binliang2021 / aifun

AI for Fun

Overview

Welcome to "AI for Fun", a public repository dedicated to exploring and demonstrating the capabilities of multi-modal AI models. This repository is designed as a resource for enthusiasts, researchers, and developers interested in the integration and application of different AI modalities such as text, image, speech, video, and more. Whether you're looking to learn, build, or simply explore, this repository offers a structured collection of model examples across various domains.

Repository Structure

The repository is organized into several folders, each dedicated to a specific type of multi-modal model. Below is the structure and a brief description of what you will find in each folder:

• Text-to-Speech: Systems that convert text into audible speech.
• Input-to-Video: Tools that create video content based on textual inputs.
• Text and Image-to-3D: Conversion tools that turn text and images into 3D outputs.

Each folder contains a mix of examples, documentation, and benchmark results for the models it includes.

How to Use This Repository

• Explore: Browse through the folders to discover different multi-modal models and their applications.
• Learn: Each model includes documentation and references to help you understand how it works and its use cases.
• Experiment: You can download and run the examples to see the models in action.
• Contribute: Contributions are welcome! Whether you're improving existing examples, adding new ones, or suggesting changes, please feel free to make a pull request.

Benchmarks and Metrics

For those interested in the performance of these models, we reference benchmarks and evaluation metrics commonly accepted in the AI community. This will help you understand the effectiveness of each model and compare them objectively.

Getting Started

To get started with the repository:

1. Navigate into the folder of interest.
2. Follow the individual READMEs and google colab in each folder for instructions on running the models.

Contributing

We encourage contributions from the community.

Acknowledgments

• Thanks to all the contributors who have invested their time in building this repository.
• Special thanks to open-source projects and organizations that provide public datasets and model architectures.