📚 PRISMA Engine Documentation

PRISMA Engine is an advanced text-to-video generation model that transforms textual descriptions into dynamic, high-quality 4D video content. Built on state-of-the-art transformer architecture, PRISMA Engine empowers creators and developers to bring ideas to life in a fully decentralized and community-driven way.

This documentation provides comprehensive guides, API references, tutorials, and best practices for seamless integration.

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🚀 Getting Started

Prerequisites

• Python 3.8+
• CUDA-enabled GPU (for optimal performance)
• pip and virtualenv recommended

Installation

Clone the repository and install dependencies:

git clone https://github.com/prisma-engine/prisma-engine.git
cd prisma-engine
pip install -r requirements.txt

⚡ Quick Start

Use PRISMA Engine in just a few lines of code:

from prisma import PrismaModel

model = PrismaModel()
video = model.generate("A beautiful sunset over the ocean")
video.save("output.mp4")

🖼️ Inference Example

Generate a video from a text prompt using the CLI:

python prisma/inference.py \
    --model-path /path/to/model/weights \
    --prompt "A futuristic cityscape at night" \
    --output output.mp4 \
    --max-frames 32 \
    --frame-size 512 512

This will load the trained model, generate a 4D video sequence, and save it as output.mp4.

🔧 API Reference

PrismaModel

model = PrismaModel(model_path=None)

Methods:

• generate(prompt: str, max_frames: int = 32, frame_size: Tuple[int, int] = (512, 512)) -> VideoObject
  • Generates a video sequence from a text prompt.
• save(filename: str) -> None
  • Saves the generated video to the specified path.

CLI Options

• --model-path: Path to the model weights.
• --prompt: Text description for video generation.
• --output: Output video file name.
• --max-frames: Number of frames to generate.
• --frame-size: Width and height of the video frames.

🧩 Advanced Usage

Fine-Tuning

Use the prisma/training.py script to fine-tune the model with your own dataset:

python prisma/training.py --dataset /path/to/dataset --epochs 10

Integration

PRISMA Engine can be integrated into web, mobile, or desktop applications via its API. See docs/integration.md for full integration examples.

🎨 Best Practices

• Use concise and descriptive prompts to guide the model effectively.
• Experiment with different frame sizes and resolutions to match your creative needs.
• Monitor GPU memory usage during high-resolution generation.
• Use the latest stable model weights from the official releases page.
• Validate outputs to ensure consistent quality before production use.

🤝 Contributing

We welcome contributions from the community!

• Fork the repository and create a new branch.
• Make your changes and ensure tests pass.
• Submit a pull request with a clear description of your changes.

Please read our CONTRIBUTING.md for more details.

📖 Documentation

• Full API Reference: docs/api.md
• Integration Guide: docs/integration.md
• Training & Fine-Tuning: docs/training.md
• Model Architecture: docs/architecture.md

📄 License

PRISMA Engine is released under the MIT License. See LICENSE for details.