OpenNovelty.org

SkipSR: Faster Super-Resolution with Token Skipping

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
video generationefficient transformerssuper-resolutiondiffusion
Abstract:

Diffusion-based super-resolution (SR) is a key component in video generation and video restoration, but is slow and expensive, limiting scalability to higher resolutions and longer videos. Our key insight is that many regions in video are inherently low-detail and gain little from refinement, yet current methods process all pixels uniformly. To take advantage of this, we propose SkipSR, a simple framework for accelerating video SR by identifying low-detail regions directly from low-resolution input, then skipping computation on them entirely, only super-resolving the areas that require refinement. This simple yet effective strategy preserves perceptual quality in both standard and one-step diffusion SR models while significantly reducing computation. In standard SR benchmarks, our method achieves up to 60% faster end-to-end latency than prior models on 720p videos with no perceptible loss in quality. Video demos are available at our anonymous project page.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes SkipSR, a framework that accelerates diffusion-based video super-resolution by identifying low-detail regions in low-resolution input and skipping computation on them. It resides in the 'Adaptive Patch Routing with Confidence Estimation' leaf, which contains three papers total (including SkipSR). This leaf sits within the broader 'Selective Processing via Patch-Level Routing and Skipping' branch, indicating a moderately populated research direction focused on learned routing mechanisms rather than fixed heuristics. The taxonomy shows this is an active but not overcrowded area, with sibling leaves exploring edge-based and threshold-based alternatives.

The taxonomy reveals neighboring research directions that share the goal of reducing video SR computation but employ different strategies. The 'Temporal Redundancy Exploitation and Frame Selection' branch (containing keyframe-based and masked attention methods) addresses efficiency through inter-frame correlation rather than intra-frame patch selection. The 'Patch-Based Reconstruction with Similarity Matching' branch uses explicit patch matching across frames, contrasting with SkipSR's learned confidence estimation. The 'Patch-Level Diffusion and Generative Models' branch explores hierarchical diffusion approaches, while SkipSR applies selective processing to existing diffusion models. These neighboring directions suggest the field is exploring multiple orthogonal axes for acceleration.

Among the 22 candidates examined via limited semantic search, none clearly refute the three core contributions. The SkipSR framework contribution examined seven candidates with zero refutable overlaps; the lightweight mask predictor examined ten candidates with zero refutations; and the mask-aware rotary positional encodings examined five candidates with zero refutations. This suggests that within the examined scope, the specific combination of low-resolution-based region identification, learned masking, and adapted positional encodings for diffusion SR appears distinct. However, the search scope is explicitly limited to top-K semantic matches and does not constitute exhaustive coverage of all patch-routing or diffusion SR literature.

Based on the limited search of 22 candidates and the taxonomy structure, the work appears to occupy a recognizable niche within adaptive patch routing, applying these ideas specifically to diffusion-based video SR. The absence of refutable prior work among examined candidates suggests novelty in the specific technical approach, though the broader concept of selective patch processing is well-established in the field. The analysis does not cover all possible related work in diffusion models, video generation, or patch-based methods beyond the examined set.

Taxonomy

Core-task Taxonomy Papers
30
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Accelerating video super-resolution through selective patch processing. The field addresses the computational burden of upscaling video by identifying and processing only the most informative or challenging image patches rather than treating all regions uniformly. The taxonomy reveals several complementary strategies: Selective Processing via Patch-Level Routing and Skipping focuses on adaptive mechanisms that decide which patches require full reconstruction versus lightweight processing, often using confidence or difficulty estimation (e.g., SkipVSR[2], Accelerating 4K Upscaling[3]). Temporal Redundancy Exploitation and Frame Selection leverages inter-frame similarity to skip or reuse computations across consecutive frames (e.g., Patch Temporal Redundancy[5]). Patch-Based Reconstruction with Similarity Matching employs nearest-neighbor or exemplar-based methods to reconstruct patches from reference frames (e.g., Flow Patch Similarity[14]). Meanwhile, Patch-Level Diffusion and Generative Models and Application-Specific Patch Processing Optimizations explore generative approaches and domain-tailored heuristics, while Training and Implementation Optimizations address model compression and deployment efficiency (e.g., Multi-scale Distillation[9]). Recent work has intensified around adaptive routing strategies that balance quality and speed. A central trade-off is deciding which patches merit expensive deep-network inference versus simpler interpolation or reuse. SkipSR[0] sits squarely within the Adaptive Patch Routing with Confidence Estimation cluster, employing learned confidence scores to route patches selectively—similar in spirit to SkipVSR[2] but differing in how confidence thresholds are calibrated or updated. Compared to earlier heuristics like Accelerating 4K Upscaling[3], which relied on hand-crafted edge or texture metrics, SkipSR[0] integrates end-to-end learning to predict patch difficulty. This positions it alongside other recent confidence-driven methods (e.g., ESSR[4], PatchVSR[1]) that seek to automate the routing decision, though the exact gating mechanisms and training objectives vary. Open questions remain about generalization across diverse content types and the interplay between patch-level skipping and temporal redundancy exploitation.

Claimed Contributions

SkipSR framework for accelerating video super-resolution

The authors introduce SkipSR, a method that identifies low-detail regions in video from low-resolution inputs and skips computation on these regions entirely during super-resolution, only processing areas that require refinement. This approach preserves perceptual quality while significantly reducing computational cost.

7 retrieved papers
Lightweight mask predictor for identifying complex regions

The authors develop a lightweight predictor network that operates in the VAE latent space to classify patches as skippable or requiring refinement. This predictor enables selective processing by routing only complex patches through the transformer while simple patches bypass the model entirely.

10 retrieved papers
Mask-aware rotary positional encodings for sparse attention

The authors modify rotary positional encodings (RoPE) to preserve spatial and temporal position information for non-contiguous patches after masking. This ensures that patches processed by the transformer remain aware of their original relative positions despite being spatially discontinuous.

5 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SkipSR framework for accelerating video super-resolution

The authors introduce SkipSR, a method that identifies low-detail regions in video from low-resolution inputs and skips computation on these regions entirely during super-resolution, only processing areas that require refinement. This approach preserves perceptual quality while significantly reducing computational cost.

Contribution

Lightweight mask predictor for identifying complex regions

The authors develop a lightweight predictor network that operates in the VAE latent space to classify patches as skippable or requiring refinement. This predictor enables selective processing by routing only complex patches through the transformer while simple patches bypass the model entirely.

Contribution

Mask-aware rotary positional encodings for sparse attention

The authors modify rotary positional encodings (RoPE) to preserve spatial and temporal position information for non-contiguous patches after masking. This ensures that patches processed by the transformer remain aware of their original relative positions despite being spatially discontinuous.

Table of Contents