Enhancing Text-to-Image Models through Direct Preference Optimization

Table of Contents

• Introduction
• Motivation
• Installation
• Usage
• Features
• Dependencies
• Configuration
• Documentation
• Examples
• Troubleshooting
• Contributors
• License

Introduction

This project aims to bridge the gap between advancements in text-based model optimization and the domain of visual data, by applying the Direct Preference Optimization (DPO) framework to the ImageReward dataset. This novel application challenges the adaptability of text-oriented optimization methods to the complexities of visual data interpretation and sets the stage for a potential breakthrough in multimodal AI research.

Motivation

The inspiration for this project comes from the pursuit of human-aligned artificial intelligence, as evidenced by the innovative works presented at NeurIPS 2023. The DPO framework, known for its stability, efficiency, and computational simplicity, is being adapted for use in image preference alignment, venturing into the less chartered territory of enhancing text-to-image models with human preference data.

Installation

Instructions on installing this project, including prerequisites and environment setup, will be provided here.

Usage

This section will include instructions on how to use the project, covering command-line arguments, configuration options, and examples of common use cases.

Features

• Adaptation of the DPO algorithm to accommodate image data.
• Fine-tuning of pre-existing text-to-image models based on human aesthetic preferences.
• Comparative analysis against models optimized using alternative approaches.

Dependencies

List of software, libraries, and tools required to run this project. Detailed installation instructions for each dependency will be provided.

Configuration

Details on how to configure the project, including setting environment variables, modifying configuration files, and adjusting parameters for optimization.

Documentation

Link to the project's full documentation, including API references, detailed setup, and usage instructions.

Examples

This section will include code snippets and step-by-step guides to demonstrate the project's capabilities and how to perform common tasks.

Troubleshooting

Common issues and their solutions, including how to diagnose problems and configurations that may cause errors.

Contributors

• Adi Asija
• Tanay Nayak

Special thanks to the authors of the foundational papers and datasets that inspired this project.

License

Information about the project's license type. Typically, this is where you'd specify if the project is under an MIT, Apache, GPL, or other licenses.