1. Bibliographic Information

1.1. Title

The central topic of the paper is "SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration." This title indicates a new approach for video restoration that leverages a Diffusion Transformer model, aiming for highly scalable and generalizable performance across various video lengths and resolutions.

1.2. Authors

The authors are:

• Jianyi Wang (Nanyang Technological University, ByteDance)

• Zhijie Lin (ByteDance)

• Meng Wei (ByteDance)

• Yang Zhao (ByteDance)

• Ceyuan Yang (ByteDance)

• Fei Xiao (ByteDance)

• Chen Change Loy (Nanyang Technological University)

• Lu Jiang (ByteDance)

  Their affiliations suggest a collaboration between academia (Nanyang Technological University) and industry (ByteDance), indicating a blend of theoretical rigor and practical application focus.

1.3. Journal/Conference

The paper is published at (UTC): 2025-01-02T16:19:48.000Z. While the specific journal or conference is not explicitly mentioned in the provided text, the arXiv link suggests it is a preprint, likely submitted to or under review for a top-tier computer vision or machine learning conference (e.g., CVPR, ICCV, NeurIPS, ICML) given the technical depth and comparison with state-of-the-art methods. These venues are highly reputable and influential in the field.

1.4. Publication Year

The publication timestamp is 2025-01-02T16:19:48.000Z, indicating it was published on January 2, 2025.

1.5. Abstract

The paper introduces SeedVR, a novel diffusion transformer designed for generic video restoration, particularly addressing the challenges of fidelity and temporal consistency in real-world degradations. Existing diffusion-based restoration methods often struggle with generation capability and sampling efficiency. SeedVR overcomes these limitations through several key innovations:

1. Shifted Window Attention: Facilitates effective restoration on long video sequences.

2. Variable-Sized Windows: Supports arbitrary length and resolution by adapting window sizes near spatial and temporal boundaries, unlike traditional window attention.

3. Causal Video Autoencoder (CVVAE): Efficiently compresses video data.

4. Mixed Image and Video Training: Enhances generalizability.

5. Progressive Training: Accelerates convergence.

   Extensive experiments show SeedVR achieves highly competitive performance on synthetic, real-world, and AI-generated video benchmarks, demonstrating superiority over existing methods. Despite its large 2.48B parameters, SeedVR is reported to be over $2 \times$ faster than current diffusion-based video restoration approaches.

1.6. Original Source Link

• Original Source Link: https://arxiv.org/abs/2501.01320
• PDF Link: https://arxiv.org/pdf/2501.01320v4.pdf The paper is available as a preprint on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem SeedVR aims to solve is generic video restoration (VR), which involves reconstructing high-quality (HQ) videos from low-quality (LQ) inputs, especially in the presence of complex and unknown degradations encountered in real-world scenarios. This is a critical task in computer vision with broad applications.

Current diffusion-based image and video restoration methods, while promising for addressing issues like over-smoothing prevalent in earlier CNN-based approaches, face significant limitations:

1. Resolution Constraints: Many methods rely on full-attention layers within U-Net architectures. These architectures struggle with computational costs and performance degradation when processing resolutions different from training data, limiting their applicability for long-duration, high-resolution videos.

2. Sampling Inefficiency: To handle arbitrary resolutions, existing diffusion-based VR often resorts to patch-based sampling (dividing videos into overlapping spatial-temporal patches and fusing them). This process, especially with large overlaps needed for coherence, leads to considerably slow inference speed, making them impractical for real-world use (e.g., VEnhancer taking 387 seconds for 31 frames, Upscale-A-Video taking 414 seconds for the same).

3. Limited Generative Capability: While fine-tuning from diffusion priors offers efficiency, it inherits limitations, including basic autoencoders without temporal compression, which leads to inefficient training and inference and limits the quality of reconstructed videos.

   The paper's entry point is to design a Diffusion Transformer (DiT) model, SeedVR, that can efficiently handle arbitrary length and resolution for generic video restoration, overcoming the resolution constraints and sampling inefficiency of prior diffusion-based methods while maintaining high generative quality.

2.2. Main Contributions / Findings

The primary contributions of SeedVR are:

1. Arbitrary Resolution Handling with Shifted Window Attention: SeedVR proposes a Diffusion Transformer block based on a shifted window attention mechanism (Swin-MMDiT) that effectively handles inputs with arbitrary resolutions and lengths in diffusion-based VR. This design uses large, non-overlapping window attention, significantly reducing computational costs compared to full attention or patch-based sampling. It also supports variable-sized windows near spatial and temporal boundaries using 3D rotary positional embedding (RoPE).

2. Efficient Causal Video Autoencoder (CVVAE): SeedVR develops a novel causal video autoencoder that significantly improves both training and inference efficiency. Unlike previous autoencoders that lack temporal compression or sufficient latent channels, SeedVR's CVVAE uses causal 3D residual blocks, increases latent channels to 16, and applies a temporal compression factor of 4, achieving strong reconstruction quality and efficiency.

3. State-of-the-Art Performance through Large-scale Training: By leveraging large-scale joint training on images and videos (10M images, 5M videos), multi-scale progressive training, and precomputed latents and text embeddings, SeedVR achieves state-of-the-art performance across diverse synthetic and real-world benchmarks. It serves as the largest-ever diffusion transformer model for generic VR, demonstrating superior visual realism and detail consistency.

4. Improved Efficiency: Despite having 2.48B parameters (over $3.5 \times$ more than some baselines), SeedVR is over2 \timesfaster than existing diffusion-based VR methods like VEnhancer and Upscale-A-Video, thanks to its efficient architecture and training strategies.

   The key findings are that SeedVR consistently outperforms existing methods across various VR benchmarks, demonstrating superior degradation removal, texture generation, and temporal consistency. Its design effectively addresses the challenges of scalability, efficiency, and quality in generic video restoration, pushing the boundaries of advanced VR.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand SeedVR, several foundational concepts in deep learning, particularly in computer vision and generative models, are essential:

• Diffusion Models: Diffusion models (or Denoising Diffusion Probabilistic Models - DDPMs) are a class of generative models that learn to reverse a diffusion process. In the forward diffusion process, noise is gradually added to data until it becomes pure noise. In the reverse process, the model learns to denoise the data step-by-step, transforming noise back into coherent data. This iterative denoising capability makes them powerful for generating high-quality images and videos. For restoration tasks, they learn to denoise a degraded input conditional on the input, effectively "restoring" it.

• Transformers: Transformers are neural network architectures introduced by Vaswani et al. (2017) that rely heavily on the attention mechanism. They have revolutionized natural language processing and are increasingly dominant in computer vision. Unlike recurrent neural networks (RNNs) or convolutional neural networks (CNNs), transformers process data in parallel and capture long-range dependencies efficiently.

  • Attention Mechanism: The core of a transformer. It allows the model to weigh the importance of different parts of the input sequence when processing a specific part. The standard self-attention mechanism calculates attention scores between all pairs of tokens in an input sequence. The self-attention calculation is: $ \mathrm{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $ Where:

    • $Q$ (Query), $K$ (Key), $V$ (Value) are matrices derived from the input embeddings.
    • $Q K^T$ calculates similarity scores between queries and keys.
    • $\sqrt{d_k}$ is a scaling factor, where $d_k$ is the dimension of the key vectors, used to prevent large dot products from pushing the softmax function into regions with tiny gradients.
    • $\mathrm{softmax}$ normalizes the scores to create weights.
    • The weighted sum of $V$ matrices is the output.

  • Window Attention: A variant of self-attention designed to reduce computational complexity, especially for high-resolution images or long sequences. Instead of calculating attention across the entire input, window attention divides the input into non-overlapping "windows" and computes self-attention independently within each window. This reduces the quadratic complexity of full attention to a linear or quasi-linear complexity with respect to the input size.

  • Shifted Window Attention: Introduced by Swin Transformer, shifted window attention enhances the window attention mechanism by allowing information flow between windows across different layers. In one layer, windows are partitioned regularly. In the next layer, the window partition is shifted (e.g., by half the window size), allowing tokens that were in different windows in the previous layer to interact. This helps capture global context while maintaining computational efficiency.

• Diffusion Transformers (DiT): Diffusion Transformers integrate the transformer architecture into diffusion models. Instead of using U-Net backbones (common in earlier diffusion models), DiT models use a transformer to predict the noise or denoised output. This brings the scalability and modeling capabilities of transformers to generative tasks. MMDiT (Multi-Modality Diffusion Transformer) further extends this by integrating multiple modalities (e.g., visual and text) within the transformer block, allowing for richer conditional generation.

• Video Autoencoders (VAEs): Variational Autoencoders (VAEs) are a type of generative model that learns a compressed, lower-dimensional representation (latent space) of data. An autoencoder consists of an encoder that maps input data to a latent representation and a decoder that reconstructs the data from this latent representation. Video VAEs apply this concept to video data, learning to compress and reconstruct video frames, often in a causally constrained manner for temporal coherence.

• Positional Embeddings: Since transformers do not inherently process sequential order, positional embeddings are added to input embeddings to inject information about the relative or absolute position of tokens in the sequence. Rotary Positional Embeddings (RoPE) are a type of positional encoding that encodes relative position information directly into the attention mechanism, offering better generalization to longer sequences and variable lengths.

3.2. Previous Works

The paper contextualizes SeedVR by discussing related work in three main areas:

• Attention Mechanism in Restoration:

  • Early CNN-based approaches [4-6, 22, 23, 46, 50, 55] struggled with long-range dependencies due to limited receptive fields.
  • Transformer-based methods [7, 11, 31, 33, 61, 83, 84] introduced attention to restoration, improving performance.
  • To mitigate the quadratic complexity of self-attention [52], many adopted window attention [11, 31, 33, 83, 84]. For instance, SwinIR [31] uses Swin Transformer with an $8 \times 8$ window, and SRFormer [83] and SRFormerV2 [84] increased this to $24 \times 24$ and $40 \times 40$.
  • Limitation: These methods often use small window sizes, limiting receptive fields. Existing diffusion-based restoration methods [20, 45, 54, 60, 70, 82] often rely on full attention to incorporate text guidance, which is computationally expensive and resolution-constrained.
  • SeedVR's distinction: SeedVR uses a significantly larger attention window ($64 \times 64$ in $8 \times$ compressed latent space) and variable-sized windows near boundaries, allowing it to handle text prompts and long-range dependencies without relying on tiled sampling strategies.

• Diffusion Transformer (DiT):

  • DiT [40] established transformers as the backbone for diffusion models [8-10, 17, 19, 25, 27, 30, 34, 41, 65, 78].
  • Approaches for efficiency: Separate temporal/spatial attention [78], token compression [8], multi-stage generation [25].
  • FIT [10] interleaves window and global attention but struggles with variable-sized inputs.
  • Inf-DiT [65] uses local attention autoregressively for variable image sizes but has a finite receptive field.
  • VideoPoet [27] uses three types of 2D window attention for video super-resolution, requiring full attention along one axis and still struggling with arbitrary shapes.
  • SeedVR's distinction: SeedVR introduces a more flexible 3D window attention that can effectively be applied to VR with varying resolutions without requiring full attention along any axis.

• Video Restoration (VR):

  • Early works [4, 5, 12, 29, 32, 33, 55, 69] focused on synthetic data, limiting real-world effectiveness.
  • Later approaches [6, 61, 77] moved to real-world VR but struggled with realistic textures due to limited generative capabilities.
  • Diffusion-based VR [20, 64, 82] emerged with impressive performance but inherited limitations from their diffusion priors.
    • They often use basic autoencoders without temporal compression, leading to inefficient training/inference.
    • Their reliance on full attention imposes resolution constraints and increases inference cost.
  • SeedVR's distinction: SeedVR redesigns the entire architecture with an efficient causal video autoencoder and a flexible window attention mechanism to achieve effective and efficient VR with arbitrary length and resolution. It addresses the core inefficiencies and resolution limitations inherent in prior diffusion-based VR models.

3.3. Technological Evolution

The field of video restoration has evolved significantly:

1. CNN-based Methods: Initially, Convolutional Neural Networks (CNNs) were dominant, offering improved performance over traditional image processing techniques. However, their limited receptive fields made it hard to capture long-range dependencies, leading to issues like over-smoothing and difficulties with complex degradations.

2. Transformer Integration: The success of Transformers in NLP led to their adoption in computer vision, including restoration. Introducing attention mechanisms allowed models to capture longer-range dependencies, addressing a key limitation of CNNs. However, full self-attention is computationally expensive for high resolutions.

3. Windowed Attention for Efficiency: To mitigate the quadratic complexity of self-attention, window attention and shifted window attention (e.g., Swin Transformer) were developed, allowing for efficient processing of high-resolution inputs while still capturing local and some global context.

4. Diffusion Models for Generative Restoration: Diffusion models emerged as powerful generative models capable of producing highly realistic images and videos, effectively tackling the problem of perceptual quality and realistic texture generation that traditional PSNR/SSIM-driven methods often struggled with.

5. Diffusion Transformers (DiT): Combining Diffusion Models with Transformers resulted in Diffusion Transformers (DiT), which leverage the strong generative capabilities of diffusion with the scalability and modeling power of transformers, moving beyond U-Net backbones.

6. Current Challenges in Diffusion VR: Despite these advancements, diffusion-based VR still faces challenges: computational cost, difficulty in handling arbitrary resolutions and lengths efficiently (often relying on slow patch-based sampling), and inefficient video autoencoders.

   SeedVR's work fits into this timeline by addressing these current challenges in diffusion-based video restoration. It advances the state-of-the-art by specifically designing a Diffusion Transformer with innovative window attention and a dedicated causal video autoencoder to handle arbitrary video lengths and resolutions efficiently, pushing towards truly generic and scalable video restoration.

3.4. Differentiation Analysis

Compared to the main methods in related work, SeedVR's core differences and innovations are:

1. Arbitrary Resolution and Length Handling:

   • Previous diffusion VR (e.g., VEnhancer [20], Upscale-A-Video [82]) relies on full attention or patch-based sampling with significant overlap, leading to resolution constraints and slow inference.
   • SeedVR's innovation: It directly tackles this with a shifted window attention mechanism (Swin-MMDiT) using large non-overlapping windows ($64 \times 64$ in latent space) and variable-sized windows at boundaries. This eliminates the need for slow tiled sampling and allows for direct application to videos of any length and resolution.

2. Efficient Video Encoding:

   • Previous diffusion VR often fine-tunes image autoencoders for video by adding 3D convolutions without temporal compression, resulting in inefficient training and inference and limited reconstruction quality due to few latent channels (e.g., 4).
   • SeedVR's innovation: It trains a custom causal video autoencoder (CVVAE) from scratch. This CVVAE incorporates causal 3D residual blocks for long video handling, increases latent channels to 16 for higher capacity, and applies a temporal compression factor of 4, significantly improving efficiency and reconstruction quality.

3. Scalable and Generalizable Training:

   • Previous VR approaches [20, 64, 82] are often trained on limited resources, hindering generalization to complex real-world scenarios.
   • SeedVR's innovation: Employs large-scale mixed image and video training (10M images, 5M videos), precomputed latents and text embeddings for faster training, and a multi-stage progressive training strategy to handle high resolutions and durations. This enables state-of-the-art performance across diverse synthetic, real-world, and AI-generated video benchmarks.

4. Computational Efficiency vs. Model Size:

   • Despite being a much larger model (2.48B parameters, $>3.5 \times$ Upscale-A-Video [82]), SeedVR is over2 \timesfaster than existing diffusion-based methods like VEnhancer and Upscale-A-Video. This demonstrates that its architectural innovations (especially window attention) and efficient VAE design translate into practical speedups, making it more viable for real-world applications.

     In essence, SeedVR fundamentally re-architects the Diffusion Transformer for video, moving beyond ad-hoc solutions for resolution handling and inefficient VAEs, to provide a more principled, scalable, and efficient approach to generic video restoration.

4. Methodology

4.1. Principles

The core idea behind SeedVR is to develop a highly scalable and efficient Diffusion Transformer (DiT) model for generic video restoration (VR) that can handle arbitrary video lengths and resolutions without compromising fidelity or temporal consistency. The theoretical basis is rooted in leveraging the powerful generative capabilities of diffusion models combined with the efficient long-range dependency modeling of transformers, specifically adapted for video data. The key intuitions are:

1. Overcoming Full Attention Bottleneck: Replacing computationally expensive full attention with window attention to enable processing of high-resolution and long video sequences efficiently.
2. Addressing Window Attention Limitations: Enhancing window attention with shifting and variable-sizing to allow for information flow across windows and handle arbitrary input dimensions at boundaries.
3. Efficient Video Representation: Using a specialized causal video autoencoder (CVVAE) to compress video into a compact latent space, reducing computational load for the diffusion model.
4. Robust Training for Generalization: Employing large-scale mixed-modality training (images and videos) and progressive training to ensure the model generalizes well to diverse real-world degradations and varied input characteristics.

4.2. Core Methodology In-depth (Layer by Layer)

As depicted in Figure 1a (not provided directly, but its description suggests the overall architecture), SeedVR follows a common latent diffusion model structure. A pretrained autoencoder compresses the input video into a latent space, and the corresponding text prompt is encoded by three pretrained, frozen text encoders. The core of SeedVR is a Diffusion Transformer (specifically, a modified MMDiT) that operates in this latent space.

4.2.1. Shifted Window Based MM-DiT (Swin-MMDiT)

The Diffusion Transformer backbone of SeedVR is a modified MMDiT block, termed Swin-MMDiT. MMDiT [17] is an effective transformer block that applies separate weights to visual input and text modalities, enabling a bidirectional flow of information. However, the original MMDiT uses full attention, which is computationally expensive and unsuitable for arbitrary lengths and resolutions in video. SeedVR addresses this by introducing a shifted window attention mechanism.

Given a video feature $X \in \mathbb{R}^{T \times H \times W \times d}$ (where $T$ is temporal dimension, $H$ is height, $W$ is width, and $d$ is feature dimension) and a text embedding $C_{text} \in \mathbb{R}^{L \times d}$ (where $L$ is text sequence length), the process in Swin-MMDiT is as follows:

1. Video Feature Flattening: The video feature $X$ is first flattened to $X' \in \mathbb{R}^{THW \times d}$, following the NaViT scheme [15]. This treats the 3D video feature as a sequence of tokens.

2. Attention Mechanism: Instead of the full attention used in standard MMDiT, Swin-MMDiT employs two types of window attention:

   • Regular Window Attention: In the first transformer block, attention is calculated within regular, non-overlapping windows. The video feature $X$ is conceptually divided into $t \times h \times w$ windows (where t, h, w are the temporal, height, and width dimensions of a window in latent space).

   • Shifted Window Attention: In the subsequent transformer block, the window partition is shifted by half the window size (e.g., $(\frac{t}{2}, \frac{h}{2}, \frac{w}{2})$) before attention is calculated. This allows for information exchange between windows across layers, enhancing global context modeling.

     The paper states that for attention calculations, it uses separate attention mechanisms for video and text features, as shown in Figure 2. This contrasts with the single multi-modality attention in the original MMDiT.

   As shown in Figure 2b, within each window:

   • Queries ($Q_{video}$), Keys ($K_{video}$), and Values ($V_{video}$) are derived from the video window features.
   • Queries ($Q_{text}$), Keys ($K_{text}$), and Values ($V_{text}$) are derived from the text features $C_{text}$.
   • The keys of the video window features and text features are concatenated: $\mathrm{Cat}(K_{video}, K_{text})$.
   • Similarly, the values are concatenated: $\mathrm{Cat}(V_{video}, V_{text})$.
   • Attention is then computed by calculating the similarity between:

     • $Q_{video}$ and $\mathrm{Cat}(K_{video}, K_{text})$, followed by a weighted sum with $\mathrm{Cat}(V_{video}, V_{text})$ to update video features.

     • $Q_{text}$ and $\mathrm{Cat}(K_{video}, K_{text})$, followed by a weighted sum with $\mathrm{Cat}(V_{video}, V_{text})$ to update text features. (The paper focuses on the video restoration aspect, so the explicit update of text features might be for consistency or for internal representation).

       The main advantage of this window-based approach is handling variable input sizes. The original Swin Transformer [35, 36] requires a cyclic shifting strategy with masking to make window sizes divisible by feature map size. However, SeedVR's Swin-MMDiT leverages the flexibility of NaViT [15] and Flash attention [14]. The partitioned window features are flattened into a concatenated 2D tensor, and attention is calculated within each window, eliminating the need for complex masking strategies on the 3D feature map. This design allows for variable-sized windows near the boundaries of both spatial and temporal dimensions, effectively overcoming the resolution constraints of traditional window attention.

   The following image illustrates the Swin-MMDiT architecture within the Diffusion Transformer.

   Figure 2: (a) The overall architecture of the Diffusion Transformer, showing the encoder-decoder structure for latent video processing and multimodal input. (b) Details of the Swin-MMDiT block, illustrating the shifted window video and text attention mechanisms with concatenated keys and values for cross-modal interaction.

3. Positional Encoding: SeedVR replaces the absolute 2D positional frequency embeddings used in SD3 with 3D relative rotary positional embeddings (RoPE) [48] within each window. This avoids resolution bias and better handles varying-sized windows at boundaries. RoPE encodes relative positional information directly into the attention mechanism, offering better generalization.

4.2.2. Causal Video VAE (CVVAE)

To efficiently process video input and overcome the limitations of existing video autoencoders (which often fine-tune image autoencoders with limited temporal compression and latent channels), SeedVR proposes a custom Causal Video VAE (CVVAE).

The CVVAE incorporates the following improvements:

1. Causal 3D Residual Block: Instead of vanilla 3D blocks, the CVVAE uses causal 3D residual blocks. This design ensures that the encoding of a frame only depends on past and current frames (not future frames), which is crucial for handling long videos by cutting them into clips and maintaining temporal coherence during generation.

2. Increased Latent Channels: Following SD3 [17], the CVVAE increases the latent channels to 16 (compared to 4 in many existing approaches). This provides higher model capacity for better reconstruction quality.

3. Temporal Compression: The CVVAE applies a temporal compression factor of 4, meaning that for every 4 input frames, it generates 1 latent frame. This significantly reduces the temporal dimension of the latent representation, leading to more efficient video encoding, training, and inference, especially for high-resolution videos. The CVVAE also uses a spatial compression factor of 8.

   The CVVAE is trained from scratch on a large dataset using a combination of losses:

• $\ell_1$ loss: Measures the absolute difference between the reconstructed and original video pixels, focusing on pixel-level fidelity.

• LPIPS loss [75]: Learned Perceptual Image Patch Similarity loss, which measures the perceptual distance between images using features from a pretrained deep network, ensuring perceptual quality.

• GAN loss [18]: Generative Adversarial Network loss, which involves a discriminator network trying to distinguish between real and generated videos, pushing the generator to produce more realistic outputs.

  The overall architecture of the CVVAE is shown in Figure 3:

  Figure 3: Architectural diagram of the Causal Video VAE (CVVAE), showcasing its encoder-decoder structure with causal 3D residual blocks and spatial-temporal compression capabilities, designed for high-quality video reconstruction.

4.2.3. Large-scale Training

Training a large-scale VR model on millions of high-resolution videos is complex. SeedVR incorporates several strategies for efficient and effective large-scale training:

1. Large-scale Mixed Data of Images and Videos:

   • The model is trained on a diverse dataset comprising 10 million images and 5 million videos.
   • Images vary in resolution, mostly exceeding $1024 \times 1024$.
   • Videos are 720p, randomly cropped from higher-resolution sources. Cropping is found to yield better performance than resizing.
   • High-quality data is ensured by filtering out low-quality samples using metrics like LAION-Aesthetics [1], [26], [53], [58].
   • This mixed data approach allows the model to learn from both static images and dynamic videos, enhancing its generalizability.

2. Precomputing Latents and Text Embeddings:

   • Encoding high-resolution videos into latent space using a pretrained VAE and processing text prompts with text encoders is time-consuming during training.
   • To overcome this, high-quality (HQ) and low-quality (LQ) video latent features, along with text embeddings, are precomputed. This dramatically speeds up training, achieving a

4 \times`speedup`.
    *   Precomputing also allows for a `larger batch size` by freeing up GPU memory that would otherwise be occupied by VAE and text encoder models.
    *   `Diverse degradations` are applied during precomputation to generate `LQ conditions`, crucial for training real-world `VR models`.

3.  **Progressively Growing Up of Resolution and Duration**:
    *   Directly training on high-resolution, long videos is challenging. SeedVR adopts a `multi-stage progressive training strategy`.
    *   The model is initialized from `SD3-Medium` [17] (2.2B parameters).
    *   Training starts with `short, low-resolution videos` (5 frames at $256 \times 256$).
    *   It progressively increases to `longer durations and higher resolutions` (e.g., 9 frames at $512 \times 512$, then 21 frames at $768 \times 768$).
    *   The final model is trained on data with `varying lengths and resolutions`. This strategy accelerates convergence.

4.  **Injecting Noise to Condition**:
    *   Synthetic `LQ-HQ video pairs` are created for training, but a `degradation gap` exists between synthetic and real-world `LQ videos`.
    *   To bridge this gap without weakening the model's generative ability, `random noise` is injected into the `latent LQ condition` [3, 82]. This is done using the formula:
        $C_{\mathrm{LQ}}^{\tau} = \alpha_{\tau} C_{\mathrm{LQ}} + \sigma_{\tau} \epsilon$
        Where:
        *   $C_{\mathrm{LQ}}^{\tau}$ is the noisy low-quality latent condition at noise level $\tau$.
        *   $C_{\mathrm{LQ}}$ is the original low-quality latent condition.
        *   $\epsilon \sim \mathcal{N}(0, I)$ is Gaussian noise sampled from a standard normal distribution.
        *   $\tau$ is the noise level, corresponding to early steps in the noise schedule defined by $\alpha_{\tau}$ and $\sigma_{\tau}$. This means the LQ condition is diffused slightly, making it more robust to variations.

    *   The model also uses `flexible text encoder usage` by randomly replacing text input with `null prompts` for each of the three text encoders, similar to `SD3` [17]. This enhances the model's ability to operate without explicit text guidance. However, injecting noise into LQ conditions to boost generative capability too much was found to reduce output fidelity, so this specific approach was not included for LQ conditions in the final model.

# 5. Experimental Setup

## 5.1. Datasets
SeedVR evaluates its performance on a comprehensive set of datasets, covering both synthetic and real-world scenarios, as well as AI-generated content.

*   **Synthetic Datasets**: These datasets provide `LQ-HQ pairs` for quantitative evaluation with full-reference metrics. Degradations are synthesized to mimic real-world conditions.
    *   **SPMCS [68]**: A dataset for progressive fusion video super-resolution, often used for spatio-temporal correlation tasks.
    *   **UDM10 [49]**: A dataset designed for detail-revealing deep video super-resolution, focusing on intricate details.
    *   **REDS30 [38]**: Part of the NTIRE 2019 challenge, used for video deblurring and super-resolution, known for its diverse scenes and motions.
    *   **YouHQ40 [82]**: A synthetic dataset specifically used for evaluating real-world video super-resolution, using degradations similar to those applied during SeedVR's training.

*   **Real-world Dataset**:
    *   **VideoLQ [6]**: A dataset designed for investigating tradeoffs in real-world video super-resolution. This dataset captures genuine degradations encountered in natural environments, making it suitable for assessing generalization to practical scenarios.

*   **AI-generated Videos**:
    *   **AIGC38**: A custom-collected dataset consisting of 38 AI-generated videos. This unique dataset allows for evaluating SeedVR's performance on content that might exhibit different characteristics and degradation patterns than natural videos, showcasing its versatility.

        All testing videos are processed to `720p` resolution while maintaining their original length to ensure fair comparisons.

## 5.2. Evaluation Metrics

For a comprehensive evaluation, SeedVR employs a variety of metrics, categorized into `full-reference` (requiring ground truth) and `no-reference` (not requiring ground truth) metrics.

### 5.2.1. Full-Reference Metrics (for Synthetic Datasets with LQ-HQ pairs)

1.  **Peak Signal-to-Noise Ratio (PSNR)**:
    *   **Conceptual Definition**: `PSNR` measures the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. It is a common metric to quantify image and video quality, where higher values indicate better quality. It's often used to compare reconstruction quality against an original image.
    *   **Mathematical Formula**:
        $\mathrm{PSNR} = 10 \cdot \log_{10}\left(\frac{\mathrm{MAX}_I^2}{\mathrm{MSE}}\right)$
        Where:
        *   $\mathrm{MAX}_I$ is the maximum possible pixel value of the image (e.g., 255 for an 8-bit grayscale image).
        *   $\mathrm{MSE}$ is the Mean Squared Error between the original and the reconstructed image.
        *   $\mathrm{MSE} = \frac{1}{mn} \sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - K(i,j)]^2$
            *   $I$ represents the original image.
            *   $K$ represents the reconstructed (noisy) image.
            *   $m$ and $n$ are the dimensions of the images.
    *   **Symbol Explanation**:
        *   $\mathrm{MAX}_I$: Maximum possible pixel value.
        *   $\mathrm{MSE}$: Mean Squared Error.
        *   `I(i,j)`: Pixel value at position `(i,j)` in the original image.
        *   `K(i,j)`: Pixel value at position `(i,j)` in the reconstructed image.
        *   `m, n`: Dimensions (height, width) of the image.

2.  **Structural Similarity Index Measure (SSIM)**:
    *   **Conceptual Definition**: `SSIM` [76] is a perceptual metric that quantifies the similarity between two images. It is designed to model the human visual system's perception of structural information, luminosity, and contrast. Values range from -1 to 1, with 1 indicating perfect similarity.
    *   **Mathematical Formula**:
        $\mathrm{SSIM}(x, y) = \frac{(2\mu_x\mu_y + c_1)(2\sigma_{xy} + c_2)}{(\mu_x^2 + \mu_y^2 + c_1)(\sigma_x^2 + \sigma_y^2 + c_2)}$
        Where:
        *   $x$ and $y$ are the two image patches being compared.
        *   $\mu_x$ and $\mu_y$ are the average (mean) pixel values of $x$ and $y$.
        *   $\sigma_x^2$ and $\sigma_y^2$ are the variances of $x$ and $y$.
        *   $\sigma_{xy}$ is the covariance of $x$ and $y$.
        *   $c_1 = (K_1 L)^2$ and $c_2 = (K_2 L)^2$ are small constants to avoid division by zero, where $L$ is the dynamic range of the pixel values (e.g., 255) and $K_1, K_2 \ll 1$.
    *   **Symbol Explanation**:
        *   $\mu_x, \mu_y$: Mean intensity of image `x, y`.
        *   $\sigma_x^2, \sigma_y^2$: Variance of image `x, y`.
        *   $\sigma_{xy}$: Covariance of image `x, y`.
        *   $c_1, c_2$: Small constants.

3.  **Learned Perceptual Image Patch Similarity (LPIPS)**:
    *   **Conceptual Definition**: `LPIPS` [75] is a metric that measures perceptual similarity between two images, often correlating better with human judgment than traditional metrics like PSNR or SSIM. It calculates the distance between deep features extracted from a pretrained neural network (e.g., VGG, AlexNet) when fed the reference and distorted images. Lower LPIPS values indicate higher perceptual similarity.
    *   **Mathematical Formula**:
        $\mathrm{LPIPS}(x, x_0) = \sum_l \frac{1}{H_l W_l} \sum_{h,w} \|w_l \odot (\phi_l(x)_{h,w} - \phi_l(x_0)_{h,w})\|_2^2$
        Where:
        *   $x$ is the reference image and $x_0$ is the distorted image.
        *   $\phi_l$ is the feature stack from layer $l$ of a pretrained network.
        *   $w_l$ is a learnable weight vector for layer $l$.
        *   $H_l, W_l$ are the height and width of the feature maps at layer $l$.
        *   $\odot$ denotes element-wise multiplication.
    *   **Symbol Explanation**:
        *   $x$: Reference image.
        *   $x_0$: Distorted image.
        *   $\phi_l(\cdot)$: Feature map extracted from layer $l$ of a pretrained network.
        *   $w_l$: Learnable scaling weight for layer $l$.
        *   $H_l, W_l$: Dimensions of the feature map at layer $l$.

4.  **DISTS [16]**:
    *   **Conceptual Definition**: `DISTS` (Deep Image Structure and Texture Similarity) is an image quality assessment metric that unifies structure and texture similarity, aiming to better align with human perception. It extracts deep features from a pretrained network and computes structural and textural similarity separately before combining them. Lower DISTS values indicate better quality.
    *   **Mathematical Formula**:
        $\mathrm{DISTS}(x, y) = \sum_{l=1}^L \left( \alpha_l \cdot \mathrm{D}_{str}(F_l(x), F_l(y)) + \beta_l \cdot \mathrm{D}_{tex}(F_l(x), F_l(y)) \right)$
        Where:
        *   `x, y` are the reference and distorted images.
        *   $F_l(\cdot)$ denotes the feature map extracted from layer $l$ of a pretrained VGG network.
        *   $\mathrm{D}_{str}$ measures structural similarity based on feature means and variances.
        *   $\mathrm{D}_{tex}$ measures texture similarity based on Gram matrices of features.
        *   $\alpha_l, \beta_l$ are learnable weights for each layer.
    *   **Symbol Explanation**:
        *   $x$: Reference image.
        *   $y$: Distorted image.
        *   $F_l(\cdot)$: Feature map from VGG layer $l$.
        *   $\mathrm{D}_{str}$: Structural distance component.
        *   $\mathrm{D}_{tex}$: Texture distance component.
        *   $\alpha_l, \beta_l$: Learnable weights.

### 5.2.2. No-Reference Metrics (for all Datasets, especially Real-world and AIGC)

These metrics do not require a ground truth reference and evaluate image/video quality based on observable characteristics.

1.  **Naturalness Image Quality Evaluator (NIQE)**:
    *   **Conceptual Definition**: `NIQE` [37] is a no-reference image quality metric that evaluates image naturalness by modeling images as multivariate Gaussian distributions (MVGDs) of locally sampled and perceptually relevant features. A lower NIQE score indicates better quality, implying the image looks more "natural."
    *   **Mathematical Formula**:
        $\mathrm{NIQE}(\mathbf{x}) = \sqrt{(\mathbf{v}_1 - \mathbf{v}_2)^T (\Sigma_1 + \Sigma_2)^{-1} (\mathbf{v}_1 - \mathbf{v}_2)}$
        Where:
        *   $\mathbf{v}_1, \Sigma_1$ are the mean vector and covariance matrix of the naturalistic image patches from a database of pristine images.
        *   $\mathbf{v}_2, \Sigma_2$ are the mean vector and covariance matrix of the image being evaluated.
    *   **Symbol Explanation**:
        *   $\mathbf{x}$: Image being evaluated.
        *   $\mathbf{v}_1, \Sigma_1$: Mean vector and covariance matrix from a pristine image dataset.
        *   $\mathbf{v}_2, \Sigma_2$: Mean vector and covariance matrix of the test image.

2.  **Multi-scale Image Quality Transformer (MUSIQ)**:
    *   **Conceptual Definition**: `MUSIQ` [26] is a `no-reference image quality assessment` model based on a transformer architecture. It processes image patches at multiple scales and aggregates information to predict quality scores. Higher MUSIQ scores indicate better quality.
    *   **Mathematical Formula**: (As a deep learning model, MUSIQ doesn't have a simple closed-form mathematical formula like PSNR/SSIM. Its core is the transformer architecture and its learned weights. The output is a quality score predicted by the model.)
    *   **Symbol Explanation**: `MUSIQ` is a black-box model. Its output is a scalar quality score.

3.  **CLIP-IQA [53]**:
    *   **Conceptual Definition**: `CLIP-IQA` leverages the `CLIP (Contrastive Language-Image Pretraining)` model to assess image quality. It uses the semantic understanding capabilities of `CLIP` to evaluate how well an image aligns with quality-related textual descriptions. Higher `CLIP-IQA` scores generally indicate better quality.
    *   **Mathematical Formula**: (Similar to MUSIQ, CLIP-IQA is a model-based metric. It typically involves computing the cosine similarity between the CLIP embedding of an image and the CLIP embedding of a "high-quality" or "pristine" text prompt, or a learned mapping from CLIP features to quality scores.)
    *   **Symbol Explanation**: `CLIP-IQA` utilizes `CLIP` embeddings to derive a quality score.

4.  **DOVER [59]**:
    *   **Conceptual Definition**: `DOVER` (Deep Optimized Video Quality Evaluator) is a `no-reference video quality assessment` model designed for `User-Generated Content (UGC)`. It considers both `aesthetic` and `technical` aspects of video quality, aiming to align with human perception in diverse real-world video scenarios. Higher `DOVER` scores indicate better quality.
    *   **Mathematical Formula**: (DOVER is a complex neural network model, so no simple closed-form formula is provided. It combines features from various sub-networks to predict a comprehensive quality score.)
    *   **Symbol Explanation**: `DOVER` is a neural network that outputs a scalar video quality score.

### 5.2.3. VAE-Specific Metric

1.  **Fréchet Video Distance (FVD)**:
    *   **Conceptual Definition**: `FVD` [51] is a metric used to evaluate the quality and diversity of generated videos by comparing the statistics of features extracted from real videos and generated videos. It is the video equivalent of `Fréchet Inception Distance (FID)` for images. A lower FVD score indicates that generated videos are more realistic and diverse, closer to real videos. `rFVD` likely refers to a reference-based FVD or a specific implementation.
    *   **Mathematical Formula**:
        $\mathrm{FVD}(\mathcal{N}(\mu_r, \Sigma_r), \mathcal{N}(\mu_g, \Sigma_g)) = \|\mu_r - \mu_g\|_2^2 + \mathrm{Tr}(\Sigma_r + \Sigma_g - 2(\Sigma_r \Sigma_g)^{1/2})$
        Where:
        *   $\mu_r, \Sigma_r$ are the mean and covariance of feature representations for real videos.
        *   $\mu_g, \Sigma_g$ are the mean and covariance of feature representations for generated videos.
        *   $\mathrm{Tr}$ denotes the trace of a matrix.
    *   **Symbol Explanation**:
        *   $\mu_r, \mu_g$: Mean feature vectors for real and generated video distributions.
        *   $\Sigma_r, \Sigma_g$: Covariance matrices for real and generated video distributions.
        *   $\mathrm{Tr}(\cdot)$: Trace of a matrix.

## 5.3. Baselines
The paper compares SeedVR against several state-of-the-art methods in video restoration and related areas:

*   **For Video Restoration (Table 1)**:
    *   **Real-ESRGAN [56]**: A classic real-world blind super-resolution model, known for its ability to handle complex degradations.
    *   **SD x4 Upscaler [2]**: A diffusion-based upscaler, likely an image-based method adapted for video.
    *   **ResShift [74]**: An efficient diffusion model for image super-resolution using residual shifting.
    *   **RealViFormer [77]**: A transformer-based model specifically investigating attention for real-world video super-resolution.
    *   **MGLD-VSR [64]**: Motion-guided latent diffusion for temporally consistent real-world video super-resolution.
    *   **Upscale-A-Video [82]**: A diffusion model designed for temporal-consistent real-world video super-resolution.
    *   **VEhancer [20]**: A generative space-time enhancement method for video generation.

*   **For Causal Video VAE (Table 2)**:
    *   **SD 2.1 [45]**: An image-based latent diffusion model's autoencoder, typically used as a baseline for video VAEs by adding 3D convolutions.
    *   **VEnhancer [20]**: The VAE component from VEhancer, which is fine-tuned from an image autoencoder.
    *   **Cosmos [44]**: A suite of image and video neural tokenizers, implying a VAE component.
    *   **OpenSora [80]**: A model for efficient video production, likely with its own VAE.
    *   **OpenSoraPlan v1.3 [28]**: A plan for OpenSora, with a VAE component.
    *   **CV-VAE (SD3) [79]**: A compatible video VAE designed for latent generative video models, specifically for SD3.
    *   **CogVideoX [66]**: A text-to-video diffusion model with an expert transformer, implying a VAE component.

        These baselines represent a range of approaches, including traditional deep learning methods, image-based diffusion models adapted for video, and recent video-specific diffusion models and VAEs. They are representative of the state-of-the-art in their respective domains, making for robust comparisons.

# 6. Results & Analysis

## 6.1. Core Results Analysis
SeedVR demonstrates significantly superior performance across a wide array of benchmarks for generic video restoration. The results highlight its effectiveness in terms of perceptual quality and generalizability across diverse data types, including synthetic, real-world, and AI-generated videos.

As seen in Table 1, SeedVR achieves the best or second-best performance across most metrics and datasets. Notably, it excels in perceptual quality metrics such as `LPIPS ↓`, `DISTS ↓`, `NIQE ↓`, `MUSIQ ↑`, `CLIP-IQA ↑`, and `DOVER ↑`. This indicates that SeedVR produces outputs that are not only structurally similar but also perceptually more realistic and pleasing to human observers, a common strength of diffusion models.

For example, on the UDM10 dataset, SeedVR achieves the highest `DOVER` score (10.508) and the lowest `LPIPS` (0.231) and `DISTS` (0.116) scores, showing strong perceptual quality. Similarly, on the AIGC38 dataset (AI-generated videos), SeedVR has the highest `DOVER` (13.424) and `MUSIQ` (65.91) scores, underscoring its ability to restore high-quality AI-generated content.

The paper acknowledges that SeedVR, like other diffusion-based methods, might show limitations on certain metrics like `PSNR` and `SSIM` on some benchmarks. This is because these metrics primarily measure pixel-level fidelity and structural similarity, while diffusion models often prioritize `perceptual quality` and `generative capability` over exact pixel reproduction. Despite this, SeedVR remains competitive even on these metrics in many cases. The consistent superiority across datasets from various sources demonstrates the effectiveness and generalizability of SeedVR.

The following are the results from Table 1 of the original paper:




Datasets
Metrics
Real-ESRGAN [56]
SD ×4 Upscaler [2]
ResShift [74]
RealViFormer [77]
MGLD-VSR [64]
Upscale-A-Video [82]
VEhancer [20]
Ours




SPMCS
PSNR ↑
22.55
22.75
23.14
24.19
23.41
21.69
18.20
22.37


SSIM↑
0.637
0.535
0.598
0.663
0.633
0.519
0.507
0.607


LPIPS ↓
0.406
0.554
0.547
0.378
0.369
0.508
0.455
0.341


DISTS ↓
0.189
0.247
0.261
0.186
0.166
0.229
0.194
0.141


NIQE ↓
3.355
5.883
6.246
3.431
3.315
3.272
4.328
3.207


MUSIQ ↑
62.78
42.09
55.11
62.09
65.25
65.01
54.94
64.28


CLIP-IQA ↑
0.451
0.402
0.598
0.424
0.495
0.507
0.334
0.587


UDM10
DOVER ↑
8.566
4.413
5.342
7.664
8.471
6.237
7.807
10.508


PSNR ↑
24.78
26.01
25.56
26.70
26.11
24.62
21.48
25.76


SSIM ↑
0.763
0.698
0.743
0.796
0.772
0.712
0.691
0.771


LPIPS ↓
0.270
0.424
0.417
0.285
0.273
0.323
0.349
0.231


DISTS ↓
0.156
0.234
0.211
0.166
0.144
0.178
0.175
0.116


NIQE ↓
4.365
6.014
5.941
3.922
3.814
3.494
4.883
3.514


MUSIQ ↑
54.18
30.33
51.34
55.60
58.01
58.31
46.37
59.14


REDS30
CLIP-IQA ↑
0.398
0.277
0.537
0.397
0.443
0.458
0.304
0.524


DOVER ↑
7.958
3.169
5.111
7.259
7.717
9.238
8.087
10.537


PSNR ↑
21.67
22.94
22.72
23.34
22.74
21.44
19.83
20.44


SSIM↑
0.573
0.563
0.572
0.615
0.578
0.514
0.545
0.534


LPIPS ↓
0.389
0.551
0.509
0.328
0.271
0.397
0.508
0.346


DISTS ↓
0.179
0.268
0.234
0.154
0.097
0.181
0.229
0.138


NIQE ↓
2.879
6.718
6.258
3.032
2.550
2.561
4.615
2.729


YouHQ40
MUSIQ ↑
57.97
25.57
47.50
58.60
62.28
56.39
37.95
57.55


CLIP-IQA ↑
0.403
0.202
0.554
0.392
0.444
0.398
0.245
0.451


DOVER ↑
5.552
2.737
3.712
5.229
6.544
5.234
5.549
6.673


PSNR ↑
22.31
22.51
22.67
23.26
22.62
21.32
18.68
21.15


SSIM ↑
0.605
0.528
0.579
0.606
0.576
0.503
0.510
0.554


LPIPS ↓
0.342
0.518
0.432
0.362
0.356
0.404
0.449
0.298


DISTS ↓
0.169
0.242
0.215
0.193
0.166
0.196
0.175
0.118


VideoLQ
NIQE ↓
3.721
5.954
5.458
3.172
3.255
3.000
4.161
2.913


MUSIQ ↑
56.45
36.74
54.96
61.88
63.95
64.450
54.18
67.45


CLIP-IQA ↑
0.371
0.328
0.590
0.438
0.509
0.471
0.352
0.635


DOVER ↑
10.92
5.761
7.618
9.483
10.503
9.957
11.444
12.788


NIQE ↓
4.014
4.584
4.829
4.007
3.888
3.490
4.264
3.874


AIGC38
MUSIQ ↑
60.45
43.64
59.69
57.50
59.50
58.31
52.59
54.41


CLIP-IQA ↑
0.361
0.296
0.487
0.312
0.350
0.371
0.289
0.355


DOVER ↑
8.561
4.349
6.749
6.823
7.325
7.090
8.719
8.009


NIQE ↓
4.942
4.399
4.853
4.444
4.162
4.124
4.759
3.955


MUSIQ ↑
58.39
56.72
64.38
58.73
62.03
63.15
53.36
65.91


CLIP-IQA ↑
0.442
0.554
0.660
0.473
0.528
0.497
0.395
0.638


DOVER ↑
12.275
10.547
12.082
10.245
11.008
12.857
12.178
13.424




## 6.2. Qualitative Comparisons

The qualitative results in Figure 4 further reinforce SeedVR's superiority. The visual examples demonstrate that SeedVR excels at both `degradation removal` and `texture generation` across real-world and AI-generated videos.

For instance, in scenes with `severely degraded video inputs`, SeedVR effectively recovers `detailed structures` like building architectures, where other methods might produce blurry or artifact-ridden results. When restoring `AI-generated videos`, SeedVR faithfully reconstructs `fine details`, such as the subtle textures of a panda's nose or the intricate facial features of a terracotta warrior. In contrast, competing approaches often yield blurred or less distinct details in these areas. This visual evidence supports the quantitative results, particularly the high scores in perceptual metrics, indicating that SeedVR produces outputs that are not only numerically superior but also visually more realistic and detailed.

The following image illustrates qualitative comparisons:

![该图像是视频恢复方法对比图，展示了四个不同场景下多种超分辨率方法的视觉效果。图中重点比较了SeedVR与其他六种方法在细节恢复和清晰度方面的差异，SeedVR在多个细节区域表现出更优的还原质量。](/files/papers/690c6c590de225812bf932dc/images/4.jpg)
*Figure 4: Qualitative comparison of video restoration methods. SeedVR demonstrates superior detail recovery and clarity on both real-world and AI-generated videos (e.g., building structures, panda's nose, terracotta warrior's face) compared to other methods that often produce blurrier results.*

## 6.3. Ablation Studies / Parameter Analysis

### 6.3.1. Effectiveness of Casual Video VAE
The ablation study on the `causal video VAE (CVVAE)` (Table 2) highlights its critical role in SeedVR's performance. The `CVVAE` is not only efficient but also achieves superior video reconstruction quality compared to state-of-the-art VAEs specifically designed for video generation and restoration.

SeedVR's VAE achieves the lowest `rFVD` score (1.85), which is $69.5\%$ lower than the second-best performer (CogVideoX with 6.06). A lower `rFVD` indicates that the latent space learned by SeedVR's VAE better captures the real video distribution, leading to more realistic and diverse reconstructions. Furthermore, SeedVR's VAE achieves the best `LPIPS` score (0.0517) and competitive `PSNR` (33.83) and `SSIM` (0.9643) relative to CogVideoX, further underscoring its superior reconstruction capability in terms of both perceptual quality and fidelity. This validates the design choices of using `causal 3D residual blocks`, increased `latent channels (16)`, and `temporal compression (4)`.

The following are the results from Table 2 of the original paper:




Methods(VAE)
Params(M)
TemporalCompression
SpatialCompression
LatentChannel
PSNR ↑
SSIM ↑
LPIPS ↓
rFVD ↓




SD 2.1 [45]
83.7
-
8
4
29.50
0.9050
0.0998
8.14


VEnhancer [20]
97.7
-
8
4
30.81
0.9356
0.0751
11.10


Cosmos [44]
90.2
4
8
16
32.34
0.9484
0.0847
13.02


OpenSora [80]
393.3
4
8
4
27.70
0.8893
0.1661
47.04


OpenSoraPlan v1.3 [28]
147.3
4
8
16
30.41
0.9280
0.0976
27.70


CV-VAE (SD3) [79]
181.9
4
8
16
33.21
0.9612
0.0589
6.50


CogVideoX [66]
215.6
4
8
16
34.30
0.9650
0.0623
6.06


Ours
250.6
4
8
16
33.83
0.9643
0.0517
1.85




### 6.3.2. Window Size for Attention
The choice of `window size` for attention is critical for both `training efficiency` and `restoration performance`. This ablation explores how different spatial and temporal window sizes impact `training time` and `DOVER` scores.

**Training Efficiency (Table 3)**:
The following are the results from Table 3 of the original paper:




Temp. Win.
Spat. Win. Size


Length
8×8
16 × 16
32 × 32
64 × 64




t = 1
t =5
455.49
138.29
110.01
58.37
46.49
23.68
20.29


345.78




This table shows that `smaller window sizes significantly increase training time`. For instance, a $1 \times 8 \times 8$ window takes 455.49 seconds per iteration, which is approximately $19.24 \times$ longer than a $1 \times 64 \times 64$ window (23.68 seconds). This efficiency difference stems from how text guidance is integrated: each window is assigned a text prompt for attention computation. `Larger window sizes` reduce the number of individual windows needing text token attention, thus improving both `training and inference efficiency`.

**Restoration Performance (Table 4)**:
The following are the results from Table 4 of the original paper:




Temp. Win.Length
Spat. Win. Size


Length
32 × 32
64 × 64
Full





t = 1
11.947
10.690
10.799


t = 3
11.476
10.429
9.145


t =5
10.558
11.595
8.521




This table (measured by `DOVER` on YouHQ40) reveals several key observations:
1.  **Full Spatial Attention Degradation**: The performance of `full spatial attention` (`Full` column) `declines` as `temporal window length increases` (from 10.799 for $t=1$ to 8.521 for $t=5$). This is attributed to the `high token count` in full attention, requiring a much longer training period for convergence, a need amplified by larger temporal windows.
2.  **Smaller Spatial Windows vs. Full Attention**: `Smaller spatial windows`, e.g., $32 \times 32$, `outperform full attention` for shorter temporal lengths but still show a performance drop as temporal length increases. This suggests that smaller windows allow for faster initial convergence, but might struggle to capture sufficient `temporal dependencies` over longer sequences without extensive training.
3.  **Optimal Window Size ($64 \times 64$)**: For a `spatial window size of`64 \times 64

, performance is comparable at shorter temporal lengths ($t=1, 3$). However, increasing the temporal window length tot=5notably improves results (11.595, which is the best performance in the table). This indicates that the larger window size effectively captures long-range dependencies and enhances semantic alignment between text prompts and restoration, especially when given sufficient temporal context.

These observations validate SeedVR's design choice of using a `large`5 \times 64 \times 64`attention window` (temporal $\times$ height $\times$ width in latent space). This configuration strikes an optimal balance between `training efficiency` (due to fewer windows) and `restoration performance` (due to better long-range dependency capture and semantic alignment), especially for longer video sequences.

7. Conclusion & Reflections

7.1. Conclusion Summary

SeedVR introduces a novel Diffusion Transformer architecture specifically designed for generic video restoration capable of handling arbitrary resolutions and lengths. Its key innovations include a shifted window attention mechanism (Swin-MMDiT) with variable-sized windows and 3D rotary positional embeddings to overcome resolution constraints and enable efficient processing of long video sequences. Furthermore, a causal video autoencoder (CVVAE) with increased latent channels and temporal compression significantly boosts training and inference efficiency while maintaining high reconstruction quality. By leveraging large-scale mixed image and video training and progressive training strategies, SeedVR achieves state-of-the-art performance across diverse synthetic, real-world, and AI-generated video benchmarks. Critically, despite its large parameter count, SeedVR demonstrates superior speed (over $2 \times$ faster) compared to existing diffusion-based methods, making it highly practical.

7.2. Limitations & Future Work

The authors acknowledge that while SeedVR achieves significant advancements, there are still areas for improvement:

• Sampling Efficiency: The paper states that future work will focus on improving the sampling efficiency of SeedVR. While the model is faster than previous diffusion-based VR methods, diffusion models are inherently iterative, which can still be slower than feed-forward networks for real-time applications.
• Robustness: Enhancing the robustness of SeedVR is also identified as a future research direction. This might involve improving its performance under even more extreme or novel degradation scenarios or ensuring stability across a wider range of input characteristics.

7.3. Personal Insights & Critique

SeedVR represents a substantial step forward in the field of generic video restoration. Its strength lies in its holistic approach, addressing not just the core generative model but also the critical aspects of efficient video representation (CVVAE) and scalable architecture (Swin-MMDiT) for arbitrary inputs.

• Innovations in Scalability: The shifted window attention with variable-sized windows and 3D RoPE is a particularly elegant solution to the long-standing problem of handling arbitrary input dimensions in Transformers, especially for 3D video data. This moves beyond the patchwork solutions of tiled sampling that plagued previous diffusion-based VR methods.

• Practical Impact: The focus on efficiency (both inference speed and training speed through precomputation and progressive training) despite a massive parameter count is highly commendable. This makes SeedVR a more practical solution for real-world applications where resources and latency are crucial.

• Generative vs. Fidelity Trade-off: The paper frankly discusses the inherent trade-off between perceptual quality (where diffusion models excel) and traditional fidelity metrics like PSNR/SSIM. This is an important nuance, as ultimately, human perception is the gold standard for restoration, and SeedVR's high scores on LPIPS, DISTS, NIQE, MUSIQ, and DOVER indicate success in this regard.

• Broader Applicability: The techniques developed for SeedVR, particularly the Swin-MMDiT and the causal video autoencoder, could potentially be transferred or adapted to other video generation or processing tasks where arbitrary length/resolution handling and efficiency are critical, such as video editing, compression, or even general video understanding. The mixed image and video training also highlights a valuable strategy for leveraging existing vast image datasets for video tasks.

  One potential area for further exploration or a minor critique could be the complexity of managing and integrating such a large-scale training pipeline, including dataset curation and the multi-stage progressive training. While effective, it suggests significant engineering overhead. Additionally, sampling efficiency remains a common challenge for all diffusion models. While SeedVR is faster than its predecessors, further breakthroughs in non-iterative or few-step sampling for video could unlock even wider applications. The ability to control the restoration process with finer-grained parameters (beyond just text prompts) could also be an interesting future direction.

Overall, SeedVR provides a robust, highly performing, and architecturally sound framework for the next generation of generic video restoration, setting a new benchmark and inspiring future research in large vision models.