Part A : The Power of Diffusion Models!

Part 0: Setup

-> Used seed = 2024

As the number of inference steps increase, rendered image quality increases. Image rendered with inference steps of 100 is more detailed and clearer.

Part 1: Sampling Loops

1.1 Implementing the Forward Process

1.2 Classical Denoising

1.3 One-Step Denoising

1.4 Iterative Denoising

1.5 Diffusion Model Sampling

1.6 Classifier-Free Guidance (CFG)

1.7 Image-to-image Translation

1.7.1 Editing Hand-Drawn and Web Images

1.7.2 Inpainting

1.7.3 Text-Conditional Image-to-image Translation

1.8 Visual Anagrams

1.9 Hybrid Images

Part B: Diffusion Models from Scratch!

Part 1: Training a Single-Step Denoising UNet

1.1 Implementing the UNet

1.2 Using the UNet to Train a Denoiser

1.2.1 Training

1.2.2 Out-of-Distribution Testing

Part 2: Training a Diffusion Model

2.1 Adding Time Conditioning to UNet

2.2 Training the UNet

2.3 Sampling from the UNet

2.4 Adding Class-Conditioning to UNet

2.5 Sampling from the Class-Conditioned UNet