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license: apache-2.0
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README.md
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Step 1: Core Architecture Design
The model combines:

Hierarchical Video Encoder (V-JEPA inspired)

Contextual Text Encoder (LLM-based)

Joint Embedding Space

Diffusion-Based Decoder


### Key Components:
1. **Cognitive Hierarchy:**
   - Video encoder extracts spatiotemporal features at multiple scales
   - Text encoder provides semantic context
   - Fusion transformer establishes cross-modal relationships

2. **Diffusion-Based Prediction:**
   - Conditional UNet generates future frames
   - Training via masked future prediction

3. **Contextual Reasoning:**
   - Joint embedding space enables multimodal understanding
   - Temporal coherence through video-text alignment

### Requirements:
- PyTorch 2.0+
- Hugging Face Transformers
- Diffusers library
- CUDA 11.7+

This architecture provides a foundation for building world models that understand temporal dynamics and contextual relationships through multimodal fusion and generative prediction.