Stable Diffusion

Welcome to the Stable_Diffusion_Scratch repository! This project contains a Diffusion Model built from scratch, designed to provide a hands-on learning experience in diffusion model fundamentals. The repository is structured to enable contributors to build, train, and experiment with custom diffusion model implementations, and enhance them with various functionalities.

Introduction

This repository contains a Text-to-Image generation model based on Diffusion Models, a powerful generative technique that progressively transforms random noise into realistic images. Unlike other generative models like GANs, diffusion models operate through a process of denoising, which makes them particularly good at generating high-quality, coherent outputs.

Diffusion Model Breakdown

Diffusion models generate images through two key processes:

1. Forward Diffusion: Noise is gradually added to the input data until it becomes unrecognizable.
2. Reverse Diffusion: The model is trained to reverse this process, removing noise step-by-step to reconstruct the original image.

The model is trained to learn this reverse process, so it can generate new data from noise.

Prerequisites

• Python 3.8+: Ensure you have an up-to-date version of Python.
• PyTorch 1.7+: This project is built on PyTorch, a popular deep learning framework.
• CUDA (Optional): GPU support is recommended for faster training times with CUDA.

Installation

1. Clone the Repository:

   git clone https://github.com/wncc/Hello-FOSS-ML-Diffusivity.git
   cd Stable_Diffusion_Model/Stable_Diffusion_Scratch
   

2. For the other dependencies, you can install them with:

   pip install numpy torch torchvision matplotlib
   

Project Structure

• models:

  • training.py: This file contains the main training code for the diffusion model. It includes essential functions for setting up the training loop, managing the optimizer, and evaluating model performance.
  • sampling.py: This file implements the sampling process, which generates images from noise by applying the learned diffusion process.

• utils:

  • unet.py: This file defines the U-Net architecture used in the diffusion model for image generation and denoising.
  • diffusion_utilities.py: This file provides utility functions related to the diffusion process and U-Net structure.

Issues Tab

The Issues tab in this repository contains a series of tasks designed to guide contributors through various improvements and feature extensions. These tasks are organized to cover different aspects of the model, allowing contributors to work on enhancements, optimizations, and new functionalities.

Contributing

We welcome contributions from beginners and experts alike. This project is aimed at providing a learning experience, so don’t hesitate to try out the issues and contribute your solutions. Feel free to create new issues or pull requests if you have ideas for improving this repository.