A Step-by-Step Guide to Developing Diffusion Models

From Concept to Code: Building a Diffusion Model to Generating Numbers

Diffusion models are a type of Generative AI to create new high-qualit yimages by gradually refining random noise

Diffusion Model: From random noise to realistic images

This article implements the Diffusion model using the MNIST dataset, which consists of handwritten digits, and I trained it in Google Colab Pro using L4 GPU.

Read an intuitive explanation of Diffusion models here on How AI Turns Random Noise into Breathtaking Images?

Diffusion Model are trained to transform clear images into random noise using a controlled noise-adding process and then reversing the process to generate coherent, realistic images using a two step process:Forward and Backward process

Diffusion models learn to create realistic data through a two-step process:

1. Forward Process, the model methodically applies controlled noise to real images. With each step, the images become progressively more distorted and blurry.
2. The Reverse process…