Training a diffusion model for image generation

In this section, we’ll implement a diffusion model from scratch using PyTorch. By the end, this model will be able to generate realistic, high-quality images. Besides PyTorch, we’ll use Hugging Face (an open-source platform that offers diverse AI tools and a collaborative hub for sharing and accessing pre-trained AI models and datasets) to load an image dataset. Besides the dataset, we’ll use the diffusers library [3] from Hugging Face, which provides implementations for models such as UNet and DDPM. We’ll also use Hugging Face’s accelerate library [4] to speed up the diffusion training process by utilizing the Graphical Processing Unit (GPU). We’ll learn more about Hugging Face in Chapter 19, PyTorch and Hugging Face.

Note

GPUs might not be readily available to you. In that case, you can access GPUs via Google Colab: https://colab.google/.

All code for this section...