Diffusion Models | PyTorch Implementation

Video Description

Diffusion Models are generative models just like GANs. In recent times many state-of-the-art works have been released that build on top of diffusion models such as #dalle , #imagen or #stablediffusion . In this video I'm coding a PyTorch implementation of diffusion models in a very easy and straightforward way. At first I'm showing how to implement an unconditional version and subsequently train it. After that I'm explaining 2 popular improvements for diffusion models: classifier free guidance and exponential moving average. I'm also going to implement both updates and train a conditional model on CIFAR-10 and afterwards compare the different results. Code: https://github.com/dome272/Diffusion-Models-pytorch #diffusion #dalle2 #dalle #imagen #stablediffusion 00:00 Introduction 02:05 Recap 03:16 Diffusion Tools 07:22 UNet 13:07 Training Loop 15:44 Unconditional Results 16:05 Classifier Free Guidance 19:16 Exponential Moving Average 21:05 Conditional Results 21:51 Github Code & Outro Further Reading: 1. Paper: https://arxiv.org/pdf/1503.03585.pdf 2. Paper: https://arxiv.org/pdf/2006.11239.pdf 3. Paper: https://arxiv.org/pdf/2102.09672.pdf 4. Paper: https://arxiv.org/pdf/2105.05233.pdf 5. CFG: https://arxiv.org/pdf/2207.12598.pdf 6. Timestep Embedding: https://machinelearningmastery.com/a-gentle-introduction-to-positional-encoding-in-transformer-models-part-1/ Follow me on instagram lol: https://www.instagram.com/dome271