Deep Learning Image Generation with GANs & Stable Diffusion

About this course

Image generation has come a long way, back in the early 2010s generating random 64x64 images was still very new. Today we are able to generate high quality 1024x1024 images not only at random, but also by inputting text to describe the kind of image we wish to obtain.

In this course, we shall take you through an amazing journey in which you'll master different concepts with a step by step approach. We shall code together a wide range of Generative adversarial Neural Networks and even the Diffusion Model using Tensorflow 2, while observing best practices.

You shall work on several projects like: Digits generation with the Variational Autoencoder (VAE), Face generation with DCGANs,then we'll improve the training stability by using the WGANs andfinally we shall learn how to generate higher quality images with the ProGAN and the Diffusion Model.

From here, we shall see how to upscale images using the SrGAN Final Project: AI Interior Designer

You will build an application that can take any photo of an empty room and breathe life into it. We will architect a pipeline that truly understands the space.

Step 1: Scene Understanding. First, we’ll use the Depth Anything model to generate a precise depth map, giving our AI an understanding of the room's 3D geometry.

Step 2: Intelligent Masking. Next, we'll use a powerful combination of Grounding DINO and Segment Anything (SAM) to automatically detect and create masks for key areas like the door, and windows.

Step 3: Controlled Generatio

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