Scalable Diffusion Models with Transformers | DiT Explanation and Implementation

In this video, we’ll dive deep into Diffusion with Transformers (DiT), a scalable approach to diffusion models that leverages the transformer architecture. We will first get an overview of vision transformer, then see the changes the author make to get to DiT. We will look in detail the different block designs that the DiT authors explore for Diffusion Transformers and also see the results of experiments with regards to diffusion transformer architecture and scaling, that the authors do. Finally we will look at an implementation of Diffusion Transformer(DiT) in Pytorch. ⏱️ Timestamps 00:00 Intro 01:10 Vision Transformer Review 04:08 From VIT to Diffusion Transformer 09:10 DiT Block Design 14:01 Experiments on DiT block and scale of Diffusion Transformer 21:50 Diffusion Transformer (DiT) implementation in PyTorch 📖 Resources Diffusion Transformer (DiT Paper) - https://tinyurl.com/exai-dit-paper My Github Implementation Link - https://tinyurl.com/exai-dit-implemen... DiT Official Implementation - https://tinyurl.com/exai-dit-official 🔔 Subscribe: https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - explainingai.official@gmail.com