2. Inner Working of Diffusion Models: A Beginner’s Guide to Image Generation
Image generation has become an intriguing field, and diffusion models are making remarkable strides in this domain. With approaches like variational autoencoders, generative adversarial models (GANs), and auto regressive models, deep learning techniques are mimicking and creating visual content. Let’s dive into this beginner’s guide to understand the inner workings and potential of diffusion models for image generation.
TABLE OF CONTENTS:
- INTRODUCTION TO IMAGE GENERATION-VAEs, GANs, & Autoregressive Models
- DIFFUSION MODELS-INTRODUCTION
- INNER WORKING OF DIFFUSION MODELS
- FORWARD DIFFUSION
- REVERSE DIFFUSION
- IMAGE GENERATION
INTRODUCTION TO IMAGE GENERATION
Image generation has been a fascinating field of study, and recent advancements in diffusion models have shown remarkable promise in this area. While numerous approaches have been explored, some of the most notable ones include variational autoencoders and generative adversarial models (GANs).
- VAEs: Variational autoencoders employ a two-step process for image generation. Initially, they compress the input images into a lower-dimensional representation and subsequently decode them back to the original size. Throughout this process, the models learn the underlying distribution of the data, enabling them to generate new images that capture the essence of the training set.
- GANs: On the other hand, GANs have gained significant popularity due to their unique design. These models employ two neural networks that compete against each other. The generator network creates images, while the discriminator network evaluates whether an image is…
