---
license: cc-by-nc-4.0
tags:
- SVECTOR
- SPECTRO
pipeline_tag: text-to-video
---
 
# 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.

---

## **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:
   ```bash
   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.