This project implements a state-of-the-art text-to-video generation model using transformer architecture and diffusion models.

• Text-to-Video Generation: Generate videos from natural language descriptions
• Transformer Architecture: Modern attention-based model for temporal consistency
• Diffusion Process: Denoising diffusion probabilistic models for high-quality generation
• Modular Design: Clean, extensible codebase with separate modules for different components
• Training Pipeline: Complete training loop with logging and checkpointing
• Inference Script: Easy-to-use inference for generating videos from text

The model consists of several key components:

1. Text Encoder: CLIP-based text encoding for understanding text descriptions
2. Video Encoder: 3D CNN for processing video frames
3. Temporal Transformer: Attention mechanism for temporal consistency
4. Diffusion Model: Denoising process for video generation
5. Decoder: Convolutional layers for final video output

pip install -r requirements.txt

python train.py --config configs/training_config.yaml

python inference.py --text "A cat playing with a ball" --output_path output_video.mp4

├── models/           # Model architectures
├── data/            # Data loading and preprocessing
├── training/        # Training utilities
├── configs/         # Configuration files
├── utils/           # Utility functions
├── train.py         # Main training script
├── inference.py     # Inference script
└── requirements.txt # Dependencies

The model uses a hybrid approach combining:

• CLIP Text Encoder: For text understanding
• 3D Convolutional Networks: For spatial-temporal feature extraction
• Transformer Blocks: For temporal attention and consistency
• Diffusion Process: For high-quality video generation

The model can be trained on various video datasets:

• Kinetics-400/600/700
• UCF-101
• Custom video datasets

• Resolution: 256x256, 512x512, or 1024x1024
• Frame Rate: 8-30 FPS
• Duration: 1-10 seconds
• Quality: High-fidelity video generation

MIT License