Spectro-2B: Advanced Video Generation Model by SVECTOR

Spectro-2B is a state-of-the-art video generation model with 2 billion parameters, designed to produce high-quality, transformer-based video outputs at 24 FPS. It combines innovative transformer techniques with advanced 3D modeling to generate, process, and understand video data. Below, we detail its architecture, internal workings, and the technical aspects of this groundbreaking model.

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Key Features

• Transformer-Based: Utilizes a powerful Transformer3DModel for processing high-dimensional spatiotemporal data.
• High Resolution: Generates videos at 768x512 resolution with seamless transitions and realism (24 FPS).
• 24 FPS Output: Smooth frame generation for real-world video applications.
• Advanced Latent Compression: Leverages a CausalVideoAutoencoder for efficient latent representation and generation.

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Model Architecture

Transformer3DModel

The heart of Spectro-2B is the Transformer3DModel. This module processes the video data across both spatial and temporal dimensions using multi-head attention, ensuring contextual coherence.

Specifications

Parameter	Value
Activation Function	gelu-approximate
Attention Bias	true
Attention Head Dimension	64
Cross-Attention Dimension	2048
Number of Attention Heads	32
Number of Layers	28
Positional Embedding	rope
Normalization	rms_norm

The positional embedding system (rope) ensures that the model efficiently encodes spatial and temporal relationships, with a theta parameter of 10,000 to balance precision and scale.

Working Principle

1. Input Encoding: The raw video data is broken into frames, and positional embeddings are applied to represent spatial and temporal information.
2. Multi-Head Attention: Attention heads focus on different regions and times within the video, enabling the model to understand both local and global context.
3. Layer Stacking: 28 transformer layers refine the intermediate representations, progressively building a high-quality video output.

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CausalVideoAutoencoder

The CausalVideoAutoencoder (VAE) handles latent space compression and decompression, ensuring computational efficiency and high fidelity in output.

Specifications

Parameter	Value
Latent Channels	128
Patch Size	4
Scaling Factor	1.0
Normalization	pixel_norm
Latent Log Variance	uniform

Working Principle

1. Compression: The raw video is converted into a compact latent representation using compress_all blocks.
2. Residual Connections: res_x and res_x_y blocks preserve essential video features during compression.
3. Reconstruction: The latent representation is decoded back into video frames, ensuring high fidelity and temporal consistency.

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Technical Workflow

1. Data Preprocessing

   • Input videos are divided into 3D tensors: [Time, Height, Width].
   • Positional embeddings are applied to encode spatiotemporal relationships.

2. Transformer Processing

   • Multi-head attention layers capture inter-frame relationships and spatial details.
   • Residual connections prevent vanishing gradients and enhance feature propagation.

3. Latent Space Compression

   • The VAE compresses video features into a smaller latent space for efficient computation.

4. Video Generation

   • The model reconstructs video frames from the latent space, ensuring smooth transitions and high realism.

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Internal Workings: Key Innovations

1. Positional Embeddings

• rope (Rotary Positional Embedding) allows flexible and efficient encoding of both spatial and temporal positions.

2. Attention Mechanisms

• Cross-attention layers enable the model to incorporate global context into localized regions.
• Self-attention layers refine intra-frame and inter-frame relationships.

3. Efficient Latent Representation

• The autoencoder design optimizes computational resources, allowing high-quality video generation with minimal overhead.

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Applications

• Video Content Creation: Generate professional-grade videos for entertainment, education, and advertising.
• Real-Time Simulations: Ideal for gaming, VR, and AR environments.
• AI-Assisted Video Editing: Automate video enhancements and transformations.

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Model Details

Attribute	Value
Model Name	Spectro-2B
Created By	SVECTOR
Parameter Count	2 Billion
Framework Version	0.25.1 (Diffusers)
Resolution	768x512
Frame Rate	24 FPS
Transformer Type	Transformer3DModel
Encoder Type	CausalVideoAutoencoder

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How to Use

1. Clone the repository:

   git clone https://huggingface.co/SVECTOR-CORPORATION/Spectro-2B.git
   

2. Install dependencies:

pip install -r requirements.txt

3. Run the model:

python generate_video.py --input "input_data.mp4" --output "output_video.mp4"

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Contact & Support

For more information, visit SVECTOR: https://www.svector.co.in or email support at support@svector.co.in

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Spectro-2B: Redefining the Future of Video Generation.