Diffusion Models - PyTorch Implementation

Explore a comprehensive PyTorch implementation of Diffusion Models in this 22-minute tutorial video. Dive into the world of generative models, including popular examples like DALL-E, Imagen, and Stable Diffusion. Learn to code an unconditional version and train it step-by-step. Discover two key improvements: classifier-free guidance and exponential moving average. Implement these updates and train a conditional model on CIFAR-10, comparing various results. Follow along with code examples, gain insights from relevant research papers, and understand concepts like timestep embedding. Perfect for those interested in state-of-the-art machine learning techniques and their practical applications in image generation.

Introduction
Recap
Diffusion Tools
UNet
Training Loop
Unconditional Results
Classifier Free Guidance
Exponential Moving Average
Conditional Results
Github Code & Outro