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Deep Learning Image Generation with GANs & Stable Diffusion
Rating: 4.3 out of 5(60 ratings)
874 students

Deep Learning Image Generation with GANs & Stable Diffusion

Face Generation with GANs, WGANs and ProGANs. Image super-resolution with SRGAN, Interior Design with Stable Diffusion
Created byNeuralearn Dot AI
Last updated 11/2025
English

What you'll learn

  • Understanding how variational autoencoders work
  • Image generation with variational autoencoders
  • Building DCGANs with Tensorflow 2
  • More stable training with Wasserstein GANs in Tensorflow 2
  • Generating high quality images with ProGANs
  • Building mask remover with CycleGANs
  • Image super-resolution with SRGANs
  • Advanced Usage of Tensorflow 2
  • Image generation with Diffusion models
  • How to code generative A.I architectures from scratch using Python and Tensorflow

Course content

9 sections • 37 lectures • 10h 15m total length

  • Welcome1:41
  • General Introduction4:37

    Trace the evolution from variational autoencoder and generative adversarial networks to diffusion models, detailing generator and discriminator dynamics, training instability, and practical implementations.

  • What you'll learn3:17

    Design and train deep learning image generators by building TensorFlow models, exploring variational autoencoders, GANs, Wasserstein GANs, CycleGAN, and diffusion models, and apply to image super-resolution and face mask removal.

  • Link to the Code0:14

Requirements

  • Basic Knowledge of Python
  • Basic Knowledge of Tensorflow
  • Access to an internet connection, as we shall be using Google Colab (free version)

Description

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 and

  • finally 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 Generation. Finally, we will feed the original image, the depth map, and the segmentation masks into ControlNet with a Stable Diffusion Inpainting model. This allows us to tell the AI, "Generate a modern sofa here on the floor, respecting the room's depth and leaving the windows untouched." The result is a stunning, realistic, and context-aware interior design.

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.


Enjoy!!!

Who this course is for:

  • Beginner Python Developers curious about Deep Learning.
  • People interested in using A.I and deep learning to generate images
  • People interested in generative adversarial networks (GANs) , other more advanced GANs and DIffusion Models
  • Practitioners interested in learning to building GANs and Diffusion models from scratch
  • Anyone who wants to master Image super-resolution using GANs
  • Software developers who want to learn how state of art Image generation models are built and trained using deep learning.

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