One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution
Authors: Yujing Sun
Lingchen Sun
Shuaizheng Liu
Rongyuan Wu
Zhengqiang ZHANG
Lei Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Real-world Video Super-Resolution
Diffusion Models
Primary area: applications

Abstract: It is a challenging problem to reproduce rich spatial details while maintaining temporal consistency in real-world video super-resolution (Real-VSR), especially when we leverage pre-trained generative models such as stable diffusion (SD) for realistic details synthesis. Existing SD-based Real-VSR methods often compromise spatial details for temporal coherence, resulting in suboptimal visual quality. 
We argue that the key lies in how to effectively extract the degradation-robust temporal consistency priors from the low-quality (LQ) input video and enhance the video details while maintaining the extracted consistency priors.
To achieve this, we propose a Dual LoRA Learning (DLoRAL) paradigm to train an effective SD-based one-step diffusion model, achieving realistic frame details and temporal consistency simultaneously.
Specifically, we introduce a Cross-Frame Retrieval (CFR) module to aggregate complementary information across frames, and train a Consistency-LoRA (C-LoRA) to learn robust temporal representations from degraded inputs.
After consistency learning, we fix the CFR and C-LoRA modules and train a Detail-LoRA (D-LoRA) to enhance spatial details while aligning with the temporal space defined by C-LoRA to keep temporal coherence. 
The two phases alternate iteratively for optimization, collaboratively delivering consistent and detail-rich outputs. During inference, the two LoRA branches are merged into the SD model, allowing efficient and high-quality video restoration in a single diffusion step. Experiments show that DLoRAL achieves strong performance in both accuracy and speed. Code and models will be released.

One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution
Yujing Sun 1.2.* Lingehen Sun 1,2.*, Shuaizheng Liu 1.2
Rongyuan Wul.2, Zhengqiang Zhang Lei Zhang 1.2,
'hee Hong Kong Polytechnic University
OPPO Research Institute
(yukki.sun, ling-chen.sun, shuaizheng. liu, rong-yuan. zhengqiang.zhang) Baanggiianghhanggeoomeeeee @connect.polyu.hl,
esizhang @ caschang@comppplln comp.
Equal contribution
CCoreesponding author
Abstract
It is a challenging problem reproduce rich spatial details while maintaining temporal consistency in real-world video super-resolution (Real-VSR), especially when we leverage pre-trained generative models such as stable diffusion (SD) for realistic details synthesis. Existing SD-based Real-VSR methods often compromise spatial details for temporal coherence, resulting in suboptimal visual quality, We argue that the key lies in how to effectively extract the eegradation-robus temporal consistency priors from the low-quality (I.Q) input video and enhance the video details while maintaining the extracted consistency priors. To achieve this, we propose a Dual LoRA Learning (DLoRAL) paradigm to train an effective SD- based one-step diffusion model, achieving realistic frame details and temporal consistency simultancously. Specifically, we introduce Cross-Frame Retrieval (CFR) module to aggregate complementary information across frames, and train a Consistency-LoRA (C-LoRA) to learn robust temporal representations from degraded imputs. After consistency learning, fix the CFR and C-LoRA modules and train a Detail-LoRA (D-LoRA) to enhance spatial details while aligning with the temporal space defined by C-LoRA to keep temporal coherence. The two phases alternate iteratively for optimization, collaboratively delivering consistent and detail-rich outputs. During inference, the two LoRA branches merged into the SD model, allowing efficient and high-quality video restoration in a single diffusion step. Experiments show that DLoRAL achieves strong performance both accuracy and speed. Code and models are available at pattps://githie thub com/yj Jumm/yyjunnn/DLL.ALL..
Introduction
Video super-resolution (VSR) aims to reconstruct high-quality (HQ) videos from low-quality (LQ) inputs. Traditional VSR methods typically rely convolutional neural network (CNN)-based B2 7 and Transformer-basel designs [5] 19], trained with pixel-wise L2 or L1 losses. While effective in some metrics (e.g., PSNR), these methods often produce over-smoothed results without fine details. To improve perceptual quality, generative adversarial network (GAN)-based VSR methods incorporate the adversarial loss [1]] during training encourage sharper details restoration
B, 21 4 41]. However, many VSR models 42 [1 [53] are trained under simplified degradation assumptions (e.g., bicubic downsampling), limiting their performance on real-world LQ videos with complex and unknown degradations. Additionally, GAN-based methods can produce unnatural
Thhis work is supported by the PolyU-OPPO Joint Innovative Research Center.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

CLIPIQA
MUSIQ
10TH
10'M
of total
Paramiters
30
Upscale A Viciooo
NGLD
MANIQA
BRISQUE
Better
STAR
Better
DOVER
202
2200
2200
5200
4202
inference Time 1s)
Quality comparison
(b) Efficiency Comparison
Figure Quality and efficiency comparison among SD-based Real-VSR methods. (a) Quality comparison on the VideoLQ benchmark [8]. (b) Efficiency comparison tested on an A100 GPU (512 512 input with 50 frames for VSR). DLoRAL achieves the best perceptual quality with only one diffusion step, about 10x faster than Upscale-A A-Video [54]. MGLD [4]] and STAR [40].
artifacts and generalize poorly to diverse video content. Recently, pre-trained diffusion feafusioo--baeed text-to- image (T2I) models such as Stable Diffusion (SD) [25 have shown impressive results in real-world
image super-resolee repee-resolution (Real-ISR) [3 38
48
26 27 34) with realistic textures. One line of
research treats the LQ image as a control signal and employs ControlNet-like structures [51 to guide
generation 31 48 38 26]. and another line of research directly fine-tunes the SD model with LoRA
13
for efficient one-step restoration
37 [27]
The success of SD in Real-ISR inspired exploration of diffusion models for real-world video super- resolution (Real-VSR). Although the powerful generative priors of SD enhance details, they can introduce inconsistencies among frames when the generated textures sometimes deviate from the content of the LQ inputs [3 227. To alleviate this issue, existing SD-based Real- methods typically suppress such fluctuations at the cost of perceptual quality. These methods, such as Upscale- A-Video (54] and MGLD-VSR 4.. incorporate temporal modules into pre-trained SD models and adopt frame-wise losses to balance spatial detail and temporal consistency. Despite the significant progress achieved, these methods have two major limitations. First, these approaches optimize detail and consistency jointly in single model, resulting in suboptimal trade-offs. Improving one objective usually harms the other due to their conflicting nature. Second, the temporal consistency existing in real-world LQ videos is ignored, which can be effectively leveraged to help anchor detail generation on a consistent temporal basis.
To address these issues, we propose a Dual LoRA Learning (DLoRAL) framework for Real- Our method built on one-step residual diffusion model [37 27). which significantly reduces inference time while maintaining strong generative capability. Inspired by PiSA-SR [27). which learns two LoRA modules to achieve adjustable Real-ISR results, we design two decoupled LoRA branches within the shared diffusion UNet to resolve the conflict between spatial detail and temporal coherence. Specifically, a Consistency-Lote
(C-LoRA) is designed learn temporal consistency representation, and Detail-LoRA (D-LoRA) is designed to restore high-frequency spatial details. To exploit the inherent temporal consistency in LQ videos, we introduce a Cross-Frame Retrieval (CFR) module, which extracts pructure-aligned temporal features from adjacent degraded frames, helping the model Iearn degradation-roll robusst representations. CFR not only provides a stable and informative intermediate representation for C-LoRA to build upon, but also serves the anchor for the subsequent detail enhancement stage to maintain temporal alignment.
Instead of optimizing both objectives jointly, we adopt dual-stage training strategy. The training begins from the temporal consistency stage, in which we fine-tune C-LoRA and CFR modules using consistency- related losses. In the detail enhancement stage, we freeze C-LoRA and CFR, and train D-LoRA to refine high-frequency details with the additional classifier score distillation (CSD) [2 loss. These two stages are alternatively trained to allow each branch to specialize in its objective. During inference, the two LoRA modules can integrated in one-step diffusion. As illustrated in Fig. our DLoRAL method achieves both high temporal consistency and superior visual quality,

---

outperforming previous Real-VSR methods in overall quality, as well as inference speed (about 10x
speedup over current methods [54 44 40), as illustrated in Fig. [b)).
Our main contributions are summarized as follows. (1) We propose a Dual LoRA Learning (DLoRAL)
paradigm for Real-VSR which decouples the learning of temporal consistency and spatial details into two dedicated LoRA modules under a unified one-step diffusion framework. (2) We introduce a Cross-Frame Retrieval (CFR) module to extract degradation-robust temporal priors for Consistency- LoRA (C-LoRA) training, providing structure-aligned intermediate representations that guide the subsequent training of Detail-LoRA (D-LoRA) for high-fidelity restoration. (3) Our DLoRAL model achieves state-of-the-an performance on Real- ESR benchmarks, producing visually realistic frame details and stable temporal consistency.
2 Related Work
typically rely on simply synthesized data bicubic downsampling), leading to significant performance gap when applied to real-word world videos. Early works [45] 35 addressed this by collecting real-world LQ-HQ video pairs, such as the
Real-World VSR. Conventional VSR methods 11
iPhone-captured dataset [45] However, these datasets are limited by device bias and scalability. The
following works [33 8 simulated realistic degradations by combining blur, noise, and compression, while others enhanced robustness through architectural design. For instance, RealVSR 45 introduces a domain adaptation mechanism that aligns feature distributions between synthetic and real domains through adversarial learning. RealBasie refines the restoration process through iterative correction modules. Despite these advances, existing methods still struggle to recover fine details and generalize across diverse real-world scenarios, often producing over-smoothed outputs.
[8] proposes degradation modeling framework that
Diffusion Based Real-VSR.
Recent advances in diffusion models for image restoration [1 23 49)
50 have inspired the extension to Real-VSR tasks [54] 44 17 40). A common approach is to adapt pre-trained T2I models by injecting temporal modules to ensure both perceptual quality and temporal consistency. For example, Upscale-A-Ve Video [54] integrates temporal layers into the pre-trained diffusion model and proposes a flow-guided recurrent latent propagation module. MGLD-VSH [4 guides the diffusion process with a motion-guided loss and inserts a temporal module into the diffusion decoder. The other directions include decomposing the complex learning burden into staged training phases 117 and reformulating attention mechanisms in diffusion transformers
30 to process
videos of arbitrary length. Rather than leveraging the pre-trained T2I model, STAR 40) leverages compressed temporal representations from text-to-video (T2V) models Despite these efforts, balancing spatial detail and temporal consistency remains key challenge. Most existing methods enforce frame-leved constraints to improve consistency by sacrificing visual fidelity. In this work, we propose decoupled learning strategy: first learning degradation-robie temporal priors from LQ inputs then guiding HQ generation with these features. This design ensures both high-quality detail restoration and stable temporal coherence.
Real-VSR Paradigms. Recent VSR methods follow two main paradigms: sliding-windows based 42 16] and eecurrent-based [6 7 [5] 20
32
11
[4 - 40). beeddng-windowwbassee methods
reconstruct each output frame using a set of neighboring frames, capturing fine-grained local details and short-term temporal dependencies. In contrast, recurrent-based methods propagate features across frames sequentially, offering higher efficiency, but are prone to error accumulation and detail degradation. Most diffusion- based Real-VSR methods [54] 44 40) adopt the recurrent design for its inference efficiency. In this work, we build on and temporal details. To mitigate the computational overhead, we adopt a one-step diffusion strategy that eliminates redundancy while maintaining high reconstruction quality.
window framework to better preserve spatial
3 Methodology
3.1 Preliminary
Diffusion models 25 simulate a forward process where clean latent code z0 is gradually noised into 2i using Gaussian noise: = Va.- 20 + v1 dt -e, with ~ (O, 7) and ã, following a predefined schedule. During training, a model (t,,, 2,) is trained to predict the added noise at each timestep I. During inference, 20 is recovered from pure noise ør NN(0, 1) by iterative denoising. However,
3

---

this multi-step process is slow and stochastic, limiting its efficiency and stability in super-resolution (SR) tasks that demand fast and reliable reconstruction. To address this issue, recent works [37 27 propose one-step diffusion that skips iterative sampling by directly refining an IQ latent into its HQ counterpart. To further improve controllability, PiSA-SR (27] introduces a residual learning formulation that allows the model to focuses on high-frequency corrections:
: =n = - - co(="0)
where 20 and 2 "º represent the latent codes of LQ and HQ respectively.
Most existing VSR methods [34 44 40 rely on multi-step diffusion, resulting in high computational cost. In this work, we make the first attempt to apply one-step diffusion framework to VSR, improving efficiency while preserving restoration quality by adapting the residual learning formulation in Eq. ( to accelerate convergence. To this end, we introduce VSR- -Se-specific modules and learning strategies to produce detail-rich and temporally consistent results.
3.2 Dual LoRA Learning Network for Real-
Motivation. There is fundamental challenge in Real-VSR: how to balance the preservation of spatial details and the enforcement of temporal consistency. To simultaneously achieve both objectives, begin by analyzing the characteristics of real-world LQ videos and the limitations of current SD-based VSR methods, which motivate the design of our proposed framework.
Temporal Consistency in Degraded Videos. Despite degradations, such as noise, blur, and compres- sion, real-world LQ videos retain stable information across frames, preserving inherent structural and semantic consistency. Leveraging these consistent representations provides strong foundation for reconstructing HQ videos with realistic details and temporal coherence. To exploit this, we propose a Cross-Framd Retrieval (CFR) module to aggregate complementary information across frames to enhance consistency. addition, we design a Consistency-Lond LoRA (C-LoRA) further improve reconstruction by reinforcing temporal alignment and structural integrity. This stage lays the groundwork for more accurate guidance the subsequent detail enhancement phase.
Conflict in Optimizing Details and Consistency. To adapt pre- pre-trained diffusion models for VSR and balance spatial detail and temporal coherence, existing methods [25] 46) typically introduce trainable layers optimized jointly with diffusion and temporal losses. However, detail generation and consistency preservation are inherently conflicting objectives, and joint optimization often results in suboptimal trade-offs. To address this, we propose two decoupled weight spaces: one for temporal consistency modeling and another for detail enbancement. Rather than training two networks, which is costly, we adopt a decoupled scheme inspired by PiSA-SR (27), embedding two specialized LoRA branches into shared SD UNet. This lightweight design enables alternative refinement, allowing each branch to focus on its objective.
F Framework Overview. Building on the above insights, we design Dual LoRA Learning (DLoRAL) framework to generate HQ video outputs from degraded inputs. Given an LQ sequence of N frames, [_Q = (TLQ n = 11 1..... N). our model Go generates a corresponding HQ sequence IHQ = HQ n 1,..,,, NJ. To utilize information from neighboring frames, we adopt a wliding-window strategy (1,6) where each HQ frame T is generated from two adjacent LQ frames:
the current n-th frame I!-2 and its preceding frame r_2 For the first frame /&Q which lacks previous frame, we adopt a self-replication approach to generate 1-0
As illustrated in Fig. 2 our generator Go leverages the pre-trained SD model, which consists of VAE encoder Eg, an SD UNet cg, and VAE decoder Dg. Our DLORAL framework employs two specialized training stages, i.e.. temporal consistency stage and detail enhancement stage. In the temporal consistency stage, the CFR module retrieves inter-frame relevant information from degraded inputs, then the UNet is finetuned by C-LoRA for further reinforcement of temporal aligmment. In the detail enhancement stage, D-LORA is optimized to improve spatial visual quality. These two stages are trained alternately in iterative manner progressively refine both temporal consistency and spatial quality, ultimately leading to coherent and detail-preserve video restoration. During inference, the C-LoRA and D-LoRA merged into the SD UNet to ensure efficient deployment.
Temporal Consistency Stage. This stage is to establish a temporally coherent and robust representa- tion from the LQ video sequence 14Q before enhancing details. This stage involves two main steps: temporal feature fusion using CFR module and fine-tuning the SD UNet to improve consistency.

---

Frozen Trainable
Gradient Flow
69 Stage. Transfer
Lyuz
Cross F Frame
rieval
Gonsistency-LoRA
Detail-LoRA
Cept
Casiitancy
VAE Encoden
Consistency Stage
VAE becodar
Enhoncement Stage
2
Cross FussFFocc Frame
Retrivul
Fand Flow Estimator
Consistency- LoRA
Detail-LoRA
Estimator +
Erge
Less Grow
O
Figure 2: The training pipeline of our proposed DLoRAL. The Cross-Frame Retrieval (CFR) and Consistency-LoRA (C-LoRA) modules are optimized in the consistency stage, while the Detail- LoRA (D-LoRA) is optimized in the enhancement stage. Both stages alternately trained to ensure temporal coherence and visual quality.
To unlock the inherent consistency among degraded inputs, CFR improves the current latent rep- resentation 2.!2 by employing specialized attention mechanism that integrates complementary information from the previous latent feature r22 Specifically, with encoded features 244Q and 2 CFR first warps them into the same coordinate space with SpyNet (24) following a common frame (denoted as Fup). The current latent features 244 and aligned latent features (= are then projected into query (Qn). key (Kn-1), 1). and value (Vn-11 12-1) embeddings through 1 x convolutions (denoted as parameterized by Wo, W K. and Wv, shown below:
..Q...
alignment procedure 332 +
Qn WQ 2n KA-1 WK Fup (211)) Vn-1 Wy F
12
(2)
With obtained embeddings extracted from adjacent frames, CFR employs two mechanisms to en- hance fusion quality. First, for each query position p, selectively attends only the top-k most similar positions (denoted as Fropk (p)) in the aligned previous frame, avoiding perturbations from uncorrelated noises. Second, for each query position p, learnable threshold T.(p)] predicted via a lightweight MIP. dynamically adapts to regional characteristics. enforcing stricter filtering in detail-rich areas while being more permissive in flat regions, ensuring that only confident matches could contribute to the final fusion. The fused feature [p] is computed as:
(Qulp). K (q)) ( Vd
2Q [p] 2.0 + Σ
T Tolpl) Vv-119),
@EFiopa
where d(.) is non-negattvv gating function (e.g., ReLU [2].. and is the channel dimension.
The latent feature z4Q is then processed by the UNet to generate the HQ latent =!Q this stage, only the C-LoRA is trainable, while D-LoRA remains frozen. The final HQ frame is reconstructed via the VAE decoder by H2 Do (2 #Q). All trainable components in this stage, including the CFR module and C-LoRA, are optimized using the consistency loss Lcons- which is designed to ensure both the quality of individual frames and the temporal consistency across the sequence. It combines the pixel-level loss (fpix), LPIPS loss (Lipips), and optical flow loss (Lap), shown below:
Lcons = -pix Cpix + >Apppps + Aape Copts
Cop | OGT
||| F(T (HI F(IGT IST
Here, the & loss is adopted as the Lrxxx and fop measures the L,, distance between optical flow maps estimated from generated and ground-truth frame pairs, promoting motion alignment and smooth
5

---

transitions. The loss weights Apix, Alpips, and Aope are empirically set to balance spatial accuracy, perceptual quality, and temporal consistency.
Detail Enhancement Stage. Different from the temporal consistency stage, which yields aligned and coherent latent representations, the detail enhancement stage focuses restoring high-frequency visual details. this stage, adjacent latent features 2.4, and 2, are processed by the frozen CFR module to reapply the learned alignment and fusion, thus the temporal consistency learned in the consistency stage maintained without introducing new variations.
The resulting temporally enriched latent =7 is then fed into the diffusion UNet <g. We employ a decoupled finetuning strategy: only the D-LoRA parameters, responsible for detail synthesis, trainable, while the C-LoRA parameters, associated with consistency, remain frozen. This setting allows the D-LoRA focus solely on detail synthesis without compromising the temporal structure previously established. The output HQ latent ,HQ is then decoded using the frozen decoder Dy to produce the final super-resolved frame I.R..
To guide this detail enhancement while preserving the structure learned previously, the loss function Leuh combines several components follows:
(5) We retain Cpix. Liiipss and Lopt used in the consistency stage (as Eq. serving as anchors to maintain spatial fidelity and motion coherence. Furthermore, we introduce the Classifier Score Distillation (CSD) loss [27] Lest, which encourages the generation of richer and finer details.
Lenh = Cpix + Alpips Clpips + AoptCopt + Cesd-
3.3 Training and Inference
Dynamic Dual-Stage Training. We adopt dynamic dual-stage training scheme. The training begins with the consistency stage, aiming learning degradation-roban robust features and establishing strong temporal coherence among frames. In this stage, only the CFR and C-LoRA modules are trainable, while the D-LoRA is fixed. Once the model converges the consistency stage, the training switches to refine high-frequency spatial details, guided by Lenh with the additional CSD loss. In this stage, only the D-LoRA parameters are trainable, while the CFR module and C-LoRA are fixed. Such an alternative training is iterated, allowing the model to dynamically converge toward solution that balances temporal coherence and visual fidelity.
Smooth Transition Between Training Stages. Compared to the consistency stage, the enhancement stage introduces an additional loss function Lesd for enriching semantic details. Directly switching between the full loss functions Leons and Leah can lead to instability due to the abrupt change in learning targets. To prevent this, we employ a re-weighting strategy that progressively shifts the loss objective, ensuring a smooth transition between stages. Taking the transition from the consistency stage to the enhancement stage as an example, after the consistency stage, the two loss functions are interpolated as the optimization objective for warm-up phase of steps, as shown below:
(()) = (1
8 St
Leoms +
Lenh.
E [0, 8e),
(6)
where denotes the current step within the transition. Symmetric interpolation is applied when switch back from the enhancement stage to the consistency stage.
Inference Phase. At test time, both C-LoRA and D-LoRA are activated and merged into the frozen diffusion UNet. single diffusion step used enhance the LQ input to HQ video frames.
4 Experiment
4.1 Experimental Settings
Implementation Details. We adopt the pre-trained Stable Diffusion V2.1 as the backbone of denoising U-Net. Training is carried out with batch size of 16, sequence length of 3, and video resolution of 512 x 512. All models are trained using the PyTorch framework on NVIDIA A100 GPUs We use Adam optimizer with an initial learning rate of 5 x 10-5, For inference, both C-LoRA and D-LoRA are activated simultaneously a frozen UNet. Videos are processed in sliding sequences to fit GPU memory limits.

---

Training Datasets. To support the decoupled training design of our DLoRAL framework, we construct two training datasets for the consistency and enhancement stages, respectively.
For the consistency stage, the training data needs to contain realistic motion while maintaining rea- sonable image quality. To this end, we select 44,162 high-quality frames from the REDS dataset [2 which offers professionally captured sequences with rich dynamics, and a curated set of videos (39 from P chosen based on aesthetic and temporal smoothness criteria. These sequences provide necessary temporal priors for learning degradation-robust representations.
For the enhancement stage, the training data should prioritize visual quality. Thus, we select the LSDIR [18] dataset, known for its rich textures and more fine-grained details than existing public video datasets. To preserve the learned consistency modeling capability and enable the optical flow regularization among frames, we generate simulated video sequences based on LSDIR. Specifically, for each ground-truth image in LSDIR, we apply random pixel-level translations to it to generate multiple shifted images. The resulting pseudo-video sequences inherently support consistency constraints through synthetic motion, while surpassing real video datasets in visual quality.
The data in both stages are degraded using the RealESRGAN 33 degradation pipeline. We apply identical degradation parameters across frames within the same video, while using random parameters for different video sequences.
Testing Datasets. We evaluate our method on both synthetic and real-world datasets, including UDMI0 [47, SPMCS [28]. RealVSR 455 and Videol. [ Among them, UDM10 contains 10 sequences, each having 32 frames. SPMCS contains 30 sequences, each having 31 frames. RealVSR contains 50 real-world sequences, each having 50 frames. Videol.Q contains 50 real-world sequences with complex degradations. For the synthetic dataset (UDM10 and SPMCS), we synthesize LQ-HQ pairs following the same degradation pipeline in training. For real-world datasets (RealVSR and VideoLQ), we directly adopt the given LQ-HQ pairs.
Evaluation Metrics. A set of full-reference and no-reference metries are selected to evaluate different real-world VSR methods. The full-reference metrics include PSNR and SSIM, and perceptual quality with LPIPS [52] and DISTS (9] No-reference quality assessment involves MUSIQ [4].
MANIQA (3). CLIPIQA 29 and the video quality assessment metric DOVER [3).. Compared to Real-ISR, Real-VSR places greater emphasis on temporal consistency. Following prior works [44 53
we use the average warping error Emarp to quantitatively assess temporal consistency: Ewory
N-1 N-1 H Fw @ I|1. For the test datasets with GT, optical flow in Fup is estimated from GT frames. For real-world datasets without GT (e.g., VideoLQ test set), we use the flow estimated from predicted frames.
4.2 Experimental Results
To demonstrate the effectiveness of our DLoRAL algorithm, we compare it with seven represnetative and state-of-the-art methods, including three Real-ISR models (RealESRGAN (33), StableSR (3I,
and the one-step model OSEDiff (37), a discriminative VSR model (RealBasic VSR [8). and three
diffusion-based VSR models (Upscale-A- A-Video (54, MGLD-VSR
44 and STAR 40).
Quantitative Comparison. We show the quantitative comparison on both synthetic and real-world video benchmarks (where real-world testing videos were centrally cropped to 128 x 128 resolution) in Tab T from which several key observations can be made, First, non-diffusion-base methods (e.g., RealESRGAN and RealBasic VSR) VSR) perform worse than diffusion-based methods no-reference perceptual quality metrics, such as MUSIQ and CLIPIQA, mainly because they lack the strong image priors provided by pre-trained SD models, leading over-smoothed results. Second, SD- based Real-ISR methods (StableSR and OSEDiff) can achieve comparable or even better perceptual quality scores than existing Real-VSR methods. In particular, OSEDiff achieves the best DOVER scores on both the UDM10 and SPMCS datasets. However, its warping error evaluated by E Ejary is worse. This is because the Real-ISR methods generate details for each frame without considering the fiterfframe consistency. Finally, compared to the existing Real-VSR methods, our DLoRAL consistently ranks first or second across range of perceptual quality metrics. including LPIPS. DISTS, MUSIQ, CLIPIQA, MANIQA, and DOVER, demonstrating its strong alignment with human perception. At the same time, AL does not compromise temporal consistency, as evidenced
https://www wwt.:/pwwels.comm

---

Real-ISR Methods
Real-VSR Methods
Datasets
Metrics
RealESRGSN
StableSR
OSEDiff
RealBasic VSR
Upscale-A- Video
MGLD
STAR
DLoRAL
PSNR
21.345
22.042
23.761
24.334
22.364
24.192
24.451
23,975
SSIM
0,565
0,568
0.696
0.723
0,584
0.685
0.714
0.710
LPIPS
0.451
0.455
0.367
0.363
0,410
0.335
0.417
0.327
DISTS
0.175
0.185
0.175
0.204
0.198
0.176
0.230
0.179
BRISQUE
29.843
26,310
20.718
14.129
17.607
22.701
36,910
16.250
UDMI0
MUSIQ
49.838
47,805
63.146
62.360
61.046
61.309
40,789
65.620
CLIPIQA
0.474
0.445
0.574
0.474
0.445
0.453
0.267
0.652
MANIQA
0.330
0.319
0.334
0.330
0.318
0.291
0.244
0.373
Egap 4
7,580
8,440
5,220
4.670
5,790
4.610
3,510
4.720
DOVER
36,860
30.470
48.404
37,572
37.694
40.045
30,384
42.871
PSNR
21.660
19.260
20.650
21.580
19.030
21.260
20.730
21.240
SSIM f
0.569
0.585
0.696
0.545
0.386
0.515
0.489
0.524
LPIPS
0.444
0.432
0.354
0.404
0.485
0.384
0.606
0.375
DISTS
0,246
0.235
0.229
0.237
0.274
0.234
0.342
0.222
BRISQUE
25.240
26.310
19.471
12.048
19.784
23.184
27.902
11.030
SPMCS
MUSIQ
53.221
47.805
64.619
66.683
66,912
$5.0079
33.247
67.390
CLIPIQA f
0.515
0.445
0.526
0.515
0,517
0.437
0.240
0.581
MANIQA
0,308
0.319
0.308
0.308
0.443
0.312
0.237
0.340
E
7.570
8,430
7,500
5,400
7.570
4,410
4.080
6.250
DOVER
32.151
30.470
40.160
30,953
32.151
31.118
17.220
34.895
PSNR
21.340
18.950
19,920
22,270
20.060
21.120
15.080
20,360
SSIM
0.565
0.583
0.588
0.720
0.591
0.646
0.433
0.606
LPIPS
0.451
0.225
0.282
0.193
0.263
0.219
0.409
0.242
DISTS
0.175
0.154
0.164
0. 0.160
0.158
0.151
0.279
0.150
BRISQUE
29.843
28.250
31,794
30,362
25.476
39
62.750
27.893
RealVSR
MUSIQ
49.838
69,962
64.101
71.413
67.714
70.734
67.947
70.908
CLIPIQA
0.474
0.546
0.370
0.532
0.617
MANIQA t
0.330
0.345
0.341
0.384
0.414
0.499
0.438
0.386
Equp J
17,580
25.010
18.300
18.720
18.200
19.210
24.600
17.300
DOVER
36,860
46.846
42.138
46.439
36,136
42.044
30.214
49.646
BRISQUE
29.605
22.337
26.403
24.790
25.101
9966
42.582
23.039
MUSIQ
53.138
52,975
58.95 38,959
59,475
57.489
53.092
49,305
63.846
CLIPIQA
0,334
0.478
0.499
0.393
0.377
0.315
0.333
0.567
VideoLQ
MANIQA
0.232
0.278
0.254
0.312
0.328
0.254
0.268
0.344
Exup
7,580
8,430
8,406
8.108
7.586
7.409
7,280
7.897
DOVER 28,400
30.470
37.580
34.772
36.860
31,899
29 29.400
38.505
Table I:
Comparison of various Real-ISR and Real-VSR methods across different datasets. The best and second best results of each metric are highlighted red and blue, respectively.
by its superior performance on the E ggopp metric. For example, on the RealVSR dataset, DLoRAL achieves state-of-the-an results in DISTS, CLIPIQA, and DOVER, while ranking among the top in Ewarp: highlighting its ability to produce visually pleasing and temporally coherent outputs.
It should be mentioned that although Eaap is widely used to assess temporal consistency, it does not correlate well with human perception. For example, blurry Real-VSB outputs can achieve lower warping errors but exhibit poorer visual quality. DLoRAL may report slightly larger Engary values than some methods (e.g., STAR), but this is because DLoRAL better preserves fine details that the warping error metric tends penalize.
Qualitative Comparison. To further demonstrate the effectiveness of DLoRAL, we visualize the Real-VSR results in Fig. 3 One can see that DLORAL. can remove complex spatial-variant degradations and generate realistic details, significantly outperforming other Real-VSR models. Specifically, for the severely degraded facial region (first row), RealBasic fails to reconstruct the facial structural, and Upscale-A- A-Video and STAR lose facial details. MGLD produces sharper outputs, but suffers from severe structural distortions, particularly around the eye regions. In contrast, DLoRAL successfully recovers fine facial features while maintaining structural integrity. The second row highlights the performance in texture reconstruction, where our method restores sharper and more legible texture patterns compared to the blurry or distorted outputs from other algorithms.
To better compare the consistency, we plot the temporal profiles of the VSR results produced by competing methods Fig. Real-ISR approaches such as StableSR and OSEDiff restore sharper details but suffer from severe temporal instability, as shown by the erratic fluctuations in their profiles,
8

---

resulting in unpleasant flickering that harms the video quality. On the other hand, while existing Real-VSR methods can offer better temporal consistency than Real-ISR methods, this comes the cost of blurred details (see the results of Upscale-A-Videss MGLD and STAR in the left case of Fig. 4 or intra-frame artifacts (see the results of RealBasic VSR, Upscale-A- A-iieA and STAR the right case of Fig. E. In comparison, our DLoRAL produces smooth and stable transitions across frames, as reflected by its consistent temporal profiles. This qualitative evidence aligns with our quantitative results, demonstrating DLoRAL's ability to preserve fine visual details while ensuring natural temporal consistency, More visual comparisons can be found in the Appendix.
Zoomed I.Q
RSaR Upecale-A- A-Vide
MGLD
STAR
DLORAL
Figure 3: Qualitative comparison of VSR models real-world VideoL dataset.
LR
Rei V
Upocale
Vided
MIGUU
STAR
S
STAR
Subusa
DLORAL
USEDED
DLORA
Figure 4: Temporal profiles of competing Real-ISR and Real- methods.
Real-ISR Methods
RSR Methods
StableSR
OSEDin
Upscale-A A-Video
MGLD
STAR
DLoRAL
Inference Step
200
1
15
Inforence Time (s/50 frames)
32800
340
3640
4146
2830
346
# Total Param (M)
1150
1294
14442
1430
2492
1300
Table 2: Complexity comparison among different methods. All methods are evaluated using 50
512 x 512 frames for the x4 VSR task. Inference time is measured on an A100 GPU and includes the entire pipeline: data loading, processing, and result storage.
Complexity Comparison. We compare the inference steps, model size, and inference time of competing diffusion based models in Tab. The inference time of the whole pipeline (including data loading, data processing, and result storage) is reported, which measured on the x4 VSR task with 50 frames of 512 x 512 LQ images on single NVIDIA A100 80G GPU, Compared with Real-ISR methods, DLoRAL (346s) achieves strong balance between quality and complexity, delivering superior visual quality and temporal consistency while maintaining similar speed to OSEDiff (340s). Among the Real-V VSR methods, DLoRAL achieves the fastest inference time and the lowest parameter count, benefiting from its efficient one-step design. Specifically, DL is more 10x faster than Poxcale-A-Video MGLD, and 8x faster than STAR, while maintaining superior visual quality.
Ablation Study. To validate the effectiveness of the proposed components in our model, we conduct ablation studies by selectively removing each of the three key modules: (i) CFR, (ii) C-LoRA, and (iii) D-LoRA, while keeping all other settings identical. For this analysis, we adopt VidcoLQ subset of four representative sequences with diverse scenes and motions from the VideoLQ dataset. As summarized in Tab. 3 removing either CFR or C-LoRA leads to weaker temporal consistency (i.e., higher warping error), indicating their complementary roles in maintaining temporal coherence. In contrast, removing D-LoRA significantly impairs all perceptual metrics, confirming its core contribution to fine-grained detail enhancement, Further ablations are provided in the Appendix.
SSpecifically, VideoLQ4 contains the 013, 015, 020, and 041 clips, each consisting of 100 frames.

---

MUSTQ 1
CLIP-IQA f
MANIQA 1
F warp
Ours (Full)
66.6174
0.5475
0.3791
1,51 10
W/o CFR
64.5732
0.5148
0.3386
1.58 x 10 3
W/o C-LoRA
64.2623
0.5492
0.3520
1.61 X 10
W/o D-LoRA
54.0769
0.3654
0.2471
1.48 x 10
Table 3: Ablation study on key modules on VideoLQ4 dataset.
User Study. We also conduct a user study to further examine the effectiveness of DLoRAL in comparison with existing RealVSR methods. We invited ten volunteers to participate in a user study. Our DLoRAL method was compared with the other three diffusion-based Real- VSR methods: Upscale-A-Video [54], MGLD [4] and STAR [40]. We randomly selected 12 real-world LQ videos with complex degradations and motions from the VideoLQ dataset [8], whose scenes are shown in Fig. 5Ja). Each LQ video and its corresponding HQ videos generated by the competing Real-VS VSRR methods were presented to the participants who were asked to select the best HQ result by considering two equally weighted factors: the perceptual quality and temporal consistency of the video.
The results of the user study are shown in Fig. 5(()). DLoRAL received 93 votes, significantly outperforming the other methods, with MGLD, STAR, and Upscale-A- Video receiving 14, and 5 votes, respectively. This overwhelming preference for DLoRAL. highlights its effectiveness in addressing the challenges of weal-world video restoration. Note that the selected videos include variety of motion scenarios. In scenarios with complex motion, DLoRAL is able to achieve superior visual quality while maintaining temporal consistency comparable to other methods. In relatively static scenes, DLoRAL demonstrates stable temporal consistency along with equally sharp and clear visual quality.
O
DLoRAL: 77.5% (93)
Upscale-A -hscall-A-Vidoo: 4.2% (5)
STAR: 6.7% (8)
MGLD: 11.6% (14)
(a) LQ vidcos used in user study
(b) Voting results from 10 volunteers
Figure 5: LQ videos used in user study and the voting results.
5 Conclusion
We proposed DLoRAL to achieve temporally consistent and detail-rich Real-VSR results. To effectively extract degradation-robut robuust temporal priors from low-quality input videas while enhancing details without compromising these priors, we first developed a CFR module and consistency-Lood to generate robust temporal representations, and then developed a detail-LoRA to enhance spatial details. We optimized these two objectives alternatively and iteratively, where the results of the previous stage served model demonstrated significantly superior performance to previous Real-VSR methods, achieving rich spatial details without compromising the temporal coherence.
anchor to provide priors for the next stage. The resulting DLoRAL
Limitations. Despite its strong performance, DLoRAL still has certain limitations. First, since inherits the 8x downsampling VAE from SD, DL faces difficulties in restoring very fine-scale details such as small texts. Second, this heavy compression of VAE may disrupt temporal coherence. making it harder to extract robust consistency priors. A VAE specifically designed for Real-VSR tasks could help to address these issues. We leave this challenge for future investigation.
10

---

References
[1] Yuang Ai, Xiaoqiang Zhou, Huaibo Huang, Xiaotian Han, Zhengyu Chen, Quanzeng You, and Hongxia Yang. Dreamclear: High-capacity real-world image restoration with privacy-safe dataset curation. Advances in Neural Information Processing Systems, 37:55443-55469 2024.
[2] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Do- minik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffuuiio: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:231 2023.
[3] Adrian Bulat and Georgios Tzimiropoulos. Super-fan: Integrated facial landmark localization and super-resolution of real- world low resolution faces in arbitrary poses with gans. In Pro- ceedings of the IEEE conference on computer vision and pattern recognition, pages 109-117, 2018.
[4] Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos. To learn image super-resolution, use gan to learn how to do image degradation first. In Proceedings of the European conference on computer vision (ECCV), pages 185-200, 2018.
[5] Jiezhang Cao, Yawei Li, Kai Zhang, and Luc Van Gool. Video super-resolution transformer. arXiv preprini arXiv:2 2021.
[6] Kelvin CK Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basievsr: The
search for essential components in video super-resolution and beyond. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4947-4956, 2021.
[7] Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basievsr++: Improv-
ing video super-resolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 5972-5981, 2022.
[8] Kelvin CK Chan, Shangehen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 5962-5971, 2022.
[9] Keyan Ding, Kede Ma, Shiqi Wang, and Ecro P Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE transactions on pattern analysis and machine intelligence, 44(5):2567-2588 25881, 2020.
[10] Linwei Dong, Qingnan Fan, Yihong Guo, Zhonghao Wang. Qi Zhang, Jinwei Chen, Yawei Luo, and Changqing Zou. Tsd-sr: One-step diffusion with target score distillation for real-world image super-resolution. arXiv preprint arXiv:2411.1826. 2024.
[11] Dario Fuoli, Shuhang Gu, and Radu Timofte. Efficient video super-resolution through recurrent latent space propagation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 3476-3485. IEEE, 2019.
[12] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks, In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 315-323. JMLR Workshop and Conference Proceedings, 2011.
[13] Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen -hu, Yuanzhi Li, Shean Wang. Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
[14] Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. Musiq: Multi-scale image quality transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5148-5157, 2021.
[15] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE conference computer vision and pattern recognition, pages
2017.

---

[16] Wenbo Li, Xin Tao, Taian Guo, Lu Qi. Jiangbo Lu, and Jiaya Jia. Mucan: Multi Multi-correspoodence aggregation network for video super-resolution. In Computer Vision- ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23-28,, 2020, Proceedings, Part X 16, pages 335-351. Springer, 2020.
[17] Xiaohui Li, Yihao Liu, Shuo Cao. Ziyan Chen, Shaobin Zhuang, Xiangyu Chen, Yinan He, Yi Wang, and Yu Qiao. Diffvsr: Enhancing real-world video super-resolution with diffusion models for advanced visual quality and temporal consistency. arXiv preprint arXiv:2501. 1015001111000 10, 2025.
[18] Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong. Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, et al. Lsdir: A large scale dataset for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1775-1787, 2023.
[19] Chengxu Liu, Huan Yang, Jianlong Fu, and Xueming Qian. Learning trajectory trans- former for video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5687-5696, 2022.
[20] Chengxu Liu, Huan Yang. Jianlong Fu, and Xueming Qian. Learning rajectory-awwae trans- former for video super- super-tesolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5687-5696, 2022.
[21] Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin. Pulse: Self- supervised photo upsampling via latent space exploration of generative models. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 2437-2445 2020.
[22] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge video deblurring and super-resolution: Dataset and study. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0 0, 2019.
[23] Yunpeng Qu, Kun Yuan, Kai Zhao, Qizhi Xie, Jinhua Hao, Ming Sun, and Chao Zhou. Xpsr: Cross-modal priors for diffusion-based image super-resolution. In European Conference Computer Vision, pages 285-303. Springer. 2024.
[24] Anurag Ranjan and Michael J Black. Optical flow estimation using spatial pyramid network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4161-4170, 2017.
[25] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10699 10695, 2022.
[26] Lingchen Sun, Rongyuan Wu, Jie Liang, Zhengqiang Zhang, Hongwei Yong, and Lei Zhang. Improving the stability and efficiency of diffusion models for content consistent super-resolution. arXiv preprint arXiv:2401.00877 2023.
[27] Lingchen Sun, Rongyuan Wu, Zhiyuan Ma. Shuaizheng Liu, Qiaosi Yi, and Lei Zhang. Pixel- level and semantic-level adjustable super-resolution: A dual-lora approach. arXiv preprint arXiv:2412.032 2024.
[28] Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiyy Jia. Detail-revealing deep video super-resolution. In Proceedings of the IEEE international conference on computer vision, pages 4472-4480, 2017.
(29) Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 2555-2563, 2023.
[30] Jianyi Wang, Zhijie Lin, Meng Wei, Yang Zhao, Ceyuan Yang, Fei Xiao, Chen Change Loy, and Lu Jiang. Seedvr: Seeding infinity in diffusion transformer towards generic video restoration. arXiv preprint 2:Xi::250..01320, 2025.
12

---

[31] Jianyi Wang. Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(12):5529-5944 2024.
[32] Xintao Wang. Kelvin CK Chan, Ke Yu, Chao Dong, and Chen Change Loy. Edvr: Video restoration with enhanced deformable convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0-0, 2019.
[33] Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVE international conference on computer vision, pages 1905-1914, 2021.
[34] Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu. Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: single step. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 25796-25805, 2024.
image super-resolution in
[35] Pengxu Wei, Yujing Sun, Xingbei Guo, Chang Liu, Guanbin Li, Jie Chen, Xiangyang Ji, and Liang Lin. Towards real-world burst image super-resolution: Benchmark and method. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13233- 13242, 2023.
[36] Haoning Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jingwen Hou, Annan Wang, Wenxiu Sun, Qiong Yan, and Weisi Lin. Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 100144201555 2023.
[37] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang. One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Processing Systems, 37:92529-92553, 2024.
[38] Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, and Lei Zhang. Seesr: Towards semantics-aware real-world image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 25456-25467, 2024.
[39] Yuhui Wu, Liyi Chen, Ruibin Li, Shihao Wang, Chenxi Xie, and Lei Zhang. Insvie-Ims Effective instruction-based video editing with elaborate dataset construction. arXiv preprint arXiv:2503.20287, 2025.
[40] Rui Xie, Yinhong Liu, Penghao Zhou, Chen Zhao, Jun Zhou, Kai Zhang, Zhenyu Zhang, Jian Yang, Zhenheng Yang, and Ying Tai. Star: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution. arXiv preprint urXiv:2501.02976 2025.
[41] Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, fia-Bin Huang, and Difan Liu. Videogigagan: Towards detail-rich video super-resolution. arXiv preprint 24Xiv:2404.10 12388, 2024.
[42] Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, and William T Freeman. Video enhancement with trsk-trie flow. International Journal of Computer Vision, 127:1106- 2019.
[43] Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujuu Yang. Maniqa: Multi-dimension attention network for no-reference image quality assessment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1191-1200, 2022.
[44] Xi Yang, Chenhang He, Jianqi Ma, and Lei Zhang. Motion-guided latent diffusion for temporally consistent real-world video super-resolution. In European Conference on Computer Vision, pages 224-242. Springer, 2024.
[45] Xi Yang, Wangmeng Xiang, Hui Zeng, and Lei Zhang. Real-world video super-resolution: benchmark dataset and decomposition based learning scheme. In Proceedings of the IEEE/CVF international conference computer vision, pages 4781-4790 2021.
13

---

[46] Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang. Wenyi Hong. Xiaohan Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with expert transformer. arXiv preprini arXiv:2408.
2024.
[47] Peng Yi, Zhongyuan Wang, Kui Jiang, Junjun Jiang, and Jiayi Ma. Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. Proceedings of the IEEE/CVF international conference computer vision, pages 3106-3115 2019.
[48] Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang. Jingwen He, Yu Qiao, and Chao Dong. Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 25680, 2024.
[49]
Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, and Chao Dong. Scaling up excellence: Practicing model scaling for photo-realistic image restoration in the wild. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 25669-25680 2024.
[50] Zongsheng Yue, Kang Liao, and Chen Change Loy, Arbitrary-steps image super-resolution via diffusion inversion. arXiv preprint arXiv:2412.09013, 2024.
[51] Lymin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages
2023.
[52] Richard Zhang, Phillip Isola. Alexei Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018
[53] Zhengqiang Zhang, Ruihuang Li, Shi Guo, Yang Cao, and Lei Zhang. Tmp: Temporal motion propagation for online video super-resolution. IEEE Transactions Image Processing, 2024.
[54] Shangchen Zhou, Peiqing Yang. Jianyi Wang, Yihang Luo, and Chen Change Loy. Upscale- a-video: Temporal-consister diffusion model for real-world video super-resolution. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Yes, the main claims in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We discuss limitations in the final section (after the conclusion).
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't It acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
15

---

Justification:
Yes, the paper provides full set of assumptions and proof for each theoretical
result.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Yes, paper fully discloses all necessary information to reproduce the main experimental results, supporting the main claims and conclusions.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
16

---

reproducible, they Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: All data are publicly available. We will release the codes and new data if the paper is accepted.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.co cc/
for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/
Cooee//mdeeeSbmmmcia ssi onPolicy) for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Yes, the paper provides all necessary details, including hyperparameters, evaluation metrics and etc. to fully understand the results.
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented in the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer:
[No]
Justification:
We do not report error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
17

---

The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful figures symmetric error bars that would yield results that are out of range (e.g. negative rates).
show in tables or
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We detail the type of compute resources in experimental settings.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't 't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification: Yes, the research the paper fully conforms to the NeurIPS Code of Ethics in every respect.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive socictal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Yes, we analyze the potential social impact in appendix.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
18

---

Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), faireess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper poses such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g.. code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: Yes, clearly indicate the baseline methods and testing data used in the paper. Their licenses permit use with academic scope.
Guidelines:
The answer NA means that the paper does use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
19

---

If assets are released, the license, copyright information, and terms of use in the thcode. has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
package should be provided. For popular datasets, paperswi
latasets.paperssi
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification:
Code and dataset will be release if the paper is accepted.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowd sourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowd sourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
20

---

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors adhere the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: Our core method focuses on video super-resolution and does not involve LLMs part.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM for what should or should not be described.
21


Reviews:
[{"review_id": "g1wjagGqA8", "reviewer": "Reviewer_dmAC", "summary": "This paper is interesting. The authors add two LORA to enhance the temporal consistency and details of the original one step diffusion SR network.", "questions": "Perhaps ablation studies can be given to show the contribution of each LORA.", "limitations": "Can such design be applied to other image-based diffusion networks? such as image generation network.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Experimental results show obvious improvments with slight computational increments.\n\nStrengths\n1. Decoupled Learning for Conflicting Objectives: By separating temporal consistency (C-LoRA) and spatial detail (D-LoRA) into dedicated modules, DLoRAL avoids the trade-off inherent in joint optimization, allowing each component to specialize in its target.\n2. Effective Extraction of Temporal Priors: The CFR module leverages inherent consistency in LQ videos (despite noise/blur) to aggregate structure-aligned cross-frame information, providing stable guidance for both modules.\n3. Efficient One-Step Inference: Built on a one-step diffusion framework, DLoRAL reduces inference time by ~10× compared to multi-step diffusion methods (e.g., Upscale-A-Video, MGLD), with comparable or better quality.\n4. Strong Performance Across Metrics: DLoRAL achieves state-of-the-art results in perceptual metrics (MUSIQ, CLIPIQA), fidelity (PSNR, SSIM), and temporal consistency (warping error), balancing detail and coherence effectively.\nAlternate Training Strategy: Iterative optimization of consistency and detail stages, with smooth loss transitions, ensures each module converges without interfering with the other.\n\nWeaknesses\n1. Limitations of VAE Compression: Inherits the 8× downsampling VAE from Stable Diffusion, which struggles to restore ultra-fine details (e.g., small text) due to aggressive compression.\n2. Temporal Coherence Disruption: The VAE’s heavy compression may disrupt temporal consistency in LQ frames, making it harder to extract robust temporal priors. A VAE specifically designed for Real-VSR could mitigate this.\n3. Dependence on Pre-trained Models: Relies on the pre-trained Stable Diffusion backbone, which may limit generalization to highly specialized video degradation types not covered by the original model’s training data.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns. And we adopt VideoLQ4, as detailed in Appendix Section2, as our default test set for the reported experiments.\n\n**[W1 and W2]: Limitations of VAE.**  \n\n**[Response]**:  We sincerely thank the reviewer for pointing out the limitation of using the pre-trained VAE from the Stable Diffusion (SD) model. This is indeed a well-known issue, which we have also explicitly discussed and identified as our limitation in our manuscript (Page 9, Lines 339–343). The aggressive 8× downsampling in the VAE can compromise the reconstruction of fine structures (*e.g.,* small text and textures). Additionally, a heavily compressed VAE may struggle to extract accurate temporal priors. Therefore, improving the VAE is an important research direction, which we plan to explore in future work.\n\nIn this paper, however, we aim to strike a balance between leveraging the natural image prior of SD model and achieving temporal consistency for video super-resolution (VSR) task; thus, the original VAE is kept for compatibility with the pre-trained UNet. To jointly maintain visual quality and temporal consistency within this framework, we propose Cross-Frame Retrieval and Dual-LoRA Learning. As shown in Table 1 and Figure 4 of the main paper, our method achieves strong results across both consistency and quality metrics, demonstrating its ability to maintain temporal coherence while preserving high visual quality under the heavily compressed VAE.\n\nLooking forward, recent works, such as TVT [1], have attempted to employ a VAE with a lower downsampling rate in SD-based models. These approaches significantly mitigate the challenges of reconstructing fine structures and alleviating temporal coherence disruption caused by severe compression, while retaining the generative capability of pre-trained diffusion models. We find this direction highly promising and plan to explore VAE designs specifically tailored for Real-VSR, such as TVT-like strategies, in our future work. This line of research is complementary to our current work and may further enhance the applicability of diffusion-based VSR models in real-world scenarios.\n\n**[W3]: Generalization capability.**\n\n**[Response]:**  We sincerely thank the reviewer for raising this important concern about the generalization ability of our method. Our approach builds upon the pre-trained SD model to leverage its strong natural image generative priors, which have been validated to have high generative capacity in prior SR tasks [2,3,4].\n\nWe fully acknowledge that the SD model was trained on large-scale image datasets, which may not cover many real-world video degradation types. This potential distribution mismatch could indeed affect generalization in unseen scenarios. To address this limitation and enhance the robustness of our model, instead of using SD’s original training data, we construct dedicated datasets for Real-VSR training.  These datasets simulate diverse degradation types and scene content, aiming to better align the Real-VSR needs. To further evaluate the generalization performance of our method, we conduct the following experiments:\n\n1. **Cross-dataset evaluation**:  \nWe extensively test DLoRAL on datasets with varying degradation characteristics, including synthetic benchmarks (UDM10, SPMCS) and real-world datasets (RealVSR, VideoLQ). RealVSR contains mobile-captured videos, while VideoLQ features authentically degraded Internet content. As shown in Table 1 and Figure 3, our method consistently achieves strong performance across all datasets, demonstrating robustness under a wide range of degradation patterns.\n\n2. **User study**:  \nWe also perform user studies (Figure 3 of the Appendix) to assess the perceptual quality in challenging conditions, such as low light, fast motion, and compression artifacts. The results indicate that DLoRAL maintains good perceptual quality and generalizes well in these difficult scenarios.\n\nNonetheless, we agree that there can still be failure cases for degradations outside of our training data. \n\n**[Q1]: More ablation studies of each LoRA.**\n\n**[Response]:**  We thank the reviewer for the insightful suggestion regarding an ablation study on the two LoRA branches. We conduct a dedicated experiment with all models trained from scratch, comparing (i) C-LoRA only, (ii) D-LoRA only, and (iii) the full C+D configuration. The results are presented in the table below.\n\n|                  | MUSIQ ↑   | CLIP-IQA ↑       | MANIQA ↑         | Warping Error ↓          |\n|------------------|-----------|------------------|------------------|---------------------------|\n| C-LoRA only      | 50.5394   | 0.3052           | 0.2147           | **1.48 × 10⁻³**           |\n| D-LoRA only      | 65.8557   | 0.5420           | 0.3412           | 1.62 × 10⁻³              |\n| C+D LoRA (Ours)  | **66.6174** | **0.5475**      | **0.3791**       | 1.51 × 10⁻³              |\n\nThe above results confirm that C-LoRA primarily enhances temporal consistency, while D-LoRA recovers fine spatial details; combining both yields the best overall performance. These findings substantiate the complementary contributions of each LoRA and justify the proposed dual‑stage design.\n\n**[Q2]: Application to other networks.**\n\n**[Response]:**  If we correctly understand the reviewer's question, it concerns whether our proposed design can be applied to other diffusion networks, such as DiT-based models (*e.g.*, Stable Diffusion 3). To verify this, we conduct additional experiments using Stable Diffusion 3 as the pre-trained model, one model trained with our proposed strategies (*i.e.*, CFR and Dual-LoRA learning) and the other model trained without the strategies. \n\n|                    | MUSIQ ↑     | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|--------------------|-------------|----------------|----------------|------------------------|\n| SD3 w/ our strategy  | **69.0547** | **0.5812**     | **0.4015**     | **1.55 × 10⁻³**        |\n| SD3 w/o our strategy | 58.3651     | 0.4217         | 0.3243         | 1.62 × 10⁻³            |\n\n\nThe results show that our method can be successfully integrated into other diffusion models and leads to improved VSR results.\n\n**Reference:**\n\n[1] Q. Yi, S. Li, R. Wu, L. Sun, Y. Wu, L. Zhang, \"Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training,\" Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025.\n\n[2] J. Wang, Z. Yue, S. Zhou, K. C. Chan, C. C. Loy, \"Exploiting Diffusion Prior for Real-world Image Super-resolution,\" International Journal of Computer Vision (IJCV), 2024.\n\n[3] X. Lin, J. He, Z. Chen, Z. Lyu, B. Dai, F. Yu, Y. Qiao, W. Ouyang, C. Dong, \"DiffBIR: Toward Blind Image Restoration with Generative Diffusion Prior,\" Proceedings of the European Conference on Computer Vision (ECCV), 2024.\n\n[4] L. Sun, R. Wu, Z. Ma, S. Liu, Q. Yi, L. Zhang, \"Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach,\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025."}, {"review_id": "6U8w9yU459", "reviewer": "Reviewer_mZuh", "summary": "This paper addresses the challenge of Real-World Video Super-Resolution (Real-VSR), where the goals of generating rich spatial details and maintaining temporal consistency are often in conflict. The authors propose a novel framework, Dual LoRA Learning (DLORAL), which leverages a pre-trained Stable Diffusion model to perform high-quality VSR in a single diffusion step. The core idea is to decouple the learning of temporal consistency and spatial detail into two specialized, low-rank adaptation (LoRA) modules: a Consistency-LoRA (C-LORA) and a Detail-LoRA (D-LORA). The model first learns robust temporal priors from degraded inputs using a Cross-Frame Retrieval (CFR) module and the C-LORA. Subsequently, it enhances spatial details using the D-LORA while keeping the consistency modules frozen. These two training stages are alternated iteratively to find an optimal balance. During inference, the LoRAs are merged, enabling efficient, one-step restoration that, as experiments show, outperforms existing methods in both perceptual quality and speed.", "questions": "I believe the paper is strong, but my final evaluation could be improved if the authors address the following points:\n\n- Regarding training complexity, could you please provide an ablation study or at least a discussion on the necessity of the alternating, dual-dataset training scheme? Specifically, how does this approach compare to a simpler baseline that performs joint training on a combined dataset, using a single, fixed loss function (e.g., $L_{enh}$ from Eq. 5) from the start? This would help clarify whether the performance gains are truly from the decoupled optimization or from other factors.\n\n- The CFR module's reliance on only the single preceding frame (t−1) seems potentially fragile. How does the model perform in video scenes with significant challenges to this assumption, such as rapid motion, severe occlusions, or scene cuts where the t−1 frame provides little useful information? Have you considered extending the CFR module's temporal window to incorporate information from more distant frames (e.g., t−2, t−5)?\n\n- The generation of motion via random pixel shifts on still images is an interesting choice. How crucial is this simulated motion for the performance of the Detail-LoRA? What happens if D-LORA is trained on high-quality still images alone, without the optical flow regularization term ($L_{opt}$)? Does this significantly degrade temporal consistency, or does the frozen C-LORA and CFR module provide sufficient temporal guidance on their own? Answering this could potentially simplify the data preparation pipeline significantly.", "limitations": "Yes, the authors have included a limitations section.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The central contribution—decoupling the optimization of temporal consistency and spatial detail via a dual-LoRA, alternating training paradigm—is highly original and significant. This method directly confronts the fundamental trade-off that plagues many generative VSR models. The resulting ability to produce both temporally stable and detail-rich videos marks a notable advancement in the field.\n\n- The paper demonstrates state-of-the-art performance across a wide range of quantitative metrics and on multiple challenging real-world and synthetic datasets. The qualitative results presented in Figures 3 and 4 are particularly impressive, showing clear superiority over competing methods in restoring facial structures and fine textures. Critically, the model achieves this while being remarkably efficient, performing inference in a single step, which is approximately 10x faster than other diffusion-based VSR models. This combination of quality and speed is a major strength.\n\n- The technical execution is solid. The Cross-Frame Retrieval (CFR) module is well-designed, employing mechanisms like top-k attention and a learnable gating threshold to robustly aggregate information from noisy adjacent frames, rather than using a naive fusion. The dynamic, iterative training process with a smooth loss transition strategy is also a thoughtful detail to ensure stable convergence.\n\nWeaknesses:\n\n- The proposed training pipeline is notably complex, involving two separate stages, two different training datasets, alternating optimization, and a carefully scheduled loss interpolation for transitions. While this yields impressive results, the paper lacks an ablation study to justify this complexity. It is unclear how sensitive the final performance is to this specific training curriculum. A simpler joint-training approach with a well-weighted composite loss might potentially achieve comparable results with greater ease of implementation and training stability.\n\n- The Cross-Frame Retrieval (CFR) module, which is critical for establishing consistency, operates with a sliding window of only two frames (the current and the immediately preceding one). This architectural choice may limit the model's ability to handle more complex temporal dynamics, such as occlusions, dis-occlusions, rapid non-linear motion, or re-emerging objects after several frames. The reliance on only the previous frame for temporal priors is a potential bottleneck for long-term consistency.\n\n- For the detail enhancement stage, the authors generate \"video sequences\" by applying random pixel-level translations to single high-quality images from the LSDIR dataset. While this enables the use of an optical flow loss, this synthetic motion is highly simplistic and not representative of real-world camera or object motion. It is unclear if training D-LORA on this artificial data is truly beneficial or if it might bias the model in subtle ways.", "was_revised": true, "final_justification": "The added comparisons convincingly show that (i) the dual-stage, dual-dataset schedule outperforms a joint-training baseline, (ii) a temporal window = 1 in the CFR module offers the best trade-off between perceptual quality and temporal coherence without excessive memory/time cost, and (iii) the lightweight synthetic-motion curriculum is crucial for preserving temporal consistency during detail enhancement. These results directly address my earlier concerns about training complexity, window size, and data realism.\n\nSome open questions remain—e.g., performance on extremely rapid-motion scenes and practical guidance for hyper-parameter tuning—but the paper now provides solid evidence of a state-of-the-art VSR method that combines high visual quality with single-step inference speed. I therefore raise my score to 4.", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns. And we adopt VideoLQ4, as detailed in Appendix Section2, as our default test set for the reported experiments.\n\n**[W1] and [Q1]: Clarification about the training complexity.**\n\n**[Response]:**  We appreciate the reviewer's comment. While our method involves two training stages and two datasets, we would like to clarify that each stage is purposefully paired with a single task-specific dataset and the dedicated losses, while the two stages are trained independently in an alternating manner. This design is actually not that complex, but rather a deliberate choice to decouple the optimization of temporal consistency and spatial detail via a dual-LoRA framework. \n\nSpecifically, the C-LoRA is trained exclusively on degraded real-world videos to learn robust temporal priors. The D-LoRA is trained only on high-quality images (with synthetic motion) to enhance spatial detail.\nThis decoupled training results in a stable training process and improved final performance than the joint-training approach with a mixed dataset and a combined loss, as shown in our experiments in Appendix Section2: Ablation studies on training strategies in DLoRAL. Observed from the Figure 1 of the Appendix, while a joint-training scheme with a carefully balanced loss may appear simpler, it will result in conflicts between competing objectives, leading to slower convergence and weaker overall performance. In contrast, our dual-space, dual-stage design separates the learning of detail and consistency, enabling higher visual quality and more stable temporal alignment.\n\n**[W2] and [Q2]: Concerns on CFR.**\n\n**[Response]:**  We sincerely thank the reviewer for raising this important concern. For challenging scenes where the $t{-}1$ frame provides limited useful information, the absolute attention mechanism in our CFR module selectively integrates relevant features while effectively filtering out noise from the previous frame. This design ensures that the reconstruction of the current frame remains robust without being adversely affected by irrelevant information.\n\nRegarding the temporal window size, we evaluate the use of a larger temporal window in the CFR module. And the corresponding results are shown below.\n\n|                          | MUSIQ ↑     | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|--------------------------|-------------|----------------|----------------|------------------------|\n| Temporal window = 0 (w/o CFR) | 64.5732     | 0.5148         | 0.3386         | 1.58 × 10⁻³            |\n| Temporal window = 1 (Ours)    | **66.6174** | **0.5475**     | **0.3791**     | 1.51 × 10⁻³        |\n| Temporal window = 3           | 65.4470     | 0.5452         | 0.3645         | 1.51 × 10⁻³        |\n| Temporal window = 5           | 62.1562     | 0.5019         | 0.3204         | **1.50 × 10⁻³**            |\n\nFrom the table, we observe that a temporal window size of 1 achieves a good balance between temporal consistency and visual quality. When the temporal window is increased to 5, there is a slight drop in the warping error, indicating better temporal consistency. However, this comes at the cost of noticeably reduced visual quality, as reflected by the decrease in metrics such as MUSIQ and CLIP-IQA. On the other hand, when the temporal window is set to 0 (*i.e.,* without CFR), the warping error is significantly worse, highlighting the importance of cross-frame interactions introduced by the CFR module.\n\nFurthermore, larger temporal window corresponds to higher computational cost. This is due to the increased number of cross-frame interactions (*i.e.,* cross-attention operation), which leads to quadratic growth in memory and computation requirements.\n\nIn conclusion, while a larger temporal window can slightly improve temporal consistency, the trade-offs in visual quality and computational efficiency make it less attractive. Therefore, we adopt a temporal window of 1 in our final design.\n\n**[W3] and [Q3]: Training data of enhancement stage.**\n\n**[Response]:**  We appreciate the reviewer's thoughtful attention to the design of our training data for detail enhancement. Simulated motion is used to prevent the model from forgetting previously learned temporal consistency. To further validate its effectiveness, we retrain the model by adopting high-quality still images and removing the optical flow loss in the second stage. The results are shown in the table below.\n\n|                         | MUSIQ ↑     | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|-------------------------|-------------|----------------|----------------|------------------------|\n| W/ simulated motion (Ours)   | **66.6174** | **0.5475**     | **0.3791**     | **1.51 × 10⁻³**        |\n| W/o simulated motion         | 66.5229     | 0.5243         | 0.3617         | 1.65 × 10⁻³            |\n\n\nThe two models achieve comparable scores on visual metrics, indicating similar perceptual quality. However, the model trained without simulated motion exhibits a noticeably higher warping error, suggesting degraded temporal consistency. This gap confirms that—even though the synthetic motion is simplistic—it plays an important role in preventing the model from forgetting previously learned temporal consistency during detail enhancement."}, {"review_id": "Z5yBcg3O6g", "reviewer": "Reviewer_Bv1v", "summary": "The authors propose a method called DLoRAL to achieve faster, more consistent, and detail-enhanced video super-resolution. DLoRAL consists of a CFR module, C-LoRA, and D-LoRA, which are designed to extract cross-frame complementary information, maintain temporal consistency, and restore image details, respectively. Experiments are conducted to demonstrate the effectiveness of DLoRAL. Compared to previous methods, DLoRAL not only achieves improved performance but also requires less inference time.", "questions": "See Weaknesses.\n\nI may consider increasing the score once all concerns are addressed.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and easy to understand.\n2. The design of Cross-Frame Retrieval is interesting, utilizing the top-k most similar K V pairs to compute cross-frame attention.\n3. The proposed method employs a single-step diffusion model, resulting in faster inference speed compared to baselines.\n\nWeaknesses:\n1. Why use the image model SD for video super-resolution tasks? There are already several advanced video generation models, such as Hunyuan Video and Wan2.1 1.3B, with acceptable LoRA training costs. It seems more straightforward to perform video super-resolution tasks on video generation models.\n    - Furthermore, based on my experience, the temporal inconsistency issue may stem from using SD for video generation. Using video generation models might significantly alleviate this issue and does not seem to require specific fine-tuning to enhance temporal generation capabilities, which is necessary when using SD. Therefore, I am concerned about the practical significance of the motivations behind this paper.\n2. The proposed method is based on the UNet architecture, and I am uncertain if it can be adapted to the current DiT architecture.\n3. How can it be ensured that the two designed LoRA implementations achieve true decoupling? The paper claims that D-LoRA is intended to learn high-frequency details, but since the supervision signal comes from video, it will inevitably learn motion information. Moreover, D-LoRA's loss also uses the loss from the Temporal Consistency Stage (C-LoRA).\n4. Will training on constructed pseudo-video data for the enhancement stage affect performance? Since pseudo-videos lack reasonable motion information.\n5. I noticed that the appendix includes some ablation studies on the training strategy; however, the paper lacks detailed ablations for each proposed module, as well as explanations of how these modules contribute to overall performance.\n6. Does the proposed method maintain its faster inference speed when applied to multi-step diffusion models? Or, if the compared baseline uses a single-step diffusion model, is there a significant difference in inference speed compared to the proposed method?", "was_revised": true, "final_justification": "The author's rebuttal addresses my concerns, and given that the authors promise to incorporate the experimental results in the rebuttal into the revised version, my final score is borderline accept.", "rebuttal": "We sincerely thank this reviewer for the constructive comments. We hope our following point-to-point responses can address this reviewer's concerns. And we adopt VideoLQ4, as detailed in Appendix Section2, as our default test set for the reported experiments.\n\n**[[W1] and [Q1]]: The reason of choosing T2I model.**\n\n**[Response]:**  We agree that video generation models (T2V) [1,2] have made remarkable progress and can offer stronger temporal consistency. However, we employ the text-to-image (T2I) model primarily due to its superior image quality in the context of Video Super-Resolution (VSR).\n\nTo support our choice, we conduct a direct comparison by training our VSR model using the SD 2.1 T2I model and the Wan2.1-1.3B T2V model on the same VSR training data with identical training settings (*i.e.*, joint training with composite consistency and enhancement losses). The results are shown below:\n\n|         | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|---------|---------------|----------------|----------------|------------------------|\n| Wan2.1  | 43.0117       | 0.3615         | 0.2641         | **1.47 × 10⁻³**        |\n| SD2.1   | **54.1642**   | **0.4873**     | **0.3428**     | 1.50 × 10⁻³            |\n\nWe see that SD2.1 achieves much higher image quality while maintaining comparable temporal consistency (measured by warping error).  This result may stem from the following two factors:\n\n1. **Training data**: The per-frame quality of video data used to train T2V models is generally lower than that of images used to train T2I models so that T2V models are harder to generate fine-grained visual details. \n\n2. **Model architecture**: T2V models typically adopt video VAEs that compress both spatial and temporal dimensions. This leads to a heavier compression compared to the VAE used in T2I models. causing more information loss.\n\nWhile T2V models can offer better temporal coherence, VSR tasks require both high visual quality and temporal consistency. In our experiments, T2I-based models provide a better trade-off between the two goals. STAR [3] in Table 1 of the main paper is based on a T2V model (*e.g.,* CogVideo [4]), which also reflects this quality gap.\n\n**[W2] and [Q2]: Adaption to DiT**.\n\n**[Response]:**  To answer this concern, we implement our approach on a DiT-based T2I model (*i.e.,* SD 3) and train the VSR model with and without our proposed strategies. The experimental results are shown below.\n\n|                       | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|-----------------------|---------------|----------------|----------------|------------------------|\n| SD3 w/o our strategy  | 58.3651       | 0.4217         | 0.3243         | 1.62 × 10⁻³            |\n| SD3 w/ our strategy   | **69.0547**   | **0.5812**     | **0.4015**     | **1.55 × 10⁻³**        |\n\nWe see that our method can be successfully integrated into the DiT architecture and consistently improves both perceptual quality and temporal consistency. This demonstrates the generality of our approach beyond UNet-based models (*i.e.,* SD 2.1).\n\n**[W3] and [Q3]: Decoupling of dual LoRA**.\n\n**[Response]:**  Thanks for the question. We would like to clarify that the term **decoupling** in our method refers to **functional specialization**, rather than entirely separate training processes. \n\nPlease kindly note that D-LoRA is trained on high-quality images from the LSDIR dataset, not on real-world video data as used by C-LoRA. It enables D-LoRA to focus on enhancing perceptual details.  The temporal loss inherited from C-LoRA serves to retain the learned temporal priors and prevent forgetting, rather than to model motion.\n\nWe further validate this design with two experiments. First, as shown in the table below, adding D-LoRA to a fixed C-LoRA during inference significantly improves visual quality (higher MUSIQ), while maintaining comparable temporal coherence (similar warping error). This demonstrates that D-LoRA effectively enhances perceptual quality without interfering with C-LoRA in maintaining temporal consistency.\n\n|                   | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|-------------------|---------------|----------------|----------------|------------------------|\n| C-LoRA only       | 56.8002       | 0.3124         | 0.2296         | **1.49 × 10⁻³**        |\n| C+D LoRA (Ours)   | **66.6174**   | **0.5475**     | **0.3791**     | 1.51 × 10⁻³        |\n\nSecond, the table below shows that removing synthetic motion and the consistency loss ($L_{opt}$) from D-LoRA training results in worse temporal consistency (*i.e.*, higher warping error), even though the visual quality (*e.g.*, MUSIQ) remains comparable. \n\n|                       | LPIPS ↓      | MUSIQ ↑       | CLIP-IQA ↑     | Warping Error ↓       |\n|-----------------------|--------------|----------------|----------------|------------------------|\n| W/o $L_{opt}$         | 66.5229      | 0.5243         | 0.3617         | 1.65 × 10⁻³            |\n| W/ $L_{opt}$ (Ours)   | **66.6174**  | **0.5475**     | **0.3791**     | **1.51 × 10⁻³**        |\n\nTherefore, though C-LoRA and D-LoRA share some common training elements, their functional roles are decoupled, where C-LoRA maintains the temporal consistency and D-LoRA enhances the details.\n\n**[W4] and [Q4]: Pseudo-video training data for enhancement stage**.\n\n**[Response]:**  While existing video datasets [5, 6, 7, 8] offer diverse motion patterns, they often suffer from limited visual quality compared to image datasets.  To prioritize perceptual quality in the enhancement stage, we use high-quality image data with synthetic motion as the training data. This design allows D-LoRA to focus on visual detail enhancement without being hindered by the lower quality of real video frames.  \n\nHowever, this does not mean we ignore realistic motion. The consistency stage is trained on real videos with complex, natural motions, ensuring strong temporal priors. This decoupled design allows us to leverage the strengths of both data sources — real motion from videos and fine details from images — achieving a better overall performance of temporal coherence and perceptual quality.\n\nTo assess the impact of this choice, we retrain the enhancement stage on the Pexel video dataset [5], one of the highest-quality public video datasets. As shown in the table below, training on real video datasets leads to reduced image quality (*e.g.,* lower MUSIQ) while maintaining comparable temporal consistency (*i.e.*, similar warping error). This confirms that pseudo-video training offers better perceptual quality without sacrificing consistency, and is thus more suitable for the enhancement stage.\n\n|                                 | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|---------------------------------|---------------|----------------|----------------|------------------------|\n| Video-supervised enhancement stage | 57.5231       | 0.3658         | 0.2745         | **1.50 × 10⁻³**        |\n| Image-supervised enhancement stage | **66.6174**   | **0.5475**     | **0.3791**     | 1.51 × 10⁻³        |\n\n**[W5] and [Q5]: Ablation of each module**.\n\n**[Response]:**  Thank for the nice suggestion. In response, we add a new ablation study to analyze the contribution of each of our proposed modules:  (i) C-LoRA,  (ii) D-LoRA, and  (iii) CFR.  We compare the full model (denoted as ours) with three variants, each removing one module while keeping the others unchanged.\n\n|              | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|--------------|---------------|----------------|----------------|------------------------|\n| Ours (Full)  | **66.6174**   | 0.5475         | **0.3791**     | 1.51 × 10⁻³            |\n| W/o CFR      | 64.5732       | 0.5148         | 0.3386         | 1.58 × 10⁻³            |\n| W/o C-LoRA   | 64.2623       | **0.5492**     | 0.3520         | 1.61 × 10⁻³            |\n| W/o D-LoRA   | 54.0769       | 0.3654         | 0.2471         | **1.48 × 10⁻³**        |\n\nAs shown in the table above, removing either CFR or C-LoRA leads to weaker temporal consistency (*i.e.,* higher warping error), indicating their complementary roles in maintaining temporal coherence. In contrast, removing D-LoRA significantly impairs all perceptual metrics, confirming its core contribution to fine-grained detail enhancement.\n\n**[W6] and [Q6]: Adaption to multi-step diffusion models**.\n\n**[Response]:**  We thank the reviewer for the question. If we understand correctly, the concern is whether our faster inference speed mainly comes from using a single-step diffusion model. This is correct — our method is built on a one-step framework, which naturally runs faster than its multi-step counterparts. While this design provides clear efficiency benefits, our main contribution lies in enabling detail-rich and temporally consistent video super-resolution within this lightweight framework.\n\nTo ensure a fair comparison, we also evaluate against other single-step baselines. As shown in Tables 1 and 2 of the main paper, our method achieves comparable inference speed to OSEDiff, a state-of-the-art single-step model, while delivering better perception quality and temporal consistency.\n\n**Reference:**\n\n[1] Kong et al. HunyuanVideo: Large Video Generative Models. arXiv, 2024.\n\n[2] Wan et al. WAN: Large-scale Video Generation. arXiv, 2025.\n\n[3] Xie et al. STAR: Text-to-Video Models for VSR. ICCV, 2025.\n\n[4] Hong et al. CogVideo: T2V Generation via Transformers. ICLR, 2023.\n\n[5] Pexels. https://www.pexels.com/\n\n[6] Nan et al. OpenVid-1M: High-Quality T2V Dataset. ICLR, 2025.\n\n[7] Bain et al. Frozen in Time: Joint Video-Image Encoder. ICCV, 2021.\n\n[8] Zhou et al. Upscale-A-Video: Diffusion for Real-World VSR. CVPR, 2024."}, {"review_id": "F8kvq6dQwP", "reviewer": "Reviewer_1Rdb", "summary": "This paper proposes a new diffusion-based video super-resolution pipeline that synthesizes high-resolution videos in one-step diffusion sampling. The author proposes a dual-LoRA learning scheme, consisting of one for retrieving information across frames and one for enhancing video consistency. Extensive results show the effectiveness of their pipeline.", "questions": "I wonder why the author selects image diffusion model as their solution instead of video diffusion models, which are effective in solving video tasks.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "This paper is well-organized and easy to follow. The results in the video demo are convincing and impressive. However, at the same time, I have some concerns about it:\n1. My main concern about this paper is the novelty. The core contribution of this work is the dual-stage LoRA tuning. However, the loss functions of the proposed dual-stage training are very similar, except that the consistency stage utilizes an additional classifier score distillation loss. However, this loss term is not well introduced in the manuscript, and I cannot easily find how and why such a design brings performance gain against existing methods.\n\n2. Missing ablation study. Because the dual-stage LoRA fine-tuning is a core contribution, more visual and numeric comparisons should be provided to demonstrate that such a design leads to prior performance. I see one ablation in the supplementary, but hope to see with/ and w/o each stage.\n\n\nMinor suggestions:\n1. Please add \"Ours\" to the proposed method in the figures when comparing with other methods (e.g., Figure 1).\n\n2. Please keep the method's name and the position of zoomed regions in the video demo consistently. Please do not use brown color that is similar to the background, try some vivid ones like red, light yellow, or green for better visualization. Try to stop at images with the best visualization in the demo to show differences more clearly. \n\nThe paper can be better organized to make it more complete and convincing. I give my current rating based on the assumption that the dual-stage training mechanism brings performance improvements and is unique compared with existing techniques. I will change my rating according to authors' feedback.", "was_revised": true, "final_justification": "I want to raise my score to accept as the author has addressed my concerns and questions through the rebuttal period.", "rebuttal": "We sincerely thank this reviewer for the constructive comments. We hope our following point-to-point responses can address this reviewer's concerns. And we adopt VideoLQ4, as detailed in Appendix Section2, as our default test set for the reported experiments.\n\n**[W1]: Clarity of contributions**.\n\n**[Response]:** Thanks for the question, and we are sorry if we did not make our contributions clear enough in the main paper. Regarding the CSD loss [1], it draws on the idea of score distillation from pre-trained text-to-image diffusion models. It compares two predictions from the frozen base model: one conditioned on the text prompt and one unconditioned. The difference guides optimization toward outputs that better align with the textual semantics and exhibit higher visual quality. This loss is applied only in the enhancement stage, which focuses on detail enhancement; while in the consistency stage, we emphasize on temporal coherence, making CSD less relevant.\n\nIn addition to the design of loss functions, the novelty of our proposed two-stage framework lies in the use of distinct training data, the Cross-Frame Retrieval (CFR) module, the integration of dual LoRA parameter spaces, and the alternative training strategy. Specifically, the consistency stage is trained on real-world videos with complex motion, enabling the use of consistency loss and focusing on temporal coherence. While the enhancement stage uses high-quality image data with synthetic motion. It is guided by inherited consistency loss components to retain learned priors, and also by the CSD loss to enhance fine-grained details. This decoupling allows us to define two specialized and trainable parameter spaces, each leveraging the strengths of different data types and loss functions, which is rarely explored in prior works. By alternating the two phases during iterative optimization, the framework works collaboratively to achieve both temporal consistency and high-quality detail enhancement.\n\nWe acknowledge that the description of the CSD loss is not sufficiently detailed in the original manuscript, and we will add a clear explanation of its motivation and implementation in the revised version. We note that the contributions of dual stages can be reflected in the ablation study provided in our response to W2. We acknowledge that this key experiment is missing from the original manuscript, and we will include it in the revised version to improve clarity.\n\n\n\n**[W2]: Ablation of each stage**.\n\n**[Response]:**  We thank the reviewer for the insightful suggestion regarding an ablation study on the two stages. We conducted a dedicated experiment with all models trained from scratch, comparing (i) consistency-stage only, (ii) enhancement-stage only, and (iii) dual-stage design (ours). Due to the policy of NeurIPS rebuttal, we are unable to include visual results here. We will incorporate these results in the revised manuscript to provide a more comprehensive evaluation.\n\n|                         | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|-------------------------|---------------|----------------|----------------|------------------------|\n| Consistency-stage only  | 50.5394       | 0.3052         | 0.2147         | **1.48 × 10⁻³**        |\n| Enhancement-stage only  | 65.8557       | 0.5420         | 0.3412         | 1.62 × 10⁻³            |\n| Dual-stage design (Ours)| **66.6174**   | **0.5475**     | **0.3791**     | 1.51 × 10⁻³            |\n\nThese results confirm that the temporal consistency is primarily enhanced in the consistency stage, while fine spatial details are recovered in the enhancement stage; combining both yields the best overall performance. These findings substantiate the complementary contributions of each stage and justify the proposed dual‑stage design.\n\n**[W3] and [W4]: Suggestions of visual presentation**.\n\n**[Response]:**  We thank the reviewer for these valuable presentation suggestions. We will (i) label our method explicitly as “ours” in relevant figures, and (ii) adjust the demo video for better visual presentation.\n\n**[Q1]: The reason of choosing T2I model**.\n\n**[Response]:**  Thank you for the thoughtful question. We agree that recent video generation models (T2V) [2,3] have made remarkable progress and can offer stronger temporal consistency. However, we chose to train our model based on the T2I model primarily due to its superior image quality, which is important for VSR.\n\nTo support our choice, we conduct a direct comparison by training our VSR model with a T2I model (SD2.1) and a T2V model (Wan2.1-1.3B). Both VSR models are trained on the same video dataset with identical training settings (*i.e.*, joint training with composite consistency and enhancement losses).  And the results are shown below:\n\n|         | MUSIQ ↑       | CLIP-IQA ↑     | MANIQA ↑       | Warping Error ↓       |\n|---------|---------------|----------------|----------------|------------------------|\n| Wan2.1  | 43.0117       | 0.3615         | 0.2641         | **1.47 × 10⁻³**        |\n| SD2.1   | **54.1642**   | **0.4873**     | **0.3428**     | 1.50 × 10⁻³            |\n\nWe see that the T2I model (SD2.1) achieves much higher image quality while maintaining comparable temporal consistency (measured by warping error).  This result may stem from the following two factors:\n\n1. **Training data**: The per-frame quality of video data used to train T2V models is generally lower than that of images used to train T2I models so that T2V models are harder to generate fine-grained visual details. \n\n2. **Model architecture**: T2V models typically adopt video VAEs that compress both spatial and temporal dimensions. This leads to a heavier compression compared to the VAE used in T2I models. causing more information loss.\n\nWhile T2V models can offer better temporal coherence, VSR tasks require both high visual quality and temporal consistency. In our experiments, T2I-based models provide a better trade-off between the two goals. STAR [4] in Table 1 of the main paper is based on a T2V model (*i.e.,* CogVideo [5]), which also reflects this quality gap.\n\n**Reference:**\n\n[1] L. Sun, R. Wu, Z. Ma, S. Liu, Q. Yi, L. Zhang, \"Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach,\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.\n\n[2] W. Kong, Q. Tian, Z. Zhang, R. Min, Z. Dai, J. Zhou, J. Xiong, X. Li, B. Wu, J. Zhang, \"HunyuanVideo: A Systematic Framework for Large Video Generative Models,\" arXiv preprint arXiv:2412.03603, 2024.\n\n[3] T. Wan, A. Wang, B. Ai, B. Wen, C. Mao, C.-W. Xie, D. Chen, et al., \"Wan: Open and advanced large-scale video generative models,\" arXiv preprint arXiv:2503.20314, 2025.\n\n[4] R. Xie, Y. Liu, P. Zhou, C. Zhao, J. Zhou, K. Zhang, Z. Zhang, J. Yang, Z. Yang, Y. Tai, \"STAR: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution,\" Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025.\n\n[5] W. Hong, M. Ding, W. Zheng, X. Liu, J. Tang, \"CogVideo: Large-scale pretraining for text-to-video generation via transformers,\" Proceedings of the International Conference on Learning Representations (ICLR), 2023."}]

Author rebuttal:

Meta-review: This paper tackles Real-World Video Super-Resolution (Real-VSR), where generating fine spatial details often conflicts with maintaining temporal consistency. The authors introduce Dual LoRA Learning (DLORAL), a framework built on a pre-trained Stable Diffusion model that achieves high-quality VSR in a single diffusion step. DLORAL separates the learning of temporal consistency and spatial detail into two LoRA modules: C-LoRA for consistency and D-LoRA for detail. Using a Cross-Frame Retrieval module, C-LoRA learns temporal priors, after which D-LoRA enhances spatial details while freezing consistency learning. The two modules are trained alternately to strike a balance, then merged at inference for efficient one-step restoration. Experiments show DLORAL surpasses existing methods in both perceptual quality and speed.

Reviewers' primary concerns revolved around the limitations of VAE compression, the complexity of the training pipeline, and the explanation of the loss term. The author provided a careful rebuttal and new ablations, which successfully addressed most concerns. 

Overall, the paper is recommended for acceptance. Some questions still need to be addressed, as the reviewer mZuh mentioned.
Decision: Accept (poster)