Hydra Video is an extension of the Mamba v2 architecture, specifically designed for efficient video processing. This model introduces a novel approach to handling the unique characteristics of video data, combining the strengths of Mamba v2 with tailored modifications for spatial and temporal dimensions.

1. Hybrid Directionality:

   • Spatial Dimensions: Utilizes bi-directional modeling, similar to the Hydra architecture.
   • Temporal Dimension: Employs causal (unidirectional) modeling, reflecting the inherent forward progression of time in video sequences.

2. Efficient Processing:

   • Leverages the state space model efficiency of Mamba v2.
   • Adapts the architecture to handle the additional complexities of video data.

3. Intuition-Driven Design:

   • Recognizes that spatial information in videos benefits from bi-directional context.
   • Acknowledges the causal nature of frame progression in the time dimension.

4. Balance of Context and Causality:

   • Allows for rich spatial understanding through bi-directional processing.
   • Maintains temporal coherence and causality in the time dimension.

This approach aims to provide a more nuanced and efficient way of processing video data, potentially leading to improved performance in various video-related tasks such as action recognition, video classification, and temporal event detection.

Generation of video can be inherently expensive. Given the nature of SSMs recurrence which we can take advantage of perhaps this challenge
can be surmounted. By using a combination of autoregressive and parallel processing we should be able to lower the bar.

graph TD
    A[Start] --> B[Parallel Sampling Generation]
    B -->|Bidirectional| C[Cache Forward and Backward State]
    C --> D[Update Forward State]
    D --> E[Project Updated Forward State and Cached Backward State]
    E -->|Repeat N times| D
    E -->|Repeat M times| B
    E --> F[End]

    subgraph "Causal Processing (x N)"
        D
        E
    end

    subgraph "Overall Process (x M)"
        B
        C
        D
        E
    end

• Create hydra video module
• Write unit test for hydra video module
• Write VAE quantizer code
• Create VQVAE tokenizer built with hydra-video blocks
• Create video decoder built with hydra-video blocks
• Create training code for VQVAE Tokenize
• Create training code for video decoder
• Write generation code for parallel sampling and autoregressive generation