Denoising Diffusion Pytorch

Denoising Diffusion Process

Welcome to the documentation for Denoising Diffusion Pytorch, a comprehensive implementation of Denoising Diffusion Probabilistic Models (DDPMs) and their many variants. This project provides a clear, concise, and powerful toolkit for researchers and developers interested in generative modeling.

Overview

Denoising Diffusion Probabilistic Models are a class of generative models that have shown remarkable results, rivaling and sometimes surpassing Generative Adversarial Networks (GANs) in image synthesis quality. The core idea is to systematically corrupt data with noise in a forward process, and then learn a neural network to reverse this process, denoising the data to generate new samples.

This library implements the foundational DDPM paper and a wide array of subsequent improvements and architectural variations, making it a valuable resource for exploring the cutting edge of diffusion models.

Key Features

  • Core DDPM Implementation: A faithful implementation of the model described in Ho et al., 2020.
  • Multiple Architectures: Includes the standard U-Net, as well as advanced variants like the magnitude-preserving Karras U-Net and the U-ViT from the Simple Diffusion paper.
  • Advanced Techniques: Provides implementations for numerous cutting-edge techniques:
    • Classifier-Free Guidance
    • Elucidated Diffusion
    • Inpainting with RePaint
    • Support for 1D and 3D data.
  • Ease of Use: A high-level Trainer class abstracts away the training loop, allowing you to train a model on a folder of images with just a few lines of code.
  • Multi-GPU Ready: Built-in support for distributed training using 🤗 Accelerate.
  • Modular and Extensible: The code is designed to be easily understood, modified, and extended for your own research.

Getting Started

Ready to dive in? Here's how you can get started:

  1. Installation: Get the library and its dependencies set up in your environment.
  2. Quick Start: Follow a simple tutorial to train your first diffusion model on an image dataset.
  3. Core Concepts: Understand the fundamental components of the library, including the diffusion process and the U-Net architecture.
  4. Advanced Models: Explore the various advanced models and techniques implemented in this repository.