Fine-Tuning GPT-2 for Stable Diffusion Prompts

Overview

This GitHub project aims to improve prompts for stable diffusion using fine-tuned GPT-2 models. The goal is to generate high-quality prompts that lead to accurate and reliable predictions in diffusion-related scenarios.

Table of Contents

• Background
• Getting Started
• Data Collection
• Data Preprocessing
• Model Fine-Tuning
• Evaluation
• Contributing
• License

Background

Diffusion processes play a crucial role in various scientific and engineering domains. Ensuring stable and accurate predictions requires well-designed prompts to guide the generation process. This project aims to enhance the capabilities of GPT-2 in providing effective prompts for stable diffusion scenarios.

Getting Started

To contribute to this project, follow these steps:

Data Collection

For successful fine-tuning, high-quality training data is essential. Collect data related to stable diffusion scenarios from reputable sources. Consider academic papers, research articles, and domain-specific datasets. Organize the data into a suitable format for preprocessing.

Data Preprocessing

Prepare the collected data for fine-tuning by:

1. Cleaning and formatting the text data.
2. Tokenizing the text into GPT-2 compatible format.
3. Splitting the dataset into training, validation, and test sets.

Model Fine-Tuning

Fine-tune the GPT-2 model using the prepared dataset. Use the transformers library for seamless integration. Define appropriate fine-tuning hyperparameters and train the model over several epochs. Monitor the training process and adjust parameters as needed.

python fine_tune.py --data_path data/ --model_name gpt2 --epochs 5