arxiv: 2509.23980 · v2 · submitted 2025-09-28 · 💻 cs.CV

Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution

Jinpei Guo , Yifei Ji , Shengwei Wang , Zheng Chen , Yufei Wang , Sizhuo Ma , Yong Guo , Baiang Li

show 3 more authors

Jusheng Zhang Yulun Zhang Jian Wang

This is my paper

Pith reviewed 2026-05-18 11:39 UTC · model grok-4.3

classification 💻 cs.CV

keywords video super-resolutiondiffusion modelsattention specializationredundancy reductionone-step diffusionprogressive training

0 comments p. Extension

The pith

OASIS uses attention specialization routing in a one-step diffusion model to reduce redundancy for efficient real-world video super-resolution.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to show that generative diffusion models adapted to video super-resolution suffer from redundancy because low-quality inputs already contain much of the needed content. By introducing attention specialization routing that assigns heads to different patterns and a progressive training strategy, the model can adapt without extra computational burden or loss of pretrained knowledge. This leads to stronger performance and much faster inference. A sympathetic reader would care because it makes high-quality video enhancement more practical for real-world applications where speed matters.

Core claim

OASIS is an efficient one-step diffusion model that incorporates attention specialization routing to assign attention heads to different patterns according to their intrinsic behaviors, mitigating redundancy while preserving pretrained knowledge, and uses a progressive training strategy starting with temporally consistent degradations then shifting to inconsistent ones to facilitate learning under complex degradations.

What carries the argument

Attention specialization routing that assigns attention heads to different patterns according to their intrinsic behaviors.

If this is right

OASIS achieves state-of-the-art performance on both synthetic and real-world datasets for video super-resolution.
It offers a 6.2× speedup over one-step diffusion baselines such as SeedVR2.
The approach allows diffusion models to better adapt to VSR tasks.
The progressive training helps handle complex degradations effectively.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar specialization techniques could reduce redundancy in other diffusion-based image or video tasks.
Applying this to multi-step diffusion models might further improve efficiency in generative video tasks.
Testing on longer video sequences could reveal if the temporal consistency from progressive training scales well.

Load-bearing premise

Low-quality videos already preserve substantial content information, creating redundancy that attention specialization routing can mitigate without losing pretrained knowledge or performance.

What would settle it

If applying attention specialization routing to a diffusion VSR model results in no improvement in speed or a drop in performance on real-world datasets, the central claim would be falsified.

Figures

Figures reproduced from arXiv: 2509.23980 by Baiang Li, Jian Wang, Jinpei Guo, Jusheng Zhang, Shengwei Wang, Sizhuo Ma, Yifei Ji, Yong Guo, Yufei Wang, Yulun Zhang, Zheng Chen.

**Figure 2.** Figure 2: Overview of OASIS. Given an input LQ video, a pixel-unshuffle operation maps it into the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Head-level specialization in diffusion trans [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Visual comparisons on synthetic and real-world datasets for [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Comparison of temporal consistency. The temporal profile is obtained by stacking the red line across frames. Our method produces smoother frame transitions that closely resemble the ground truth. Method Step Params (B) Time (s) MACs (T) Upscale-A-Video 30 1.09 283.70 9,084.73 MGLD-VSR 50 1.57 429.48 8,528.70 VEnhancer 15 2.50 122.48 3,056.16 STAR 15 2.49 176.53 4,281.67 SeedVR 50 3.40 207.13 8,243.13 Seed… view at source ↗

**Figure 6.** Figure 6: Visual comparisons between ASR and different attention patterns. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Effect of global-head ratio ρ on PSNR and E∗ warp metrics. The results are evaluated on the MVSR4x dataset. Global Attn refers to the baseline using the global attention only. Progressive Training Strategy. We compare standard training against our progressive training in Tab. 3c. Training with stage 1 (S1) alone results in poor temporal consistency, while training with stage 2 (S2) alone also leads to sub… view at source ↗

read the original abstract

Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, since low-quality videos already preserve substantial content information. Such redundancy leads to increased computational overhead and learning burden, as the model performs superfluous operations and must learn to filter out irrelevant information. To address this problem, we propose OASIS, an efficient $\textbf{o}$ne-step diffusion model with $\textbf{a}$ttention $\textbf{s}$pecialization for real-world v$\textbf{i}$deo $\textbf{s}$uper-resolution. OASIS incorporates an attention specialization routing that assigns attention heads to different patterns according to their intrinsic behaviors. This routing mitigates redundancy while effectively preserving pretrained knowledge, allowing diffusion models to better adapt to VSR and achieve stronger performance. Moreover, we propose a simple yet effective progressive training strategy, which starts with temporally consistent degradations and then shifts to inconsistent settings. This strategy facilitates learning under complex degradations. Extensive experiments demonstrate that OASIS achieves state-of-the-art performance on both synthetic and real-world datasets. OASIS also provides superior inference speed, offering a $\textbf{6.2$\times$}$ speedup over one-step diffusion baselines such as SeedVR2. The code will be available at \href{https://github.com/jp-guo/OASIS}{https://github.com/jp-guo/OASIS}.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

OASIS claims a practical efficiency fix for diffusion video super-resolution via attention routing and progressive training, but the abstract supplies no metrics or ablations to support the SOTA and 6.2x speedup assertions.

read the letter

The paper's main contribution is OASIS, a one-step diffusion approach for video super-resolution that routes attention heads according to their intrinsic patterns and uses progressive training to move from simple to complex degradations. The goal is to cut redundancy because low-quality inputs already hold most of the content, letting the model adapt pretrained diffusion weights without extra overhead. That framing of the redundancy problem is direct and relevant for anyone trying to run generative models on video tasks with limited compute. The routing idea and the staged training schedule are the concrete new pieces; they target a real adaptation issue rather than just scaling up existing models. Releasing code is also useful so others can inspect the implementation. The soft spots are straightforward. The abstract states state-of-the-art results on synthetic and real-world data plus a 6.2x speedup over SeedVR2, yet it contains no PSNR, SSIM, LPIPS values, no runtime tables under matched conditions, and no ablation that isolates the routing mechanism or shows attention patterns before and after specialization. Without those controls it is impossible to tell whether the routing actually reduces redundant computation or simply trades one set of artifacts for another. The core assumption that low-quality video preserves enough information for safe specialization is plausible but remains untested in the provided text. This paper is for people working on efficient generative video enhancement, particularly those adapting diffusion models to real-time or edge settings. A reader looking for targeted efficiency tweaks rather than entirely new architectures could extract the routing and training ideas. I would send it to peer review because the topic is timely and the proposed components are specific enough to evaluate once full experiments, ablations, and hardware details are supplied. The authors should be asked to add quantitative evidence for the redundancy reduction claim in any revision.

Referee Report

2 major / 1 minor

Summary. The paper introduces OASIS, an efficient one-step diffusion model for real-world video super-resolution. It features an attention specialization routing mechanism that assigns attention heads to different patterns to reduce redundancy in adapting diffusion models to VSR, since low-quality videos already contain substantial content information. Additionally, a progressive training strategy is proposed, beginning with temporally consistent degradations and progressing to inconsistent ones. The authors assert that this results in state-of-the-art performance on synthetic and real-world datasets and a 6.2× inference speedup compared to one-step diffusion baselines like SeedVR2, while preserving pretrained knowledge.

Significance. If the empirical results support the claims, this work could be significant for efficient video super-resolution by showing how attention specialization routing and progressive training can mitigate redundancy in diffusion models, enabling faster inference and stronger adaptation to real-world degradations while preserving or improving quality.

major comments (2)

Abstract: The abstract states that OASIS achieves state-of-the-art performance on both synthetic and real-world datasets and provides a 6.2× speedup over baselines such as SeedVR2, but supplies no quantitative tables, metrics (e.g., PSNR, SSIM, LPIPS), error bars, ablation studies, or derivation details. This makes it impossible to evaluate whether the attention specialization routing and progressive training support the central claims of redundancy reduction and performance preservation.
Abstract: The core assumption that low-quality videos preserve substantial content information, enabling safe mitigation of redundancy via attention specialization routing without loss of pretrained knowledge, is stated but not supported by any analysis, such as attention pattern comparisons or ablation isolating the routing mechanism.

minor comments (1)

The use of bold text to highlight the OASIS acronym in the abstract is a minor presentation issue that could be revised for consistency with standard academic formatting.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We address the major comments point by point below, clarifying the role of the abstract as a summary while noting that detailed results appear in the full manuscript.

read point-by-point responses

Referee: Abstract: The abstract states that OASIS achieves state-of-the-art performance on both synthetic and real-world datasets and provides a 6.2× speedup over baselines such as SeedVR2, but supplies no quantitative tables, metrics (e.g., PSNR, SSIM, LPIPS), error bars, ablation studies, or derivation details. This makes it impossible to evaluate whether the attention specialization routing and progressive training support the central claims of redundancy reduction and performance preservation.

Authors: Abstracts are designed to be concise overviews and conventionally omit detailed tables, metrics, error bars, or full ablation results to maintain brevity. The full manuscript contains these elements in the experimental section, including quantitative tables reporting PSNR, SSIM, and LPIPS on both synthetic and real-world datasets, direct comparisons demonstrating the 6.2× speedup relative to SeedVR2, ablation studies isolating the contributions of attention specialization routing and progressive training, and supporting analyses for the redundancy reduction claims. We maintain that this organization allows proper evaluation of the claims through the complete paper rather than the abstract alone. revision: no
Referee: Abstract: The core assumption that low-quality videos preserve substantial content information, enabling safe mitigation of redundancy via attention specialization routing without loss of pretrained knowledge, is stated but not supported by any analysis, such as attention pattern comparisons or ablation isolating the routing mechanism.

Authors: The assumption is introduced in the abstract and motivated in the introduction of the full manuscript. Supporting evidence, including attention pattern comparisons across heads and ablations that isolate the routing mechanism's effect on redundancy while preserving performance and pretrained knowledge, is presented in the method and experimental sections. These analyses directly address how the routing assigns heads to intrinsic behaviors to reduce overhead without compromising adaptation to VSR. revision: no

Circularity Check

0 steps flagged

No circularity: abstract proposes architectural changes without equations or self-referential reductions

full rationale

Only the abstract is available, which describes OASIS as a one-step diffusion model incorporating attention specialization routing and progressive training to reduce redundancy in VSR. No equations, parameters fitted to subsets then relabeled as predictions, self-citations, uniqueness theorems, or ansatzes are present. The SOTA and 6.2× speedup claims are stated as empirical results from experiments, not derived by construction from inputs. The derivation chain is therefore self-contained with independent content; no load-bearing step reduces to its own definition or prior self-citation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review supplies no explicit free parameters, mathematical axioms, or invented physical entities; the main additions are the named routing mechanism and training schedule whose details are not provided.

invented entities (1)

attention specialization routing no independent evidence
purpose: assign attention heads to different patterns according to intrinsic behaviors to reduce redundancy
Introduced in the abstract as the core mechanism of OASIS.

pith-pipeline@v0.9.0 · 5787 in / 1279 out tokens · 54749 ms · 2026-05-18T11:39:42.299000+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 10 internal anchors

[1]

Edgefusion: On-device text-to-image generation.arXiv preprint arXiv:2404.11925,

Thibault Castells, Hyoung-Kyu Song, Tairen Piao, Shinkook Choi, Bo-Kyeong Kim, Hanyoung Yim, Changgwun Lee, Jae Gon Kim, and Tae-Ho Kim. Edgefusion: On-device text-to-image generation.arXiv preprint arXiv:2404.11925,

work page arXiv
[2]

Basicvsr++: Improving video super-resolution with enhanced propagation and alignment

Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video super-resolution with enhanced propagation and alignment. InCVPR, 2022a. Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Investigating tradeoffs in real-world video super-resolution. InCVPR, 2022b. Bin Chen, Gehui Li, Rongyuan Wu, Xindong Zhang, J...

work page arXiv
[3]

Compression-aware one-step diffusion model for jpeg artifact removal.arXiv preprint arXiv:2502.09873, 2025a

Jinpei Guo, Zheng Chen, Wenbo Li, Yong Guo, and Yulun Zhang. Compression-aware one-step diffusion model for jpeg artifact removal.arXiv preprint arXiv:2502.09873, 2025a. Jinpei Guo, Yifei Ji, Zheng Chen, Kai Liu, Min Liu, Wang Rao, Wenbo Li, Yong Guo, and Yulun Zhang. Oscar: One-step diffusion codec across multiple bit-rates.arXiv preprint arXiv:2505.1609...

work page arXiv
[4]

VideoPoet: A Large Language Model for Zero-Shot Video Generation

Dan Kondratyuk, Lijun Yu, Xiuye Gu, Jos ´e Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung, Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, et al. Videopoet: A large language model for zero-shot video generation.arXiv preprint arXiv:2312.14125,

work page internal anchor Pith review Pith/arXiv arXiv
[5]

HunyuanVideo: A Systematic Framework For Large Video Generative Models

Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603,

work page internal anchor Pith review Pith/arXiv arXiv
[6]

Diffusion adversar- ial post-training for one-step video generation.arXiv preprint arXiv:2501.08316,

Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, and Lu Jiang. Diffusion adversar- ial post-training for one-step video generation.arXiv preprint arXiv:2501.08316,

work page arXiv
[7]

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003,

work page internal anchor Pith review Pith/arXiv arXiv
[8]

Ultravsr: Achieving ultra-realistic video super-resolution with efficient one-step diffusion space.arXiv preprint arXiv:2505.19958,

Yong Liu, Jinshan Pan, Yinchuan Li, Qingji Dong, Chao Zhu, Yu Guo, and Fei Wang. Ultravsr: Achieving ultra-realistic video super-resolution with efficient one-step diffusion space.arXiv preprint arXiv:2505.19958,

work page arXiv
[9]

Decoupled Weight Decay Regularization

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101,

work page internal anchor Pith review Pith/arXiv arXiv
[10]

SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

work page internal anchor Pith review Pith/arXiv arXiv
[11]

Denoising Diffusion Implicit Models

11 Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502,

work page internal anchor Pith review Pith/arXiv arXiv 2010
[12]

Consistency Models

Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models.arXiv preprint arXiv:2303.01469,

work page internal anchor Pith review Pith/arXiv arXiv
[13]

Asymrnr: Video dif- fusion transformers acceleration with asymmetric reduction and restoration.arXiv preprint arXiv:2412.11706, 2024a

Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, Zhao Jin, and Dacheng Tao. Asymrnr: Video dif- fusion transformers acceleration with asymmetric reduction and restoration.arXiv preprint arXiv:2412.11706, 2024a. Xibo Sun, Jiarui Fang, Aoyu Li, and Jinzhe Pan. Unveiling redundancy in diffusion transformers (dits): A systematic study.arXiv preprint arXiv:2411.13588, ...

work page arXiv
[14]

Wan: Open and Advanced Large-Scale Video Generative Models

Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314,

work page internal anchor Pith review Pith/arXiv arXiv
[15]

Exploring clip for assessing the look and feel of images

Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. InAAAI, 2023a. Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, et al. Seedvr2: One-step video restoration via diffusion adversarial post-training.arXiv preprint arXiv:2506....

work page arXiv
[16]

Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity.arXiv preprint arXiv:2502.01776,

12 Haocheng Xi, Shuo Yang, Yilong Zhao, Chenfeng Xu, Muyang Li, Xiuyu Li, Yujun Lin, Han Cai, Jintao Zhang, Dacheng Li, et al. Sparse videogen: Accelerating video diffusion transformers with spatial-temporal sparsity.arXiv preprint arXiv:2502.01776,

work page arXiv
[17]

Star: Spatial-temporal augmentation with text-to-video models for real-world video super-resolution.arXiv preprint arXiv:2501.02976,

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 arXiv:2501.02976,

work page arXiv
[18]

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Xi Yang, Chenhang He, Jianqi Ma, and Lei Zhang. Motion-guided latent diffusion for temporally consistent real-world video super-resolution. InECCV, 2024a. 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 an expert transfo...

work page internal anchor Pith review Pith/arXiv arXiv
[19]

Fast video generation with sliding tile attention.arXiv preprint arXiv:2502.04507, 2025

Hui Zhang, Tingwei Gao, Jie Shao, and Zuxuan Wu. Blockdance: Reuse structurally similar spatio- temporal features to accelerate diffusion transformers. InCVPR, 2025a. Peiyuan Zhang, Yongqi Chen, Runlong Su, Hangliang Ding, Ion Stoica, Zhengzhong Liu, and Hao Zhang. Fast video generation with sliding tile attention.arXiv preprint arXiv:2502.04507, 2025b. R...

work page arXiv
[20]

Effortless efficiency: Low-cost pruning of diffusion models

Yang Zhang, Er Jin, Yanfei Dong, Ashkan Khakzar, Philip Torr, Johannes Stegmaier, and Kenji Kawaguchi. Effortless efficiency: Low-cost pruning of diffusion models.arXiv preprint arXiv:2412.02852,

work page arXiv
[21]

Dynamic diffusion transformer

Wangbo Zhao, Yizeng Han, Jiasheng Tang, Kai Wang, Yibing Song, Gao Huang, Fan Wang, and Yang You. Dynamic diffusion transformer.arXiv preprint arXiv:2410.03456,

work page arXiv
[22]

Open-Sora: Democratizing Efficient Video Production for All

Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. Open-sora: Democratizing efficient video production for all. arXiv preprint arXiv:2412.20404,

work page internal anchor Pith review Pith/arXiv arXiv
[23]

A-sdm: Accelerating stable diffusion through redundancy removal and performance optimization.arXiv preprint arXiv:2312.15516,

Jinchao Zhu, Yuxuan Wang, Xiaobing Tu, Siyuan Pan, Pengfei Wan, and Gao Huang. A-sdm: Accelerating stable diffusion through redundancy removal and performance optimization.arXiv preprint arXiv:2312.15516,

work page arXiv