2K [3], with 900 images, which follows the RealESRGAN [38] degradation process. For evaluation, we apply both synthetic and real-world datasets. The synthetic datasets include UDM10 [34], SPMCS [53], and YouHQ40 [63], using the same degradations as training. For real-world datasets, we apply RealVSR [51], MVSR4x [37], and VideoLQ [5]. RealVSR and MVSR4x contain real-world LQ-HQ pairs captured via mobile phones, while VideoLQ is Internet-sourced without HQ references. All experiments are conducted with a scaling factor × 4.

Evaluation Metrics. We adopt multiple evaluation metrics to 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 [42]. We also use 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 E ∗ warp , refers to E warp ( × 10 -3 ) [12], to assess temporal consistency. Through these metrics, we conduct a comprehensive evaluation of video quality.

Implementation Details. Our DOVE is based on the text-to-video model, CogVideoX1.5 [52]. We use an empty text as 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 β 1 =0 . 9 , β 2 =0 . 95 , and β 3 =0 . 98 . In stage-1, training is conducted on video data. The videos have a resolution of 320 × 640 and a frame length of 25. The model is trained for 10,000 iterations with a learning rate of 2 × 10 -5 . In stage-2, both video and image data are used, with φ = 0.8 ( i.e. , images comprising 80% of the input). All inputs have a resolution of 320 × 640. The model is trained for 500 iterations with a learning rate of 5 × 10 -6 . The loss weights λ 1 and λ 2 are set to 1.

4.2 Ablation Study

We investigate the effectiveness of the proposed latent-pixel training strategy and video processing pipeline. All training configurations are kept consistent with settings described in Sec. 4.1. We evaluate all models on UDM10 [34]. Results are presented in Tab. 1.

Training Strategy. We study the effects of the latent-pixel training strategy, as shown in Tab. 1a. In the stage-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 stage-2 (S2), perceptual metrics ( i.e. , LPIPS, CLIP-IQA, 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.

Image Ratio ( φ ). We further investigate the impact of the image data ratio in stage-2. The results are presented in Tab. 1b. 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-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. , φ = 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. 1c. For OpenVid-1M [25], we select videos with a resolution higher than 1080p (1080 × 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. 1d. First, we apply the filtering steps ( i.e. , + Filter), including metadata, scene, and quality filtering) to the raw dataset ( i.e. , OpenVid-1M [25]). Although OpenVid 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], RealBasicVSR [5], Upscale-AVideo [63], MGLD-VSR [50], VEnhancer [9], and STAR [48].

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YouHQ40: 036

HR

LR

ResShift [56]

Upscale-A-Video [63]

MGLD-VSR [50]

VEnhancer [9]

STAR [48]

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LR

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RealBasicVSR [5]

Upscale-A-Video [63]

MGLD-VSR [50]

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STAR [48]

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20P465

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GT

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STAR

DOVE (ours)

MGLD-VSR

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Quantitative Results. We present quantitative comparisons in Tab. 2. Our DOVE achieves outstanding performance across diverse datasets. For fidelity metrics ( i.e. , PSNR and SSIM), our model achieves the best performance on most datasets. For perceptual metrics ( i.e. , LPIPS, DISTS, and CLIP-IQA), the DOVE ranks first or second on five datasets. Furthermore, for video-specific metrics regarding quality ( i.e. , FasterVQA and DOVER) and consistency ( e.g. , E ∗ warp ), the DOVE also performs strongly. Besides, we provide more comparison results in the supplementary material.

Qualitative Results. We provide visual comparisons on both synthetic ( i.e. , YouHQ40) and realworld ( i.e. , VideoLQ) 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-Video [63] and STAR [48]) or blurring ( e.g. , MGLD-VSR [50]). In contrast, our method achieves excellent temporal consistency, with smooth transitions and rich details across frames. This is due to the strong prior of the pretrained model and our effective latent-space 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 in Tab. 3. For fairness, all methods are measured running time on the same A100 GPU, generating a 33-frame 720 × 1280 video. Our method is approximately 28 × 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 superresolution (VSR). DOVE is constructed based on the pretrained video generation model, CogVideoX. 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 the 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 is 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 (2021SHZDZX0102), 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 2025B01W0005.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAn 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \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?\n\nQUESTIONS:\nComparison 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.\n\nLIMITATIONS:\nPlease see weaknesses and questions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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)\n\nQUESTIONS:\n* 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}