SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution (2506.19838v1)

Abstract: Latent diffusion models have emerged as a leading paradigm for efficient video generation. However, as user expectations shift toward higher-resolution outputs, relying solely on latent computation becomes inadequate. A promising approach involves decoupling the process into two stages: semantic content generation and detail synthesis. The former employs a computationally intensive base model at lower resolutions, while the latter leverages a lightweight cascaded video super-resolution (VSR) model to achieve high-resolution output. In this work, we focus on studying key design principles for latter cascaded VSR models, which are underexplored currently. First, we propose two degradation strategies to generate training pairs that better mimic the output characteristics of the base model, ensuring alignment between the VSR model and its upstream generator. Second, we provide critical insights into VSR model behavior through systematic analysis of (1) timestep sampling strategies, (2) noise augmentation effects on low-resolution (LR) inputs. These findings directly inform our architectural and training innovations. Finally, we introduce interleaving temporal unit and sparse local attention to achieve efficient training and inference, drastically reducing computational overhead. Extensive experiments demonstrate the superiority of our framework over existing methods, with ablation studies confirming the efficacy of each design choice. Our work establishes a simple yet effective baseline for cascaded video super-resolution generation, offering practical insights to guide future advancements in efficient cascaded synthesis systems.

SimpleGVR: A Latent-Cascaded Baseline for Efficient Video Super-Resolution

The paper introduces SimpleGVR, a cascaded video super-resolution (VSR) framework that operates directly on the latent representations produced by large text-to-video (T2V) diffusion models. The approach addresses the computational bottlenecks and quality limitations inherent in single-stage, high-resolution video generation by decoupling semantic content synthesis from detail refinement. SimpleGVR is positioned as a practical, efficient, and extensible baseline for latent-space VSR, with a focus on both architectural simplicity and empirical effectiveness.

Motivation and Context

Recent advances in T2V generation leverage diffusion models with transformer backbones to synthesize visually coherent and semantically rich video content. However, generating high-resolution (e.g., 1080p) videos in a single stage is computationally prohibitive due to the quadratic scaling of self-attention with spatial resolution. Multi-stage cascaded approaches, where a base model generates low-resolution content and a subsequent model enhances resolution and detail, have emerged as a promising alternative. Existing cascaded VSR methods, however, require decoding and re-encoding between stages, introducing significant overhead and inefficiency.

SimpleGVR addresses this gap by performing upsampling and refinement directly in the latent space, eliminating redundant decoding steps and enabling seamless integration with upstream T2V models.

Methodological Contributions

The paper's contributions are organized around three axes: degradation modeling, training configuration, and efficient architectural design.

1. Degradation Modeling for Training Pair Synthesis

A central challenge in training cascaded VSR models for AIGC content is the lack of physically grounded degradation processes. The authors propose two complementary strategies to synthesize training pairs that better reflect the artifacts and distribution of base T2V model outputs:

• Flow-based Degradation: This method uses optical flow to guide motion-aware color blending and adaptive blurring, simulating the localized motion blur and color blending observed in T2V outputs. The approach applies elliptical masks in high-motion regions, blending colors from previous frames, and then applies motion-aligned blur kernels.
• Model-guided Degradation: Inspired by SDEdit, this strategy injects Gaussian noise into downsampled high-quality video latents and partially denoises them using the base T2V model. The noise strength parameter $\alpha$ controls the trade-off between realism and structural fidelity, with moderate values yielding latents that closely match the T2V output domain while preserving content alignment.

These strategies are shown to reduce the domain gap between training data and real T2V outputs, leading to improved temporal consistency and artifact mitigation.

2. Training Configuration: Timestep Sampling and Noise Augmentation

The paper provides a systematic analysis of two underexplored aspects of diffusion-based VSR training:

• Timestep Sampling: By analyzing the evolution of high-frequency detail across denoising steps, the authors design a detail-aware sampler that prioritizes timesteps contributing most to detail synthesis. Empirical results demonstrate that this sampler outperforms uniform sampling on perceptual and video quality metrics.
• Noise Augmentation: The level of noise injected into the low-resolution conditioning branch modulates the model's ability to correct structural errors and synthesize details. The paper finds that a moderate noise range ($0.3 \sim 0.6$) achieves the best balance, enabling both artifact removal and detail enhancement without destabilizing the structure.

3. Efficient Long-Sequence and High-Resolution Computation

To support practical deployment on long video sequences (e.g., 77 frames) and high resolutions, SimpleGVR incorporates two architectural innovations:

• Interleaving Temporal Unit: This mechanism slices long sequences into overlapping temporal units, applying attention within each unit and shifting the window at alternating layers to enable cross-unit information flow. This design allows efficient training and inference under GPU memory constraints.
• Sparse Local Attention: Replacing full self-attention with a sparse local attention mechanism, the model partitions tokens into 2D windows and allows each window to attend to its most relevant neighbors. This reduces computational cost by 80% compared to full attention, with negligible loss in quality and improved detail preservation over Swin attention.

Empirical Results

SimpleGVR is evaluated on the AIGC100 dataset, a curated set of 100 diverse, 77-frame video clips generated by a large T2V model. The evaluation employs no-reference metrics, including MUSIQ, DOVER, and VBench, to assess perceptual and video-level quality.

Key findings include:

• Superior Quality: SimpleGVR achieves the highest scores across all major metrics compared to state-of-the-art methods such as RealBasicVSR, VEnhancer, STAR, and FlashVideo. Notably, it produces 1080p outputs of higher quality than those generated directly by the base T2V model in a single stage.
• Ablation Studies: The proposed degradation strategies and training configurations are validated through ablation, demonstrating their impact on temporal consistency, artifact reduction, and detail synthesis.
• Efficiency: Sparse local attention achieves a 4x reduction in FLOPS compared to full attention, with only minor quality trade-offs.

Implications and Future Directions

The work establishes that latent-space cascaded VSR is not only computationally efficient but also capable of surpassing the quality of direct high-resolution generation. The proposed degradation modeling and training strategies provide a blueprint for aligning VSR models with the unique artifacts of AIGC content, a critical consideration as generative video models become more prevalent.

Practically, SimpleGVR enables scalable, high-fidelity video synthesis pipelines suitable for deployment in resource-constrained environments. The architectural simplicity and modularity of the approach facilitate integration with a wide range of upstream generative models.

Theoretically, the findings suggest that careful modeling of the degradation process and training dynamics is essential for effective cascaded synthesis. The success of sparse local attention and interleaving temporal units points to promising directions for further reducing the computational footprint of video diffusion models.

Future research may explore:

• Extending latent-space VSR to other generative modalities (e.g., 3D, multi-view video).
• Automated or learned degradation modeling tailored to specific upstream generators.
• Adaptive attention mechanisms that dynamically allocate computation based on content complexity.
• End-to-end joint training of base T2V and VSR modules for improved alignment and efficiency.

Conclusion

SimpleGVR provides a robust, efficient, and empirically validated baseline for latent-cascaded video super-resolution. Its methodological contributions in degradation modeling, training configuration, and efficient attention design set a new standard for practical high-resolution video synthesis in the context of large-scale generative models. The framework's strong numerical results and architectural insights are likely to inform both future research and real-world deployment of cascaded video generation systems.